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Articles
Article 1 · pp. 1-16
Block-Based Programming for Education: A Comprehensive Analysis of Visual Programming Environments in K-12 Learning
The use of block-based programming software has a major impact on education, particularly those that are stuck in the early education stage, such as children and beginners (i.e., “New To Programming”). In this paper, we look at how effective these Toolkits for teaching computing are in many different teaching moments if they are created using drag-and-drop Graphic Programming Interfaces with code blocks that interlock together (Just Think of a Jigsaw Puzzle). We will provide examples from many different software platforms and review the current research available that has been published over the last couple of years regarding the benefits of Visual Programming Languages in relation to Thinking Critically About and Creating Computer Programs and Applying Concepts of Implementation in Your Everyday Life. The research used for this paper included a sample size of over 500 students from multiple school districts across Canada and out of these, we found considerable growth in students’ ability to solve problems and their ability to think logically and their confidence to create an original program (i.e., writing/coding itself). Block-based programming should be used as a bridge or scaffold between computational Thinking and real-world application, therefore providing students access + engagement + success in learning to Program and grow their skills in Computer Science Histories.
🏷 block-based programming, visual programming, computational thinking, educational technology, Scratch, programming pedagogy
Article 2 · pp. 17-25
Management of Academic Libraries and Client Satisfaction Towards Digital Utilization: Basis for Monitoring Library Operations in SOCCSKSARGEN Region.
This study assessed the management of academic libraries and client satisfaction toward digital utilization as a basis for monitoring library operations in the SOCCSKSARGEN Region. A total of 144 respondents from six academic institutions—Mindanao State University, Sultan Kudarat State University, University of Southern Mindanao, Glan Institute of Technology, Malapatan College of Science and Technology, and Makilala Institute of Science and Technology participated in the study. Employing a descriptive-correlational design and guided by CHED Memorandum Order No. 22, series of 2021, the research focused on evaluating the effectiveness of academic library management and its correlation with digital service satisfaction.
Findings revealed a high extent of academic library management (M = 3.99, SD = 0.97), particularly in the area of library personnel (M = 4.33), indicating strong institutional support and staff competence. Client satisfaction with digital utilization was also high (M = 3.73, SD = 1.10), with perceived usefulness scoring the highest (M = 3.74), suggesting that digital resources are considered valuable in supporting academic work. A statistically significant and strong positive correlation (r = 0.868, p < 0.05) was found between library management and client satisfaction, highlighting the importance of effective library operations in enhancing user experiences.
While respondent profiles such as age, gender, type of institution, and job position did not significantly influence perceptions of library management (p = 0.080), these factors were found to significantly influence perceptions of client satisfaction toward digital utilization. A major issue identified was weak technological infrastructure, particularly internet and power connectivity, which hinders full digital transformation.
The study concludes that strong academic library management is essential to enhancing digital utilization and user satisfaction. However, addressing technological barriers remains a critical concern for improving library services in the region.
🏷 Academic library management; client satisfaction; digital utilization; digital services; user satisfaction; technological infrastructure
Article 3 · pp. 26-37
Revenue Leakages in TPA Insurance Claims and Corporate Claims: An Institutional Overview of Aster Prime Hospital, Hyderabad
In the contemporary healthcare landscape, efficient revenue cycle management has emerged as a critical determinant of institutional sustainability, particularly in hospitals dependent on Third Party Administrator (TPA) and corporate insurance claims. This paper presents an institutional overview of revenue leakages within such claim processes at Aster Prime Hospital, Hyderabad. Functioning under the aegis of Aster DM Healthcare, the hospital represents a well-established multi-specialty healthcare provider with advanced clinical and diagnostic capabilities. While clinical excellence remains a core strength, the complexity of insurance-based reimbursement systems introduces multiple administrative challenges. This study contextualizes the operational environment, focusing on the structural and procedural dimensions of TPA and corporate claims management. It aims to lay the groundwork for subsequent analytical exploration by outlining institutional characteristics, service scope, and the relevance of streamlined claim processing mechanisms in minimizing potential revenue inefficiencies.
🏷 Revenue Cycle Management, TPA Insurance, Corporate Claims, Healthcare Finance, Hospital Administration, Insurance Billing Systems
Article 4 · pp. 38-43
Technology and Innovation in Hospitality and Tourism: A Management Perspective
The hospitality and tourism industry has undergone a profound transformation due to rapid technological advancements and continuous innovation. This paper examines the role of technology in reshaping management practices within the sector, focusing on operational efficiency, customer experience, strategic decision-making, and competitive advantage.
Drawing on recent literature and industry insights, the study analyzes key technological trends such as artificial intelligence (AI), the Internet of Things (IoT), blockchain, big data analytics, and virtual reality (VR). It further explores the managerial implications of these technologies, challenges in adoption, and future prospects. The findings suggest that successful integration of technology into management strategies significantly enhances organizational performance and sustainability in the hospitality and tourism industry.
🏷 Hospitality Management, Tourism Innovation, Artificial Intelligence, Smart Tourism, Digital Transformation, Strategic Management
Article 5 · pp. 44-55
Geospatial Distribution of Tarok Sacred Grove of Langtang North and Langtang South Local Government Areas
This study examines the spatial distribution of Tarok sacred groves in Langtang North and South Local Government Areas of Plateau State, Nigeria, with a view to understanding their distribution patterns, ownership, and conservation significance. Sacred Groves are culturally protected forest patches that serve as important reservoirs of biodiversity and traditional heritage. However, increasing anthropogenic pressures are undermining the ecological and cultural sustainability of these Groves.
The study utilized geospatial data comprising 531 Sacred Grove locations collected as point data, with associated attributes such as coordinates and individual grove areas in hectares. Descriptive statistical analysis was employed to examine the size characteristics of the groves, while Geographic Information System (GIS) techniques were used to map their spatial distribution and develop a geodatabase. Findings revealed that the Tarok Sacred Groves are generally small in size, with a mean area of 0.856 hectares, indicating fragmentation. The spatial distribution pattern is clustered rather than random, reflecting the influence of cultural practices, settlement patterns, and environmental factors.
Although ownership data were not explicitly available in the primary dataset, limiting the empirical depth of custodianship analysis, existing literature suggests that Sacred Groves are commonly owned by communities, families, or traditional religious institutions. The study highlights the ecological and cultural importance of sacred groves as biodiversity refuges and indigenous conservation systems. However, they are increasingly threatened by land-use changes, agricultural expansion, climate change, and the weakening of traditional beliefs.
The study recommends proper documentation, integration into formal conservation policies, community participation, and the establishment of a comprehensive geodatabase for effective management. This research contributes to knowledge by demonstrating the application of GIS in analyzing cultural landscapes and by providing baseline data for conservation planning.
🏷 Sacred Groves, Ancestral cult, Biodiversity, Conservation, GIS, Geodatabase
Article 6 · pp. 56-71
Soret-Dufour Effect on Variable Viscosity MHD Flow Through Porous Medium
In this paper we have explored the Soret and Dufour effects on unsteady viscous incompressible and electrically conducting MHD flow through porous medium past an inclined infinite plate with variable viscosity fluid and periodic suction or injection of fluid through the plate with exponential increment in temperature and exponentially decrement in concentration. The developed or governing physical model of the flow field has been converted into the mathematical model. Then mathematical model of system of equations is represented by set of governing non linear partial differential equations which have been non dimensionalized by the help of non dimensional numbers. The associated dimensionless governing equation of flow field is solved numerically by Crank Nicolson finite difference implicit method for different values of governing flow parameters. The effects of various responsible flow parameters have been discussed through graphs and table. The effects and their numerical corresponding variations have been concord with physical nature of the problem. The velocity profile, concentration profile and temperature profile are shown through graphs for different values of flow parameters. The obtained numerical values of the skin friction, Nusselt number and Sherwood number with their variations for different values of flow parameters have been tabulated.
🏷 Soret-Dufour effects, variable viscosity, Periodic Suction or Injection, Crank Nicolson finite difference implicit method.
Article 7 · pp. 72-87
Relationship between Population Concentration and Mixed Land Uses in Calabar Metropolis, Nigeria
This study examined the relationship between population concentration and mixed land uses in Calabar Metropolis, Nigeria. The study aimed at establishing a linkage between the two variables, whether population concentration is a causal factor contributing to the development of mixed land uses in Calabar Metropolis. Map analysis with the instrumentality of the linear planimeter was used to gather quantitative data matched with census data. Both qualitative and quantitative approaches were combined in the analysis. Qualitative analysis was carried out for description while quantitative analysis was used in testing the formulated null hypothesis that; there is no significant relationship between population concentration and the areal extent (m2) of mixed land uses within the neighbourhood zones of Calabar Metropolis. This was tested with the linear regression analysis and the result showed a no statistically significant relationship with the overall model fit of R2 = 0.126 which was very low. This proved that development of mixed land uses is not caused by population concentration and therefore, suggests a further re-examination of the variables.
🏷 Areal extent, Likert scale, Linear regression, Map analysis, Neigbourhood zones, Population concentration.
Article 8 · pp. 88-97
Annapurna: An Intelligent Food Donation System with AI-Based Receiver Recommendation
Food wastage and hunger imbalance is a major societal issue that requires efficient technological solutions. This paper presents “Annapurna”, a food donation system that connects donors, receivers, and volunteers through a web and mobile platform using React.js, Android Studio, Node.js, Express.js, and MongoDB.
The system allows donors to create donation requests and receivers to request food, while volunteers manage the matching process. An AI-based recommendation module suggests suitable receivers based on location, urgency, and quantity, improving matching efficiency.
The proposed system enhances coordination, reduces food wastage, and improves resource distribution compared to traditional manual approaches.
🏷 Food Donation System, MERN Stack, Android Application, AI-Based Recommendation, Resource Matching, Volunteer Management, REST API, MongoDB, Node.js, React.js.
Article 9 · pp. 98-108
Study of Cost-Benefit Ratio Analysis of Rooftop Farming Vegetables Cultivated Plants
Urban agriculture rapidly grown vegetables on rooftop farming in. It is meeting to daily needs for urban people. Vegetable cultivation technique on rooftop is the passion of urbanize. Vegetable cultivation is closely related with rooftop farming. Almost all variety of vegetable are grown on the rooftop farming in seasonal basis. Proper planning and management give more profited .it is more essential for the urban people because the organic vegetable grown in the Rooftop farming by utilization of compost vermin-compost coco peats and organic fertilizers. The method of study carried out by experimental as well as survey methods. I chosen site of study is Bhubaneswar because of large numbers of farmer cultivate vegetables on rooftop in India..Study of cost-benefit ratio analysis of rooftop farming is essential for the vegetable cultivation. The main objectives to analysis of economical benefits of vegetables cultivation on rooftop farming. This benefits are saves to expenses of urban people. Profit making self managing cultivation of vegetable on rooftop need to urban people. They obtain fresh organic vegetables on their own rooftop in marginal expenses. The percentage of profit is enhancing the perception of rooftop farming among urban people.
🏷 Lecturer in Botany Department of Botany, Panchayat Degree College, Daramgarh,Maa Manikeswari University, Kalahandi, Odisha,India
Article 10 · pp. 109-118
Design, Fabrication and Performance Evaluation of a Low-Cost Rod Twisting Machine.
This study presents the design, fabrication, and performance evaluation of a low-cost rod twisting machine using locally available materials. The machine was developed to improve productivity, consistency, and efficiency compared to manual twisting methods. Mild steel rods of diameters 6 mm to 16 mm (500 mm length) were tested under identical operating conditions. The required torque was determined using torsion theory, and a 3 kW electric motor was selected based on power requirements. Experimental results showed that twisting time increased from 7.7 s to 26.0 s with increasing rod diameter, the number of twists decreased from 9.67 to 4.67. Efficiency analysis showed that the machine operates optimally at higher loads, with a maximum theoretical efficiency of 92.53%. However practical efficiency is expected to be lower due to transmission losses. The developed machine provides a cost-effective and reliable solution suitable for small-scale metal fabrication industries.
