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Articles
Article 1 · pp. 1-9
Enhancing Formation Control of Multi Agent Systems Using Ann Based Technique
The persistent power failure that paralyzed business activities were caused by these factors that could not attain threshold, Inter-agent spacing error (RMS), Maximum formation shape error, Relative position estimation error ,Formation centroid tracking error , Relative velocity mismatch , Formation convergence time , Communication latency and Energy consumption deviation . This constant power failure observed in the country led to introduction of enhancing formation control of multi agent systems using ANN based technique. To perfectly achieve this, it was done in this manner, formation control of multi agent systems was characterized and the causes of poor formation control of multi agent systems was established and a conventional SIMULINK model for formation control of multi agent systems was designed. Then ANN was trained in the causes of poor formation control of multi agent systems for an effective reduction of the causes of poor formation control of multi agent systems and an algorithm was developed to implement the process. However, a SIMULINK model for enhancing formation control of multi agent systems using ANN based technique was designed and the results obtained were validated and justified. The results obtained were the conventional Inter-agent spacing error (RMS) that causes poor formation control of multi agent systems was 0.4 m. On the other hand, when ANN based technique was integrated into the system, it automatically changed 0.2 m. and the conventional Energy consumption deviation that causes poor formation control of multi agent systems was 27%. Meanwhile, when ANN based technique was integrated into the system, it drastically reduced to 20 %.Finally, with these results obtained the percentage enhancement formation control of multi agent systems when an ANN was integrated in to it was7%.
🏷 enhancing, formation, control, multi agent, systems, ANN based, technique
Article 2 · pp. 10-20
Improving Sliding Mode Control with Chattering Reduction using Fuzzy Based Technique
The constant power failure in our society this time around that had jeopardized business activities were caused by the following factors that could not attain their respective thresholds, Steady-State Tracking Error , Chattering Amplitude ,Chattering Frequency ,Settling Time ,Overshoot , Boundary Layer Thickness ,Actuator Position Oscillation, Actuator Torque Oscillation and Sensor Noise Influence . This consistent power failure in the society was surmounted by introducing improving sliding mode control with chattering reduction using fuzzy based technique. To vividly achieve this, it was done in this manner sliding mode control with chattering reduction was characterized and the causes of poor sliding mode control with chattering reduction was established and a conventional SIMULINK model for sliding mode control with chattering reduction was designed. Then, a fuzzy rule base for minimization of poor sliding mode control with chattering reduction was developed and an algorithm that would implement the process was equally developed. later, a SIMULINK model for improving sliding mode control with chattering reduction using fuzzy based technique was designed and the results obtained were validated and justified. The results obtained were, the conventional Steady-State Tracking Error that causes poor sliding mode control with chattering reduction was 6% which did not fall within the bench mark. On the other hand, when Fuzzy based technique was integrated into the system, it simultaneously became4.8% which is within the threshold and the conventional Sensor Noise Influence that causes of poor sliding mode control with chattering reduction was18dB. Meanwhile, when Fuzzy based technique was incorporated into the system, it immediately increased to27.5dB. Finally, with these results obtained, it meant that the percentage improvement in sliding mode control with chattering reduction when fuzzy based technique was integrated was52.8%
🏷 Improving, sliding, mode ,control, chattering, reduction, fuzzy, based ,technique
Article 3 · pp. 21-29
Cooking Quality, Fasting Blood Glucose, Glycemic Index and Load of High–Fiber Noodles Made from Wheat, Tiger Nut Residue and Cassava Flour Blends
High-fiber composite noodles were made from the combination of wheat, tiger nut residue and cassava flour blends. High fiber inclusion (up to 20 %) was achieved presenting an excellent option for formulating healthier noodles. The cooking quality and fasting blood glucose of the noodles were evaluated and the data generated were subjected to analysis of variance (ANOVA) and means were separated using Duncan multiple range test (DMRT) to establish the significant difference (p <0.05). Available carbohydrate, glycemic index and load were also determined. The result of the cooking quality of the noodles generated include; cooking time (38–40 min.), cooking loss (0.22–0.82 g), water uptake (0.14–0.17 %), and bulk density (0.26–0.53 g/ml). Fasting blood glucose (85.85–110.37 mg/dL), glycemic index (36.14–49.17), glycemic load (9.36–14.93) and available carbohydrate (25.90–30.37 g/100g) values for noodles remained lower than wheat controls, confirming reduced postprandial glycemic response and overall functional suitability. The composite noodles performed favourably when compared with the control (100 % wheat flour).
🏷 Cooking quality, fasting blood glucose, glycemic index, glycemic load, high-fiber noodles
Article 4 · pp. 30-36
Matrix Rhythm Therapy Versus Interferential Therapy Combined with Lumbar Stabilization Exercises in Chronic Non-Specific Low Back Pain: A Randomized Comparative Trial
Background: Chronic non-specific low back pain is a persistent musculoskeletal condition associated with pain, disability, impaired movement confidence, and reduced participation in daily life. Lumbar stabilization exercises are widely used in rehabilitation; however, the added value of different adjunct modalities remains uncertain.
Objective: To compare the effects of Matrix Rhythm Therapy and Interferential Therapy, each combined with lumbar stabilization exercises, on pain intensity, disability, and multidimensional pain perception in individuals with chronic non-specific low back pain.
Methods: Thirty participants with chronic non-specific low back pain were allocated to Matrix Rhythm Therapy plus lumbar stabilization exercises (MRT + LSE; n=15) or Interferential Therapy plus lumbar stabilization exercises (IFT + LSE; n=15). Interventions were delivered three times weekly for six weeks. Outcomes included the Numeric Pain Rating Scale (NPRS), Oswestry Disability Index (ODI), and McGill Pain Questionnaire, assessed at baseline and after intervention.
Results: Both groups improved after treatment. The MRT + LSE group showed larger mean reductions in NPRS (5.13 points), ODI (28.80 points), and McGill Pain Questionnaire scores (29.86 points) than the IFT + LSE group, which improved by 2.33, 18.00, and 17.27 points, respectively. Summary-based between-group analyses favored MRT + LSE for NPRS, ODI, and McGill outcomes.
Conclusion: Both interventions were associated with reductions in pain and disability; however, Matrix Rhythm Therapy combined with lumbar stabilization exercises produced greater improvements across all reported outcomes. Larger trials with corrected demographic reporting, allocation concealment, assessor blinding, and long-term follow-up are needed to confirm these findings.
🏷 Chronic low back pain; Matrix Rhythm Therapy; Interferential Therapy; Lumbar stabilization; Physiotherapy; Rehabilitation
Article 5 · pp. 37-46
Formulation and Sensory Evaluation of Functional Cake Prepared from Sweet Potato Powder
The present study aimed to develop and evaluate a value-added cake formulated with sweet potato powder, eggs, cocoa powder, date syrup, baking soda, baking powder, vanilla essence, and oil. Sweet potato powder was incorporated as a functional ingredient to enhance the nutritional quality of the product while maintaining acceptable sensory attributes. The prepared cake was subjected to sensory evaluation for color, texture, flavor, taste, and overall acceptability using a standard hedonic scale. The results indicated that the formulated cake possessed desirable sensory characteristics and good consumer acceptability. The inclusion of sweet potato powder contributed to improved nutritional value by increasing dietary fiber and providing natural bioactive compounds, while date syrup acted as a natural sweetener and flavor enhancer. Cocoa powder and vanilla essence helped in masking the characteristic flavor of sweet potato and improved palatability. The combined use of baking powder and baking soda ensured proper leavening and satisfactory cake volume and crumb structure. The study concludes that sweet potato powder can be successfully utilized in cake formulation to develop a nutritionally enriched bakery product with good sensory quality and potential for application in functional food development.The developed sweet potato powder cake showed good sensory quality and overall acceptability. Sweet potato powder improved the nutritional value by increasing dietary fibers, while date syrup enhanced sweetness and flavor. Cocoa powder and vanilla essence improved palatability, and proper leavening agents provided good cake texture and volume. Overall the cake proved to be a nutritious and acceptable functional bakery product.
🏷 Sweet Potato Powder, tray drying, cake, sensory attributes
Article 6 · pp. 47-56
Predicting Diabetes Risk using Anomaly-Based Modeling of Physiological and Lifestyle Data.
Diabetes is a major global health burden that is rapidly expanding and needs to be detected early and prevention strategies to be effective. Identifying the risk of diabetes early is very important to prevent complications such as cardiovascular diseases, kidney failure and nerve damage. But, conventional predictive methods aren't always able to find subtle and complex patterns in patient data, which makes them less effective when it comes to early diagnosis. Research’s objective is to seek innovative, accurate and strong strategies for early detection of high risk individuals. The aim of this study is to create an anomaly based machine learning model for diabetes risk prediction based on physiologic and lifestyle data. Parameters measured and included in the data are key physiological parameters such as blood glucose, BMI, blood pressure, insulin level, age and cholesterol, as well as lifestyle parameters such as physical activity, smoking status, alcohol consumption, sleep length, and dietary habits. The target variable is the outcome of diabetes (positive or negative). For anomaly detection, the study employs modelling algorithms based on anomalies (One-Class SVM, Local Outlier Factor (LOF), Autoencoders, and Hybrid Model which is combination of several of these). The methodology involves data preprocessing, feature selection, model construction and evaluation using accuracy, precision, recall, F1 score and ROC-AUC. The results demonstrated the highest accuracy and ROC-AUC value of the Hybrid Model, suggesting it effectively performed in detecting high-risk diabetes cases. Important predictors are blood glucose and BMI, additional factors are lifestyle behaviours. In conclusion, the proposed anomaly-based method improves diabetes risk prediction and aids in the detection of anomalies, which may be beneficial for diabetes prevention services and clinical actions.
🏷 Diabetes Risk Prediction, Anomaly Detection, Machine Learning, Physiological and Lifestyle Data.
Article 7 · pp. 57-79
Personal Innovativeness and Initial Trust in AI-Enabled BIM Adoption Using UTAUT in Malaysia
The construction industry is undergoing a significant digital transformation driven by Construction 4.0 technologies, with Building Information Modeling (BIM) and Artificial Intelligence (AI) emerging as key enablers of improved productivity, collaboration, and decision-making. The integration of AI with BIM has given rise to AI-enabled BIM systems, which combine BIM's data-rich environment with advanced capabilities such as machine learning, predictive analytics, natural language processing, and intelligent automation. Despite the potential benefits of AI-enabled BIM, its adoption within the Malaysian construction industry remains limited, and empirical evidence on the factors influencing its acceptance is scarce.
This study examines the determinants of behavioral intention to adopt AI-enabled BIM systems among construction professionals in Malaysia. Grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT), the study extends the model by incorporating personal innovativeness and initial trust as additional predictors. Data were collected from 261 professionals employed by CIDB Grade 7 (G7) construction firms randomly selected using probability sampling and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM).
The results indicate that performance expectancy, effort expectancy, social influence, and personal innovativeness have significant positive effects on behavioral intention to adopt AI-enabled BIM systems. Personal innovativeness was a particularly important predictor, exerting both direct effects on behavioral intention and indirect effects through performance expectancy and effort expectancy. Initial trust did not directly influence behavioral intention; however, it significantly enhanced perceptions of usefulness and ease of use, which in turn indirectly affected adoption intention through these mediating constructs. The model demonstrated satisfactory explanatory and predictive power.
This study contributes to the literature by extending UTAUT to the emerging context of AI-enabled BIM adoption and by providing one of the first empirical investigations of this technology in Malaysia. The findings offer practical implications for construction firms, policymakers, and software developers seeking to accelerate AI-enabled BIM adoption through user-centered design, digital skills development, organizational support, and targeted policy interventions.
🏷 AI, Building Information Modeling, Personal Innovativeness, Initial Trust, UTAUT.
Article 8 · pp. 80-90
Assessment of Motivational Factors as Correlates of Librarians’ Job Satisfaction in Libraries of Two Federal Universities in South-South, Nigeria
Purpose: This study investigated motivational factors as correlates of librarians’ job satisfaction in libraries of two federal universities in South-South, Nigeria. The study sought to establish the nature and strength of the relationship between two selected motivational factors — training and development, and salary/wages — and the job satisfaction of librarians.
Design/Methodology/Approach: A correlational research design was adopted for the study. The entire population of 60 librarians across the University of Calabar and the University of Benin constituted the study sample, using a census enumeration technique. Data were collected through a researcher-designed rating scale (RSMFCLIS) comprising 35 items rated on a four-point Likert scale. Mean scores, standard deviations, Pearson Product-Moment Correlation Coefficient, t-test statistics, and F-test multiple regression were used to answer the research questions and test the hypotheses at the 0.05 level of significance.
Findings: Results revealed a significant positive relationship between training and development and librarians’ job satisfaction (r = .61, R² = 0.37; tcal = 9.68 > tcrit = 1.96, p < 0.05). A significant positive relationship was also found between salary/wages and librarians’ job satisfaction (r = .67, R² = 0.45; tcal = 11.34 > tcrit = 1.96, p < 0.05). Both null hypotheses were rejected. Salary/wages emerged as the stronger correlate, accounting for 45% of the variation in librarians’ job satisfaction.
Implication: The findings imply that the adequacy of training and development opportunities and the timely disbursement of competitive salaries are critical determinants of librarians’ job satisfaction in federal university libraries. Where these motivational factors are inadequate, job dissatisfaction, declining service quality, and low morale are likely consequences, with negative implications for the academic mission of the university.
Originality/Value: This study contributes to the limited body of literature on motivational factors and librarian job satisfaction specific to the South-South geopolitical zone of Nigeria. It provides empirical evidence and practical recommendations for university library management and policy makers on the motivational strategies most likely to improve the job satisfaction and performance of academic librarians.
🏷 Motivational Factors; Job Satisfaction; Training and Development; Salary/Wages
Article 9 · pp. 91-99
Impact of Exchange Rate Fluctuations on India’s Import and Export Sector
This study examines the impact of exchange rate movements on India’s import and export sectors. Using secondary data from RBI (Reserve Bank of India), EXIM (Export Import) Bank, government reports and research journals, this study finds out that Rupee depreciation improve export-oriented sectors like IT, textiles and agricultural sectors by making Indian products and services competitive in global market. However, import-dependent industries like petroleum, electronics, gems and jewellery face increasing costs, inflation, and higher debt burdens. This research highlight the role of factors like inflation, interest rates and geopolitical tensions in exchange rate fluctuations. This study recommends hedging strategies, domestic substitutes, MSME support and Rupee internationalization to reduce dependency of international currency like Dollar.
🏷 Exchange rate, Fluctuation, Inflation, Currency, Depreciation, Appreciation, Export, Import.
Article 10 · pp. 100-114
A Comprehensive Review of Intrusion Detection Systems for Routing Attacks in Mobile Ad Hoc Networks
Mobile Ad Hoc Networks (MANETs) are wireless networks that do not require any fixed infrastructure or central administration. The high vulnerability of MANETs to routing based security attack is mainly due to their dynamic topology, limited bandwidth, and distributed architecture, including black hole, wormhole, gray hole, rushing, and Sybil attacks. IDS (Intrusion Detection Systems) are crucial in detecting attacks and securing communications in MANETs. Machine Learning (ML) and Deep Learning (DL) methods have greatly enhanced the intrusion detection capability in recent years by allowing intelligent attack classification and anomaly detection. This review paper summarizes the existing IDS techniques for MANETs routing attack detection system. It presents the traditional IDS techniques, machine learning enabled IDS, and deep learning intrusion detection framework techniques. Existing research contributions are also tabulated and presented in a detailed comparative analysis based on the techniques used, attacks detected, datasets used, performance measures used, advantages and limitations. Moreover, the study identifies the significant research challenges and future directions for developing efficient, lightweight and intelligent IDS design for MANET security.
🏷 MANET, Intrusion Detection System, Routing Attacks, Machine Learning, Deep Learning, Network Security, Black Hole Attack, Wormhole Attack, IDS.
Article 11 · pp. 115-130
Project ATLAS: An Integrated Learning and Development Management System with Automated CPD/PD Credit Tracking and Competency-Based Talent Mapping Using ISO/IEC 25010:2023 Quality Metrics
Traditional educational training frameworks in public sector governance often rely on disconnected learning platforms, slow manual workflows for tracking professional development (CPD/PD) credits, and fragmented visibility between training initiatives and workforce skill profiles. To address these bottlenecks, this study designed, developed, and validated Project ATLAS (Advanced Training and Learning Administration System), an integrated Learning and Development Management System featuring automated CPD/PD credit tracking, role-stratified administrative dashboards, and competency-based talent mapping engines. Built on a custom Model-View-Controller (MVC) architecture in PHP, a lightweight server-side hypermedia stack, MySQL relational database, and secured through Cloudflare Zero Trust outbound tunnels, the system replaces error-prone manual processes with reliable automation. The platform's architectural quality and functional suitability were evaluated using a descriptive engineering design aligned with the ISO/IEC 25010:2023 software product quality model. A purposive sample of 15 technical experts and administrative evaluators assessed the system, yielding an overall quality score of 3.91 out of 4.00 (SD = 0.11). Security (M = 3.96, SD = 0.05) and Safety (M = 3.94, SD = 0.08) dimensions received the highest ratings. These findings demonstrate that custom MVC backend layers combined with optimized relational database constraints deliver enterprise-grade security, minimized network latencies, and high administrative efficiency in resource-constrained public educational settings. Project ATLAS offers a scalable, compliant, data-driven architecture that mitigates administrative backlogs, preserves institutional memory, and aligns workforce capabilities with institutional needs.
🏷 Design Science Research, ISO/IEC 25010:2023, Competency Mapping, Learning Management System, Professional Development Tracking.
Article 12 · pp. 131-140
The Price–Experience Trade-off in Organized Retail: A Comparative Study of Consumer Satisfaction, Store Choice, and Brand Advocacy in D-Mart and Competing Retail Chains
Consumer preferences, satisfaction, and store choice factors of organized retail in Bhopal were the subjects of this study. Using a descriptive research design, primary survey data was collected and analyzed at various levels to compare the prominent discount retailer, D-Mart, with alternative retailers. The results indicate that D-Mart's strong preference in the market is due to both aggressive pricing and product mix but does not correlate with a commitment to brand loyalty (high overall recommendation scores). There were operational bottlenecks that created friction between customers (crowd control/billing). Therefore, the cost-experience trade-off suggests that even though value-based pricing would attract customers and ultimately drive sales, for continued customer loyalty businesses will need to find a combination of affordability with the operational excellence of providing great experiences. This study suggests that the increasing importance of experience-driven variables affects how customers view and recommend products and services to others. This research extends knowledge on organized retail by examining Tier-2 urban markets rather than larger metropolitan markets. Retailers should be aware of the implications for their ability to provide both a low-cost and exceptional service or customer-centric operation.
🏷 Organized Retail, Consumer Satisfaction, Store Choice, D-Mart, Brand Advocacy, Customer Loyalty, Price–Experience Trade-off, Consumer Behaviour
Article 13 · pp. 141-144
The Missing Spark: Examining Motivational Gaps in Education Through Maslow's Hierarchy and Herzberg's Two-Factor Theory
Academic learning requires constant mental work, but many observers say that undergraduate students show less energy and more exhaustion as their studies continue. Experts claim that current research about motivation stays mostly inside the corporate world, which means a large space exists in the knowledge regarding higher education, specifically for undergraduate students in the Indian academic context. The research examines the motivation of undergraduate students by using Maslow's Hierarchy of Needs and Herzberg's Two-Factor Theory at the same time. Motivation is evaluated so that the problems of the undergraduate students are understood.
Information was gathered using a quantitative research design. Many researchers believe that primary data were gathered from 43 undergraduate students, including 22 female, 17 male, and 4 who did not say their gender. A structured questionnaire was shared through Google Forms, while responses were marked on a five-point Likert scale using 18 items. Because the data was collected digitally, the undergraduate students could answer from different places.
Results show that esteem needs (3.38/5) and physiological needs (3.36/5) have better satisfaction. Many people believe that undergraduate students feel sure about their mental skills and find the school buildings good enough. A middle score (3.29/5) was reached for social needs, even though self-actualization (3.06/5) and safety needs (2.90/5) reached the lowest levels of satisfaction. These low scores show that gaps exist in academic security and the way rules are made. When the gender-based analysis was finished, the undergraduate students who did not share their gender showed much lower motivation, especially regarding safety needs (1.69/5).
This research suggests that the leadership of the educational institution must change its focus toward making academic rules clear. Many scholars believe that fair testing methods and project -based learning should be created to help undergraduate students. Better mentorship is needed so that undergraduate students find chances for personal growth. Recognition systems are organized to help the undergraduate students reach self-actualization. When these changes are made, the motivation of undergraduate students will improve because the environment becomes more supportive.
🏷 Student Motivation, Maslow's Hierarchy of Needs, Herzberg's Two-Factor Theory, Education, Academic Burnout
Article 14 · pp. 145-155
Firm Size and Environmental Disclosure Among Firmlisted on the Nairobi Securities Exchange
This study examined the relationship between firm size and environmental disclosure among firms listed on the Nairobi Securities Exchange (NSE). Environmental disclosure has become an important aspect of corporate accountability due to increasing global concerns regarding environmental sustainability. However, environmental reporting among Kenyan firms remains largely voluntary because of limited regulatory frameworks and absence of mandatory disclosure standards. The study adopted an explanatory research design and utilized secondary data collected from annual reports of firms listed on the NSE for the period 2018–2022. Content analysis was employed to extract environmental disclosure information, while descriptive and inferential statistics were used to analyze the data. Environmental disclosure was measured using an environmental disclosure index developed from a disclosure checklist. Firm size was measured using the logarithm of total annual sales turnover. Regression analysis findings revealed that firm size had a positive and significant relationship with environmental disclosure (β = 0.147, p = 0.010). Pearson correlation results also confirmed a significant association between firm size and environmental disclosure (r = 0.217, p = 0.001). The study concludes that larger firms are more likely to disclose environmental information than smaller firms due to increased public visibility and stakeholder pressure. The study recommends the establishment of mandatory environmental disclosure guidelines to improve transparency and sustainability reporting among listed firms in Kenya.
🏷 Firm Size., Environmental Disclosure., Sustainability Reporting, Stakeholder Theory, Nairobi Securities Exchange, Kenya.
Article 15 · pp. 156-173
CP-IMOS: A Cross-Platform Imbalance-Aware Methodology for Sentiment Classification of MOOC Reviews in IT Education
MOOC review datasets offer a practical way to study course strengths, learner frustrations, and platform-level issues. However, sentiment models built from these data can give a distorted picture when positive ratings dominate the corpus. This paper presents CP-IMOS, a Cross-Platform Imbalance-Aware MOOC Sentiment Classification methodology, using public review data from Coursera, Udemy, and Stepik. The Coursera file contained 1,454,711 review rows; after cleaning and exact duplicate removal, 498,401 review records remained. Keyword filtering of course identifiers and titles then yielded 258,828 IT-related Coursera reviews from 215 course identifiers. For Udemy, a 200,000-comment Kaggle sample was processed with Course_info.csv to support second-platform validation. After cleaning, 46,377 comments from Development and IT & Software courses were retained from 15,836 course identifiers. The Stepik Russian-language corpus was included to test multilingual ingestion, duplicate removal, and Cyrillic-character validation during retrieval, but it was excluded from supervised sentiment training because it did not provide rating or sentiment labels. CP-IMOS maps rating-derived labels into five classes that correspond to ratings 1 through 5 and adds the Platform Sentiment Imbalance Index (PSII) to measure majority-class dominance before model interpretation. The classifier stage uses TF-IDF features with transparent machine-learning components. In the Coursera analysis sample, SGD Logistic Regression obtained the highest macro-F1 (0.383), while Multinomial Naive Bayes produced higher accuracy (0.759) but a weaker macro-F1 (0.250). In the Udemy validation set, SGD Logistic Regression also produced the highest macro-F1 (0.430). The findings show that cross-platform MOOC review data are strongly skewed toward positive ratings, although Udemy contains more non-positive ratings than Coursera. The main contribution is a reproducible retrieval-to-classification workflow that uses PSII, macro-F1, per-class metrics, and platform-specific interpretation before sentiment results are used in IT education analytics.
🏷 CP-IMOS; MOOC reviews; sentiment classification; IT education; class imbalance
Article 16 · pp. 174-185
Link-Spam-Resistant Domain Ranking: A Comparative Evaluation of PageRank, HITS, and TrustRank Under Simulated Link-Farm Attacks Using Common Crawl Web Graphs
Link-based ranking is still an important part of web information retrieval, but it remains vulnerable to coordinated link-farm attacks. This study examined the resistance of PageRank, HITS, and TrustRank to link spam using simulated attacks on domain-level web graphs extracted from Common Crawl. A quantitative, comparative, and controlled experimental design was used. Five independent base graphs were prepared, with each graph containing 100,000 legitimate domain nodes. Four link-farm structures were tested: star-centered, bounded-density clique, bipartite, and mixed. The attacks were applied at four intensity levels: 1%, 3%, 5%, and 10% of the legitimate graph size. Across all conditions, the experiment produced 480 attacked graph instances and 1,440 verified algorithm-level runs. The algorithms were compared using spam target promotion, spam score share, top-1000 spam infiltration, legitimate-domain displacement, ranking stability, execution time, peak memory use, and iteration count. The results showed that PageRank was the most vulnerable algorithm. It had the highest spam target promotion, highest spam score share, measurable top-1000 spam infiltration, greater legitimate-domain displacement, and lowest ranking stability. HITS showed the strongest overall resistance and computational efficiency. It recorded near-zero spam score share, zero top-1000 infiltration, zero legitimate-domain displacement, highest stability, fastest runtime, and fewest iterations. TrustRank also blocked top-1000 infiltration and legitimate-domain displacement, although it allowed higher target promotion than HITS. The bounded-density clique structure caused the most damage to PageRank, while higher attack intensity increased spam impact in most cases. The findings show that link-spam resistance depends on the ranking algorithm, attack structure, attack intensity, and evaluation metric used. A single ranking score or attack model is not enough to evaluate resistance to link-farm manipulation.
🏷 Web information retrieval, link spam, PageRank, HITS, TrustRank
Article 17 · pp. 186-191
Effective Communication in an Electronic Environment Among Students of Ekiti State University, Ado-Ekiti
This study examined effective communication in electronic environments among students of Ekiti State University, Ado-Ekiti, Nigeria. Anchored on Media Richness Theory and the Technology Acceptance Model, the study sought to assess students’ use of electronic platforms, the challenges encountered, and the factors influencing communication effectiveness. A descriptive survey design was adopted; a sample of 384 undergraduate students were selected through multistage sampling procedure. Data were collected using a validated questionnaire titled, “Electronic Communication Effectiveness Questionnaire” ECEQ, with a reliability coefficient of r = 0.86. Descriptive statistics, t-test, and multiple regression analysis were used to analyze data at 0.05 level of significance. Findings revealed that WhatsApp, Telegram, and Google Classroom were the most frequently used platforms for academic interaction, while email and institutional Learning Management Systems were underutilized. Students perceived electronic communication as effective for information sharing and group collaboration, but ineffective for complex academic discussions due to poor network access, digital distractions, and low digital literacy skills. Regression results showed that digital literacy β = .32, p < .001, stable internet access β = .28, p < .001, and lecturers’ responsiveness β = .21, p < .01 significantly predicted effective communication in electronic environments, accounting for 47% of the variance. Significant differences existed in perceived effectiveness based on level of study. The study concluded that while electronic communication enhances flexibility and access to learning resources among EKSU students, its effectiveness is constrained by infrastructural and skill-related challenges. It recommended that the university management should invest in reliable internet infrastructure, integrate digital literacy training into the General Studies curriculum, and train lecturers on effective online facilitation strategies to optimize electronic communication for academic purposes.
🏷 Electronic Communication, Digital Literacy, Netiquette, Online Learning, Media Richness
Article 18 · pp. 192-201
Hirelink: A Web Based Campus Recruitment Platform
Traditional campus recruitment processes in educational institutions often involve manual coordination, spreadsheet management, and repetitive communication between students, recruiters, and placement officers. These practices lead to inefficiencies, delays, data inconsistency, and lack of transparency in recruitment activities. This paper presents HireLink, an intelligent AI-assisted web-based campus recruitment and placement management platform designed to automate and optimize the end-to-end recruitment workflow. The proposed system integrates student profile management, AI-based resume screening using Applicant Tracking System (ATS) scoring, recruiter job management, round-wise selection tracking, analytics dashboards, and alumni networking features within a centralized platform. The system is developed using React.js for the frontend, Django for backend processing, and MySQL for secure data management. Additionally, the platform incorporates automated eligibility filtering and resume ranking techniques to improve recruitment efficiency and decision-making. Experimental evaluation demonstrates significant reduction in administrative workload, faster candidate shortlisting, and improved operational transparency compared to traditional placement methods. The proposed platform provides a scalable, secure, and efficient solution for modern campus recruitment ecosystems.
🏷 Campus Recruitment, Web Application, Placement Automation, React.js, Django, Applicant Tracking System (ATS),
Article 19 · pp. 202-224
“Development of an Integrated Information Security Governance Maturity Assessment Framework for Higher Education Institutions Using COBIT 2019 and ISO/IEC 27001: A Design Science Research Approach”
The adoption of digital technologies, online learning platforms, and electronic services has significantly changed the functioning of higher education institutions. Universities and colleges now rely extensively on digital platforms for teaching, learning, research, and administrative functions. As a result, these institutions manage large quantities of sensitive data, including student records, financial information, research findings, and other vital assets.
In parallel, the rise in cybersecurity threats has underscored the necessity for enhanced information security governance to protect these resources and ensure secure and efficient technology use across campuses. Although established frameworks such as COBIT 2019 and ISO/IEC 27001 offer valuable guidance on governance and information security management, they are often implemented independently. This separation can cause gaps, overlaps, and inconsistencies in governance practices. To address this issue, this study developed an Integrated Information Security Governance Maturity Assessment Framework specifically for Higher Education Institutions by merging the governance principles of COBIT 2019 with the information security management demands of ISO/IEC 27001.
The research employed a Design Science Research (DSR) methodology to develop a framework that is robust in theory and applicable to the higher education sector. This framework was built through a comprehensive analysis of COBIT 2019, ISO/IEC 27001:2022, relevant academic sources, and existing governance maturity models. The resulting framework encompasses five assessment dimensions: Governance and Leadership, Risk and Compliance Management, Security Operations and Control Management, Monitoring and Performance Evaluation, and Continuous Improvement and Governance Optimization.
Additionally, a five-level maturity model was crafted to enable institutions to evaluate their current governance capability and identify areas needing further development. Findings indicate that integrating COBIT 2019 and ISO/IEC 27001 results in a more systematic and thorough approach to information security governance. The framework provides higher education institutions with a practical tool for assessing governance maturity, planning improvement efforts, and enhancing cybersecurity governance. It also lays the groundwork for further implementation, testing, and empirical validation in real university settings.
🏷 Information Security Governance, Cybersecurity Governance, COBIT 2019, ISO/IEC 27001, Governance Maturity, Maturity Assessment Framework, Higher Education Institutions
Article 20 · pp. 225-247
Effects of Moisture on Some Engineering Properties of Dried Date Fruits (Phoenix Dactylifera L.) Relevant To Its Handling, Processing and Storage
Date fruits arrive in the high-humidity southern Nigerian market in a dried state and are highly hygroscopic. Some engineering properties (EP) relevant to the design of handling, processing, and storage equipment for this hygroscopic dried fruit were studied as a function of moisture content. The EP determined were: length, width, and thickness (L, W, T); geometric mean diameter (Dg); arithmetic mean diameter (Da); square mean diameter (Ds); equivalent mean diameter (De); surface area (S); aspect ratio (AR); specific surface area (Ss); sphericity (φ); mass (M); volume (V); density (D); porosity (ε); angle of repose (ζ); and coefficient of static friction (CoF) on three structural surfaces — glass (CoFg), plywood (CoFp), and galvanised steel (CoFgs) — at five moisture content levels (MCL) ranging from 6.08 to 16.23% (wet basis). Each EP was evaluated on 500 replicates, comprising 100 replicates per MCL. All measurements followed standard procedures reported in the literature. Mean values (±SD) were: L = 35.47 ± 3.40 mm; W = 17.53 ± 1.65 mm; T = 15.65 ± 1.53 mm; Dg = 21.32 ± 1.69 mm; Da = 22.88 ± 1.85 mm; Ds = 21.98 ± 1.75 mm; De = 22.06 ± 1.76 mm; S = 20257.69 ± 4864.71 mm²; AR = 0.496 ± 0.044; Ss = 6.03 ± 1.60 mm²/ml; M = 6.03 ± 1.60 g; V = 6.43 ± 1.60 cm³; D = 0.94 ± 0.06 g cm⁻³; ε = 40.42 ± 3.93%; ζ = 35.35 ± 2.82°; CoFg = 0.357 ± 0.047; CoFp = 0.338 ± 0.070; and CoFgs = 0.268 ± 0.034. All EP increased with increasing moisture content, with the exception of ζ, which showed an inverse relationship until the penultimate MCL before rising at 16.23%. Analysis of variance confirmed that the effect of moisture content was statistically significant (p ≤ 0.05) for thickness, density, porosity, and CoF on all three surfaces. CoFp exhibited the highest values at all MCL studied. The results of this study are intended to support food engineers and processing equipment designers working with dried date fruits.
🏷 Engineering properties; dried dates, Phoenix dactylifera L.; angle of repose; aspect ratio; coefficient of friction.
Article 21 · pp. 248-269
Eventease: Design and Implementation of a GUI-Based Python Event Registration and Ticketing System with Sqlite Integration
Efficient management of event registration and ticketing is crucial for the success of academic, corporate, and social events. Traditional manual methods, including paper-based logs and unintegrated spreadsheets, are prone to errors, duplicate entries, and operational delays, especially during high-volume registration periods. This study presents EventEase, a modern, GUI-based Event Registration and Ticketing System engineered using Python and integrated with a locally isolated SQLite relational database engine.
The application utilizes advanced visual toolkits like customtkinter to deliver a professional user experience featuring real-time input normalization and an interactive data visualization backend powered by matplotlib. Architecturally, the system separates roles into administrative control channels and customer booking gateways. Administrators have access to full event creation mechanisms supporting multi-tiered ticket rates (Regular, VIP, Student), granular category and venue configuration utilities, real-time attendee gate verification with explicit timestamp logging, and automated transaction backup systems running on Write-Ahead Logging (WAL) parameters. Simultaneously, the front-end customer client handles seamless profile creation, dynamic seat availability checks, cash-tender calculation wizards, and instant receipt generation.
Functional testing confirmed that all database joins, user-role authentications, and reporting modules operate with zero record degradation. EventEase demonstrates how desktop applications can be built to optimize operational capacity, remove data redundancy, and secure transactional integrity within institutional environments.
🏷 EventEase, Event Registration, Ticketing System, Python, GUI, SQLite, Database Management, Automation
Article 22 · pp. 270-288
Smartcampus Hub: A Python-Based Student Enrollment and Event Ticketing Management System Using Object-Oriented Programming
The SmartCampus Hub: A Python-Based Student Enrollment and Event Ticketing Management System Using Object-Oriented Programming is a desktop application developed using Python, Tkinter for the graphical user interface, and a database management system (such as SQLite or MySQL) for data storage and retrieval. The system is designed to automate student enrollment processes and campus event ticketing operations, reducing manual paperwork and improving administrative efficiency.
The application provides modules for student registration, course enrollment, event creation, ticket issuance, attendee tracking, and report generation. By applying Object-Oriented Programming (OOP) principles, the system achieves better code organization, maintainability, scalability, and reusability. Database integration enables persistent storage of student records, enrollment transactions, event details, and ticket information, while Tkinter provides an interactive and user-friendly interface for administrators and students.
The system was developed following standard software development practices, including requirements analysis, design, implementation, testing, and evaluation. Testing results indicate that the application can efficiently manage enrollment and event-related transactions while maintaining data accuracy and consistency. The study demonstrates that combining Python, Tkinter, and a relational database can produce a practical, cost-effective, and scalable campus management solution suitable for educational institutions.
🏷 Python Programming, Tkinter, Database Management System, SQLite/MySQL, Object-Oriented Programming (OOP)
Article 23 · pp. 289-296
"Microfinance and Rural Market Dynamics: An Empirical Case Study of SHG-Led Commercial Activities in Holenarasipura Taluk"
Micro Finance is recognized as a financial service which provides small loans to the needy by exploring different savings from poor and low-income group people who does not have any kind of access to regular banking services. This type of financial service provides loans to those individuals who would like to start small and micro businesses. It intends to improve their income levels by enhancing their standard of living. The prime objective of this kind of finance is to support small entrepreneurs by providing financial assistance without bargaining for a mortgage. It tries to promote rural entrepreneurship and intends to develop socio-economic conditions of the lowest strata of the society.
The current study intends to examine the influence of Micro-Finance on the development of commercial activities undertaken by the Self Help Group Members in Holenarasipura Taluk of Hassan District. It also tries to understand the market dynamics involved in this process. It is based on a questionnaire survey of selected SHG members involved in various commercial activities such as, tailoring, petty shops, dairy farming, small handicrafts, fisheries, agricultural oriented businesses located in Holenarasipura Taluk. A total of 100 questionnaires were served and evaluated using various statistical tools such as, percentage analysis, mean score analysis, and factor analysis, chi-square test, correlation and regression analysis.
The findings of this study reveal that any kind of access to micro-finance has contributed significantly in financial inclusion of rural masses. It has resulted in enhancing rural entrepreneurship, increased rural house hold income and expanded market opportunities for rural farming. It is also found in the process of analysis that there exists a positive relationship between availability of credit and growth of business. Self Help Groups have facilitated in creating saving habits, rural employment generation, empowering rural women and there by contributing towards socio-economic growth of rural areas. This has contributed significantly in sustainable rural development. It is suggested that strengthening financial literacy programs, increasing credit support through SHGs and linking rural markets to the main stream markets will maximize the participation of rural entrepreneurs.
🏷 Micro-Finance, Self Help Group, Financial Engineering, Rural Markets, Commercial Banks, Financial Literacy
Article 24 · pp. 297-316
Design and Evaluation of a Web-Based Personal Financial Management System for Young Professionals
This study design and evaluation of a web-based personal financial management system for young professionals. The system was developed to support expense tracking, budgeting, bill monitoring, savings goal management, and visual reporting through an accessible web platform. A developmental and descriptive research design was used. The prototype was built using an Agile development approach and evaluated using a software quality instrument based on functionality, usability, reliability, performance, and supportability. Twenty-seven young professionals evaluated the user-facing system, while three information technology professionals assessed technical quality. Weighted means and qualitative comments were used to interpret the results. Findings showed that the system was generally rated positively by young professional respondents, with an overall weighted mean of 3.61, interpreted as Strongly Agree. Usability received the highest mean score of 3.69, followed by reliability at 3.63, performance at 3.61, functionality at 3.60, and supportability at 3.50. IT professionals also gave favorable ratings, particularly in functionality, where the system received a mean of 4.00. The results suggest that analytics-supported features, including dashboards, category-based spending summaries, and budget alerts, can help young professionals understand their financial behavior and improve personal financial monitoring. However, the evaluation also identified areas for improvement, including clearer user guides, faster loading of reports, more consistent reminders, and possible integration with banks or e-wallets. The study contributes to applied financial technology research by demonstrating a practical prototype that combines personal finance management functions with basic data analytics for young professionals.
🏷 Personal financial management system; young professionals; usability evaluation; financial technology
Article 25 · pp. 317-335
Shopease: An Integrated Python-Based Shopping Cart System
In recent years, technology has transformed business operations, with e-commerce platforms enabling customers to browse products, place orders, and complete transactions digitally. Traditional shopping methods, which rely on physical presence, manual billing, and limited operating hours, are often inefficient and prone to human error. This study presents Shopease, an integrated Python-based shopping cart system that combines user registration, product management, shopping cart functionality, checkout processing, and transaction tracking through a MySQL database. The system employs Object-Oriented Programming (OOP) principles and a GUI built with Tkinter and CustomTkinter to provide a user-friendly and interactive interface. Functional testing demonstrated that all modules, including login/registration, product catalog, shopping cart operations, checkout, and admin panel management, performed as expected. Shopease improves efficiency, accuracy, and accessibility compared to manual or console-based systems, offering a reliable platform for managing products, customers, and transactions in an educational or small-scale business environment. Limitations include the absence of online payment integration, delivery tracking, email notifications, and advanced security protocols, which provide opportunities for future enhancements.
🏷 Shopease, Python, MySQL, Shopping Cart System, GUI, Object-Oriented Programming, E-commerce
Article 26 · pp. 336-348
From Traditional HRM to AI-Augmented HRM: A Conceptual Perspective
Objective: This paper aims to develop a comprehensive conceptual framework for integrating artificial intelligence (AI) into human resource management (HRM). It seeks to capture both the opportunities and challenges of AI adoption, emphasizing performance management, employee engagement, workforce planning, and the evolving role of HR professionals. Ethical, organizational, and societal considerations, such as transparency, privacy, and algorithmic bias, are central to this framework.
Methodology: The study employs a conceptual approach based on an extensive review of interdisciplinary literature, including empirical research, theoretical models, and recent advances in AI technologies (e.g., predictive analytics, federated learning, generative AI). Key theoretical lenses—Resource-Based View, Sociotechnical Systems Theory, Algorithmic Management, Responsible AI, and Human-AI Collaboration—are integrated to analyze AI-HRM interactions and implications.
Results / Contributions: The chapter proposes a multidimensional framework highlighting four interconnected dimensions: (1) technological capabilities, (2) human and organizational factors, (3) ethical and regulatory environment, and (4) strategic alignment for value creation. This framework guides HR scholars and practitioners in responsibly adopting AI to enhance decision-making, efficiency, and employee experiences while mitigating risks such as bias, dehumanization, and resistance. It provides actionable insights for ethical governance, workforce transformation, and human-AI collaboration, and identifies future research avenues in contextual, industry-specific, and longitudinal studies of AI in HRM.
🏷 Human Resource Management (HRM), Artificial Intelligence (AI), conceptual framework
Article 27 · pp. 338-3347
Total Quality Management as a Strategic Operational Framework for Quality Assurance and Sustenance in Indian Higher Education Institutions: A Conceptual Review
For three decades, one recurring event has shaped how quality assurance works in Indian higher education: the periodic external assessment visit, run either by the National Assessment and Accreditation Council (NAAC) or by the National Board of Accreditation (NBA). That structure has done real work — it has made quality a formal institutional obligation, one that colleges and universities cannot simply ignore. What it has not reliably done is turn quality into a continuous operational habit. This paper argues that Total Quality Management (TQM), reframed as a strategic operational framework rather than a periodic compliance exercise, is what allows an institution to bridge that divide. Curriculum design, faculty performance, research output, student support and institutional governance become, on this reading, everyday sites of quality practice rather than evidence assembled hastily before an assessment cycle. The paper draws on TQM's industrial roots in Deming, Juran and Crosby, and on the education-specific scholarship built by Sallis, by Owlia and Aspinwall, and by Sahney, Banwet and Karunes, to synthesise six interlocking components: a transformation-process view of the institution, SERVQUAL-QFD gap analysis, accreditation-aligned criteria mapping, a leadership and process-management stream, the Owlia-Aspinwall quality dimensions, and excellence-model-based approaches. These six pieces are woven together as a single framework and read against India's shifting regulatory landscape, including the proposed Viksit Bharat Shiksha Adhishthan (VBSA) Bill, 2025, on the premise that a TQM system built around institutional operation, rather than around any single named evaluator, stays relevant no matter who ends up doing the evaluating. The paper wraps up by working through what this means for institutional leadership and policy, being upfront about the limits of a purely conceptual treatment, and flagging the empirical groundwork a subsequent primary study still needs to cover.
🏷 Total Quality Management; quality assurance; NAAC; Indian higher education; accreditation; institutional governance; quality sustenance
Article 28 · pp. 349-362
Reverse Logistics, Regulatory Environment and Supply Chain Efficiency in Kenyas Remanufacturing Sector: A Meta Analysis
The paper undertakes a Meta-analysis on research done on the topic of reverse logistics and the effect it bears on the Kenyan remanufacturing sector, while considering the moderating effect of the regulatory environment as a result of limited adoption of the practice and studies undertaken on the area of interest. The study is pegged on the resource-based view that puts emphasis on the capabilities of the firms in reverse logistics and transport cost economics, which explains the challenges in governance of return networks. The paper reviews literature systematically through a meta-analysis methodology following the PRISMA guidelines, where articles are searched in prestigious academic databases. The research obtained 80 articles from SAGE, Science Direct, and Wiley between periods of 2020 and 2026. Duplicates and publications that were not based on reverse logistics, not empirically undertaken, and publications from predatory journals were eliminated, leaving 30 that were further subjected to the inclusion criteria, leaving 14 peer-reviewed articles whose empirical and theoretical findings were used to evaluate the effect of reverse logistics on the efficiency of Kenyans' remanufacturing supply chain. The study adopted the Hunter-Schmidt method to synthesize correlation coefficients from multiple studies. The findings of the study support the theoretical framework that links reverse logistics and supply chain efficiency in the remanufacturing sector on average (r=0.42) globally, in developed countries (r) =0.60, In Kenya, the r coefficient is below 0.42, showing a weak correlation, as infrastructure and the informal sector dominance, limits the adoption of reverse logistics from reaching its preferred potential. The moderation analysis determined that the regulatory environment, in terms of policy regulations and laws (average r of 0.51), moderates the relationship between reverse logistics and supply chain efficiency. The studies imply the existence of a need for the adoption of strategic regulations that target policy coherence, the incorporation of technology, and alignment of the stakeholders to enhance significant efficiency gains. The findings enrich and confirm the theoretical discourse on the manner in which the business environment interacts with the constructs of reverse logistics to drive efficiency in remanufacturing sector supply chains.
🏷 efficiency, reverse logistics, circular economy, remanufacturing, Regulatory environment
Article 29 · pp. 363-388
KAINAN: Design and Implementation of a Gui-Based Python Restaurant Ordering and Billing System with SQLite Integration
Efficient management of restaurant orders and billing is critical for providing quality service, minimizing errors, and improving operational workflow. Traditional manual billing systems are prone to inaccuracies, delays, and difficulty in tracking orders, particularly during peak hours. This study presents KAINAN, a GUI-based restaurant ordering and billing system developed using Python and integrated with an SQLite database. The system allows restaurant staff to create, view, update, and delete orders while automatically calculating bills and managing inventory. Object-Oriented Programming (OOP) principles ensure modularity, code reusability, and maintainability, while the graphical user interface (GUI) provides an intuitive and user-friendly environment. Functional testing confirmed that all modules, including order management, billing computation, and record updates, perform as expected. KAINAN improves accuracy, reduces human error, and streamlines restaurant operations, demonstrating the practical application of Python programming, GUI development, and database management in small to medium-scale food service establishments. Limitations include the absence of online payment integration and multi-user network functionality, which can be incorporated in future versions.
🏷 KAINAN, Restaurant Ordering System, Billing System, Python, GUI, SQLite, Object-Oriented Programming, Inventory Management
Article 30 · pp. 389-401
Assessment of Households’ Access to Domestic Water Supply in Keffi Urban Area, Nasarawa State
This study assessed household water accessibility in Keffi, Nigeria, with emphasis on sources, service conditions, consumption, and socioeconomic determinants. A cross-sectional survey of 382 households was conducted using structured questionnaires, and data were analysed using descriptive and inferential statistics. Results showed that boreholes were the dominant water source (42.9%), followed by private piped connections (18.3%) and vendor/sachet water (16.2%), while government supply accounted for only 14.1%. Although 57.1% of households accessed water within 500 m and 59.6% spent ≤10 minutes reaching sources, only 20.9% had daily supply, indicating poor service reliability. Average water consumption was approximately 85 L/person/day, below the recommended 120 L standard, with 56.6% of households consuming less than 100 L/day. Water affordability was a major concern, as households spent about 10% of income on water, exceeding the 5% benchmark. Access to improved water sources increased significantly with income, from 47.0% among low-income households to 86.5% among high-income groups. Statistical analysis confirmed a significant relationship between income and water accessibility (χ² = 102.38, p < 0.001), with income emerging as the strongest predictor (β = 0.383, p < 0.001; R² = 0.233). Perception analysis revealed general dissatisfaction with water reliability, affordability, and government performance (overall mean = 2.95), alongside strong demand for improved planning (mean = 4.74). The study concludes that water accessibility in Keffi is constrained primarily by service inefficiencies and socioeconomic inequality rather than physical distance, highlighting the need for improved infrastructure and equitable water policies.
🏷 Water accessibility; Domestic water supply; Improved water sources; Water affordability; Service reliability; Nigeria.
Article 31 · pp. 402-412
Development of an Online School Document Processing for Registrar and Cashier in Pangasinan State University Alaminos City Campus
The main offices that require improvement are the Registrar and Cashier offices of Pangasinan State University – Alaminos City Campus. These offices are responsible for the requests for documents transactions among the students. The manual and/or semi-manual procedures and usage of these offices are inefficient and result in delays and inconsistencies. Thus, this study aims to create an effective and efficient way of addressing this problem by creating an "Online School Document Processing System." The system was created by utilizing a Web-based platform where students can access the documents they need and perform payment verification. The system can contribute greatly to the efficient management of documents by the university. Furthermore, based on the testing and assessment conducted, the system proves that it is an effective and efficient way of creating and utilizing documents. The system can help the offices of the registrant and cash register with their tasks and may result in more student satisfaction. Conclusion Thus, this study proves that the creation and usage of the system are indeed effective. The system may help the offices of the registrant and cash register with their tasks and may result in more student satisfaction. The system can also contribute.
🏷 Online Document Processing, Web-Based Application, School Management System
Article 32 · pp. 413-423
Sustainable Finance Beyond Green Bonds: Emerging Sustainable Finance Instruments for India's Green
Traditionally, expansion in emerging economies such as India has come at the cost of environmental damage and climatic change. In recent years, concerns with sustainability and climate threats have intensified, highlighting the need for alternative development strategies. In this respect, green finance has been recognized as a pivotal mechanism for fostering sustainable economic growth by directing financial flows towards environmentally responsible and climate-friendly enterprises. This article analyses the role, difficulties and prospects of green finance in Indian economy.
The secondary data utilised in this qualitative study is derived from diverse sources, including official reports, policy documents, and the latest research on sustainable and eco-friendly finance. It highlights the relevance of green finance in India for development of renewable energy, sustainable infrastructure, climate smart agriculture, rural development and expansion of environmentally sustainable financial instruments, including green bonds and investments based on Environmental, Social, and Governance (ESG) criteria.
Government policies, regulatory frameworks, and institutional support have further strengthened the green finance ecosystem in the country. Besides this, the study identifies several issues like lack of awareness, low financial access, regulatory vacuum, absence of standardized credit ratings and high transaction costs that are hampering the growth of green financing in India.
However, despite these limitations, green financing presents a significant opportunity to help facilitate India’s transition to a carbon-efficient and sustainable development path. to harness the promise of green finance, India must expand policy support, improve institutional coordination and enhance private sector participation.
🏷 sustainable finance, renewable energy, green bonds, Indian economy, climate change
Article 33 · pp. 424-445
Efficient Record-Keeping: A GUI-Based Python Library Management System
Manual library record-keeping often leads to misplaced records, slow book searching, inaccurate borrower tracking, and difficulty in monitoring available and borrowed books. To address these problems, this study developed Efficient Record-Keeping: A GUI-Based Python Library Management System, a desktop-based application designed to help manage library book records and borrowing transactions in a faster, more organized, and more accurate way. The system was developed using Python as the main programming language, a graphical user interface for user interaction, and SQLite as the database management system for permanent record storage. The system includes essential library management features such as adding book records, viewing available books, searching book information, editing book details, deleting records, borrowing books, returning books, and generating reports. Through database integration, book and borrower records remain saved even after the program is closed. The system also reduces manual work by allowing users to retrieve records quickly and update book status efficiently. Overall, the developed Library Management System provides a practical and user-friendly solution for improving library record-keeping, book monitoring, and borrowing management.
🏷 Library Management System, Python, SQLite, Graphical User Interface, Book Records, Borrowing Management, Record-Keeping
Article 34 · pp. 446-454
The Effect of Sustainable Dairy Cattle Value Chain Management Practices on Farmers’ Income in Kikatsi Subcounty, Kiruhura District
The study focused on evaluating the effect of sustainable dairy cattle value chain management practices on farmers' income in Kikatsi Subcounty, Kiruhura District. It was guided by one objective to identify the factors influencing the adoption of sustainable dairy cattle value chain management practices in Kikatsi Subcounty. The study employed a cross-sectional research design. Primary data were collected from 238 respondents using a questionnaire and an interview guide. The study employed simple random sampling and purposive sampling to select the correct categories of respondents. The data was analysed using SPSS version 23.0. Results revealed that the adoption of sustainable dairy cattle value chain management practices in Kikatsi Subcounty is mainly driven by technological and input access, education level, access to credit facilities, institutional and extension support, farmers’ income, and market accessibility, all of which showed positive and significant effects. This indicates that when farmers have access to improved technologies, financial resources, education, and reliable markets, they are more likely to embrace sustainable dairy practices. The results explain that improvements in milk yield and quality, market access and prices, cost reduction through efficient resource use, and improved breeding and animal health significantly raise farmers’ earnings. The study recommends that; agricultural extension officers and cooperatives should intensify farmer training on sustainable feeding, breeding, and milk handling practices to sustain and expand gains in yield and quality. Policymakers should integrate sustainable dairy value chain practices into national agricultural programs, emphasizing incentives for farmers who adopt environmentally friendly and resource-efficient systems.
🏷 Sustainable Dairy Cattle, Value Chain Management Practices, Farmer's Income, Kikatsi Sub county, Kiruhura District.
Article 35 · pp. 455-461
A Review Paper on Dual-Mode Emotion Recognition Systems Using Facial Analysis and Interactive Questioning
Emotion recognition has become a significant research area in computer vision and affective computing due to its growing applications in human computer interaction. Early approaches mainly relied on facial expression analysis; however, recent studies emphasize multimodal and contextual information for improved robustness. The review paper analyses recent advancements in emotion recognition systems with a focus on dual mode emotion approaches combining facial and speech-based emotion recognition. The study reviews deep learning techniques, system architectures, and commonly used datasets, particularly RAVDESS, for speech emotion recognition. It discusses an implemented facial expression recognition module to connect theory with practical use. The paper highlights existing research gaps concerning real-time performance, robustness, and computational efficiency. Finally, it outlines future research directions, focusing on efficient multimodal fusion, improved accuracy, and systems that can recognize emotion in real time.
🏷 Emotion recognition, facial expression recognition, speech emotion recognition, deep learning, multimodal emotion analysis, human-computer interaction
Article 36 · pp. 462-467
MindCare - AI Based Mental Wellness Voice Based Companion
Mental health disorders such as stress, anxiety, and depression are increasing rapidly across all age groups. Many individuals do not receive timely support due to limited access to professional mental healthcare services. Artificial intelligence–based voice assistants provide a new opportunity to offer emotional support and continuous mental health monitoring. This paper surveys voice-based artificial intelligence mental health companions integrated with emotion tracking systems. The study reviews existing technologies, emotion detection techniques, system architectures, and practical applications. It also discusses major challenges including privacy, ethical responsibility, and emotional accuracy. The survey highlights that combining voice interaction with emotion tracking can significantly improve accessibility and user engagement in mental healthcare systems. This paper aims to provide a comprehensive overview for researchers and developers working in the field of intelligent mental health support systems.
🏷 Artificial Intelligence, Mental Health, Voice Assistant, Emotion Tracking, Natural Language Processing, Human Computer Interaction
Article 37 · pp. 468-476
The Relationship Between Governance Accounting Practices and Risk Management among Non-Governmental Organizations in Kajiado County, Kenya
Non-Governmental Organizations (NGOs) operate in increasingly complex environments characterized by donor dependency, accountability pressures, financial uncertainty, and operational risks, making effective governance and risk management essential for organizational sustainability. Governance accounting practices such as board oversight, internal audit, compliance monitoring, transparency, and accountability structures are expected to strengthen institutional control and support risk management. However, empirical evidence on the relationship between governance accounting and risk management within NGOs in Kenya remains limited. This study examined the relationship between governance accounting practices and risk management among NGOs in Kajiado County, Kenya. A cross-sectional mixed-methods design was adopted, involving 93 respondents drawn from registered NGOs through a census approach. Data were collected using structured questionnaires and key informant interviews and analysed using descriptive statistics, Pearson correlation, and simple linear regression, alongside thematic analysis of qualitative data. The descriptive findings indicated high levels of governance accounting adoption among NGOs, particularly in relation to internal audits and financial controls (M = 4.59, SD = 0.56), compliance with statutory and regulatory requirements (M = 4.55, SD = 0.63), and board oversight structures (M = 4.51, SD = 0.56). Correlation analysis revealed a positive but statistically insignificant relationship between governance accounting and risk management (r = 0.186, p = 0.074). Similarly, regression analysis showed that governance accounting had a positive but statistically insignificant relationship with risk management (β = 0.306, p = 0.074), explaining 3.5% of the variation in risk management outcomes (R² = 0.035). Qualitative findings suggested that governance accounting contributed to institutional stability and accountability but was more effective when embedded in routine organizational decision-making rather than maintained primarily for compliance. The study concludes that governance accounting is an important accountability and control mechanism within NGOs, but its independent contribution to risk management is limited when examined in isolation. The study recommends stronger integration of governance systems into strategic risk oversight, institutional decision-making, and organizational accountability frameworks.
🏷 Governance accounting, risk management, NGOs, accountability, internal controls, Kenya.