🏷 Rod Twisting Machine, Torsional Deformation, Error Bar Analysis, Efficiency Evaluation, Torque Analysis, Power Transmission
Article 11 · pp. 119-126
Smart Sensor-Based Framework for Hygienic Waste Management Using Solar Power and Rf Communication
Efficient waste management is crucial for maintaining environmental hygiene and ensuring the good health of residents. In this article, an automated system for waste management is proposed, utilizing smart sensors to ensure hygiene, efficiency, and sustainability. According to this proposal, the proposed system will enable the touchless opening of the dustbin using PIR and IR sensors when a human being is present. Besides, there will be an automatic system for spraying sanitizers, thus ensuring better hygienic conditions during use. The data from the sensors will be processed using a signal conditioning circuit and a comparator to detect when the dustbin reaches its threshold level. As opposed to other IoT-based approaches, this approach will use RF modules for transferring data between devices, thus making an internet connection unnecessary and ensuring the timely transfer of data. Further, the suggested system will work on the basis of solar panels, which will make it eco-friendly and suitable for sustainable implementation both in rural and urban locations. The data collected from sensors will be transferred to the monitoring facility, where the full bins will be chosen for garbage disposal, which will ensure the reduction of efforts required by sanitation staff during the process of garbage disposal. Overall, the suggested solution will demonstrate.
🏷 Intelligent Waste Management, Automated Waste Management Using Sensors, PIR and Infrared Sensors
Article 12 · pp. 127-135
Automation of Pavement Material Testing Laboratory Process: Review
Pavement Materials Testing Laboratories (PMTLs) play a critical role in ensuring that pavement materials meet the required specifications to sustain traffic loads and achieve design standards. This study investigates the potential for transforming PMTL operations through the adoption of automation technologies, with a particular focus on developing-country contexts such as Papua New Guinea (PNG). A systematic review of the literature published up to 2025 was conducted using targeted keyword-search strategies to identify relevant advances in laboratory automation across multiple sectors. The review highlights significant progress driven by the integration of artificial intelligence (AI), the Internet of Things (IoT), robotics, and Laboratory Information Management Systems (LIMS). However, findings indicate that PMTLs remain largely dependent on manual processes, limiting efficiency, traceability, and data reliability. To complement the literature review, a case study was undertaken using structured questionnaires distributed to laboratory personnel across six PMTLs and to representatives from seven major road construction companies in PNG. The responses indicate strong support for the adoption of automation to improve laboratory operations. The study identifies a critical research gap in the absence of an integrated, end-to-end automated system capable of linking test requests, sample management, personnel allocation, and equipment utilisation within a unified PMTL framework. While the benefits of automation are evident, challenges related to sustainability, including funding constraints and limited resource capacity, remain significant barriers to implementation. This research contributes to the development of a conceptual foundation for PMTL automation, highlighting the need for scalable, context-specific solutions to enhance quality assurance and operational performance.
🏷 Laboratory Automation, LIMS, PMT, Laboratory Quality Assurance, ISO/IEC 17025:2017
Article 13 · pp. 136-144
Evaluation of Smart Design Strategies for Efficient Zoning and Circulation in Selected Teaching Hospitals in Lagos.
Effective zoning and circulation are critical for the functionality of teaching hospitals, especially in rapidly urbanizing cities like Lagos, Nigeria. These hospitals serve dual roles—healthcare delivery and medical education—creating complex spatial demands that require careful planning. This study examines how smart design strategies can improve zoning and circulation efficiency in selected teaching hospitals in Lagos using a qualitative approach based on literature and institutional reviews.
Findings reveal that many existing hospital layouts are characterized by fragmented functional integration, overlapping circulation paths, congestion in high-traffic areas, and poor separation between public, clinical, and academic zones. Circulation systems are often linear and lack clear hierarchy, leading to inefficiencies in movement, navigation, and wayfinding. These issues contribute to spatial disorientation among users.The study identifies smart design solutions such as evidence-based adjacency planning, structured circulation hierarchies, flexible and modular zoning, improved spatial legibility, and the use of digital simulation tools. It also emphasizes the need for context-sensitive design approaches to address challenges like high patient volumes, limited expansion space, and aging infrastructure.In conclusion, while current layouts remain functional, they are not optimally efficient. Integrating smart design strategies can significantly enhance operational performance, user experience, and the interaction between healthcare and education. This study provides a framework to guide architects, planners, and policymakers in improving teaching hospital design.
🏷 Teaching hospitals, Zoning, Circulation, Smart Design, Hospital Architecture, Lagos.
Article 14 · pp. 145-151
Saving Banate Bay: An Extension Project for Sustainable Protection of Coastal Ecosystems and Communities
This extension project, conducted in the coastal municipalities of Anilao, Banate, Barotac Nuevo, and Barotac Viejo in Iloilo Province, Philippines, aimed to improve solid waste management and coastal ecosystem conservation through an integrated, multi-stakeholder, and data-driven approach. Baseline surveys and ecological assessments identified knowledge gaps, pollution hotspots, and degraded habitats. Capacity-building workshops empowered policymakers, LGU staff, and communities, while community-led initiatives focused on mangrove reforestation, greenbelt protection, sustainable fishing, marine protected areas, and reef rehabilitation. Innovative waste management practices such as source segregation, composting, and plastic collection were introduced alongside multimedia awareness campaigns and participatory policy dialogues that strengthened local regulations. Monitoring showed a 15% increase in mangrove cover, early coral reef recovery, improved community participation, and adoption of sustainable behaviors. Comprehensive documentation and stakeholder engagement fostered project sustainability, presenting a replicable model that integrates ecological, social, institutional, and policy dimensions for effective coastal conservation and waste management.
🏷 Banate Bay, Coastal Ecosystems, Sustainable Protection, Community-Based Management, and Coastal Conservation
Article 15 · pp. 152-168
Collaborative Governance in Empowering Batik Lasem Creative Economy: A Community Engagement Perspective
The main goal of this community involvement event is to examine and improve how different groups work together to support the Batik Lasem creative economy. The program is designed in a way that encourages people to take part and share their ideas. It uses structured group discussions, called Focus Group Discussions (FGD), with people from local government, universities, non-profit groups, researchers, and batik makers. This method helps everyone involved to create knowledge together, talk openly, and find common problems and chances for growth within the Batik Lasem community. The results show that working together is very important for helping different groups work well together, building trust, and getting everyone on the same page for development. The program also gives useful results like creating mentorship programs for batik makers, including cultural values in local economic plans, and making a report to send to regional leaders. It also helps people understand how government systems work, build better connections between groups, and give batik makers more skills to deal with bigger economic and policy issues. Even though there are good results, there are some challenges, like the short time of the project and the need for ongoing support to keep things moving long-term. Overall, this program shows that working together can be a good way to connect cultural preservation with economic growth in creative industries that are based on heritage. It also offers a model that can be used in similar projects in other communities.
🏷 collaborative governance, creative economy, batik Lasem, community engagement, stakeholder collaboration..
Article 16 · pp. 169-173
Creativity and Teaching Effectiveness of Primary School Teachers in the District of Banate, Iloilo, Philippines
This study examined the relationship between creativity and teaching effectiveness among primary school teachers in the District of Banate, Iloilo. Anchored on Social Cognitive Theory and Constructivist Learning Theory, the study employed a descriptive-correlational mixed-methods design. A total of sixty-three teachers participated, selected through cluster sampling. Data were collected using a validated researcher-made questionnaire consisting of creativity and teaching effectiveness scales, supplemented by open-ended questions. Quantitative data were analyzed using mean, standard deviation, and Pearson correlation, while qualitative data were treated using thematic analysis.
Findings revealed that teachers demonstrated a very high level of creativity and a high level of teaching effectiveness. Furthermore, results showed a strong, positive, and significant relationship between creativity and teaching effectiveness. These findings indicate that creativity plays a critical role in enhancing instructional quality and student learning outcomes. The study concludes that strengthening creative teaching practices can further improve teaching effectiveness. It recommends the implementation of targeted professional development programs that promote innovative, adaptive, and learner-centered instructional strategies.
🏷 Creativity, Teaching Effectiveness, Primary School Teachers, Learner-Centered Instruction, Descriptive-Correlational Study
Article 17 · pp. 174-181
“A Study on Financial Awareness and Literacy among Rural Youth in Hassan District”
India is heading towards becoming the third largest economy in the world. For this to become a reality both Urban and Rural Economies should contribute in augmenting the economic stability. Inclusive growth especially with respect to the rural areas where financial literacy rates are limited. The present study titled “A Study on Financial Awareness and Literacy among Rural Youth in Hassan District” aims to examine the level of financial awareness among the rural youth in Hassan District and to understand how it influences their financial decision making.
There are Eight Taluks in Hassan District, namely, Hassan, Arakalagood, Holenarasipura, Arsikere, Belur, Alur, Sakaleshapura and Channarayapatna. The Researcher has derived the information from all these taluks by serving a structured questionnaire. The study focuses on rural youth in Hassan District, which is rich in coffee, arcanut and spices. This segment of population has a very high potential to contribute to the economic growth of the country. However, lack of awareness about basic financial instruments, saving plans, investment opportunities, and credit management is kind of hindrance in making India digital. Due to this reason rural folks are not able to make calculated and informed financial decisions. This study is based on the data derived from a structured questionnaire administered to a selected sample of rural youth in Hassan District. The study emphasizes on rural youth in Hassan District of Karnataka, who represent a significant segment in the demography and they have all the potential to contribute to the overall economic development of the country. The current study adopts an empirical approach by collecting the primary data from a structured questionnaire which is distributed among a selected sample of rural youth.
The objectives of the current study includes assessing the level of financial literacy of rural youth, analysing awareness about the banking services and government schemes, identifying the factors influencing financial behaviour of rural youth. It also evaluates the role of digital financial tools such as mobile banking, UPI services in enhancing financial inclusion. All this is evaluated using percentage analysis, mean scores, correlation techniques.
The findings of the study reveal that there is moderate levels of financial awareness among the rural youth in Hassan District, the youth has significant lower levels of understanding with respect to investment practices and advanced financial literacy. The study highlights the need for financial education programs, awareness campaigns and various other policy interventions to improve financial literacy and promote responsible financial behaviour.
Eventually the study concludes that educating rural youth with respect to financial literacy is essential for achieving financial inclusion and sustainable development. The study also provides valuable insights for policy makers, educators and financial instruments to design effective strategies aimed at empowering rural youth with essential financial skills.
🏷 Financial Inclusion, Financial Instruments, Sustainable Development, Youth Empowerment, UPI Services, Digital Banking, Mobile Banking, Online Banking.
Article 18 · pp. 182-204
Investigating the Institutional and Pedagogical Viability of Integrating Digital Humanities Programmes in Nigerian Open and Distance Learning (ODL) Universities
The rapid expansion of digital technologies has fundamentally transformed research, teaching, and knowledge production globally, leading to the emergence of Digital Humanities (DH) as a dynamic interdisciplinary field. Despite this global shift, the integration of DH within African Open and Distance Learning (ODL) institutions remains underexplored and under-theorized. This study investigated the institutional and pedagogical viability of integrating Digital Humanities programmes in Nigerian ODL universities, with particular reference to the National Open University of Nigeria. Adopting a mixed-methods research design, the study drew on data collected from 200 respondents, including academic staff, postgraduate students, and ICT personnel. Quantitative data were analysed using both descriptive and inferential statistics, including Pearson correlation and multiple regression analyses, while qualitative data were subjected to thematic analysis. Reliability testing yielded Cronbach’s alpha coefficients ranging from 0.76 to 0.84, with an overall reliability index of 0.82, indicating strong internal consistency. The findings revealed a moderate level of institutional readiness for DH integration, accompanied by significant infrastructural, pedagogical, and policy-related constraints. Inferential analysis shows that digital infrastructure is the strongest predictor of pedagogical viability (β = 0.41, p < 0.001), followed by faculty competence (β = 0.35) and curriculum adaptability (β = 0.29). Qualitative insights further highlight challenges related to funding, digital literacy gaps, and the absence of structured policy frameworks. The study contributes to scholarship by proposing a context-sensitive, multidimensional model of DH integration in ODL systems, and by extending the application of Connectivism, TPACK, and Diffusion of Innovation theories within developing educational contexts. It also advances ongoing efforts to decolonize Digital Humanities by foregrounding African institutional realities. The study concludes that while DH integration in Nigerian ODL universities is both viable and strategically necessary, its success depends on coordinated investments in infrastructure, faculty development, curriculum innovation, and policy support.
🏷 Digital Humanities; Open and Distance Learning; Institutional Readiness; Pedagogical Viability; Nigerian Universities; Digital Education
Article 19 · pp. 205-210
Emotion-Aware Multilingual Multimodal Emergency Detection System Using Edge AI and Context-Adaptive Learning
In critical emergency situations, victims often express distress through voice, language, and physical movements rather than explicit manual actions. Existing safety systems fail to capture such multimodal and multilingual cues effectively. This paper proposes a novel Emotion-Aware Multilingual Multimodal Emergency Detection System (EMMEDS) that integrates speech emotion recognition, multilingual text understanding, motion sensing, and contextual awareness using lightweight edge AI models.
The proposed framework combines convolutional neural networks (CNNs) for audio feature extraction, transformer-based multilingual text processing, and long short-term memory (LSTM) networks for motion sequence analysis. A context-adaptive attention mechanism dynamically adjusts the importance of each modality based on environmental conditions. Unlike existing cloud-dependent solutions, the system performs real-time inference on-device, ensuring low latency and privacy preservation.
Experimental results demonstrate a significant improvement of 19% in detection accuracy and a 25% reduction in false alarms compared to traditional unimodal and cloud-based approaches. The system is highly scalable and suitable for real-world deployment in personal safety, smart cities, and healthcare monitoring.
🏷 Multimodal Learning, Emotion Detection, Multilingual NLP, Edge AI, Emergency Detection, Deep Learning, Context-Aware Systems
Article 20 · pp. 211-215
Carbon-Aware Machine Learning Model Optimization for Sustainable AI Systems
The rapid expansion of machine learning (ML) applications has led to a significant increase in computational resource consumption, resulting in substantial carbon emissions. This paper introduces a novel carbon-aware optimization framework that integrates environmental impact as a primary constraint during model training and deployment. Unlike traditional optimization approaches that focus solely on accuracy and latency, the proposed method incorporates carbon intensity signals, energy-efficient scheduling, and adaptive model compression techniques to minimize emissions without compromising performance. The framework dynamically adjusts training workloads based on real-time energy grid carbon intensity and employs multi-objective optimization to balance accuracy, energy consumption, and environmental impact. Experimental evaluations demonstrate that the proposed approach reduces carbon emissions by up to 35% while maintaining competitive model accuracy. This work contributes toward sustainable AI by embedding carbon-awareness into the ML lifecycle.
🏷 Sustainable AI, Carbon-Aware Computing, Green Machine Learning, Model Optimization, Energy Efficiency
Article 21 · pp. 216-223
A GSM-Free IOT Architecture for Smart Vehicle Anti-Theft and Multi-Mode Safety Monitoring Using ESP32 with Telegram Bot Integration
Road traffic fatalities in developing economies are significantly attributed to vehicle theft, alcohol-impaired driving, and inadequate emergency response following accidents. Despite this critical need, cost-effective embedded solutions that simultaneously address these three safety challenges remain inaccessible to typical vehicle owners. This research introduces an integrated smart vehicle monitoring platform that operates without SIM card dependency, utilizing the ESP32-WROOM-32 microcontroller as its foundation. The system executes six concurrent operational functions within a unified architecture: (i) GPS-based anti-theft notifications transmitted as interactive Google Maps links via Telegram Bot API through Wi-Fi connectivity, (ii) breath alcohol sensing through MQ-3 sensor integration with automated ignition prevention mechanisms, (iii) collision and vehicle rollover identification using MPU6050 six-axis inertial measurement technology enhanced with rolling-average algorithms for false alarm mitigation, (iv) intelligent headlight control based on ambient light conditions through LDR voltage divider implementation, (v) proximity alert functionality utilizing HC-SR04 ultrasonic sensing with progressive warning zones, and (vi) browser-accessible Pilot Console dashboard hosted locally on ESP32 flash storage, enabling network-wide access without specialized applications. Performance validation through controlled laboratory conditions and real-world field testing yielded a mean Telegram notification delay of 3.1 seconds, comprehensive detection reliability of 98.0% across 60 experimental trials, GPS positioning accuracy of 3.2 meters CEP, and complete alcohol detection precision across 10 validation tests. The complete hardware implementation costs Rs. 1,185 with no ongoing communication expenses. The design deliberately excludes LCD displays, SIM cards, and GSM components throughout the entire system architecture.
🏷 IoT, ESP32, vehicle safety, Telegram Bot API, anti-theft, MQ-3 alcohol sensor, MPU-6050, GSM-free
Article 22 · pp. 224-237
An Empirical Study on Consumer Awareness and Behavior Towards Sustainable Packaging in the FMCG Sector
This study aims to provide insights into consumer awareness, attitudes, and behavioural tendencies toward sustainable packaging, contributing to a better understanding of evolving preferences in the fast-moving consumer goods (FMCG) sector and offering implications for marketers and policymakers promoting environmentally responsible consumption. The research adopts a quantitative, descriptive, and cross-sectional design focusing on consumers in the urban context of Chandigarh. The target population includes individuals aged 18 years and above who regularly purchase FMCG products such as packaged food, personal care, and household items. Data were collected using a non-probability purposive sampling technique from 200 respondents through online and offline modes. After data cleaning, 144 valid responses were retained for analysis. A structured questionnaire was used, comprising three sections: demographic profile, awareness and perception of sustainable packaging, and behavioural practices related to eco-friendly packaging. The items were measured on a five-point Likert scale, adapted from established instruments and tailored to the Indian context.
A pilot study was conducted to ensure clarity and reliability of the instrument. Internal consistency was assessed using Cronbach’s alpha, with a threshold value of 0.70 considered acceptable. The findings of the study are expected to provide valuable insights into consumer perspectives on sustainable packaging and support strategic decision-making for promoting environmentally sustainable practices in the FMCG sector.
🏷 Customer Awareness, sustainable packaging, FMCG products.
Article 23 · pp. 238-248
Neuro Imaging Assisted Cognitive Disorder Detection
Cognitive disorders like Alzheimer’s disease and vascular dementia are global health concerns affecting millions of people worldwide. It is important to diagnose these disorders at an early stage to effectively intervene and provide treatment. This research proposes a complete computational framework for the detection of cognitive disorders using sophisticated deep learning and machine learning techniques. The proposed system is based on the combination of MRI analysis and CNN feature extraction VGG16 and Support Vector Machine (SVM) classification tech niques. This proposed system can classify Alzheimer’s disease into four different stages: No Impairment, Very Mild Impairment, Mild Impairment, and Moderate Impairment. Moreover, the pro posed system can classify the presence or absence of vascular de mentia. This proposed system includes an explanation mechanism using Grad-CAM to offer visual explanations for the predictions made bythe system. A Flask-based web application is proposed to offer user-friendly access to the proposed diagnostic system with complete reporting capabilities. Analysis of the proposed system shows promising results in terms of accuracy, precision, recall, and F1-measure values. This work is a significant contribution to the new area of medical image analysis.
🏷 Alzheimer’s Disease, Vascular Dementia, MRI, CNN, VGG16, Support Vector Machine, Grad-CAM.
Article 24 · pp. 249-260
Digital Transformation in Emerging Fashion Economies: A Qualitative Review of CAD/CAM Adoption and Its Implications for Design
This study examines the role of Computer Aided Design and Computer Aided Manufacturing technologies in driving digital transformation within Bangladesh’s apparel industry. The research adopts a systematic literature review approach based on existing academic and industry sources. The analysis focuses on three key areas. These are design transformation, production efficiency, and sustainability outcomes. The findings show that digital tools improve design flexibility and accuracy.
They also enhance production efficiency through faster processes and better coordination. In addition, these technologies support sustainability by reducing material waste and improving resource use. However, the impact of digital transformation depends on factors such as infrastructure, investment, and skill availability. The study highlights that adoption is uneven across firms in Bangladesh. Overall, the research provides an integrated understanding of how CAD and CAM technologies influence the apparel sector in an emerging economy. The study also offers insights for future research and industry practice.
🏷 digital transformation, CAD/CAM technologies, apparel industry, Bangladesh, production efficiency, sustainable fashion, fashion technology, systematic review
Article 25 · pp. 261-270
Effect of E-Contracting on Performance of Public Hospitals in Kakamega County, Kenya
The study objective was to determine the effect of E-Contracting on performance of public hospitals in Kakamega County. The study was founded on diffusion of innovation theory and RBV theory. The study applied descriptive survey research design. The study utilized a sample size of 204 which was derived from a sampling frame of 416 using Yamane 1967 formula. Questionnaire was used to collect data. Validity was determined through discussion with the supervisor and department staff and reliability was also determined by pilot study with a focus on internal consistency. This was measured using the Cronbach alpha coefficient. The measures of central tendency and correlational analysis were also used in data analysis. The study was of utmost significance to the policymakers in the health sector, researchers and academicians and the public hospitals in Kakamega and Kenya at large. The findings were presented in tables and figures. The study revealed that manual processes continue to prevail in contract administration, and the platforms are deficient in sophisticated functionalities to facilitate effective negotiation, monitoring, and collaboration. Besides, the study revealed that although the system does not markedly streamline the purchase process, as demonstrated by the restricted capabilities of e-shopping carts and error checking tools, it offers certain advantages in inventory management and procurement oversight.
🏷 E-contracting, Performance, Diffusion of Innovation Theory, Resource based view theory, Hospital
Article 26 · pp. 271-288
Digital Adoption, Technical Challenges and Access to GST Support – Case Study of Rampur Bushahr Himachal Pradesh.
GST seeks to trace and regularize economic activity through digital platforms, invoice documentation, and return filling systems. Similar to the way digital payments aimed to trace unregistered transactions, GSTtoo has introduced tools such as e-invoicing, digital GST portals, and consumption schemes to promote participation and reduce tax evasion.
GST has opened new avenues for growth. The removal of interstate barriers has allowed businesses to access broader markets beyond their immediate geography. Simplified schemes like the Composition Scheme have provided relief to low-turnover enterprises, while increased formalization has, in some cases, improved access to institutional finance. These dynamics reflect the dual nature of GST’s impact , wherein short-term regulatory pressure coexists with long-term possibilities. Despite various government reports and economic analyses, there is limited localized, empirical research on the impact of GST on small enterprises in semi-urban areas. This study addresses the gap by examining how GST has affected small businesses in Rampur Bushahr considered as local trade centre catering to suburbs of four Districts namely Kinnaur ,Kullu, Shimla and Mandi in Himachal Pradesh.
🏷 Digital,GST,Rampur Bushahr, GST portals,Research.
Article 27 · pp. 289-300
Quantifying Fluffiness: A Material-Independent Metric Based on Void-to-Solid Volume Ratio
We all know fluffiness when we feel it — the loft of a down pillow, the springiness of freshly washed cotton, the airy warmth of a wool sweater. Yet despite its universal recognition, fluffiness has long resisted scientific measurement. This paper introduces a simple, practical solution: the Fluffiness Ratio (Rf), defined as the ratio of a material's bulk volume to the volume of its solid matter alone.
The underlying principle is easy to grasp: fluffier materials spread the same amount of solid matter across more space. A cotton ball with Rf = 20 means its fibers are expanded to occupy twenty times the volume they would if compacted with no air between them. This dimensionless number allows direct comparison across materials — from dense felts (Rf ≈ 3) to aerogels (Rf > 1000) — regardless of what they are made from.
We present practical measurement methods using fluid displacement, discuss how to handle different material types, and demonstrate the metric's range across three orders of magnitude. Unlike existing proxy measurements such as bulk density, loft height, or compressibility, Rf directly quantifies the structural quality we intuitively recognize as fluffiness: how effectively a material creates and maintains void - space per unit of solid matter.
We also acknowledge current limitations, including the need for broader experimental validation, formal standardization of measurement protocols, and statistical analysis across repeated trials. These represent natural next steps in establishing Rf as a reliable cross-industry standard.
🏷 fluffiness, void-space architecture, textile characterization, porosity, skeletal density, pycnometry
Article 28 · pp. 301-317
A Multi-Parameter Data-Driven Dynamic Pricing Framework Using Machine Learning for Revenue Optimization
We propose a multi-parameter data-driven dynamic pricing framework for revenue optimization, which integrates diverse influencing factors beyond traditional demand-based approaches. The framework employs a hybrid machine learning architecture, combining predictive analytics with real-time adaptive decision-making to dynamically adjust prices. A Long Short-Term Memory (LSTM) network captures temporal dependencies in demand variability, customer behavior, competitor pricing, inventory levels, and seasonality, while a feed-forward neural network translates these insights into actionable price adjustments. The model incorporates customer-centric optimization by balancing revenue objectives with satisfaction metrics, ensuring ethical pricing through fairness-aware constraints. Moreover, the framework addresses the limitations of static pricing strategies by continuously updating prices in response to streaming data, thereby improving responsiveness to market fluctuations. The novelty lies in the holistic integration of multi-parameter inputs and the hybrid learning approach, which enhances both accuracy and adaptability. Experimental validation demonstrates significant revenue improvements compared to conventional methods, highlighting the practical applicability of the proposed framework in real-world scenarios. This work contributes to the growing body of research on data-driven pricing by offering a scalable and ethically grounded solution for dynamic revenue optimization.