Article 38 · pp. 477-486
Evaluation of Market Acceptability and Consumer Perception of Tofu Goodies in Bay, Laguna
This study evaluated the market acceptability and consumer perception of Tofu Goodies, an innovative plant-based tofu nugget enterprise paired with signature homemade sauces in Barangay Paciano Rizal, Bay, Laguna. Using a quantitative descriptive-correlation design, data were gathered from 289 targeted snack consumers within the locality. Demographic profiling showed that the core consumer segment consists of young adults aged 18–24 (43.25%) and students (44.29%) living on a limited monthly allowance below ₱5,000 (35.99%). Market acceptability achieved a favorable overall composite mean of 3.03 on a 4-point Likert scale, with product quality scoring highest (Sub-Mean = 3.10), led by the flavor profile of the homemade sauces (x̄ = 3.15).While consumers held highly positive perceptions regarding health benefits (Sub-Mean = 3.25) and environmental sustainability (Sub-Mean = 3.07), they expressed reservations regarding sensory attribute replication (Sub-Mean = 2.77). One-Way Analysis of Variance (ANOVA) proved that market acceptability was uniform across all demographic profiles (p > 0.05), failing to reject the null hypothesis. Crucially, Pearson correlation analysis revealed a non-significant relationship between consumer perception and actual product acceptability (r = -0.0186, p = 0.7534). This mathematically demonstrates that ideological alignment with healthy or green diets does not automatically translate into real-world purchasing behavior. Intense market competition (Sub-Mean = 2.87) and limited localized storefront accessibility (Sub-Mean = 2.67) emerged as primary operational challenges. These insights informed a highly structured action plan designed to secure long-term business sustainability.
🏷 Tofu nuggets, market acceptability, consumer perception, plant-based alternatives, descriptive-correlation, ANOVA, Bay, Laguna
Article 39 · pp. 487-495
Market Acceptability and Consumer Perception of Corn-Based Coffee: A Descriptive Study of Café de Maiz in Bay, Laguna
This study evaluated consumer perceptions and market acceptability of Café de Maiz, an innovative, non-caffeinated corn-based coffee substitute produced by a social enterprise in Bay, Laguna. Utilizing a quantitative descriptive-evaluative design, data were gathered from 236 active coffee consumers selected through purposive and convenience sampling. The primary market demographic was identified as young adult females (18–25 years old) operating within a monthly allowance of ₱5,000–₱10,000. Descriptive analysis revealed strong overall market acceptability (x̄ = 3.10, SD = 0.84), driven primarily by promotional outreach and affordable pricing. Consumer perception was highly positive (x̄ = 3.11, SD = 0.82), with respondents valuing the beverage’s non-acidic properties and its support for local agricultural sustainability. One-Way ANOVA confirmed that market acceptability did not vary significantly across demographic segments (p > 0.05), indicating broad market appeal. Crucially, Pearson correlation analysis revealed a statistically significant, weak negative relationship between consumer perception and market acceptability (r = -0.1574, p = 0.0157). This finding highlights a distinct attitude-behavior gap: while consumers highly approve of the health and ethical benefits of corn coffee, their actual transactional engagement is constrained by operational barriers, primarily localized product availability (x̄ = 2.78) and deep-seated habitual preferences for commercial instant coffee (x̄ = 2.74). These results inform a strategic action plan focused on decentralized distribution, sensory-driven marketing, and value-added social advocacy to ensure enterprise sustainability.
🏷 Corn Coffee, Market Acceptability, Consumer Perception, 4Ps Marketing Mix, Social Enterprise, Bay, Laguna.
Article 40 · pp. 496-518
Micro- and Nanoplastic Contamination in Surface and Groundwater Sources of Aifam Owukpa, Ogbadibo Lga, Benue State, Nigeria: A First Exploratory Scan
The micro- and nanoplastic (MP/NP) contamination of freshwater systems has become a rapidly growing environmental and public health concern globally, yet rural groundwater and surface water of sub-Saharan Africa remain critically understudied. This first exploratory study in Aifam Owukpa, Ogbadibo Local Government Area (LGA), Benue State, Nigeria, characterises MP/NP contamination across 12 purposively selected water sources (streams, springs, and hand-dug wells) sampled during the rainy season (July–August 2025). Multi-stage membrane filtration (5 µm and 0.45 µm), hydrogen peroxide digestion, and sodium chloride density separation were applied for particle extraction. Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy (Nicolet iS50, 4,000–400 cm⁻¹) was used for polymer identification, and Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy (SEM-EDS; JEOL JSM-6610LV) provided morphological and elemental characterisation. Physicochemical parameters (pH, turbidity, electrical conductivity, biochemical oxygen demand, and total dissolved solids) were measured and benchmarked against WHO drinking-water quality guidelines. Microplastics were detected in 85% of sampling sites (11 of 12) at concentrations of 45–210 particles/L (mean: 112 ± 45 particles/L), with stream sites substantially exceeding wells and springs. Dominant polymers were polyethylene (40%), polypropylene (30%), polystyrene (15%), polyethylene terephthalate (10%), and polyvinyl chloride (5%). Morphological analysis identified fragments (55%), fibres (30%), and films (15%). Nanoplastic presence was inferred in 40% of samples via sub-micrometre spectral broadening; direct quantification using Py-GC/MS or nano-FTIR is strongly recommended for future work. Turbidity showed a strong positive correlation with MP abundance (r = 0.78, p < 0.01). SEM-EDS confirmed high carbon content (65–75% C) and weathering-consistent morphologies indicative of local secondary fragmentation. A GIS-based contamination hotspot map (Figure 11) spatially delineates high-, moderate-, and low-risk zones. The single-season design and absence of contaminant adsorption data are acknowledged limitations; future studies should incorporate comparative dry- and wet-season sampling, quantitative nanoplastic analysis, and heavy metal/persistent organic pollutant (POP) adsorption experiments. These findings establish a critical contamination baseline, underscoring the need for community-level plastic waste governance and integration of MP monitoring into Benue State water quality frameworks.
🏷 Microplastics, nanoplastics, ATR-FTIR, SEM-EDS, GIS hotspot mapping, freshwater contamination, Nigeria, groundwater, rural water quality, polymer characterisation, seasonal variability, Benue State.
Article 41 · pp. 519-526
Challenges Faced by Artisanal Fish Farmers Along the River Niger in Kogi State
This study examined the activities, challenges, and sustainability strategies of artisanal fish farmers along the River Niger using 2025 survey data. Descriptive statistics were used to analyse respondents’ perceptions. Findings indicate that artisanal fish farming remains a key livelihood, with 83% of respondents affirming its importance. The sector is still largely traditional (81%), though 74% reported a gradual shift toward modern aquaculture practices. About 72% depend on natural water bodies, while 68% engage in fish processing and preservation. Production trends appear positive, as 90% observed increased fish output over the past five years. Most respondents also reported adequate resources (82%), year-round availability (87%), and general abundance of fish in the river system (87%). However, several constraints persist, including inadequate technical knowledge (83%), limited access to equipment (91%), climate change impacts (84%), flooding (88%), and weak government support (82%). Farmers respond through collaboration (87%), use of indigenous knowledge (85%), and income diversification (82%), though access to credit remains limited (74%). Overall, the study suggests that while artisanal fish farming supports livelihoods and food security, its sustainability depends on addressing key technical, environmental, and institutional challenges.
🏷 Artisanal fish farming, River Niger, fish production growth, climate change impacts, aquaculture challenges, livelihood sustainability
Article 42 · pp. 527-535
Assessment of Artisanal Fish Farming Activities Among River Niger Communities in Kogi State
This study examined the major activities and production trends of artisanal fish farming among communities along the River Niger using survey data collected in 2025. Descriptive statistics were employed to analyse respondents’ perceptions. Findings reveal that artisanal fish farming is a key livelihood activity, with 83% of respondents agreeing that it constitutes their primary source of income. Traditional fishing methods remain dominant, as indicated by 81% agreement, while 72% reported reliance on natural water bodies for fish breeding and rearing. However, 74% acknowledged a gradual increase in the use of modern aquaculture techniques. Fish preservation and processing practices were less consistent, with 68% participation and 29% indicating non-involvement. Regarding production trends, 90% of respondents observed a significant increase in fish production over the past five years, while 82% agreed that available resources support high production levels. Furthermore, 87% reported year-round fish availability, and 82% indicated that supply meets local demand. Notably, 87% affirmed that the River Niger still provides abundant fish resources. The study concludes that artisanal fish farming remains vital and moderately evolving, though constrained by limited modernization and infrastructural gaps, highlighting the need for sustainable management and technological advancement.
🏷 Artisanal fish farming, River Niger communities, traditional fishing methods, modern aquaculture adoption, fish production trends, livelihood sustainability.
Article 43 · pp. 536-544
Experimental Evaluation of the Effect of Additive on Vegetable Oil-Based Lubricants
This study investigates the physicochemical properties of vegetable oil-based lubricants derived from sesame and groundnut oils, with emphasis on the effect of a composite chemical additive. The additive formulation consisted of a fine mixture of aluminum oxide (Al₂O₃) and magnesium sulfate heptahydrate (MgSO₄·7H₂O) combined with hydrogen peroxide and oleum, applied at varying concentration levels (10%, 20%, 30%, 40%, and 50% by volume). Key properties evaluated include kinematic viscosity, pour point, flash point, cloud point, specific gravity, thermal conductivity, acid value, free fatty acid, and saponification value, following established ASTM standards. Results indicate that increasing the additive concentration enhances the thermal conductivity of both oils, with sesame oil demonstrating superior thermal performance compared to groundnut oil. Conversely, groundnut oil exhibits better viscosity characteristics at higher additive concentrations, retaining greater lubricity, whereas sesame oil’s viscosity decreases with increasing additive levels. Both oils, however, exhibit declining thermal conductivity and lubricity at elevated temperatures (50–70°C), revealing a significant limitation for high-temperature industrial applications. Physicochemical analysis further confirms that groundnut oil has a higher flash point (265°C) and saponification value, while sesame oil presents a lower pour point (−14.67°C), indicating better cold-temperature performance. These findings suggest that optimizing composite additive formulations can significantly improve the performance of bio-lubricants; nonetheless, further research is required to address high-temperature stability before these lubricants can be adopted for broader industrial use.
🏷 Additives; lubricants; Groundnut oil; Sesame oil; Thermal conductivity; Vegetable oil-based lubricants; Viscosity
Article 44 · pp. 545-559
Consultancy Frameworks for AI-Driven Medical Imaging: Advancing Early Diagnosis in Healthcare Systems
While the integration of AI (artificial intelligence), its application in medical imaging is rapidly changing the world of diagnostics, some health systems have yet to develop specific design guides to offer guidance for how we make AI usage in medical imaging safe and ethically sound, whilst in a way that could scale to a wider context with good security. We now present a consultancy system for AI-driven medical imaging for early diagnosis in Saudi Arabia. Based on international standards like SPIRIT-AI, CONSORT-AI, CLAIM, DECIDE-AI and the WHO Ethics and Governance of AI for Health, the framework covers essential obligations for responsible AI use.
An expert perspective approach was used to gather the expertise in the field by utilizing qualitative design based on healthcare professionals, radiology practitioners, biomedical engineering and digital health specialists deployed in Saudi Arabia. Some of the more specific domains identified in this analysis relate to readiness assessment, workflow integration, interoperability, data governance, cybersecurity, explainable AI (XAI), federated learning, regulatory alignment with FDA, AI Act, and continuous metrics. The results indicate that the successful introduction of AI in medical imaging should be achieved within an organisational consultancy model, with ethical restrictions and technical validation, adaptation of clinical workflow and stakeholder participation. The framework developed provides applicable recommendations for healthcare institutions working to incorporate AI-assisted imaging tools to optimize early diagnosis, enhance efficiency in radiology, as well as to promote global AI governance best practices. We add to the growing digital health literature by providing a contextualized evidence-informed model aimed at supporting strategizing judgments and fast-tracking the move to AI enabled diagnostic platforms in Saudi healthcare systems.
🏷 Medical imaging AI-enabled; consultancy frameworks; early diagnosis tools; radiology workflow integration
Article 45 · pp. 560-575
The Effect of Pollution on the Freezing Point of Dal Lake: A Comprehensive Analysis
Dal Lake, located in Srinagar, Jammu and Kashmir, is one of India's most iconic freshwater bodies. However, it has undergone severe environmental degradation due to anthropogenic activities, particularly sewage discharge, houseboat waste, and agricultural runoff. This research paper examines how pollutants in Dal Lake water alter its freezing point compared to pure water. Through analysis of existing water quality data, freezing point depression principles, and field observations, we demonstrate that dissolved impurities—primarily nitrogen compounds, phosphorus, calcium, magnesium, and dissolved salts—lower the freezing point of Dal Lake water to approximately −11°C, significantly below the standard 0°C freezing point of pure water (Atkins, 2010; Castellan, 1983; Organic Biotech, 2025). This phenomenon has profound implications for the lake's winter ecology, aquatic life cycles, and regional climate patterns. The paper integrates colligative property theory with empirical data from Dal Lake water quality assessments conducted between 2005 and 2024 (GeoJournal, 2005; IWA Publishing, 2024).
🏷 Dal Lake, freezing point depression, water pollution, eutrophication, colligative properties, aquatic ecology
Article 46 · pp. 576-588
Topic- Sustainable and Ergonomical Packaging Solution Towards Hindu Worship Rituals (A Case Study of Redefining Community Performance in Kashi Vishwanath Temple, Varanasi, India).
The research was initiated to identify sustainable alternative to existing recycled plastic packaging of worship offerings (puja) in temples. As a case study Kashi Vishwanath Temple, Varanasi was the focal point of the research. In the process the craft clusters in the region were studied and analyzed. The methodology was based on qualitative and naturalistic approach. Moonj grass basketry craft practiced nearby was narrowed down. A pilot design and development activity was conducted to gauge the acceptance of new concepts in the community of craftswomen. The undesirable aspects in the craft clusters were mapped and classified during the process. Strategies were formed to create a consolidated system giving birth to a replicable micro enterprise which collaborates with the temple economy. The research addresses towards awareness, self-empowerment and capacity building of the craftswomen enhancing their performance and making the craft practice professionalized.
🏷 Ecological Sustainability, Social Sustenance, Women- Empowerment, Packaging Solution, Capacity Building.
Article 47 · pp. 589-593
Artificial Intelligence in the Construction Industry in India: Opportunities, Challenges & Future Prospects
The construction industry in India, a key driver of economic growth contributing significantly to GDP and employment, faces persistent issues such as project delays, cost overruns, labour shortages, safety concerns, and low productivity. Artificial Intelligence (AI) offers transformative potential through applications in project management, predictive analytics, design optimization, risk assessment, safety monitoring, and supply chain management. With 54% of Indian construction firms already adopting AI and ML as of recent reports, India leads in digital adoption within the sector regionally. Supported by government initiatives like the National Strategy for Artificial Intelligence and the India AI Mission, the sector is poised for rapid growth. This paper examines key opportunities, major challenges, and future prospects, highlighting pathways for sustainable and efficient infrastructure development.
🏷 Artificial Intelligence, Construction Industry India, Project Management, Predictive Analytics, Digital Transformation, Smart Cities, Industry 4.0.
Article 48 · pp. 594-609
Tension-Driven Innovation: Reconceptualising Sustainable Entrepreneur-ship Through Paradox Fields
Sustainable entrepreneurship serves at the nexus of economic viability, ecological stewardship, and social responsibility. However, the prevailing Triple Bottom Line (TBL) framework, which treats these aspects as harmoniously aligned, fails to adequately capture the ongoing conflicts entrepreneurs face when balancing sustainability objectives. This limitation has created a theoretical gap in understanding how sustainability-related contradictions can generate entrepreneurial innovation. Therefore, this study aims to examine the limitations of the TBL framework, synthesise insights from paradox theory and the sustainable entrepreneurship literature, and propose a novel conceptual model that explains how sustainability tensions can become sources of innovation.
The study is based on conceptual theory-building research design grounded in a systematic review and synthesis of the literature on sustainable entrepreneurship, paradox theory, and the Triple Bottom Line. Through thematic analysis and theory synthesis, key concepts, tensions, and mechanisms underlying sustainable entrepreneurial action were identified and integrated.
The study proposes the Paradox Field Model of Sustainable Entrepreneurship. Economic sustainability, social sustainability, ecological sustainability, and entrepreneurial innovation comprise the four interrelated dimensions of the three-dimensional tetrahedral framework. The model identifies six persistent paradoxical tensions and explains how entrepreneurs transform these tensions into innovative outcomes through five mechanisms: tension recognition, rethinking cognitive, both/and thinking, dynamic cycling, and tetrahedral integration.
The study concludes that sustainability tensions should not be viewed as obstacles to be resolved but as productive forces that stimulate entrepreneurial innovation, offering a new theoretical foundation for future research and practice in sustainable entrepreneurship.
🏷 Entrepreneurship, Sustainable entrepreneurship, Paradox Theory, Triple Bottom Line, Entrepreneurial innova-tion, Sustainability Tensions, conceptual mode
Article 49 · pp. 610-631
Design and Evaluation of a GUI-Based GPA Tracker System Using Python and Sqlite for Student Academic Record Management
compute student academic records efficiently. Manual recording of student grades often results in errors, misplaced records, and time-consuming computations, especially when handling multiple students. To address these concerns, the proposed system was developed using Python, Tkinter/CustomTkinter for the graphical user interface, and SQLite3 for database management. The system allows users to add, edit, search, delete, and view student records while automatically computing the Grade Point Average (GPA) based on encoded grades and subject units. Input validation was also integrated to prevent incomplete entries, invalid grades, duplicate student IDs, and other common recording errors. The results of the system testing showed that the GPA Tracker successfully performed its intended functions, including accurate GPA computation, record storage, data retrieval, report generation, and error handling. Overall, the system provides a more organized, accurate, and user-friendly approach to managing student academic records compared to manual methods. This project may also serve as a practical reference for future developers and students studying GUI design, database integration, and academic record management systems.
🏷 GPA Tracker, Python, SQLite3, Graphical User Interface, Student Records, Academic Record Management
Article 50 · pp. 632-646
An IT Governance Framework for Blockchain-Enabled Information Integrity in Philippine Private Higher Education Institutions
Information integrity is a critical priority for private higher education institutions (PHEIs) as it supports institutional credibility, graduate employability, and compliance with accrediting and regulatory bodies. Academic recordkeeping in Philippine PHEIs continues to evolve as part of broader digital transformation initiatives. Although blockchain technology offers features such as immutability, decentralization, and verifiability, its adoption should not be approached solely as a technical undertaking. Effective implementation requires IT governance mechanisms that align technological initiatives with institutional goals, ensure responsible resource allocation, manage organizational and security risks, and facilitate coordination among key stakeholders. This study proposes an adaptable IT Governance Framework for blockchain-enabled information integrity in Philippine private higher education institutions. Developed from the perspectives of stakeholders within a PHEI context, the framework highlights the coordinating role of the IT Department while integrating the responsibilities of the Registrar, Finance Office, Accreditation or Quality Assurance Unit, and Senior Management. The framework combines blockchain capabilities with governance practices involving strategic alignment, resource and investment management, risk management and security, roles and accountability, and performance and compliance monitoring. Using a mixed-method approach involving surveys and interviews with key institutional stakeholders, the findings provide empirical support for governance-driven blockchain adoption characterized by clear policies, phased implementation, stakeholder collaboration, and adequate resource allocation. The proposed framework offers a practical roadmap for institutional leaders seeking to strengthen information integrity while advancing digital transformation in a manner that promotes trust, accountability, and regulatory compliance. The study concludes that blockchain adoption should be regarded not merely as a technology upgrade but as an IT governance initiative that can be adapted by Philippine PHEIs operating within similar governance and regulatory environments.
🏷 IT Governance, Blockchain, Information Integrity, Academic Recordkeeping, Resource Management
Article 51 · pp. 647-652
Consumer Perception and Patronage of Nigerian-Made Consumer Electronics
In Nigeria, locally manufactured consumer electronics face low patronage due to consumer perceptions regarding value, risk, quality, and innovativeness, despite government initiatives promoting domestic products, with imported alternatives dominating the market, highlighting the need to understand the psychological and behavioral determinants of local product adoption. This study examines the influence of consumer perception on the patronage of Nigerian-made electronics, aiming to identify factors that encourage initial purchase, actual purchase behavior, and repurchase intention. A descriptive quantitative research design was employed, targeting adults aged 18 years and above residing in Surulere, Lagos State, with a sample of 305 respondents selected using convenience sampling. Data were collected via structured questionnaires measured on a five-point Likert scale and analyzed using SPSS, incorporating descriptive statistics and linear regression to examine relationships between perception dimensions and patronage outcomes. Results indicate that perceived value positively and significantly influences patronage intention (β = 0.358, p < 0.001), perceived risk negatively affects actual purchase behavior (β = -0.290, p < 0.001), and perceived quality positively predicts repurchase intention (β = 0.346, p < 0.001), demonstrating that consumer perceptions play a pivotal role in both initial and repeat patronage of Nigerian-made electronics. The study concludes that enhancing perceived value, mitigating perceived risk, and maintaining consistent product quality are critical strategies for increasing adoption and loyalty, and recommends that manufacturers implement value-based marketing, robust after-sales support, and continuous quality improvement, while policymakers should support initiatives that promote domestic products and build consumer confidence.
🏷 Consumer Perception, Nigerian-Made, Consumer Electronics
Article 52 · pp. 653-660
Carotid Artery Diameter and Blood Pressure: A Weak and Sex-Dependent Association in a Normotensive Nigerian Population
Background: The relationship between systemic arterial blood pressure (BP) and carotid artery diameter is often derived from hypertensive cohorts. This study explores this relationship in a normotensive Nigerian population to understand vascular physiology in the absence of pathology.
Methods: We analyzed data from 104 healthy adults. Carotid artery diameters were measured via ultrasonography. Pearson's correlation and ANOVA were used to assess relationships with systolic blood pressure, diastolic blood pressure and their mean arterial pressures (MAP).
Results: Mean age of the cohort was 28 years. In the overall cohort, correlations between BP and carotid diameter were largely absent or weak. A sex-stratified analysis revealed a distinct pattern: in females, there were significant positive correlations between MAP and the right ECA (r=0.453, p=0.003) and left ECA (r=0.335, p=0.030). No significant correlations were observed in males at all. No correlations existed between the larger common and internal carotid arteries with blood pressure parameters
Conclusion: The relationship between arterial blood pressure and carotid artery diameter in healthy, normotensive individuals is weak and exhibits sexual dimorphism, being evident only in females. This suggests that the strong correlations reported in literature are likely a feature of pathological hypertensive remodeling rather than normal physiology. The female-specific association warrants further investigation.
🏷 Blood pressure, Carotid artery diameter, Normotensive, Correlation, Healthy Nigerian population.
Article 53 · pp. 661-678
Direction-Aware Telematics Crash Detection and Injury Plausibility Scoring for False Injury Claim Mitigation
Modern connected-vehicle programs enable insurers and fleet operators to receive high-frequency inertial signals during collisions, supporting both rapid crash detection (for emergency response and First Notice of Loss) and post-loss analytic validation of claimed injuries. However, many deployed pipelines treat crash severity as a scalar (e.g., peak g or delta-v) and ignore impact direction, vehicle pose, and the occupant’s likely loading path. This omission reduces both safety performance (missed oblique and side impacts) and claim integrity performance (false positives and insufficient context for injury plausibility decisions). This paper proposes a direction-aware, end-to-end telematics architecture that jointly estimates (i) crash occurrence, (ii) impact direction in the vehicle reference frame, and (iii) injury plausibility scores for claim triage. The method fuses accelerometer and gyroscope streams with road context (map-matching, speed limit, surface condition proxies) and optional vehicle signals (airbag, belt, ADAS). A physics-based module derives biomechanically motivated bounds over expected injury risk given delta-v, pulse duration, and direction, while a machine-learning model learns residual patterns associated with suspicious injury narratives. The design supports stream processing for real-time alerts and batch re-scoring for audits, and includes governance controls for privacy and regulated data handling. Using representative evaluation scenarios constructed from mixed telematics datasets, the proposed direction-aware model improves suspicious-claim detection AUC by ~0.06 over severity-only baselines while keeping real-time scoring within sub-second latency. We discuss implementation considerations, failure modes, and responsible deployment practices.
🏷 Telematics, crash detection, impact direction, delta-v, injury biomechanics, fraud analytics, FNOL, claim triage, streaming ML.
Article 54 · pp. 679-687
Kernel-Level Performance Evaluation of Windows Server 2022 During High-Concurrency Access to Educational Applications in the Schools Division Office of Passi City
Institutional web application infrastructure within regional educational administrative sectors frequently relies on monolithic web runtimes deployed natively on single bare-metal compute hosts. This study presents an empirical, kernel-level performance evaluation of the localized bare-metal server infrastructure orchestrating daily logistics for the Schools Division Office (SDO) of Passi City. The environment hosts a production suite of enterprise educational and administrative platforms—including DocWatch (Document Tracking System), Baol sang Kaalam (Localized Educational Resources), and BioSync (Attendance Management System)—operating natively on an Apache, PHP, and MariaDB (XAMPP) stack. To bypass impractical hypervisor abstraction layers, this research applies an experimental research design to evaluate core operating system kernel mechanics under stress. Specifically, it measures symmetric multiprocessing (SMP) process management, thread scheduling frequencies, and memory working set boundaries under high-concurrency conditions. Using Apache JMeter (Apache Software Foundation, 2024), the ecosystem was subjected to parameterized concurrent thread loads ranging from 100 to 1,000 users. An initial baseline test revealed that reaching 1,000 users triggered severe thread pool exhaustion, causing transaction error rates to rise to 3.47% despite average CPU saturation dropping to 40.70%. To resolve this operational blockage, a subsequent multi-iteration ablation study was executed by applying targeted Multi-Processing Module (MPM) and database buffer optimizations. These targeted interventions successfully resolved the hardware-level bottleneck, reducing process lock contention and allowing the operating system to comfortably sustain 1,000 concurrent users with a near-zero transaction error rate (<0.01%) while stabilizing average CPU utilization down to 31.66%. Building upon these successful optimization outcomes, this study proposes a Dynamic Thread-Scaling Framework (DTSF)—a zero-cost, software-defined automation framework designed in alignment with modern ISO/IEC performance efficiency standards (International Organization for Standardization, 2024) to dynamically adjust thread allocation vectors based on real-time OS performance counters. Finally, an unmitigated boundary stress test utilizing a synthetic extreme load of 10,000 concurrent users successfully identified the absolute hardware exhaustion limit of the physical compute node, characterized by sustained 100% CPU utilization and a 17.56% application failure rate. The captured empirical telemetry serves as a data-driven configuration blueprint proving that low-level kernel optimization can significantly extend physical infrastructure lifecycles in resource-constrained public-sector ecosystems.
🏷 Bare-Metal Server, High Concurrency, Kernel Performance, Process Scheduling, Windows Server 2022
Article 55 · pp. 688-703
AI-Based and Compressed-Domain Video Steganography: A Systematic Review and Comparative Analysis (2017–2025)
Artificial intelligence has significantly transformed video steganography by improving the security, robustness, and adaptability of secret data embedding techniques. This paper presents a systematic review and comparative analysis of AI-based and compressed-domain video steganography techniques published between 2017 and 2025. Relevant studies were collected from major scientific databases and critically analyzed based on embedding domain, deep learning architecture, dataset, payload capacity, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), mean square error (MSE), computational complexity, robustness, and resistance to steganalysis attacks. The review highlights the advantages and limitations of convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), transformer-based, and compressed-domain approaches. Comparative analysis indicates that AI-driven techniques generally achieve superior imperceptibility and security compared to conventional methods, although higher computational requirements remain a significant challenge. The study further identifies existing research gaps, discusses practical implementation challenges, and outlines future research directions including lightweight deep learning models, explainable AI, federated learning, and real-time secure multimedia communication systems.
🏷 Deep Learning Steganography, GAN, CNN, Video Steganography, Compressed Domain Embedding, Diffusion Models, Multimedia Security
Article 56 · pp. 704-707
Implementation of Artificial Intelligence (AI) in The Food Industry: Basis for an AI Comprehensive Model
Artificial Intelligence (AI) has emerged as a major disruptive and transformative force within the food and retail industries, reshaping supply chains, quality metrics, and organizational operational paradigms. This study evaluated the extent of AI implementation within the food industry based on software quality dimensions—Functionality, Reliability, Usability, Efficiency, Maintainability, and Portability—and determined the core operational challenges encountered during deployment. Utilizing a quantitative research design, data were collected from food industry stakeholders and analyzed using frequency distribution, percentages, weighted means, and One-Way Analysis of Variance (ANOVA). The findings revealed that AI implementation is highly realized across key metrics, with Functionality scoring highest (Overall Weighted Mean = 3.29, Highly Implemented) and Portability presenting stable operational compliance (Overall Weighted Mean = 3.17, Implemented). Inferential testing verified that there are no statistically significant variances in the level of AI implementation across workforce demographic markers or structural configurations such as company size (p > 0.05). However, significant organizational, infrastructure, and technical expertise limitations persist. Based on these empirical results, a comprehensive structural AI Model is proposed to provide a practical foundation for sustainable, ethical, and strategic AI optimization within food industrial systems.
🏷 Artificial Intelligence, Food Industry, Industry Acceptance, Technological Capabilities, Software Quality Model, Structural AI Model.
Article 57 · pp. 708-730
Meditrack: A Gui-Based Clinic Appointment and Patient Record Management System with MYSQL Storage and Excel Backup
Manual management of clinic appointments and patient records can cause delays, misplaced files, duplicate entries, inaccurate information, and difficulty in retrieving patient data. To address these problems, this study developed MediTrack, a GUI-based Clinic Appointment and Patient Record Management System designed to help clinic staff manage patient information and appointment records in a faster, more organized, and more reliable manner. The system was developed using Python as the main programming language, Tkinter for the graphical user interface, MySQL for permanent database storage, and OpenPyXL for automatic Excel backup. MediTrack includes important features such as secure login, adding patient records, viewing records, searching patient information, updating appointment status, deleting records, clearing input fields, and safely exiting the system. It also applies input validation to prevent common errors, such as blank fields, duplicate patient IDs, invalid names, incorrect age input, and contact numbers that do not contain exactly eleven digits. Patient data are stored in a MySQL database while a duplicate copy is automatically saved in an Excel file for backup and reporting purposes. The results of system testing showed that MediTrack successfully performed its intended functions and prevented invalid information from being saved. Overall, MediTrack provides a practical, user-friendly, and efficient solution for improving clinic appointment scheduling and patient record management.
🏷 MediTrack, Clinic Management System, Patient Record System, Appointment System, Python, Tkinter, MySQL, Excel Backup
Article 58 · pp. 731-745
Dinecart System: An Integrated Python-Based Restaurant Ordering, Billing, and Online Shopping Cart Management System
Efficient management of restaurant operations and online orders is essential for enhancing customer experience, reducing human error, and improving business productivity. Traditional manual or semi-digital systems often lead to inaccurate billing, delayed order processing, and inventory mismanagement. This study introduces DineCart System, a Python-based application integrating restaurant ordering, billing, and an online shopping cart with an SQLite backend database. Utilizing Object-Oriented Programming (OOP) principles, the system provides a graphical user interface (GUI) for seamless order management, real-time inventory updates, accurate billing, and customer-friendly shopping cart functionality. Functional testing demonstrated that the system correctly handles order creation, transaction processing, inventory adjustments, and receipt generation. By consolidating multiple restaurant management tasks into a single platform, DineCart System streamlines operations, improves accuracy, and enhances the overall efficiency of restaurant service and online ordering processes.
🏷 DineCart, Restaurant Ordering System, Online Shopping Cart, Python, GUI, SQLite, Billing System, Object-Oriented Programming
Article 59 · pp. 746-769
Pos²: A Gui-Based Mini Point-of-Sale and Inventory Management System for a Mini Grocery Store
Manual sales and inventory management in mini grocery stores often results in inaccurate calculations, delayed transactions, inconsistent stock monitoring, missing records, and difficulty in generating sales reports. To address these challenges, this study developed POS²: A GUI-Based Mini Point-of-Sale and Inventory Management System for a Mini Grocery Store. The system was designed to automate sales transactions, inventory monitoring, refund processing, product record management, activity logging, and sales report generation. It was developed using Python as the main programming language, Tkinter for the graphical user interface, and SQLite as the database management system. The system includes essential features such as PIN authentication, sales and checkout processing, product adding, viewing, updating, and deleting, stock adjustment, refund handling, activity log monitoring, and report generation. The SQLite database stores important information such as administrator accounts, inventory records, sales history, and activity logs, allowing data to remain available even after the application is closed. Through its graphical interface, users can manage store operations more easily without relying on handwritten records or manual computations. Testing showed that the system successfully performed its major functions with proper database integration. Overall, POS² provides a simple, organized, and efficient system that can help mini grocery stores improve transaction accuracy, inventory reliability, and business record management.
🏷 Department of Computer Engineering, Eulogio “Amang” Rodriguez Institute of Science and Technology (EARIST) Nagtahan, Manila, Philippines
Article 60 · pp. 770-787
Development of A BB84-Based Quantum Security System
The rapid advancement of quantum computing poses a serious threat to traditional cryptographic systems such as RSA and Elliptic Curve Cryptography (ECC), which rely on computational difficulty for security. Quantum algorithms like Shor’s and Grover’s have the potential to break or weaken these methods, putting modern communication systems at risk. To address this challenge, this work presents a quantum-safe com-munication framework that combines the BB84 Quantum Key Distribution (QKD) protocol with Post-Quantum Cryptography (PQC) techniques, aiming to ensure secure data exchange in the presence of quantum-capable adversaries.
In this system, the BB84 protocol is simulated to enable secure key exchange between two parties over a virtual quantum channel. The generated key is refined through processes such as basis matching, error correction, and privacy amplification using SHA-256 to ensure reliability and secrecy. For secur-ing communication, One-Time Pad (OTP) encryption is used alongside HMAC-SHA3-256 to provide both confidentiality and message integrity. Additionally, file encryption is handled using the XChaCha20-Poly1305 algorithm, selected for its efficiency and strong protection against potential vulnerabilities like nonce reuse.
To further enhance security, the framework integrates a hybrid key management approach using the Kyber-512 Key Encapsulation Mechanism (KEM) combined with a Key Deriva-tion Function (KDF), merging classical and quantum-resistant techniques. Experimental results demonstrate the effectiveness of the system, achieving a Quantum Bit Error Rate (QBER) of 2.5% and successful performance in both message and file en-cryption. Overall, this work contributes toward the development of practical and resilient communication systems designed for the emerging quantum era.
🏷 Quantum Key Distribution (QKD), BB84 Pro-tocol, Quantum Cryptography, Tensor Networks, Quantum Sim-ulation, QBER Analysis, Eavesdropping Detection, Secure Com-munication Systems
Article 61 · pp. 788-794
Deep Learning Approaches for Automatic Recognition of Textile Weave Structures
The Fabric weave structure is an essential component in the design and manufacture of premium fabric. The structure or pattern used in the fabric's weaving significantly impacts its overall appearance, texture, durability, and drape. Traditional methods of Weave pattern recognition are relied heavily on manual visual inspection, which is time-consuming, prone to human error, struggles with complex patterns and sensitive to lighting conditions Therefore, an automated system is necessary for the classification of woven cloth to improve production efficiency. In this paper we proposed a deep learning model particularly Transfer Learning model that employs data augmentation and transfer learning methods for the classification and identification of woven textiles and compared various also compared MobileNet V2 model with ResNet 50. The model use a MobileNet-V2 to autonomously extract and categorize fabric texture features in an end-to-end fashion. The model is trained using a custom dataset with various weave types, including plain, twill and satin. The experimental findings indicate that the suggested model is resilient and attains cutting-edge precision. We compared our findings with other baseline methods, demonstrating that the suggested technique attained superior accuracy when accounting for rotational orientations in fabric and appropriate illumination conditions.
🏷 Deep learning, MobileNet-V2, Pattern recognition, ResNet-50, Transfer learning, Woven fabric
Article 62 · pp. 795-809
How E-Commerce Shapes Cross-Border Trade in the Digital Era
This study explores the transformative effects of e-commerce on cross-border trade in the digital age, with a particular focus on the Balkan countries (Kosovo, Albania, Bulgaria, Bosnia and Herzegovina, Montenegro, Romania, Croatia, Serbia, northern Macedonia, Greece and Slovenia). It analyzes how the integration of electronic commerce reshapes traditional international trading practices by reducing geographical barriers and introducing new cost dynamics. Utilizing survey data from 300 e-commerce SMEs, the research investigates the impact of factors such as distance, language barriers, and emerging e-commerce costs on trade expenses. Through regression analysis, the study reveals that online transactions significantly lower distance-related costs, aligning with the "death of distance" concept. However, language barriers continue to pose a challenge. The findings also indicate that improvements in cross-border payment systems could lead to substantial increases in e-commerce activity. Furthermore, information-oriented business models and digital marketing strategies are identified as key drivers of sales growth and market success. The paper concludes with policy recommendations aimed at enhancing digital trade and fostering economic integration in the region. The research contributes to the understanding of e-commerce dynamics and its role in shaping future trade landscapes.
🏷 E-commerce; Cross-border trade; SMEs; Digital transformation; Balkan region
Article 63 · pp. 810-823
Development of a Predictive Model for Forecasting Customer Satisfaction Levels in Enugu Electricity Distribution Company (EEDC) Using Data Mining Techniques.
Customer satisfaction is vital for optimizing service delivery within the electricity distribution sector. However, many power distribution firms in Nigeria, such as the Enugu Electricity Distribution Company (EEDC), rely primarily on reactive complaint-handling mechanisms. To enable proactive service management, this study developed a predictive model for forecasting customer satisfaction levels within EEDC using data mining techniques. A comprehensive dataset comprising billing records, outage histories, metering information, complaint logs, payment delays, and customer feedback was analyzed. Three machine learning algorithms, Decision Tree, Logistic Regression, and Random Forest, were implemented and evaluated. System development followed the Object-Oriented Analysis and Design Methodology (OOADM), while the data mining pipeline adhered to the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. The predictive system was built using Python, Flask, SQLite, Scikit-learn, and a responsive frontend comprising HTML, CSS, JavaScript, and Bootstrap. The architecture integrates core modules for secure authentication, predictive analytics, dashboard visualization, complaint analysis, historical tracking, and automated recommendation generation. Performance evaluation utilized Accuracy, Precision, Recall, and F1-Score metrics along with confusion matrices. The empirical findings demonstrated that the Random Forest algorithm achieved the highest classification accuracy at 94.6%, outperforming both the Decision Tree and Logistic Regression models. Key drivers of customer dissatisfaction were identified as frequent power outages, estimated billing practices, delayed resolutions, and negative sentiment in feedback. This study concludes that data mining effectively forecasts customer satisfaction, providing power utilities with a practical, intelligent decision-support tool to transition from reactive workflows to proactive, data-driven service delivery.
🏷 Predictive Analytics, Customer Satisfaction, Electricity Distribution Sector, Data Mining, Random Forest, CRISP-DM, EEDC Nigeria.
Article 64 · pp. 824-846
Relationship Between Workspace Ergonomics and Psychological Stress in Lasustech Architecture Studios
Lagos State University of Science and Technology’s architecture studio serves as an intensive academic and creative environment where students spend prolonged hours engaging in design-related activities. However, inadequate ergonomics, such as uncomfortable furniture, poor lighting, insufficient ventilation, overcrowding, and prolonged sitting, contributed to psychological stress among architecture students. This study therefore examined the relationship between workspace ergonomics and psychological stress in LASUSTECH architecture studios to improve students’ well-being and create healthier learning environments. The study adopted a mixed-method descriptive research approach. Data were gathered through questionnaires and physical observations, conducted with architecture students in selected studio spaces at LASUSTECH. Statistical tools such as frequency distribution, mean score analysis, and correlation analysis were used to analyse the data collected. The findings revealed that poor ergonomics in LASUSTECH architecture studios significantly affected students’ psychological stress levels, leading to fatigue, anxiety, reduced concentration, emotional exhaustion, and decreased academic performance. The study also identified the major ergonomic factors contributing to stress among architecture students. The study concludes that workspace ergonomics plays a significant role in enhancing students’ psychological well-being, comfort, and productivity within architecture studios. Proper ergonomic considerations in studio planning and design are therefore essential to achieving conducive, student-friendly learning environments. The study recommends that policymakers, university administrators, and educational planners establish ergonomic standards for architecture studios by improving furniture quality, lighting systems, ventilation, spatial organisation, and indoor environmental conditions within higher institutions.
🏷 LASUSTECH Architecture Studios, Learning Environment, Psychological Stress, Student Well-being, Workspace Ergonomics
Article 65 · pp. 847-867
Hybrid Transformer–LSTM Forecasting and PPO-Based Intelligent Kubernetes Auto-Scaling Framework for Cloud Data Science Pipelines Using Predictive Workload Analytics and Multi-Objective Performance Optimization
With the rise of cloud-native data science pipelines, dynamic infrastructure is needed to cope with the variable nature of AI/ML workloads, however, traditional auto-scalers based on Kubernetes use reactive threshold mechanisms leading to inefficient resource utilization and service level agreement (SLA) violations. In this paper, we introduce a novel prediction-based framework with an integrated intelligent auto-scaler based on a Hybrid Transformer-LSTM forecasting model and a Proximal Policy Optimization (PPO) reinforcement learning (RL) agent. The forecasting engine leverages historical time series data on performance metrics, such as CPU, memory, GPU utilization, and request latency, to provide accurate workload predictions. These predictions are used by the RL agent to determine optimal scaling decisions, pod placement and resource allocation strategies based on multiple objectives including minimizing latency, maximizing throughput, energy savings and lowering cloud expenditures. In extensive evaluations performed using multiple node clusters performing distributed deep learning, stream processing and real-time inference pipelines, the proposed solution outperforms conventional HPA/VPA and baseline ML scalers by offering improved accuracy, speed of scaling, reducing unnecessary allo-cations by 38% and meeting strict SLA requirements for bursty workloads.
🏷 Kubernetes; Intelligent Auto-Scaling; Hybrid Transformer–LSTM; Proximal Policy Optimization; Cloud-Native AI; Multi-Objective Optimization
Article 66 · pp. 868-879
Links in the Micropolitical Networks That Determine Teaching Practice in Secondary Education Teachers
This research explains how secondary school teachers, through their actions, articulate connections that allow them to lend representativeness to their ideological discourses within the established framework, thus giving rise to the expression of group identity. A qualitative approach, using an ethnographic design, is employed to explore, examine, and understand the intersubjective links within the school organization and to produce profound interpretations and cultural meanings from the participants' perspectives. The information is gathered using semi-structured techniques, such as participant observation and interviews, as an approach to the interactions between teachers that constitute their daily performance, which then becomes an object of study. The starting point is that group dynamics act as a filter for ideology, because they go beyond simply being a group, incorporating the production expressed in its cohesion and fragmentation. The concluding argument is that connection as an institutional bond configures a habitus that generates a structure of collective behavior from which teaching practice emerges, established in the bonds of connection with the groups from which education is instituted.
🏷 social connection, hierarchies and complementarities, territorial distribution, performance of the everyday, intersubjective bond
Article 67 · pp. 880-966
Task Delegation Practices and Challenges Encountered by the School Heads in Public Schools
This study examined the effectiveness of task delegation practices and the challenges encountered by school heads, with the aim of identifying factors that influence leadership efficiency and operational outcomes in public schools. Specifically, the research sought to determine the effectiveness of task delegation practices, the nature of challenges experienced by school heads, and whether these practices and challenges significantly vary according to demographic characteristics such as sex, age, position, years in service, and highest educational attainment. The study addressed a research gap on the interplay between delegation effectiveness and operational challenges in school leadership, particularly in the context of schools with experienced administrators holding advanced degrees. Data were collected from school heads using structured questionnaires and analyzed using descriptive and inferential statistics to explore patterns, differences, and relationships between delegation practices and encountered challenges.
Findings showed that school heads generally practiced effective task delegation but faced challenges like communication gaps, trust issues, unclear roles, and limited training. Delegation effectiveness and challenges varied by demographics such as years of service and educational attainment, underscoring the role of experience and qualifications. However, no direct link was found between effectiveness and challenges, indicating that strong practices alone may not resolve operational difficulties. To address this, recommendations and a program proposal were developed to improve clarity, trust, competence, monitoring, and professional development. These measures aim to strengthen delegation, reduce inefficiencies, and enhance performance. Future research may broaden scope and sample to refine results.
🏷 task delegation, school leadership, operational challenges, educational attainment, professional development
Article 68 · pp. 967-972
Design and Implementation of a Centralized Mosque Management Portal for Masjid Angullia
The researchers developed the Centralized Mosque Management Portal for Masjid Angullia to improve the administration and dissemination of information within the mosque. Traditional management practices at Masjid Angullia rely on manual methods such as paper-based records, social media postings, and spreadsheets, which often lead to delays in communication, disorganized records, and increased administrative workload. To address these challenges, the study proposes a centralized web-based portal that integrates essential mosque management functions into a single digital platform. The system provides an organized and accessible hub for managing announcements, prayer schedules, events, and donation records, specifically optimized to minimize the workload for a single administrative user. It aims to enhance operational efficiency, transparency, and community engagement by systematically managing information and enabling mosque members to conveniently access updates. To meet the immediate needs of the community, the development process utilized an accelerated deployment timeline, rapidly transitioning from the initial design and foundational chapters directly to implementation and evaluation. The system was evaluated using the ISO/IEC 25010 software quality model, focusing on functionality, usability, reliability, performance efficiency, and maintainability. Evaluation results indicated that the developed portal is highly effective in supporting mosque operations. Overall, the study demonstrates that implementing a centralized digital solution enhances administrative efficiency, strengthens communication, and supports better community engagement within Masjid Angullia.
🏷 centralized mosque portal, digital donation tracker, automated financial computation, information dissemination
Article 69 · pp. 973-990
Systematic Review on Improving Performance of a Single-Spool Gas Turbine System Using Combustion Chamber Direct Water Injection
This study synthesizes prior research on performance enhancement of single-spool gas turbine systems through direct water injection in the combustion chamber. The review establishes that water injection effectively reduces nitrogen oxides (NOx) emissions by suppressing peak flame temperatures and modifying combustion kinetics. Numerical and experimental investigations indicate that increasing water-to-fuel ratios improves combustion stability, augments mass flow rate, and enhances power output, albeit with a corresponding rise in specific fuel consumption. Comparative assessment of alternative optimization strategies, including regeneration, combined cycles, and inlet air cooling, reveals inherent trade-offs between efficiency gains, environmental performance, and operational constraints. Despite extensive studies, a critical gap remains in defining optimal injection parameters under varying operating conditions. This work therefore proposes a simulation-based framework for optimizing water injection in a 135 MW single-spool gas turbine, targeting improved thermal efficiency, reduced emissions, and enhanced overall performance.
🏷 Gas turbine; Single spool; Combustion chamber; Direct water injection, etc
Article 70 · pp. 991-998
Assessing the Potential Use of Porous Asphalt Pavements under Timor-Leste's Climatic Conditions
Timor-Leste’s tropical climate, characterized by high temperatures, strong solar radiation, and seasonal heavy rainfall, poses significant challenges to conventional asphalt pavements, including rutting, moisture damage, and accelerated aging. Porous asphalt pavements, widely adopted in tropical and subtropical regions, offer enhanced drainage capacity and potential environmental benefits. This study evaluates the applicability of porous asphalt under Timor-Leste’s climatic conditions through a narrative literature review of evidence reported in comparable tropical environments. The analysis highlights the hydraulic and thermal advantages of porous asphalt, such as improved stormwater management, reduced hydroplaning risk, and potential mitigation of urban heat accumulation. However, concerns regarding durability, clogging, maintenance requirements, and economic feasibility remain critical. Findings suggest that porous asphalt could contribute to climate-resilient infrastructure in Timor-Leste, but local validation through laboratory testing, pilot-scale field studies, and life-cycle cost assessments is essential before large-scale implementation.
🏷 porous asphalt, tropical climate, pavement engineering, drainage, Timor-Leste
Article 71 · pp. 999-1005
Construction of Q- Rung Orthopair (M, N) Uncertainty Level Subgroup Structures
The main objective of this article is to introduce the idea of q-rung orthopair (m, n)-fuzzy subgroups of a finite group. We discuss the concept of q-rung orthopair (m, n) – fuzzy subgroups as a combination of intuitionist fuzzy subgroup and Pythagorean fuzzy subgroup. We provide the definition of q-rung orthopair (m, n) – fuzzy subgroup and examine various properties associated with it. Finally we analyze q-rung orthopair (m, n)- fuzzy cosets, (m, n) – fuzzy normal subgroups and (m, n)- fuzzy level subgroups.
🏷 intuitionist fuzzy set, Pythagorean fuzzy set, q-rung orthopair (m, n)- fuzzy subgroup, q-rung orthopair (m,n)- fuzzy Coset, normal subgroup, homomorphism.
Article 72 · pp. 1006-1024
Influence of Virtual Influencer Credibility, AI-Generated Content Quality, Parasocial Interaction and Digital Trust on Purchase Intention
The increasing use of artificial intelligence in digital marketing has transformed the way consumers interact with brands and make purchasing decisions. Virtual influencers, AI-generated content, and personalized digital experiences have emerged as powerful marketing tools that influence consumer attitudes and behaviors. This study investigates the impact of Virtual Influencer Credibility, AI-Generated Content Quality, Parasocial Interaction, and Digital Trust on consumers’ Purchase Intention. Drawing upon contemporary digital marketing and consumer behavior literature, the study proposes a conceptual framework to examine the relationships among these constructs (Lou & Yuan, 2019; Dwivedi et al., 2023). A quantitative research design was adopted, and primary data were collected from 400 respondents through a structured questionnaire using a five-point Likert scale. SmartPLS 4 was employed to assess the measurement and structural models. The measurement model demonstrated satisfactory reliability and validity, with Cronbach’s Alpha and Composite Reliability values exceeding 0.70 and Average Variance Extracted (AVE) values above 0.50. The structural model results revealed that Virtual Influencer Credibility (β =0.288, p < 0.001), AI-Generated Content Quality (β = 0.451, p < 0.001), Parasocial Interaction (β = 0.253, p < 0.001), and Digital Trust (β = 0.526, p < 0.001) have significant positive effects on Purchase Intention. The model explained 57.10% of the variance in Purchase Intention (R² = 0.571), indicating moderate predictive power. The findings suggest that consumers are more likely to develop purchase intentions when they perceive virtual influencers as credible, AI-generated content as valuable, and digital platforms as trustworthy. The study contributes to the growing body of knowledge on AI-driven marketing and provides practical implications for marketers seeking to enhance consumer engagement and purchase behavior through emerging digital technologies.
🏷 Virtual Influencer Credibility, AI-Generated Content Quality, Parasocial Interaction, Digital Trust, Purchase Intention.
Article 73 · pp. 1025-1034
Automatic Agriculture Robot
The sector is also facing increased demands from the availability of labor to inefficient resource utilization. This project presents a self-guiding IoT-powered robot for irrigating crops. It utilizes with help the mobile robot frame and its control via the Arduino framework. The decision-making mechanism in the system enables automated irrigation based on soil condition, and the ultrasonic sensors provide collision detection for obstacle avoidance when moving through the farm.
Environmental information transmits wirelessly and allows the farmer to monitor the field from anywhere. Experiments under the controlled agricultural environment revealed that the robot operates effectively, with accuracy of soil moisture measurement above 92%, and there is up to a 28% traditionalmethods.
This project provides a low-cost approach for adopting precision agriculture techniques, particularly suitable for small to medium-sized farms. Future developments can focus on using machine learning algorithms to predict plant health and GPS for navigation.
🏷 Automatic Agriculture Robot, Precision Agriculture, Smart Irrigation, Internet of Things (IoT), Soil Moisture Monitoring, Autonomous Navigation, Embedded Systems
Article 74 · pp. 1035-1045
Leveraging Technology for Gender-Inclusive Governance in Nigeria: Assessing Digital Strategies to Advance Gender Equality
This study examines the role of technology in advancing gender-inclusive governance in Nigeria, with a focus on evaluating digital strategies that can promote gender equality and enhance women's participation in governance. Despite Nigeria’s commitment to international frameworks such as the Beijing Declaration and Sustainable Development Goal 5, systemic gender disparities persist in political representation, economic opportunities, and social inclusion. Traditional policy approaches have often fallen short due to entrenched cultural norms, limited access to resources, and inadequate institutional mechanisms. In response, this research investigates how technological innovations, including e-governance platforms, mobile applications, and data analytics, can serve as transformative tools for addressing these challenges. The study employs a mixed-methods approach, combining quantitative analysis of existing digital initiatives with qualitative insights from interviews with policymakers, civil society actors, and women leaders. Key findings reveal that digital tools have the potential to amplify women’s voices, streamline access to government services, and facilitate evidence-based policymaking through disaggregated data collection. However, significant barriers remain, including the digital gender divide, low levels of digital literacy among women, and infrastructural limitations in rural areas. To address these gaps, the study recommends targeted investments in digital infrastructure, capacity-building programs for women, and the integration of gender-responsive design principles into technological solutions. By leveraging technology strategically, Nigeria can move closer to achieving gender equality and fostering inclusive governance systems that reflect the needs and aspirations of all citizens. This research contributes to ongoing global discussions on the intersection of technology, governance, and gender equity.
🏷 Technology, Gender - Inclusive Governance, Gender Equality, Digital Strategies, E-Governance, Women’s Participation, Nigeria
Article 75 · pp. 1046-1057
Biological Control of Meloidogyne incognita using Mycorrhizal Fungi in Central Sudan
There are many pests and diseases damaging both the quality and quantity of tomato production. Plant parasitic nematodes are one of them. They represent an important constraint on the delivery of global food security. Damage caused by plant-parasitic nematodes has been estimated at US$ 80 billion per year. Effects of three concentrations of mycorrhizal fungi were studied for control of Root Knot Nematode. Different tomato accessions were screened for Root-Knot Nematode susceptibility. All the tested concentrations of mycorrhizal fungi on nematode infected plants were produce positive effects i.e. nematode numbers were highly decreased compare to control which also appear as improvement of the growth of infected plants at various degrees. Among three tested concentrations, generally, middle concentration 5 ml of mycorrhiza is most effective followed by high one 6 ml of mycorrhizea and the lowest effect produced by lower concentration 4 ml of mycorrhizea. Mycorrhizea fungi appear as most effective control measures of root-knot nematode in tomato.
🏷 Meloidogyne, Mycorrhizal, Mycorrhizal Central Sudan
Article 76 · pp. 1058-1066
To Determine the Influence of Organisation Culture on Strategic Planning Implementation on Performance of Public Universities
Strategic planning is an important management approach that increases the efficiency of the organisation, service delivery and competitiveness especially in public institutions operating in dynamic settings. Strategic plans have been broadly embraced by public universities, yet many still find it difficult to convert strategic objectives into better organizational performance. Existing studies have mostly focused on strategic planning as a general management technique with little attention paid to the integrated impact of implementation elements such as resource allocation, performance rating systems, and organisational culture notably in the context of Kenyan public universities. Hence, this study sought to investigate the influence of strategic planning implementation on organizational performance at Rongo University, Kenya by focusing on resource allocation, performance appraisal systems and organizational culture. The study was based on an Organizational Culture Theory which states that shared values, norms and attitudes influence organizational behaviour and effectiveness. The study used a descriptive case study research approach. The study population included administrative staff, heads of departments, directors, deans and members of the university management board. Data were obtained through structured questionnaires from primary and secondary sources and analyzed using descriptive statistics and multiple regression analysis with the use of SPSS software.The results demonstrated that a considerable positive relationship exists between strategic planning execution and organizations’ performance. There is efficient allocation of resources that has boosted the performance through enough financial support, efficient monitoring and auditing systems, optimal utilization of physical resources, and availability of qualified individuals. However, successful execution has been hampered by poor adoption of information technology. Clear procedures, supportive policies and participatory evaluation processes in performance appraisal systems boosted organizational performance, but lack of regular review sessions hindered the utilization of appraisal results for staff development and continuous improvement. The study also revealed a considerable effect of the organizational culture on the implementation of strategic planning and on the efficacy of the institution. The implementation was also facilitated by employee participation in decision-making, reporting structures, delegation of responsibilities and common organisational principles. Inflexible hierarchical structures impeded decision-making and compromised flexibility. Regression results indicated that organizational culture had a substantial beneficial effect on organizational performance (β = 0.334, p = 0.002). The study shows that the successful implementation of the strategic plans has a considerable positive impact on the performance of public universities. It recommends the strengthening of resource allocation mechanisms, enhancement of information technology adoption, improvement of performance appraisal practices, promotion of participatory management and the fostering of flexible organizational structures to enhance strategic plan implementation and institutional performance.
🏷 Organization Culture, Performance Appraisal, Strategic Planning.
Article 77 · pp. 1067-1073
Predictive Model for Smart Healthcare Systems Using Random Forest Classifier
Health care systems in developing countries often face serious challenges, including limited resources, poor infrastructure, and delays in patient care. This study presents the development of a predictive model designed to assist in early health risk detection, particularly in resource-constrained settings. In this study, an improved predictive model for smart healthcare systems using Random Forest Classifier was created and embedded in a simple web interface. The model was trained on synthetic medical data and achieved an accuracy of 91% during testing. Health workers and others were able to use the system effectively, even with minimal digital skills. The platform provided real-time predictions, that will help users make quicker clinical decisions.
🏷 Smart health care, Predictive model, Random Forest Classifier, Machine Learning, Decision Support System, Real-time Prediction
Article 78 · pp. 1074-1079
A Design-Based Comprehensive Digital Twin Framework for Intelligent Detection
Chikungunya (CGN) is a rapidly expanding mosquito-borne viral disease which causes a significant burden of disease in global health and socio-economics. The issues of delayed detection, symptomatic overlap with other arboviruses and lack of capability to integrate real time patient information with macro-environments are significant challenges for traditional reactive public health systems. The present paper proposes a novel proactive, IoT-enabled Digital Twin (DTW) intelligent healthcare monitoring framework to enhance the continuous monitoring and early outbreak forecasting. It combines the telemetry of real-time vital sign monitoring from Internet of Medical Things (IoMT) with the ecological monitoring of vector habitats by sensors. Unique temporal granulation techniques are applied to the data to optimize processing times: Within Attribute Granulation (WAG), Between Attribute Granulation (BAG), and Cross Dataset Granulation (CDG). A pipeline for unsupervised classification of patient cohorts into functional infectious or non-infectious classes is based on the similarity of clinical features and implemented using a k-Means Clustering (k-MC) procedure. The pipeline for unsupervised classification of patient cohorts into functional class of infectious or non-infectious is based on the similarity of clinical features and implemented through a k-Means Clustering (k-MC) procedure. Prognostic modeling relies on Patient Severity State (PSS) vector that is evaluated by combining a Bayesian Belief Model (BBM) for uncertainty management with an Artificial Neural Network (ANN) powered by back propagation. The proposed dual-paradigm framework obtained an outstanding average classification Precision (93.97%) and a Sensitivity (93.57%) in the simulated test set and a 48% mean decrease in overall localized prognostic prediction errors compared to the traditional baseline systems simulating the rural agricultural region of India (N = 62,325).
🏷 Chikungunya, Digital Twin, Internet of Medical Things (IoMT), k-Means Clustering, Artificial Neural Network, Bayesian Belief Model.
Article 79 · pp. 1080-1087
The Logical Relationship Between Determinism and Pragmatism: Implications for Creative Education
Education, as a value-laden activity, is rooted in philosophical assumptions about human nature, knowledge, and truth. Despite considerable scholarly attention to determinism and pragmatism as independent philosophical theories, most studies tend to present determinism as a rigid system of universal causal laws, and pragmatism as an experience-based philosophy that emphasizes change and the practical utility of ideas. However, insufficient attention has been given to clarifying how these two concepts intersect or complement each other in relation to creative education.This paper, therefore, explores the logical relationship between determinism and pragmatism in order to examine how they interact in fostering creativity. To achieve this, the paper begins with an exposition of the logically necessary conditions of these concepts, demonstrating how they are interconnected in such a way that each concept relates meaningfully to the other. The methodological approach adopted is philosophical, aimed at drawing out the implications for creative education. The paper argues that the relationship between determinism and pragmatism allows for flexibility and can be understood as complementary within the framework of creative education. It further highlights that creative education flourishes and innovation emerges at the intersection of determinism and pragmatism, rather than in their separation. The study affirms that a synthesis of deterministic awareness and pragmatic orientation provides a balanced philosophical foundation for fostering creativity in education. Finally, the paper recommends that educators and curriculum designers should recognize the influence of natural laws while promoting experimental learning approaches to enhance creativity through innovation and critical thinking.
🏷 Determinism, Pragmatism, Creative education, Experimental learning, Creativity
Article 80 · pp. 1088-1104
SMART-Learning School Information System: A Design Science-Based Framework for IT Governance and Auditing in Senior High Schools
This study addresses the persistent lack of structured, education-specific information technology (IT) governance and auditing frameworks within Senior High Schools (SHSs). While corporate entities heavily leverage mature frameworks such as COBIT 2019 to enforce accountability, maintain system stability, and guarantee security compliance, secondary education systems frequently manage fragmented implementations of Learner Information Systems (LIS) and Learning Management Systems (LMS) devoid of equivalent oversight. Guided by Design Science Research (DSR), this study develops and evaluates the SMART - Learning School Information System (SMART - LSIS) Framework. The framework provides a structured approach to IT governance and auditing, designed to enhance management, monitoring, and accountability of school information systems in Senior High Schools.
A purposive sample of 25 school ICT stakeholders - including ICT Coordinators, School Administrators, Registrars, and Support Staff - evaluated the institutionalized system infrastructure across five core domains. Descriptive statistical analysis revealed a Grand Mean of 4.05 (Agree), demonstrating strong stakeholder validation regarding the utility and structural composition of the artifact. However, Gap Analysis exposed a critical vulnerability: IT Auditing exhibited the lowest evaluation score (M = 3.87) and the highest discrepancy relative to the ideal standard (Gap = 1.13). These empirical insights substantiate the necessity of an integrated framework to consolidate school governance, system tracking, risk mitigation, and performance monitoring into a unified, continuous-improvement architecture.
🏷 IT Governance, IT Auditing, School Information Systems, Learner Information Systems (LIS), Learning Management Systems (LMS), Educational ICT Compliance, Risk Management.
Article 81 · pp. 1105-1119
An Incident-Based Ethical Risk Screening Matrix for AI-Based Information Systems Using Public AI Incident Records
This study developed an incident-based ethical risk screening matrix for AI-based information systems using 100 publicly reported AI incident records from 2020 to 2026. It used descriptive content analysis and criterion-based purposive sampling. Each incident was coded by year, system domain, ethical risk type, affected stakeholder, ethical principle violated, severity level, and evidence strength. The study aimed to identify common ethical risk patterns and translate them into a practical screening tool for IT managers and organizations. Results showed that the most common ethical risks were misinformation or deception, followed by misuse or malicious use, safety failure, privacy violation, and accountability failure. Generative AI and chatbots had the highest number of incidents. Notable risk exposure was also found in law enforcement and surveillance, finance, government and public service, education, and social media platforms. The most affected stakeholder group was the general public. Most cases were assessed as high severity, while some were assessed as critical severity. Another supporting pattern was the prevalence of deepfakes and synthetic media, mainly linked to impersonation, fraud, misinformation, privacy harm, and reputational damage. The study concludes that AI-based information systems need ethical screening early in adoption, deployment, or expansion. The proposed matrix helps users identify warning signs, ask targeted screening questions, and apply management actions. It also gives organizations a structured way to connect documented AI failures to practical review steps. The matrix is not a substitute for a full technical, legal, or cybersecurity review. It uses documented AI failures as practical evidence to support early risk identification and responsible IT management.
🏷 AI ethics, AI incidents, ethical risk, information systems, screening matrix
Article 82 · pp. 1120-1133
Paradigm Shift from Traditional to Viral Marketing: Cost Challenges, Cultural Dynamics and New Strategies in Asian Film Marketing
The global film industry has undergone a profound transformation in its marketing strategies, accelerated by the proliferation of Web 2.0 technologies and the dominance of social media platforms. Traditional marketing channels, television, radio, and print have been progressively supplanted by viral marketing strategies that promise large-scale audience reach at substantially lower costs. This paper examines the paradigm shift from traditional to viral marketing within the Asian film industry, with particular attention to cost challenges, cultural dynamics, and emerging technological strategies. Employing a qualitative approach grounded in systematic document review, this study applies a structured two-stage screening and quality appraisal procedure informed by PRISMA best practices to synthesize existing scholarship from Scopus-indexed academic journals and peer-reviewed scholarly books on social media marketing, viral communication, electronic word-of-mouth (eWOM), cultural dimensions of film consumption, and technology-driven marketing innovation. Findings reveal that while platforms such as TikTok, Instagram, Weibo, and Douyin offer unprecedented cost efficiency and organic reach, viral campaigns are constrained by the ephemerality of audience interest, the so-called ‘15 minutes of fame’ phenomenon. Critically, the study demonstrates that viral marketing dynamics, cost structures, and platform cultures differ substantially across Asian national markets, including China, Malaysia, India, South Korea, Indonesia, and Vietnam, and that no single regional model adequately captures this heterogeneity. The emergence of Artificial Intelligence (AI), Big Data analytics, predictive modelling, and blockchain-based marketing systems further reshapes the strategic landscape. While the exclusive reliance on secondary sources is acknowledged as a limitation, the study incorporates comparative case evidence from multiple Asian film industries to ground theoretical insights in documented real-world outcomes. This study contributes to the growing body of scholarship on digital film marketing in Asian contexts and offers practical implications for producers, distributors, and marketing professionals operating in the post-Web 2.0 media economy.
🏷 viral marketing, Asian film industry, social media marketing, eWOM, cultural dimensions, digital marketing strategy, AI in film marketing, cost challenges
Article 83 · pp. 1134-1139
A note on Likelihood and Likelihood Based Inference
Statistical inference aims to draw conclusions about unknown parameters using observed data. Among the various inferential frameworks, likelihood-based inference occupies a central position because it provides a direct measure of the support that observed data give to competing parameter values. This paper presents a concise review of the likelihood function and its role in statistical inference. The concept of likelihood is introduced for both discrete and continuous probability models, emphasizing its interpretation as a measure of relative evidence rather than a probability distribution for the parameter. Several illustrative examples, including binomial, normal, and exponential models, demonstrate the construction and interpretation of likelihood functions. The paper discusses important properties of likelihood, including invariance under multiplication by positive constants and the combination of independent likelihoods through multiplication. Connections between likelihood and Bayesian inference are examined, showing how posterior information can be viewed as the product of prior and current likelihood information. Fundamental principles of likelihood-based inference, including the Likelihood Principle and the Stopping Rule Principle, are reviewed with classical examples. The role of maximum likelihood estimation as a practical method for parameter estimation is also highlighted. The discussion illustrates how the likelihood function serves as a compact and informative summary of statistical evidence, providing a unified framework for estimation and inference.
🏷 Likelihood Function; Likelihood-Based Inference; Likelihood Principle; Maximum Likelihood Estimation; Bayesian Inference; Stopping Rule Principle; Statistical Evidence; Parameter Estimation.
Article 84 · pp. 1140-1147
AgriTrack Digital Ordering Monitoring System for Ttangkkuet Green Farm.
The AgriTrack Digital Ordering Monitoring System for Ttangkkuet Green Farm is designed to resolve the challenges faced by the farm owner in managing their business operations. The system focuses on improving the way orders are handled by providing features such as order management, receipt printing, order tracking, customer reviews, and product customization. This study follows the Agile Methodology, which allows the system to be built step by step, with regular feedback to make sure it meets the needs of the users. The system includes important features such as a responsive web-based platform, real-time product availability tracking, manual confirmation and notifications, OTP verification, and digital payment options. To better understand the problems faced by the farm owner—such as receiving orders and writing receipts by hand—the researchers conducted interviews, direct observations, and reviewed related studies. A purposive sampling method was used to choose the right people to take part in the study. A total of 9 participants were selected, including the farm owner, business partners, and customers. To measure how well the system is accepted and used, a Likert Scale survey will be given to the participants. After collecting the responses, statistical analysis will be done to check the usability and performance of the system. By solving the issues in the current process, the AgriTrack Digital Ordering Monitoring System aims to improve how reservations and orders are managed. This will help make operations smoother and increase customer satisfaction at Ttangkkuet Green Farm.
🏷 Tracking order, Managing Orders, customer reviews
Article 85 · pp. 1148-1174
“Systematic Review of the Toy Industry in India: Trends, Challenges, and Global Integration"
Purpose: This study examines the intellectual evolution of the toy industry through a domain-based Systematic Literature Review (SLR). The review is guided by four research questions: (RQ1) What are the publication trends in toy-related research? (RQ2) What thematic clusters define the intellectual structure of the toy domain? (RQ3) How do policy and economic developments influence the evolution of the toy industry? and (RQ4) What future research directions emerge from existing literature?
Research Design: The review follows PRISMA 2020 guidelines and the SPAR-4-SLR protocol. Peer-reviewed articles were systematically retrieved from the Scopus database to capture historical developments and structural shifts within the toy industry. Bibliometric techniques were applied using Biblioshiny (R) and VOSviewer, including descriptive analysis, keyword co-occurrence mapping, cluster analysis, and factorial analysis (PCA).
Key Findings: The findings reveal a steady Compound Annual Growth Rate (CAGR) of 6.65% in toy-related research. The literature is organized into ten thematic clusters, which merged together as three core areas such as; core toys, smart and digital toys and toy production & design, . Keywords related to data-driven technologies, such as deep learning, machine learning, and IoT, appear prominently, reflecting the growing integration of digital and analytical tools within toy research. Recent studies increasingly focus on wooden toys, smart toys, and technology-enabled play products.
Originality and Implications: To the best of the authors’ knowledge, this is the first domain-wide bibliometric review of the toy industry. The study offers a structured understanding of research trends and thematic evolution, providing useful insights for scholars, industry practitioners, and policymakers. The findings support evidence-based decision-making for innovation, policy formulation, and future academic inquiry in the toy sector. The findings will also help governments, startups to identify the core changes of the toy industry in recent years, which will help the government to make a policy framework; practitioners to identify new trends and clusters.
🏷 Toy Industry; Toy Manufacturing; Bibliometric Analysis; Toy Innovation; Data Analytics Paper Type: Systematic Literature Review
Article 86 · pp. 1175-1183
Tofu Goodies: A Strategic Business Plan for the Production and Marketing of Plant-Based Nuggets with Homemade Sauces
This study evaluated the market acceptability and consumer perception of Tofu Goodies—an innovative, plant-based nugget paired with signature homemade sauces—in Barangay Paciano Rizal, Bay, Laguna. Using a descriptive-quantitative research design, data were gathered from 289 purposively selected respondents, primarily composed of students and young professionals aged 18–24 (43.25%) operating on limited budgets. The empirical findings revealed a high level of overall market acceptability (Mean = 3.03), driven primarily by excellent product quality (Mean = 3.10) and an affordable pricing structure ranging from ₱11.00 to ₱55.00 (Mean = 3.06).
While respondents highly praised the health benefits (Mean = 3.25) and eco-sustainability (Mean = 3.07) of the product, sensory imitation benchmarks scored lower (Mean = 2.77). Inferential analysis via One-Way ANOVA confirmed no statistically significant difference in product acceptability across diverse demographic cohorts (p > 0.05). Crucially, Pearson correlation analysis revealed no significant relationship between consumer perception and actual market acceptability (r = -0.0186, p = 0.7534), mathematically proving that intellectual appreciation for healthy food does not automatically drive purchasing decisions. Market competition (Mean = 2.87) and localized geographical accessibility (Mean = 2.67) were identified as the primary operational challenges. Based on these insights, a Strategic Marketing Action Plan utilizing a hub-and-spoke distribution model and sensory-focused digital marketing is proposed to optimize business sustainability and local market penetration.
🏷 Tofu plant-based nuggets, Market acceptability, Consumer perception, Marketing mix, Strategic marketing, Bay Laguna.
Article 87 · pp. 1184-1207
Smartpos Budgeteer: Design and Implementation of a Python-Based Sales, Inventory, and Expense Management System Using Oop Principles
Manual management of sales, inventory, and expenses in small retail establishments often results in inaccurate calculations, delayed transactions, inconsistent stock tracking, missing records, and challenges in report generation. These issues can negatively affect business operations, financial monitoring, and decision-making processes. To address these challenges, this study developed SmartPOS Budgeteer, a Python-based, Graphical User Interface (GUI)-driven system that integrates Point-of-Sale (POS), inventory management, and expense tracking functionalities into a single platform. The system was designed and implemented using Object-Oriented Programming (OOP) principles to ensure modularity, maintainability, scalability, and efficient code organization.
The graphical user interface was developed using Tkinter, providing an intuitive and user-friendly environment for business owners and staff. Data management and storage were implemented through SQLite and MySQL databases to ensure secure, accurate, and permanent record keeping. The system includes modules for sales processing, inventory monitoring, expense recording, refund management, activity logging, and report generation. Automated calculations and real-time database updates help minimize human errors while improving operational efficiency.
A system development approach based on the Software Development Life Cycle (SDLC) was utilized throughout the planning, design, implementation, testing, and deployment phases. Functional testing was conducted to evaluate the performance, reliability, and accuracy of each module. Results showed that the system successfully processed sales transactions, updated inventory records, recorded expenses, handled refund transactions, validated user inputs, and generated accurate reports. All test cases passed, demonstrating the reliability and effectiveness of the system in supporting retail business operations.
The findings indicate that SmartPOS Budgeteer provides a unified, efficient, and user-friendly solution for small retail establishments. By integrating POS, inventory, and expense management functionalities, the system improves accuracy, accountability, record organization, and business decision-making. Furthermore, the implementation of OOP principles and database integration contributes to system maintainability, data integrity, and long-term usability. The study demonstrates the potential of Python-based business management systems in enhancing operational productivity and supporting the digital transformation of small retail enterprises.
🏷 SmartPOS Budgeteer, Point-of-Sale, Inventory Management, Expense Tracking, Python, Tkinter, ObjectOriented Programming, SQLite, MySQL, Retail Management System.
Article 88 · pp. 1208-1216
From Whistle to Algorithm: Human AI Collaboration in Coaching Practice in Zimbabwe
Background: Artificial Intelligence (AI) Technologies Are Increasingly Integrated Into Sport Through Performance Analytics Platforms That Provide Detailed Insights Into Athlete Workload, Physiological Responses, And Tactical Efficiency. While These Systems Offer Unprecedented Precision, They Cannot Replicate The Human Elements Of Coaching Such As Judgment, Motivation, And Contextual Understanding. Objective: This Study Investigates How AI‑Driven Performance Analytics Can Support Coaches In Tailoring Training Programs, While Emphasizing The Irreplaceable Role Of Human Expertise In Interpreting Data And Fostering Athlete Development. Methods: A Mixed‑Methods Conceptual Analysis Was Conducted Using Survey And Interview Data From 55 Participants (20 Coaches, 30 Athletes, And 5 Sport Scientists) Drawn From Both Elite And Developmental Sport Contexts. AI Applications Including Predictive Workload Modelling, Injury Risk Profiling, And Tactical Pattern Recognition Were Examined Alongside Qualitative Accounts Of Coach-Athlete Interactions To Highlight The Interplay Between Algorithmic Insights And Human Decision Making. Results: Quantitative Findings Revealed Strong Confidence In AI Reliability, With 82% Of Coaches And Athletes Agreeing That Predictive Analytics Improved Training Personalization. However, 88% Of Respondents Emphasized The Indispensability Of Human Judgment In Contextualizing AI Outputs. Correlation Analysis Indicated A Moderate Positive Relationship () Between Athletes’ Trust In AI Systems And Their Perception Of Training Effectiveness. Qualitative Interviews Reinforced These Results, Showing That Athletes Valued AI‑Enabled Personalization But Relied On Coaches For Motivation, Trust, And Ethical Guidance. Analysts Highlighted The Importance Of Feedback Loops, Where Coaches Refine AI Outputs Based On Athlete Responses, Thereby Improving Predictive Accuracy. Conclusion: Human-AI Collaboration In Coaching Represents A Transformative Paradigm Where Algorithms Augment, But Do Not Replace, The Whistle. By Combining Data‑Driven Insights With Human Relational Expertise, Coaching Practice Can Evolve Toward More Personalized, Ethical, And Effective Athlete Support. Future Research Should Investigate Frameworks For Integrating AI Into Coaching Curricula And Explore Long‑Term Impacts On Athlete Performance And Well‑Being.
🏷 Coaching, Artificial Intelligence, Performance Analytics, Human-AI Collaboration, Athlete Development, Training Personalization
Article 89 · pp. 1217-1241
A Digital Student Enrollment and Registration System: Design, Implementation, and Database Integration Using Python and SQLite
Manual student enrollment and registration processes in educational institutions are often time-consuming, prone to errors, and inefficient. This study developed a Digital Student Enrollment and Registration System using Python and SQLite to automate student record management and course registration. The system allows administrators to add, view, update, delete, and search student records, as well as manage enrolled subjects in a structured database. The application uses Object-Oriented Programming (OOP) principles and a command-line interface for ease of operation. Functional testing demonstrated that the system successfully performed all core operations, maintained data integrity, and minimized clerical errors. By implementing database integration, the system ensures permanent storage, reliable retrieval, and organized management of student enrollment information. The study demonstrates how digital systems can improve administrative efficiency and enhance the overall enrollment experience for both students and registrar personnel.
🏷 Student Enrollment, Registration System, Python, SQLite, Database Integration, OOP, Academic Records
Article 90 · pp. 1242-1259
AutoRentPro: Design and Implementation of a GUI-Based Python Car Rental Management System with SQLite Integration
The management of car rental operations, including vehicle inventory, customer reservations, and rental transactions, is often prone to errors when conducted manually. Delays in booking, inaccurate record-keeping, and inefficient tracking of vehicle availability can lead to customer dissatisfaction and operational inefficiency. This study presents AutoRentPro, a GUI-based Python Car Rental Management System integrated with an SQLite database to automate rental processes and enhance data accuracy. The system allows administrators to manage vehicle information, process customer bookings, track rental transactions, and generate reports on vehicle usage and availability. Object-Oriented Programming (OOP) principles were applied to ensure modularity, scalability, and maintainability of the code. Functional testing confirmed that the system successfully handles vehicle management, customer registration, booking processing, and report generation, while the GUI facilitates an intuitive user experience. AutoRentPro improves efficiency, reduces human error, and provides a reliable platform for managing car rental operations in small to medium-sized enterprises. Limitations include the absence of online payment integration and fleet telematics, which may be considered for future enhancement.
🏷 AutoRentPro, Car Rental System, Python, SQLite, GUI, Object-Oriented Programming, Vehicle Management, Reservation System
Article 91 · pp. 1260-1269
Q-Fuzzy Implementations of N- Lie Ideals Structures Over Lie Subalgebas -A New Approach
In this paper, we study the concept of q-fuzzy n-lie sub algebras (ideals) of lie algebras and investigate some of their properties. We discuss the relationship between q-fuzzy lie sub algebra (respectively ideals) and Lie sub algebras (respectively ideals). For a finite number of q-fuzzy n-lie sub algebras, we construct new q-fuzzy n-lie sub algebras on their direct sum. Finally, we analyze the homomorphism concept of q-fuzzy n-lie sub algebra over ideals.
🏷 Q-fuzzy set, sub algebra, q-fuzzy n-ideal, direct sum, homomorphism, Lie algebra, vector space.
Article 92 · pp. 1270-1278
A Systematic Review of Machine Learning Approaches for Predicting Heat-Related Skin Diseases under Climate Change
Climate change has intensified the frequency, duration, and severity of extreme heat events, posing significant risks to human health, particularly through the increasing burden of heat-related skin diseases. Elevated temperatures, prolonged ultraviolet radiation exposure, air pollution, and changing environmental conditions have contributed to the rising incidence of conditions such as heat rash, sunburn, skin infections, dermatitis, and skin cancer, with children and older adults being especially vulnerable. Recent advances in machine learning (ML) have enabled the development of predictive models for disease diagnosis and health risk assessment; however, existing studies predominantly focus either on general heat-related health outcomes or on skin disease classification using clinical images, with limited integration of climatic, environmental, and demographic factors for forecasting heat-related skin disease risk under future climate scenarios. The survey presents a comprehensive review of the current literature on climate-driven heat-related skin diseases and the application of machine learning techniques for their prediction. The review examines major climatic and environmental determinants, vulnerable population groups, commonly used datasets, feature selection methods, and predictive algorithms, including Random Forest, Support Vector Machine, Decision Tree, Artificial Neural Networks, Gradient Boosting, and Deep Learning models. It further compares model performance, interpretability, and limitations. The survey highlights emerging research directions for developing robust and interpretable predictive frameworks capable of forecasting future heat-related skin disease risks under climate change.
🏷 Climate Change; Heat-Related Skin Diseases; Machine Learning; Predictive Modeling; Environmental Determinants; Skin Disease Risk Assessment; Extreme Heat; Artificial Intelligence.
Article 93 · pp. 1279-1287
Mapping the Catastrophic Wear Zone: A Threshold-Based Re-Analysis of Taguchi-Optimized HSS Tool Life in Dry Aluminum Turning
This study re-examines experimental data from a Taguchi L9 orthogonal array (cutting speed: 34–134 m/min, feed: 0.1–0.3 mm/rev, depth of cut: 1–3 mm) to identify a threshold-based "catastrophic wear zone" for HSS tool operation in dry turning of aluminum. While conventional Taguchi optimization ranks cutting speed as the most influential factor affecting tool life (delta S/N = 16.14 vs. 6.87 for feed and 1.10 for depth), a closer inspection reveals a non-linear deterioration pattern. All three experiments conducted at 134 m/min produced the lowest tool life responses (45, 26, and 17 minutes), representing a 73.6% reduction compared to the optimal parameter combination. The S/N ratio collapses from 44.80 (at 90 m/min) to 28.66 (at 134 m/min)—a 36% deterioration in signal-to-noise performance. This threshold behavior establishes 134 m/min as a critical speed boundary beyond which tool life becomes both significantly shorter and highly inconsistent. Based on these findings, a recommended safe operating window is proposed: cutting speed ≤ 90 m/min, feed ≤ 0.2 mm/rev, and depth of cut ≤ 2 mm, with a red-flag warning against simultaneous use of 134 m/min and 0.3 mm/rev. The practical implications for TVET workshops, where unplanned tool failure disrupts training schedules and increases material costs, are discussed.
🏷 Tool wear; Taguchi method; HSS tool; threshold analysis; aluminum turning; catastrophic wear
Article 94 · pp. 1288-1298
TQM as a Strategic Operational Framework for Quality Assurance and Sustenance in Indian Higher Education Institutions
An Internal Quality Assurance Cell that exists on paper, meets once before a NAAC peer team arrives, and goes quiet for the remaining four years and eleven months of the accreditation cycle is not a quality mechanism — it is a filing system. Across a large share of Indian universities and colleges, this is precisely what the IQAC has become, and the gap between what it is mandated to do and what it actually does represents one of the most consequential governance failures in Indian higher education today. This study examines that gap through the lens of Total Quality Management, a management philosophy whose defining claim is that quality cannot be inspected into an organisation at periodic intervals but must be built into the everyday processes and working culture of the institution itself. The investigation was carried out across fourteen NAAC-accredited universities in Uttar Pradesh between January and June 2025, combining structured questionnaire surveys administered to 1,140 faculty members and administrative staff with in-depth institutional documentation analysis covering IQAC minutes, Annual Quality Assurance Reports, feedback data, and accreditation-period correspondence. The purpose was not to measure quality outputs — NAAC grades do that, imperfectly — but to examine the quality processes and governance behaviours that produce or fail to produce those outputs on a continuous basis. Findings reveal that TQM's five functional dimensions — leadership commitment to quality, process standardisation through the Plan-Do-Check-Act cycle, stakeholder feedback integration, data-driven institutional decision-making, and the cultivation of an organisation-wide quality culture — are either partially implemented or entirely absent in eleven of the fourteen institutions studied. Only three institutions demonstrated functioning quality governance systems that operated with any consistency outside the accreditation window. The study develops a TQM-anchored operational model adapted to the specific regulatory architecture of Indian higher education, maps it against NAAC criteria and NEP 2020 reform requirements, identifies the institutional and systemic barriers that make sustained implementation difficult, and proposes a phased transition pathway that accounts for the resource and governance realities of Indian universities rather than assuming conditions that do not exist.
🏷 Total Quality Management, Higher Education Institutions, NAAC, IQAC, Quality Assurance, NEP 2020, PDCA Cycle, Continuous Improvement, Quality Culture, Indian Universities
Article 95 · pp. 1299-1337
Explainable Transfer Learning Framework for Water Quality Prediction in Data-Scarce Developing Regions
Safe drinking water access continues to be a preva-lent public health challenge for developing nations, in which continuous water quality monitoring becomes infeasible due to the lack of resources, laboratory facilities, and sensors. Machine learning methods have been successfully applied for predicting water quality metrics such as dissolved oxygen (DO), turbidity, and pH. Nonetheless, traditional supervised machine learning models require sufficient amounts of labeled data (greater than 1000 instances) for optimal generalization. Due to the data-scarcity problem, only 100–200 historical instances are usually available for training. As a result, overfitting becomes inevitable. This paper proposes an Explainable Transfer Learning (XTL) approach for water quality prediction called XTL-WQ. We intro-duce three techniques: (i) a pre-trained Long Short-Term Mem-ory (LSTM) model; (ii) Maximum Mean Discrepancy (MMD) for domain adaptation; and (iii) SHapley Additive exPlanations (SHAP) for interpretability. On DO prediction, we achieve an RMSE of 0.42 mg/L with XTL-WQ, compared to 0.61 mg/L for LSTM baseline (+31% improvement). Under extreme data scarcity (less than 30 instances), our model achieves an RMSE of 0.51, while baselines exceed 0.75 mg/L.
🏷 Learning, Framework, Water Quality
Article 96 · pp. 1338-1346
Comparative Analysis of PWM and Svpwm Techniques for Transformer Transient Mitigation in Series Voltage Compensation Systems
The increasing integration of power electronic devices in electrical power systems has intensified the need for effective control techniques capable of minimizing transformer transient disturbances in series voltage compensation applications. This study investigates and compares the performance of Pulse Width Modulation (PWM) and Space Vector Pulse Width Modulation (SVPWM) techniques in controlling transformer transients and improving overall compensation system performance. The analysis focuses on critical parameters such as transient response characteristics, harmonic distortion, voltage stability, switching efficiency, and power quality enhancement.
A detailed simulation model of a transformer-based series voltage compensator is developed using MATLAB/Simulink to evaluate the operational behaviour of both modulation strategies under different loading and disturbance conditions. The comparative results indicate that the SVPWM technique achieves improved voltage utilization, faster dynamic response, and reduced Total Harmonic Distortion (THD) when compared with conventional PWM methods. In contrast, PWM demonstrates easier implementation and reduced computational requirements, which may be advantageous in less complex applications. The research further highlights the influence of modulation strategies on transformer stress reduction and transient mitigation within modern compensation systems.
The outcomes of this work provide a useful reference for the design and optimization of efficient series voltage compensation systems aimed at enhancing power system reliability, stability, and power quality.
🏷 Pulse Width Modulation (PWM), Space Vector Pulse Width Modulation (SVPWM), Transformer Transients
Article 97 · pp. 1347-1363
Minimizing Mobile and Wireless Electronic Nuisance in Classes and Affiliated Malpractices in Examination Centres Through a Tri-Band Detection System
This research outlines the creation of a tri-band radio-frequency (RF) detection and monitoring system aimed at addressing the increasing abuse of mobile phones and other wireless electronic gadgets in classrooms and exam venues. The system employs a passive detection method to ensure compliance with regulatory standards while safeguarding vital communication services, especially during emergencies. The system constantly monitors the surroundings to identify RF signals from devices functioning within the cellular, Wi-Fi, and Bluetooth frequencies. Detected signals are analyzed using parameters such as Received Signal Strength Indicator (RSSI), spectral occupancy, and temporal features to enable precise categorization of wireless activity. The hardware setup includes a microcontroller system, NRF24 transceiver modules for detecting 2.4 GHz Wi-Fi and Bluetooth, and a 1N34A germanium diode-based RF detector for sensing cellular signals in the 900 MHz to 2.4 GHz spectrum. A TFT display interface, along with an alert system, enables real-time tracking and alerts. The system is developed to encompass standard classroom and exam hall settings (150 m² to 450 m²) and accommodates adjustable sensitivity and time-controlled functionality. Experimental findings showed that Wi-Fi signals display consistent behavior, Bluetooth signals manifest as sporadic spikes, and cellular signals present as burst transmissions, facilitating dependable classification. The proposed solution provides an affordable, flexible, and unobtrusive system for improving academic honesty and minimizing wireless examination misconduct.
🏷 Transceiver, Germanium diode, Radio frequency, RSSI, Detector
Article 98 · pp. 1364-1372
Generative AI for IoT and Edge Computing: Enhancing Intelligent Edge Systems
The rapid expansion of the Internet of Things (IoT) has resulted in massive volumes of data being generated by interconnected devices across various domains. Traditional cloud-centric architectures often struggle with issues such as high latency, bandwidth constraints, and data privacy risks when processing this data. Edge computing has emerged as an effective solution by enabling data processing closer to the data source, thereby improving response time and reducing network dependency. In recent years, Generative Artificial Intelligence (GenAI) has gained significant attention for its ability to generate insights, predictions, and adaptive responses from complex and dynamic datasets. This paper examines the integration of Generative AI with IoT and edge computing to enhance intelligent edge systems capable of real-time analytics and autonomous decision-making. It explores architectural frameworks, potential applications in areas such as smart cities, healthcare, industrial automation, and autonomous systems, as well as the advantages of improved efficiency, scalability, and privacy preservation. Additionally, the paper discusses the technical challenges associated with deploying generative models at the edge, including resource constraints, model optimization, security, and data management. Finally, it outlines future research directions aimed at developing scalable, secure, and energy-efficient GenAI-enabled edge computing ecosystems.
🏷 Generative AI, Edge Computing, Enhancing Intelligent
Article 99 · pp. 1373-1386
Secure UPI for Web3: Integration of Solana with Unified Payments Interface
To make the payment system robust and user friendly, decentralized based Scan and Pay system need to be designed. This paper integrates the Unified Payments Interface (UPI) of India with the Solana-based Blockchain to make the payment system decentralized. Solana offers a high throughput and low-cost based decentralized infrastructure which is combined with the simple and reliable UPI system. So, the proposed system enables cryptocurrency transactions linked to UPI while maintaining user friendliness, scalability, and regulatory compliance. The designed method uses a secure architecture powered by smart contracts and modular design. It offers a viable bridge between centralized financial networks and emerging Web3 ecosystems. Proposed Solana-based UPI is compared with the Non-Solana based UPI which is using Blockchain. Results show that there is improvement of 91% in transaction latency and 95% in transaction cost as compared to the Non-Solana based UPI system.
🏷 UPI, Blockchain, Solana, Web3, Cryptocurrency, Digital Payments
Article 100 · pp. 1387-1428
Beyond Generative Intelligence: A Comprehensive Review of Emerging Artificial Intelligence Paradigms, Explainability Challenges, Ethical Risks, and Future Directions
The artificial intelligence landscape has undergone a profound transformation from narrow, task-specific automation to sophisticated, multi-paradigm systems capable of autonomous reasoning, emotional understanding, and creative generation. This systematic literature review synthesizes 141 peer-reviewed studies published between 2018 and 2026 to map the evolution of AI paradigms beyond the dominant Generative AI breakthrough. Following PRISMA guidelines, we analyzed 4,250 initial records across six major academic databases, ultimately including 141 studies that address seven emerging AI paradigms: Generative AI, Emotional and Empathetic AI, Social AI, Agentic AI, Multimodal AI, Explainable AI (XAI), and Responsible AI. Quality assessment was conducted using a modified Mixed Methods Appraisal Tool (MMAT) with a 0–10 scoring rubric, achieving excellent inter-rater reliability (ICC = 0.87, 95% CI: 0.83–0.91). Thematic synthesis followed a rigorous three-phase approach yielding 47 first-order codes, 18 second-order descriptive themes, and 5 overarching analytical clusters. Our bibliometric analysis reveals a decisive shift from purely technical AI research to socio-technical integration, with five dominant thematic clusters emerging: Intelligence and Learning, Generative Ecosystems, Human-Centric AI, Governance and Trust, and Autonomous Systems. Key findings indicate that while Generative AI has achieved remarkable capabilities in content creation and reasoning, critical challenges persist in explainability, algorithmic bias, and governance. The Black Box problem remains a fundamental barrier to trust in high-stakes domains such as healthcare, finance, and criminal justice, despite advances in XAI techniques including SHAP, LIME, and attention visualization. Concurrently, Dark AI threats—encompassing deepfakes, AI-powered cyberattacks, autonomous weapons, and surveillance systems—pose unprecedented risks requiring urgent international governance frameworks. We propose an Integrated AI Ecosystem Framework comprising six interdependent layers: Intelligence, Creation, Human Interaction, Autonomous, Governance, and Security, with Trustworthy AI serving as the integrating principle. This framework is positioned against existing AI governance frameworks (EU High-Level Expert Group Trustworthy AI, NIST AI Risk Management Framework, IEEE Ethically Aligned Design) and uniquely integrates technical architecture, governance principles, and an evolutionary pathway from current capabilities toward AGI. However, unlike established frameworks that have undergone extensive stakeholder validation, the proposed framework remains conceptual and its empirical validation represents a key future research priority. This framework positions Generative AI as the foundation for an evolutionary pathway toward Emotional AI, Agentic AI, Cognitive AI, and ultimately Artificial General Intelligence (AGI). Our analysis identifies eleven critical research propositions addressing gaps in multimodal integration, emotional intelligence validation, agentic system safety, XAI standardization, global AI governance, and framework empirical validation. This review contributes a unified conceptual model for understanding AI's convergent evolution and provides actionable recommendations for researchers, practitioners, and policymakers navigating the transition from isolated AI capabilities to integrated, trustworthy, human-centric AI ecosystems.
🏷 Artificial Intelligence; Generative AI; Emotional AI; Agentic AI; Explainable AI; Black Box AI; Dark AI; Responsible AI
Article 101 · pp. 1429-1464
A World Without Lies: A Critical Study on the Social, Psychological, Political, and Ethical Implications of Absolute Truthfulness
Lying is a pervasive element of human communication, deeply embedded in interpersonal relationships, social interaction, political processes, economic transactions, psychological adaptation, and cultural practices. Although deception is generally regarded as morally questionable, it also performs important functional roles by maintaining social harmony, protecting individual privacy, managing interpersonal relationships, mitigating conflict, and facilitating social adaptation. The ability to distinguish truth from falsehood constitutes one of the defining characteristics of human communication and social organization. Conversely, truthfulness has long been regarded as one of the fundamental virtues of ethical communication and civilized society. Against this backdrop, this study critically examines the hypothetical scenario in which all human beings suddenly lose the ability to lie. Rather than treating this question merely as a philosophical thought experiment, the paper presents a systematic interdisciplinary conceptual analysis of the potential social, psychological, political, economic, ethical, and communicative consequences of absolute truthfulness, exploring how the disappearance of deception might fundamentally transform individuals, institutions, and society as a whole. The study adopts a qualitative conceptual research design grounded in critical interdisciplinary analysis and philosophical thought experimentation. It does not rely on empirical data; instead, it synthesizes established theories and scholarly literature from communication studies, psychology, sociology, political science, economics, philosophy, ethics, and classical Indian thought. The analysis is organized through a thematic comparative analytical framework, which systematically examines the functions currently served by deception and evaluates how their absence might reshape human behaviour and social institutions. Existing theoretical perspectives on trust, impression management, self-deception, governance, organizational behaviour, media ethics, and moral philosophy provide the analytical foundation for assessing both the potential benefits and unintended consequences of a lie-free society.
🏷 Truth, Lying, Deception, Honesty, Communication, Ethics, Morality, Society, Social Trust, Transparency, Human Behaviour, Psychology, Sociology, Philosophy, Political Communication, Governance, Democracy, Privacy
Article 102 · pp. 1465-1471
Web Information Retrieval: A Literature Review of Search Engines, Semantic Search, Neural Information Retrieval, and Generative Artificial Intelligence
The growth in digital information available via the World Wide Web has made it more important than ever to develop efficient and intelligent information retrieval solutions. Despite the effectiveness of keyword-based search in matching terms accurately, traditional methods fall short in capturing intent and semantics. As a result, web information retrieval has gone through significant progress due to semantic search, machine learning, deep learning, knowledge graphs, transformers, and generative AI technologies. This literature review focuses on the developments in web information retrieval from 2016 to 2026. It discusses advancements in retrieval models, semantic search techniques, neural information retrieval, recommender systems, large language models, and generative AI-enabled retrieval systems. Major findings include a shift from keyword-based retrieval to context-based, intent-based, and knowledge-based retrieval approaches. Although there have been numerous developments in web information retrieval to ensure enhanced accuracy and user experience, the difficulties associated with scalability, misinformation, bias, interpretability, privacy, and computational efficiency continue to be considerable.
🏷 web information retrieval, search engines, semantic search, neural information retrieval, deep learning, generative AI, information retrieval systems
Article 103 · pp. 1472-1484
A Study to Assess the Knowledge on Menstrual Blood Banking Among Graduation Students at Selected Women’s College, in Varanasi, Uttar Pradesh, India
Background: Menstrual blood banking is an emerging area of regenerative medicine that utilizes menstrual blood-derived stem cells for potential therapeutic applications in tissue repair and the treatment of various degenerative diseases. Despite increasing scientific advances, awareness regarding menstrual blood banking among young women remains limited, highlighting the need to assess their level of knowledge.
Objectives: To assess the knowledge regarding menstrual blood banking among female undergraduate students and determine its association with selected demographic variables.
Methods: A quantitative, descriptive cross-sectional study was conducted among 100 female undergraduate students at Purnodaya Mahila Mahavidyalaya College, Varanasi, Uttar Pradesh. Participants were selected using a non-probability convenience sampling technique. Data were collected using a structured questionnaire consisting of demographic variables and a 15-item knowledge questionnaire on menstrual blood banking. Descriptive and inferential statistics were used for data analysis.
Results: The study revealed that 69% of participants had moderate knowledge, 18% had adequate knowledge, and 13% had inadequate knowledge regarding menstrual blood banking. No statistically significant association was observed between knowledge scores and selected demographic variables (p > 0.05).
Conclusion: The findings indicate that most female undergraduate students possess only moderate knowledge regarding menstrual blood banking. Structured educational programmes and awareness initiatives are recommended to improve knowledge and promote informed decision-making regarding menstrual blood stem cell banking.
🏷 Menstrual blood banking, menstrual blood-derived stem cells, knowledge, female undergraduate students, regenerative medicine, stem cell banking.
Article 104 · pp. 1485-1493
Application of Group V Base Oils in the Development of Advanced Engine Oil Formulations
The continuous evolution of internal combustion engines toward higher efficiency and reduced emissions has necessitated the development of ultra-low viscosity engine oils, such as 0W-16 and 0W-20 grades. While Group III and Group IV (polyalphaolefins or PAOs) base stocks provide excellent foundations for these formulations due to their high viscosity indices and oxidative stability, their non-polar nature introduces critical challenges regarding additive solubility and seal compatibility. Group V base oils, comprising a diverse class of synthetic fluids including esters, polyalkylene glycols (PAGs), and alkylated naphthalenes, have emerged as indispensable high-performance enhancers in modern lubricant chemistry. Rather than serving as the primary bulk fluid, these highly polar synthetic stocks are strategically blended into Group III/IV matrices to resolve inherent physiochemical deficiencies, fundamentally altering the performance profile of the final formulation.
This paper presents a comprehensive investigation into the application of Group V base oils for advanced engine oil development, focusing on their mechanisms of action as co-base stocks. By examining their role as additive solvents, particularly in enhancing the dispersancy and stability of polyisobutylene-bis-succinimide (PIBSI) additives, this research highlights how ester-fortified formulations manage high-temperature deposit formation. Furthermore, we propose a structured methodological framework for formulating and evaluating Group V-enhanced lubricants, emphasizing solvency, seal compatibility, and thermal resistance. Through hypothetical evaluation plans and rigorous discussion of practical and ethical deployment considerations, this work provides a foundational roadmap for next-generation engine oil design.
🏷 Group V base oil, Additives chemistry, Engine oil, Esters, PAG, PAO, Base stock, Boundary Lubrication, Deposit Control & Cleanliness,
Article 105 · pp. 1494-1509
Integrated Renewable Hydrogen and Electric Mobility for Grid Flexibility: A Sector-Coupled Framework for Developing Economies
Developing economies face a dual challenge of rapidly increasing transportation demand and the urgent need to decarbonize energy systems. While renewable energy deployment is accelerating globally, the intermittent nature of solar and wind generation presents significant challenges for grid stability and reliability. Simultaneously, transportation sectors remain heavily dependent on fossil fuels, contributing substantially to greenhouse gas emissions.
This paper proposes a sector-coupled energy infrastructure framework that integrates renewable hydrogen production, electric mobility systems, and smart grid technologies to enhance grid flexibility and accelerate the transition toward net-zero transportation in developing economies. The framework leverages surplus renewable electricity for hydrogen production through electrolysis while utilising electric vehicles (EVs) as distributed energy storage resources through vehicle-to-grid (V2G) technologies. By coupling power, transportation, and hydrogen sectors, the proposed model addresses renewable energy curtailment, grid balancing, energy security, and transportation decarbonisation simultaneously.
The study examines technological pathways, infrastructure requirements, policy mechanisms, and economic considerations for implementation. The proposed framework demonstrates how integrated hydrogen-electric mobility systems may improve renewable energy utilisation, reduce grid congestion, enhance system resilience, and support sustainable economic development through coordinated sector coupling between power, transport, and hydrogen systems. The paper concludes by outlining strategic recommendations for policymakers and stakeholders seeking to establish flexible, low-carbon transportation and energy infrastructures in developing economies.
🏷 Renewable hydrogen, Electric mobility, Grid flexibility, Net-zero transportation, Developing economies
Article 106 · pp. 1510-1522
Effect of Organic Nutrient Management on Growth Parameters of Knol-Khol (Brassica Oleracea L. Var. Gongylodes) Under Bundelkhand Conditions
The present investigation was carried out during the Rabi season of 2026 to evaluate the effect of different organic nutrient sources on the growth performance of knol-khol (Brassica oleracea L. var. gongylodes) under Bundelkhand conditions. The Organic Agriculture Farm, Karguan Ji, Bundelkhand University, Jhansi, under a semi-arid subtropical climate on sandy loam soil with neutral to slightly alkaline pH (7.48) and medium fertility status. The initial soil contained 0.48% organic carbon, 236.5 kg ha⁻¹ available nitrogen, 19.2 kg ha⁻¹ available phosphorus, and 247.8 kg ha⁻¹ available potassium, providing suitable conditions for evaluating organic nutrient management. The uniform soil fertility and favorable climatic conditions ensured reliable treatment comparisons, as reflected by the low coefficients of variation and highly significant treatment effects observed in the ANOVA. The experiment was laid out in a Randomized Block Design (RBD) comprising eight treatments with three replications. The treatments consisted of Panchagavya (4%), Jivamrita (20%), Vermiwash (1:5 dilution), their different combinations, Recommended Dose of Fertilizers (RDF: 120:30:30 kg NPK ha⁻¹), and an untreated control. Growth parameters such as plant height, number of leaves per plant, and leaf area were recorded at harvest. The results indicated significant differences among treatments. The combined application of Panchagavya (4%) and Jivamrita (20%) (T4) recorded the highest plant height (35.6 cm), maximum number of leaves per plant (18.7), and largest leaf area (400.5 cm²), followed by Panchagavya (4%) + Vermiwash (1:5 dilution) (T5). The control treatment recorded the lowest values for all growth attributes. The superior performance of integrated organic treatments was attributed to improved nutrient availability, enhanced microbial activity, increased production of plant growth-promoting substances, and improved soil fertility. The study concludes that integrated application of Panchagavya and Jivamrita is an effective and sustainable nutrient management strategy for enhancing vegetative growth of knol-khol under Bundelkhand agro-climatic conditions.
🏷 Knol-khol, Brassica oleracea var. gongylodes, Organic nutrition, Panchagavya, Jivamrita, Vermiwash, Growth parameters, Sustainable agriculture.
Article 107 · pp. 1523-1530
Intra-Gender Exploitation Among Women at Workplace: A Socio-Organizational and Psychological Analysis
The phenomenon of intra-gender workplace exploitation—specifically women targeting other women—presents a complex, multi-layered challenge within modern corporate and academic environments. While historical gender studies have focused extensively on patriarchal, inter-gender power dynamics, contemporary organizational research demonstrates that women face distinct, often invisible psychological and professional hurdles from their female peers and superiors. Evaluating this behavior strictly through an individual psychological lens risks oversimplifying complex workplace realities. This paper examines intra-gender exploitation not as isolated interpersonal friction, but as a systemic pathology shaped by institutional power structures, structural scarcity, and intersecting socio-demographic variables including age, cultural background, and hierarchical status.
🏷 Intra-Gender, Workplace, Psychological, Analysis
Article 108 · pp. 1531-1545
An Investigation into Socio-Political Constraints on Decarbonization in Akwa Ibom State, Nigeria
This study investigated socio-political constraints on decarbonization in Akwa Ibom State, Nigeria, with emphasis on how governance structures, institutional frameworks, political interests, and stakeholder dynamics influence policy implementation and outcomes. A descriptive survey research approach was used in the research, covering three oil-producing Local Government Areas (LGAs) in the state: Eket, Ibeno and Eastern Obolo. The primary data for the study were obtained using structured questionnaires distributed to a sample of 400 respondents selected using a multistage sampling procedure, while field observations were also used. Descriptive statistics, Pearson Product-Moment Correlation (PPMC) and multiple regression analyses were used for analysing the data collected from the field at a 0.05 level of significance. From the analysis, it was found that socio-political elements such as political interest in oil revenues, socio-political instability, and economic dependency on fossil fuels significantly hinder decarbonization processes. Perceived lack of effectiveness of government stakeholders, community stakeholders, and industry stakeholders in the push for decarbonization was established through stakeholder analysis, while it is agreed upon that there is a need for collaboration and awareness for better results. Also, the respondents highlighted the poor progress in the realisation of decarbonization outcomes, such as low levels of reductions in carbon emissions, inadequate use of renewable energy and continued deterioration of the environment. Inferential statistics revealed a statistically significant correlation between socio-political factors and the implementation of decarbonization policies (r = 0.808; p < 0.05), between governance and institutional factors and decarbonization activities (F = 97.609; p < 0.05) and finally between stakeholder roles/perceptions and decarbonization outcomes (r = 0.833; p < 0.05). The regression analysis shows that stakeholder perceptions/roles accounted for 39.7% of the variability in decarbonization outcomes. In conclusion, decarbonization activities in Akwa Ibom State are greatly hampered by the prevailing socio-political factors, institutional weaknesses and ineffective stakeholder involvement in achieving poor environmental transition outcomes.
🏷 Decarbonization; Socio-political constraints; Governance structures; Institutional frameworks; Stakeholder engagement; Akwa Ibom State; Nigeria.
Article 109 · pp. 1546-1563
The Impact of ITA-Wogba Creek’s Flooding on the Socio-Economic Activities of Port Harcourt, Rivers State, Nigeria.
Flood is one of the recurrent environmental problems that has affected urban communities in Port Harcourt, Rivers State, Nigeria. This study focused on the effect of flood in the Ita-Wogba Creek area on the socio-economic activities of people living in selected flood-prone communities in Port Harcourt Metropolis. This study was done to identify the causes of floods, examine the socio-economic effects of floods, find out the coping measures employed by people, and investigate the relationship between the occurrence of floods and socio-economic losses. The research adopted a descriptive survey design in conducting this study. Data was collected from 400 respondents using a multi-stage sampling method from four communities around the Ita-Wogba Creek area. The findings revealed that improper disposal of solid waste, inadequate drainage systems, poor urban planning, and rapid urbanisation are the major causes of flooding in the study area. Flooding was found to negatively affect livelihoods, damage residential and commercial properties, increase health risks, disrupt educational activities, and impose financial burdens on households. Temporary evacuation, construction of makeshift barriers, and elevation of household belongings were identified as the most common coping strategies adopted by residents. The study further established that flooding contributes to the prevalence of waterborne and vector-borne diseases, including cholera, typhoid fever, and malaria. Correlation analysis revealed a strong positive and significant relationship between flooding frequency and socio-economic losses (r = .881, p < .001), indicating that increased flood occurrence leads to greater socio-economic damage. The study concludes that flooding around Ita-Wogba Creek is largely driven by human-induced factors and poses significant socio-economic and public health challenges. It recommends improved drainage infrastructure, effective waste management, sustainable urban planning, and strengthened community-based flood management strategies.
🏷 Flooding, Community Resilience, Socio-Economic Impacts, Coping Strategies, Health Risks, Urban Planning, Disaster Management, Environmental Health
Article 110 · pp. 1564-1582
Bridging Legal Language Barriers Using Explainable AI: Outcome Prediction and Multilingual Knowledge based answer retrieval for Indian Law
This study presents an integrated legal AI platform that combines interpretable case outcome prediction with multilingual, retrieval-grounded legal question answering to improve access to Indian law. The work is motivated by the difficulty ordinary citizens face in understanding legal language, the scarcity of trustworthy guidance, and the need for tools that work across India’s major languages. To address this, the authors built two connected components: a prediction module for Supreme Court case outcomes and a question-answering module based on statutory retrieval and generation. For prediction, they compiled 26,688 Indian Supreme Court judgments from 1950 to 2024 and represented each case using TF-IDF text features, case-type encodings, and temporal metadata, then trained an interpretable logistic regression model. For legal QA, they indexed 21 Indian legal acts with sentence-transformer embeddings and FAISS, and used a locally hosted Mistral model to generate simplified answers grounded in retrieved legal passages. The system was designed for English, Hindi, and Tamil, with translation, speech input, speech output, and interactive visualizations to make legal information more accessible. The prediction model achieved 91.3% accuracy and 0.919 ROC-AUC, while confidence calibration showed a strong correlation between predicted and actual accuracy. In the QA module, the system reached 78.4% precision@5, 86% answer correctness, and only 7% hallucination, a substantial improvement over baseline generative approaches. User evaluation with 35 participants reported 4.05/5 overall satisfaction, with multilingual support and explainability among the most valued features. Overall, the study concludes that transparent machine learning, retrieval-augmented generation, and multilingual interfaces can work together to build a practical and trustworthy legal assistance system for Indian users.
🏷 Explainable artificial intelligence, Indian Supreme Court, Legal judgment prediction, Logistic regression
Article 111 · pp. 1583-1601
Family Structure and Academic Performance: A Correlational Study Among Junior High School Students
Family structure plays a vital role in shaping the social, emotional, and academic development of students. This study examined its impact on the academic performance and perceptions of Junior High School students at Silanga National High School, Schools Division of Catbalogan City, during the School Year 2025–2026, using a descriptive-correlational design. Findings revealed that among the profile variables, only sex and parents’ occupation showed significant relationships with family structure. Parents’ occupation demonstrated a moderate association, while sex showed a minimal relationship. The null hypothesis regarding the relationship between family structure and students’ perceptions of parental roles, decision-making, emotional support, discipline, and family unity was rejected, indicating that students from non-intact families perceived these aspects slightly less positively; however, economic support showed no significant relationship. Conversely, the null hypothesis on academic performance was accepted, suggesting minimal to no impact of family structure. Most students came from intact families and achieved satisfactory to very good academic performance, although many experienced limited time with parents. Recommendations include strengthening parental engagement, implementing gender-sensitive programs, enhancing emotional support, providing academic interventions, offering targeted counseling, fostering school-community collaboration, and establishing recognition programs. Future research may explore additional variables, longitudinal designs, and qualitative approaches.
🏷 Family Structure, Academic Performance, Parental Roles, Emotional Support, Student Perceptions
Article 112 · pp. 1602-1620
Integrated transport planning for low-emission urban mobility: case studies from U.S cities
As urban areas account for over 70% of global carbon dioxide emissions, integrated transport planning has emerged as a critical mechanism for achieving low-emission urban mobility. This study examines the integration of land use and transportation planning as a strategy for sustainable mobility through a descriptive case study analysis of four major U.S. cities: New York City, San Francisco, Boston, and Los Angeles. The findings demonstrate a clear correlation between compact cities and sustainable modal shares. Compact cities such as New York City, San Francisco, and Boston successfully leverage high public transit modal shares to drive down per capita emissions. In contrast, sprawling metropolitan areas like Los Angeles remain heavily car-dependent. The findings also support existing discourse on sustainable urban mobility that city-wide electrification alone will not achieve the required long-term climate obligations, but rather depends on an interplay of compact city design, boosting public transit use, and accelerating the adoption of electric vehicles. Thus, this study proposes the ABC roadmap as a long-term strategy for cities to achieve their respective climate change obligations
🏷 Integrated, transport planning, low-emission
Article 113 · pp. 1621-1635
Evaluation of Traffic Load Variation in Onitsha Following the Construction of the Atani–Obosi Access Route of the Second Niger Bridge, Nigeria
Road transportation infrastructure is fundamental to regional mobility, economic growth, and urban development. However, increasing traffic demand within rapidly urbanizing cities often exceeds the carrying capacity of existing road networks, resulting in chronic congestion, travel delays, increased transportation costs, and reduced operational efficiency. The construction of the Atani–Obosi Access Route of the Second Niger Bridge was undertaken to improve regional accessibility and redistribute vehicular traffic previously concentrated along the existing Niger Bridge corridor into Onitsha. This study evaluated variations in traffic load resulting from the construction of the access route by comparing traffic conditions before the project with current traffic flow patterns into Onitsha. The study adopted a transportation assessment approach integrating Geographic Information Systems (GIS), field traffic observations, and statistical analysis to evaluate traffic redistribution associated with the new transportation corridor. Traffic volume datasets obtained during the study were analysed to determine changes in traffic intensity, spatial distribution of vehicular movement, and transportation efficiency before and after construction of the access route. Comparative analyses were undertaken to determine the extent to which the new corridor influenced vehicular movement into Onitsha and surrounding communities. The findings indicate that the Atani–Obosi Access Route has significantly altered traffic distribution within the study area by providing an alternative transportation corridor that reduced dependence on the traditional route into Onitsha. Traffic flow became more spatially distributed, thereby reducing traffic pressure along previously congested sections of the existing road network. The study further demonstrates that the access route has improved regional accessibility, enhanced transportation efficiency, and strengthened interconnectivity between communities within Anambra State and neighbouring regions. The study concludes that the Atani–Obosi Access Route has contributed significantly to traffic redistribution and improved transportation performance within the study area. It recommends the integration of continuous traffic monitoring, intelligent transportation planning, and evidence-based infrastructure management into future highway development projects to ensure sustainable transportation systems.
🏷 Traffic Load, Traffic Redistribution, Transportation Infrastructure, Second Niger Bridge, Access Route, Onitsha, GIS, Traffic Flow, Mobility, Transportation Planning.
Article 114 · pp. 1636-1653
An Exploration of the Implementation of Digital Education Policies in Public Secondary Schools in Tanzania: A Case of Kilimanjaro Region
The employment of technologies for learning purposes has attracted much concern in many nations across the world, but the gap between policy formation and implementation has been a challenge in many developing nations. In this research, the implementation of digital education policies in public secondary schools in Tanzania has been examined with emphasis on Kilimanjaro.
Using the theoretical framework of Policy Enactment Theory (Ball, Maguire & Braun, 2012) and Street Level Bureaucracy Theory approach (Lipsky, 1980), this study adopted an exploratory qualitative case study approach in which the data was collected through semi-structured interviews conducted with 24 interviewees comprising district education officers, school heads, ICT teachers and classroom teachers, observation and review of documents.
Though there have been some successes in the implementation of ICTs in teaching and learning in many public schools, most of them face challenges like inadequate infrastructure, insufficient digital skills of the teachers, unfavorable attitudes towards use of technology in learning, insufficient time to integrate ICT in teaching, poor technological support services, and low digital skills of students.