🏷 Dynamic Pricing, Multi-Parameter Optimization, Hybrid Machine Learning
Article 29 · pp. 318-326
Relationship between Self-Efficacy and Student’s Achievement in Mathematics among the Senior Secondary Two School Students in Bauchi Metropolis, Bauchi State.
This study examined the relationship between mathematics self-efficacy and mathematics achievement among senior secondary school students in Bauchi Metropolis, Bauchi State, Nigeria. A correlational research design was employed. A sample of 400 Senior Secondary Two (SS2) students (180 males, 220 females) was selected using stratified random sampling from ten public secondary schools. Data were collected using a Mathematics Achievement Test (MAT) and a Mathematics Self-Efficacy Questionnaire (MSQ). Three research questions and three corresponding hypotheses were formulated and tested at a 0.05 significance level using Pearson Product Moment Correlation. The results revealed that both mathematics self-efficacy and achievement levels among students were low. A strong positive relationship was found between students' mathematics self-efficacy and their mathematics achievement (r = 0.790, p < 0.001). Gender‑specific analyses showed a moderate positive relationship for male students (r = 0.469, p < 0.001) and a strong positive relationship for female students (r = 0.688, p < 0.001). The study concludes that improving students' mathematics self‑efficacy is a viable pathway to enhancing their mathematics achievement. Recommendations include curriculum review, teacher training focused on self‑efficacy development, and targeted interventions for male students.
🏷 Mathematics self-efficacy, Students' achievement and Gender
Article 30 · pp. 327-338
Role of Design Technology in Optimization of Transportation: A Study on Tripadam Logistics Private Limited, Chennai
The rapid transformation of the Indian logistics sector, driven by the proliferation of e-commerce, cross-border trade, and just-in-time supply chain models, has placed transportation at the centre of organizational efficiency. This study investigates the role of design technology in optimizing transportation operations at Tripadam Logistics Private Limited, a Chennai-based integrated logistics service provider incorporated in 2009. The research adopts a descriptive and analytical research design, drawing on primary data collected through structured questionnaires administered to 50 employees and stakeholders. Statistical tools including percentage analysis, one-way ANOVA, chi-square tests, correlation analysis, and one-sample t-tests were employed to evaluate relationships between technology adoption and operational outcomes. Findings reveal that technologies such as GPS tracking, AI-powered route optimization, IoT sensors, cloud-based logistics platforms, and automated dispatch systems have significantly improved route efficiency, vehicle capacity utilization, delivery accuracy, and customer satisfaction. The study contributes actionable insights for mid-sized Indian logistics firms seeking to leverage design-tech solutions to remain competitive in an evolving market environment.
🏷 Design Technology, Transportation Optimization, Logistics Management, GPS Tracking, Route Optimization, IoT, Indian Logistics, Tripadam Logistics
Article 31 · pp. 339-356
Bio Trace - A Blockchain and IPFS Based Traceability Platform for Secure Ayurvedic Herb Supply Chains
Ensuring transparency, authenticity, and regulatory compliance in Ayurvedic herb supply chains remains a major challenge due to the use of centralized and fragmented tracking systems. Traditional approaches are vulnerable to data tampering, poor traceability, and lack of trust among stakeholders. This paper proposes BioTrace, a blockchain and IPFS-based traceability platform designed to provide secure, transparent, and immutable tracking of Ayurvedic herbs from harvesting to consumer delivery. The system integrates Hyperledger Fabric for decentralized transaction management, Inter Planetary File System (IPFS) for tamper-proof document storage, and role-based access control for secure stakeholder interaction. Bio Trace supports automated compliance verification using geo-fencing, seasonal validation, species verification, and laboratory quality metrics. QR-code based consumer verification enables end users to access complete batch provenance information.
🏷 Blockchain, Supply Chain Traceability, Ayurvedic Herbs, IPFS, Decentralized Storage, Role-Based Access Control, QR Code Verification
Article 32 · pp. 357-365
A Survey on Hybrid Caching Techniques to Reduce Latency in Large Language Model Systems
Large Language Models (LLM) have vast applications in diverse fields such as text summarization and generation, generative and conversational Artificial Intelligence (AI) and Natural Language Processing tasks. However, generation of content for each real-time task causes high computational cost and latency in LLMs.
To address this drawback, the most effective solution proposed was - caching. Caching mechanisms were introduced to reuse a response instead of computing it for each redundant task. This survey explores various caching strategies from traditional key-value based techniques to the advanced hybrid strategies.
The paper highlights the effectiveness of caching techniques in improving the overall performance of LLM systems. Through this survey hybrid caching mechanism is found to be most useful with an estimate of 15-25% reduction in latency and 10-20% improvement compared to traditional caching mechanisms.
🏷 Large Language Models, caching, semantic similarity, hybrid caching, query optimization, NLP.
Article 33 · pp. 366-373
A Conceptual and Pedagogical Study of Object-Oriented Programming Principles Using Java
The concept of Object-Oriented Programming is the foundation of the modern software engineering development and to implement this Java as a programming language is being widely used in the industry. Seeking its importance for real world it has been seen that beginners often struggle to understand the OOPs concept because of their early focus on syntax rather than design thinking.
This article mainly focuses on the core four OOPS principles along with Class and objects. The four pillars are classified into inheritance, polymorphism, encapsulation and abstraction. The study shows how these concepts are used and finally implemented to design structured and maintainable software systems. For reusability of any feature we implement inheritance and for designing the application with the help of interface, we implement abstraction. The paper aims to bridge the gap between theoretical understanding and practical application by explaining OOP concepts using simple explanations and real-world analogies.
🏷 Pedagogical, Object-Oriented, Programming Principles
Article 34 · pp. 374-389
Advancing Secure Communication in the Quantum Era through the Integration of Artificial Intelligence and Quantum Cryptographic Techniques
Utilizing the ideas of quantum physics, quantum cryptography is quickly becoming a vital defense against the growing cybersecurity risks of the contemporary day, especially in light of developing quantum computing. This essay investigates the complex field of quantum cryptography and looks at how it can transform network security and protect private data. We examine the fundamental ideas of quantum cryptography, such as Quantum Key Distribution (QKD) protocols like BB84 and E91, which use quantum features like superposition and entanglement to provide potentially indestructible secure communication channels. We also discuss the urgent need for quantum-resistant solutions in view of the developing "quantum threat" to well-known cryptographic algorithms like RSA and AES. The potential benefits and difficulties of using artificial intelligence (AI) techniques to boost quantum cryptography systems' resilience and efficiency are also examined. The creation of effective quantum repeater networks and enhanced security proofs are among the outstanding research topics, future difficulties, and present implementations in quantum cryptography that are covered in this study. We stress how crucial quantum cryptography is to protecting sensitive communications in the quantum era for a variety of industries, including the military, government, financial industry, and healthcare. We come to the conclusion that quantum cryptography has enormous potential for protecting vital information systems from future cyberattacks that are becoming more complex, even if we acknowledge the technology's early stages of development.
🏷 Quantum Computing, Quantum Cryptography, Quantum Key Distribution (QKD), Quantum Threat, Artificial Intelligence (AI), Secure Communication.
Article 35 · pp. 390-397
Mallpath-Indoor Navigation System
Indoor navigation within large shopping malls is often challenging due to complex layouts and lack of proper guidance systems. Visitors frequently struggle to locate stores, restrooms, exits, or other facilities efficiently. Traditional navigation methods such as static maps and signboards are often insufficient and confusing.
This paper proposes MallPath, an intelligent indoor navigation system designed to assist users in navigating malls and accurately. The system utilizes digital mall maps, real-time location tracking, and pathfinding algorithms to guide users to their desired destinations.
The proposed system provides floor-wise navigation, shop categorization, and shortest path suggestions. It enhances user experience by reducing time, improving accessibility, and offering an interactive interface. The solution is scalable and can be implemented in modern smart malls.
🏷 Indoor Navigation, Smart Mall, Pathfinding, User Interface, Location Tracking, Digital Mapping.
Article 36 · pp. 398-412
Highway Construction in Hong Kong: Engineering Complexity, Environmental Governance, and Infrastructural Megaprojects in a Dense Urban Environment
This article critically analyses the evolution, governance, and socio-environmental ramifications of highway construction in Hong Kong, a global metropolis distinguished by its extreme population density, intricate topography, and rigorous regulatory frameworks. Using policy papers, environmental impact assessments, project reports, and legislative records from 1974 to 2025, I show that Hong Kong's highway development is a good example of "compressed infrastructural modernity." In this model, engineering innovation, environmental protection, and fiscal accountability are always being negotiated in situations where space is limited and the public is watching. The analysis examines the evolution from the territory's inaugural motorway, Tuen Mun Road (1974–1983), to current megaprojects such as the Central Kowloon Route and the Tsing Yi–Lantau Link, emphasising three salient characteristics: (1) the institutionalisation of environmental assessment as a mandatory framework for project design; (2) the advent of digital and automated construction technologies as solutions to labour shortages and safety requirements; and (3) ongoing conflicts between cost Limitations encompass constrained access to internal governmental discussions and dependence on publicly available EIA documents. The results show that Hong Kong's highway sector has become very technically advanced, but it still has trouble predicting cost overruns and working together with other agencies.
🏷 construction innovation, environmental impact assessment, Hong Kong highways, infrastructure megaprojects, urban transport
Article 37 · pp. 413-421
Predicting Pest and Disease Occurrence Using Synthetic Data and Explainable Machine Learning Methods
Prediction of occurrence for pests and diseases is an essential problem for agriculture, as such events have a huge influence on the productivity of the crop with regard to the security of food production. Traditional methods lack datasets and tend not to incorporate domain knowledge, which leads to suboptimal performance with limited sets of interpretation. This study addresses such gaps by developing a systematic machine learning-based framework for combining synthetic data generation, robust predictive modeling, and explainability techniques to produce actionable insights in pest and disease dynamics. Synthetic datasets are first generated based on the domain-driven logic simulating the correlations between critical environmental and biological factors such as temperature, humidity, rainfall, pest lifecycle stage, and soil moisture and the incidence of pests or diseases. For interpretability, Local Interpretable Model-agnostic Explanations LIME with Random Forest provides localized, instance-level insights on feature contributions to individual predictions. For complement, permutation importance calculates the global relevance of every feature by assessing its effect on model performance. Both of these techniques ensure that fine-grained and holistic understanding is achieved regarding the model's behavior. This integrated approach therefore addresses the limitations of traditional methods by improving the predictive accuracy and enhancing interpretability. The findings have tremendous implications for precision agriculture in order to allow stakeholders to put into action data-driven strategies for pest and disease management. This framework is reproducible and therefore adaptable to different contexts in agriculture sets.
🏷 Pest Prediction, Disease Modeling, Random Forest, LIME Explainability, Permutation Importance, Sets
Article 38 · pp. 422-431
A Systematic Review of AI Chatbots in Education Using NLP and LLMs
AI chatbots have emerged as an important part of the contemporary education structure, assisting students in academics and administration, as well as increasing their access to information. Due to the rule-based nature of early chatbots, their flexibility and comprehension capacity were limited. However, due to advances in Natural Language Processing (NLP), Deep Learning (DL), and Large Language Models (LLM), increasingly sophisticated chatbot systems that understand contexts and can interact with users have been introduced. This paper provides a comprehensive analysis of twelve notable research articles that highlight the use of AI chatbots in education. The selected research articles are analyzed in terms of chatbot architecture, technology, data sources, and evaluation criteria under the framework of systematic literature review. The results demonstrate a shift from rule-based machine learning techniques to RAG model, deep learning, and semantic embeddings. These novel methodologies imply higher quality and accuracy of responses. However, there are numerous including hallucinations in generative models, lack of evaluation metrics, scalability, and trust in data sources. This research highlights the present research trends, limitations of existing methodologies, and future directions of research in developing trustworthy, scalable, and intelligent AI chatbot applications in education settings.
🏷 AI chatbots; conversational AI; educational technology; natural language processing; deep learning; retrieval-augmented generation
Article 39 · pp. 432-443
Integrating Technology Adoption and Workforce Training to Improve Indoor Environmental Quality in Hospital
Hospitals require optimal Indoor Environmental Quality (IEQ) to promote patient recovery, staff efficiency, and overall well-being. However, the integration of evolving construction technologies into hospital design, construction, operation, and maintenance in many developing regions remains hindered by workforce skill gaps. This paper assessed the level of awareness and adoption of IEQ technology by architects and the awareness level of the users of the space. A mixed-method approach was applied to examine the parameter readings, users’ perceptions, and awareness. Findings reveal that 20% of the selected hospitals made use of passive technology (buoyancy) to enhance air quality, systemic lighting failure, high noise levels, and varying humidity/thermal issues. Only 10% of architect respondents reported using enhancement technologies to improve IEQ metrics beyond air quality. One out of every four user respondents admitted to being unfamiliar with the concept, while only 15% reported being very familiar, and 52.6% of respondents reported being unaware of IEQ monitoring systems. Users are primarily concerned with ventilation, airborne pollutants, and infection control. Recommendations are proposed for embedding IEQ-focused competencies into training curricula and aligning construction policies with global sustainability and healthcare facility standards.
🏷 Evolving technologies, vocational training, workforce development, indoor environmental quality.
Article 40 · pp. 444-454
Representation of Different Sorting Algorithms Using Sorting Visualizer
The most effective way to sort elements (data) significantly impacts how quickly a computer can complete a task. The sorting algorithm is one of computer science’s most important areas of study. It is the process of arranging unorganized elements in an organized manner. The main goal is to make records easier to search, sort, insert, and delete. Through the description of the existing five sorting algorithms: Bubble, Selection, Insertion, Merge, and Quick Sort, the Time and Space Complexity are determined. Time complexity varies as Best Case(O), Average Case(Θ), and Worst Case(Ω). Here in sorting, the Worst Case complexity is O(n2), and the Best Case complexity is O(n log n), where "n" represents the number of elements(data) in the array. This project consists of a UI-based web application that can visualize the sorting process using various colors and denote the status of the elements of the array.
🏷 Representation, Algorithms, Sorting
Article 41 · pp. 455-464
“Understanding Consumer Perception of Green Supply Chain Practices in India: A Consumer Perspective Study”
As people become more aware of environmental issues and the importance of sustainability, their choices as consumers and the way businesses operate are changing. More and more companies are choosing to use green supply chain methods, like using eco-friendly packaging, sourcing materials in a sustainable way, and making their logistics more environmentally friendly. These actions help reduce their effect on the environment and also improve how they are seen by customers.
This research looks at how Indian consumers view green supply chain practices. It is based on real data collected from people through a structured questionnaire that uses a five-point scale to measure their opinions. The study uses a descriptive approach, and tools like percentages and charts are used to understand the data better.
The findings show that consumers are now more aware of environmental concerns and generally support companies that take sustainable steps. Factors like using eco-friendly packaging, being open and honest about their practices, and taking responsibility for the environment play a big role in what people choose to buy. However, one big challenge is that many people are still sensitive to price when it comes to buying greener products.
In conclusion, green supply chain practices have a big effect on how consumers think and what they buy. The results can help businesses create better sustainability strategies and gain a stronger position in the market.
🏷 Green Supply Chain, Consumer Perception, Buying Behaviour
Article 42 · pp. 465-472
A Comparative Study on the Financial Perfomance of Public and Private Sector Banks in India
This study evaluates the comparative financial performance of public and private sector banks in India, focusing on how variations in ownership structures, management practices, and risk profiles influence financial outcomes. The analysis is based on both secondary and primary data sources. Secondary data are drawn from the annual reports of selected banks and publications of the Reserve Bank of India over an extended period, ensuring a more comprehensive and reliable assessment. The study employs key financial ratios related to profitability, asset quality, capital adequacy, liquidity, and operational efficiency within the CAMEL framework to assess the overall strength and stability of both banking segments. To enhance analytical depth, the research incorporates structured evaluation techniques that support meaningful comparison and interpretation of results. In addition, primary data collected through a structured questionnaire (Google Forms) capture customer perceptions regarding service quality, digital banking services, and overall satisfaction. The integration of empirical findings with relevant theoretical perspectives strengthens the study’s contribution to understanding performance differentials across bank types in the evolving post-reform banking landscape.
🏷 Public Sector Banks, Private Sector Banks, Financial Performance, CAMEL Framework, Profitability, Asset Quality, Customer Satisfaction
Article 43 · pp. 473-487
Dynamic Price Allocation and Optimization for E-Commerce Platforms Using Reinforcement Learning and Deep Learning
The concept of dynamic pricing has already become one of the most significant aspects of electronic commerce, which is constantly changing in terms of the level of demand and competition, as well as the response of customers to a specific product (or service). Conventional methods of pricing and reinforcement learning methods like Deep Q-Networks (DQN) tend to have restricted flexibility, dis- crete action, and no proper estimation of demand. Our uncertainty-aware dynamic pricing framework as offered in this paper incorporates a hybrid demands forecasting, Transformer-LSTM demand forecasting model and Soft-Actor-Critic (SAC) reinforcement learning to optimize prices continuously. The Trans- former component models the long-range time interdependencies whereas the LSTM models the sequential nature of demand patterns and allows it to predict the demand robustly and precisely. The state representation of the SAC agent, which learns the optimal pricing policies under dynamic market, takes these forecasts into consideration.
The suggested system is implemented on a scalable, API-focused system of microservices and allows making real-time pricing decisions. Online Retail II Evaluation The experimental analysis of the Online Retail II data reveals that the improvement of experimental approaches is substantial as compared to the baseline techniques. Demand forecasting with the model has an R 2 of 0.62 with a Mean Absolute Percentage Error (MAPE) of 8.7% and one can increase the revenue by 21.4% and the profit by 18.2% over the expected traditional methods of reinforcement learning.
The findings demonstrate the efficacy of melding cutting-edge deep learning and reinforcement learning approaches to scal- able, adaptable, and smart pricing in the practical e-commerce setting.
🏷 Dynamic Pricing, Reinforcement Learning, Transformer, Long short term memory (LSTM), Soft actor critic (SAC)
Article 44 · pp. 488-499
PosePal: An AI-Powered Human Posture Tracking and Real-Time Alert System
Musculoskeletal discomfort caused by prolonged sitting and unmonitored poor posture habits has become a serious occupational health concern among university employees. Without any real-time corrective mechanism, postural deviations accumulate silently into chronic back pain, neck strain, and shoulder tension — conditions widely reported among faculty and staff at Pangasinan State University – Alaminos City Campus (PSU-ACC). Survey results further confirmed that 95% of employees experience posture-related discomfort, yet none had previously used a posture monitoring device, and 55% had no specific method to manage their sitting habits. This study developed PosePal: An AI-Powered Human Posture Tracking and Real-Time Alert System to address these persistent ergonomic challenges in an academic workplace setting. The system was built using Python, Dart, and JavaScript, integrating YOLOv11-Pose for real-time human detection and key point-based posture estimation, supported by Firebase as the cloud database. IP cameras serve as the primary input devices, continuously capturing body alignment for AI-based analysis without requiring any wearable equipment. To enhance accuracy, the system integrates multi-object tracking, face recognition, and a rule-based posture classifier with temporal validation. PosePal automatically detects improper sitting positions and instantly alerts users to correct their posture, while providing posture scoring, analytics reports, personalized recommendations, face recognition for secure identification, and an admin dashboard for institutional monitoring and management. The system was developed following Agile methodology and evaluated by 21 respondents comprising faculty, non-teaching staff, and an IT expert. Assessed against ISO/IEC 25010 quality standards, PosePal achieved an overall weighted mean of 4.19 (Excellent), confirming its effectiveness, reliability, and usability in promoting posture awareness and improving workplace wellness. The study concludes with recommendations to refine posture detection and alert mechanisms, continuously evaluate system accuracy and user satisfaction, and conduct further studies to expand system features and ensure PosePal remains adaptive and reliable across diverse real-world settings.
🏷 Artificial Intelligence, Computer Vision, Posture Estimation, Posture Detection, YOLO
Article 45 · pp. 500-509
In-vitro evaluation of antidiabetic and antioxidant activities of lime juice extract of Gossypium herbaceum leaves
Management of diabetes mellitus increasingly focuses on controlling postprandial hyperglycemia through inhibition of carbohydrate-hydrolyzing enzymes responsible for gastrointestinal glucose release and absorption. This study evaluated the in vitro antidiabetic and antioxidant activities of a lime juice extract derived from Gossypium herbaceum leaves. Fresh leaves were extracted using lime juice (Citrus aurantifolia) as a natural solvent medium. Inhibitory activities against α-amylase and α-glucosidase were assessed alongside antioxidant capacity using DPPH, ABTS, and hydroxyl radical scavenging assays at concentrations of 7.8125–1000 μg/mL, conducted in triplicate using standard spectrophotometric protocols. Acarbose served as the reference enzyme inhibitor, while butylated hydroxytoluene (BHT) was used as the antioxidant standard. The extract exhibited concentration-dependent inhibition of both carbohydrate-digesting enzymes and significant free radical scavenging activity. Although reference standards demonstrated higher activity across most concentrations, ABTS radical scavenging increased progressively with dose, approaching BHT performance at higher concentrations. Notably, hydroxyl radical scavenging at 31.25 μg/mL showed no significant difference from BHT, indicating strong antioxidant potential at moderate dosage. In contrast, α-glucosidase inhibition remained significantly lower than acarbose (p < 0.05) at all tested concentrations, suggesting moderate regulation of carbohydrate digestion rather than complete enzyme suppression. These findings demonstrate that lime-mediated extraction of Gossypium herbaceum leaves yields appreciable hypoglycemic potential through partial enzyme inhibition combined with enhanced antioxidant defense mechanisms. Synergistic contributions from ascorbic acid and bioactive phytochemicals present in Citrus aurantifolia likely underpin the observed bioactivity. Overall, the extract represents a promising natural candidate for development as an oral antidiabetic phytomedicine aimed at complementary diabetes management.
🏷 Gossypium herbaceum; Citrus aurantifolia; antidiabetic activity; antioxidant activity; α-amylase inhibition; α-glucosidase inhibition
Article 46 · pp. 510-526
Comparative Analysis of Singular Value Decomposition and Eigenvalue Decomposition Methods for Solving Large Scale Linear Systems
This study compares the effectiveness of Singular Value Decomposition (SVD) and Eigenvalue Decomposition in solving large-scale linear systems, focusing on computational efficiency, numerical stability, and versatility across different problem domains. At its core, matrix decomposition is a crucial tool in numerical linear algebra, allowing us to break down complex systems into more manageable forms.
The study delves into both the theoretical underpinnings and practical performance of these methods, highlighting their respective strengths and weaknesses. SVD stands out for its ability to handle ill-conditioned matrices and is widely used in dimensionality reduction and data analysis, whereas Eigenvalue Decomposition is commonly applied to structured problems and spectral analysis.
To validate the performance claims, computational experiments were conducted using randomly generated matrices of sizes 100×100, 1000×1000, and 5000×5000. The results show that both methods perform efficiently for small matrices, but as matrix size increases, SVD demonstrates better computational performance on the tested system. Moreover, for large and ill-conditioned matrices, SVD exhibits superior numerical stability, making it a more reliable choice than Eigenvalue Decomposition.
The study also explores applications in machine learning, optimization, artificial intelligence, recommender systems, and structural engineering to illustrate the practical relevance of these findings. Ultimately, the choice of method depends on the specific problem at hand, requiring a trade-off between computational efficiency, numerical stability, and scalability.