Other variables considered by this research include better cooperation among the public and private sectors, focus on the need for ongoing professional development, local adaptation of digital materials, and good mechanisms of monitoring and evaluation as methods that could be used to address the challenges in implementing successful transformation of digital education. This paper has reached the conclusion that for the transformation of the process of learning into a digital one in Tanzania and Sub-Saharan Africa, a multi-sectoral approach is necessary.
🏷 Digital Education Policies; Policy Implementation; Public Secondary Schools;
Article 115 · pp. 1654-1661
GST 2.0 and Its Impact on Economic Growth: A Structural and Macroeconomic Analysis
By collapsing the five-tier structure into three slabs (5%, 18%, and 40%), the reform aims to reduce classification disputes and correct inverted duty structures. Using a mixed-method approach—combining secondary data (Q3–Q4 FY2025–26) with sectoral case studies in automobiles, FMCG, and healthcare—this study finds an initial fiscal gap of ₹45,000 crore offset by an 8.1% surge in monthly collections. Econometric projections suggest GST 2.0 contributed to India’s 7.3% GDP growth in FY2026, alongside lower inflation and improved MSME liquidity. Positioned within global VAT/GST experiences, India’s reform illustrates how simplification can stimulate consumption-led growth while raising challenges of fiscal federalism and sin-tax dependency.
🏷 GST 2.0; Rate Rationalization; Fiscal Federalism; Inverted Duty Structure; Macroeconomic Stability; Sectoral Analysis
Article 116 · pp. 1662-1667
Natural Radioactivity in Farmland Soils and Soil-to-Cereal Transfer of Primordial Radionuclides in Chikun Local Government Area, Kaduna State, Nigeria
Radionuclides that are naturally present in agricultural soils have the potential to spread to food crops and provide a route for radiation exposure to humans. In Chikun Local Government Area, Kaduna State, Nigeria, this study assessed the activity concentrations of 40K, 226Ra, and 232Th in farming soils and their transfer to certain cereal crops. Four farming villages (Kujama, Rido, KRPC Perimeter, and Sabon Tasha) provided samples of composite soil and related cereal crops (sorghum, millet, and maize). Gamma-ray spectroscopy was used to measure radioactive activity concentrations, and soil-to-cereal transfer factors (TFs) were computed to evaluate radionuclide uptake. Spatial and crop-related changes were assessed using descriptive statistics and one-way analysis of variance (ANOVA). With activity concentrations ranging from 365.24 ± 28.61 to 421.68 ± 34.92 Bq kg⁻¹, the data indicated that the most common radionuclide in the soils was 40K. This was followed by 232Th (46.53 ± 7.85–58.91 ± 9.36 Bq kg⁻¹) and 226Ra (31.82 ± 5.74–41.76 ± 7.63 Bq kg⁻¹). Geological enrichment was suggested by the fact that 232Th surpassed the global norm, although 40K and 226Ra were generally within the global reference range. The order of the soil-to-cereal transfer factors was 40K > 226Ra > 232Th, with maize showing the lowest radioactive uptake and sorghum the highest. Significant variations in transfer factors across cereal crops (40K: p = 0.012; 226Ra: p = 0.021; 232Th: p = 0.038) and radionuclide activity concentrations among sampling locations (40K: p = 0.014; 226Ra: p = 0.028; 232Th: p = 0.007) were found using one-way ANOVA. The data reveal that local geology greatly influences radioactive distribution and that radionuclide transmission to cereals is crop-dependent. The produced baseline data are useful for radiological risk assessment, environmental radioactivity monitoring, and the creation of evidence-based plans to guarantee food safety and sustainable agricultural output in the research region.
🏷 Naturally occurring radionuclides; Farmland soil; Soil-to-cereal transfer factor; Gamma-ray spectrometry; Environmental radioactivity; Chikun LGA.
Article 117 · pp. 1668-1679
Financial Literacy, Financial Self-Efficacy, and Financial Stress Among Graduate Students: A Conceptual Analysis of Financial Well-Being in Emerging Educational Regions of India
Financial stress has become an increasingly important concern among graduate students due to rising educational costs, increasing living expenses, limited financial resources, and uncertainty regarding future employment opportunities. While previous studies have examined financial literacy, financial self-efficacy, and financial well-being separately, limited research has integrated these constructs within a single conceptual framework, particularly in the context of emerging educational regions of India. This study addresses this research gap by developing an integrated conceptual understanding of the relationships among financial literacy, financial self-efficacy, financial resilience, financial stress, and financial well-being. The study adopts a conceptual and descriptive research design based on a systematic review of secondary literature collected from peer-reviewed journal articles, books, academic reports, and scholarly publications. The review indicates that financial literacy and financial self-efficacy significantly influence financial behaviour, budgeting practices, financial decision-making, and students’ ability to cope with financial stress. Educational expenses, accommodation costs, educational debt, family financial responsibilities, and socioeconomic vulnerability are identified as major contributors to financial stress among graduate students. Students with higher financial knowledge and stronger financial confidence are generally better able to maintain financial stability and adapt to financial challenges. The study highlights the importance of financial education initiatives, counselling support, and institutional interventions in improving financial resilience and overall financial well-being. The novelty of this study lies in proposing an integrated conceptual framework that links financial literacy, financial self-efficacy, financial resilience, financial stress, and financial well-being, providing a foundation for future empirical research in emerging educational regions of India.
🏷 Financial Literacy, Financial Self-Efficacy, Financial Stress, Financial Well-Being, Behavioural Finance, Graduate Students, Higher Education.
Article 118 · pp. 1680-1695
Mobile Application Deployment of CNN-based Food Classification System for Visually Impaired Individuals: A Real-Life Application for Nigerian Swallow Foods
The study addresses the significant challenge of food identification faced by visually impaired individuals, particularly in culturally specific contexts like Nigerian cuisine. A convolutional neural network (CNN) classification system was developed to categorize Nigerian swallow foods and assess their freshness (Fresh, 4 Hours Old, 1 Day Old) across 60 fine-grained classes using a dataset of 22,823 items. Six light CNN architectures were trained and evaluated based on metrics such as accuracy, macro-averaged precision, recall, F1 score, and AUC-ROC, alongside their parameter count and inference latency for deployment suitability. Among these, MobileNetV2 emerged as the top performer, achieving a test accuracy of 99.78%, a macro F1 score of 0.9979, and a perfect AUC-ROC of 1.0000, while maintaining a moderate parameter count of 2.33 million, making it suitable for mobile applications. LeNet-5 and ShuffleNetV2 also demonstrated strong performances with accuracies above 98.9%. In contrast, CCFNN exhibited poor performance with only 23.54% accuracy due to capacity issues. The best-performing model, MobileNetV2, was integrated into an application named Naija Food Eye, which facilitates food identification and freshness assessment for the visually impaired, providing confidence scores, top-5 predictions, and quick response times. The findings confirm that lightweight CNN models can effectively assist visually impaired individuals in recognizing foods within a culturally rich culinary landscape, offering a viable, deployable solution.
🏷 Assistive Technology, Convolutional Neural Network, Deep Learning, Food Classification, Mobile Application, Nigerian Swallow Foods, Transfer Learning, Visually Impaired.
Article 119 · pp. 1696-1700
The Bio-Physiological Impact of Nidra Vega Dharana (Suppression of the Sleep Urge): An Ayurvedic and Modern Clinical Review
In both ancient Eastern medicine and modern Western physiology, sleep is recognized not as a passive state of rest, but as an active, vital process of biological restoration. Ayurveda categorizes Nidra (sleep) as one of the Trayopastambha (the three pillars of life, alongside food and controlled sensory or sexual energy). When the biological urge to sleep arises, it is termed a Vega (a natural, impending bodily reflex). Forcible suppression of this urge is called Nidra Vega Dharana (or Nidra Vega Vidharana). This review explores the systemic causes, multi-organ consequences, and integrative preventive measures of suppressing the sleep urge from both Ayurvedic and modern clinical perspectives.
🏷 Bio-Physiological, Dharana, Nidra Vega, Clinical Review
Article 120 · pp. 1701-1718
Job Preparedness, Professional Competence and Work Efficiency Among Administrative Officers in the Department of Education
This study examined the relationship between job preparedness, professional competence, and work efficiency among Administrative Officers in the Department of Education. Using a quantitative descriptive-correlational design, data were gathered from 106 Administrative Officers in the Schools Division of Capiz through a validated survey questionnaire.
Frequency counts, percentages, means, standard deviations, Spearman's rho, and other appropriate statistical analyses were used. The findings showed that the respondents demonstrated high levels of job preparedness, professional competence, and work efficiency.
More importantly, the results revealed significant positive relationships among the variables. Job preparedness showed the strongest association with professional competence (r = 0.790, p < .01), followed by the relationship between professional competence and work efficiency (r = 0.585, p < .01), and job preparedness and work efficiency (r = 0.552, p < .01).
These findings suggest that when administrative officers are better prepared for their roles, they are more likely to demonstrate stronger professional competence, which also contributes to improved work efficiency. The study underscores the value of sustained professional development and organizational support in strengthening administrative performance within the Department of Education.
Article 121 · pp. 1719-1728
The Influence of Government Sponsorship on the Academic Performance of Sponsored Students in Public Secondary Schools in Lugari Sub-County, Kenya.
Government sponsorship programmes have become an important policy intervention to improve access to secondary education for learners from economically disadvantaged households in Kenya. Despite the expansion of bursary schemes, concerns remain regarding their effectiveness in improving students' academic performance. This study examined the influence of government sponsorship on the academic performance of students in public secondary schools in Lugari Sub-County, Kakamega County, Kenya. The study employed a mixed-methods research design, combining quantitative and qualitative approaches to provide a comprehensive understanding of the relationship between sponsorship and academic achievement. A sample of 376 respondents comprising sponsored students, class teachers, and parents was selected using purposive sampling. Data were collected using structured questionnaires and interview schedules. Quantitative data were analysed using descriptive statistics, Pearson correlation analysis, and multiple regression analysis with the aid of the Statistical Package for the Social Sciences (SPSS), while qualitative data were analysed thematically. The findings revealed that government sponsorship had a statistically significant positive influence on students' academic performance. Pearson correlation analysis demonstrated a strong positive relationship between government sponsorship and academic performance (r = 0.818, p < .001). Qualitative findings further showed that bursary support reduced financial constraints, improved school attendance, enhanced access to learning materials, and enabled learners to concentrate on their studies. The study concludes that government sponsorship contributes significantly to improved educational outcomes by reducing financial barriers that hinder academic success. The study recommends increasing bursary allocations, ensuring timely disbursement of funds, and strengthening monitoring mechanisms to maximize the effectiveness of government sponsorship programmes.
🏷 Government sponsorship, academic performance, bursary programmes, secondary education, socio-economic status.
Article 122 · pp. 1729-1756
The Landscape of Mobile Banking Channels: Exploring Their Diverse Characteristics
Government sponsorship programmes have become an important policy intervention to improve access to secondary education for learners from economically disadvantaged households in Kenya. Despite the expansion of bursary schemes, concerns remain regarding their effectiveness in improving students' academic performance. This study examined the influence of government sponsorship on the academic performance of students in public secondary schools in Lugari Sub-County, Kakamega County, Kenya. The study employed a mixed-methods research design, combining quantitative and qualitative approaches to provide a comprehensive understanding of the relationship between sponsorship and academic achievement. A sample of 376 respondents comprising sponsored students, class teachers, and parents was selected using purposive sampling. Data were collected using structured questionnaires and interview schedules. Quantitative data were analysed using descriptive statistics, Pearson correlation analysis, and multiple regression analysis with the aid of the Statistical Package for the Social Sciences (SPSS), while qualitative data were analysed thematically. The findings revealed that government sponsorship had a statistically significant positive influence on students' academic performance. Pearson correlation analysis demonstrated a strong positive relationship between government sponsorship and academic performance (r = 0.818, p < .001). Qualitative findings further showed that bursary support reduced financial constraints, improved school attendance, enhanced access to learning materials, and enabled learners to concentrate on their studies. The study concludes that government sponsorship contributes significantly to improved educational outcomes by reducing financial barriers that hinder academic success. The study recommends increasing bursary allocations, ensuring timely disbursement of funds, and strengthening monitoring mechanisms to maximize the effectiveness of government sponsorship programmes.
🏷 Government sponsorship, academic performance, bursary programmes, secondary education, socio-economic status.
Article 123 · pp. 1757-1764
Enabling Factors for Effective Implementation and Sustainability of Community-Based Rehabilitation (CBR) in Malaysia
This study investigates the enabling factors that contribute to the effective implementation and sustainability of Community-Based Rehabilitation (CBR), locally known as Program Pemulihan Dalam Komuniti (PDK), in Malaysia. Despite the expansion of PDK centres nationwide, challenges remain in sustaining high-quality rehabilitation services. Drawing on prior literature, this research examines the roles of workforce motivation, caregiver support, stakeholder collaboration, community participation, and service accessibility in fostering programme continuity. A qualitative methodology involving semi-structured interviews and focus group discussions with key stakeholders provided in-depth insights into operational and social determinants of PDK sustainability. The findings reveal that motivated and well-trained staff, active caregiver engagement, strong multi-stakeholder partnerships, inclusive community involvement, and accessible services are critical factors underpinning effective implementation. Workforce motivation and stakeholder collaboration emerged as particularly influential, shaping both programme quality and long-term viability. The study further highlights the need for structured caregiver support and enhanced service accessibility to ensure equitable rehabilitation outcomes. These results have implications for national policy and programme design, aligning with the United Nations Sustainable Development Goals (SDGs) by promoting inclusive education (SDG 4), decent work (SDG 8), good health and well-being (SDG 3), reduced inequalities (SDG 10), and partnerships for the goals (SDG 17). The research contributes to the literature by integrating human, social, and institutional factors into a comprehensive sustainability framework for PDK.
🏷 Community-Based Rehabilitation, Sustainability, Workforce Motivation, Caregiver Support, Stakeholder Collaboration, SDGs
Article 124 · pp. 1765-1776
A Comprehensive Carbon Footprint Monitoring Framework for Sustainable Apparel Manufacturing
This study proposes the design, implementation, and evaluation of a software-based carbon footprint monitoring system developed specifically for the apparel manufacturing industry. As one of the major contributors to global greenhouse gas (GHG) emissions, the sector faces increasing pressure to monitor and reduce its environmental impact arising from energy-intensive activities such as fabric production, dyeing, garment manufacturing, and transportation. Existing carbon monitoring approaches often depend on hardware-based systems that provide historical data but offer limited support for real-time intervention and decision-making. The proposed platform consolidates production information, energy consumption records, material utilization, and logistics data into a unified digital system. Using standardized emission factors, it continuously calculates carbon dioxide (CO₂) emissions and provides real-time visibility into environmental performance. This enables manufacturers to identify emission hotspots, implement corrective actions promptly, and improve overall sustainability outcomes. The study also examines market readiness and adoption trends for digital carbon monitoring solutions. Growing environmental regulations, sustainability reporting requirements, and increasing consumer demand for eco-friendly products have accelerated the need for effective carbon management tools. Major apparel-producing countries, including Bangladesh and China, are increasingly adopting such technologies to meet international trade expectations and sustainability commitments. Evidence from industry case studies indicates that digital monitoring systems contribute to measurable reductions in energy use and carbon emissions while supporting operational efficiency. Furthermore, the research highlights the influence of carbon taxation policies, sustainability standards, and financial incentives such as subsidies and green financing in promoting technology adoption. The findings demonstrate that real-time software-based carbon monitoring systems can generate both environmental and economic benefits, helping apparel manufacturers achieve regulatory compliance, improve competitiveness, and advance long-term sustainability goals.
🏷 Greenhouse gas, Carbon Footprint, System Architecture, Database Management
Article 125 · pp. 1777-1786
Awareness, Perception & Involvement of Child Ragpickers in Swachh Bharat Abhiyan
Child ragpicking is a type of informal waste collection practiced by children to survive, and this practice exposes them to dangerous situations and leaves them outside of formal sanitation systems, The aim of the study is to critically examine how far child ragpickers understand Swachh Bharat Abhiyaan(SBA) and how they see themselves in the mission and whether their hygiene habits and lived experience are consistent with the mission goals. It also aims to analyse whether SBA policies acknowledge or favour this vulnerable population and evaluates their hopes of education, security and dignified livelihood beyond ragpicking. A survey of 479 child rag pickers under the age of 14 years was conducted in four districts of Punjab, Bathinda, Mansa, Sangrur and Barnala with the help of a structured interview schedule. The research measures five fundamental dimensions, namely, SBA awareness, involvement in cleanliness activities, hygiene and sanitation behaviour, perception of waste management, and future aspirations. In exploring these dimensions, the research will draw attention to the lack of connection between the objectives of the programme and the actual experiences of children who work at the bottom of the waste economy, and therefore, the necessity to formally incorporate them, provide them with welfare, and protect them with policies in the Indian sanitation system.
🏷 Child Ragpickers, Swachh Bharat Abhiyaan, Waste Management, Sanitation Awareness, Child Labour, Punjab.
Article 126 · pp. 1787-1793
Spatio-Temporal Dynamics of Water Quality in Bheemanakuppe Lake Amidst Anthropogenic Alterations, Bangalore District, Karnataka
Freshwater lakes are indispensable economic, ecological, and cultural lifelines. Urgent action against degradation and biodiversity loss is critical to ensure sustainable water resources, protect human health, and safeguard national economies. In this study, water quality parameters such water quality parameters such as Temperature (27 ± 0.5), pH (7.4 ± 0.1), Total Dissolved Solids (TDS) (360 ± 10), Electrical Conductivity (EC) (460 ± 10), Total Hardness (TH) (122.33 ± 2.52), Dissolved Oxygen (DO) (7.5 ± 0.3), Biological Oxygen Demand (BOD) (1.17 ± 0.15), Chemical Oxygen Demand (COD) (27.67 ± 2.52), Chloride (32.33 ± 2.52), Calcium (82.33 ± 2.52), Magnesium (31.67 ± 1.53), Sodium (27.67 ± 2.52), and Potassium (22 ± 2) were analyzed at Bheemanakuppe Lake in 2024-25. The data were analyzed using Pearsons's correlation matrix and Water Quality Index (WQI) to evaluate the overall health and suitability of lake water. Highly positive correlations were observed between Chloride (Cl) and Sodium (Na) (0.92), as well as between COD and Chloride (0.92). A strong positive correlation was also found between BOD and pH (0.85). In contrast, strong negative correlations were observed between Total Alkalinity (TA) and Sodium, Chloride, and COD (ranging from −0.85 to −0.89). Additionally, Potassium (K) showed moderate negative correlations with BOD and Sodium (approximately −0.75). The Water Quality Index (WQI) was used to condense these parameters into a single, interpretable value, which remained within the “Good” category, with an average of 45.1 during the 2024–2025 period. The study emphasizes the importance of continuous monitoring and the implementation of strategic measures for the preservation and sustainable management of Bheemanakuppe Lake. It also provides valuable insights for local communities, enabling them to make informed decisions to protect and safeguard this vital water resource.
🏷 Physiochemical parameters, Pearson correlation, Water quality index, Bheemanakuppe Lake, Sustainable management.
Article 127 · pp. 1794-1807
Transformative Roles of Generative Artificial Intelligence as a Co-Instructor: A PRISMA-Guided Systematic Literature Review for Personalized and Sustainable Higher Education
The rapid advancement of Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs) such as ChatGPT, has significantly transformed instructional practices in higher education. Beyond serving as instructional support tools, GenAI systems are increasingly conceptualized as collaborative co-instructors capable of assisting educators in curriculum planning, personalized instruction, formative assessment, and learning analytics. Despite growing scholarly interest, existing reviews often examine these applications independently and provide limited integration of pedagogical, ethical, governance, and sustainability perspectives. This study addresses this gap through a PRISMA-guided systematic literature review of 20 empirical and evidence-based scholarly publications published between 2023 and 2025. Articles were retrieved from Scopus, Web of Science, ERIC, and Google Scholar using predefined search protocols and were analyzed through directed qualitative content analysis. The synthesis identified three dominant instructional functions of GenAI: co-planning, co-instruction, and co-assessment, each demonstrating measurable improvements in instructional efficiency, learner engagement, personalized learning, and formative feedback. Simultaneously, the review highlights significant challenges related to academic integrity, algorithmic bias, data privacy, teacher autonomy, and student overreliance on AI-generated content. Comparative analysis across studies indicates that effective implementation consistently depends on sustained human oversight through a Teacher-in-the-Loop (TiTL) framework, which positions educators as pedagogical decision-makers while leveraging AI to augment instructional effectiveness. The review further demonstrates that responsible GenAI integration contributes to sustainable higher education by improving resource efficiency, reducing faculty workload, expanding equitable access to learning, and supporting resilient digital education ecosystems. The study contributes a synthesized conceptual perspective that integrates pedagogical, ethical, governance, and sustainability dimensions into a unified framework for responsible AI-augmented higher education. The findings provide evidence-based guidance for educators, institutional leaders, and policymakers seeking to implement GenAI responsibly while preserving academic quality, educational equity, and human-centered teaching.
🏷 Generative Artificial Intelligence (GenAI); Artificial Intelligence in Higher Education; Teacher-in-the-Loop
Article 128 · pp. 1808-1816
Optimizing Building-Integrated Renewable Energy Systems (BIRES) For Climate and Environmental Security in South-East Nigeria
The built environment in South-East Nigeria presents significant opportunities for advancing climate action through building-integrated renewable energy systems (BIRES). This study examined the optimization of BIRES for climate and environmental security among builders and civil engineers in Abia, Anambra, Ebonyi, Enugu, and Imo States. Using a cross-sectional survey design, data were collected from 150 registered professionals selected through stratified random sampling. Findings revealed low BIRES adoption and optimization awareness, with major barriers including lack of tropical climate design guidelines, absence of building code requirements, inadequate technical capacity, limited government incentives, and the high cost of imported components. Despite limited optimization practices, respondents strongly acknowledged the climate and environmental security benefits of BIRES, particularly in reducing greenhouse gas emissions, improving energy resilience, and enhancing sustainable building performance. Two-way ANOVA analysis showed that professionals with higher optimization awareness reported significantly greater perceptions of climate security benefits, while those lacking lifecycle optimization practices perceived higher environmental security risks. Both hypotheses were not accepted at p < 0.001. The study concludes that inadequate optimization knowledge and limited lifecycle practices hinder the effective contribution of BIRES to sustainable energy and environmental security. It recommends mandatory professional training on BIRES optimization, stronger institutional support, financial incentives, and the integration of lifecycle assessment standards into regional building regulations and professional practice frameworks.
🏷 Building-Integrated Renewable Energy Systems (BIRES), Climate Security, Environmental Security, Renewable Energy, Sustainable Buildings.
Article 129 · pp. 1817-1826
Development of Smart Water IoT System to Assess and Monitor the Quality of Indian River Water
The growing threats to river water quality demand innovative approaches for effective monitoring and protection. This paper proposes an Internet of Things (IoT)-based river water quality monitoring system to address this challenge. The proposed system utilizes a network of sensors strategically deployed within the river, measuring crucial parameters like temperature, pH, dissolved oxygen, turbidity, and conductivity. Sensor data is continuously transmitted wirelessly to a central hub for processing and analysis. Utilizing cloud computing platforms, the system enables real-time data visualization and analysis, allowing for prompt identification of potential pollution events. Additionally, the system integrates alerting mechanisms to notify relevant authorities, facilitating timely interventions. This paper presents the design, implementation, and field testing of the proposed system, along with a thorough evaluation of its performance. The results demonstrate the system's effectiveness in capturing comprehensive water quality data, facilitating real-time monitoring, and enabling proactive water management strategies.
🏷 Water Quality Index (WQI), Total Dissolved Solids (TDS), Turbidity, Extreme Gradient Boosting (XGBoost)
Article 130 · pp. 1827-1842
A Hybrid Machine Learning Framework for Early Detection of Hiv-Associated Opportunistic Infections
The increasing burden of OIs among people living with HIV necessitates accurate and timely predictive systems to support clinical decision-making. The framework leverages clinical, demographic, and treatment-related variables extracted from a real-world dataset obtained from the University of Uyo Teaching Hospital. Data preprocessing techniques, including normalization, feature engineering, and dimensionality reduction were applied to enhance model performance. The proposed model was evaluated using multiple performance metrics, including accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrix analysis. Experimental results show that the best-performing configuration achieved an accuracy of 96.79% with strong sensitivity and specificity, demonstrating its effectiveness in distinguishing between infected and non-infected cases. Furthermore, the model demonstrated the use of the ReLU-based activation function. SHAP analysis further enhanced model interpretability by identifying key predictive features such as CD4 count, treatment type, and age. Overall, the results confirm that the proposed hybrid framework is a reliable and clinically meaningful tool for early detection and risk stratification of HIV-associated opportunistic infections.
🏷 HIV, Infection, Machine Learning, Opportunistic Infections, DNN
Article 131 · pp. 1843-1851
Evaluating the Rest Function of a Smart Pressure Reducing Mattress in Promoting Comfortable Rest Among Bedridden Individuals: An Initial Experiment in Healthy Adults
A bedridden condition with long-term bed rest often results in a pressure ulcer or decubitus, primarily in protruding body parts. Aiming at minimizing the risk and preventing the development of an advanced grade decubitus, an innovative smart pressure-reducing mattress was developed and tested in healthy adults to evaluate the rest function, basically. Back to the basic function of a mattress, this study aimed at evaluating the rest function of a smart mattress by analyzing the indicators of relaxation, rest facilitation, and sleep inducement, and their correlation with comfort after 40 minutes of utilization in the supine position. This was an experimental study employing the one-group post-test only design. The sample was 29 female nursing students (n=29). Instruments included a smart pressure-reducing mattress and the rest function questionnaire. Descriptive statistics and the Spearman Rank correlation test were employed in data analysis (α<0.05). The majority gave maximum scores for indicators of relaxed feeling (69%), rest facilitation (69%), sleep inducement (65.5%), and comfort (79.3%). Comfort (M ± SD = 81.03 ± 6.38; 2.79 ± 0.41) was strongly correlated with relax feeling (M ± SD = 3.69 ± 0.47) and rest facilitation (M ± SD = 3.66 ± 0.55) [p<0.05; ρ=0.523 and ρ=0.662, respectively]. Although most respondents fell asleep after 40 minutes lying down on the smart mattress, comfort was not correlated with sleep inducement (M ± SD = 3,52 ± 0,78; p=0.290). The rest function of the smart mattress is working very well. Utilizing a smart mattress for 40 minutes in supine position can promote comfortable rest by improving relax feeling and facilitating rest in healthy adults. There is a need for a future randomized controlled trial (RCT), testing this smart mattress directly against a regular mattress.
🏷 air mattress, decubitus, pressure reduction, pressure ulcer, smart mattress.
Article 132 · pp. 1852-1863
Hall and ion slip effects on MHD free convective flow of chemical reaction - applications
Hall and ion-slip impacts on unsteady MHD free convective laminar flow of an incompressible viscous, electrically conducting and heat generation/ absorbing fluid enclosed with a semi-infinite porous plate within a rotating frame has been premeditated. The plate is assumed to be moving with a constant velocity in the direction of fluid movement. A uniform transverse magnetic field is applied at right angles to the porous surface, which is absorbing the fluid with a suction velocity changing with time. The non-dimensional governing equations for present investigation are solved analytically making use of two term harmonic and non-harmonic functions. The graphical results of velocity, temperature and concentration distributions on the analytical solutions are displayed and discussed with reference to pertinent parameters. There is an intense world wide activity in the development of instrumentation for medical diagnosis and bio screening based on biological labeling and detection of nano particles. The skin friction coefficient increases in Hall and ion slip parameters. Out comes disclose that the impact of thermal convection of nano particles has increased the tempertature distribution, which helps in destroying the cancer cells during the drug delivery process.
🏷 Hall effection effect, MHD,Chemical reaction,Nano
Article 133 · pp. 1864-1876
Towards Inclusive Knowledge Organizational Systems for Multilingual Communities and Conceptual Models in Federal Universities Libraries in Nigeria
Knowledge organization systems (KOS)—including classification schemes, subject heading lists, thesauri, and metadata frameworks—remain predominantly monolingual and shaped by epistemological traditions developed in Anglo-American and European contexts, posing significant access barriers for multilingual communities served by academic libraries in linguistically diverse nations such as Nigeria. This paper examines the conceptual and practical challenges of designing inclusive KOS for multilingual users within Nigerian higher education institutions, where English serves as the official language of instruction alongside numerous indigenous languages spoken by students, researchers, and host communities. Drawing on the literature on classification theory, multilingual subject access, indigenous knowledge organization, international library policy instruments, and the technological context of Nigerian academic libraries, this paper proposes a layered conceptual model—the Inclusive Multilingual Knowledge Organization (IMKO) framework—that integrates terminology mapping, parallel vocabularies, community-informed indexing, and adaptive metadata layers within existing library systems. The framework is examined against the operational realities of a composite Nigerian academic library, including its predominant reliance on the Koha open-source integrated library system (ILS), highlighting infrastructural, financial, and human-resource considerations documented in prior research. The paper concludes with recommendations for library and information science education, professional bodies, and policy actors—including the National Universities Commission and the Tertiary Education Trust Fund—to support the gradual adoption of more linguistically inclusive cataloguing and classification practices.
🏷 knowledge organization systems; multilingualism; academic libraries; Nigeria; subject access; inclusive cataloguing; indigenous knowledge; Koha
Article 134 · pp. 1877-1885
PROJECT DATA-SPORT (Developing Analytical Thinking and Academic Skills through Sports Performance Observation, Reflection, and Tracking): Enhancing Literacy, Numeracy, and Life Skills Competencies through Fitness and Sports Data Integration in Senior High
Physical Education provides opportunities for integrating academic competencies such as analytical thinking, literacy, and numeracy through performance-based activities. However, many Physical Education programs focus primarily on physical participation without maximizing the use of sports performance data as a learning resource. This study examined the effectiveness of PROJECT DATA-SPORT (Developing Analytical Thinking and Academic Skills through Sports Performance Observation, Reflection, and Tracking) in enhancing literacy, numeracy, and life skills competencies among Senior High School students through sports and fitness data integration.
This study utilized a quantitative school-based action research design conducted among forty (40) Senior High School students during the School Year 2025–2026. The intervention involved structured observation, recording, analysis, and reflection of sports performance indicators such as time, distance, repetitions, and fitness scores. Data were collected using a researcher-made four-point Likert scale questionnaire, performance tracking sheets, and reflection logs. The collected data were analyzed using weighted mean and descriptive statistics.
Results revealed that the implementation of PROJECT DATA-SPORT significantly enhanced students’ analytical thinking, numeracy, and life skills competencies. Students demonstrated a very high level of analytical thinking through sports data analysis (overall mean = 3.37), indicating their ability to interpret and evaluate performance indicators. The project also improved students’ numeracy skills in computing and organizing sports performance data (overall mean = 3.31). In addition, students developed stronger life skills such as self-discipline, goal setting, and self-monitoring (overall mean = 3.40). Engagement in sports data observation and reflection activities was also rated very high (overall mean = 3.38), suggesting that integrating performance tracking increased students’ motivation and participation in Physical Education classes.
The findings conclude that integrating sports performance data in Physical Education significantly enhances students’ analytical thinking, numeracy skills, and life skills development. PROJECT DATA-SPORT demonstrates that Physical Education can serve as an interdisciplinary platform for promoting academic competencies while improving physical fitness engagement. Schools are encouraged to institutionalize sports data integration strategies to support holistic learning and strengthen literacy and numeracy development in Senior High School students.
🏷 sports data integration, analytical thinking, numeracy, literacy, physical education, performance tracking, senior high school
Article 135 · pp. 1886-1894
Risk Associated with Management of Engineering Projects Using Intelligent Based Control System
Some abandoned Engineering projects observed in some parts of the country this present era has arisen as a result of these factors Scope Creep, Schedule Delays, Budget Overruns, Safety Hazards, Technical Challenges and Additional Risk Factors. This is surmounted by introducing reducing risk associated with management of engineering projects using intelligent based control system. To achieve this, it was done in this manner, characterizing and establishing the causes of the risk associated with management of engineering projects, designing a conventional SIMULINK model for the risk associated with management of engineering projects, designing management rule base that will reduce the percentage of the risk associated with management of engineering projects, training ANN in the management rule base to enhance the efficacy of reducing the percentage of the risk associated with management of engineering projects, developing an algorithm that will implement the process, designing a SIMULINK model for reducing risk associated with management of engineering projects using intelligent based control system and validating and justifying the percentage improvement in the reduction of risk associated with management of engineering projects with and without intelligent based control system. The results obtained are the conventional Scope Creep that caused risk associated with management of engineering project was12%. On the other hand, when an intelligent based control system was introduced in the system, the percentage of risk observed in management of engineering project was reduced to10.4%, the conventional Safety Hazards that caused high risk associated with management of engineering projects was7%. However, when an intelligent based control system was integrated in the system the risk associated with management of engineering projects reduced to6.07% and the conventional Additional Risk Factors that caused risk associated with management of engineering projects was32% while when an intelligent based control system was imbibed in the system, it wittingly reduced to27.75%. Finally, with these results obtained, it shows that the percentage improvement in the reduction of risk associated with management of engineering projects when an intelligent based control system was integrated in the system over the conventional competitor was 4.25%.
🏷 Reducing, risk, associated, management, engineering, project, intelligent, based, control, system
Article 136 · pp. 1895-1905
Machine Learning-Based Early Prediction of Opportunistic Infections Among People Living with HIV
The early prediction of opportunistic infections (OIs) among people living with HIV (PLWH). Despite the widespread use of antiretroviral therapy (ART), opportunistic infections remain a major cause of morbidity and mortality, particularly in low- and middle-income settings. Early identification of individuals at high risk is therefore essential for improving clinical outcomes and optimizing healthcare resource allocation. comprehensive dataset comprising 3,982 patient records and 88 clinical, demographic, laboratory, and treatment-related features was utilized. The dataset included categorical, numerical, and text-based variables capturing diverse aspects of patient health status. Principal Component Analysis (PCA) was applied for dimensionality reduction, identifying key contributing factors such as current weight, age, ART refill patterns, and engagement in enhanced adherence counseling sessions. Several machine learning algorithms were explored, with XGBoost emerging as the best-performing model. The model achieved an accuracy of 97.17%, with an AUC of 0.994 and an Average Precision score of 0.979, demonstrating strong discriminative ability. Evaluation metrics further confirmed sensitivity and specificity, with minimal false negatives, which is critical for clinical safety in HIV care.The proposed framework demonstrates the potential of machine learning to support early risk stratification and clinical decision-making for opportunistic infections in HIV-positive populations.
🏷 HIV, Infection, Machine Learning, Opportunistic Infections, XGBoost
Article 137 · pp. 1906-1922
Review on Proton Radioactivity in the Actinide Region: Experimental status, Theoretical Approaches and Future perspectives
Proton radioactivity is one of the most distinctive manifestations of quantum tunnelling in atomic nuclei and provides a powerful probe of nuclear structure beyond the proton drip line. Since its theoretical prediction by Goldanskii in 1960 and subsequent experimental confirmation, proton emission has become an important spectroscopic tool for investigating single-particle configurations, shell evolution, nuclear deformation, and the interplay between nuclear and Coulomb interactions in proton-rich systems. While proton radioactivity has been extensively studied in light, medium-mass, and rare-earth nuclei, comparatively little attention has been devoted to the actinide region despite its unique structural and decay properties.This review presents a comprehensive overview of proton radioactivity in actinide nuclei (89 ≤ Z ≤ 103), with emphasis on recent theoretical developments and future experimental prospects. The historical evolution of the field, experimental status, theoretical models, and decay systematics are critically discussed. Particular attention is given to the prediction of proton-emitting actinides, the influence of proton-decay energy and quantum tunnelling on half-lives, Geiger–Nuttall-type correlations, and the competition between proton emission, α decay, β decay, spontaneous fission, and cluster radioactivity. The predictive performance of widely used theoretical approaches, including the Coulomb and Proximity Potential Model (CPPM), Generalized Liquid Drop Model (GLDM), Effective Liquid Drop Model (ELDM), Density-Dependent Michigan 3-Yukawa (DDM3Y) interaction, Jeukenne–Lejeune–Mahaux (JLM) interaction, and related microscopic models, is also evaluated.The review highlights the most promising candidate proton emitters in the actinide region and discusses the prospects for their experimental observation using next-generation rare-isotope facilities. By integrating current theoretical predictions with available experimental knowledge, this article provides a comprehensive assessment of proton radioactivity in heavy nuclei and underscores its importance for understanding the limits of nuclear stability and the structure of proton-rich actinides.
🏷 Proton radioactivity; Actinides; Proton drip line; Quantum tunneling; Nuclear structure
Article 138 · pp. 1923-1934
Exploring the Impact of Artificial Intelligence (AI) and Digital Connectivity on Educational Management in Nigerian Polytechnics.
The integration of Artificial Intelligence (AI) and digital connectivity is driving a significant shift in education systems worldwide, offering unprecedented opportunities to improve efficiency, effectiveness, and decision-making processes, particularly in areas such as planning, administration, and student engagement. In Nigeria, the polytechnic education sector is at a critical juncture, with digital transformation presenting significant opportunities to enhance educational management practices. This study examines how AI tools and digital connectivity frameworks can improve administrative efficiency, data-driven decision-making, student support, and teaching methods within Nigerian polytechnics. Using a mixed-methods approach, the research combines surveys and interviews with around 200 participants from five polytechnics, including administrative staff, ICT officers, and academic leaders from selected polytechnic schools. Data analysis, using Likert-scale evaluations, shows increasing awareness and interest in AI solutions. However, challenges such as poor infrastructure, limited funding, and a lack of digital skills slow down widespread adoption. The study highlights the need for strategic policies, greater investment in ICT infrastructure, and targeted AI training programs. By sharing these findings, the research supports ongoing policy talks on digital transformation in higher education. It offers practical suggestions for developing smart, connected, and effective educational management systems in Nigeria’s polytechnic sector.
🏷 Artificial Intelligence, Digital Connectivity, Transformation, Data, Likert Scale, ICT, Survey, Data analysis
Article 139 · pp. 1935-1950
Digital Detoxing Among Students: A Systematic Review
Smartphones, social media and constant screen time has entered the lives of adolescents and young adults and there has been a growing incidence of anxiety, sleep disruption, attentional problems and inactivity. Digital Detoxing has become a new form of self-regulation, with the goal of reducing or eliminating digital device use for a specific period of time and for specific purposes. This paper systematically synthesizes: neurological mechanisms underlying excessive digital engagement, and the efficacy of digital detox interventions across primary outcomes falling in the categories mental-health, cognitive, and behavioural outcomes in student and young-adult populations. In all 68 studies were short listed that met with inclusion criteria in the literature covered from 2010 - 2025. Pooled estimates revealed significant benefits of digital detox on anxiety, depression, sleep quality and subjective well-being. Cognitive attention outcomes showed moderate effects. Moderator analyses indicated that multicomponent interventions combining behavioural strategies with psychoeducation and mindfulness yielded the largest effects. Duration of 14–28 days and guided/therapist-supported formats significantly showed the largest effects. Digital detox interventions demonstrate consistent small-to-moderate efficacy across multiple outcomes in student populations. Structured, multicomponent programs of 2–4 weeks represent the current evidence-based standard.
🏷 digital detox interventions, screen time reduction, neurological mechanisms, excessive digital engagement, primary outcomes, moderator analyses
Article 140 · pp. 1951-1957
Self-Assembled Monolayers as Hole Transport Layer in Inverted Perovskite Solar Cells
SAMs have become one of the most essential materials in the modern inverted PSCs‚ setting the stage for molecular engineering at interfaces through simple scalable processing․ In this review‚ we summarize the advancements that exploit SAMs as hole transport layers in the inverted (p-i-n) PSCs‚ addressing the outstanding challenges of molecular packing‚ adhesion to metal oxides‚ and wetting from polar precursors of the perovskite layer․ We will highlight seven recent works from Chen et al․‚ Li et al․‚ Du et al․‚ Ahmmed et al․‚ Wang et al․‚ Yuan et al․‚ and Huang et al․ for passivating surface defects with ligands‚ changing the core of the SAM to improve its dipole and binding energy‚ coupling SAMs to polymer or carbonaceous layers to exploit their advantages and compensate for their weaknesses‚ and replacing incompatible carbazole-based anchoring groups with stronger ones․ All these strategies have delivered PCEs beyond 26% and meaningful operational stability improvements under accelerated aging․ In this Review we describe what worked‚ what didn't and argue that‚ rarely‚ a single fix solves all the problems․ Rather the most effective interventions often use a combination of approaches․ We conclude the review with some thinking about where the field is currently challenged‚ and where future gains are likely․
🏷 inverted p-i-n PSC, optoelectronics, self-assembled monolayer perovskite
Article 141 · pp. 1958-1964
A Correlational Study on the Level of Disaster Preparedness and Emergency Response Confidence Levels of Nursing Students of St. Mary’s College of Baliuag, Inc., A.Y. 2025–2026
The development of disaster preparedness and emergency response competencies is one of the most important areas of education that all nursing students need to acquire because they will be on the front lines during a disaster. Therefore, this study investigated Nursing students' preparedness behavior, knowledge of disaster related terms, and their confidence to respond in emergencies. In addition, it examined the relationships between the three variables. Descriptive and correlational methods were used. Altogether 215 BSN students completed a written survey designed to collect data. The results indicated a high degree of BSN students' preparedness behavior (mean percent = 61.86%), primarily through receiving information about disasters. However, there were some low scores when students answered questions about their preparation for planning and having access to emergency telephone numbers. The BSN students had moderate knowledge regarding disaster preparedness (overall mean = 57.21%). These findings indicate that the students have limited knowledge about the specifics of what to do during a disaster. Although the BSN students stated they were confident in responding to emergencies (overall mean = 3.98), the correlation between their knowledge and confidence was not significantly related (r = .369). The implications of these findings suggest that confidence cannot be solely based on theory or knowledge; therefore, including hands-on practice, simulations, and drills should be included in the BSN program to develop both competency and readiness for disaster situations.
🏷 Disaster preparedness; Emergency Response; Knowledge; Confidence; BSN Students; Correlational Study
Article 142 · pp. 1965-1978
Digital Product Innovation Strategies and Organizational Performance of Public Universities in Kenya
This paper adopts a conceptual and desk-review approach in examining the influence of digital product innovation strategies on organizational performance of public universities in Kenya. The increasing pressure on universities to remain competitive in an increasingly digital environment has necessitated the adoption of innovative approaches aimed at improving organizational performance. This study examined the influence of digital product innovation strategies on organizational performance of public universities in Kenya. Digital product innovation strategies were operationalized through research and development initiatives, innovation centers and hubs, and intellectual property management. Organizational performance was measured using student enrollment, employee turnover, and access to information. The study focused on Resource-Based View Theory proposed by Barney (1991) and Dynamic Capabilities Theory advanced by Teece et al. (1997). These theories explain how organizations use internal resources, innovation skills, and flexible abilities to gain a lasting edge over competitors. Recent research has highlighted the importance of these theories in understanding organizational performance and digital changes in fast-moving environments. (Civelek et al., 2023; Gohr et al., 2023); Heaton et al., 2023). This study utilized Desktop review methodology through narrative literature synthesis. Findings indicate that digital product innovation strategies positively influence organizational competitiveness, accessibility of university services, and institutional attractiveness to prospective students.
🏷 Digital product innovation, organizational performance, public universities, innovation strategies, higher education
Article 143 · pp. 1979-1987
Sustainable Consumer Behaviour in Fashion: Determinants and Implications for Generation Z
As one of the most environmentally intensive industries, fashion shows a persistent gap between Gen Z’s pro-environmental values and actual purchasing behaviour, a gap fast fashion’s throwaway culture has normalised. This study examines the determinants of sustainable fashion consumption among India’s Gen Z consumers, comparing the influence of consumer knowledge, perceived behavioural control, attitude, and external influence on sustainable purchasing.
A quantitative, cross-sectional survey was conducted using a structured online questionnaire adapted from Joshi et al. (2023) and Banytė et al. (2022). The seven-section instrument used a five-point Likert scale. Of 208 responses, 187 were retained after cleaning. PLS-SEM in SmartPLS-4 was used for analysis; the measurement model showed AVE above 0.59, composite reliability above 0.83, and SRMR of 0.086.
The structural model explained 67% of the variance in sustainable consumer behaviour (R² = 0.670, Q² = 0.617). Perceived behavioural control was the strongest predictor (β = 0.398, p < 0.001), confirming that affordability, availability, and convenience drive sustainable purchasing. Attitude (β = 0.315) and consumer knowledge (β = 0.262, both p < 0.001) were also significant, with knowledge exerting an indirect effect via attitude (β = 0.104). External influence had no direct effect on behaviour but operated indirectly through attitude (β = 0.132), shaping behaviour only through attitudes.
The dominance of perceived behavioural control suggests that reducing structural barriers, such as price premiums and limited availability, is more impactful than attitude-change campaigns alone. The knowledge effect supports investment in standardised, verified eco-labels, while the absent direct external influence effect implies social media promotions are unreliable behaviour-change levers; transparency and certification would serve consumers better. The study ranks four predictors within one model, clarifies the mediated role of external influence, and contributes evidence from an underrepresented Indian Gen Z context.
🏷 sustainable fashion, sustainable consumption, sustainable consumer behaviour, Generation Z
Article 144 · pp. 1988-1998
Influence of Asset Tangibility on Financial Performance of Non-Listed Building and Construction Firms in Kenya
Asset tangibility constitutes a fundamental determinant through which business entities secure financing and enhance their operational capacity, thus necessitating strategic management of tangible asset portfolios. The principal challenge resides in optimizing the utilization of fixed assets to generate superior financial returns while maintaining adequate collateral value for borrowing purposes. Kenya’s building and construction industry recorded performance metrics of 3.2/3.5 in third order during the 2014/2016 study period, representing a notable decline when juxtaposed against the 5.8 and 6.0 percentages achieved during 2005/2006. This suboptimal performance trajectory within the construction sector, according to extant literature, was attributable to the asset composition and utilization patterns among industry participants. This circumstance prompted the current investigation into the effect of asset tangibility on the financial performance of non-listed building and construction enterprises operating in Kenya. The research endeavor sought to assess the influence of asset tangibility ratios on profitability metrics of these non-listed construction firms. The study employed Return on Assets (ROA) and Return on Equity (ROE) as proxies for financial performance evaluation. The investigation covered the period spanning 2014 to 2016. The theoretical framework incorporated Pecking Order theory. A descriptive survey design was adopted as the research methodology. Secondary data were extracted from consolidated financial statement records maintained at the National Construction Authority and the Kenya Association of Manufacturers. The target population encompassed all 10 Tier 1 non-listed building and construction firms registered with the National Construction Authority during the three-year study window. The sample size corresponded to the entire target population, with secondary data serving as the primary information source. Analytical techniques comprised mean calculations, correlation regression modeling, and ANOVA (F-test). Findings were interpreted and presented through tabular and graphical representations, utilizing SPSS software version 2.1 for efficient data processing. The outcomes derived from this analysis demonstrated that asset tangibility exerts a discernible influence on the financial standing of non-listed building and construction firms in Kenya. The evidence indicated that financial performance tends to improve when tangible assets are effectively managed and utilized within the capital structure of the respective firm. This observation provides compelling justification for strategic investment in fixed assets as opposed to maintaining excessive liquid holdings. Proper asset management proved to enhance operational efficiency as it facilitates access to credit through collateralization while generating productive capacity that exceeds the benefits expected of holding non-productive assets. The findings of this study will enable management and financial practitioners to evaluate corporate growth characteristics, asset utilization efficiency, and financial performance metrics to project future enterprise value. The research recommends that business entities should work on optimizing asset tangibility ratios within their capital structures as a strategy for enhancing financial performance and maximizing shareholder wealth creation.
🏷 asset tangibility, financial performance, non-listed building and construction firms, Kenya
Article 145 · pp. 1999-2008
Optimization of Unit-Load Formation and Facility Design: A Comprehensive Guide to Pallet Selection and Inventory Logistics
Effective facility design and unit-load formation are critical factors in optimizing modern warehouse operations and minimizing supply chain costs. While various handling equipment options exist, pallets serve as the primary structural foundation for maximizing storage and transport efficiency. This study outlines a systematic approach to equipment selection and dimension optimization within a large-scale industrial warehouse design. It evaluates pallet material performance across wood, plastic, and metal alternatives. To overcome the challenges of standardizing handling procedures, two distinct optimization workflows—Linear Programming (LP) and Space Utilization Efficiency (SUE)—are examined to identify the ideal footprint for uniform racking configurations. Comprehensive inventory modeling is then applied to calculate total pallet capacity constraints under specified load weights, height boundaries, and stack ability limits.
Based on structural and economic parameters, premium wooden pallets measuring 1000mm X 900mm X 150mm were selected, achieving an optimal spatial efficiency of up to 94.4% for core inventory components. To fully support the facility's inventory demands across nine distinct material classes, the total fleet requirement was mathematically determined. The logistics analysis indicates that the warehouse requires a total infrastructure capacity of 185,663 standardized pallets, comprised of 181,376 units for primary cycle and safety stock alongside 4,287 units for dedicated buffer operations. Implementing this uniform pallet standard minimizes floor space loss, simplifies shelf design, and establishes a highly streamlined material handling workflow.
🏷 Facility Design, Unit-Load Formation, Space Utilization Efficiency (SUE), Linear Programming, Warehouse Logistics, Inventory Standardization.
Article 146 · pp. 2009-2017
Implementation of Ethno-Ergonomics through the Synergy of Dewata Nawa Sanga Manifestations to Improve Physiological Stability and Health
Ethno-ergonomics is a branch of health science that discusses the physiology of work that considers human abilities, capabilities, and limitations and their implementation according to certain community groups. Dewata Nawa Sanga are nine Gods with their respective characteristics. The characteristics of the nine Gods are synergized with the anatomical and physiological conditions of humans related to their health. The method used is through a quasi-experimental. The research design uses a randomized pre and post test group design involving 32 subjects. The research process is: identification of problems, prioritization of problems, and formulation of an action plan. The independent variable, the implementation of ethno-ergonomics through the synergy of the manifestation of Dewata Nawa Sanga and the dependent variables: (1) understanding and caring attitude towards the management of physiological conditions and (2) health quality seen from musculoskeletal complaints, fatigue, and stress due to work. The data obtained were analyzed descriptively by looking for the percentage of change and continued with a paired t-test at a significance level of 5%. The results showed an increase in: (a) physiological stability as seen from a 27.87% decrease in musculoskeletal complaints, a 24.88% decrease in fatigue, and a 28.42% decrease in work-related stress; (2) an increase in understanding of health governance by 17.77%; and (3) an increase in caring attitudes toward health governance by 23.93%. The conclusion is that the implementation of ethno-ergonomics through the synergy of Dewata Nawa Sanga manifestations can improve physiological stability, understanding, and the community's caring attitudes toward health governance.
🏷 Ethno-ergonomics, Governance, and Health.
Article 147 · pp. 2018-2016
Understanding Data Privacy: Assessing Students’ Perceptions and Attitudes Toward Data Privacy in Higher Education Institutions of Agartala City
In the contemporary digital landscape, data privacy has become an essential area of knowledge, particularly for young individuals, including students. The rapid advancement of information technology has significantly transformed the education system, offering substantial opportunities for students in higher education. However, concerns regarding data privacy and security have emerged alongside these opportunities. This study examines the level of awareness among students regarding data privacy and the measures they take to safeguard their personal information. A semi-structured questionnaire was used to collect the data needed to meet the aims. Employing a mixed-methods research design, the study integrates quantitative techniques—utilising tabulation with percentages—alongside qualitative approaches, such as case studies and theoretical descriptive analysis. The findings provide critical insights into students' awareness levels, contributing to the broader discourse on data privacy education in higher learning institutions.
🏷 Data Privacy, Cybersecurity, Higher Education, Student Awareness, Social Attitudes.
Article 148 · pp. 2027-2047
Anti-Corruption Research Dissemination and Private Sector Sustainability: Evidence from African Anti-Corruption Agencies
Corruption remains one of the most pervasive obstacles to sustainable development across Africa, imposing substantial costs on the private sector through distorted markets, unfair competition, and eroded stakeholder trust (Transparency International, 2025). Recent evidence indicates that 64% of South Africans believe most business leaders are corrupt, highlighting the magnitude of the challenge facing the continent (Human Sciences Research Council, 2025). Although the private sector is increasingly acknowledged as both a casualty of corruption and a critical collaborator for attaining the 2030 Agenda for Sustainable Development, the methods by which anti-corruption information is put into practice by businesses are not well understood (Nicaise and Rahman, 2025).
In Africa, Anti-Corruption Agencies (ACAs) have devised creative methods for involving commercial enterprises, which encompass educational campaigns, reward programs for law-abiding firms, and collaborations between public and private entities (Basel Institute on Governance, 2024). Emerging research demonstrates that anti-corruption compliance programmes correlate with improved market share, profit margins, and sustainable growth (Jenkins and Ishikawa, 2025). However, the effectiveness of disseminating anti-corruption research findings as a predictor of sustainable business delivery has received limited empirical attention.
Analysis of firm-level data across sub-Saharan Africa identifies corruption as among the most severe impediments to firm growth, frequently surpassing infrastructure deficiencies as a binding constraint (International Monetary Fund, 2025). The challenge is particularly acute for small and medium-sized enterprises, which face disproportionate barriers and are often excluded from formal anti-corruption frameworks (Pillay, 2025). Furthermore, the effectiveness of regulatory enforcement and knowledge dissemination is contingent upon institutional trust, with the possibility of bribery diluting the effectiveness of regulatory interventions regardless of entrepreneurs' trust levels (Nyemi, 2025).
This study addresses the gap by examining the extent to which the dissemination of anti-corruption research findings by ACAs predicts sustainable delivery in the private sector across African countries. Drawing on institutional theory and knowledge translation frameworks, we investigate dissemination practices, mediating mechanisms, and contextual moderators. By offering evidence-based insights, this paper contributes to strengthening the business case for integrity and optimising ACA strategies for sustainable private sector development in Africa.
🏷 Anti-corruption research dissemination, sustainable service delivery, private sector compliance, experiential paradigm, ACA, corruption prevention, DUA Model
Article 149 · pp. 2048-2063
A Multidimensional Conceptual Framework of Generative AI As a Co-Instructor: Measurement Formulation for Personalized and Sustainable Higher Education
Generative artificial intelligence (GenAI) is transforming higher education by evolving from a content automation tool into a collaborative instructional partner. However, its conceptualization as a co-instructor remains fragmented and lacks a validated measurement structure. This study develops a multidimensional conceptual framework and measurement formulation that positions GenAI as a structured pedagogical collaborator supporting personalization, sustainability, and ethical integrity. Using a theory-building approach grounded in interdisciplinary literature, the study defines key constructs, specifies relationships, and proposes indicators for future structural equation modeling. The framework identifies three instructional domains co-planning, co-instruction, and co-assessment mediated by pedagogical implementation effectiveness and moderated by ethical governance readiness. An eight-construct model and 37-item instrument are proposed for empirical validation. The study contributes conceptual clarity by advancing a systematic, ethically grounded approach to human–AI collaboration in higher education and recommends institutionally guided, teacher-in-the-loop implementation strategies.
🏷 generative AI, co-instructor model, measurement development, higher education, ethical AI governance
Article 150 · pp. 2064-2086
"Impact-X: A Causally-Grounded Interpretable Multimodal Deep Learning Framework for Transparent Early Disease Detection Using Imaging, Clinical, and Genomic Data."
Recent developments in multimodal deep learning have brought great progress to early disease detection; yet, wide-scale implementation of such models in clinics is hindered by the inherently inscrutable reasoning of existing methods. Current frameworks often employ post-hoc explanations that are not cross-modal consistent and are unable to disambiguate between causality and correlation, compromising both clinician trust and patient safety. In order to resolve these key issues, we introduce IMPACT-X, a novel Causally-Grounded Interpretable Multimodal Deep Learning Framework. IMPACT-X fuses mul-tiple heterogeneous modalities—medical imaging with Vision Transformers, medical records with Tabular Transformers, and genetic sequences with Graph Neural Networks—into a single and interpretable model.
Our framework includes a novel Causal Multimodal Fusion Layer (CMFL) which leverages cross-modal attention alignment in order to align the representation in a dynamic manner. Fur-thermore, an SCM module with DAG learning capabilities helps identify latent confounders and ensures the causally-consistent nature of the predictions. An uncertainty-aware decision-making layer estimates epistemic uncertainty through Monte Carlo Dropout in order to produce confidence scores. A unique cross-modal interpretability alignment loss function ensures coherent explanations across multiple modalities. The experimental results show that IMPACT-X achieves an SOTA performance with AUC-ROC score of 0.94, beating the best black-box baseline by 5.2%. Quantitative evaluation shows that IMPACT-X is 40% better in terms of faithfulness than traditional attention mechanism-based explanation approaches. A qualitative study with practicing medical professionals shows the benefits of causality-grounded predictions by increasing the level of physician trust in the system output. With its combination of high prediction accuracy and causal interpretability, IMPACT-X can pave the way for the development of a regulatory compliant and interpretable paradigm of medical AI that can safely be implemented in clinics, while enabling more accurate personalized medicine practices.Index Terms—Multimodal Deep Learning; Causal Inference; Interpretability; Early Disease Detection; Clinical Decision Sup-port; Genomic Integration
🏷 Causally-Grounded, Interpretable, Deep Learning
Article 151 · pp. 2087-2098
The Effect of Mobile Service Quality on Continuance Intention Mediated by Perceived Value and Customer Satisfaction Among Users of the Mytelkomsel App in Banda Aceh
This study aims to examine the effect of mobile service qualityon continuance intention in the MyTelkomsel application, with perceived value and customer satisfaction as mediating variables. Data were collected through an online questionnaire distributed to 200 respondents who are users of the MyTelkomsel application in Banda Aceh, using a non-probability sampling technique with purposive sampling method. The results of the analysis indicate that mobile service qualityhas a positive and significant direct effect on continuance intention, perceived value, and customer satisfaction. Perceived value does not have a positive and significant direct effect on continuance intention and therefore does not mediate the relationship between mobile service qualityand continuance intention. Meanwhile, customer satisfaction has a positive and significant direct effect on continuance intention and partially mediates the relationship between mobile service qualityand continuance intention.