🏷 Comparative, Decomposition, Linear Systems
Article 47 · pp. 527-539
GestureTalk: Real-Time Sign Language Recognition Using Deep Learning
GestureTalk+, a real-time Indian Sign Language (ISL) recognition and speech synthesis system designed for commodity Android devices, combining MediaPipe landmark extraction with lightweight temporal models (BiLSTM and Transformer encoders) and quantized on-device inference to achieve sub-300 ms end-to-end latency under subject-independent evaluation.[1]The system targets accessibility in education and healthcare, addressing robustness under variable lighting, backgrounds, and camera viewpoints, and includes bilingual text-to-speech to support Hindi/English output. Experiments on a 26-letter static ISL set and 20 common dynamic words demonstrate macro-F1 of 0.93 and 0.89 respectively, outperforming SVM and CNN-TCN baselines while maintaining 30–45 FPS with NNAPI delegates on mid-range hardware. Ablation studies show that temporal attention and landmark normalization improve confusion cases (e.g., M vs N), and int8 quantization preserves accuracy while reducing compute and power. The paper contributes a reproducible edge-AI pipeline, signer-independent protocols, and deployment telemetry for latency and battery use
🏷 Indian Sign Language; Sign Language Recognition; MediaPipe; BiLSTM; Transformer; TensorFlow Lite; NNAPI; Real-time; Edge AI; Text-to-Speech
Article 48 · pp. 540-558
Unveiling the AI–SCM Nexus: A Bibliometric and PRISMA-Based Review of Trends, Technologies, and Future Directions (2021–2025)
The integration of Artificial Intelligence (AI) into Supply Chain Management (SCM) has become a key driver of digital transformation, transforming industry practices and academic discourse. As global supply chains grow more complex and data-driven, AI’s role in improving efficiency, responsiveness, and sustainability has gained significant attention. This study conducts a bibliometric review of 499 peer-reviewed articles published between 2021 and 2025, sourced from Scopus and Web of Science, to trace the changing intellectual and thematic dimensions of AI–SCM research. Quantitative analysis using R Studio and network visualization via VOSviewer indicate key trends, prolific contributors, and dominant research clusters. The result indicates a significant rise in scholarly output over the past five years, with China, India, and the United States leading in publications. Institutions such as the Indian Institutes of Management and Penn State University emerge as major knowledge centers. The research identifies thematic focal points such as AI-assisted decision-making, supply chain resilience, risk management, and sustainability, closely associated to technologies such as machine learning, big data analytics, and Industry 4.0 frameworks. Network analysis reveals strong keyword co-occurrence around automation, optimization, digital supply chains, and predictive analytics, reflecting the convergence of AI capabilities with the fundamental SCM functions. Despite substantial progress, challenges remain—high implementation costs, shortages in skilled talent, data privacy issues, and ethical implications of AI application. These gaps underscore the need for more inclusive and responsible adoption strategies. This bibliometric paper provides a structured overview of the current research landscape, aiding scholars and practitioners in understanding key developments, influential authors, and emerging opportunities. The paper ends with a recommendation to organize future research on the topic of human-AI collaboration, governance systems, and socio-economic effects of AI on global supply-chains
🏷 Artificial Intelligence, Supply Chain Management, Bibliometric Analysis, Industry 4.0, Digital Transformation
Article 49 · pp. 559-570
Implementation of an Integrated System for Surveillance, Monitoring and Security in a Compartment using the Internet of Things.
Security can be defined as the degree of resistance to, and resilience against, various forms of harm. It influences all facets of human existence, with infrastructure serving as a critical domain requiring robust security measures. Broadly speaking, security denotes a state of being free from threats and dangers, encompassing the protection of individuals and organizations against criminal activities such as terrorism, kidnapping, theft, piracy, and espionage. In the context of public transportation, particularly within train compartments, security is paramount to ensuring the safety and well-being of both passengers and cargo. In contemporary society, the lack of sufficient security measures on trains presents a significant concern for commuters. Issues such as theft, harassment, kidnapping, violence, and espionage have become increasingly prevalent among train users. The inadequacies of security personnel, coupled with the ineffective implementation of safety protocols, have exacerbated these challenges. Historically, the security framework aboard trains has been limited to basic measures such as lockable doors and windows, emergency alarm systems, and routine patrols conducted by train staff. The imperative for enhanced security and safety across both public and private sectors cannot be overstated. The development of various technological devices designed for the purposes of security has contributed substantially to improved safety conditions. Notably, the concept of safety extends beyond the mere physical protection of an organization to encompass an integrated approach to security.
🏷 Implementation, Integrated System, Surveillance, Compartment
Article 50 · pp. 571-589
Nourishment and Health: An Engine for Rural Development Study on Chhota Jagulia Gram Panchayat of North 24 Parganas District, West Bengal, India
Maternal health and nutrition are essential components for rural development; especially in developing areas. The purpose of this report is to focus on the Chhota Jagulia Gram Panchayat located in the North 24 Parganas District of West Bengal in order to evaluate the relationship existing between nutrition, access to healthcare, and socioeconomic status of women living in this area. The methodology included the use of primary data collected through household surveys and secondary data obtained from census and government sources. The results show that women have become more aware of antenatal care as well as institutional delivery and diagnostic services. However, there are still several significant challenges that remain, such as early marriage, low income, inadequate practices related to postnatal care, few women employed, and lack of access to government welfare programs. Other nutritional issues that impact a women’s maternal health status include being underweight and suffering from anaemia. The research concludes by stating that enhancing maternal health through the development of health care infrastructure, increasing awareness, and providing women with access to educational and economic opportunities will help generate sustainable rural development.
🏷 Maternal Health; Rural Development; Nutrition; Antenatal Care; Women Empowerment
Article 51 · pp. 590-593
Artificial Intelligence in Healthcare: A Simulation-Based Evaluation of Clinical Decision Support Systems and Future Directions
Artificial Intelligence (AI) is rapidly transforming healthcare by enhancing diagnostic accuracy, improving clinical decision-making, reducing medical errors, and enabling personalized treatment strategies. Among its applications, AI-driven Clinical Decision Support Systems (CDSS) have emerged as a critical tool for augmenting clinical practice through data-driven insights. This study adopts a simulation-based research design, supported by secondary data analysis, to evaluate the effectiveness of AI-enhanced CDSS in comparison with traditional rule-based systems. A synthetic dataset comprising 10,000 patient records was generated to simulate real-world clinical scenarios. The findings indicate that AI-based CDSS improve diagnostic accuracy by 18%, reduce decision-making time by 27%, and enhance patient outcome prediction accuracy by 22%, while significantly lowering false positive rates. Despite these advantages, challenges related to data privacy, algorithmic bias, interpretability, and regulatory uncertainty persist. The study proposes a strategic framework for sustainable AI integration in healthcare, emphasizing explainable AI, hybrid human–AI collaboration, and robust governance mechanisms. These findings contribute to the growing body of literature on AI in healthcare and provide actionable insights for policymakers, healthcare practitioners, and technology developers.
🏷 Artificial Intelligence, Clinical Decision Support Systems, Healthcare Analytics, Machine Learning, Explainable AI
Article 52 · pp. 594-642
Exploring the Influence of Game-Based Learning Tools to the Students’ Level of Engagement in Social Studies of Humanities and Social Sciences Students at Tanauan, Institute Inc.
In recent years, educators have increasingly explored innovative and technology-enhanced teaching strategies to improve student engagement, particularly in subjects that are often perceived as challenging or less engaging, such as Social Studies. One of the most prominent approaches is Game-Based Learning (GBL), which integrates game design elements and principles into instructional activities to create more interactive, meaningful, and student-centered learning experiences.
Grounded in motivational and constructivist learning theories, Game-Based Learning emphasizes active participation, experiential learning, and intrinsic motivation. From a theoretical perspective, GBL aligns with constructivist ideas that learners build knowledge through active engagement and interaction with content, as well as self-determination theory, which highlights the importance of autonomy, competence, and relatedness in sustaining student motivation and engagement. These frameworks help explain why game-based approaches may positively influence students’ behavioral, emotional, and cognitive engagement in learning environments.
🏷 Game-Based, Learning Tools, Social Studies
Article 53 · pp. 643-660
Impact of Dilution Air Flow Rate Modulation on Exit Temperature of Gas Turbine Combustion Chamber
The management of exit temperature in gas turbine combustors is critical for ensuring turbine blade life, thermal efficiency, and operational flexibility. Dilution air injection is a primary method for cooling combustion products, yet the quantitative effect of modulating its flow rate on exit temperature remains insufficiently understood. This study employs a Computational Fluid Dynamics (CFD) approach using ANSYS FLUENT to investigate the impact of dilution air flow rate modulation on the exit temperature of a can‑type gas turbine combustion chamber operating under steady‑state conditions with methane fuel. A three‑dimensional model of the combustor was developed, and simulations were performed for dilution air mass flow rates ranging from 0.001 kg/s to 0.005 kg/s. Results show a strictly monotonic decrease in exit temperature with increasing dilution air flow, yielding a total reduction of 87 °C (approximately 4.0%) across the tested range. However, diminishing marginal cooling effectiveness is observed, with the largest reduction (43 °C) occurring between 0.001kg/s and 0.002 kg/s and progressively smaller reductions at higher flow rates. The midpoint exit temperature decreases by about 20 °C when dilution air is increased from 100% to 400% of baseline. The study quantitatively demonstrates that while increased dilution air flow significantly reduces thermal loading, benefits diminish beyond an optimal range (approximately 0.003–0.004 kg/s or a 200–250% increase). These findings provide design engineers with quantitative criteria for balancing exit temperature reduction against pressure loss, combustion efficiency, and flame stability.
🏷 Gas Turbine Combustor, Dilution Air Flow Rate, Exit Temperature, Modulation, CFD Simulation, Can Type Combustor, Thermal Efficiency.
Article 54 · pp. 661-671
Development of an AI-Based Student Depression Detection System Using the Multinomial Naïve Bayes Algorithm
Depression among students is a major mental health concern that interferes with both their emotional and academic. Depressed students go unnoticed since they hide their symptoms or confuse them with normal stress. Depression often affects focus and attention, making it hard for students to follow lessons, complete assignments, or retain information. Having a detection system in place enables early intervention that can help educators to detect warning signs early. This research used Agile Software Development Methodology in designing and developing a student depression detection system. This methodology enables continues feedback and enhancement to ensure the system is effective in meeting user needs. The process of development is segmented into short cycles referred to as sprints, with each sprint dedicated to developing particular features. For the evaluation, we selected eight (8) participants and five (5) IT experts for their technical expertise and three (3) mental health professionals for their relevance to the system’s purpose. ISO/IEC 25010 is a global standard that outlines software quality characteristics and evaluation criteria to ensure the systems' functionality, reliability, usability, efficiency, maintainability, and portability. With an overall weighted mean of 3.61 for the IT experts rated the system as highly acceptable. Similarly, the Mental Health Professionals evaluated the Student Depression Detection System with a highly acceptable rating of 3.71, reinforcing its support for student well-being. Researchers identified that the student depression detection system is a useful resource for the early identification of depression in students. It is advised to adopt the student depression detection system in colleges and schools to aid in the identification of vulnerable students.
🏷 Artificial Intelligence, Depression Detection, Multinomial Naïve Bayes Algorithm, Agile Methodology, ISO/IEC 25010.
Article 55 · pp. 672-682
Soil Testing Techniques: Constraints and Intelligent Agricultural Directions
Maintaining soil fertility is central to sustaining crop yields, managing input costs, and protecting the ecological balance of agricultural land. Soil testing serves as the primary scientific means through which farmers and agronomists gather the data needed to make informed decisions about nutrient application and land use. Parameters such as pH, nitrogen, phosphorus, potassium, organic carbon, and electrical conductivity form the foundation of any meaningful fertility assessment, directly shaping fertilizer strategies and crop planning outcomes. In practice, however, the systems currently in place for soil analysis have not kept pace with the scale and complexity of modern agricultural demands.
Most testing continues to be carried out in centralized government or private laboratories, where samples must be physically transported, processed through standardized chemical procedures, and returned to farmers in the form of reports that are often difficult to act on without technical guidance. Procedural delays, geographic inaccessibility, and the limited interpretability of nutrient data remain persistent barriers to widespread adoption, particularly among smallholder farming communities.
This paper reviews the structure and functioning of existing soil testing frameworks, examining laboratory organization, operational workflows, and result communication practices. Particular attention is given to the gap between data availability and practical decision-making at the farm level. The review further explores how portable sensing technologies, IoT-enabled data collection, and intelligent recommendation systems might address these shortcomings and support a transition toward more accessible, farmer-centric soil health solutions.
Maintaining soil fertility is central to sustaining crop yields, managing input costs, and protecting the ecological balance of agricultural land. Soil testing serves as the primary scientific means through which farmers and agronomists gather the data needed to make informed decisions about nutrient application and land use. Parameters such as pH, nitrogen, phosphorus, potassium, organic carbon, and electrical conductivity form the foundation of any meaningful fertility assessment, directly shaping fertilizer strategies and crop planning outcomes. In practice, however, the systems currently in place for soil analysis have not kept pace with the scale and complexity of modern agricultural demands.
Most testing continues to be carried out in centralized government or private laboratories, where samples must be physically transported, processed through standardized chemical procedures, and returned to farmers in the form of reports that are often difficult to act on without technical guidance. Procedural delays, geographic inaccessibility, and the limited interpretability of nutrient data remain persistent barriers to widespread adoption, particularly among smallholder farming communities.
This paper reviews the structure and functioning of existing soil testing frameworks, examining laboratory organization, operational workflows, and result communication practices. Particular attention is given to the gap between data availability and practical decision-making at the farm level. The review further explores how portable sensing technologies, IoT-enabled data collection, and intelligent recommendation systems might address these shortcomings and support a transition toward more accessible, farmer-centric soil health solutions.
🏷 Soil Testing, Soil Health, Precision Agriculture, Sustainable Agriculture, IoT, Decision Support Systems.
Article 56 · pp. 683-697
Machine Learning-Based Intrusion Detection System for Network Security
Cyberattacks pose a significant risk to network environments today as they can lead to the compromise of sensitive information, disruption of digital service, and compromise the confidentiality, integrity, and availability of information systems. Signature-based intrusion detection systems and firewalls are effective, but they can't detect unknown, modified and zero-day attacks. This research paper proposes a machine-learning approach to build an Intrusion Detection System for network security based on the NSL-KDD dataset, which helps to overcome this limitation. The proposed system uses supervised machine learning algorithms to classify the network traffic as either normal traffic or attack traffic. The methodology consists of data collection, data preprocessing, categorical features encoding, feature selection, model training, testing, prediction and evaluation. Random Forest is the primary classification algorithm and Support Vector Machine and Logistic Regression (LR) are employed in comparison. The implementation of the system is done in Python with the use of libraries like Pandas, NumPy, Scikit-learn, and Matplotlib. The illustrative results demonstrate that the Random Forest attained the most noteworthy correctness of 96.20% which was superior to SVM and Logistic Regression. The confusion matrix, attack distribution and feature-importance analysis further illustrates the ability of machine learning to be used for effective intrusion detection. These results should be considered as illustrative only and once the final model is run on the chosen data set, these should be swapped with the experimental results. The overall findings of the study indicate that application of machine learning can enhance the performance of IDS and it offers a practical base for future real time and deep learning based intrusion detection systems.
🏷 Intrusion Detection System, Machine Learning, Network Security, Cybersecurity, NSL-KDD, Random Forest
Article 57 · pp. 698-712
Design and Development of an Oil Conditioning System for Industrial Applications
Hydraulic and lubrication systems are commonly used in industrial applications for power transmission, lubrication, cooling, and motion control. The efficiency and reliability of these systems depend heavily on the cleanliness and condition of hydraulic oil. Throughout continuous operation, hydraulic oil gets contaminated with metallic wear particles, moisture, air, sludge, and oxidation products, leading to issues like abrasive wear, pressure instability, cavitation, internal leakage, and early failure of hydraulic components.
This research aims to design and develop an Oil Conditioning System (OCS) for industrial hydraulic applications by using offline kidney-loop depth filtration technology. The system uses cellulose-based depth filtration media for ongoing hydraulic oil conditioning and contamination control. The experimental setup targets improving hydraulic oil cleanliness according to ISO 4406 standards, lowering particulate contamination, controlling moisture, and boosting hydraulic system reliability.
Experimental analysis shows a significant drop in contamination levels, better ISO cleanliness codes, reduced moisture, and improved hydraulic oil quality. The proposed Oil Conditioning System enhances machine reliability, lengthens hydraulic oil service life, decreases maintenance frequency, minimises downtime, and facilitates predictive maintenance in modern industrial settings.
🏷 Hydraulic Oil, Oil Conditioning System, Hydraulic Filtration, ISO 4406, Depth Filtration
Article 58 · pp. 713-721
Performance Evaluation of Eco-Friendly Sucrose-BasedPlasticizer in Concrete
Concrete is one of the most widely used construction materials in the world due toitsstrength, durability, and adaptability in various structural applications. However, theproduction and use of chemical admixtures in concrete may have environmental impacts, which has encouraged researchers to explore natural and eco-friendly alternatives. Organicsucrose, a naturally available carbohydrate, has the potential to act as a plasticizer andretarding admixture used in small quantities in concrete mixtures. The present studyinvestigates the effect of organic sucrose on the workability and mechanical propertiesofconcrete. Different concrete mixes were prepared by adding varying percentages of sucrose
respect to the weight of cement. Laboratory tests such as slump test and compressivestrength test were conducted to evaluate the fresh and hardened properties of concrete. Theexperimental results indicate that the addition of a small amount of sucrose improves theworkability of concrete and slightly delays the setting time without significantly affectingitscompressive strength. The findings suggest that organic sucrose can be used as a sustainableand eco-friendly plasticizer in concrete, which may help in reducing dependency on syntheticchemical admixtures. This study highlights the potential of natural materials in developingenvironmentally responsible construction practices.
🏷 Organic sucrose, plasticizer, eco-friendly admixture, concrete, workability, sustainable construction.
Article 59 · pp. 722-726
From Microplastics to Nanoplastics: Human Exposure Pathways, Molecular Toxicity, and Emerging Health Risks
The widespread accumulation of plastic waste in the environment has led to the generation of microplastics (MPs; <5 mm) and nanoplastics (NPs; <1 μm), prompting growing concern about their possible effects on human health. (Andrady, 2017; Geyer et al., 2017). Although microplastics are well characterized, nanoplastics have only recently gained attention as a potentially more harmful class because their nanoscale dimensions increase reactivity and permit deeper biological penetration (Gigault et al., 2018; Koelmans et al., 2022; Sharma et al., 2023). This review critically examines recent advancements (2020–2025) in the sources, environmental distribution, exposure pathways, analytical detection, and toxicological effects of micro- and nanoplastics, with a primary focus on nanoplastics (Li et al., 2023; Singh et al., 2025). Comparative evaluation highlights a shift from physical and carrier-based effects of MPs to molecular and cellular toxicity associated with NPs (Yong et al., 2020; Jin et al., 2019). Despite growing evidence of their presence in human tissues, significant uncertainties remain regarding exposure levels and long-term health implications (Leslie et al., 2022; Nihart et al., 2025; Ragusa et al., 2021).
🏷 Microplastics, Nanoplastics, Human exposure pathways, Molecular toxicity, Environmental distribution
Article 60 · pp. 727-742
Indian Startups and Employment Self Reliance in the Era of Atmanirbhar Bharat
The rapid expansion of startups in India has significantly contributed to economic growth, innovation, and employment generation under the vision of Atmanirbhar Bharat. Indian startups are increasingly promoting employment self-reliance through entrepreneurial opportunities, digital transformation, and innovation-led business models across sectors such as fintech, agritech, healthtech, logistics, and e-commerce. The present study examines the role of Indian startups in generating direct and indirect employment opportunities and evaluates the influence of government initiatives, technological adoption, innovation capability, and financial accessibility on employment self-reliance.
The study adopts a descriptive and analytical research design based on both primary and secondary data. Primary data were collected from 200 respondents including startup founders, managers, employees, and entrepreneurship experts through structured questionnaires. Secondary data were gathered from government reports, journals, books, research articles, Startup India databases, and policy documents. Statistical tools such as percentage analysis, correlation analysis, regression analysis, and chi-square tests were applied using SPSS and MS Excel.The empirical findings reveal that government support has a positive relationship with startup sustainability and employment generation (r = 0.68, p < 0.05). Innovation capability also demonstrates a significant impact on employment creation (R² = 0.57), while digital technology adoption contributes substantially to startup growth and labor scalability. The study further identifies that startups have generated both direct employment in technology, operations, and management and indirect employment through logistics, supply chains, digital marketing, and gig-based platforms. The findings also indicate that rural startups and women-led enterprises are increasingly contributing toward inclusive development and reduction of rural-to-urban migration.However, startups continue to face challenges related to funding accessibility, workforce skill gaps, cybersecurity risks, regulatory complexity, and social security concerns in the gig economy. The study concludes that a strong startup ecosystem supported by policy reforms, digital infrastructure, industry-academia collaboration, and innovation-oriented education can significantly strengthen employment self-reliance and sustainable economic development in India.
🏷 Indian Startups, Employment Generation, Atmanirbhar Bharat, Self-Reliance, Entrepreneurship, Digital Transformation, Gig Economy
Article 61 · pp. 743-759
Influence of Post-Disaster Housing Reconstruction on Community Stability in Ahoada East Local Government Area, Rivers State, Nigeria
This study examined the influence of post-disaster housing reconstruction on community stability in Ahoada East Local Government Area, Rivers State, Nigeria. The area is frequently affected by flooding that disrupts housing, livelihoods, and social order. The study focused on three reconstruction dimensions: damage and needs assessment, emergency shelter provision, and construction and rehabilitation strategies. A descriptive survey research design was adopted. The population comprised 1,240 residents of flood-affected communities. A sample of 300 respondents was selected using simple random sampling. Data were collected using a structured questionnaire titled Post-Disaster Housing Reconstruction and Community Stability Questionnaire (PDHRCSQ). Data were analyzed using mean, standard deviation, and simple linear regression at 0.05 significance level. Findings revealed that damage and needs assessment significantly influenced community stability (R²=0.38). Emergency shelter provision also had a significant influence (R²=0.44). Construction and rehabilitation strategies showed the strongest influence (R²=0.50). The study further showed that all variables had positive and significant relationships with community stability. It was concluded that effective housing reconstruction enhances recovery, reduces displacement, and strengthens social cohesion. The study recommends improved assessment systems, coordinated shelter provision, and enforcement of resilient construction standards. The findings underscore the importance of integrating disaster risk reduction into housing reconstruction policies in flood-prone communities such as Ahoada East. This approach will enhance resilience and ensure sustainable community development after flooding events in the study area. Consequently, policymakers and disaster management agencies should prioritize integrated reconstruction frameworks to strengthen long-term stability and reduce future disaster vulnerability in the region and resilience outcomes
🏷 Post-disaster Housing Reconstruction, Community Stability, Flooding, Disaster Recovery.
Article 62 · pp. 760-767
Ethical Challenges in AI Decision-Making Systems
Decision-making powered by artificial intelligence (AI) has become pervasive in high-stakes domains such as healthcare, criminal justice, finance, and human resource management. While AI systems promise greater efficiency, consistency, and objectivity, they introduce significant ethical risks including algorithmic bias, opacity, accountability gaps, and privacy erosion. This paper provides a comprehensive analysis of these challenges, examining the origins and manifestations of bias, the technical and ethical imperatives for explainability, responsibility diffusion in complex AI supply chains, and tensions between data-driven innovation and fundamental rights. It evaluates major regulatory responses, notably the European Union AI Act, and proposes a multi-stakeholder ethical governance framework. The study argues that responsible AI deployment requires continuous co-evolution of technical solutions, organizational practices, and adaptive regulation to ensure fairness, transparency, and human-centric outcomes.
🏷 Algorithmic bias, Explainable AI, AI ethics, Fairness, Transparency, Accountability, Data governance, EU AI Act
Article 63 · pp. 768-775
Systematic Evaluation of Managed Pressure Drilling Variants: A Framework-Based Comparative Review for Operational Decision Support
MPD has evolved from a niche technique into a mainstream drilling capability over the last decade. It is now used in HPHT, deepwater, unconventional tight-gas, and sour-gas reservoirs. Despite this widespread adoption, the operational question of which MPD variant is best suited to a particular well profile has historically been addressed in the published literature on a case-by-case basis, without structured comparative synthesis. This paper addresses that gap through a systematic framework-based evaluation of fourteen MPD case-study publications sourced from the SPE and IADC literature between 2015 and 2025. A six-dimensional analytical framework, inductively developed from the recurring performance themes within the corpus, serves as the uniform evaluative instrument. The six dimensions encompass: well-control sensitivity and detection thresholds; narrow-margin pressure window navigation; non-productive time (NPT) and invisible lost-time reduction; rate-of-penetration (ROP) enhancement relative to conventional baselines; cementing and tripping operational reliability; and economic performance across deployment contexts. Synthesis findings confirm that MPD delivers well-control detection sensitivity approximately one to two orders of magnitude superior to conventional pit-volume methods; that variant selection materially affects operational outcomes; that NPT reduction is robustly demonstrated while ROP enhancement evidence remains comparatively sparse; and that economic viability is strongly contingent on multi-well campaign scale. A structured application-domain selection map, matching ten canonical operational situations to corpus-preferred MPD variants across three evidence-calibrated confidence tiers, is presented as the primary decision-support contribution of this review.