🏷 mobile service quality, perceived value , customer satisfaction, continuance intention, MyTelkomsel
Article 152 · pp. 2099-2119
Entrepreneurial Ecosystems and Sustainable SME Growth in Ghana: The Mediating Role of Innovation Capability
Small and Medium Enterprises (SMEs) are foundational to economic development in Sub-Saharan Africa, yet their sustainable growth potential remains persistently constrained by inadequate ecosystem conditions and limited innovation capacity. This study investigates how entrepreneurial ecosystems, operationalized through six dimensions comprising community support, university support, government support, access to finance, institutional quality, and infrastructure, influence sustainable SME growth in Ghana, with innovation capability serving as a mediating mechanism and entrepreneurial purpose as a moderating variable. Grounded in Dynamic Capabilities Theory and the Resource-Based View, a quantitative cross-sectional survey design was employed, gathering data from 350 SME owners and managers across selected regions of Ghana. Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied for hypothesis testing. Measurement model assessment confirmed strong construct reliability (Cronbach's α range: 0.80–0.91; composite reliability range: 0.85–0.93), adequate convergent validity (AVE range: 0.55–0.65), and acceptable discriminant validity, with all Heterotrait-Monotrait (HTMT) ratios below the 0.85 threshold. The structural model explained 72.4% of variance in innovation capability (R² = 0.724) and 67.1% of variance in sustainable SME growth (R² = 0.671), with predictive relevance confirmed (Q² = 0.463 for innovation capability; Q² = 0.407 for sustainable SME growth). Model fit was acceptable (SRMR = 0.058; NFI = 0.908). The findings confirm that all six ecosystem dimensions directly and positively influence both innovation capability and sustainable SME growth. Innovation capability significantly mediates the relationship between the entrepreneurial ecosystem and sustainable SME growth, while entrepreneurial purpose positively moderates the innovation capability–sustainable growth nexus. This study extends entrepreneurial ecosystem literature to the Sub-Saharan African context and provides actionable insights for policymakers, university administrators, and SME practitioners seeking to foster innovation-driven sustainable growth in developing economies.
🏷 Entrepreneurial Ecosystem, Innovation Capability, Sustainable SME Growth, Community Support, University Support
Article 153 · pp. 2120-2129
Efficacy Trial of Chromolaena odorata and Coconut Vinegar as an Alternative Herbicide
This study evaluated the herbicidal efficacy of aqueous extracts from different parts of Chromolaena odorata (L.) enhanced with coconut vinegar for the control of common weeds under field conditions. Specifically, the study aimed to determine which plant part (leaves, roots, or stems) exhibited the greatest weed control efficacy compared with a positive control (sterile distilled water) and a negative control (glyphosate). The experiment was conducted using a Randomized Complete Block Design (RCBD) with five treatments and three replications. The treatments consisted of T1 – Sterile Distilled Water (positive control), T2 – C. odorata leaves + coconut vinegar + sterile distilled water, T3 – C. odorata roots + coconut vinegar + sterile distilled water, T4 – C. odorata stems + coconut vinegar + sterile distilled water, and T5 – Glyphosate (negative control). Results showed that the C. odorata-based formulations significantly suppressed weed growth compared with the untreated control. Seven days after application, Treatment 2 recorded the highest weed control rating among the botanical treatments (7.33 ± 0.58), followed by Treatment 4 (7.00 ± 1.00) and Treatment 3 (6.67 ± 1.15), while glyphosate achieved a mean rating of 8.00 ± 0.00. Fourteen days after application, weed control ratings declined for the botanical treatments, with Treatment 2 recording 5.33 ± 0.58, Treatment 4 recording 5.00 ± 1.00, and Treatment 3 recording 4.67 ± 0.58, whereas glyphosate achieved complete weed control (10.00 ± 0.00). Analysis of variance (ANOVA) revealed highly significant differences among treatments at both evaluation periods (p < 0.001). Tukey's Honestly Significant Difference (HSD) test indicated that all C. odorata-based treatments significantly outperformed the untreated control, while no significant differences were observed among the leaf, root, and stem extracts. However, glyphosate provided significantly greater weed control than the botanical formulations after 14 days. Among the weed species evaluated, Cleome rutidosperma was the most susceptible to the C. odorata extracts, whereas Physalis angulata was the least affected. The findings demonstrate that aqueous extracts of C. odorata enhanced with coconut vinegar possess promising herbicidal activity and can effectively suppress common weeds, particularly during the early stages of weed growth. Although their efficacy was lower than that of glyphosate over time, the comparable performance of the leaf, root, and stem extracts suggests that allelopathic compounds are distributed throughout the plant. These results highlight the potential of C. odorata as a sustainable botanical herbicide and provide a basis for further optimization of formulation, concentration, and application strategies for integrated weed management.
🏷 Chromolaena odorata, allelopathy, botanical herbicide, coconut vinegar, weed management, glyphosate, bioherbicide.
Article 154 · pp. 2130-2149
Wired Antenna Arrays For 5G Applications in Cellular Base Stations: A Comparative Performance Analysis
The deployment of fifth-generation (5G) cellular networks demands advanced antenna systems capable of delivering high gain, wide bandwidth, low return loss, and efficient beamforming. This article presents a comprehensive comparative analysis of wired antenna arrays specifically dipole arrays, Yagi-Uda arrays, and log-periodic dipole arrays (LPDAs) designed for 5G base station applications at 3.5 GHz. Using CST Microwave Studio simulations, single-element and multi-element configurations were evaluated against key performance metrics including return loss, VSWR, gain, directivity, bandwidth, and radiation pattern characteristics. The ten-element dipole array achieved a gain of 11.79 dBi with a narrow beamwidth of 16.4°, representing a 9.5 dB improvement over a single dipole. The ten-director Yagi-Uda demonstrated superior return loss (-25.22 dB) and gain (11.7 dBi) compared to the three-director version (8.9 dBi). The four-element LPDA array exhibited enhanced bandwidth (0.508 GHz) and improved VSWR (1.255) relative to its single-element counterpart. These findings provide quantitative guidelines for selecting optimal wired antenna configurations for specific 5G deployment scenarios.
🏷 5G base station, dipole array, Yagi-Uda antenna, log-periodic dipole array, antenna array, beamforming, CST simulation, sub-6 GHz
Article 155 · pp. 2150-2155
Using ICA Neuroimaging Techniques to Detect Neurological Abnormalities.
EEG (Electroencephalogram) measures electrical potentials on the scalp surface that occur as a result of dynamic brain function. This paper presents the method to extract EEG Data and various methods used to perform EEG Neuroanalysis, and why the author has chosen to work on ICA method. EEG data is collected by placing sensors on a person’s scalp at different positions, amplifying data by 100db, digitizing it with a sampling rate of 256HZ and using BLUETOOTH Technology to wirelessly transmit it to the computer. EEGLAB, a C library and tool integrated in MATLAB, has been described. The methodology used to obtain the EEG datasets and implement both ICA methods on the data has been presented. Baseline wander and power line interference were also removed from baseline wander and power line interference from the EEG data. The time taken for ICA and FAST ICA has been compared. Finally, neurological abnormalities were detected abnormalities in brain, using ICA, by detecting spikes and sharp waves in EEG.
🏷 EEGLAB, ICA, Artifacts, Whitening, Sphering
Article 156 · pp. 2156-2171
Assessment of Future Hydrological Flow Regime Analysis Under Changing Climate in a Tropical River Basin
Understanding how river flow regimes may evolve under changing climatic conditions is fundamental for sustainable water resources planning, particularly in tropical monsoon river basins where streamflow exhibits strong seasonal variability. This study presents a novel framework for assessing future hydrological flow regimes at a 10-daily timescale under near-future climate scenarios and subsequently examines the associated seasonal variability in streamflow and baseflow components. The framework was implemented in the Brahmani River Basin (39,116 km²) located in eastern India, a region characterized by pronounced monsoonal hydrology. The semi-distributed Soil Water Assessment Tool (SWAT) model was employed to simulate historical streamflow dynamics and demonstrated satisfactory performance in reproducing observed hydrological behaviour. Future climate projections for the basin were obtained from the REMO regional climate model under three Representative Concentration Pathways (RCP2.6, RCP4.5, and RCP8.5). Changes in future flow regimes were analysed using flow duration curves (FDCs) developed at a 10-daily timescale, while long-term trends in streamflow and baseflow were evaluated using the non-parametric Mann-Kendall test and Theil-Sen slope estimator. Baseflow separation was performed using the Recursive Digital Filter (RDF) method based on hydrograph recession characteristics. The results reveal a tendency towards increasing streamflow and baseflow during the near-future period (2021-2045), suggesting enhanced water availability in the basin under projected climatic conditions. By capturing hydrological variability at a finer temporal resolution, the proposed framework provides improved insights into future water availability and seasonal flow characteristics. The methodology is readily transferable to other river basins and offers a practical basis for climate-resilient water resources planning and management in monsoon-dominated regions.
🏷 Baseflow; Climate Scenarios; Flow duration curve; Hydrologic regime; SWAT
Article 157 · pp. 2172-2181
Development of Spatial and Land Planning Applications in North Sumatra Province
The development of information technology drives digital transformation in the implementation of spatial planning, particularly in the management of spatial data and the provision of information to the public. The management of Regional Spatial Planning (RTRW) data that is still scattered across various documents makes the process of information retrieval and public services less effective. This study aims to develop a WebGIS-based application of the North Sumatra Spatial Planning and Land Information System (SIPINTAR SUMUT) as a medium for integrating spatial and non-spatial data to support the planning, utilization, and control of spatial utilization in North Sumatra Province. The study uses the Design Science Research (DSR) method which includes the stages of problem identification, goal determination, system design and development, demonstration, evaluation, and communication of results. The research data consists of base maps, thematic maps, plan maps, RTRW documents, and other supporting data integrated into the WebGIS-based system. The results show that SIPINTAR SUMUT has been successfully developed with main features such as a homepage, interactive maps, information on spatial structure, spatial patterns, forest areas, marine zones, user authentication, and a public complaint page that supports public participation in monitoring spatial utilization. The application's implementation can improve ease of access to spatial planning information, accelerate spatial data retrieval, support transparency in public services, and increase the efficiency of spatial planning data management within the North Sumatra Provincial Government. Therefore, SIPINTAR SUMUT can serve as a model for implementing WebGIS-based spatial planning digitization, supporting effective, transparent, and participatory governance.
🏷 Land Spatial Planning; WebGIS; SIPINTAR SUMUT; Design Science Research.
Article 158 · pp. 2182-2188
Re-Thinking Organisational Ambidexterity Through Artificial Intelligence: A Dimensional Approach
In the contemporary business landscape, characterised by the increasing prevalence of artificial intelligence (AI), organisations of various sizes are striving to understand its role within their operational frameworks. Companies striving to remain competitive in this dynamic environment are compelled to investigate the potential of AI, raising critical questions about the significance, timing, and level of investment required for AI-enabled resources. This paper proposes a debate to include use of AI into the metrics used to assess organizational ambidexterity. A review of pertinent literature on AI and ambidexterity suggests that AI holds considerable importance, and further research on including AI in the measurement scale of both the dimensions of organisational ambidexterity, exploration and exploitation, becomes all the more logical and befitting.
🏷 Organisational Ambidexterity, Artificial Intelligence, Dynamic Capabilities, Exploration, Exploitation
Article 159 · pp. 2189-2203
Experimental Investigation of the Hydraulic Characteristics and Forced-Convection Thermal Performance of Porous Copper Alloy Fins: Effects of Airflow Velocity, Pore Density, and Porosity
Porous metallic fins have emerged as a promising alternative to conventional solid extended surfaces because their interconnected pore networks permit fluid penetration and offer a substantially larger effective heat transfer area. The present study reports a systematic experimental investigation of the hydraulic and thermal characteristics of porous copper alloy fins under forced convection. Eight sintered porous copper alloy specimens with pore densities of 10, 20, and 40 PPI, porosities ranging from 0.379 to 0.624, and thicknesses of 5 mm and 10 mm were tested in a controlled wind-tunnel facility over superficial air velocities of approximately 0.7–5.7 m/s.
Two objectives were pursued: (i) quantification of the influence of airflow velocity on pressure drop, permeability, inertia coefficient, and friction factor; and (ii) evaluation of the forced-convection heat transfer performance in terms of the convective heat transfer coefficient. The measured pressure drop increased nonlinearly with velocity in accordance with the Darcy–Forchheimer model, and rose with increasing pore density, decreasing porosity, and increasing specimen thickness, with pressure losses spanning approximately 20–970 Pa across the tested conditions. The experimentally determined permeability varied from 0.59 × 10⁻⁸ m² to 8.30 × 10⁻⁸ m² and behaved as an intrinsic structural property, decreasing with pore density and with reducing porosity, while the inertia coefficient increased with pore density and decreasing porosity and was only weakly sensitive to velocity.
The friction factor decreased monotonically with the permeability-based Reynolds number (ReK ≈ 4–112), reflecting the transition from viscous- to inertia-dominated transport. The convective heat transfer coefficient increased continuously with airflow velocity and with pore density, the 40 PPI specimen delivering the highest thermal performance, whereas porosity exhibited a competing influence between airflow penetration and solid-phase conduction, indicating the existence of an optimum void fraction.
The combined results demonstrate a clear thermo-hydraulic trade-off and provide experimentally validated permeability and inertia-coefficient data suitable for porous-media modelling and the design of compact heat sinks, electronics cooling modules, and battery thermal management systems.
🏷 porous fins; copper alloy foam; forced convection; pressure drop; permeability; inertia coefficient; friction factor; heat transfer enhancement
Article 160 · pp. 2204-2212
SAR Backscatter, not Optical Indices or Phenology, Drives Cropland Classification Accuracy: A Multi-Sensor Feature Ablation Study in the Abuja Municipal Area Council, Nigeria
Cropland mapping in rapidly urbanizing tropical regions increasingly relies on multi-sensor Earth observation, yet the individual contribution of different sensor and feature types to classification accuracy is rarely quantified explicitly. Building on a prior comparative study of Random Forest (RF) and Gradient Tree Boosting classifiers using a 20-band Sentinel-1/Sentinel-2 feature stack over the Abuja Municipal Area Council (AMAC), Nigeria, this study conducts a systematic feature ablation to isolate the marginal contribution of spectral indices, NDVI-derived phenological metrics, and Synthetic Aperture Radar (SAR) backscatter to cropland classification accuracy. Six feature-stack combinations, ranging from optical-only to the full fused stack, were classified using an RF classifier with fixed hyperparameters and the same stratified reference dataset (373 validation points) used in the original study. Overall accuracy, Kappa coefficient, and cropland-specific precision, recall, and F1-score were compared across combinations, and McNemar's test was used to assess whether accuracy differences between stacks were statistically significant. Results show that SAR backscatter, not spectral indices or phenological metrics, was the dominant driver of accuracy gains: the optical-plus-SAR combination achieved the highest overall accuracy (74.8%) and Kappa (0.685) of all six combinations tested, exceeding even the full fused stack (73.5%, κ=0.668), while adding spectral indices and phenological metrics without SAR reduced accuracy below the optical-only baseline (67.0% vs. 68.9%). McNemar's tests confirmed the full stack was not significantly different from the optical-only baseline or the optical-plus-SAR combination (p > 0.05), but was significantly more accurate than any combination lacking SAR (p < 0.05). Gini-based variable importance ranked VH backscatter and VH temporal variance as the two most important predictors among all 20 bands, and this ranking was corroborated by a permutation-importance cross-check on an independently trained model, which agreed with Gini importance on both top-ranked SAR bands despite the two methods diverging further down the ranking indicating SAR's dominance is a robust finding rather than an artifact of Gini importance's known bias toward continuous variables. These findings indicate that, in this tropical savanna setting, radar data delivers the largest return on investment for operational cropland monitoring, while spectral indices and phenological metrics contribute comparatively little value on their own.
🏷 Feature Ablation, Random Forest, Sentinel-1, Sentinel-2, SAR-Optical Fusion, Variable Importance, Cropland Mapping
Article 161 · pp. 2213-2218
A Brief Analysis of How Digitalization Impacts Human Resource Practices and Challenges to it
Every business has changed its strategy in the era of technology and stiff competition. To have a competitive edge, it has become substantial for businesses to adopt digitalization as it saves time and creates new opportunities. However, this disruption does not occur at the organizational level; it has societal and environmental implications. At the business or organizational level, it has fundamentally transformed every functionality, and the HR function is one of them. It offers new tools and work methodologies to enhance employee efficiency, engagement, and decision-making. Digital technology has streamlined various HR processes such as recruitment, selection, training and development, performance management, etc. Tools like artificial intelligence and HR analytics have revolutionized workforce planning and management, offering real-time feedback and increasing efficiency. It has enabled HR professionals to make informed decisions based on extensive data insights. Besides offering benefits, digitalization also presents challenges that need to be considered. The firms must adopt digital tools and tactics to improve their HR operations and stay competitive. The present study explores the impact of digitalization on HR practices and offers insights into the challenges and opportunities it presents. This article attempts to determine how digitalization impacts HR practices and its significant challenges. This theoretical discussion gives a brief review of the impact and challenges of HR digitalization in the current scenario. For this study, the researcher reviewed the most recent research findings from the scientific literature and business practice and used an integrative research method. This research article is becoming an essential voice in HR digitalization and its impact on HR practice. It will allow HR professionals and academia to develop more insights into reaping the benefits of digitalization and concentrating on how it can be strategically advantageous for business organizations in this technological era.
🏷 Digitalization, Impact of Digitalization, Human Resource Practices, Challenges and Trends
Article 162 · pp. 2219-2226
Advanced Information Security and Assurance: A Literature Review of Zero Trust Architecture, Artificial Intelligence, and Cybersecurity Resilience
The quick uptake of cloud computing, artificial intelligence (AI), Internet of Things (IoT), and hybrid workplace solutions has changed the cybersecurity needs in a way that renders the traditional notion of perimeter defense inefficient in addressing sophisticated attack scenarios such as ransomware, insider threats, and advanced persistent threats (APTs). This literature review analyzes advancements in the field of Advanced Information Security and Assurance from 2016 to 2026.
Key advancements include NIST SP 800-207 published in 2020, widespread use of Zero Trust Architecture (ZTA), and incorporation of AI into security analytics. The reviewed sources show that Zero Trust drastically minimizes attack surfaces using continuous authentication, least-privilege access, and microsegmentation. Also, AI is beneficial in improving threat detection through predictive analytics, behavioral anomalies detection, and automation of the incident response process.
On the other hand, AI also poses emerging risks such as adversarial machine learning, automated API reconnaissance, AI phishing scams, and intelligent malware. Additionally, cyber resilience, explainable AI, and adaptive governance are the identified research areas important for protecting future digital infrastructures. While significant advances have been made, there are still many challenges related to complexity, interoperability, staffing shortage, privacy, and governance.
🏷 information security, cybersecurity assurance, zero trust architecture, artificial intelligence, cloud security, cyber resilience
Article 163 · pp. 2227-2235
Advanced Content Networking: A Literature Review of Information-Centric Networking, Edge Computing, and Intelligent Content Delivery
The explosive growth in multimedia data flow, cloud-based services, mobile apps, and IoT devices is changing the needs of today's network infrastructures. The classical Internet designs based on hosts are facing difficulties with meeting the need for reduced latency, improved scalability, effective content delivery, and overall user experience. Thus, advanced content networking has become an essential research field associated with content delivery in the context of content-centric architecture, edge computing, intelligent caching, and software defined networks. This literature review analyzes scientific progress in advanced content networking between 2016 and 2026, paying attention to Information-Centric Networking, Content-Centric Networking, Named Data Networking, edge computing, cloud-to-edge continuum architectures, and artificial intelligence content delivery. Comparative findings from the reviewed literature indicate that Information-Centric Networking architectures, particularly Named Data Networking, improve network performance through in-network caching and content-based routing. Studies report cache hit ratio improvements of approximately 20–40%, reductions in backbone traffic, and lower end-to-end latency compared with conventional host-centric architectures. However, these benefits are offset by challenges related to forwarding-table scalability, name-based routing complexity, and cache management, highlighting the performance trade-offs that remain barriers to Internet-scale deployment.
🏷 advanced content networking, information-centric networking, content-centric networking, edge computing, caching, named data networking, cloud-edge continuum
Article 164 · pp. 2236-2250
The Relationship Between AI Content Exposure and Responsible Usage: Toward A Framework for Informed Digital Engagement
The rapid proliferation of artificial intelligence (AI)-generated content across digital platforms has fundamentally altered the information landscape in ways that demand critical scholarly attention. As AI technologies become increasingly embedded in everyday media consumption, educational settings, and professional environments, questions surrounding how exposure to such content shapes users' attitudes and practices regarding responsible usage have gained considerable urgency. This paper examines the relationship between AI content exposure and responsible usage by drawing on social cognitive theory, digital literacy scholarship, and emerging AI ethics frameworks. Through a conceptual synthesis of existing literature, the study argues that exposure to AI-generated content does not inherently cultivate responsible use behaviours; rather, the nature, frequency, and context of exposure, mediated by users' critical awareness, digital literacy, self-efficacy, institutional support structures, and platform design, collectively determine whether AI engagement becomes a vehicle for informed participation or uncritical dependency.
This revised and expanded version introduces a multi-variable conceptual framework that maps the pathways between exposure and responsible usage, a synthesizing table of key literature, a dedicated discussion of emerging AI technologies including multimodal AI, autonomous agents, and generative video, a limitations section acknowledging the conceptual scope of the inquiry, and an expanded future research agenda. The paper concludes with practical recommendations for educators, developers, regulators, and organisations committed to fostering responsible AI citizenship in an increasingly automated information environment.
🏷 artificial intelligence, AI-generated content, responsible usage, digital literacy, social cognitive theory, AI ethics
Article 165 · pp. 2251-2277
Geological Hazard Prediction and Prevention: A Review of Mechanisms, Monitoring, and Mitigation
As industrialization expands, modern infrastructure increasingly collides with volatile geological environments, driving a sharp escalation of complex, cascading geohazards. These destructive hazard chains represent a critical global threat to structural and economic resilience. Despite this escalating risk, current early warning systems predominantly rely on fragmented, static methodologies that fail to capture the dynamic reality of temporal hazard evolution. Furthermore, contemporary predictive models are bifurcated between deterministic physical models demanding exhaustive geotechnical parameters and data-driven artificial intelligence algorithms functioning as opaque black boxes devoid of physical interpretability. To resolve these limitations, this review systematically evaluates the modern geohazard landscape through a coupled active-passive conceptual model. This framework systematically evaluates how primary high-energy failures trigger subsequent instability in surrounding geomaterials. The synthesis reveals that integrating space-air-ground multi-scale monitoring including orbital InSAR, UAV photogrammetry, and distributed ground sensors establishes a vital surveillance continuum for early hazard identification. Because these heterogeneous data streams possess significant environmental noise, rigorous multi-source data fusion remains essential to minimize false alarms and resolve spatial discontinuities. Analytically, physically based models provide indispensable mechanical transparency by explicitly simulating material deformation, whereas data-driven ensemble algorithms excel at processing high-dimensional, nonlinear geospatial inputs. Ultimately, transitioning toward proactive, real-time risk reduction demands the integration of physics-informed neural networks (PINNs) that embed geomechanical constraints into computational pipelines. Coupling these hybrid architectures with interactive digital twin technologies will transform static hazard mapping into dynamic virtual environments, empowering engineers to successfully mitigate evolving disaster chains globally.
🏷 Geological hazards; Disaster risk reduction; Real-time monitoring; Data-driven forecasting; Early warning systems; Physics-informed neural networks (PINNs).
Article 166 · pp. 2278-2294
The Effectiveness of Current Management Strategies Utilized by the Nigerian Airspace Management Agency (Nama)
The study assessed the adequacy of Nigerian Airspace Management Agency’s Current Management Strategies towards Sustainable Radar Service Delivery. The study was based on the observed operational challenges associated with the Agency’s radar infrastructure despite huge capital investments made on modern ATC equipment over the years. It was quantitatively carried out and adopted survey research design. Three hundred survey questionnaires were administered to selected respondents from Lagos, Abuja, Port Harcourt, and Kano radar centres of NAMA. The respondents are drawn from radar operators, ATCOs, Aviation Engineers and Management Staff. Two hundred Fifty-Five (255) usable questionnaires were retrieved and used for the study. The information gathered was analyzed using descriptive statistical tools such as frequency counts, percentages, mean and standard deviation. The study hypothesis were tested at 0.05 level of significance using one-sample t-test. Findings from the study showed that Current management strategies adopted by NAMA has positively impacted the operations of RADAR services in ensuring acceptable standard of Airspace Management while unstable power supply; aging infrastructure, unavailable dedicated fund for maintenance; shortage of certified technical manpower and weak interconnectivity between radar stations located in different regions were identified as critical operational challenges. The study also revealed radar failure have major consequences on Controllers workload, flight delay, excess fuel burn, operation cost and safety of flight. Evidence from the study also confirmed that Modernization of RADAR equipment through ADS-B technology implementation strategy; centralize database and digital Signal Processing Concepts along with Predictive Maintenance programs and Automated Fault Detection/Alert Systems will significantly improve radar surveillance coverage, tracking accuracy and reliability rate. All the hypotheses were found to be statistically significant at 0.05 level of significance as against the alpha level of 0.10, therefore, all the null hypotheses are rejected. It can be concluded that although NAMA have laid a solid foundation for Radar services operation, for sustainable RADAR services delivery, efforts should be geared towards infrastructure upgrade/modernization, available funding for maintenance upgrades, continuous technical capacity building program, alternative power supply system and independent regulatory body to keep checking mate operations in line with global safety regulation.
🏷 Nigerian Airspace Management Agency (NAMA), radar service delivery, management strategies, air traffic management, aviation safety
Article 167 · pp. 2295-2307
Impact of Instagram Reels vs. YouTube Shorts on Brand Engagement among Gen Z Changes
Social media has transformed how brands communicate with consumers, with short-form video platforms emerging as key marketing channels. This study examines the impact of Instagram Reels and YouTube Shorts on brand engagement among Gen Z, focusing on brand discovery, engagement, and purchase intention, and comparing each platform’s effectiveness in building awareness.
Prior research links short-form videos to brand visibility and purchase decisions, but most studies examine platforms in isolation, leaving gaps in direct comparison, platform-driven differences in student interaction, and links between platform preference and purchase intention among young Indian consumers.
A descriptive design was adopted. Primary data were collected via a structured Google Forms questionnaire from 300 Gen Z respondents, mainly college students aged 17–26, using convenience sampling, supplemented by secondary data from journals and reports. Data were analysed using frequency, percentage, Chi-square, ANOVA, and Spearman correlation.
Instagram Reels emerged as the most preferred platform (40%), while 35.7% used both equally. Most respondents (57.3%) watched brand-related short videos often, with 57.3% agreeing such videos aid brand discovery and 64% finding them more engaging. YouTube Shorts proved effective at retaining attention (59.7% watched until the end), and 58% credited algorithms for brand discovery. On purchase intention, 57% agreed short video ads influence product trials, and 52.7% reported higher purchase likelihood after viewing. All four hypotheses were supported, confirming significant links between brand discovery and engagement, engagement and purchase intention, platform preference and perceived engagement, and algorithms and trial decisions.
Both platforms significantly shape brand engagement and purchase intention among Gen Z. Instagram Reels leads in preference and visual content, while YouTube Shorts shows stronger retention, with algorithms acting as a key bridge between content and consumer. Marketers should prioritise authentic, creative short-form content to engage young audiences. Future research could expand the sample, include other platforms, and examine brand loyalty.
🏷 short-form videos; brand engagement; Gen Z consumers; Instagram Reels; YouTube Shorts; social media marketing; purchase intention
Article 168 · pp. 2308-2314
“The Effectiveness of Informational Booklet on Knowledge & Attitude Towards Human Milk Bank Among Postnatal Mothers in Selected Hospital”
Human milk banks play an important role in providing safe and nutritious breast milk to infants who are unable to receive milk directly from their mothers, especially premature and sick newborns. Adequate knowledge and a positive attitude among postnatal mothers are essential for the acceptance and utilization of human milk banking services. The present study was conducted to assess the effectiveness of an informational booklet on knowledge and attitude towards human milk banks among postnatal mothers in a selected hospital. METHODOLOGY: A quantitative research approach with a pre-experimental one-group pre- test–post-test design was adopted for the study. The study was conducted among 60 postnatal mothers selected through a non-probability convenient sampling technique from the postnatal ward of a selected hospital. The independent variable of the study was the informational booklet, and the dependent variables were the knowledge and attitude of postnatal mothers towards human milk banks. Data were collected using a structured knowledge questionnaire and a Likert attitude scale. Results :The findings of the study revealed that in the pre- test, 48.33% of postnatal mothers had poor knowledge and 35% had average knowledge regarding human milk banks. After the intervention, the post-test results showed significant improvement, with 63.33% of mothers demonstrating excellent knowledge and 36.67% showing good knowledge. The mean knowledge score increased from 6.87 to 15.87. Similarly, the attitude of mothers improved significantly from a pre-test mean score of 24.97 to a post- test mean score of 37.05, indicating a highly favourable attitude after receiving the informational booklet. Statistical analysis using the paired t-test showed highly significant results (p < 0.0001) for both knowledge and attitude. Conclusion: The study concluded that the informational booklet was highly effective in improving the knowledge and attitude of postnatal mothers towards human milk banks. Educational interventions such as informational booklets can be an effective strategy to increase awareness and promote acceptance of human milk banking, ultimately contributing to improved neonatal health outcomes.
🏷 Human Milk Bank, Postnatal Mothers, Knowledge, Attitude, Informational Booklet, Breast Milk Donation, Neonatal Care.
Article 169 · pp. 2315-2328
Role of Strategic Digital Marketing Tools (Smm, Seo, and Content Marketing) in Restaurant Competitiveness and Brand Positioning
The rapid digitalization of the foodservice industry has transformed how restaurants communicate, compete, and position their brands in contemporary markets. With consumer decision-making increasingly shaped by online cues such as social media content, search visibility, and digital storytelling, strategic digital marketing tools have become central to restaurant competitiveness. This empirical study investigates the role of Strategic Social Media Marketing (SSMM), Search Engine Optimization Visibility (SEOV), and Content Marketing Effectiveness (CME) in shaping Brand Visibility Perception (BVP), Brand Positioning Effectiveness (BPE), and ultimately Restaurant Competitiveness Rating (RCR) among restaurants in the Indore Division of Madhya Pradesh, India. Using existing quantitative data collected from restaurant customers (N = 600), the study employs reliability analysis, factor analysis, and multiple regression techniques to examine the structural relationships among key constructs. Findings demonstrate that strategic SMM and content marketing significantly enhance brand visibility and brand positioning, while SEO-driven visibility emerges as a critical predictor of competitiveness in markets where digital search behaviour is dominant. The study highlights the importance of integrated digital marketing strategies, emphasizing that restaurants leveraging cohesive social media presence, optimized search rankings, and compelling content narratives achieve stronger market positioning and competitive advantage. Practical implications include a digital branding framework for restaurants, and theoretical implications situate digital marketing tools within contemporary branding and competitiveness models. This research contributes to 2024–25 literature by offering evidence from an emerging urban market and underscores the strategic role of digital platforms in shaping restaurant brand value and competitive success.
🏷 Digital Marketing, Tools, Brand Positioning
Article 170 · pp. 2329-2334
Improving Packaging Warehouse Operational Efficiency Through Relayout Strategy and Material Handling Standardization in Indonesian Chemical Companies
The speed of warehouse order processing is a key industry imperative for customer satisfaction. One way warehouse managers can achieve this is by comprehensively optimizing warehouse operations. Several researchers have observed that warehouse order processing activities contribute a significant portion of warehouse time and total costs. This study used the packaging materials warehouse of a chemical company in Tangerang City, Indonesia. The main problems identified in this study included long picking times, the risk of component picking errors, and low stock visibility. Several methodologies were used to address these issues. The process began with mapping SKUs based on service frequency and quantity (fast-moving/slow-moving). Based on the collected data, changes were made to the item layout. Furthermore, additional areas for packaging support components, such as lids, were added near the packaging to reduce errors during picking. Furthermore, 5S practices were implemented to remove unused items from the packaging warehouse. This also helped the warehouse expand its storage space. The results showed a 50% reduction in picking time, from the initial standard time of 4 seconds per item to 2 seconds per item. Several unsafe storage areas were also removed, improving operator safety during picking and contributing to increased warehouse capacity. Furthermore, the implementation of a pail storage system also facilitated faster receipt and service of goods. The incidence of damaged pails during storage was also reduced. The results of this study concluded that the 5S method and determining the layout based on the movement of goods in the warehouse can have a significant impact on warehouse productivity.
🏷 warehouse, operational efficiency, Relayout, 5S
Article 171 · pp. 2335-2343
Effective Communication and Startup Performance among Young Entrepreneurs
Effective communication is increasingly recognized as a critical factor in the success and performance of startup ventures. However, many young entrepreneurs encounter communication-related challenges, including unclear information, weak feedback systems, and delays in communication, which can adversely affect employee coordination, decision-making, customer relations, and overall business performance. This study examined the influence of communication clarity, feedback mechanisms, and communication timeliness on startup performance among young entrepreneurs. A quantitative research approach was adopted, and data were analyzed using Structural Equation Modeling (SEM) to assess the relationships among the study variables. Model fitness was evaluated using multiple goodness-of-fit indices, including Chi-square statistics, Root Mean Square Error of Approximation (RMSEA), Standardized Root Mean Square Residual (SRMR), Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), and the Coefficient of Determination (CD). Three hypotheses were tested to determine the effects of clarity of information, feedback mechanisms, and communication timeliness on startup performance using path coefficients, standard errors, z-values, and p-values. The findings revealed that clarity of information (β = 0.2373, p < 0.05), feedback mechanisms (β = 0.3395, p < 0.01), and communication timeliness (β = 0.2808, p < 0.01) exert positive and statistically significant effects on startup performance. The results suggest that clear communication enhances employee understanding and operational effectiveness, effective feedback mechanisms support continuous improvement and goal alignment, while timely communication facilitates responsiveness and efficient decision-making. The study concludes that effective communication strategies are essential for improving startup performance among young entrepreneurs. It recommends that startup founders prioritize clear information sharing, establish structured feedback systems, and ensure timely communication to enhance business growth, operational efficiency, and long-term sustainability.
🏷 Effective Communication, Communication Clarity, Feedback Mechanisms, Communication Timeliness, Startup Performance, Young Entrepreneurs
Article 172 · pp. 2344-2354
Political Economy of Solid Minerals Governance in Nasarawa State, Nigeria: A Rentier State Analysis (1999 - 2020)
Nasarawa State is widely described as the “home of solid minerals” because metallic, industrial, and gemstone resources are distributed across its thirteen local government areas. Despite this abundance, the governance of solid minerals in Nigeria places ownership and control under the Federal Government. Anchored in Rentier State Theory, this paper examines the political economy of solid minerals governance in Nasarawa State between 1999 and 2020, focusing on how dependence on external oil rents shapes subnational incentives to exploit local mineral wealth. The study adopted a mixed-method design combining a cross-sectional survey with key-informant interviews, supported by documentary and institutional analysis. The survey covered 400 respondents drawn from thirteen purposively selected mining-affected wards and villages, and from relevant institutions; 376 questionnaires returned were used for analysis while unstructured interviews with selected stakeholders provided additional insight. Documentary evidence from official reports, policy documents, budgets, and published sources was used. The study finds that the state is permitted to participate only as a corporate actor and revenue collector, but not as a regulator, and has largely relied on federation account instead of building a robust solid minerals economy. Evidence attributes the state’s weak interest in solid minerals to its dependence on oil revenue, while existing state-level instruments for mineral revenue collection are perceived as ineffective. The paper concludes that Nasarawa’s status as a subnational rentier state explains the paradox of mineral abundance and underdevelopment and calls for reforms that strengthen state capacity, improve transparency in derivation, and create incentives for solid minerals governance.
🏷 Economic Development; Federalism; Governance; Rentier State; Solid Minerals;
Article 173 · pp. 2355-2366
Generating Livelihood through Amazing Make-Up (GLAM): A Needs-Based Evaluation of a Community-Based TVET Intervention for Out-of-School Youth and 4Ps-Monitored Students in Nueva Ecija
This study presents a needs-based evaluation of Project GLAM (Generating Livelihood through Amazing Make-Up), a community extension intervention implemented by the College of Education of the Nueva Ecija University of Science and Technology (NEUST) in partnership with the Department of Social Welfare and Development (DSWD), local government units, and private-sector partners. The project addresses the documented vulnerability of out-of-school youth (OSY) and students monitored under the Pantawid Pamilyang Pilipino Program (4Ps) by equipping them with marketable competencies in hair styling, make-up artistry, personal grooming, and entrepreneurship. The study synthesizes three primary sources: a DSWD needs assessment of 4Ps case-management data covering nine municipalities in Nueva Ecija; the NEUST Accomplishment and Terminal Report documenting the first wave of training across three pilot sites (Guimba, Quezon, and Talavera) from March 16 to 27, 2026; and the instructional module developed for the beauty care and make-up artistry curriculum. Findings show that poverty-driven child labor (58.9%), financial concern (30.3%), early pregnancy or fatherhood (5.5%), and early marriage (5.3%) were the leading drivers of school dropout among 3,049 identified children, of whom 1,822 were classified as high-risk. The project trained 55 participants across 72 hours of instruction, achieving 100% completion across all logical-framework output indicators and an overall client-satisfaction rating of 4.84 (Excellent, Very Satisfied). Pre-training competency self-ratings rose from 2.46 to 3.44 following the intervention. The evaluation is situated within human capital theory and the capability approach, and is read against Philippine and international literature on TVET effectiveness, conditional cash transfers, and higher-education extension. The study concludes that competency-based, entrepreneurship-integrated beauty and wellness training is a feasible and well-received mechanism for converting short-term conditional cash transfers into pathways toward sustainable, community-based livelihoods, while noting that self-reported, single-source, output-level evidence from a three-site pilot warrants caution in generalizing to outcome-level and program-wide claims. Recommendations include a second wave of training to reach a wider pool of identified high-risk beneficiaries, pursuit of formal TESDA National Certificate II accreditation, establishment of a mobile training unit for remote barangays, and longitudinal tracking of graduate income and employment outcomes.
🏷 Project GLAM; out-of-school youth; 4Ps; TVET; livelihood training; beauty and wellness industry; community extension; needs assessment; capability approach
Article 174 · pp. 2367-2376
Society Demands Quality Life of products in Marketing
The study briefly describes Marketing Management factors of social responsibility i.e. product, survival, price and profit which are involved in the marketing practices in Cachar district, among them 96 unit under the study. Marketing has an important role to play in the growth and development of underdeveloped economy. A rapid development of an underdeveloped country it is possible only through the modern techniques of marketing. Importance of marketing goes on increasing in these countries with every growth in industrialization and urbanization because marketing is a significant tool for producing the goods and services at large scale and for selling this production successfully in the market. Marketing is considered to be the most important activity of all the business activities. It has become a necessity for the successful operation of all other business sources. In recent years, the functions of marketing have undergone tremendous changes. It is being recognized as a profession based on a systematic part of knowledge. These changes have increased the liabilities of marketing in international markets. It exercises a definite and extensive influence on our economic and social life styles. As a socio-economic institution a market-oriented company has to perform all tasks involved in the development and delivery of goods and services from production to consumption. As a social institution, marketing is responsible to deliver a standard of living and maximise life quality. Now life quality means not only the quantity and quality of consumer goods and services but also enriched quality of the environment.
🏷 Product, survival, social responsibility and Profit.
Article 175 · pp. 2377-2387
Tensile Strength, Tool Degradation, and Cost Efficiency: Integrating Physical Material Properties into Management Accounting Models
This study examines the empirical integration of physical material properties, specifically tensile strength, into management accounting models, focusing directly on tool degradation and overall cost efficiency within modern manufacturing environments. While traditional cost accounting systems heavily emphasize standard financial and operational variables, limited academic and practical attention has been given to how intrinsic material characteristics directly influence bottom-line production costs, waste generation, and long-term organizational performance outcomes.
To address this critical gap, this study adopts a quantitative research design. Empirical data were collected via structured questionnaires from 210 production engineers, cost accountants, and operations managers working across various manufacturing firms in Nigeria. Both descriptive statistics and multiple regression analysis were employed to rigorously analyze the gathered data.
The findings reveal that tensile strength significantly and profoundly influences tool degradation rates (β = 0.72, p < 0.001) and cost efficiency (β = 0.68, p < 0.001). Specifically, processing higher tensile strength materials was strongly associated with accelerated tool wear, elevated maintenance expenses, and reduced cost efficiency when these physical variables are not optimally managed or accounted for during strategic budget planning and forecasting.
Consequently, the study concludes that seamlessly integrating key materials science variables into contemporary management accounting frameworks significantly enhances the accuracy of cost predictions and strategic operational decision-making. Ultimately, this paper contributes to interdisciplinary research by successfully linking engineering material properties with traditional cost accounting models. Based on these insights, it recommends that manufacturing firms actively incorporate material-based cost drivers into their performance evaluation systems and comprehensive variance analysis protocols to optimize real-time production workflows, minimize machine downtime, and improve cost control strategies.
🏷 Tensile strength, Tool degradation, Cost efficiency, Management accounting, Material properties, Manufacturing.
Article 176 · pp. 2388-2409
Static Analysis Meets Artificial Intelligence: A Hybrid Framework for PHP Vulnerability Detection Using DeepSeek LLM and Machine Learning
The fast adoption of Large Language Models (LLMs) for code generation has revolutionised the process of constructing online applications; nevertheless, the security implications of AI-generated PHP code are still not fully understood. Combining static analysis, machine learning, and LLM-driven intelligence, this research provides a comprehensive hybrid vulnerability detection system that can identify security weaknesses in PHP web applications. The method is designed to identify vulnerabilities in PHP web applications. Utilising eleven different vulnerability categories that are connected to the CWE and OWASP Top 10 standards, our system is more secure. In addition, a trainable Random Forest classifier that utilises TF-IDF vectorisation is utilised, and DeepSeek LLM is incorporated for the purpose of providing intelligent explanations and remediation guidance. We demonstrate that the framework finds 15 vulnerabilities across 8 CWE categories from a single configuration file by conducting exhaustive assessments on vulnerable PHP code samples. As a result, the framework achieves a Security Score of 62%. A total of eight medium vulnerabilities, two critical vulnerabilities (SQL Injection), four high vulnerabilities (Hardcoded Credentials, Authentication Bypass, and Broken Access Control), and one low vulnerability were discovered by the static analysis engine. With the help of DeepSeek, the neuro-symbolic pattern generating component was able to attain an average accuracy of 76.6% while simultaneously lowering the amount of time required to produce patterns from weeks to hours. Authentication Security (0%-27% coverage) and HTTP Security Headers (0% coverage) were found to have major gaps in coverage due to the risk assessment that was conducted across five different LLM models. The fact that the whole open-source Streamlit application provides interactive vulnerability reporting along with the ability to export in both JSON and HTML demonstrates that hybrid AI-driven static analysis is an essential tool in contemporary DevSecOps workflows.
🏷 Static Analysis, PHP Security, Vulnerability Detection, Machine Learning, DeepSeek LLM
Article 177 · pp. 2410-2432
Game-Based Learning and its Role in Boosting Students’ Motivation and Academic Achievement
Game-based learning, an instructional approach that integrates interactive games to enhance student engagement and learning outcomes, has gained increasing attention for its potential to improve motivation and academic performance. This study examined the role of game-based learning in enhancing students’ motivation and academic achievement among Key Stage 2 learners in the District of Wright I, Schools Division of Samar, during School Year 2025–2026. Using a quantitative descriptive-correlational research design, the study explored relationships among students’ profile variables, perceptions of game-based learning implementation, motivation, and academic performance. Findings revealed that most students demonstrated moderately high academic performance, with 67.14 % achieving mean grades between 85 and 89. Among the profile variables, only access to gadgets at home and positive attitudes toward game-based learning were significantly correlated with perceived implementation and motivation, while age, sex, and frequency of gadget use showed no significant relationships. Student and teacher perceptions of implementation were not significantly associated with academic achievement, although positive learning attitudes showed a weak relationship with slightly higher performance. Teachers identified difficulty in clarifying instructions and rules as a major implementation challenge. Recommendations include enhancing teacher training, improving access to technology, fostering positive learning attitudes, and conducting further studies on long-term outcomes.
🏷 Game-based learning, Student motivation, Academic achievement, Technology access, Key Stage 2 learners
Article 178 · pp. 2433-2437
Caste and Community in the Novels of Mulk Raj Anand: A Critical Rereading of Untouchable and Coolie
This paper undertakes a close critical rereading of caste and community in two of Mulk Raj Anand's early novels, Untouchable (1935) and Coolie (1936), read together as a diptych on the structural production of human waste under colonial modernity. Rather than treating caste and class as parallel or interchangeable systems of exclusion, the paper argues that Anand's fiction traces a specific historical hinge: the moment at which a ritual hierarchy of purity and pollution, inherited from pre-colonial social organisation, is absorbed into and partially reconfigured by an emerging colonial-capitalist economy of wage labour. Untouchable compresses this argument into the single day of Bakha, a young sweeper in the fictional cantonment town of Bulashah, whose body is marked as permanently polluting regardless of his conduct or intention; Coolie extends it across the itinerant, multi-year odyssey of Munoo, an orphaned hill boy whose successive employments in Sham Nagar, Daulatpur, Bombay and Simla show caste stigma mutating into, without ever being replaced by, the disposability of the unskilled worker. Drawing on close reading, postcolonial theory, and the historical record of colonial governmentality, the paper contends that Anand's much-discussed 'solutions'--conversion, Gandhian reform, and the machine, on the one hand, and trade-union solidarity, on the other--are staged not as answers but as unresolved provocations, and that this formal irresolution is itself the novels' most enduring political argument. The paper closes by situating this reading against more recent Dalit-critical scholarship that has questioned the adequacy of a non-Dalit, upper-caste author's authorial voice to represent Dalit interiority, arguing that Anand's fiction is best read today as a historically significant but structurally limited intervention rather than a final or sufficient account of caste.
🏷 Mulk Raj Anand; Untouchable; Coolie; Caste; Dalit Literature; Colonial India; Social Realism; Class and Labour; Postcolonial Narrative; Gandhian Reform; Literary Representation of Marginality.
Article 179 · pp. 2438-2445
Examining The Influence of Entrepreneurial Education on Entrepreneurial Intention: Evidence from Postgraduate and Undergraduate Students with an Entrepreneurial Mindset as a Mediator
This study explores the impact of entrepreneurial education on entrepreneurial intention, with a focus on the mediating role of entrepreneurial Mindset among postgraduate students. The study aims to assess the reliability and validity of the key constructs—entrepreneurial education, entrepreneurial Mindset, and entrepreneurial intention—while also examining the predictive relationships among them through regression analysis. Data were collected from a sample of 203 postgraduate and undergraduate students enrolled in autonomous colleges affiliated to Mangalore University and deemed universities in Mangalore that offer Entrepreneurship as a compulsory course. Standardised scales were employed, and the analysis was conducted using SPSS. Reliability was confirmed through Cronbach’s alpha, while construct validity was established using factor analysis. Regression results indicate a positive relationship between entrepreneurial education and entrepreneurial intention, with entrepreneurial Mindset playing a significant mediating role. These findings provide preliminary support for the theoretical model and underscore the importance of fostering both educational and psychological components in nurturing entrepreneurial intent among students. The results also serve as a foundation for further large-scale empirical studies in this domain.
🏷 Entrepreneurial education, entrepreneurial intention, entrepreneurial Mindset, postgraduate students, entrepreneurial intent.
Article 180 · pp. 2446-2473
Analyzing the overarching trends and developments of OTT platforms: A Bibliometric analysis
This study aims to examine the overall trends and development of the OTT Platforms that have emerged as the most accessed platforms for entertainment, leaving behind the television industry. OTT platforms have increasingly become a favorable source of entertainment. A total of 343 articles were retrieved from the Scopus database from 2008 to 2024, and then reduced to 272 documents (articles), after limiting to some keywords; a total of 270 articles were reviewed. A method for bibliometric analysis that employs cluster analysis and scientific mapping, utilizing the bibliographic coupling of documents. The study underscores the growth of Over-the-top platform services during the COVID-19 pandemic. With the help of bibliographic coupling, seven different clusters have been made and identified as Cluster 1 Transformative Impact of Streaming Media, Cluster 2 Evolution of Media Consumption, Cluster 3 User-Centric Revenue Models in the Entertainment Industry, Cluster 4 Media Investment and Streaming Dynamics, Cluster 5 Transformation in Television and Media Culture in the Digital Age, Cluster 6 Evolving Video Consumption and Delivery Models, Cluster 7 Impact of OTT Services on Telecom Industry. The research identified several elements that enhance the appeal of OTT platforms, including ease of access, cost-effectiveness, the ability to binge-watch, quality of service, personalization, and targeted offerings. This study adds to the current body of knowledge by examining new trends, developments, and areas that require further research.
🏷 OTT, Binge-watching, Subscription video on demand, Cord-cutting, Bibliometric analysis, Technological advancement.
Article 181 · pp. 2474-2486
Machine Learning-Based Multi-Cancer Diagnostic System for Early Detection and Accurate Classification Across Diverse Cancer Types
Cancer is one of the major causes of death worldwide, mostly owing to late discovery and hence restricted treatment choices. Existing screening approaches are primarily invasive and often associated with complicated, long and expensive procedures. In biomedicine and bioinformatics, several research groups have examined the use of machine learning methods to solve the important challenge of categorizing cancer patients into high- and low-risk categories. These methodologies have thus been used to mimic the onset and treatment of cancer. The ability of ML algorithms to detect important characteristics in complex datasets further highlights their importance. Many of these approaches like as Decision Trees, Logistic Regression (LR), Support Vector Machines and K-Nearest Neighbours have been widely employed in cancer research to generate prediction models that aid decision makers to make better and more trustworthy decisions. ML methods are indeed able to improve our understanding of cancer formation, but need adequate validation to be regarded for application in ordinary clinical practice. Hence, an ML approach was utilized to simulate the progression of cancer. The prediction models shown here are based on several ML approaches and a broad variety of input features and Data Samples. The proposed framework incorporates data preprocessing, feature selection, and advanced classification algorithms to enhance diagnostic accuracy and facilitate timely clinical decision-making. The study emphasizes the potential of artificial intelligence in advancing precision oncology and improving healthcare outcomes.
🏷 Machine Learning, Kaggle Multi Cancer Dataset, Predictive Models, Cancer Disease, Medical Images, Classification.
Article 182 · pp. 2487-2494
Precision Farming Technologies: A Comprehensive Review of Drone and Sensor Applications for Sustainable Crop Management
The agriculture sector is undergoing a technological revolution in order to cope with various emerging challenges like food insecurity, resource scarcity, climate variability, and environmental degradation. Traditional agricultural systems based on random usage of agricultural inputs and laborious farming practices are becoming less sustainable in the face of changes in climatic and socio-economic environments. Precision agriculture is defined as a technology-based and site-specific approach in agriculture, which is based on the application of digital technologies for crop cultivation while making the optimal use of natural resources. Unmanned aerial vehicles (drones) and intelligent sensor systems are some of the modern agricultural technologies. Multispectral, hyperspectral, thermal and RGB imaging technologies allow drones to instantly assess plant health, incidence of pests and diseases, nutrient deficiencies, weed problems, watering needs, and yield estimates. At the same time, various sensors in soils and environment measure important parameters such as soil moisture, nutrient content, temperatures, humidity, soil pH, and electrical conductivity. Combining all the above technologies with GIS, GPS, AI, ML, and IoT provides precise and automated agricultural interventions tailored to individual plots that increase production, decrease costs, optimize resource use, and minimize negative impacts on the environment. In this review paper, an in-depth evaluation of the concepts behind precision agriculture, types and functioning of drone technology and sensors, usage of drones in agriculture, advantages of drones in agriculture in terms of economy and environment, limitations of using drones in agriculture, and future directions for research are done. In addition to that, current status, policies related to precision agriculture, and adoption of precision agriculture in India and elsewhere are elaborated upon.
🏷 Precision farming, drones, sensors, crop management, UAV, IoT, smart agriculture, GIS
Article 183 · pp. 2495-2502
Zero Budget Natural Farming (ZBNF): A Comprehensive Review of Sustainable Crop Production, Soil Health, and Environmental Conservation
Zero Budget Natural Farming (ZBNF) is an eco-friendly and sustainable agriculture technique formulated as an alternative approach to the current conventional agriculture practices that make intensive use of chemicals. ZBNF was introduced by Indian Agriculturist Subhash Palekar. ZBNF is an agricultural technique that seeks to minimize cost of cultivation, restore soil health, preserve biodiversity, and increase farmers' income through the discontinuation of dependence on artificial fertilizers and pesticides. The process makes use of natural biological processes, indigenous knowledge systems, cow-based bio-formulations, mulching, biodiversity and water conservation. Over the past few decades, there has been an excessive use of chemicals in agriculture leading to serious environmental degradation, decreased soil fertility, pollution of ground water, high costs of cultivation and increased farmer indebtedness. ZBNF was formulated in order to address these issues and has received wide acclaim as an innovative technique for sustainable agriculture, especially in India. The present article gives a comprehensive account of ZBNF in terms of its history, philosophy, science, practice, socioeconomic importance, ecological importance, problems, policies, and future prospects.
🏷 Zero Budget Natural Farming, Sustainable crop production, Soil health, Environmental conservation
Article 184 · pp. 2503-2515
Instructional Leadership Practices and their Influence on Curriculum Implementation in Basic Education
This study examined instructional leadership practices of school leaders and their influence on curriculum implementation in secondary basic education. Anchored in the premise that school leadership effectiveness is central to improving teaching and learning outcomes, this analysis examines how principals and school heads exercise instructional supervision, set academic goals, support teacher professional development, and foster a conducive learning environment. Using a mixed-methods design, the study gathered qualitative data from interviews with school leaders (4) and quantitative survey responses from teachers (64) to examine leadership practices and curriculum delivery alignment. Findings indicate that strong instructional leadership significantly enhances curriculum implementation when leaders actively engage in classroom observations, provide constructive feedback, and promote collaborative professional learning among teachers. The area of research is instructional leadership and curriculum implementation in secondary basic education. Findings indicate that a strong, positive, and statistically significant relationship exists between instructional leadership and effective curriculum implementation when leaders actively engage in classroom observations, provide constructive feedback, and promote collaborative professional learning among teachers. Effective school leadership also strengthens alignment between curriculum standards and classroom practices, improving instructional quality and learner outcomes in secondary basic education. The study's outputs are empirical evidence that proactive instructional leadership marked by regular classroom observation, constructive feedback, and collaborative professional learning has a positive, statistically significant effect on curriculum implementation in secondary basic education, and a set of practical recommendations for school leaders to protect instructional time
🏷 Educational Management and Development, Descriptive Correlational, Instructional leadership, Curriculum Implementation, Academic performance, Alicia Bohol, Philippines
Article 185 · pp. 2516-2522
From Maternal Trauma to Social Mission: Transforming Personal Loss into Social Support
The loss of a child is one of the most traumatic events, capable of significantly impacting a mother's psycho-emotional state, social roles, and life prospects. Even while caring for a seriously ill child, the lives of parents, and especially mothers, change dramatically, accompanied by high levels of stress, emotional exhaustion, social isolation, and significant financial strain on the family. However, despite the high level of psycho-emotional trauma, some people are able to transform their own difficult experiences into a source of personal development, recovery, or the formation of prosocial activities and even leadership. This is especially evident in the processes of establishing charitable foundations and organizations.
The purpose of this article is to analyze the relationship between the traumatic experience of loss and the process of subsequent personal growth in the mother, as well as the formation of a social mission through charitable work. Particular attention is paid to:
The mechanisms of finding meaning and purpose after the loss of a child;
The role of altruistic activity in the process of psychological rehabilitation;
The study explores the potential and specific features of charitable organizations in providing support to families and mothers in crisis situations.
The methodological basis of this work is an analysis of contemporary psychological, sociological, and medical-social research on maternal grief, post-traumatic rehabilitation, and personal growth. A practical example of the implementation of these theoretical principles is the case of Tatyana Artemyeva and her creation of the charitable foundation "NeuroChildren" for children with brain diseases. Ms. Artemyeva was able to transform her own experience of loss into systemic assistance for children, mothers, and families facing difficult life circumstances.
These results confirm the role of charitable activity not only as a mechanism for providing social support to others but also as a key factor in reconstructing one's own identity, creating a new meaning in life, and developing social leadership after trauma.
🏷 post-traumatic growth, maternal trauma, charity, social support, leadership.
Article 186 · pp. 2523-2540
Moderating Role of Digital Literacy on the Nexus between Electronic Human Resource Management Practices and Organizational Performance of Federal Universities in North-Western Nigeria
Technology adoption enhances human resource capabilities to contribute to organizational efficiency and competitive advantage. This study examined how electronic human resources management (e-HRM) practices, moderated by digital literacy, improve organizational performance in North-Western Nigeria. The study was guided by the Technology Acceptance Model, complemented by Resource Base View and Human Capital Theories. Using a cross-sectional survey, data were collected by questionnaire from 592 employees of seven federal universities in North-Western Nigeria. A total sample size of 448 respondents was used. Descriptive and inferential statistics tests using the Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to analyse the data. The findings revealed that digital literacy, e-communication and e-performance appraisal have positive and statistically significant effects on organizational performance, while e-recruitment has a negative statistically significant effect on organizational performance. This suggests that the existing e-recruitment systems may not be adequately developed, properly utilized, or effectively integrated into institutional recruitment processes. Also, digital literacy has a significant moderating role in the relationship between e-performance appraisal, e-communication, and organizational performance. However, digital literacy exhibited negative but significant moderating influence on the relationship between e-recruitment and organizational performance. This would imply that possessing digital competencies alone is insufficient to guarantee improved recruitment outcomes when the supporting technological infrastructure and recruitment systems are ineffective Based on these findings, the study recommends that the management of federal universities should establish technical support and mentoring systems to ensure staff continuously improve their digital capabilities and adapt to evolving technologies.
🏷 Digital Literacy, e-Communication, Electronic Human Resource Management, Organizational Performance, e-Performance Appraisal
Article 187 · pp. 2541-2550
BFL-Guard: A Blockchain-Enabled Federated Learning Framework with Zero-Knowledge Gradient Verification and Tokenized Incentives
Federated Learning (FL) enables collaborative model training across decentralized participants without sharing raw data. However, existing FL systems remain vulnerable to Byzantine attacks and suffer from a lack of accountability, verifiability, and economic incentives for honest participation. We present BFL-Guard, a novel blockchain-orchestrated federated learning framework integrating: (i) zk-SNARK-based zero-knowledge gradient proofs, (ii) an on-chain Byzantine-tolerant aggregation smart contract, and (iii) a tokenized incentive protocol (FedToken). BFL-Guard stores model checkpoints as IPFS hashes anchored on Ethereum, ensuring tamper-evident auditability. Experiments on CIFAR-10 and Shakespeare benchmarks demonstrate 95.2% and 87.6% accuracy in IID and Non-IID settings, surpassing all baselines while converging 12.4% faster even under 30% Byzantine injection.
🏷 Blockchain, Federated Learning, Byzantine Fault Tolerance, Zero-Knowledge Proofs, Smart Contracts, Incentive Mechanism, Decentralized AI, IPFS, zk-SNARK
Article 188 · pp. 2551-2562
Greening the Grind: Reimagining Work-Life Balance in India's it Sector through Remote work and Sustainable Environments
Rationale: It is a conceptual-exploratory study that investigates how remote work arrangement and green environmental factors converge and influence work-life balance (WLB) of IT sector employees. This paper explores the interplay between these two rather than them acting on their own to impact employee well-being, productivity and life satisfaction, in the context of the institutionalization of hybrid and remote work and the increased salience of organizational environmental sustainability post-pandemic.
The methodology used is non-empirical, literature based approach, the study is synthesis of literature from related studies which are obtained in management literature, environmental psychology, organizational behaviour and human resource management. A narrative-thematic review approach is used to summarise the literature by highlighting recurring constructs, gaps in theory and patterns that emerge from the studies in peer-reviewed journals, industry reports and policy documents (2019–2026) which culminate in the proposed ECOFLEX Framework.
Synthesis: Remote work autonomy with access to a green/biophilic environment (home gardens, natural light and low-pollution surroundings) leads to compounding positive effects on WLB which is greater than the effects of either autonomy alone or access to a green/biophilic environment alone. Without access to green spaces, on the other hand, remote working can increase the sense of isolation and lack of boundaries.
Implications: Findings provide IT organisations with a foundation for rethinking their remote work practices by adding environmental wellness elements (green workspace allowances, biophilic design guidelines, sustainability related flexibility). Policymakers could think about urban planning and housing aspects of the expanding remote IT workforce.
Limitations: This paper is a conceptual study and does not include primary empirical evidence; the proposed ECOFLEX framework needs to be quantitatively and qualitatively tested with further research in various IT organizational settings and across different geographies.
Contribution & Novelty: The originality of the study consists of considering the inter-action between the influence of remote work and green environmental variables on WLB, in a manner that was not touched by previous studies, which have mostly considered them as separate antecedents. The ECOFLEX framework is a new integrative lens for future empirical investigation.
This article will be relevant to keywords such as Remote Work, Green Environment, Work-Life Balance, IT Sector, Sustainable Workplace Practices.
🏷 Work-Life, Sustainable, Environments
Article 189 · pp. 2563-2575
Determinants Impacting Employee Moonlighting: A Conceptual Push-Pull & SMART ISM Perspective
Moonlighting has been in practice for a long time, but post-pandemic, the inclination of white-collar workers towards moonlighting has tremendously increased. This study aims to explore the factors that affect the moonlighting of employees in the post-COVID work environment. The data for the present study are all secondary data from peer-reviewed journal articles, company reports, news articles, corporate policy statements, HR publications, and publicly available organisational communications in relation to employee moonlighting. Considering Push-Pull Theory and SMART Interpretive Structural Modelling (SMART-ISM), the study identifies and analyses the interrelationships between the key determinants identified through an extensive literature review. Eleven determinants were identified and categorised under three main heads, namely financial, job-related, and opportunity-driven. The Structural Self-Interaction Matrix (SSIM) was built based on the expert opinions and a hierarchical SMART-ISM was constructed. It shows that recession/inflation, wage stagnation and financial frictions are at the baseline, whereas entrepreneurship opportunities and participation in the ‘gig-economy' appear to be outcome-level factors. The study provides a framework which helps to understand the moonlighting behaviour in a structured manner and also provides practical implications to HR managers and policymakers.
🏷 Moonlighting, push factor, pull factor, gig economy, digital economy, economic
Article 190 · pp. 2576-2588
Challenges Faced by Women Investors in Equity Market Participation - An Empirical Study
Although women's financial inclusion has been on the rise, their involvement in equity markets remains limited. In order to promote financial inclusion and economic empowerment, it is necessary to gain insight into the influencing factors associated with women's investment choices. This study is designed to investigate the issues of the women's participation in the equity markets, where the impact of investment knowledge, risk perception, and financial literacy, Investor Confidence, Family's Influence, Social Influences, Information Accessibility, Market Complexity and Time Constraints on Equity Market Participation have been analyzed. This research used the strategy quantitative research and data collection techniques is primary data which is obtained from the respondents of this research, namely female investors who are 250 respondents who have been taken. Partial Least Squares Structural Equation Modeling was used to analyze the gathered data. Research results show that Financial Literacy, Information Accessibility, Investor Confidence, Investment Knowledge and Family Influence are important factors which have positive impacts on women's involvement in equity markets. On the other hand, Risk Perception, Market Complexity and Time Constraints were found to have significant negative impacts as these are the main obstacles for investment participation. There was no significant effect of Social Influence on participation decision. The model was strong in explaining the variation of Equity Market Participation with the R square value of 61.55%. The findings indicate that financial education, better access to investment information, greater investor confidence and a streamlined investment process can help to promote women investors' participation. This study helps the growing body of behavioural finance literature by offering a unified model for understanding women's investment decisions and contributes to the expanding corpus of research on developing investor education strategies to encourage women's investment in equity markets as a pathway to financial empowerment and reach gender parity in financial decision-making.
🏷 Women Investors; Equity Market Participation; Financial Literacy; Risk Perception; Investor Confidence.
Article 191 · pp. 2589-2599
Mental Wellbeing at Workplace: An Emerging Driver of Employee Performance and Organisational Success
Mental health of workforce is a significant aspect of sustainable organisational performance. The study highlights the rising trend of mental health issues post pandemic that is creating an alarm for organisations to address the issue on urgent basis. The study tries to identify the critical factors affecting mental health of an employee. Inputs from published articles, research papers, global health surveys and organisational case studies shows that mental wellbeing reduces stress, anxiety, burnout, and absenteeism and promotes greater job satisfaction and organisational productivity. The study concludes that organisations should integrate mental health and wellbeing initiatives into strategic practices to build a resilient and productive workforce for greater competitive advantage.
🏷 Mental health, Employee wellbeing, pandemic, productivity, strategic initiatives
Article 192 · pp. 2600-2620
Challenges and Opportunities for Large-Scale Adoption of Interlocking Stabilized Soil Block Technology in Urban Housing Development
Rapid urbanization and the escalating demand for affordable urban housing have intensified the search for sustainable, cost-effective building materials. Interlocking Stabilized Soil Block (ISSB) technology offers a promising, low-carbon alternative to conventional fired bricks and concrete blocks by utilizing locally sourced soil compressed with a minimal stabilizing agent (such as cement or lime). However, despite its proven economic and environmental advantages, widespread adoption in urban housing developments remains limited. This paper investigates the critical challenges and emerging opportunities surrounding the large-scale integration of ISSB technology in urban contexts. Through a mixed-methods approach—combining a comprehensive literature review, comparative cost-benefit analyses, and stakeholder interviews within the construction sector—the study identifies major barriers to adoption. These include rigid or outdated building regulatory frameworks, deeply entrenched negative public perceptions associating soil-based materials with low-income or temporary housing, and supply chain inconsistencies regarding block quality and production scale. Conversely, the research highlights significant opportunities, particularly driven by shifting green building policies, rising carbon tax pressures, and the urgent need for climate-resilient urban infrastructure. The paper concludes with a strategic framework aimed at policymakers, developers, and engineers to standardize ISSB production, update municipal building codes, and incentivize sustainable development, positioning ISSB technology as a viable cornerstone for future eco-friendly urban housing.
🏷 Interlocking Stabilized Soil Blocks (ISSB), Sustainable Construction Materials, Urban Housing Development, Affordable Housing, Green Building Codes, Low-Carbon Infrastructure
Article 193 · pp. 2621-2632
Improving Prediction of Dengue Outbreaks Using Attention-based LSTM Model with Honey Badger Optimization for Hyperparameter Tuning
Climate Change Poses a Significant Challenge to the Current Dynamics of Disease Outbreaks. This Study Improves Outbreak Prediction Using an Attention-Based LSTM Model Optimized by the Honey Badger Algorithm (HBA) for Hyperparameter Tuning.
Using Disease, Climate, and Geographic Data From 2015–2024 in Different Barangays in Koronadal, South Cotabato, the Model Predicts Incidence Over 1-, 3-, 6-, and 12-Month Horizons. Attention Mechanisms Enhanced Long-Term Pattern Detection, While HBA Reduces Overfitting and Boosts Accuracy. Results Show the HBA-LSTM Reduces Mean Squared Error by 43.7% Over Standard LSTM and 22.2% Over Attention Models. Similar Reductions are Seen in RMSE, MAE, and MAPE. Though Effective, Further Tuning and Alternative Architectures are Suggested for Improved Generalization.
🏷 LSTM, overfitting, attention mechanism, deep learning, honey badge, optimization, fine-tuning
Article 194 · pp. 2633-2644
Industrial Practices in Repair, Maintenance, and Performance Evaluation of three-Phase Induction Motors: A Practical Case Study
Repair and rewinding of three-phase induction motors is a routine but technically demanding activity in industrial maintenance workshops, and the quality of that work has a direct bearing on motor efficiency, reliability, and service life. While international standards (e.g., IEEE 43, IEC 60034 series, NEMA MG-1, ISO 21940-11) define acceptance criteria for insulation resistance, surge/impulse withstand, and rotor balancing, published literature rarely documents how these criteria are applied together on a single motor as it moves through a workshop repair cycle. This paper addresses that gap through a practical case study of a burnt-out three-phase induction motor processed at an industrial motor-servicing facility.
The objective of the study is to document, in a structured and reproducible format, the diagnostic and corrective sequence applied to the motor — direct current resistance (DCR) testing, insulation resistance (Megger) testing, surge testing, rotor static and dynamic balancing, mechanical reconditioning, and stator rewinding — and to compare the pre-repair (faulty) and post-repair (healthy) results against recognised acceptance limits. The motor was diagnosed with a grounded, turn-to-turn shorted stator winding (Megger reading of 0 MΩ and a shorted phase pair) and unbalance readings far above the workshop's 9 g acceptance threshold. Following stripping, rewinding with Class F insulation and vacuum-pressure impregnation (VPI), and two-plane dynamic balancing, the motor achieved an insulation resistance of 998–1000 MΩ, a maximum inter-phase DCR deviation of 0.2 Ω, superimposed surge waveforms across all three phases, and a final rotor unbalance below 9 g on both correction planes.
The novelty of this work lies in presenting a single, fully-instrumented before/after case record that links electrical diagnostics, mechanical balancing, and winding-design considerations (coil pitch, connection type) within one workshop cycle, and in framing the acceptance criteria used against established international standards rather than workshop convention alone. The findings are intended to serve as a practical reference for motor-repair technicians, junior engineers, and students, and as a template for reporting repair-quality data in a form suitable for later statistical or predictive-maintenance analysis.
🏷 Three-phase induction motor; motor repair and rewinding; insulation resistance testing; direct current resistance (DCR) test; surge test; rotor balancing; predictive maintenance; industrial case study.
Article 195 · pp. 2645-2653
Design and Development of an IoT-Enabled Arduino Uno-Based Intelligent Medical Waste Collection and Monitoring System with Android Application Support
Biomedical waste management has become a critical environmental and public health challenge due to the rapid increase in hazardous waste generated by hospitals, diagnostic laboratories, and rural healthcare centres. Inefficient monitoring and delayed waste collection often result in waste overflow, infectious disease transmission, hazardous gas emissions, and increased occupational health risks. Despite the availability of conventional waste management practices, there remains a significant research gap in the development of low-cost, real-time, and intelligent monitoring systems suitable for resource-constrained healthcare facilities. This study aims to design, model, implement, and validate an intelligent biomedical waste monitoring system based on the Arduino Uno microcontroller integrated with an Android mobile application. The proposed system utilizes ultrasonic sensors for waste level measurement, MQ-135 gas sensors for hazardous gas detection, and Bluetooth communication for real-time wireless monitoring. Mathematical models for ultrasonic distance estimation and gas concentration calibration are developed, and the system performance is evaluated through experimental analysis using metrics such as alert accuracy, response time, power consumption, and waste collection efficiency. Experimental results demonstrate that the proposed system significantly improves operational efficiency, minimizes waste overflow incidents, enhances safety compliance, and reduces manual monitoring requirements compared with conventional waste collection methods. The developed framework provides a scalable, reliable, and cost-effective solution for intelligent biomedical waste management, offering substantial potential for deployment in hospitals, clinics, and rural healthcare facilities, particularly in resource-limited settings.
🏷 Android monitoring system, Arduino Uno, Biomedical waste management, Embedded systems, IoT healthcare, Gas detection, Smart waste collection, Ultrasonic sensing, [1].
Article 196 · pp. 2654-2657
Transitioning Identities: The Cognitive and Psychological Evolution of Adolescent Females in Nursing Education
The transition from late adolescence to professional nursing practice involves a radical, multi-dimensional restructuring of the self (p. 1). This article examines how the rigorous nursing curriculum influences the cognitive and psychological architecture of adolescent females, moving them from self-oriented adolescent perspectives to a service-oriented professional identity (p. 1). It highlights the development of emotional intelligence, resilience, the mitigation of gender-based socialization, and the acquisition of the "nursing gaze"—a specialized clinical lens that balances deep empathy with objective scientific reasoning (p. 1).
🏷 Transitioning Identities, Cognitive, Psychological
Article 197 · pp. 2658-2663
Work Interference with Family and Family Interference with Work Among College Teachers
The teaching profession demands significant time, emotional involvement, and professional commitment, which often create conflicts between work and family responsibilities. This study examines Work Interference with Family (WIF) and Family Interference with Work (FIW) among college teachers. The study aims to understand the extent to which professional responsibilities affect family life and how family responsibilities influence work performance. Data were collected from 340 college teachers through a structured questionnaire using convenient sampling techniques. The findings reveal that heavy workload, administrative responsibilities, extended working hours, and family obligations significantly contribute to work-family conflicts. The study highlights the importance of flexible work policies, stress management practices, and institutional support systems to improve work-life balance among college teachers.
🏷 Work Interference with Family, Family Interference with Work, College Teachers, Work-Life Balance, Occupational Stress
Article 198 · pp. 2664-2669
One District One Product as a Market Access Lever for Regional Food Brands: A Founder-Led Case Study from India's Premium Snacks Segment
India's One District One Product (ODOP) initiative, coordinated by the Department for Promotion of Industry and Internal Trade, seeks to identify, brand and promote one distinctive product from every district of the country. The programme has been discussed at the level of policy design and district economies, but very little published work examines how an individual firm actually experiences ODOP: what onboarding involves, what the scheme offers a small brand in practice, and where its design assumptions sit uneasily with how modern consumer businesses are structured. This paper addresses that gap through a single case study of Patang, a bootstrapped premium snacks brand that sources ten products from seven kitchens located in their districts of culinary origin, and which was onboarded onto ODOP through Invest India in 2026. Drawing on participant observation by the founder, internal company records and programme documentation, the study traces the onboarding journey, including product-to-district mapping and the use of ODOP identity marks on packaging. Three findings emerge. First, the scheme's principal near-term value to a young brand is legitimacy: state recognition functions as a trust signal in buyer conversations, particularly in institutional and gifting channels. Second, alignment costs are negligible for firms whose sourcing is already place-anchored, which suggests that ODOP currently rewards a supply chain design choice rather than inducing it. Third, the programme's single-district framing fits producers rooted in one location but fits awkwardly with portfolio brands that aggregate products across many districts, a structural gap with implications for scheme design. The paper closes with recommendations for policymakers and for founders of provenance-led brands considering enrolment.
🏷 One District One Product (ODOP); market access; regional food brands; place-based branding; MSME policy
Article 199 · pp. 2670-2685
“Strategic Employee Engagement as a Driver of High-Performing Organizations: Evidence from Leading Indian Companies”
Employee engagement has emerged as a strategic priority for organizations seeking to improve productivity, innovation, and long-term sustainability. In today's dynamic business environment, organizations increasingly recognize that competitive advantage is not determined solely by technology or financial resources but by the commitment, motivation, and involvement of their employees. Engaged employees demonstrate greater enthusiasm, stronger organizational commitment, improved collaboration, and a willingness to contribute beyond formal job responsibilities. These behaviours significantly influence organizational performance and the ability to respond effectively to changing market conditions.
This study examines how strategic employee engagement contributes to the development of high-performing organizations by analysing publicly available evidence from four leading Indian companies: Infosys, Tata Consultancy Services (TCS), HCLTech, and Asian Paints. A qualitative multiple-case study approach was adopted using secondary data drawn from annual reports, sustainability reports, corporate websites, and recognized workplace publications. The study explores the engagement initiatives implemented by these organizations and identifies common practices that influence employee motivation, organizational culture, innovation, and business performance.
The findings indicate that strategic employee engagement extends beyond traditional human resource practices. Leadership commitment, transparent communication, continuous learning, employee recognition, well-being initiatives, and opportunities for career development consistently emerge as key drivers of organizational excellence. Although each organization adopts engagement practices suited to its context, all four companies demonstrate that sustained investment in people contributes to stronger organizational performance and resilience.
The study proposes a Strategic Employee Engagement Framework illustrating the relationship between leadership, engagement practices, employee commitment, innovation, and organizational performance. The findings offer practical insights for managers and policymakers seeking to strengthen workplace engagement while contributing to the growing body of knowledge on strategic human resource management.
🏷 Employee Engagement, High-Performing Organizations, Strategic Human Resource Management, Organizational Performance, Multiple Case Study, India.
Article 200 · pp. 2686-2705
Teachers’ Perceptions Towards Learner-Centered Techniques: A Gateway to Educational Enrichment
This research article entitled ‘Teachers’ Perceptions Towards Learner-Centered Techniques in English Language Teaching Classroom’ aims to explore secondary-level English teachers’ perceptions towards learner-centered techniques in English language teaching classrooms in terms of group work, problem-based learning, and project- based learning.
To meet the objectives of this study, ten English language teachers of secondary level from five institutional and five community schools located in Kathmandu district were selected as participants. The participants were selected using a purposive sampling strategy. The researcher collected data by using semi-structured interviews.
The collected data were first transcribed and then analyzed thematically. After analysis and interpretation of the data, it was found that the majority of the teachers understand the value of learner-centered techniques for effective teaching in ELT classrooms. This study found that teachers faced challenges while implementing learner-centered techniques in their classrooms, for example, limitation of time, a large number of students, and inadequate resource materials.
🏷 Perceptions, techniques, motivation, content, effective learning
Article 201 · pp. 2706-2714
The Effect of Cash Management Systems on the Financial Accountability of Bungoma County Government, Kenya
This study aimed to understand the impact of Cash Management Systems on financial accountability. The study used the theoretical framework of the Technology Acceptance Model (TAM) and the Resource-Based View (RBV) Theory. The descriptive research design was used. The target population consisted of 204 respondents who were representative of junior accountants, finance managers, senior accounting officers and departmental heads in different departments of County Government of Bungoma. Stratified and simple random sampling techniques were used to obtain respondents of 136. Structured questionnaires were used for data collection to gather primary data. A pilot study was carried out in Kakamega County to check the validity and reliability of the research instrument. The data were analysed descriptively, using frequencies, percentages, means and standard deviations, and inferentially with correlation and multiple regression analysis. Different normality, multicollinearity and heteroscedasticity diagnostic tests were carried out to ensure the suitability of the models. Ethical issues were adhered to throughout the study. The results indicated that cash management systems were all positively and statistically significant in determining the financial accountability of the CMSs (B=0.089 & p=0.022. The study found that the effective implementation of Cash Management Systems play a significant role in enhancing financial accountability in the County Government of Bungoma. The study advises to strengthen the cash management system, budgetary controls, financial reporting system, and implement proper record keeping techniques with continued staff training to optimize utilization of the CAM and accountability results.
🏷 Cash Management Systems, financial accountability, Technology Acceptance Model, and Resource-Based View Theory
Article 202 · pp. 2715-2727
Psychological Wellbeing, Workplace Challenges, and Work-Life Balance Among Working Women: A Systematic Review
This systematic review synthesises findings from 48 peer-reviewed studies published between 2015 and 2026 on the wellbeing, occupational stress, and workplace experiences of working women. Drawing on a Scopus-indexed corpus, the review identifies seven key thematic areas: psychological wellbeing and mental health, workplace harassment and bullying, work-life balance and family conflict, gender inequality, industry-specific occupational stress, physical health and body image, and relationships and domestic life. Findings consistently highlight that working women face compounding stressors arising from dual role demands, hostile work environments, gender-based inequalities, and inadequate organisational support. Evidence-based interventions such as mindfulness, internet-based therapies, flexible work policies, and physical activity programmes are identified as promising strategies to improve wellbeing outcomes. The review calls for targeted organisational policies and broader structural reforms to promote equitable, healthy workplaces for women across sectors and geographies.
🏷 working women; psychological wellbeing; workplace stress; harassment; work-life balance; gender inequality; occupational health
Article 203 · pp. 2728-2769
Work Readiness of BSBA OJT Students in a Private Catholic Institution: A Student-Supervisor Comparative-Correlational Assessment
Work readiness is essential in helping business students transition from academic learning to employment because it reflects their ability to apply knowledge, technical competencies, communication skills, professionalism, interpersonal abilities, self-management, and ethical responsibility in actual workplace settings. This study aimed to assess the work readiness of Bachelor of Science in Business Administration On-the-Job Training students based on their self-assessments and the evaluations of Host Training Establishment supervisors as a basis for enhancing the On-the-Job Training manual. A quantitative descriptive-comparative design was employed among 94 Bachelor of Science in Business Administration On-the-Job Training students selected through total enumeration and 50 Host Training Establishment supervisors selected through purposive sampling. Data were gathered using validated questionnaires with content validity indices of 0.93 and 0.94 and a Cronbach’s alpha coefficient of 0.979. For descriptive statistics, frequency, percentage, mean, and standard deviation were used. For inferential statistics, the t-test and analysis of variance were employed to determine significant differences, while Pearson’s correlation coefficient was used to examine the relationship between the variables. Findings revealed that the students demonstrated a generally very high level of work readiness across the seven dimensions. Most differences based on the profiles of the students and supervisors were not statistically significant. However, knowledge competency varied significantly across industry categories, while work attitude and professionalism differed significantly according to the position of the Host Training Establishment Supervisor. The assessments of the students and supervisors showed a moderate positive but statistically non-significant relationship. The study concluded that Bachelor of Science in Business Administration On-the-Job Training students were generally prepared for workplace demands. However, student self-assessments and supervisor evaluations represented distinct yet complementary perspectives that should both be considered in evaluating work readiness. The institution should enhance the On-the-Job Training manual by providing clearer competency standards, structured orientation, industry-aligned tasks, regular monitoring, consistent evaluation tools, and scheduled feedback sessions involving students, On-the-Job Training coordinators, and Host Training Establishment supervisors. Based on the findings, the Integrated Work Readiness Enhancement and Industry Bridging Program was proposed.
🏷 Work Readiness, BSBA Students, On-the-Job Training (OJT), Employability Skills, Professional Competencies
Article 204 · pp. 2770-2777
Reclaiming Indigenous Knowledge Systems in the Age of Technological Modernity: A Critical Analysis of Cultural Westernization and Epistemic Erosion in India
This paper critically examines the relationship between technological modernity and the gradual erosion of Indigenous Knowledge Systems (IKS), with specific reference to the Indian context. It argues that contemporary digital infrastructures—while offering unprecedented global connectivity and operational efficiency—simultaneously accelerate epistemic homogenization, digital colonialism, and cultural Westernization. Drawing on decolonial theory, Science and Technology Studies (STS), and recent scholarship on data sovereignty, the study situates the marginalization of IKS within a broader socio-technical framework. Employing a critical-interpretive methodology that triangulates documentary policy analysis with three India-specific case studies—the National Education Policy (NEP) 2020 and the EdTech sector, generative artificial intelligence (AI) and linguistic marginalization, and Adivasi agricultural-data extraction—the paper demonstrates how digital systems restructure cognition, commodify cultural memory, and displace indigenous epistemologies. The analysis identifies four structural mechanisms of erosion—epistemic standardization, algorithmic bias, data dispossession, and cognitive restructuring—which together produce a condition of epistemic dependency in which interpretive authority shifts from communities to privately owned platforms. Empirical indicators are integrated throughout: India recorded 19,569 raw mother-tongue returns in the 2011 Census yet officially recognizes only 121 languages, while English constitutes roughly 46% of the most recent Common Crawl corpus and over 90% of the training data underpinning large language models. The paper advances two conceptual models—a comparative epistemological paradigm and an epistemic erosion–revitalization framework—and argues for a paradigm of technological sovereignty that critically engages digital advancement while safeguarding pluralistic knowledge ecosystems. The findings carry implications for education policy, AI governance, and indigenous data rights, suggesting that hybrid pedagogies, collective-consent data protocols, and decolonial AI design can convert digital infrastructure from a vector of erosion into an instrument of epistemic revitalization.
🏷 ndigenous knowledge systems; digital colonialism; epistemic erosion; generative AI; NEP 2020
Article 205 · pp. 2778-2786
Digital Innovation and Entrepreneurship: Review of Challenges in Competitive Markets with emphasis on micro enterprises in Manipur.
This article presents a narrative review of selected studies that investigate the impact of digital innovation on entrepreneurship and its outcomes within diverse sociopolitical contexts. The review shows that improved innovation output and the ability to absorb innovative digital models are associated with higher economic growth rates. Technological changes explain a significant portion of entrepreneurial dynamics, while regional factors also play a key role. Internet usage has led to reduced operational costs, increased sales, and enhanced customer interaction for the majority of firms, enabling rapid market expansion and stronger supplier linkages. Digitalization fosters entrepreneurship’s expansion into new markets, creating a positive feedback loop between digital adoption and firm growth. The study further highlights that government policy and institutional support have more influence on entrepreneurship sustainability than technological readiness alone. Although most examples derive from broader national and global settings, these insights highlight similar digital innovation challenges and opportunities for micro‑enterprises in emerging economies such as Manipur, where digital platforms, e‑commerce adoption, and mobile internet penetration are becoming pivotal for inclusive economic development. Future research should empirically examine how digital entrepreneurship contributes to income growth, market reach, and sustainability among smaller and tribal enterprises in such regions.
🏷 Digital innovation, Entrepreneurship, Regulation, Micro‑enterprises, E‑commerce adoption.
Article 206 · pp. 2787-2800
An Integrated Explainable Deep Learning Framework for Loan Default Prediction and Credit Risk Decision Support System
The credit risk assessment remains a critical challenge for financial institutions as manual and semi-automated loan evaluation processes often produce inconsistent decisions, high default rates, and operational inefficiencies. This paper discusses an Integrated Data-Driven Loan Management Framework that comprises an Artificial Neural Network (ANN) for credit default prediction, Structured Query Language (SQL) to systematically extract and transform data, and Interactive Visual Analytics Dashboards to aid in providing transparency within the decision-making process. For the study, a public loan dataset was used to pre-process the target dataset containing a total of 38,577 records and 24 attributes with pre-processing techniques such as feature engineering, one-hot encoding and class re-balancing with the use of Synthetic Minority Oversampling Technique (SMOTE) yielding 43 model-ready inputs.
The final ANN architecture was developed with 2 hidden layers (43 and 21 neurons with ReLU activation) and a sigmoid output neuron for binary classification. SHapley Additive exPlanations (SHAP) were computed to provide interpretability for each prediction made by the model. The overall accuracy of the model is 87% and the weighted precision, recall, and F1-score are 0.87 for the held-out test set (n = 12,858). The framework is implemented as a web-based decision support tool using Flask that enables users to receive risk scores in real-time along with explainable outputs and visual dashboards. The results from this experimentation indicate that an integrated pipeline (including data querying, predictive modeling, interpretability, and visualization) provides better decision-making and stakeholder transparency than using a model in isolation; therefore, it serves as a proof-of-concept prototype for intelligent loan management in banks and other financial institutions.
🏷 Credit risk assessment, artificial neural network, loan default prediction, SMOTE, explainable AI.
Article 207 · pp. 2801-2809
Numerical Investigation of Geogrid-Reinforced Strip Foundations Subjected to Adjacent Excavation Using PLAXIS 2D
Excavation activities carried out near existing foundations can significantly alter the stress state of the surrounding soil, resulting in reduced bearing capacity and increased settlement that may compromise the stability and serviceability of structures. This issue is particularly important in urban areas, where new construction is frequently undertaken adjacent to existing buildings with limited available space. The present study evaluates the effectiveness of geogrid reinforcement in improving the performance of strip foundations subjected to the influence of nearby excavations through numerical modelling using PLAXIS 2D. A series of finite element analyses was performed by varying the excavation depth and horizontal distance from the existing strip foundation to assess their effects on foundation behaviour. Both reinforced and unreinforced soil conditions were analysed under identical loading and boundary conditions to enable a systematic comparison of their performance. The response of the foundation system was evaluated in terms of ultimate bearing capacity and settlement. The numerical results demonstrate that the incorporation of geogrid reinforcement effectively mitigates the adverse effects of adjacent excavation by improving the load-carrying capacity of the foundation soil and limiting excessive deformation. Depending on the excavation geometry, the reinforced soil exhibited an increase in ultimate bearing capacity ranging from 14% to 27%, while the corresponding settlement decreased by approximately 11% to 16% compared with the unreinforced condition. The improvement is attributed to the ability of the geogrid to provide tensile reinforcement, enhance stress distribution, and improve soil confinement beneath the foundation. Overall, the study demonstrates that geogrid reinforcement is an effective ground improvement technique for maintaining the stability and performance of existing strip foundations subjected to nearby excavation, thereby providing useful guidance for the design and rehabilitation of foundation systems in urban construction projects.
🏷 Bearing Capacity, Settlement, Geogrid, Foundation soil, Excavation.
Article 208 · pp. 2810-2816
Development of an Arduino-Based Automated Irrigation System for Sandy Soil in Igbesa, Ogun State
Agriculture relies on effective irrigation for crops to grow, especially in areas like Igbesa, Ogun State, where sandy soil is most commonly used for planting. A lot of farmers, especially in the rural communities of Igbesa, operate using the manual methods of irrigation, which results in water wastage and low farm produce yield. In light of this, this paper aims to design, implement and study the performance of an automated irrigation system for sandy soils in Igbesa, Ogun State. Sandy soil is a type of soil texture packed with lots of minerals that cannot retain moisture effectively due to its loose particles, and they are commonly found in various parts of southwestern Nigeria. This system uses an Arduino Uno R3 microcontroller with a resistive soil moisture sensor that detects the moisture changes in sandy soils and triggers a mini-DC motor pump when the moisture level of the sandy soil falls below a given threshold set in the software code in the Arduino IDE. Results indicate that effective irrigation scheduling helps the sandy soil retain moisture and the device operate effectively when the moisture readings detected by the resistive soil moisture sensor decrease within seconds of detection. The early adoption of an automated irrigation system by rural farmers in Igbesa will not only increase farm produce yields but also reduce the cost of hiring personnel to perform manual irrigation methods on farmlands.
🏷 Arduino Uno, Igbesa, Sandy Soil, Soil Moisture Sensor
Article 209 · pp. 2817-2834
A Technical Review on Optimization of a Natural Gas Conversion Process for Liquefied Petroleum Gas (LPG) Production
Liquefied Petroleum Gas (LPG) production from Natural Gas Liquids (NGLs) is an energy-intensive process that relies heavily on efficient distillation operations, particularly within deethanizer, depropanizer, and debutanizer columns. This review examines recent advances in the optimization of natural gas conversion processes for LPG production, with emphasis on improving energy efficiency, reducing operational costs, and minimizing carbon dioxide (CO₂) emissions. Various optimization approaches reported in the literature are analyzed, including thermodynamic modeling, exergy analysis, Response Surface Methodology (RSM), Genetic Algorithms (GA), Artificial Neural Networks (ANN), and driving force methods. The reviewed studies commonly employed simulation tools such as MATLAB, AVEVA PRO/II, and ChemSep for modeling and optimization of distillation systems. The reviewed works demonstrate that optimization of operating variables such as reflux ratio, column pressure, and sequence configuration can significantly reduce energy consumption while maintaining product purity requirements, but little study have been made on the simultaneous optimization of debutanizer and depropanizer columns using integrated simulation platforms such as Aspen HYSYS while considering multiple interacting variables such as: Feed tray location, column Pressure and Reflux Ratio.
🏷 Energy efficiency, Distillation column optimization, Aspen Hysys, Liquefied Petroleum Gas LPG
Article 210 · pp. 2835-2849
Real-World Applications of the Assignment Problem: A Case Study Approach
The assignment problem is one of the most fundamental optimization models in operations research, focusing on the efficient allocation of limited resources to specific tasks while minimizing total cost or maximizing overall effectiveness. Because of its mathematical simplicity and computational efficiency, the model has become an essential decision – support tool across manufacturing, logistics, healthcare, education, transportation and many other industries. This paper presents a comprehensive examination of the assignment problem through a practical case study approach. It begins with an overview of the historical evolution of the assignment problem, followed by a review of relevant literature and a discussion of its theoretical foundations. The paper further distinguishes the assignment problem from other optimization techniques, including transportation and linear programming models. To illustrate its practical applicability, a real-world-inspired machine-to-job allocation problem is formulated and solved systematically using the Hungarian Method. Each stage of the solution process is explained with appropriate tables and interpretations to enhance conceptual understanding. The study also highlights the diverse applications of assignment models across multiple industries and discusses emerging research directions involving artificial intelligence, machine learning, fuzzy optimization, and dynamic decision-making. The findings demonstrate that the assignment problem remains a powerful analytical tool for improving operational efficiency and supporting evidence-based managerial decisions in increasingly complex organizational environments.
🏷 Assignment Problem, Hungarian Method, Operations Research, Optimization, Resource Allocation, Mathematical Modelling, Case Study.
Article 211 · pp. 2850-2859
Electronic Media Taxpayer Education and Monthly Rental Income Tax Adherence Among Residential Landlords in Bungoma County, Kenya
Monthly rental income tax compliance remains a practical concern in Kenya because weak compliance constrains domestic revenue mobilization and shifts the burden onto compliant taxpayers. This article examined the influence of electronic media taxpayer education on monthly rental income tax adherence among residential landlords in Bungoma County, Kenya. The article was guided by Fiscal Exchange Theory and Social Influence Theory, with tax awareness, organizational trustworthiness, and digital competence serving as explanatory mechanisms to interpret the relationship. The research adopted a descriptive research design. From the target population of 153,653 residential rental property owners, a sample of 399 landlords was obtained using Yamane's formula and stratified random sampling. Structured questionnaires generated 326 usable responses. (Urban Planning Challenges and Community Resilience: An Empirical Assessment of Disaster Vulnerabilities in Bungoma Municipality, 2023, pp. 100-110) Data were examined using descriptive statistics, reliability analysis, Pearson correlation, and simple linear regression. Results showed that electronic media taxpayer education had a strong, positive, and statistically significant relationship with monthly rental income tax adherence, r = .838, p < .001. Regression results further indicated that electronic media taxpayer education explained 70.2% of the variation in adherence (R² = .702), F (1, 324) = 765.047, p < .001. The article concluded that radio, television, SMS alerts, email alerts, social media, and the KRA website can strengthen adherence when messages are timely, credible, and easy to act on. The article recommends segmented electronic taxpayer education that combines deadline-based reminders, radio explanations, verified social media updates, and simplified online guidance.
🏷 Electronic media, taxpayer education, monthly rental income tax, tax adherence, residential landlords, Bungoma County.
Article 212 · pp. 2860-2872
Artificial Intelligence in Human Resource Management: Transforming Recruitment, Performance Management, And Employee Retention
Artificial Intelligence (AI) is reshaping the way organisations manage their human resources by introducing greater speed, accuracy, and analytical capability into HR decision-making. Activities that were once largely administrative and dependent on manual intervention are increasingly supported by intelligent technologies capable of processing vast amounts of workforce data and generating actionable insights. As a result, Human Resource Management (HRM) is evolving into a strategic function that contributes directly to organisational performance and long-term competitiveness.
This paper examines the influence of AI across three core dimensions of the employee lifecycle: recruitment and talent acquisition, performance management, and employee retention. It reviews the growing use of technologies such as Natural Language Processing (NLP), machine learning, intelligent applicant tracking systems, conversational AI, sentiment analysis, and predictive analytics. Drawing upon recent academic literature, industry reports, and organisational case studies, the paper highlights the measurable benefits associated with AI adoption, including shorter recruitment cycles, more objective performance evaluation, improved workforce planning, and stronger employee retention outcomes.
The discussion also recognises that the implementation of AI is accompanied by important ethical, legal, and organisational challenges. Issues relating to algorithmic bias, data privacy, transparency, explainability, and employee trust continue to influence the responsible deployment of AI-enabled HR systems. The paper argues that sustainable success depends not on replacing human judgement with intelligent systems but on integrating technological capabilities with ethical governance and professional expertise. It concludes that an augmented model, in which AI enhances rather than replaces human decision-making, offers the most balanced and effective approach for future-ready Human Resource Management.
🏷 Artificial Intelligence, Human Resource Management, Recruitment, Performance Management, Employee Retention, Predictive Analytics, Machine Learning.
Article 213 · pp. 2873-2879
Enhancing Senior Faculty Members’ Research Writing Competence Through a Seminar-Workshop on AI Tools and Ethical AI Practices
The rapid integration of artificial intelligence (AI) into higher education has created both opportunities and challenges for academic research writing, particularly for senior faculty members at MSU-ILS with limited exposure to emerging AI tools. This action research examined the effectiveness of a seminar-workshop on AI tools and ethical AI practices in enhancing senior faculty members’ research writing competence. Guided by the action research cycle of planning, action, observation, and reflection, the study involved senior faculty members from a higher education institution who participated in a structured capacity-building intervention. Data were collected using a researcher-developed, five-point Likert scale questionnaire administered before and after the seminar-workshop, focusing on baseline competence, perceived improvement, and ethical awareness in AI-assisted research writing. Descriptive statistical analysis revealed that participants initially demonstrated a moderate level of knowledge and skills in using AI tools for research writing. Following the intervention, results indicated a marked improvement in faculty members’ competence, confidence, and perceptions of the usefulness and ethical application of AI tools in academic writing. Participants reported increased awareness of ethical guidelines and greater confidence in using AI responsibly without compromising academic integrity. The findings suggest that seminar-workshops integrating both technical and ethical dimensions of AI use are effective in strengthening research writing competence among senior faculty members. The study contributes to the growing discourse on responsible AI adoption in higher education and provides practical insights for designing faculty development initiatives that support ethical and effective AI-assisted scholarly writing.
🏷 artificial intelligence, research writing, faculty development, ethical AI
Article 214 · pp. 2880-2915
Working Conditions, Work Environment, Recognition, and Job Satisfaction Among Nurses
Nurse retention remains a major challenge for healthcare organizations, making it essential to understand the organizational factors associated with nurses' job satisfaction. This study examined the levels of working conditions, work environment, recognition, and job satisfaction among nurses in selected hospitals in Northern Mindanao, Philippines. Specifically, it assessed the levels of these organizational factors, determined job satisfaction levels, examined their relationships with job satisfaction, and identified predictors of job satisfaction. A descriptive-correlational research design was employed, involving 174 registered nurses selected through simple random sampling from four hospitals. Data were collected using a researcher-developed questionnaire and analyzed using descriptive statistics, Spearman's rank-order correlation, and multiple linear regression. The findings revealed that nurses generally perceived their working conditions (M = 3.42), work environment (M = 3.72), recognition (M = 3.42), and job satisfaction (M = 3.52) as high. Among the dimensions of the independent variables, educational opportunity demonstrated a statistically significant positive relationship with job satisfaction (ρ = 0.155, p = .041), whereas the remaining dimensions showed no statistically significant relationships. Multiple regression analysis indicated that the combined dimensions of working conditions, work environment, and recognition did not significantly predict job satisfaction (R² = .073, F = 1.288, p = .241), suggesting that other organizational and individual factors may contribute to nurses' job satisfaction. The study concludes that although nurses generally reported favorable perceptions of their workplace and high job satisfaction, strengthening educational and professional development opportunities may further enhance job satisfaction. Future studies should examine additional organizational and personal factors that may better explain variations in nurses' job satisfaction and retention.
🏷 Job Satisfaction, work environment, working conditions, nurse retention, recognition
Article 215 · pp. 2916-2940
Content Delivery Readiness of Philippine Higher Education Institution Websites: Evidence from HTTP Archive and Chrome UX Report
This study examined the content delivery readiness of selected Philippine Higher Education Institution websites using public performance data from the Chrome User Experience Report (CrUX) and HTTP Archive. The study covered 30 official website origins, consisting of 10 State Universities and Colleges, 10 private HEIs, and 10 Local Universities and Colleges. Each website was validated and normalized before data extraction. CrUX data for May 2026 and HTTP Archive page-level data from the May 1, 2026 crawl were collected through BigQuery. Of the 30 websites, 25 had complete data from both sources, four had CrUX data only, and one had no usable match. The readiness framework combined field indicators related to actual user experience with page-level indicators describing technical delivery conditions. Among the 25 complete-data websites, five showed high readiness, 13 moderate readiness, and seven low readiness. Private HEIs recorded the highest average score among the three groups, although the comparison was interpreted descriptively because the corpus was purposively selected. The most common issues were heavy page weight, slow visual loading, high request count, slow Time to First Byte, and slow Largest Contentful Paint. Sensitivity testing using three alternative weighting schemes showed that 84 percent to 96 percent of classifications remained unchanged, while rank correlations ranged from 0.962 to 0.980. These results indicate that the main findings remained generally stable across the alternative models, with most category changes occurring near the score boundaries. The resulting framework provides a practical basis for identifying and prioritizing improvements in payload size, loading behavior, request management, server response, and routine website performance monitoring.
🏷 content delivery readiness, Core Web Vitals, Chrome User Experience Report, HTTP Archive, Philippine higher education websites
Article 216 · pp. 2941-2954
Handloom 4.0: An AI-Assisted D2C Framework for Sambalpuri Saree Weavers - A Participatory Design Study in Kardola Village, Odisha
Background. Kardola Village in Sambalpur district, Odisha-formally adopted by Odisha State Open University (OSOU) under the Unnat Bharat Abhiyan (UBA)-comprises forty-five weaving households (142 members) whose livelihoods depend on the production of Sambalpuri Ikat (Bandha) handloom sarees. Field data indicate monthly household incomes of ₹4,500-₹8,000, with variation driven largely by differential market access rather than craft quality. Because nearly all output is sold through local trader intermediaries, weavers realise only a fraction of the eventual retail value of their products.
Objective. This study documents the current production and marketing conditions of the Kardola weaving community and, on that empirical basis, develops and specifies the Handloom 4.0 framework: an AI-assisted, Direct-to-Customer (D2C) digital commerce model intended to reduce trader dependence and improve income realisation.
Methods. A Participatory Design methodology was applied. Primary data were collected through a household census (N = 45), semi-structured interviews with primary earning members, four focus group discussions, structured observation of production and transaction practices, and iterative co-design workshops. Qualitative data were analysed using reflexive thematic analysis; quantitative baseline indicators were triangulated across interview, observation, and group-verification sources. The framework was refined through member checking and expert review.
Findings. The baseline analysis indicates that income suppression is primarily a function of channel structure and credit dependence rather than production capacity, and that the community already possesses substantial latent digital assets (smartphone access in 38 of 45 households; active WhatsApp use in 36). The Handloom 4.0 framework is specified in five integrated layers: an AI-assisted product catalogue, WhatsApp-first social commerce, QR/UPI-based direct payment, aggregated community logistics, and a producer-company governance structure.
Projected outcomes and limitations. Using an income model derived from observed price differentials, the framework is projected-under stated adoption and demand assumptions-to raise monthly household income toward ₹12,000-₹18,000 and to reduce trader dependence over an eighteen-month horizon. These figures are analytical projections rather than measured results; empirical validation through a controlled pilot is identified as the essential next stage. The study contributes a transferable, community-centred model for artisan D2C transformation while explicitly delineating its evidentiary boundaries.
🏷 Sambalpuri saree; Handloom 4.0; direct-to-customer commerce; participatory design; AI-assisted commerce; rural artisan livelihoods; digital transformation; Odisha
Article 217 · pp. 2955-2963
Experimental Investigation of Carbon Dioxide Adsorbing Concrete Blocks Incorporating Zeolite and Dunite as Sustainable Mineral Admixtures
The construction sector is facing growing challenges to reduce greenhouse gas emissions by adopting environmentally sustainable construction materials with lower carbon footprints. Among the emerging solutions, carbon dioxide (CO₂)-adsorbing concrete has attracted considerable attention because of its ability to capture atmospheric CO₂ while maintaining the engineering properties required for structural applications. This study experimentally evaluates the potential of developing sustainable concrete blocks with enhanced CO₂ adsorption capacity through the partial replacement of conventional materials with natural zeolite and Dunite. Concrete mixes containing different replacement proportions were prepared and assessed for both fresh and hardened properties. Workability, compressive strength, water absorption, and density were determined in accordance with the relevant Indian Standard specifications. In addition, the CO₂ adsorption capacity of the concrete blocks was evaluated to examine the effect of these mineral additives on carbon sequestration performance. The findings reveal that incorporating zeolite and Dunite improves the CO₂ adsorption capability of concrete while preserving satisfactory mechanical properties when used within optimal replacement limits. However, higher replacement levels reduce workability and compressive strength, primarily due to increased pore volume and weaker cementitious bonding within the concrete matrix. Overall, the results indicate that a balanced combination of zeolite and Dunite can produce sustainable concrete blocks with enhanced carbon capture potential and acceptable structural performance. The outcomes of this research provide valuable insights into the development of eco-friendly construction materials that can contribute to carbon mitigation efforts while supporting the long-term sustainability of the built environment.
🏷 Sustainable concrete, Carbon sequestration, Zeolite, Dunite
Article 218 · pp. 2964-2994
Understanding Social Loafing Among Senior High School Learners: Proposed Evidence-Based Teaching-Learning Intervention Strategies
This mixed-methods explanatory sequential study investigated the phenomenon of social loafing among Grade 12 learners at Leyte National High School in the Philippines. Recognizing that collaborative learning is a cornerstone of the Philippine Department of Education curriculum, this study addressed a critical gap in local literature by exploring the extent, causes, consequences, and solutions to social loafing within this specific institutional context. Quantitative data were collected through surveys, followed by qualitative data gathered via in depth interviews to provide deeper explanation of the initial findings.
The findings reveal a notable self-other discrepancy, where learners rarely identify themselves as loafers but readily recognize loafing behavior in their peers. Key causes of social loafing include the absence of individual grading, the perception that personal input is unnecessary for group success, large group sizes, and unclear individual accountability. Qualitative analysis uncovered additional personal factors such as laziness, lack of interest, lack of competence, fear of judgment, and low academic ambition. Group dynamic factors include poor communication and permissive leadership, while external factors encompass home responsibilities and technological constraints.
Consequences of social loafing extend beyond poor academic performance to include delayed submissions, overburdened active members experiencing stress and burnout, resentment and conflict among team members, decreased motivation, and damaged group cohesion. Proposed solutions include establishing clear and equal roles, implementing individual grading and peer evaluations, applying meaningful consequences for non-contribution, fostering open communication, setting strict deadlines with monitoring, and reducing group size. The study concludes that addressing social loafing requires structural, and relational interventions to create equitable and productive collaborative learning environments.
🏷 Social Loafing, High School Learners, Teaching-Learning
Article 219 · pp. 2995-3006
Leadership Practices in Improving Employee Motivation: A Qualitative Case Study of an Indonesian Financial Leasing Branch
Employee motivation remains a critical challenge for organizations operating in highly competitive service industries, particularly within the financial leasing sector, where employees are required to achieve demanding performance targets while maintaining high-quality customer service. In this context, leadership practices play an essential role in shaping employees' work experiences and sustaining their motivation. This study aims to explore leadership practices employed by branch managers in improving employee motivation within Company X, an Indonesian financial leasing company. A qualitative case study approach was adopted to obtain an in-depth understanding of leadership practices as experienced by organizational members. Data were collected through semi-structured in-depth interviews and non-participant observations involving purposively selected participants with direct experience of the organization's leadership processes. The collected data were analyzed using thematic analysis to identify recurring patterns and meaningful themes emerging from participants' experiences. The findings revealed four major leadership practices that contributed to employee motivation: open and supportive communication, recognition and appreciation, participative decision-making, and coaching for employee development. These practices were perceived by employees as creating a supportive work environment characterized by trust, collaboration, continuous learning, and meaningful interpersonal relationships. The study demonstrates that employee motivation is influenced not only by organizational policies or financial incentives but also by leaders' consistent managerial behaviors embedded in daily workplace interactions. The findings contribute to the growing literature on leadership practices by providing qualitative evidence from the Indonesian financial leasing industry, a context that has received limited scholarly attention. From a practical perspective, the study suggests that managers can strengthen employee motivation by fostering open communication, recognizing employee contributions, encouraging collaborative decision-making, and providing continuous developmental support. These findings offer valuable implications for leadership development in financial service organizations and other service-based industries operating in dynamic and competitive environments.
🏷 Leadership Practices; Employee Motivation; Qualitative Case Study; Financial Leasing Industry; Human Resource Management
Article 220 · pp. 3007-3016
Fostering Trust and Loyalty in E-Commerce through Ethical Data Practices
This conceptual paper examines how ethical data practices foster consumer trust and loyalty in e-commerce, with particular emphasis on data privacy, data security, and consumer perceived risk. As digital platforms increasingly depend on personal and behavioural data for personalisation, payment processing, targeted marketing, and customer engagement, consumer concerns regarding transparency, informed consent, unauthorised data sharing, cybersecurity, and manipulative data use have become central to online exchange relationships. Drawing on Social Exchange Theory, the paper proposes that consumers are more willing to maintain relationships with e-commerce platforms when the exchange of personal data for convenience and personalised services is perceived as fair, beneficial, secure, and trustworthy. The paper distinguishes ethical data governance from regulatory compliance by arguing that lawful data processing represents only a minimum threshold, whereas ethical governance also requires fairness, proportionality, accountability, respect for autonomy, and consideration of consumer wellbeing. An integrated conceptual framework is developed in which ethical data practices strengthen consumer loyalty through consumer trust, while perceived privacy and security risk weakens the positive relationship between ethical data practices and trust. The discussion also considers emerging governance challenges associated with generative artificial intelligence, algorithmic personalisation, blockchain, and decentralised identity systems. The paper contributes to e-commerce and business ethics literature by explaining how, and under what conditions, responsible data governance supports sustainable digital relationships. Practical implications are presented for managers, marketers, IT teams, policymakers, and consumers, together with measurable constructs and empirical testing directions for future research.
🏷 Ethical Data Practices, Data Privacy, Data Security, Consumer Perceived Risk, Consumer Trust, Consumer Loyalty, E-Commerce
Article 221 · pp. 3017-3027
Assessment of Occupants' Satisfaction Level with Aesthetic Qualities of Selected Interlocking Stabilized Soil Block Houses in Southwestern Nigeria
Interlocking Stabilized Soil Block (ISSB) technology is gaining ground in Nigeria as a sustainable, cost-effective alternative to conventional masonry, yet its aesthetic dimension has received little empirical scrutiny. This study assesses occupant satisfaction with the aesthetic qualities of selected ISSB residential buildings in Southwestern Nigeria, drawing on structured questionnaire data from 110 respondents across three estates in Ekiti, Ogun, and Lagos states. Descriptive statistics, Pearson correlation, and multiple regression analyses were applied using SPSS. Occupants expressed strong satisfaction with the environmental harmony, natural colour, and visual distinctiveness of ISSB buildings (mean scores of 4.05, 4.00, and 3.98 respectively), while surface finishing quality (2.87) and public perception (2.95) fell within the neutral range. The grand mean of 3.65 confirms general aesthetic satisfaction. Regression analysis identified environmental harmony as the strongest predictor of overall satisfaction (β = 0.298, p < 0.001), and surface finishing quality as a disproportionately influential weakness (β = 0.143, p < 0.05). Targeted improvements in finishing technique, architectural detailing, and public awareness are recommended to consolidate and extend ISSB's aesthetic appeal in the Nigerian housing market.
🏷 Interlocking Stabilized Soil Blocks (ISSBs), Occupant Satisfaction, Aesthetic Quality, Sustainable Housing, Earth Construction, Southwestern Nigeria
Article 222 · pp. 3028-3038
Comprehensive Analysis of the Arduino UNO Q Single Board Computer: Architecture, Performance, And IoT Suitability
The Internet of Things (IoT) ecosystem has long operated on a bifurcated model, forcing hardware developers to choose between low-power, deterministic real-time microcontrollers (MCUs) and high-performance, non-deterministic Single Board Computers (SBCs). This paper presents a comprehensive technical review and analytical evaluation of the Arduino UNO Q, a newly released system that directly addresses this division. Citing official launch documentation and verified technical briefs, the UNO Q integrates a ”dual-brain” Heterogeneous System Architecture (HSA) combining a Qualcomm® Dragonwing™ QRB2210 microprocessor (MPU) running a Debian Linux operating system with an STM32U585 real-time microcontroller (MCU). This paper details the structural layout, describes the physical domain isolation, and derives a mathematical model for Inter-Processor Communication (IPC) latency. To address the lack of live physical benchmarking, we establish a formal MultiCriteria Decision Analysis (MCDA) framework to mathematically explain system positioning, and provide three reproducible, stepby-step physical experimental testing protocols. By clarifying the boundary between confirmed hardware capabilities and proposed validation setups, this research provides an essential, rigorous baseline for evaluating this novel class of embedded edge devices.
🏷 Internet of Things (IoT), Single Board Computer (SBC), Arduino, Arduino UNO Q, Heterogeneous System Architecture (HSA)
Article 223 · pp. 3039-3051
Hate Speech Detection on Twitter Using XGBoost: A Large-Scale Dataset Analysis with Feature Engineering and Comparative Evaluation
Social media platforms and Twitter in particular, has become a fertile channel for the rapid circulation of harmful discourse, including hateful and offensive language aimed at individuals and communities. Automatically flagging such content remains a demanding task within natural language processing, largely because micro blog text is short, informal, and heavily reliant on context for correct interpretation. This paper presents a hate-speech classification framework built around the Extreme Gradient Boosting (XGBoost) algorithm and evaluated on one of the largest publicly available labeled Twitter corpora, comprising roughly 96,973 tweets divided into three categories: hate speech, offensive language, and normal content. Each tweet is represented through a combined set of feature families: Term Frequency-Inverse Document Frequency (TF-IDF) vectors, dense word-embedding features, and tweet-level metadata such as hash tag frequency, mention count, re tweet count, and capitalization ratio. Prior to feature extraction, tweets are cleaned through tokenization, stop-word removal, lemmatization, and URL stripping to reduce noise in the raw corpus. The hyper parameters of the XGBoost classifier were selected through grid search combined with stratified cross-validation. The tuned model reached an overall accuracy of 93.7%, a macro-averaged F1-score of 0.937, and an area under the curve above 0.94 for every class, surpassing the results obtained with Naive Bayes (78.4%), Support Vector Machines (82.1%), Random Forest (85.6%), LSTM networks (88.3%), and a fine-tuned BERT model (90.1%). These outcomes indicate that gradient boosting, when paired with a carefully engineered feature set, provides an accurate and computationally efficient alternative for large-scale hate-speech detection.
🏷 Hate Speech Detection, Twitter, XGBoost, Feature Engineering, Text Classification
Article 224 · pp. 3052-3066
Board Expertise, Audit Quality, and Tax Avoidance in Bangladeshi Banks: Does Political Connection Matter?
This study explores how the tax avoidance in Bangladesh is influenced by some board characteristics such expertise of board and audit quality, with moderating impact of political connection with board. Using panel data on the banking industry listed on the DSE from 2012 to 2022, the study runs several regressions to investigate whether political connections moderate the board experts. The results revealed that both legal experts and audit quality negatively impact tax avoidance. It indicates that the presence of more legal experts on the board and the greater the use of BIG4 auditors in a firm, the lower their engagement in tax avoidance practices. Accounting experts on the board, conversely, enhance tax avoidance. Similarly, politicians on the board moderate the legal experts and weaken the legal expert’s role regarding tax avoidance decisions. The findings imply that politicians increase tax avoidance by converting the negative role of legal experts into a positive one. Further, supporting agency theory, the findings confirm a conflict between owners and managers where institutional and managerial shareholders dominate the board of directors regarding tax policy.
🏷 Tax avoidance, legal experts, accounting experts, audit quality, banking industry, Bangladesh.
Article 225 · pp. 3052-3066
Water Quality Monitoring for the Sustainable Management of Aquatic Ecosystem in the Markandeya Dam Reservoir, Kolar District, Karnataka, India
Water is an irreplaceable natural resource and the cornerstone of aquatic and terrestrial ecosystems alike. Dams and reservoirs are critical infrastructure that support biodiversity, agriculture, potable water supply, hydropower generation, flood mitigation, and recreation. Despite their ecological importance, reservoir water quality remains vulnerable to seasonal fluctuations, anthropogenic pollution, and nutrient enrichment. The present study evaluates the physico-chemical characteristics and plankton community structure of Markandeya Dam, Kolar District, Karnataka, India, across pre-monsoon, monsoon, and post-monsoon seasons from February to July 2025. Phytoplankton (22 taxa) and zooplankton (14 taxa) were enumerated and subjected to nine diversity indices. A Water Quality Index (WQI) computed via the Weighted Arithmetic Index method yielded a value of 46.57, classifying the reservoir as moderately polluted. Nygaard's Water Quality Indices (NWQI) further indicated a mesotrophic trophic status. Most physico-chemical parameters conformed to WHO and BIS drinking-water standards; however, dissolved oxygen variability and elevated faecal coliform counts signal localized organic pollution and microbial contamination risks. Shannon–Wiener diversity indices for both phytoplankton (H' = 0–1.45) and zooplankton (H' = 0–0.92) indicated moderate pollution, with dominance by Chlorophyceae and Monogononta rotifers, respectively. Seasonal patterns revealed higher diversity and species richness during cooler pre-monsoon months and greater dominance during warm transitional periods. The integration of WQI, NWQI, and multi-index plankton assessment constitutes a robust and replicable framework for the ecological monitoring and sustainable management of tropical reservoir ecosystems.
🏷 Physico-chemical parameters; Water Quality Index; Nygaard's Water Quality Indices; Phytoplankton diversity; Zooplankton community; Mesotrophic reservoir; Kolar District; Karnataka
Article 226 · pp. 3081-3089
Advancing Tilt Angle Control in Friction Stir Welding using Fuzzy Logic
Friction Stir Welding (FSW) is a solid-state joining technique renowned for its ability to produce high-quality welds in aluminum and other non-ferrous metals. A critical parameter influencing weld integrity is the tool tilt angle, which, if improperly set, can lead to defects such as tunnel voids, surface irregularities, or poor material mixing. This study presents a fuzzy logic-based control system for real-time adjustment of the tool tilt angle during FSW. The proposed system utilizes inputs such as torque, temperature, and plunge depth deviation to infer optimal tilt corrections using a Mamdani-type fuzzy inference system. Triangular membership functions and an expert-defined rule base were implemented in MATLAB to simulate adaptive tilt adjustments. Simulation results showed that for a torque input of 10 Nm, a temperature of 45 °C, and a plunge deviation of 0.5 mm, the system produced a tilt adjustment value of 0.224°, as calculated through centroid defuzzification. Surface plots confirmed that the controller responds smoothly to varying conditions, with tilt adjustments ranging from -0.4° to +0.5° based on combined sensor inputs. The proposed system offers a robust and flexible solution suitable for integration into CNC-based FSW platforms for improved automation and weld quality.
🏷 Friction Stir Welding, Fuzzy Logic, Tilt Angle, Torque, Temperature, Plunge.
Article 227 · pp. 3090-3094
Acute Non–ST Elevation Myocardial Infarction in a Young Adult with Poorly Controlled HIV Infection: A Case Report from Timor-Leste in June 2026
Background
Cardiovascular disease is increasingly recognized as an important complication among people living with HIV (PLWH). HIV infection is associated with accelerated atherosclerosis due to chronic immune activation, systemic inflammation, endothelial dysfunction, metabolic abnormalities, and prothrombotic changes with interruption of antiretroviral therapy (ART) may increase the risk of premature coronary artery disease (1,2). With the widespread availability of antiretroviral therapy (ART), PLWH have increased life expectancy; however, cardiovascular disease has emerged as an important cause of morbidity and mortality. Acute coronary syndrome (ACS) in young adults with HIV remains uncommon but carries significant morbidity and mortality.
Case Presentation
A 25-year-old male with known HIV infection presented with an 8-hour history of acute chest discomfort. The patient had a history of poor adherence to antiretroviral therapy and was considered to have uncontrolled HIV infection. Initial evaluation revealed electrocardiographic abnormalities with T-wave inversion suggestive of myocardial ischemia. Cardiac biomarkers were elevated, with troponin level of 1.6 (assay-dependent units), confirming myocardial injury. Echocardiography demonstrated regional wall motion abnormalities consistent with ischemic myocardial dysfunction. The patient was diagnosed with non–ST elevation myocardial infarction (NSTEMI) and admitted to the intensive cardiac care unit (ICCU). Despite appropriate acute management, the patient deteriorated rapidly and died several hours after admission.
Conclusion
This case highlights the occurrence of fatal NSTEMI in a very young patient with untreated HIV infection. Early cardiovascular risk assessment, optimization of ART adherence, and increased awareness of ACS among young PLWH are essential, particularly in low- and middle-income countries where access to advanced cardiac investigation and intervention may be limited such as Timor Leste.
🏷 HIV infection, antiretroviral therapy non-adherence, acute coronary syndrome, NSTEMI, young adult, myocardial infarction, Timor-Leste.
Article 228 · pp. 3095-3104
AI-Based Emission Inventory Modeling: A Comparative Study for Environmental Monitoring
Air pollution in metropolitan cities such as in India has significantly increased due to fast urbanization and consistently rising number of vehicles along with its motor activity. Pollutants such as Nitrogen Oxides (NOx), Sulphur Dioxide (SO₂), Particulate Matter (PM₂.₅, PM₁₀), Hydrocarbons (HC), Carbon Monoxide (CO) and others are important components emitted by the vehicle which have negative impact on the environment and human health. Precise emission inventory estimation is crucial for developing management plans and policies for air quality.
In the present study, an Artificial Intelligence (AI) based grid-wise vehicular emission inventory model for two prominent cities of India Nagpur and Mumbai are developed. Combines traditional bottom-up and top-down emission inventory methods with cutting-edge AI technologies to enhance emission estimation and prediction for vehicles. The use of traffic activity data, vehicle registration records, meteorological parameters, road network information and traffic imagery. Vehicle count, vehicle category, traffic density, and traffic speed information are extracted from traffic images and video using deep learning models such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and vehicles identification algorithm YOLO.
Studying this is done using a spatial grid-based approach to analysing the dispersion of emissions within urban areas and temporal variations over hourly, daily and seasonal scales. Their comparative study with the emissions produced at Mumbai shows that there are significant variations in emissions because of the population density, the traffic composition and the urban infrastructure. Incorporated with AI, this emission inventory model with enhanced accuracy and efficiency offers significant improvement from the typical approach to valuable insights for sustainable transportation planning and air pollution mitigation measures.
🏷 Vehicular Emission Inventory, Artificial Intelligence, CNN, RNN, YOLO, Grid-Based Modeling, Air Pollution, GIS, Traffic Emissions
Article 229 · pp. 3105-3116
Beyond Intended Use: Adaptive Material Strategy (AMS) in Contemporary Window Design
Architectural design has traditionally classified finishing materials according to predefined functional categories. Window sills, wall finishes, countertops, flooring, and façade materials have historically been selected based on established construction practices rather than their actual technical characteristics. However, contemporary interior architecture increasingly challenges these conventional boundaries. Advances in material engineering have created opportunities to rethink how architectural elements are designed by focusing on performance rather than intended use.
This paper introduces the concept of Adaptive Material Strategy (AMS), a design framework that proposes selecting finishing materials according to their physical, technical, and aesthetic properties instead of their conventional classification. Using window architecture as a primary example, the paper demonstrates how innovative material adaptation expands design possibilities while improving durability, maintenance, spatial perception, and architectural expression.
he proposed framework argues that contemporary architects should evaluate materials through a multidisciplinary lens that integrates engineering performance, environmental conditions, construction technology, and visual composition. Adaptive Material Strategy represents a shift from product-oriented specification toward performance-oriented architectural thinking.
🏷 Adaptive Material Strategy, window design, interior architecture, material innovation, architectural detailing, window sill design, contemporary interiors, material performance, architectural technology.
Article 230 · pp. 3117-3128
Analysis of Hotel Bookings in Terms of Occupancy and Sales across Seasons and Events, and the Development of a Proposed Smart-Pricing Mechanism
The hospitality sector increasingly relies on operational data to guide pricing, yet many operators still raise or lower prices uniformly across all units. This paper analyses hotel bookings in terms of occupancy and sales across seasons and events, and develops a proposed unit-level smart-pricing and selective-marketing mechanism. The study follows a descriptive–analytical approach using actual operational data for 95 residential hotel units operated by a hospitality company in Riyadh and Al-Madinah, Saudi Arabia, covering January to June 2026 (181 days, 9,028 occupied nights, SAR 4.22 million in confirmed sales), organised through an interactive web dashboard that is publicly accessible online. Independent-samples (Welch) t-tests were used to test the significance of the observed differences. The results show clear monthly variation, a dominant Airbnb channel, and — most importantly — that the occupancy–sales relationship is not directly proportional but is mediated by the average daily rate (ADR): some units combine low occupancy with high sales, while others show the opposite. The effect of Ramadan differs sharply by city, with occupancy and rate rising in Al-Madinah but falling in Riyadh; weekends significantly outperform weekdays; and the cancellation rate reaches 10.3%. The study concludes with a set of quantified, calibratable pricing rules that link each pricing decision to the event, season, city, day type, channel, and the actual performance of the individual unit.
🏷 Revenue management; Average daily rate; Ramadan demand; Cancellation rate; Interactive web dashboard.
Article 231 · pp. 3129-3133
Social Media Marketing and Consumer Behaviour
The rapid growth of social media platforms has transformed the marketing landscape, enabling businesses to engage directly with consumers through interactive and personalized communication. Social media marketing (SMM) has become a critical strategy for influencing consumer awareness, attitudes, purchasing decisions, and brand loyalty. This research article examines the relationship between social media marketing and consumer behaviour by reviewing existing literature and analyzing the impact of social media platforms on consumer decision-making. The study identifies key factors such as electronic word-of-mouth (eWOM), influencer marketing, online reviews, content quality, customer engagement, and personalization that significantly influence consumer behaviour. The findings suggest that effective social media marketing enhances customer trust, satisfaction, and long-term brand relationships. The article concludes with implications for marketers and recommendations for future research.
🏷 Social Media Marketing, Consumer Behaviour, Digital Marketing, Electronic Word-of-Mouth, Influencer Marketing, Brand Loyalty
Article 232 · pp. 3134-3152
The Influence of Service Quality, Store Atmosphere, and Price on Consumer Satisfaction at Gosha Kitchen & Patisserie Renon Denpasar, Bali, Indonesia
This study investigates the influence of service quality, store atmosphere, and price on consumer satisfaction at Gosha Kitchen & Patisserie Renon, Denpasar. The rapid growth of culinary businesses in Bali has intensified competition, making consumer satisfaction a critical success factor. This research employs a quantitative approach using accidental sampling with 100 respondents from a population of 105,970 consumers. Data analysis techniques include validity testing, reliability testing, classical assumption tests (normality, multicollinearity, and heteroscedasticity), multiple linear regression analysis, and hypothesis testing (F-test and t-test). The results demonstrate that: (1) service quality, store atmosphere, and price simultaneously have a positive and significant effect on consumer satisfaction; (2) service quality partially has a positive and significant effect on consumer satisfaction; (3) store atmosphere partially has a positive and significant effect on consumer satisfaction; and (4) price partially has a positive and significant effect on consumer satisfaction. The findings suggest that improving service responsiveness, enhancing visual communication through attractive menu designs and clear signage, and maintaining price transparency compared to competitors can significantly increase consumer satisfaction. This research contributes to the marketing management literature by providing empirical evidence from the Indonesian culinary industry context.
🏷 Service Quality, Store Atmosphere, Price, Consumer Satisfaction, Culinary Industry
Article 233 · pp. 3153-3170
Development of stimuli-responsive polymeric hydrogels and their applications
Electro-responsive nanocomposite hydrogels based on gelatin, partially neutralized acrylic acid (PAAc), and acid-functionalized multiwalled carbon nanotubes (MWCNT-COOH) were developed and evaluated as potential smart drug delivery systems. The successful functionalization of pristine MWCNTs was confirmed by the shift in characteristic X-ray diffraction (XRD) peaks, while the presence of MWCNT-COOH peaks in the nanocomposite hydrogel verified their effective incorporation into the polymer matrix. Scanning electron microscopy (SEM) revealed distinct morphological changes following acid functionalization and demonstrated the uniform dispersion of MWCNT-COOH within the hydrogel network, resulting in a smooth and homogeneous surface. Swelling studies showed that increasing MWCNT-COOH content reduced the swelling percentage in the absence of an electric field, whereas the application of a 10 V electric field significantly enhanced swelling, confirming the electro-responsive nature of the hydrogels. In vitro hemolysis assays indicated excellent blood compatibility, highlighting their suitability for biomedical applications. The electro-responsive release of vitamin B12 was strongly influenced by the ionic strength of the release medium, applied voltage, and MWCNT-COOH concentration. Furthermore, drug release behavior was systematically optimized using response surface methodology based on a central composite design. Overall, the developed Gelatin-g-PAAc/MWCNT-COOH nanocomposite hydrogels exhibit favorable structural, swelling, biocompatible, and electro-responsive characteristics, making them promising candidates for the design of advanced electrically controlled drug delivery systems.
🏷 Nanocomposite, hydrogel, electro-responsive, drug delivery, MWCNTs.
Article 234 · pp. 3171-3179
Artificial Intelligence and Emotional Maturity in Corporate Leadership Mentoring: A Synergistic Approach
The rapid integration of artificial intelligence into organizational processes has significantly transformed the way leaders make decisions, communicate, learn, and manage people. While AI increasingly supports analytical thinking, information processing, and operational efficiency, it cannot replace the distinctly human capacities that define sustainable leadership, including emotional maturity, ethical judgment, empathy, and the ability to build trust. These competencies remain fundamental for effective leadership in environments characterized by uncertainty, technological acceleration, and continuous organizational change.
This paper examines the evolving relationship between artificial intelligence and emotional maturity within contemporary corporate mentoring. Rather than viewing AI as a substitute for human leadership, the article proposes a synergistic perspective in which technology enhances cognitive capabilities while emotionally mature leaders provide contextual understanding, ethical reflection, and meaningful interpersonal relationships. The paper argues that the future of leadership development depends on integrating technological intelligence with psychological competence rather than prioritizing one over the other.
Drawing upon contemporary leadership research, organizational psychology, executive coaching, and emerging studies on artificial intelligence in management, this conceptual paper introduces a human-centered approach to corporate mentoring. It explores how emotionally mature leaders can effectively collaborate with AI systems while maintaining authentic communication, psychological safety, adaptive decision-making, and organizational trust.The article concludes that artificial intelligence should be regarded as an instrument that expands managerial capacity rather than replaces human leadership. Organizations that intentionally combine technological innovation with the development of emotional maturity are likely to cultivate more adaptive, resilient, and ethically responsible leaders capable of navigating the increasing complexity of modern organizational environments.
🏷 artificial intelligence, emotional maturity, executive coaching, corporate mentoring, adaptive leadership, emotional intelligence
Article 235 · pp. 3180-3190
The Impact of Road Development on Rural Employment (Farming) in Etsako West Local Government Area of Edo State
Development can be defined as improving the welfare of a society through appropriate social, political, and economic conditions including employment opportunities. This study is aimed at examining the impact of road development on rural employment (farming) in Etsako West local government area of Edo state through, taking inventory of rural employment in the study area, examining the existing road transport facilities in the area, assessing the effect of roads on rural employment and analyzing the impact of road transport on rural employment. Using both primary and secondary data for the study, the primary data were obtained through the use of questionnaires administered on 12 wards in the study area, administering 485 questionnaires in total and Scheduled interviews with the LGA secretariat. This was done using a systematic random sampling method. Findings revealed that farming accounts for 55.7% of employment, shifting cultivation is the most prominent type of agricultural practice, with represents 91.1%, Cassava, with 33. 3% is the predominant crop cultivated alongside Vegetables, Maize, Beans Groundnut and Yam, types of roads used to farms are mainly footpaths, earth roads and Tarred roads and majority of the farms are only connected with footpaths making accessibility to farm very poor and increasing the expected time spent to go to the farm and from the farm to the communities, this led to high cost of transport and abandonment of produce in farms, among others. The study therefore recommended Construction and Rehabilitation of Farm Access Roads, Adoption of Rural Road Network Planning, Provision of Maintenance Framework for Rural Roads, Subsidy and Transport Support for Farmers, Promotion of Larger and Commercial Farm Sizes and Integration of Agricultural and Road Development Policies
🏷 Rural Employment, Farming, Development, Access, Impact
Article 236 · pp. 3191-3197
Scenario of THDE model in Bianchi type VI0 Universe
In this paper, we study the anisotropic Bianchi type VI0 metric that consists of Tsallis holographic dark energy (THDE) and dark matter (DM). The hybrid expansion law for the average scale factor is used to calculate some cosmological parameters and achieve exact solutions to Einstein's field equations. These parameters geometrical and physical behaviours demonstrate how the universe eventually becomes homogeneous, flat, and isotropic. Analyzing the equation of state (EoS) parameter also reveals that our model acts like a lambda cold dark matter (Λ) scenario in the late universe.
🏷 Bianchi-VI0 type, Hybrid expansion laws, Tsallis holographic dark energy model
Article 237 · pp. 3198-3212
Perceived Greenwashing and Its Impact on Brand Trust and Purchase Intention: An Empirical Study of Herbal Shampoo Consumers in Karnataka
The market of herbal and natural shampoo in Karnataka functions within the setting of a rapidly expanding number of eco-labels that make consumers more sensitive to the genuineness of environmental advertising. This paper looks at the perceived greenwashing on brand trust and purchase intention by consumers of herbal shampoo in Karnataka through brand trust. There is a tested moderated mediation model where Environmental Knowledge (EK) is the moderator of the relationship between Perceived Greenwashing (PGW) and Brand Trust (BT), with Brand Trust (BT) being a mediator between Purchase Intention (PI) and Perceived Greenwashing (PGW). A five-point Likert scale structured questionnaire was used to collect the data of 400 respondents and was analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) with bootstrapping of 5,000-subsamples by using SmartPLS 4. All four constructs demonstrated satisfactory reliability and validity (Cronbach’s α: 0.732–0.799; CR: 0.832–0.868; AVE: 0.555–0.621). Brand Trust had a significant negative effect (β = -0.43, p < 0.001) on Perceived greenwashing, leading to purchase intention (β = +0.43, p < 0.001). Partial mediation was established: the moderating role of brand trust on purchase intention through perceived greenwashing was also significant (β = -0.177, p < 0.001) but there was also direct relationship (β = -0.221, p < 0.001).). Environmental Knowledge significantly amplified the negative effect of greenwashing on trust through the interaction term (β = 0.258, p < 0.001). The model accounted 28.9% variation in brand trust, and accounted 29.9% in purchase intention. These results can be applied to herbal FMCG brands and organizations that regulate in the booming green Indian consumer market.
🏷 Perceived Greenwashing; Brand Trust; Purchase Intention, Environmental Knowledge, PLS-SEM, Karnataka, Herbal Shampoo.
Article 238 · pp. 3213-3234
From Infrastructure Assets to Infrastructure Intelligence: Reimagining Civil Engineering for an Era of Artificial Intelligence, Climate Uncertainty, and Autonomous Infrastructure Systems
Civil engineering enters the second quarter of the twenty-first century carrying a conceptual inheritance it has rarely interrogated directly. This paper advances a single, falsifiable thesis: the defining failure of twentieth-century civil engineering was not a failure of materials, computation, or design method, but a category error in the treatment of infrastructure as a class of physical assets to be optimized rather than as a class of adaptive, decision-making systems operating under irreducible uncertainty.
We call this inheritance the Asset Paradigm and argue that it persists because it is embedded in codes, procurement, liability law, curricula, and professional identity, not because it remains effective. Building on, and formally sharpening, the emerging literature on infrastructure intelligence and resilience, we contribute: (i) necessary and sufficient conditions that distinguish Infrastructure Intelligence from smart infrastructure, digital twins, and cyber-physical systems, together with a quantitative formalization; (ii) a five-stage Infrastructure Evolution Model, classifying real systems by the locus of run-time decision authority and justified against maturity-model precedents from other engineering domains; (iii) four empirically testable propositions, each with explicit variables, candidate datasets, and validation methods; (iv) an illustrative bridge structural-health-monitoring application; (v) a phased, institutionally feasible transition pathway; and (vi) a ten-item Grand Research Agenda for 2025–2050. We position the framework against complex adaptive systems, socio-technical, resilience, adaptive-governance, and deep-uncertainty theory, and identify precisely where each breaks down under conditions of artificial intelligence, climate non-stationarity, and machine-speed autonomy. We conclude with a set of concrete claims and institutional commitments.
🏷 infrastructure intelligence; asset paradigm; complex adaptive systems; climate non-stationarity; adaptive governance
Article 239 · pp. 3235-3251
An Optimized Double-Sampling c Control Chart for Enhanced Monitoring of Process Nonconformities
Statistical Process Control (SPC) plays a vital role in monitoring manufacturing and service processes characterized by count data. The conventional Shewhart c-control chart is widely used for monitoring the number of nonconformities under the Poisson distribution; however, its performance deteriorates when detecting small and moderate process shifts. This study proposes an optimized Double-Sampling (DS) c-control chart that improves shift detection while maintaining a specified in-control Average Run Length (ARL0). The chart design is formulated as a constrained optimization problem in which the out-of-control Average Run Length (ARL1) is minimized subject to target ARL0 and inspection efficiency requirements. Exact Poisson probabilities are employed to derive the operating characteristics, while the Average Sample Size (ASS) is used to evaluate inspection effort. Extensive numerical comparisons for various process means and shift magnitudes demonstrate that the proposed DS c-chart consistently achieves substantially lower ARL1 values than the conventional single-sampling c-chart, particularly for small and moderate shifts, while maintaining the desired false-alarm performance. The proposed scheme provides an efficient, practical, and economically attractive alternative for monitoring Poisson-distributed process nonconformities.
🏷 Double-Sampling c-Control Chart; Statistical Process Control; Poisson Distribution; Average Run Length; Average Sample Size; Attribute Control Charts.
Article 240 · pp. 3252-3271
Comparative Analysis of Machine Learning and Deep Learning Models for Real-Time Driver Drowsiness Detection.
Drowsy driving is among the major factors leading to accidents on roads, especially in scenarios where long hours of traveling or working are involved. The current paper performs a comparative analysis between traditional machine learning (ML) techniques and lightweight deep learning (DL) methods concerning driver drowsiness detection using the YawDD Dataset (a yawning detection dataset). This Dataset consists of 322 videos from the mirror (rear-view mirror camera position) subset and 29 videos from its Dash (dashboard) subset, recorded from 107 drivers of diverse ages, gender and ethnicities. The videos are presented in various levels of illumination and facial occlusions (glasses and sunglasses) as well as poses. Frames were extracted from the videos at regular intervals resulting in thousands of labelled images.
This study evaluates two types of algorithms based on their performance through several parameters such as accuracy, precision, recall, and F1 score while simultaneously considering the efficiency of computation measured by inference time, CPU usage, memory, and frames per second (FPS).
According to the findings, while DL models like the convolutional neural network based on EfficientNet and TinyCNN offer better classification performance with accuracy levels surpassing 93%, these models exhibit inferior inference rates. Consequently, these DL models cannot be employed in real-time situations since they require hardware accelerators. On the other hand, traditional ML models, mainly the combination of Random Forest and Support Vector Machine, offer a good balance between accuracy and efficiency. Logistic regression provides the best processing rate but with limited accuracy.
Conclusively, the results imply that traditional ML models are still applicable for real-time tasks in resource-constrained environments compared to DL models. However, DL models are recommended for implementations where hardware accelerators are available.
🏷 Driver Drowsiness Detection, Machine Learning, Deep Learning, Real-Time Systems, Low-Power Devices, Computer Vision.
Article 241 · pp. 3272-3282
An Assessment of Accessibility of Library Services for Persons with Disability in Federal Universities in Nigeria
This study assessed the accessibility of library services for persons with disabilities in selected federal universities in Nigeria. The study was motivated by the need to determine the extent to which university libraries provide inclusive services, facilities, information resources, and assistive technologies that support equitable access to information for persons with disabilities. Specifically, the study sought to identify the library services available to persons with disabilities, determine the accessibility of library facilities and information resources, and examine the assistive technologies and support services provided in federal university libraries.
A descriptive survey research design was adopted, using a quantitative approach. The study covered six federal universities representing the six geopolitical zones of Nigeria: Ahmadu Bello University, University of Maiduguri, University of Ilorin, University of Ibadan, University of Nigeria, Nsukka, and University of Port Harcourt. The population of the study comprised 1,000 registered persons with disabilities, from which a sample size of 286 respondents was drawn using proportionate and purposive sampling techniques. Data were collected through a structured questionnaire, and 282 valid responses were analyzed using descriptive statistics, including frequencies, percentages, means, and standard deviations. The findings revealed that most conventional library services, including reference services, circulation services, internet and ICT services, online database access, and reprographic services, were available to persons with disabilities. The study further found that library facilities and information resources were moderately accessible, with electronic resources, digital databases, library entrances, and catalogues recording relatively high accessibility levels.
The study concluded that although federal university libraries in Nigeria have made notable efforts toward improving accessibility, significant gaps still exist in the provision of disability-focused services and assistive technologies. The study recommends increased investment in accessible infrastructure, specialized assistive technologies, staff training, and inclusive library policies to enhance equitable access to library services for persons with disabilities.
🏷 Assessment, Accessibility, Library Services, Persons with Disabilities, Federal Universities, Nigeria.
Article 242 · pp. 3283-3291
Scholarly Communication, Open Access Publishing, and Research Visibility in Nigerian and Sub-Saharan African Universities
Nigerian universities produce a substantial volume of research across disciplines such as agriculture, public health, education, governance, and the humanities; however, much of this scholarship remains underrepresented in global academic communication systems. This paper examines the structural conditions shaping scholarly communication in Nigerian and Sub-Saharan African universities, with particular emphasis on open access (OA) publishing as a mechanism for improving research visibility. Drawing on a systematic review of literature and institutional data from OpenDOAR, African Journals OnLine (AJOL), and the National Universities Commission (NUC), the study analyzes the effectiveness and limitations of gold, green, diamond, and bronze OA models within the Nigerian context.
The findings reveal persistent barriers to OA adoption, including the high cost of article processing charges (APCs), weak institutional repository infrastructure, the absence of coordinated national OA policies, and the growing challenge of predatory publishing. These constraints collectively reduce the global discoverability of Nigerian research and reinforce structural inequities in scholarly communication. Despite these challenges, emerging opportunities are identified, including the expansion of AJOL as a continental OA platform, the potential of diamond OA models supported by scholarly societies, and the strategic role of institutional repositories in enhancing access and visibility.
The paper concludes by proposing a national OA strategy aligned with UNESCO’s Open Science framework, emphasizing policy reform, repository mandates, and sustainable funding models to strengthen the visibility and impact of Nigerian and African scholarship.
🏷 Open Access; Scholarly Communication; Institutional Repositories; Research Visibility; Nigerian Universities; AJOL.
Article 243 · pp. 3292-3308
Bridging Vision and Reality:Public Policy and the Implementation Gap in India’s NEP 2020
The National Education Policy 2020 (NEP 2020) represents one of the most ambitious educational reform initiatives undertaken in India in the past three decades. Conceived as a transformative framework, the policy seeks to restructure the Indian education system by promoting multidisciplinary learning, flexibility in academic pathways, integration of vocational education, and the adoption of technology-driven educational practices. However, the success of any public policy is determined not only by the clarity of its vision but also by the effectiveness of its implementation at the institutional level. This study examines the implementation of NEP 2020 within undergraduate colleges affiliated with the University, focusing on the gap between policy design and institutional execution. Employing a mixed-methods research design, the study draws on quantitative survey data and qualitative insights gathered from ten affiliated colleges—two urban and eight rural institutions. The findings reveal significant structural and administrative challenges that hinder effective implementation, including inadequate infrastructure, shortages of trained faculty, limited digital readiness, and an overburdened examination system. Furthermore, the study highlights the complexities of implementing national-level reforms within a federal governance structure where educational policy is centrally formulated but largely executed by state-level institutions. The analysis suggests that while NEP 2020 provides a visionary blueprint for educational transformation, its success depends heavily on institutional preparedness, sustained financial investment, and coordinated policy execution across governance levels. Bridging the implementation gap is therefore essential to realize the transformative potential envisioned by NEP 2020.
🏷 National Education Policy 2020; Higher Education Reform; Policy Implementation; Academic Bank of Credits
Article 244 · pp. 3309-3320
Improving Diagnostic Turnaround Time and Analytical Reliability in Clinical Biochemistry Laboratories: A Quality Management Framework
Clinical biochemistry laboratories play a key role in diagnosis, triage, therapy monitoring and patient-flow decisions. Ongoing delays and inconsistent test reliability weaken their clinical value and cannot be corrected simply by increasing analyser speed. These problems often arise at poorly managed interfaces across the total testing process, including test requesting, patient identification, specimen collection and transport, accessioning, centrifugation, analysis, validation and result reporting.
Objective: To create a comprehensive quality-management framework that shortens diagnostic turnaround time (TAT) while maintaining analytical reliability in clinical biochemistry laboratories.
Methods: A narrative review was conducted using international laboratory quality standards, consensus guidance and peer-reviewed research on TAT, quality indicators, risk-based control, sigma metrics, autoverification and the total testing process.
Findings: Sustainable improvement depends on defining TAT from the user perspective, decomposing it into measurable components, redesigning bottleneck-prone pre- and post-analytical steps, and applying analytical controls matched to method risk, clinical purpose and performance. In one published single-institution blood-gas implementation, rule-based autoverification reduced TAT from 27 to 18 minutes while the reported verification error rate fell from 2.0% to 0.05%, illustrating that speed and reliability can improve together when safeguards are validated and monitored. This review offers an integrated framework linking TAT metrics, risk controls and quality-management-system governance across the full testing cycle.
Conclusion: A high-quality clinical biochemistry laboratory is not merely fast. It is a well-controlled socio-technical system that produces timely, traceable, clinically interpretable and analytically reliable results. Quality management provides the governance needed to improve speed and reliability simultaneously rather than trading one against the other.
🏷 clinical biochemistry; turnaround time; analytical reliability; ISO 15189; quality indicators; risk-based quality control; total testing process; laboratory quality management; autoverification
Article 245 · pp. 3321-3337
Generalized Additive Modeling of Photovoltaic Backsheet Tensile Strength Degradation Under Multi-Stressor Field Conditions
Photovoltaic module reliability depends on the long-term mechanical durability of polymeric backsheets that provide electrical insulation, moisture protection, and structural support. Accurate prediction of backsheet degradation is therefore essential for reliability-centered operation, maintenance scheduling, and lifecycle management of photovoltaic systems. This study develops a Generalized Additive Model (GAM) with penalized thin-plate regression splines and ridge regularization to predict tensile strength degradation under multi-stressor field conditions, benchmarked against ordinary least squares (OLS), weighted least squares (WLS), and Random Forest (RF) regression using 511 consecutive daily field observations of a PET/PET/EVA multilayer photovoltaic backsheet. The GAM achieves in-sample R² = 0.998 and RMSE = 0.8 MPa, compared with R² = 0.925 and RMSE = 4.69 MPa for OLS, and R² = 0.92 and RMSE = 4.84 MPa for WLS. RF achieves R² = 1.0 and RMSE = 0.2 MPa in-sample but captures no physically interpretable structure beyond UV dominance. Residual variance scales with cumulative UV dose at an exponent of α = 0.592 (SE = 0.105, p < 0.001). A biphasic degradation regime is identified, with tensile strength declining at 0.18 MPa/(MJ/m²) below a UV threshold of 196.5 MJ/m², and accelerating to 0.574 MPa/(MJ/m²) above it, a factor of 3.19. The GAM framework provides a physically interpretable and statistically defensible tool for forecasting photovoltaic backsheet degradation trajectories, with direct implications for predictive maintenance scheduling, module replacement planning, and reliability certification of photovoltaic systems.
🏷 photovoltaic backsheet; tensile strength degradation; generalized additive model; penalized splines; ridge regularization
Article 246 · pp. 3348-3361
Influence of Cerium (Ce) Doping on the Optical Properties of Silver Telluride (Ag2Te) Thin Films Deposited by Electrodeposition Method.
In this investigation, Silver telluride (Ag2Te) thin films have been successfully deposited onto FTO glass substrate using electro deposition method to investigate the influence of Ce doping on optical properties of Ag2Te films. Silver trioxonitrate (V) and tellurium (iv) oxide were the precursors used for silver and tellurium ions. Depositions of films made from cerium-doped silver telluride were conducted at room temperature. The percentages concentration of cerium dopant used in this investigation are 2.5%, 5.0%, 7.5% and10.0%. The optical properties of the thin films were characterized using Uv-Vis spectrometry. Absorbance results confirm that increasing Ce concentration significantly increases the absorbance of Ag2T films across the UV, VIS, and NIR regions. The 10.0% Ce-doped sample demonstrates the highest absorbance across all regions, with values of 43.36% at 365 nm, 42.68% at 400 nm, 34.00% at 700 nm, and 35.16% at 1100 nm, indicating the strong influence of cerium in enhancing light absorption. This trend reflects that as Ce concentration increases, absorbance improves significantly in all spectral regions, suggesting that Ce doping introduces additional localized states that facilitate electronic transitions, thereby enhancing the films' optical properties. The transmittance of Ag2Te and Ce-doped Ag2Te films decreases notably with increasing cerium concentration across the UV, VIS, and NIR regions. This decline in transmittance with increasing Ce concentration is consistent with the corresponding increase in absorbance suggesting that Ce doping enhances light absorption, thus reducing the amount of light transmitted through the films. Cerium doping enhances reflectance slightly, particularly in the NIR regions, likely due to increased surface roughness and scattering effects introduced by Ce incorporation. The extinction coefficient (k) of silver telluride and cerium-doped silver telluride films generally increases with wavelength, indicating higher optical absorption at longer wavelengths. Other optical results were also presented.
🏷 Silver Telluride, optical properties, Electrodeposition. Cerium doping Spectrophotometer
Article 247 · pp. 3362-3378
Emerging Challenges and Their Solutions Related to the Climate and Forest Ecosystem Due to the Metal Ore Project in Surjagarh, Gadchiroli District, Maharashtra
The Surjagarh Iron Ore Project in Gadchiroli district, Maharashtra, represents one of the largest mining initiatives in central India and has significant economic importance for regional development and the steel industry. However, large-scale extraction of iron ore in a biodiversity-rich forest landscape has raised concerns regarding climate resilience, forest degradation, biodiversity conservation, hydrological changes, and socio-economic impacts on indigenous communities. The Surjagarh hills are situated within dense tropical forests inhabited by Scheduled Tribes, including the Madia Gond community, and support numerous floral and faunal species. This paper examines the emerging environmental and climatic challenges associated with mining activities in the Surjagarh region through secondary data analysis obtained from Forest Survey of India (FSI), India State of Forest Report (ISFR), Ministry of Environment, Forest and Climate Change (MoEFCC), Geological Survey of India (GSI), and published scientific literature. The study analyses land-use changes, carbon emissions, forest fragmentation, biodiversity threats, hydrological disturbances, and socio-economic implications. Suitable mitigation strategies based on sustainable mining, afforestation, carbon sequestration, biodiversity restoration, community participation, and climate-smart governance are proposed.
🏷 Surjagarh, Iron Ore Mining, Climate Change, Forest Ecosystem, Biodiversity, Carbon Emission, Sustainable Mining, Gadchiroli.
Article 248 · pp. 3379-3392
Development and Quality Evaluation of Functional Hard Candy Enriched with Cymbopogon Citratus (Lemon Grass) Extract
The growing demand for functional confectionery products has encouraged the incorporation of natural plant-based ingredients with health-promoting properties into conventional sugar-based foods. The present study aimed to develop and evaluate a functional hard candy enriched with Cymbopogon citratus (lemongrass) extract. Fresh lemongrass leaves were processed using thermal and non-thermal extraction methods, and the extracts were incorporated into a standard hard candy formulation containing sucrose, liquid glucose, citric acid, and water. The developed candies were evaluated for their physicochemical characteristics, including moisture, total sugars, reducing sugars, ash, and titratable acidity, using standard AOAC methods. Sensory quality was assessed by twenty semi-trained panellists using a nine-point hedonic scale.
The developed lemongrass candy exhibited desirable physicochemical properties, with moisture (2.05%), total sugars (93.2%), reducing sugars (23.8%), ash (0.28%), and titratable acidity (0.52%), all of which were within the acceptable range for hard-boiled confectionery. Sensory evaluation revealed that both formulations were well accepted. The candy prepared using thermally extracted lemongrass showed higher scores for appearance, texture, and overall acceptability, whereas the non-thermally extracted formulation received higher scores for colour, aroma, and taste, indicating better retention of natural volatile compounds. These findings demonstrate that the extraction method significantly influences the sensory characteristics of lemongrass candy while maintaining desirable physicochemical quality.
The study concludes that Cymbopogon citratus can be successfully incorporated into hard candy to produce a functional confectionery with good storage stability, favourable sensory properties, and potential health benefits. The developed product offers a promising value-added herbal confectionery with potential for commercial application.
🏷 Cymbopogon citratus; Functional hard candy; Lemongrass extract; Herbal confectionery; Physicochemical properties
Article 249 · pp. 3393-3404
Patterns and Preventive Strategies for Health Risk Behaviours Among in-School Adolescents in Ado Ekiti, Ekiti State, Nigeria
Background: Health risk behaviours acquired during adolescence are a leading driver of preventable morbidity and mortality among young people worldwide, yet context-specific evidence to guide school-based prevention remains scarce in many Nigerian settings, including Ado Ekiti, Ekiti State. This study examined the prevalence and patterns of health risk behaviours, awareness of and attitudes toward preventive strategies, associated factors, and the perceived effectiveness of existing interventions among in-school adolescents in Ado Ekiti.
Methods: A descriptive cross-sectional survey was conducted among 248 in-school adolescents aged 10-19 years, selected from public and private secondary schools in Ado Ekiti through multi-stage sampling combining purposive school selection with systematic random selection of students. Data were collected using a structured, self-administered questionnaire and analysed with SPSS version 27 using descriptive statistics, chi-square tests, Pearson correlation, and independent-samples t-tests, with significance set at p < 0.05.
Results: Nearly half of respondents (49.2%) had engaged in at least one health risk behaviour, most commonly unhealthy dietary practices (55.6%) and physical inactivity (46.8%), and 29.8% reported two or more concurrent behaviours. Only 25.8% demonstrated adequate awareness of preventive strategies, although 64.5% held positive attitudes toward prevention. Health risk behaviours were significantly associated with age, gender, school type, and socioeconomic status (p < 0.05). They were significantly and inversely correlated with awareness (r = -0.412), attitude (r = -0.387), and perceived effectiveness of existing strategies (r = -0.356), all p < 0.001.
Conclusion: Health risk behaviours cluster at high prevalence among in-school adolescents in Ado Ekiti and are shaped by an interconnected set of individual, interpersonal, and contextual determinants that existing school-based interventions have not adequately addressed. Comprehensive, skills-based, peer-led, and family-inclusive prevention strategies are urgently required.
🏷 adolescents; health risk behaviours; preventive strategies; school health; Nigeria
Article 250 · pp. 3405-3412
Role of Learners’ Interests and Talents in Their Choice of Senior Secondary School Pathways in Bumula Sub County, Bungoma County, Kenya
The implementation of the Competency-Based Education (CBE) curriculum in Kenya emphasizes pathway selection based on learners' interests, talents, strengths, and career aspirations. However, limited empirical evidence exists on the extent to which learners' interests and talents influence their choice of senior secondary school pathways, particularly in rural contexts. This study examined the role of learners' interests and talents in the choice of senior secondary school pathways among Grade Nine learners in Bumula Sub-County, Bungoma County, Kenya. The study adopted a mixed methods research design. The target population comprised approximately 3,500 Grade Nine learners in public junior secondary schools, together with career guidance teachers, school heads, and selected parents. Using the Krejcie and Morgan sampling table, a sample of 347 learners was selected through stratified random sampling from twenty public junior secondary schools. Quantitative data were collected using questionnaires, while qualitative data were obtained through interviews. Quantitative data were analysed using descriptive statistics, including frequencies, percentages, means, and standard deviations, while qualitative data were analysed thematically. The findings revealed that learners' interests and talents significantly influenced pathway selection. Most respondents indicated that their personal interests influenced their pathway choices (M = 3.98), their talents matched their selected pathways (M = 3.87), they enjoyed subjects related to their preferred pathways (M = 4.01), and they were passionate about their chosen pathways (M = 3.93). Conversely, most learners disagreed that they selected pathways that did not match their interests (M = 2.39). The study concludes that learners generally choose pathways that align with their interests, talents, and aspirations, although a minority continue to be influenced by external factors such as parental expectations, school limitations, and inadequate career guidance. The study recommends that schools strengthen career guidance programmes and create opportunities to identify and nurture learners' interests and talents through co-curricular activities, mentorship, and practical learning experiences to promote informed and learner-centred pathway selection.
🏷 Competency-Based Education, learners' interests, learners' talents, pathway selection, Grade Nine learners, senior secondary school pathways
Article 251 · pp. 3429-3439
Overview of Design and Performance Analysis of a Downdraft Biomass Gasifier for Dual-Fuel Internal Combustion Engine Applications
Energy security, fossil-fuel dependence, and the need for productive utilization of agricultural residues have renewed interest in biomass gasification for decentralized power and thermal applications in India. A downdraft biomass gasifier integrated with a dual-fuel internal combustion engine offers a practical route for reducing diesel consumption while using locally available biomass resources. This paper examines the design relevance, operating principles, feedstock considerations, and performance parameters governing such systems, with emphasis on brake specific fuel consumption, brake specific energy consumption, brake thermal efficiency, diesel replacement capability, and future AI-assisted performance prediction.
🏷 Biomass gasification; downdraft gasifier; producer gas; dual-fuel engine; brake thermal efficiency; diesel substitution; AI-based prediction
Article 252 · pp. 3440-3457
Vision Transformer Based Digital Image Forgery Detection and Localization Using Global Contextual Feature Learning
Artificial intelligence has significantly improved digital image editing capabilities, making it increasingly difficult to distinguish authentic images from manipulated ones [5, 7]. This paper proposes a Vision Transformer (ViT)-based framework for digital image forgery detection and localization by leveraging global contextual feature learning [4]. Unlike conventional Convolu-tional Neural Networks (CNNs), Vision Transformers capture long-range dependencies through self-attention mechanisms, enabling more effective identification of manipulated regions [4, 9]. The proposed framework performs image preprocessing, patch extraction, positional encod-ing, transformer-based feature learning, binary classification, and forgery localization. The model is evaluated using publicly available benchmark datasets, including CASIA V2, Co-MoFoD, and FaceForensics++ [20, 48], and its performance is assessed using Accuracy, Pre-cision, Recall, F1-score, Area Under Curve (AUC), Intersection over Union (IoU), and Pixel Accuracy [17, 49]. Experimental results demonstrate that the proposed Vision Transformer framework outperforms conventional CNN-based methods in terms of detection accuracy and localization precision [16, 19]. The proposed approach provides a robust and scalable solution for modern digital image forensics [15] and can be extended to hybrid transformer architectures and video forgery detection in future work.
🏷 Digital Image Forensics, Vision Transformer, Image Forgery Detection, Image Localization, Deep Learning
Article 253 · pp. 3458-3464
The Sectoral Distribution of Commercial Banks' Credit and Economic Growth in Nigeria
The study examined the sectoral distribution of commercial banks' credit and economic growth in Nigeria. The specific objectives include to: Examine the effect of commercial bank’s loans and advances to agricultural sector on economic growth in Nigeria. Ascertain the effect of commercial bank’s loans and advances to mining and quarrying sector on economic growth in Nigeria. The study adopted pre-test estimation of descriptive statistics, unit root and bound tests to ensure that the data set is stationary and fit for the analysis. Auto-Regressive Distributed Lag Model (ARDL) method of analysis was adopted for the estimation. The data utilized for the analysis were extracted from CBN statistical bulletin. The study revealed that commercial bank’s loans and advances to agricultural sector for the period of this study has positive and insignificant impact on gross domestic product in Nigeria, commercial bank’s loans and advances to mining and quarrying sector for the period of this study has positive and insignificant impact on gross domestic product in Nigeria. Hence, sectoral distribution of commercial bank’s loans, advances have not contributed significantly to the Nigerian economic growth. Thus, the study concluded that sectoral distribution of commercial bank’s credit had positive and insignificant impact on economic growth of Nigeria. The study recommends that monetary authorities should deregulate interest rate to make funds available for agricultural sector and mining and quarrying sector to expand their business, increase productivity and boast the economy at the end. Government should devise measures to closely monitor the utilization of credits given to every sector of the Nigerian economy in order to enthrone accountability and ensure that the projects for which the credits were obtained are carried out.
🏷 Commercial Banks' Credit, Economic Growth, Auto-Regressive Distributed Lag Model
Article 254 · pp. 3465-3475
Assessment of NDMU BS Computer Science Student Trainee Performance During On-the-Job Training Using Performance Appraisal Reports (2023–2025)
This study assessed the performance of Bachelor of Science in Computer Science (BSCS) student trainees from Notre Dame of Marbel University (NDMU) during On-the-Job Training (OJT) across three summer terms (2023–2025) using a multidimensional performance appraisal instrument completed by host supervisors. A descriptive quantitative design was employed with 30 BS Computer Science trainees from NDMU (Summer 2023: n=5; Summer 2024: n=16; Summer 2025: n=9) deployed to nine host organizations in South Cotabato and surrounding regions. The performance appraisal instrument comprised three parts: Part I assessed Personal Skills and Attitude across four dimensions (Work Habits, Work Skills, Social Skills, General Attitude) using a 5-point scale; Part II evaluated Internship Job Performance across six dimensions using categorical ratings (Not Satisfactory, Satisfactory, Very Satisfactory); and Part III measured alignment with three Program Educational Objectives (PEOs) using a 5-point scale. Data were analyzed using descriptive statistics (means, standard deviations, frequencies, percentages). Part I yielded an overall mean of 4.69 (SD=0.53), with General Attitude highest (M=4.83) and Work Skills lowest (M=4.57). In Part II, Teamwork Skills received the highest Very Satisfactory rate (96.7%) while Job Specific Skills received the lowest (66.7%). Part III overall mean was 4.67 (SD=0.47), with PEO3 highest (M=4.77) and PEO2 lowest (M=4.57). A consistent upward performance trend was observed across terms (2023→2024→2025). The NDMU BS Computer Science trainees demonstrated strong overall performance particularly in professional dispositions, teamwork, and continuous learning. Findings support targeted interventions to strengthen technical skill application and ethical competency development.
🏷 On-the-Job Training, Performance Appraisal, Computer Science Education, Internship Assessment, Program Educational Objectives
Article 255 · pp. 3476-3485
Emotional Intelligence and Work-Life Balance among Employees: A Comparative Study Based on Gender and Job Type
Workplace demands have grown steadily more complex, and the capacity of employees to regulate emotion while sustaining a workable boundary between professional and personal domains has become a matter of practical as well as theoretical interest. This paper examines the association between emotional intelligence and work-life balance among 134 full-time employees drawn from technical and non-technical occupations, and additionally tests whether gender and occupational category shape either construct independently or in combination. Emotional intelligence was measured using the Wong and Law Emotional Intelligence Scale, and work-life balance was assessed through an adapted version of Hayman’s three-dimensional scale. Pearson correlation revealed a moderate, statistically significant positive association between emotional intelligence and work-life balance (r = .331, p < .001), suggesting that employees who read and manage their own emotional states with greater facility also report a more sustainable balance between work and personal commitments. A 2 × 2 factorial ANOVA indicated a significant main effect of gender on emotional intelligence, with male employees reporting somewhat higher scores than female employees, while job type produced no discernible effect and the gender-by-job-type interaction remained non-significant. Work-life balance, by contrast, did not vary meaningfully by gender, job type, or their interaction. These results are discussed with reference to the Conservation of Resources framework and existing literature on emotional competence in Indian organizational settings, and practical implications for workplace training are outlined.
🏷 emotional intelligence, work-life balance, gender, occupational type, employees
Article 256 · pp. 3486-3495
Influence of Biology Remedial Instruction on Students' Academic Achievement in Secondary Schools in Kimaeti Sub-County, Bungoma County, Kenya
The study sought to determine the effect of remedial teaching on students' academic performance in Biology. The study was guided by the Constructivist Theory of Learning advanced by Jean Piaget and Lev Vygotsky. The study adopted a descriptive survey research design. The target population comprised 315 students, 80 teachers, and 20 school administrators drawn from public secondary schools in Kimaeti Sub-County, for a total of 415 respondents. Questionnaires were used to collect quantitative data from students and teachers, while interview schedules were administered to school administrators to collect qualitative data. The validity of the research instruments was established through expert judgment, and reliability was determined using the Cronbach Alpha coefficient. Quantitative data were analyzed using the Statistical Package for the Social Sciences (SPSS) Version 26 and presented using frequencies, percentages, means, and standard deviations. Pearson Product-Moment Correlation was used to determine the relationship between remedial teaching and students' academic achievement in science. Qualitative data were analyzed thematically and presented in narrative form. The study established that remedial teaching had a positive and significant relationship with students' performance in Biology (r = 0.684, p = 0.000). The findings further revealed that remedial teaching enabled teachers to revisit difficult concepts, provide individualized instruction, and increase students' mastery of scientific concepts, thereby improving academic achievement in science. The study concluded that remedial teaching significantly contributes to improved students' academic achievement in Biology in secondary schools in Kimaeti Sub-County. The study recommends that school administrators institutionalize remedial teaching programs, the Ministry of Education formulate clear guidelines on remedial instruction, and teachers adopt learner-centered remedial approaches that address students' specific learning difficulties in science subjects.
🏷 Biology remedial instruction, academic achievement, remedial teaching, secondary schools
Article 257 · pp. 3496-3508
Grassroots Women's Networks and Community Disaster Resilience in Manipur, India
This paper analyzes how indigenous frameworks form a basis for women's response to disasters by examining the structural (historical Ima Keithel network: Mothers' Market), social (Meira Paibi: Women Torchbearers) and operational dimensions of leadership in disaster management within grassroots institutional structures embedded into socio-cultural history. A mixed-methods design using an explanatory sequential approach was employed; quantitative data were collected through structured questionnaires administered to 384 leaders from five districts (Imphal West, Imphal East, Thoubal, Churachandpur and Ukhrul) while qualitative key informant interviews of twelve respondents.
The results of SEM indicated that structural social capital ( = .342, p < .001) and cognitive social capital ( = .418, p < .001) positively predict community disaster resilience, grassroots institutional trust ( = .215, p < .01) mediates between informal women networks and operational evacuation efficiency, and there is a significant mismatch between informal disaster response capacity and formal institutional integration (87.5% respondents report zero formal representation within State and District Disaster Management Authorities (SDMA/DDMA)).
Qualitative thematic analysis showed that women networks have rapid resource mobilization and localized risk communication, but they are systematically constrained by patriarchal institutional structures, lack of financial autonomy, and non-recognition in formal policy frameworks. This study offers an empirical model for integrating grassroots, gender-led social capital into state-level DRR policies for community resilience building in complex emergency settings.
🏷 Disaster Management, Women's Leadership, Social Capital Theory, Manipur, Resilient Communities, Grassroots Institutions, Feminist Political Ecology.
Article 258 · pp. 3509-3535
Navigating Rising Living Costs: A Qualitative Study of Financial Well-Being and Household Financial Decision-Making among Working Professionals in Uttarakhand
Rising living costs have become a major socioeconomic challenge, placing increasing pressure on household financial stability and the financial well-being of working professionals. Although previous studies have extensively examined inflation, household finance, and financial well-being using quantitative approaches, limited attention has been paid to how individuals experience and manage rising living expenses in their everyday lives, particularly in the regional contexts of developing economies such as India. This study explored how rising living costs influence financial well-being and household financial decision-making among working professionals in Uttarakhand.
The study employed an interpretivist research philosophy combined with an exploratory qualitative approach. Semi-structured interviews were carried out with 25 working professionals from government and private-sector organizations in Uttarakhand. Participants were purposefully chosen to ensure a variety of perspectives on household financial management. The interview transcripts were manually analyzed through Braun and Clarke's (2006) six-phase thematic analysis.
The study showed that rising living costs have greatly affected how households plan their budgets, prioritize expenses, save money, make investment choices, and approach long-term financial planning. Participants reported increased financial stress due to higher costs for housing, healthcare, education, transportation, and other essential needs. To stay financially stable, they implemented various adaptive strategies such as stricter budgeting, cutting back on discretionary expenses, building emergency savings, and finding additional sources of income. Viewing these findings through the lens of the Family Resource Management Theory (FRMT), it becomes clear that households continuously adjust their financial resource management in response to changing economic circumstances.
The study contributes to the household finance literature by extending the application of the Family Resource Management Theory to the context of rising living costs and by providing qualitative evidence from Uttarakhand, an underexplored region in India. The findings offer practical insights for policymakers, employers, financial institutions, and financial educators seeking to strengthen household financial resilience and promote sustainable financial well-being.
🏷 Rising Cost of Living; Financial Well-being; Household Financial Behaviour; Financial Stress; Family Resource Management Theory
Article 259 · pp. 3536-3552
Electricity Supply Dynamics and Economic Growth in Nigeria: Evidence from an Error Correction Model (1980-2021).
This research study investigates the impact of electricity supply dynamics and economic growth in Nigeria: evidence from an Error Correction Model (1980 – 2021). The study examined the effects of electricity demand-supply gaps, electricity pricing per capita, lending interest rates, and disposable income on GDP. Result of ADF unit root test confirm that all variables were integrated of order one (I(1)); and this necessitated the test for long-run relationship. Thus, the cointegration result shows that there is one cointegrating equation ie trace statistics (83.73255) is greater than their respective critical value (69.81889) at 5% level of significance, hence there is a long-run equilibrium relationship among the variables. The ECM results reveal that power outages significantly reduce economic growth as a million MWh of deficit in electricity demand reduces GDP by 7600.8billion Naira, while disposable income positively and significantly drives growth. Interestingly, lending interest rates show a positive relationship with GDP, contrary to conventional expectations, possibly reflecting credit expansion during periods of economic optimism. Electricity pricing exhibited a negative but statistically insignificant effect. The findings highlight the importance of reliable power infrastructure, income-enhancing fiscal policies, and balanced energy pricing strategies for fostering inclusive economic growth in Nigeria. Therefore, the study recommends for governments’ increasing investments in Energy/Electricity infrastructure in order to enhance energy supply and hence reduce power outages, benefiting all sectors of the economy. Also, there is need to advocate for full privatization of the Nigeria’s energy sectors including electricity generation and the downstream.
🏷 Electricity supply, Economic growth, Power outages, Energy pricing, Error Correction Model, Nigeria. JEL Classification: B41, C22, C32, O47, F43, P22, Q43, Q47.
Article 260 · pp. 3553-3569
Financial Literacy as a Catalyst for Fintech Readiness: A Comparative Study of India in the Global Digital Financial Ecosystem
Financial Technology (FinTech) has transformed the global financial ecosystem by enhancing financial inclusion, innovation, and digital service delivery. India has emerged as one of the world's fastest-growing FinTech markets, driven by initiatives such as the Unified Payments Interface (UPI), Aadhaar, Digital India, and the Pradhan Mantri Jan Dhan Yojana (PMJDY). Despite these advancements, disparities in financial literacy, digital literacy, cybersecurity awareness, and financial capability continue to hinder the sustainable and inclusive adoption of digital financial services. This study examines the current status of financial literacy in India and evaluates the country's readiness for FinTech development within the global digital financial ecosystem. A descriptive and comparative research design based on secondary data from internationally recognized sources, including the OECD/INFE, World Bank, International Monetary Fund (IMF), Reserve Bank of India (RBI), National Centre for Financial Education (NCFE), Bank for International Settlements (BIS), and International Telecommunication Union (ITU), is employed. The findings indicate that India has made significant progress in digital payments, financial inclusion, and digital public infrastructure, although financial capability has not advanced at the same pace as technological innovation. The study proposes an integrated conceptual framework and a multidimensional FinTech Readiness Index (FTRI) that can be used to assess national preparedness for sustainable digital financial development. The findings provide practical policy recommendations for strengthening financial literacy, digital capability, and inclusive digital finance.
🏷 Financial Literacy; FinTech Readiness; FinTech Readiness Index; Digital Financial Literacy; Financial Inclusion; Digital Finance; India.
Article 261 · pp. 3570-3582
The Influence of Job Insecurity, Workload, and Work-Family Conflict on Work Stress of Female Workers in Central Jakarta
The purpose of this study was to examine the influence of job insecurity, workload, and work-family conflict on work stress among female workers in Central Jakarta. Drawing on the Job Demands–Resources (JD-R) theory, this study explains that work stress arises when work-related demands exceed employees' available resources to cope with them. A quantitative method with causal relationship analysis was applied. Data were collected through questionnaires distributed to 140 participants selected through purposive sampling, consisting of female workers in Central Jakarta who were married and/or had families and had worked for at least one year. All measurement indicators were adapted from established previous studies and assessed using a five-point Likert scale. The collected data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The findings indicate that job insecurity, workload, and work-family conflict have positive and significant effects on work stress, with workload exerting the strongest influence among the three independent variables. These results support the Job Demands–Resources (JD-R) theory, suggesting that excessive work demands and employment uncertainty contribute to higher levels of work stress among female workers. This study provides both theoretical and practical contributions by extending the application of the JD-R framework in the context of female workers in Central Jakarta and offering empirical evidence that can assist organizations in developing strategies to improve employee well-being through better workload management, enhanced employment security, and policies that support work-family balance. However, this study is limited to female workers in Central Jakarta and only examines three independent variables. Therefore, future research is recommended to involve more diverse respondents from different regions and occupational sectors while incorporating additional variables or mediation and moderation models to obtain a more comprehensive understanding of work stress.
🏷 Job Insecurity, Workload, Work-family conflict, Work Stress, Female Worker.
Article 262 · pp. 3583-3607
Climate Change Adaptation and Human Resource Management: Examining Workforce Resilience across Geographically Vulnerable Regions
Climate change stands out as one of the greatest global challenges facing organizations, employees, and labor markets in regions already vulnerable to environmental shifts. More frequent extreme weather like heat waves, floods, droughts, cyclones, and other climate threats throws business operations, workforce productivity, employee health, and long-term organizational stability into turmoil. Governments and companies have started to embrace climate adaptation measures, but fewer have really recognized how Human Resource Management (HRM) can play a strategic role in boosting workforce resilience to climate risks. This paper looks at ways to weave climate change adaptation into HRM practices so organizations in vulnerable areas can strengthen their workforce’s ability to adapt. By drawing on Dynamic Capabilities Theory, Organizational Resilience Theory, Human Capital Theory, and the Ability–Motivation–Opportunity (AMO) framework, the study builds a strong conceptual foundation for climate-responsive HRM. The research involves a systematic review of recent literature from 2015 to 2025, using peer-reviewed articles pulled from databases like Scopus, Web of Science, and ScienceDirect. This review highlights adaptive HRM strategies like climate-aware workforce planning, occupational health and safety measures, flexible work policies, ongoing employee training, psychological support, digital workforce management, and leadership training as essential drivers of organizational resilience. The results show that organizations in climate-vulnerable zones boost employee adaptability, cut down on disruptions, reinforce organizational commitment, and keep productivity high when they integrate climate adaptation into HR policies and strategic planning. This paper adds to the conversation on sustainable HRM by offering a framework that links climate adaptation actions with workforce resilience and organizational durability. It also lays out clear recommendations for managers and policymakers who want to build organizations that can weather future climate surprises.
🏷 Climate Change Adaptation; Human Resource Management; Workforce Resilience; Sustainable Human Resource Management
Article 263 · pp. 3608-3619
Deep Learning-Driven Intelligent System for Early Diabetes Prediction and Risk Assessment
Diabetes is one of the fastest-growing chronic diseases worldwide, posing significant health and economic challenges due to its long-term complications. Early identification of individuals at high risk of developing diabetes is essential for timely intervention, personalized treatment, and improved patient outcomes. This study proposes a Deep Learning-Driven Intelligent System for Early Diabetes Prediction and Risk Assessment that utilizes advanced deep learning techniques to accurately classify diabetic and non-diabetic individuals based on clinical and physiological attributes. The proposed framework employs a comprehensive data preprocessing pipeline, including missing value handling, normalization, feature selection, and class balancing to enhance data quality and model performance. A deep neural network (DNN) architecture with multiple hidden layers is designed to capture complex nonlinear relationships among patient characteristics such as glucose level, body mass index (BMI), age, insulin concentration, blood pressure, skin thickness, diabetes pedigree function, and other relevant medical parameters. Experimental results demonstrate that the proposed deep learning framework achieves superior predictive performance compared with conventional machine learning algorithms, enabling reliable early-stage diabetes detection and comprehensive risk assessment. Furthermore, the intelligent system can assist healthcare professionals in clinical decision-making by identifying high-risk individuals before the onset of severe diabetic complications. The proposed approach offers a scalable, cost-effective, and automated solution for preventive healthcare and has the potential to be integrated into smart healthcare platforms and telemedicine systems for continuous diabetes screening and personalized risk management.
🏷 Deep Learning; Early Diabetes Prediction; Diabetes Risk Assessment; Deep Neural Network (DNN); Artificial Intelligence (AI); Healthcare Analytics; Medical Decision Support System
Article 264 · pp. 3620-3627
Performance Comparison of Support Vector Machine and Decision Trees for the Classification of Handwritten Digits
Handwritten digit recognition remains one of the fundamental applications of machine learning and pattern recognition due to its widespread use in banking, postal services, document processing and intelligent information systems. Although deep learning approaches have demonstrated remarkable performance, classical machine learning algorithms such as Support Vector Machine (SVM) and Decision Tree (DT) remain attractive because of their simplicity, computational efficiency and interpretability. This study evaluates and compares the performance of Support Vector Machine and Decision Tree classifiers for handwritten digit recognition using the Modified National Institute of Standards and Technology (MNIST) dataset. The dataset comprises 70,000 grayscale images of handwritten digits (0–9), each with a resolution of 28 × 28 pixels. Image preprocessing involved normalization of pixel values and standard data preparation before model implementation. Both classifiers were implemented using Python and the Scikit-learn library under identical experimental conditions, and model performance was evaluated using accuracy, precision, recall and F1-score. Experimental results showed that the Support Vector Machine outperformed the Decision Tree across all evaluation metrics. The SVM achieved an accuracy of 94.32%, precision of 94.41%, recall of 94.32%, and an F1-score of 94.35%, whereas the Decision Tree recorded 87.36% accuracy, 87.42% precision, 87.36% recall and 87.35% F1-score. The findings indicate that SVM provides superior classification performance for handwritten digit recognition, while Decision Tree offers faster implementation and greater interpretability. The study concludes that SVM is more suitable for applications requiring high recognition accuracy, whereas Decision Tree remains appropriate for applications where computational simplicity and model transparency are prioritized
🏷 Handwritten Digit Recognition, Support Vector Machine, Decision Tree, MNIST Dataset, Machine Learning, Image Classification
Article 265 · pp. 3628-3636
Strategy Formulation and Organizational Performance of Selected Private Hospitals in Bungoma County, Kenya
Strategy formulation has increasingly gained recognition and traction among top and medium-level organizations because of its ability to improve organizational performance. In the current globalization era, strategic management practices have been considered among the most significant practices that distinguish organizations from others. As a key sector, healthcare continues to attract private investors who want to provide medical services and make profits just like other entrepreneurs. This study established the effect of strategy formulation on the organizational performance of selected private hospitals in Bungoma County, Kenya. The study was anchored on two theories, including management by objectives theory as the main theory, supported by Resource-based theory. The explanatory research design was used for this study. The study targeted a population of 149 respondents that includes; managers, directors, marketing officers, and board of management representatives. The sample size involved participants distributed across the thirty-eight selected private hospitals in the county. A five-point Likert scale on the data was used on the data collection instruments. Pilot study was carried out in three private hospitals in Kakamega County for validity of the research instrument. For reliability analysis, Cronbach’s alpha was used which recommends a minimum threshold of 0.7. Descriptive statistics was used to analyze data involving means, percentages and frequencies. Findings indicated that Strategy formulation (r=0.891, p=000) has a statistically significant effect on organizational performance. The study recommended that private hospitals in Bungoma should embrace strategy formulation to enhance their organizational performance.
🏷 Strategy formulation, Organizational performance, Management by Objectives theory.
Article 266 · pp. 3637-3654
Advances in Pervious Concrete: Hydraulic Performance, Durability, and Sustainable Material Applications
Pervious concrete is an advanced, ecological-oriented material, the performance of which is defined by the interactions between porosity, permeability, density and compressive strength. Though higher hydraulic efficiency is achieved but this is counterbalanced by a progressive optimization mix of the matrix with reduction in compressive strength (3-25 MPa) in accordance with increase in porosity. This is a result of reduced interparticle contact and a proportional decrease in density of about 3.5-5% per 1% increase in the void content. A significant 55-65% strength loss (as porosity increases between 20-30%) has been reported, reflecting a less compact internal structure. To obtain a balanced performance it is necessary to optimize mix design, in particular by balancing the water-cement ratio between approximately 0.25-0.30, the appropriate gradation of aggregates, and the incorporating recycled and natural aggregates. Furthermore, the addition of cementitious material and fibers will enhance strong bonding in the microstructure and durability with minimal loss in permeability. Even though pervious concrete has massive potential as a sustainable pavement material, and the beneficial effect on stormwater managing, urban heat-reduction, further development of material engineering and environment friendly construction process is steadily increasing its functional reliability, thus strengthening its contribution to sustainable and climate-resilient urban infrastructure.
🏷 stormwater; recycled aggregate; permeability; durability; porosity
Article 267 · pp. 3655-3662
The Criminal Procedure (Identification) Act, 2022: A Critical Appraisal in the Light of Right to Equality & Privacy
On April 4 and 6, Lok Sabha and Rajya Sabha had respectively passed the Criminal Procedure(Identification) Bill, 2022 following a lengthy debate. With the President giving his assent to the Bill on April 18, it was duly notified by the Government, bringing the Act into force. It is a modification of the Identification of Prisoners Act, 1920, which stands repealed through Section 10(1) of the 2022 Act. In entirety, there has been considerable evolution as compared to the previous 1920 Act which is a colonial law that permitted the collection of fingerprints, footprint impressions, and photographs of convicts and others. The Criminal Procedure (Identification) Act, 2022 was enacted with the aim of authorizing law enforcement agencies to take measurements of convicts and other persons for the purposes of identification and investigation in criminal matters. It is essential to note that the 2022 Act is digitalizing the criminal records and paving the way to make data with respect to criminals easily available through a centralized database, thereby being in accordance with the global standards adopted by various other countries. The Act widens the power of State and its enforcement agencies during a criminal investigation, with regard to the taking of biometric and other biological data of any person arrested by the police, including persons detained under preventive detention laws. While the term ‘any person’ doesn’t specify exactly who are to come under the purview of this Act, it widens the scope of its application, leaving it to the whims and fancies of the State and its enforcement agencies. The author intends to analyse the Act’s potential misuse and possible intrusion into the ambit of fundamental rights such as the right to equality and privacy of those covered by the Act. Further, the author wishes to provide a concise explanation of the Act and numerous other related issues.
🏷 Investigation, Criminal records, biological data, Centralised database, Fundamental rights
Article 268 · pp. 3663-3677
Voltage-Instability Bus Identification in Power Transmission Networks Using an Improved Strength Pareto Evolutionary Algorithm
Voltage instability remains a major operational challenge in stressed transmission networks because reactive power deficiencies and contingency events can force bus voltages outside acceptable operating limits. This paper presents an Improved Strength Pareto Evolutionary Algorithm (ISPEA) for the identification of voltage-instability buses in transmission systems under steady-state and contingency loading. A Newton–Raphson load-flow model was first used to obtain the operating point of the network. Contingency severity is represented by a 90% increase in reactive demand at load buses, after which the ISPEA ranks buses using a multi-objective fitness structure based on voltage magnitude deviation, maximum loading capacity, and generator operating constraint penalties. ISPEA runs for 200 generations with a population of 100, an archive of 100, and a Newton–Raphson convergence tolerance of 10⁻⁵ p.u. The method was evaluated on the IEEE 30-bus and Nigerian 31-bus networks using MATLAB R2023a. Under base-case conditions, the IEEE 30-bus system reaches its minimum voltage at Bus 5 (0.9360 p.u.), whereas the Nigerian 31-bus system records its lowest voltage at Bus 21 (0.9430 p.u.). After contingency loading, the number of under-voltage buses increased from 2 to 8 in the IEEE system and from 2 to 9 in the Nigerian system. ISPEA identifies Buses 5, 7, and 23 as the most critical in the IEEE 30-bus system, and Buses 5, 11, and 21 in the Nigerian 31-bus system. Compared with LVSI and VCPI screening, ISPEA yields the lowest maximum loading capacity values across all evaluated buses, confirming the superior identification of genuinely vulnerable buses for reactive power compensation siting.
🏷 Voltage instability; weak-bus identification; contingency analysis; transmission networks; multi-objective optimization;
Article 269 · pp. 3678-3683
Acute Kidney Injury Secondary to Acute Urinary Retention in an 87-Year-Old Female with Prolonged Poor Oral Intake in Hospital National Guido Valadares, Timor Leste on May 2026: A Case Report
Acute kidney injury (AKI) is a common medical emergency in elderly patients and is associated with significant morbidity and mortality. Postrenal obstruction accounts for approximately 5–10% of AKI cases but is more common among older adults. We report the case of an 87-year-old woman who presented with a 10-day history of inability to eat and drink, progressive weakness, abdominal distension, and inability to pass urine. Examination revealed a markedly distended bladder consistent with acute urinary retention. Laboratory investigations demonstrated severe AKI and Urinary Tract infection (UTI). Prompt bladder catheterization drained a large volume of urine, resulting in rapid clinical improvement and gradual recovery of renal function. This case highlights the importance of considering urinary retention as a reversible cause of AKI in elderly women, where delayed diagnosis may lead to permanent renal damage.
🏷 Acute kidney injury, acute urinary retention, obstructive uropathy, elderly female, postrenal AKI, bladder outlet obstruction, dehydration; Timor-Leste; resource-limited setting
Article 270 · pp. 3684-3692
Cloud Computing and the Digital Transformation of Public Service Delivery in Nigeria
The research aims at establishing the role that cloud computing plays in the digital transformation of public services in Nigeria by focusing on its impact in terms of improving efficiency, accessibility, transparency, and innovation. The use of cloud computing technology has proved to be an indispensable means through which digital government can be enabled through data storage, information sharing, remote access to public services, and better collaboration between government institutions. Notwithstanding these benefits, there have been numerous challenges in the implementation of cloud computing in Nigeria's public institutions. The present research has employed the use of qualitative research methodology through a thorough review and analysis of related literature covering scholarly articles, government papers, policies, and reports on cloud computing and digital transformation within the Nigerian public sector. Qualitative analysis allows for the determination of major themes that cover areas such as cloud adoption, digital governance, cyber security, service delivery, and infrastructure among others. According to the results, cloud computing plays a role in the improvement of public services delivery through increased efficiency, reduced IT costs, better access to services, data integration, inter-agency cooperation, as well as transparency and accountability in government activities. Nonetheless, issues such as digital infrastructure inadequacy, cybersecurity risks, lack of skills, conflicting policies, budgeting, and data sovereignty remain challenges in the way of implementing cloud computing. This paper has demonstrated that cloud computing has enormous possibilities to facilitate the digitalization of government services in Nigeria and ensure better access by the citizens to effective and responsive government services. The study makes recommendations on the need to enhance the development of digital infrastructure, adopt sound cybersecurity policy frameworks, develop capacity of public service personnel, create harmonized policies, and develop collaborative relationship between government agencies and ICT providers.
🏷 Cloud Computing, Digital Transformation, Public Service Delivery, Nigeria, Digital Government.
Article 271 · pp. 3693-3699
Varicose Vein Sclerotherapy Using Polidocanol for Chronic Non-Healing Leg Ulcer: A Case Report from Muhimbili National Hospital
Background: Chronic venous ulcers represent the most severe manifestation of chronic venous insufficiency and are associated with significant morbidity, particularly when longstanding and untreated. In low-resource settings, limited access to advanced vascular interventions often results in prolonged disease duration and poor outcomes. Minimally invasive techniques such as ultrasound-guided foam sclerotherapy offer a potential alternative for definitive management.
Case Presentation: We report a 26-year-old male who presented with a 10-year history of left lower limb swelling and a chronic non-healing ulcer around the ankle. The lesion initially developed as a raised skin lesion and progressed into an ulcer associated with intermittent bleeding. Clinical examination revealed varicose veins and localized swelling of the left lower limb. Duplex ultrasound demonstrated dilatation of the small saphenous vein with an incompetent sapheno-popliteal junction and multiple incompetent perforator veins, with no evidence of deep venous thrombosis.
Intervention: The patient underwent ultrasound-guided polidocanol foam sclerotherapy targeting the incompetent venous segments.
Outcome: Following treatment, progressive ulcer healing was observed, culminating in complete epithelialization and resolution of symptoms. No complications were reported.
Conclusion: This case demonstrates that ultrasound-guided polidocanol foam sclerotherapy can achieve complete healing of longstanding venous ulcers by addressing underlying venous reflux. It also highlights the feasibility and effectiveness of this minimally invasive technique in a low-resource setting and supports its broader adoption in similar healthcare environments.
🏷 Chronic venous ulcer, polidocanol, foam sclerotherapy, interventional radiology, Tanzania, venous insufficiency
Article 272 · pp. 3700-3710
Reinforcement Learning for Personalized Insulin Dosing: A Comparative Study of A2C, SAC and PPO on Real-World Clinical Data
Personalized insulin dosing for Type 1 diabetes mellitus (T1DM) remains challenging because of complex glucose-insulin dynamics and substantial patient variability. Reinforcement learning (RL) has emerged as a promising approach for adaptive insulin management, yet the reliability of learned policies depends heavily on reward design and evaluation strategy. This study compares three actor–critic RL algorithms: Soft Actor-Critic (SAC), Advantage Actor-Critic (A2C), and Proximal Policy Optimization (PPO) for personalized insulin dosing using real-world continuous glucose monitoring, insulin delivery, basal insulin, and meal intake data from the OhioT1DM dataset. A custom Gymnasium-based environment was developed, and all algorithms were trained under identical conditions for 100,000 timesteps. Performance was evaluated using cumulative reward together with clinically relevant measures, including Time in Range (TIR) and insulin dosing behaviour. Although A2C and PPO achieved higher cumulative rewards than SAC, both converged to near-zero insulin dosing policies that exploited the reward formulation rather than learning clinically meaningful glucose regulation. In contrast, SAC maintained adaptive dosing behaviour, achieving a TIR of 72.71% with an average insulin dose of 1.769 U/step. These findings show that higher cumulative reward does not necessarily correspond to better clinical decision-making in open-loop reinforcement learning environments. The study highlights the importance of behaviour-focused evaluation alongside conventional reward metrics and provides practical insights for developing safer and more reliable reinforcement learning systems for personalized diabetes management.
🏷 Reinforcement Learning, A2C, SAC, PPO, Insulin dosing, Type 1 Diabetes, OhioT1DM Dataset, Reward Hacking, Time in Range, Clinical Decision Support.
Article 273 · pp. 3711-3728
Factors Influencing Investors’ Adoption of Robo-Advisors in Bangalore: An Extrapolation from the Fiduciary Duty of Investment Advisors
Robo-advisors are one of the prominent fintech innovations seen in the era of digitization. This study intended to investigate the factors that influence robo-advisor adoption among investors in Bangalore, India. It integrates the principles of Fiduciary Duty (Care and Loyalty) with the Technology Acceptance Model (TAM). From the analysis of 301 valid responses, the research found that while all fiduciary drivers are significant, Firm Reputation acts as the strongest predictor of value, and Privacy Protection is the primary driver of trust. Interestingly, investment experience did not moderate the relationship between trust and adoption, which suggests that there is a universal trust threshold across all investor segments in the Bangalore market.
🏷 Robo-Advisors; FinTech Adoption; Fiduciary Duty; Technology Acceptance Model; Perceived Value; Trust; Privacy Protection; Firm Reputation.
Article 274 · pp. 3729-3742
Mitigating the 'Green Premium' Barrier: How Digital Information Richness and Material Transparency Enhance Customer Trust in High-Priced Sustainable Products
The global paradigm shift towards sustainable consumption is frequently hindered by the "Green Premium" the additional cost associated with eco-friendly products. While classical economic theory suggests that high prices deter purchase intention, this study posits that in the digital marketplace, the primary barrier is information asymmetry, not merely price. Integrating Signaling Theory and the Information Systems (IS) Success Model, this research investigates how the quality of information provided by e-commerce platforms moderates the negative relationship between price and customer trust. We analyzed a secondary dataset of 3,587 eco-friendly products from Amazon, employing text mining techniques to quantify "Information Granularity" (description depth) and "Material Transparency." The findings reveal a paradox: while higher prices generally diminish customer trust, this negative effect is significantly attenuated when the product page exhibits high information richness. This study contributes to the literature by demonstrating that Information Systems function as a critical validation mechanism that justifies the green premium, shifting consumer focus from cost to value.
🏷 Green Marketing, Information Systems, Customer Trust, Green Premium, E-Commerce.
Article 275 · pp. 3743-3752
Schooling Attitude and Peer-Pressure as Predictors of Examination Malpractice Tendencies in Federal Unity Colleges
This study investigated schooling attitude and peer pressure as predictors of examination malpractice tendencies in Federal Unity College. A correlational research design was adopted for the study. No sampling was done as the entire population was used. The instruments used for the study are Schooling Attitude on Examination Malpractice Scale (SAEMS), Peer Pressure on Examination Malpractice Questionnaire (PPEMQ) and Examination Malpractice Tendency Inventory (EMTI). The instruments were face-validated using three experts and construct validated using factor analysis. The reliability indices of the instruments were ascertained to be 0.87, 0.87 and 0.90 using Cronbach alpha method for SAEMS, PPEMQ and EMTI, respectively. Linear and Multiple regression were used to answer the research questions using coefficient of determination (R2) value while the hypotheses were tested using ANOVA (regression output) at 0.05 level of significance. The result shows that schooling attitude and peer pressure are significant predictors of students` examination malpractice tendencies. The result equally shows that schooling attitude and peer pressure have a significant joint predictive power (80%, F= 918.020, p<0.05) on students` examination malpractice tendencies. The study recommends among others that workshops, seminars, conferences and sensitization trainings on the danger of examination malpractice tendencies should be organized for parents, teachers, students and the general public by the government in collaboration with examination bodies and educational stakeholders in order to create awareness on the menaces and dangers of examination malpractice tendencies. This could help reduce examination malpractice.
🏷 Schooling attitude, peer-pressure, examination malpractice tendencies, stakeholders
Article 276 · pp. 3753-3756
The Influence of Cultural Congruence on Brand Trust and Purchase Intention in Nigeria According to Hofstede’s Cultural Dimensions: An Empirical Analysis of Telecommunication Giants (MTN, Glo, and Airtel) Advertising Campaigns, 2023–2025
This study investigates the associations between perceived cultural congruence in advertising and brand trust/purchase intention among Nigerian telecommunication consumers, framed by Hofstede’s cultural dimensions. A cross-sectional survey of 614 active subscribers evaluated MTN, Glo, and Airtel campaigns from 2023–2025. Cultural congruence was operationalized along collectivism, power distance, and uncertainty avoidance. Hierarchical regression and bootstrap mediation analysis (PROCESS Model 4, N = 1,842 brand evaluations) showed that the three dimensions significantly predicted brand trust (ΔR² = .47), with uncertainty avoidance as the strongest predictor (β = .34), followed by collectivism (β = .28) and power distance (β = .24). Brand trust fully mediated relationships with purchase intention. The prominence of uncertainty avoidance congruence highlights the importance of reliability appeals in a sector marked by network challenges. Findings support glocalization strategies in emerging African markets while noting limitations in generalizability.
🏷 Cultural congruence, Hofstede’s cultural dimensions, brand trust, purchase intention, telecommunication advertising, Nigeria, glocalization, emerging markets
Article 277 · pp. 3757-3768
Optimized and Explainable Air Quality Index Classification System
Air pollution has become a major environmental and public health concern due to rapid urbanization, industrial growth, and increasing vehicular emissions. High concentrations of pollutants such as PM2.5, PM10, NO₂, SO₂, CO, and O₃ can significantly impact human health and environmental sustainability. Accurate monitoring and prediction of air quality are therefore essential for effective environmental management and public safety. This paper presents AirAware, a machine learning–based system designed to predict and monitor Air Quality Index (AQI) levels using historical air pollution data and real-time environmental information. The system utilizes the XGBoost algorithm to analyze pollutant parameters and generate accurate AQI predictions and classifications. Data preprocessing techniques such as cleaning, normalization, and SMOTE-based class balancing are applied to improve model performance and ensure reliable predictions across different AQI categories. In addition, the system integrates real-time air pollution data through the OpenWeather API, enabling continuous monitoring of current environmental conditions. The predicted AQI values and pollution trends are displayed through a web-based dashboard, allowing users to visualize air quality patterns and compare real-time data with machine learning predictions. By combining machine learning techniques with real-time data integration, the proposed system provides an effective solution for air quality prediction, monitoring, and environmental awareness.
🏷 Air Quality Index, XGBoost, Explainable AI, SHAP, SMOTE, Machine Learning.
Article 278 · pp. 3769-3783
Analysis of the Effect of Celebrity Endorsement, Brand Image, and Product Quality on the Purchase Decision of Make Up Products with Purchase Intention as an Intervening Variable
The beauty industry in Indonesia has been growing rapidly along with the increasing awareness among women of the importance of self-care and appearance. This condition encourages companies to compete through product innovation in order to meet diverse consumer preferences. This study aims to analyze the influence of celebrity endorsement, brand image, and product quality on purchasing decisions of Make Over products with purchase intention as an intervening variable. Primary data were collected through questionnaires from 200 respondents, using a non-probability sampling method with purposive sampling technique. Data analysis in this study was carried out using the Structural Equation Modelling (SEM) method based on Partial Least Square (PLS), with SMARTPLS 4.0 as the testing tool. The testing stages included: validity test (convergent validity, discriminant validity, average variance extracted), realibility test (cronbach’s alpha and composite reliability), R square, model fit, and hypothesis testing (path coefficient and specific indirect effect). The results of the study show that celebrity endorsement and product quality influence purchase intention of Make Over products, while brand image does not influence purchase intention. Celebrity endorsement does not influence purchasing decisions, whereas brand image and product quality do. Celebrity endorsement and product quality influence purchasing desicions through purchase intention as an intervening variable, while brand image does not influence purchasing decisions through purchase intention as an intervening variable.
🏷 Celebrity Endorsement, Brand Image, Purchase Intention, Purchase Decision, Product Quality.
Article 279 · pp. 3784-3797
Effects of Neuromuscular Control Training and Proprioceptive Exercise on Pain, Pelvic Stability, and Functional Outcomes in Patients with Sacroiliac Joint Dysfunction
Objective: Evaluate the efficacy of neuromuscular control training combined with proprioceptive exercises on pain reduction, pelvic stability, and functional outcomes in patients with sacroiliac joint (SIJ) dysfunction compared to standard care.
Methods: A prospective, randomized controlled trial with 124 participants (intervention n=62, control n=62) conducted over 12 weeks at three academic medical centers. Intervention group received 2-3 supervised sessions per week of structured neuromuscular training targeting core stabilizers and proprioceptive exercises. Control group received standard care consisting of patient education and general exercise. Primary outcomes assessed at baseline, weeks 4, 8, 12, and 24: Visual Analog Scale (VAS) pain (0-10), Posterior Superior Iliac Spine Distance Ratio (PSIS-DR) for pelvic stability assessment, and Lower Extremity Functional Scale (LEFS, 0-80 points). Secondary outcomes included Oswestry Disability Index (ODI), Short Form-36 (SF-36) quality of life measures, proprioceptive function, and core muscular strength.
Results: Intention-to-treat analysis of 121 participants (97.6% retention). Intervention group demonstrated statistically significant and clinically meaningful improvements across all primary outcomes. VAS pain reduction: 5.2±1.8 points (77% improvement) in intervention versus 1.3±1.5 points (19% improvement) in controls (p<0.001, 95% CI: 3.1-4.9). Pelvic stability improvement: 3.4±1.1 mm (41.5% reduction in asymmetry) in intervention versus 0.6±0.8 mm (7.1% reduction) in controls (p<0.001, 95% CI: 2.4-4.4). LEFS functional improvement: 28.5±6.7 points (88% recovery) in intervention versus 7.2±5.3 points (23% recovery) in controls (p<0.001, 95% CI: 18.3-23.8). Large effect sizes (Cohen's d) demonstrated for all primary outcomes (d=1.8-2.6). Benefits sustained at 6-month follow-up with minimal regression. 68% of intervention participants achieved clinically normal pelvic alignment versus 11% in controls (p<0.001). Session adherence: 95.2% (34.2±2.1 of 36 prescribed sessions). Home exercise compliance: 78.4%. Adverse events: no serious adverse events; mild self-limiting muscle soreness in 30% of intervention participants during week 1-2.
Conclusion: Structured neuromuscular control training combined with systematic proprioceptive exercise is highly effective for managing sacroiliac joint dysfunction, providing substantial and durable improvements in pain, pelvic stability, and functional capacity. This conservative intervention demonstrated superior efficacy compared to standard care with excellent safety profile and high patient adherence. Evidence supports position as first-line management for SIJ dysfunction.
🏷 Sacroiliac joint dysfunction, neuromuscular control, proprioceptive training, core stability, pelvic stability, conservative management, rehabilitation, randomized controlled trial
Article 280 · pp. 3798-3808
The Barriers to the Adoption of Emerging Technologies in the Apparel Manufacturing Industry: A Dynamic Framework Analysis Focused on India
Despite the transformative potential of advanced technologies—such as Artificial Intelligence (AI), soft robotics, and mandated Digital Product Passports (DPPs)—the Indian apparel manufacturing sector exhibits a critical lag in achieving comprehensive, integrated digitalization. While end-to-end digital transformation is projected to shorten the production timeline by up to 40%, implementation is hampered by systemic, multi-dimensional barriers. Analysis reveals these constraints include acute data fragmentation coupled with poor IT infrastructure; severe financial stress resulting from high implementation costs with uncertain Return on Investment (ROI); and the persistent technical difficulty of automating the handling of limp, deformable fabrics. Furthermore, India-specific challenges like cyber security risk are dominant cause barriers to Industry 4.0 adoption. This report addresses this pervasive adoption gap by analyzing constraints across four integrated dimensions: Technological, Organizational, Environmental, and Dynamics (post-adoption processes). Existing models, notably the Technology-Organization-Environment (TOE) framework, are deemed insufficient as they fail to account for the unique material-specific challenges and provide limited consideration of dynamic adoption processes. This research introduces the Dynamic Apparel Technology Adoption and Supply Chain Resilience (DATAR) Framework to model the actual assimilation process necessary for industry modernization and strategic resilience.
🏷 Adoption, Emerging, Technologies
Article 281 · pp. 3809-3819
Adherence, Engagement and Participant Experiences in a Workplace Rajyoga Meditation Programme: Findings from an Eight-Week Intervention Study Among Corporate Executives
Background: Adherence and participant engagement are important determinants of the success of Adherence, Engagement and Participant Experiences in a Workplace Rajyoga Meditation Programme: Findings from an Eight-Week Intervention Study Among Corporate Executives
workplace meditation interventions. Although meditation has been widely investigated for its psychological and organisational benefits, comparatively few studies have examined attendance, practice engagement and participant experiences during programme implementation. Understanding these process-related outcomes is essential for evaluating the feasibility and acceptability of workplace meditation interventions.
Methods: This study was conducted as a secondary analysis of process-related outcomes from a larger experimental pre-test–post-test control group study among corporate executives working in the Environment, Health and Safety (EHS) function of a large agrochemical organisation in India. Ninety-four participants were randomly assigned to either a Rajyoga meditation group (n = 46) or an active control group receiving physical relaxation commentary (n = 48). Participants attended guided online sessions on weekdays for eight weeks. Attendance records, self-reported daily practice logs and participant feedback questionnaires were analysed using descriptive statistics.
Results: Participants in the Rajyoga meditation group demonstrated higher attendance and engagement than those in the control group. Mean attendance was 64.95% in the Rajyoga group compared with 55.73% in the control group, while 60.9% and 31.3% of participants, respectively, met the predefined adherence criterion of attending at least 70% of sessions. Rajyoga participants also reported greater self-practice engagement, higher numbers of good-quality and distraction-free practice sessions, and more favourable feedback regarding emotional regulation, concentration, confidence in decision-making and overall programme benefit.
Conclusion: The findings indicate that workplace Rajyoga meditation is a feasible and well-accepted intervention for corporate executives. Higher adherence, greater engagement and positive participant experiences suggest that Rajyoga meditation can be successfully integrated into organisational wellness programmes. Future research should investigate factors influencing long-term adherence and sustained practice across diverse workplace settings.
🏷 Rajyoga meditation; workplace wellness; corporate executives; adherence; participant engagement; feasibility.
Article 282 · pp. 3820-3838
Impact of Petroleum Subsidy Removal on Health Care Sector in Nasarawa State Nigeria.
The study examined the impact of petroleum subsidy removal on the healthcare sector in Nasarawa State, Nigeria. The broad objective of the study is to investigate the impact of petroleum subsidy removal on public health care delivery in Nasarawa State Nigeria. The study adopted a survey research design, both primary and secondary data were used for the study. The population of 2,520 was used from which the sample size of 345 was determined using Taro Yamane formular. Logistic multiple regression analysis (logit regression model) was employed to examine the relationship between the dependent and independent variables. The result revealed that increased fuel prices have led to higher transportation costs, reduced access to medical services especially in rural areas and a rise in operational expenses for hospitals and clinics. The study also revealed that the positive and significant outcome for patients’ accessibility (p = 0.0026) suggests that as transportation fares rise, patients find it increasingly difficult to visit healthcare facility. Therefore, the study recommended that the federal and state governments introduce targeted subsidies or grants specifically for the health sector to ensure a continuous supply of essential drugs, medical consumables, and equipment in public hospitals despite the rising cost of energy and logistics. It also recommended that the government, in collaboration with local transport unions, implement subsidized transport schemes or mobile health outreach programs in rural and semi-urban areas to improve patient access to health facilities. The study concludes that while subsidy removal may promote long-term fiscal sustainability, its short-term adverse effects on the healthcare sector are significant.
🏷 Health Care Delivery, Patients’ Accessibility, Low-income Household Accessibility, Health Workers Response, Availability of Essential Medical Services.
Article 283 · pp. 3839-3851
Autonomous Rover for Medical Requirements with Web-based Geospatial Path Planning using GPS
The Autonomous Rover for Medical Requirements with Web-Based Geospatial Path Planning Using GPS is an intelligent robotic system designed to automate the transportation of medicines, medical equipment, and emergency healthcare supplies. The project aims to improve the efficiency, accuracy, and safety of medical logistics while reducing human effort and delivery time. The rover utilizes Global Positioning System (GPS) technology for real-time location tracking and Web-Based Geospatial Path Planning to determine the shortest and safest route to the destination.The system consists of a microcontroller, GPS module, motor driver, DC motors, ultrasonic sensors, and a wireless communication module. A web application enables users to assign destinations, monitor the rover's movement, and track deliveries in real time. The integrated Obstacle Detection mechanism ensures safe navigation by identifying and avoiding obstacles encountered along the route. This solution is particularly beneficial for hospitals, healthcare campuses, rural medical centers, and disaster-affected areas where timely delivery of medicines is critical. By combining Robotics, Internet of Things (IoT), GPS Navigation, and Geospatial Technologies, the proposed system provides a reliable, cost-effective, and scalable platform for autonomous medical delivery. The project contributes to the advancement of Healthcare Automation, improving operational efficiency and supporting faster emergency response while minimizing manual intervention.
🏷 Global Positioning System (GPS), Web-Based Geospatial Path Planning, Robotics, Internet of Things (IoT)
Article 284 · pp. 3852-3860
Neuro-Adaptive Blended Learning in AI – Rich Environments: Research Focus, Outcomes and the Road Ahead
Right now, smart software learns as students do, shifting the balance away from rigid teaching formats. Instead of making learners fit the method, some tools adjust mid-step using brain research plus digital feedback loops. Outcomes begin standing out when classroom work blends with adaptive platforms tuned live through behaviour patterns. One analysis pulled findings from multiple experiments and big-picture summaries to track what happens behind the scenes. Results show stronger memory recall, sharper test outcomes, better mental effort control - around one-quarter to over one-third improvement across cases. Still, concerns about who owns data, upkeep expenses, fairness in automated choices pop up every time progress appears solid. Progress stalls unless tech grows hand-in-hand with proven education methods and access for all shapes of classrooms.
🏷 Blended Learning, Neuro Adaptive, Brain based learning, Artificial Intelligence, Educational technology.
Article 285 · pp. 3861-3870
Photovoltaic System Loads Analysis for Low/Middle Income Earners in Afikpo, Ebonyi State, Nigeria.
Unreliable electricity supply remains a persistent challenge in many developing countries, disproportionately affecting low- and middle-income households through frequent power outages and rising energy costs. This study assessed household electrical energy demand and determined the optimal sizing of stand-alone photovoltaic (PV) systems for low- and middle-income households in Afikpo, Ebonyi State, Nigeria.
A total of 100 households participated in the study, comprising 50 low-income households (monthly income of ₦50,000–₦150,000) and 50 middle-income households (monthly income of ₦150,000–₦500,000). A structured load survey was conducted to collect data on household appliances, including their quantities, rated power, and average daily operating hours. Daily energy demand was estimated from the load survey, and PV systems were designed using standard engineering procedures based on daily energy consumption, average peak sunshine hours, system de-rating factor, battery storage requirements, and inverter capacity. The economic viability of the proposed systems was evaluated using installation cost estimates and simple payback period analysis.
The results show that low-income households consume an average of 2.15 kWh/day and can be adequately served by a 600 W stand-alone PV system comprising two 300 W solar modules, a 48 V, 150 Ah battery bank, and a 1 kW pure sine wave inverter. Middle-income households recorded an average daily energy demand of 5.48 kWh/day, requiring a 1.5 kW PV system consisting of three 500 W solar modules, a 48 V, 300 Ah battery bank, and a 2 kW pure sine wave inverter. Economic analysis indicates that the proposed systems are financially attractive, with estimated payback periods of approximately three years, depending on household energy consumption and prevailing component costs.
The findings confirm that household income level significantly influences electricity demand and PV system sizing requirements. The study demonstrates that properly designed stand-alone photovoltaic systems provide a technically feasible, economically viable, and environmentally sustainable solution for improving residential electricity access in semi-urban Nigeria. The results provide practical design guidelines for engineers, researchers, policymakers, and energy planners involved in residential renewable energy deployment.
🏷 Photovoltaic systems, household energy demand, system sizing, payback period, rural electrification, Nigeria
Article 286 · pp. 3871-3882
The Blue-Green Infrastructure Paradigm in India: Landscape Design Proposal for transforming the Mahalaxmi Racecourse into a Resilient City Park, Mumbai
Accelerated urbanization in megacities around the world has resulted in an acute shortage of open spaces, the living infrastructure that modulates climate and creates community. Dense coastal cities in India e.g., Mumbai has experienced intentional paving over of the natural greens overtime bringing down already low open space per capita levels while increasing the risk of urban hydrological disasters. Current city stormwater systems that are designed on traditional discharge-based engineering are ill-equipped to handle the sudden surge of monsoon rainfall events. There is an urgent need to reimagine grey infrastructure in urban areas as hybrid green-blue landscapes. The objective of this paper is to provide landscape design strategies to transform Mumbai’s 120-acre Mahalaxmi Racecourse land parcel into a high performance, resilient public open space through application of the Sponge City concept. The design workflow consists of an initial bio-physical overlayed site analysis which determines that the Mahalaxmi Racecourse is a low-lying basin that currently collects runoff from surrounding urban catchment. After establishing opportunities for site-scale stormwater management beyond basic park aesthetics the project moves to formulate a design basis rooted in Nature-based Solutions (NbS) that can detain a 5-year peak rainfall event across most of the central part as marshes and ponds thereby lowering site’s current runoff coefficient of 0.85 to 0.45. Design guidelines are developed based on desired engineered landscape outcomes such as a Native Arboretum Core planned, and Riparian Bio-Filtration Spine that naturally purifies urban runoff through root-zone Phyto-treatment. Additionally, network-level design strategy such as the incorporation of an underground transit under-pass and an elevated walkway loop was proposed to overcome the sites disconnect from adjacent railway and major road grids respectively. Operational sustainability was designed into the project through implementation of a Closed-loop Stewardship Model that segments larger site into revenue-generating Active Cells that subsidize maintenance operations of less used Ecological Core Cells. The speculative Master Plan effectively unlocks the enormous potential of underutilized urban mega voids and provides a replicable template for transforming them into equitable, productive, self-sustaining infrastructure that enhances urban resilience and increases public access in cities of the Global South.
🏷 landscape stewardship, public space democratization, stormwater management, urban resilience
Article 287 · pp. 3883-3893
Design and Finite Element Analysis of RCC and Timber-Composite RCC Columns with Seismic Effect
This study presents the design and finite-element analysis (FEM) of three reinforced cement concrete (RCC) column configurations under seismic effect, with the objective of quantifying how a central Sal-timber core alters the structural performance of an otherwise conventional RCC column. All three columns are of identical external size (300 × 300 mm) and clear height 3.0 m, fixed at both ends, and use M30 concrete, Fe500 steel, 4–16 mm φ longitudinal bars (steel ratio 0.89 %) and 8 mm φ ties at 150 mm c/c. The configurations are a plain RCC column (C1), and two timber-composite RCC columns with 100 × 100 mm (C2) and 150 × 150 mm (C3) Sal-timber cores. Each column is idealised as a fixed-fixed line member discretised into six Euler-Bernoulli beam elements, using transformed-section properties (modular ratios m = 7.30 for steel and n = 0.456 for timber). Axial capacity is computed per IS 456:2000, modal and elastic-buckling behaviour from the FEM stiffness, and seismic demand by the equivalent-static method of IS 1893 (Part 1):2016 for Zone V. The results show that, because the timber core sits near the neutral axis, flexural rigidity falls only 0.6 % (C2) and 3.1 % (C3) and lateral drift is practically unchanged, while self-weight drops 7 % and 16 % respectively, reducing seismic base shear and end moments by a similar margin. The principal penalty is a reduction in ultimate axial capacity of 6 % (C2) and 13 % (C3). All three columns remain short (buckling never governs), satisfy the 0.4 % drift limit, and plot safely within the P–M interaction envelope. The 100 × 100 mm timber core offers the best balance of weight and seismic-demand reduction with negligible loss of capacity.
🏷 RCC column; timber-composite column; Sal-timber core; finite element analysis; seismic effect; transformed section; IS 1893; P–M interaction.
Article 288 · pp. 3894-3908
Prediction of Sustainable Concrete Strength Incorporating Fly Ash and Recycled Aggregates Using Artificial Neural Networks
Artificial Neural Networks (ANNs) have been used to predict the compressive strength of sustainable concrete as an alternative to large laboratory experiments․ Sustainable concrete mixes were designed by replacing natural coarse aggregate (NCA) with recycled coarse aggregate (RCA) at varying percentages ranging from 0% to 100%․ Ordinary Portland cement was replaced with fly ash at the rate of 0%‚ 5%‚ 10%‚ 15%‚ 20%‚ 25%‚ 30%‚ 35% and 40% to obtain samples․ Twenty-five concrete mixes were tested for compressive strength at 3‚ 14 and 28 days․
In contrast‚ for moderate fly ash replacement levels (10-20 percent)‚ strength increased at later ages due to pozzolanic reactions․ At high fly ash replacement levels‚ strength development was delayed during the curing period․ Increasing RCA content decreased compressive strength because of the presence of unhydrated mortar‚ greater porosity‚ lower bonding between the aggregate and paste matrix‚ and greater water absorption compared to normal aggregate․
An ANN model has been developed to predict the concrete strength‚ using five parameters as input (NCA content‚ RCA content‚ cement content‚ fly ash content and curing age) and the concrete compressive strength as an output parameter․ Based on the seventy-five experimental results‚ the developed model is highly accurate‚ with R²-value of 0․9935 and RMSE (Root Mean Square Error) of 0․6465 MPa․ These results show that the ANN can successfully predict the sustainable concrete compressive strength․ The proposed model is very useful for mix proportion optimization with considerable reductions in the time‚ cost‚ and effort of experimental studies․
🏷 Artificial Neural Network (ANN), Sustainable Concrete, Recycled Coarse Aggregate, Fly Ash and Compressive Strength
Article 289 · pp. 3909-3916
Tourism Marketing Strategies and Destination Branding in Southern West Bengal: A Comparative Analysis of Kolkata, South 24 Parganas, Bankura and Purulia.
Tourism marketing in emerging regional destinations demands more than generic promotional effort it requires an understanding of the unique competitive landscape, cultural assets, and infrastructure realities of each locale. This paper examines tourism marketing strategies and destination branding across four southern districts of West Bengal, India: Kolkata, South 24 Parganas, Bankura, and Purulia. Drawing on a comprehensive review of secondary data, district-level SWOT analyses, and an empirical tourist survey (n = 600), the study assesses how current marketing strategies perform across the extended 7Ps marketing mix and identifies critical gaps in branding, digital communication, and institutional coordination. Findings reveal that while Kolkata operates as a mature, multi-channel tourism destination, the remaining three districts despite possessing internationally significant cultural and natural assets suffer from weak digital presence, fragmented promotion, and inadequate destination branding. The study proposes a differentiated, district-specific branding framework grounded in each destination's Unique Selling Proposition (USP), and recommends strategic investments in digital transformation, community-based marketing, and cross-district tourism circuits. The research contributes empirically to destination marketing literature in the context of an emerging Indian tourism region and offers practical guidance for tourism planners, destination management organizations (DMOs), and policy-makers.
🏷 Destination Branding, Tourism Marketing, West Bengal, 7ps Marketing Mix, Digital Tourism, Community-Based Tourism, Swot Analysis
Article 290 · pp. 3917-3930
Toward a Regulated Future: Assessing Management of Zimbabwe's Informal Pharmaceutical Market
The study investigated the drivers of Zimbabwe's informal pharmaceutical market, focusing on its sources, distribution, and public health impact in Harare's Central Business District. This market raises significant concerns about medicinal drug quality and safety, contributing to antimicrobial resistance and undermining legitimate pharmaceutical businesses. The study employed a sequential explanatory mixed-methods design, collecting data from 51 participants, including consumers, vendors, regulators, and distributors, through questionnaires. The findings indicate that unregistered vendors source medicinal drugs primarily from locals and international suppliers. The distribution occurs directly to consumers through sachets at transport hubs, from car boots and street corners. Despite the perception among sellers and customers that these medicinal drugs are benign unless abused, the study highlights an urgent need for intervention. The study recommended policy actions, including public awareness campaigns on the dangers of unregulated medicinal drugs, strengthening border screening, and the implementation of a national health insurance policy to improve health delivery and outcomes in Zimbabwe. The study suggested an Informal Pharmaceutical Market Elimination Framework to protect consumers from unregulated medicinal products and promote the formal pharmaceutical market. The framework is legitimacy-sensitive and synchronous digital hierarchy–driven, escalates punishment for corrupt actors, and targets all involved stakeholders.
🏷 Informal Pharmaceutical Market, Unregulated Medicinal Drugs, Social Determinants of Health (SDOH), Social Network Analysis (SNA), Balloon-Squeeze Concept
Article 291 · pp. 3931-3943
Design and Development of a Web-Based Form Builder Application for Form Data Management and Processing at the Tasikmalaya City Department of Communication and Informatics
The use of digital forms at the Tasikmalaya City Department of Communication and Informatics was distributed across different platforms, resulting in decentralized data management and inefficient manual recapitulation. This study aimed to design and develop a web-based form builder application that supports centralized form creation, response collection, data management, and administrative reporting. The system was developed using the Waterfall model, consisting of problem identification and data collection, requirements analysis, system design, implementation, testing, and maintenance. Data were collected through observation, questionnaire-based needs elicitation, literature review, and documentation. The developed application provides authentication, dashboards, survey statistics, form management, a dynamic form builder, submission management, regional government organization management, access control, database backup, help documentation, and data export and import. The dynamic form builder enables authorized users to construct forms using configurable input components, including text, numerical, date, selection, checkbox, and file-upload fields, without developing a separate application for each data-collection requirement. Functional evaluation was conducted using Black Box Testing across 20 scenarios covering authentication, dashboards, form construction, field configuration, input validation, response submission, data management, access control, backup, export, import, and logout. All tested functions produced outputs consistent with the predefined requirements and were classified as passed. Preliminary User Acceptance Testing involving ten participants using super-admin access produced an overall mean score of 4.15 out of 5, equivalent to 83.0% of the maximum possible score, with a 100% positive-response rate. These findings indicate that the application successfully integrates the principal stages of form-data management within a single web-based platform and received a high level of initial user acceptance. However, further evaluation involving additional user roles, security testing, performance testing, and scalability assessment is required.
🏷 Dynamic Form Builder, Web-Based Application, Form Management, Black Box Testing, User Acceptance Testing
Article 292 · pp. 3944-3951
Web-Based Boarding House Management Information System with Dashboard Analytics and Multiple Linear Regression for Rental Income Prediction
Small boarding-house businesses commonly rely on receipts and annual notebooks to record tenants, rental periods, payments, unit availability, and income. Such practices can support daily operations, but they make historical retrieval, operational monitoring, and decision support increasingly difficult as the number of records grows. This study develops a web-based boarding house management information system that integrates operational data management, dashboard analytics, and rental-income prediction for Surapati Boarding House in Bandung, Indonesia. The application was developed with Laravel 12, PHP, and MySQL, while the predictive model was trained offline in Python using scikit-learn. Because the original tenant and payment records contain private information, a simulated historical dataset was constructed from the property’s operational characteristics. The dataset contains 54 monthly observations from January 2022 to June 2026. The independent variables were occupied Type A units, occupied Type B units, and payment arrears, while monthly rental income was the dependent variable. After cleaning currency-formatted values and removing invalid rows, the data were divided into 43 training observations and 11 testing observations. The resulting multiple linear regression model was integrated into the Laravel analytics page through its estimated intercept and coefficients. The system provides authentication, unit and tenant master data, rental transactions, payment records, operational summaries, due-date reminders, and an analytics interface that compares actual and predicted income. Model evaluation produced an R-squared value of 95.85%, a mean absolute error of IDR 250,715.83, a root mean squared error of IDR 303,605.01, and a mean absolute percentage error of 1.39%. These results indicate close agreement between predicted and simulated actual income. However, the findings are limited to the simulated case-study dataset and require validation using anonymized real operational data before broader deployment.
🏷 boarding house management system, dashboard analytics, multiple linear regression, rental income prediction, web application
Article 293 · pp. 3952-3962
Generative AI in Art and Design Education: A Systematic Review of Tools, Pedagogy, and Collaboration
Generative AI (GenAI) is rapidly emerging as a transformative force in art and design education. It not only expands the boundaries of creative expression but is also reshaping traditional teaching models. Tools such as Midjourney, DALL·E, Stable Diffusion, Leonardo, and Firefly are now widely adopted in classrooms and studios, demonstrating formidable capabilities in image generation, visual storytelling, and creative exploration. These technologies empower students to generate high-quality visual works and design sketches at unprecedented speeds, fundamentally altering learning approaches and creative workflows.
However, despite GenAI's growing adoption, related research remains in its infancy. Existing literature predominantly focuses on technical performance or creative potential, with limited systematic comparisons and analyses across different regions, disciplines, and teaching contexts. Concurrently, practical challenges and deeper educational implications have yet to be fully explored. Therefore, comprehensive research is needed to map current developments, unlock AI's untapped potential, and address emerging challenges.
Accordingly, this study categorizes AI's role in education into three core dimensions to establish a systematic analytical framework: First, Generative Tools—emphasizing their function in creating artworks, optimizing techniques, and transforming styles; Second, Pedagogical Methods—focusing on AI's application in interactive learning and instant feedback, integrating it as a pedagogical scaffold; Third, Collaborative Partners—exploring AI's participation in artistic evaluation and its potential for real-time collaborative creation with humans. This three-dimensional framework enables a more comprehensive understanding of generative AI's multifaceted roles and value within higher education in art and design.
🏷 Generative AI, Art, Education
Article 294 · pp. 3963-3965
“Enhancing Fraud Detection in Core Banking Systems Through Risk-Based Internal Controls and Forensic Auditing”
The proliferation of Core Banking Systems (CBS), digital banking, cloud computing and real-time payment platforms has made financial fraud more sophisticated. Current fraud detection techniques are not effective anymore against new cyber assaults and complex financial crimes. This paper discusses the role of forensic auditing, risk-based internal controls and emerging technologies in enhancing fraud prevention in commercial banks. It discusses the application of forensic auditing with a combination of digital forensics, artificial intelligence (AI), machine learning, behavioural analytics, blockchain, and continuous auditing to improve fraud detection, fraud investigation, and regulatory compliance. The study also discusses contemporary issues in banking fraud such as insider threats, data privacy, legacy system integration, and regulatory requirements and suggests strategic measures to enhance fraud resilience. The findings indicate that the implementation of intelligent technologies with strong governance and forensic auditing mechanisms enable proactive fraud management, strengthen operational resilience, protect customer assets, and build trust in digital banking ecosystems.
🏷 Core Banking Systems (CBS), Forensic Auditing, Financial Fraud Detection, Artificial Intelligence (AI), Blockchain Technology
Article 295 · pp. 3966-3986
Soil-to-Plant Transfer of Naturally Occurring Radionuclides in West Africa: Mechanisms, Transfer Factors, Food-Chain Contamination, And Human Health Implications
Naturally occurring radionuclides (NORMs), including uranium-238 (238U), thorium-232 (232Th), radium-226 (226Ra), and potassium-40 (40K), are widely found in terrestrial ecosystems and are a significant source of environmental radiation exposure. Their movement from soil to plants is a crucial process through which radionuclides enter the food chain, leading to internal radiation doses in humans. This review summarizes the current understanding of the environmental distribution, mechanisms of soil-to-plant transfer, transfer factors, and the radiological consequences of naturally occurring radionuclides in West Africa Peer-reviewed papers from major scientific databases were used to undertake a structured literature review, with a focus on studies reporting radiological risk assessments, soil-to-plant transfer factors, and radioactive activity concentrations. The research that is now available suggests that a variety of factors, including soil physicochemical features, radionuclide speciation, plant traits, climate, and agricultural methods, influence the uptake of radionuclides by plants. Due to its vital biological role, potassium-40 often demonstrated the largest transfer to crops, while radionuclides from the uranium and thorium family showed relatively lesser mobility but were nonetheless significant contributors to long-term internal radiation exposure. The analysis also shows that while quantitative studies on soil-to-plant transfer are still scarce and spatially unequal, environmental radioactivity evaluations dominate research in West Africa. The majority of published studies are from Nigeria and Ghana, however there are still significant knowledge gaps in many other West African nations. Regional radiological risk assessment is made questionable by the absence of long-term environmental monitoring, locally determined transfer factor databases, and standardized procedures. Harmonized radioecological studies, the creation of region-specific transfer factors for important staple crops, integrated food-chain studies, and ongoing environmental monitoring should be the top priorities for future study. By filling in these gaps, West African decision-making on public health, food safety, and environmental radiation protection will be strengthened and exposure assessment accuracy will increase.
🏷 Naturally occurring radionuclides; Soil-to-plant transfer; Transfer factor; Environmental radioactivity; Food chain; Radiological risk; West Africa.
Article 296 · pp. 3987-4003
Cadmium Chloride-Induced Hepatotoxicity: Molecular Mechanisms, Oxidative Stress, and Histopathological Alterations—A Comprehensive Review
Cadmium chloride (CdCl) is a highly dangerous heavy metal commonly used in electroplating, pigment production, battery manufacturing, plastic stabilization, and other laboratory settings. Due to the speed of industrial growth and human activities, cadmium levels in air, water, and soil have gone up dramatically, This way the compound is a leading reason for environmental and occupational contamination of health. Essential trace elements are those the body relies on but with cadmium, the body does not. In fact, its biological half-life is up to forty years, making the chemical progressively accumulate in tissues. Although the kidney has long been known as the prime organ involved in chronic lead poisoning, cadmium absorption actually causes rapid damage to the liver due to this organ's primary role in metabolism and detoxification of foreign substances (xenobiotics). CdCl liver accumulation initiates many pathological responses including overproduction of reactive oxygen species (ROS), exhaustion of the cell's antioxidant systems, mitochondrial malfunctions, release of inflammatory cytokines, cell death, and fibrosis. At a larger level of tissues and functions, all these changes cause disturbances in liver structure and function leading to hepatocellular injury. Oxidative stress and disruption of calcium homeostasis are just two mechanisms of cadmium toxicity besides other effects like antioxidant enzyme inhibition, DNA and membrane (lipid) damage, and activation of several intracellular signaling pathways such as NF-B, MAPK, and Nrf2. From a histological standpoint, changes include liver cell (hepatocyte) alterations, widened blood sinuses, cells of the immune system coming into the tissue, blood vessels in a state of congestion, spots of necrosis (cell death), and fibrosis. From a clinical point of view, liver damage caused by CdCl is seen as an increase in the levels of some biomarkers, viz. alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), and bilirubin, as indicators of disease state changes and alteration of enzyme activities for antioxidant defense (e.g. glutathione peroxidase).The article reviews the present state of knowledge on cadmium environmental exposure sources, toxicokinetic pattern of CdCl, mechanisms responsible for cadmium-induced liver damage, and the corresponding histopathological and biochemical manifestations. Comprehending the molecular events involved is a crucial step toward the designing of strategies aimed at preventing cadmium-induced liver injuries identification of biological indicators of exposure, and formulation of new ways of treatment that will lessen liver damage caused by cadmium exposure.
🏷 Apoptosis, Cadmium chloride, Hepatotoxicity, Oxidative stress, Liver injury, Reactive oxygen species, Toxicokinetics, Heavy metals, Histopathology, Inflammation
Article 297 · pp. 4313-3428
Influence of Marketing Strategies on Business Performance of Selected Small and Medium Enterprises in Abeokuta Metropolis, Ogun State, Nigeria
Marketing strategy is a substantial driving force that differentiates the success of many businesses by conceptualizing and implementing a good marketing strategy with clear demarcation. This study investigates the relationship between marketing strategies and business performance, evidence from Abeokuta metropolis. The study conceptualized a model showing the relationship among product strategy, promotional strategy, pricing strategy and business performance. This study used descriptive and inferential survey research approach. The study population was the selected SMEs owners in Abeokuta metropolis, Ogun state. Data was collected from 158 respondents through a validated and reliable questionnaire. One hundred and fifty-eight (158) copies of the questionnaire were successfully retrieved and were analyzed using descriptive and inferential statistics. The findings reveal a significant positive relationship between product strategy and business performance (PC = 0.32, N = 158, p < 0.01); No significant relationship between promotional strategy and business performance (PC = 0.28, N = 158, p < 0.05); a significant positive relationship between pricing strategy and business performance (PC = 0.30, N = 158, p < 0.03); The findings also reveal that these variables (product strategy, promotional strategy and pricing strategy collectively have significant impact on business performance. Conclusively, the finding of the study shows that product strategy, promotional strategy, and pricing strategy significantly enhance business performance among SMEs in the Abeokuta metropolis. The study recommends that SME owners should use the AIDA framework to create marketing campaigns that build awareness, generate interest, foster desire, and prompt action.
🏷 Marketing strategy, product strategy, promotional strategy, pricing strategy, business performance, AIDA.
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