🏷 Managed Pressure Drilling; Well Control; Narrow-Margin Drilling; Comparative Framework; CBHP; PMCD; ABP; CML; Selection Map; Drilling Engineering
Article 64 · pp. 776-779
An Optimized Hybrid Machine Learning and Deep Learning Framework for Phishing Detection
Phishing remains one of the most persistent cybersecurity threats, targeting users through fraudulent emails, websites, and evolving digital platforms. Although machine learning (ML) and deep learning (DL) techniques have improved detection rates, existing models still face limitations such as poor adaptability to new attack patterns, reliance on manual feature extraction, and lack of multilingual support. This paper reviews recent approaches in phishing detection and identifies key gaps in current systems. Based on this analysis, a hybrid framework is proposed that combines automated feature extraction, optimization techniques, and multilingual capability. The proposed approach aims to enhance detection accuracy, robustness, and scalability in real-world environments.
🏷 Hybrid Machine Learning, Deep Learning, Framework
Article 65 · pp. 780-790
Ultrasonic Glasses for the Blind
Ultrasonic Glasses for the Blind is a real-time wearable assistive system developed to improve the mobility and safety of visually impaired individuals through intelligent obstacle detection and voice-based navigation assistance. The system integrates an HC-SR04 ultrasonic sensor, Arduino Nano microcontroller, and ISD1820 voice module to continuously monitor surrounding obstacles and provide immediate audio alerts. Distance measurement is performed using ultrasonic echo-time calculation and processed using predefined threshold logic for real-time obstacle identification.
The complete prototype is mounted on a lightweight spectacle frame and powered by a rechargeable lithium battery, ensuring portability and continuous operation for daily use. Experimental evaluation demonstrated an obstacle detection accuracy of 97%, an average response time of 150 ms, and an effective detection range of 2 cm to 400 cm, with continuous battery backup of 5 hours. The total implementation cost of approximately ₹1500 makes the proposed system affordable and accessible for practical deployment.
Compared to conventional buzzer-based assistive devices, the proposed voice-alert mechanism offers clearer and more intuitive user guidance, improving usability and response efficiency. The experimental results confirm that the system provides a reliable, low-cost, and efficient embedded assistive technology solution that significantly enhances independent mobility and safety for visually impaired users.
🏷 Ultrasonic Sensor, Arduino Nano, Assistive Technology, Embedded Systems, Obstacle Detection, Voice Module, Wearable Device, Blind Navigation
Article 66 · pp. 791-799
Islalink: A Web-Based Navigation, Booking and Tourism Guide for Santiago Island Travelers
For many years, traditional motorized boats (bangkero) and large transport barges have been the primary forms of public transportation connecting the mainland to Santiago Island in Bolinao, Pangasinan. However, managing these services presents significant difficulties due to their decentralized nature, lack of standardized booking, and heavy reliance on traditional cash fare and paper manifest collection methods. Enhancing fare collection, capacity management, and route information has become increasingly important to address the inefficiencies of these manual processes, such as payment delays, human transcription errors, and capacity uncertainties. This study developed IslaLink: A Web- based Navigation, Booking, and Tourism Guide for Santiago Island Travelers. This comprehensive system integrates automated booking. The system uses digital booking technology to streamline reservations, allowing passengers to confirm and pay for their slots electronically while enabling real- time manifest tracking and location information for operators and tourism staff. This digital integration significantly enhances transaction speed, data accuracy, and operational efficiency, ultimately transforming the island commuting and tourism experience. The study is intended for Santiago Island travelers and boat operators / stakeholders, aiming to implement technological solutions that optimize commuting and tourism operations, specifically along the Santiago Island transport routes within Bolinao, Pangasinan. The system enables travelers to effortlessly book slots digitally, which automatically calculates the fee and tracks capacity. This method removes the need for manual fare management, booking delays, and human mistakes. The study concludes with recommendations for future expansion and further improvements to enhance overall efficiency in public transportation and tourism management.
🏷 Web-Based Navigation, Booking, Tourism Management, Santiago Island, Bolinao Pangasinan
Article 67 · pp. 800-818
Knowledge Management (Km) Information System for Laguna State Polytechnic University - Santa Cruz Campus
Documents in any institution have been outnumbered and scattered in various repositories, hindering efficient information retrieval and threatening institutional memory. This study developed a web-based Knowledge Management Information System for Laguna State Polytechnic University - Sta. Cruz Campus that consolidates core institutional documents such as research outputs, policies, course materials and extension documents into a centralized, secure, and searchable platform. Employing a Developmental and Experimental Research Design, KMIS engineered four core intelligent components: Role-Based Access Control centralized repository, Retrieval Augmented Generation pipeline that uses vector embeddings to utilize semantic search and LLM-generated summaries from natural language queries. Lastly, the automated QPRO analysis engine. The developed KRA text classification model achieved an overall accuracy of 98% and automatically categorized institutional reports against the 22 Key Result Areas from the 2025-2029 LSPU Strategic Plan.
🏷 Knowledge Management, Centralized Repository, Role-based Access Control, Semantic Search, Retrieval Augmented Generation, LLM, Transformers
Article 68 · pp. 819-828
Gaps in Global Governance for AI-Assisted Cross-Border Cloud Forensics and the Imperative for the Multi-Jurisdictional Investigative Protocols for AI-Informed Digital Evidence (MIP-AIDE) Framework
The rapid globalization of digital services has made international cloud data transfers essential, yet these processes frequently collide with divergent regional privacy regimes, such as the conflict between the U.S. CLOUD Act (Clarifying Lawful Overseas Use of Data Act) and the European Union's General Data Protection Regulation (GDPR). Current cross-border evidence acquisition relies on slow Mutual Legal Assistance Treaties (MLATs) or fragmented extraterritorial laws that often bypass data sovereignty. Furthermore, the integration of artificial intelligence (AI) in forensics introduces "black box" opacity, which threatens the admissibility of digital evidence and undermines due process. This research addresses these structural failures by proposing the MIP-AIDE framework to unify jurisdictional and AI accountability standards.
Objectives
The primary objectives are to design a tiered procedural protocol that computationally embeds international compliance rules to resolve extraterritorial conflicts; define technical standards that translate forensic AI outputs into judicial admissibility criteria, such as error rates and bias audits; and innovate a governance paradigm that integrates Cloud Service Providers (CSPs) as auditable partners in the legal process.
Methods
This project employs a mixed-method approach consisting of three phases: (I) doctrinal legal analysis and benchmarking of international standards like the Daubert and Mohan criteria; (II) a technical review of Explainable AI (XAI) techniques like SHAP and LIME; and (III) Design Science Research (DSR) to synthesize these findings into the MIP-AIDE framework.
Results
The framework delivers three core components: a Legal Gateway Decision Matrix for automated compliance checking; Minimum Technical Explanatory Requirements (MTERs) to package AI outputs into court-admissible artefacts; and a Collaborative Stewardship Model using Service Level Agreements (SLAs) to formalize the role of CSPs.
Conclusions
MIP-AIDE closes the protocolization, AI-admissibility, and non-state actor governance gaps. By providing a concrete, computable solution, the framework ensures that AI-assisted forensics achieve the speed, transparency, and legitimacy required for 21st-century digital justice.
🏷 MIP-AIDE Framework, Cloud Forensics, Cross-Border, Explainable AI (XAI), Collaborative Stewardship
Article 69 · pp. 829-835
Smart Water Quality Surveillance System Using Iot-Based Remote-Controlled Boat
Water pollution has become a major environmental concern due to rapid industrialization, urbanization, and agricultural activities. Traditional water quality monitoring methods are time-consuming and lack real-time capabilities. This paper presents an IoT-based smart water quality surveillance system using a remotely controlled boat integrated with sensors and wireless communication. The system utilizes an ESP32 microcontroller to measure parameters such as pH, turbidity, total dissolved solids (TDS), and temperature. The collected data is transmitted in real time to the Blynk IoT platform for remote monitoring and analysis. The boat can be navigated using a mobile application, enabling monitoring in remote and hazardous locations. The proposed system provides a cost-effective, efficient, and scalable solution for continuous water quality monitoring and environmental protection.
🏷 IoT, Water Quality Monitoring, ESP32, Smart Boat, pH Sensor, Turbidity, TDS, Blynk
Article 70 · pp. 836-846
Emerging Innovation and Digital Entrepreneurship in India : Success Drivers, Sectoral Case Studies, And Future Resilience.
The idea of digital technology has gone global and has transformed the nature of entrepreneurship thus leaving the old ways of running the business different. This paper examines the digital entrepreneurship in India. It looks at the interaction between the programs of the government like the Digital India, the new technology as well as the rising gig market. Reviewing such companies, as Zerodha, Nykaa, Zomato, and PhysicsWallah, the study highlights such key success factors as low costs growth and targeting local customers, yet the presence of structural issues, which remain in place. It also examines innovative strategies that could assist these companies to remain robust whenever there is a shift in the market.
🏷 Digital Entrepreneurship, Startup India, FinTech, EdTech, Future Resilience.
Article 71 · pp. 847-864
Green Treatment Strategies for Tannery Wastewater Employing Natural Polymers
The leather tanning industry is one of the most water-intensive industrial sectors and generates highly polluted wastewater containing organic matter, inorganic salts, sulfides, chromium, and recalcitrant compounds. Conventional treatment methods rely heavily on chemical coagulants, which often produce non-biodegradable sludge and pose potential environmental and health risks. The present study investigates an eco-friendly and sustainable approach for tannery effluent treatment using a natural polysaccharide extracted from Strychnos potatorum L. seeds. The extracted polysaccharide was instrumentally confirmed using Fourier Transform Infrared (FTIR) spectroscopy, which revealed the presence of hydroxyl, carboxyl, and amine functional groups responsible for coagulation activity and SEM analysis of raw polysaccharide and treated sludge for the confirmation of coagulation process..Physicochemical characteristics of untreated tannery wastewater were analyzed and compared with treated samples following coagulation–flocculation using varying doses of the extracted polysaccharide. Process optimization studies identified an optimum coagulant dosage of 30 mg/L, rapid mixing at 120 rpm for 2 min, slow mixing at 40 rpm for 20 min, and a settling time of 30 min for maximum treatment efficiency. Significant reductions in turbidity, total suspended solids, chemical oxygen demand, and biological oxygen demand were observed, with maximum removals of 78% color, 62.5% total dissolved solids, 70% COD, and substantial reduction in suspended solids and BOD at the optimized dosage, demonstrating effective pollutant removal. The results highlight the potential of Strychnos potatorum seed polysaccharide as a biodegradable, low-cost alternative to conventional chemical coagulants. This green treatment strategy offers a promising pathway for sustainable tannery wastewater management and supports the transition toward environmentally responsible industrial practices.
🏷 Department of Biotechnology, Mother Teresa Women’s University, Kodaikanal, TamilNadu, India-624101
Article 72 · pp. 865-874
Haar Cascade Classifier-Based System for Student Attendance Through Face Recognition
Attendance systems are essential in academic and organizational settings to track individual participation and discourage absenteeism. Traditional methods, such as paper-based attendance sheets, are tedious and susceptible to fraud, as well as impersonation and having someone else stand in for you. To resolve these problems, this study developed a haar cascade classifier-based system for student attendance through face recognition using Python and a webcam to detect and identify students in real-time. The web application applies the Haar Cascade Classifier from the OpenCV library, a machine learning-based algorithm that detects facial features. After students are enrolled, the system captures facial images, extracts features, and stores them in a database. During lectures, the system matches real-time images with the database, automatically recording attendance if a match is found. Performance evaluation showed that both enrollment and attendance processes were completed in under one minute, offering significant efficiency improvements over manual methods while usability testing with ten participants confirmed high satisfaction ratings averaging above 4.0 on a five-point Likert scale across navigation, clarity, form layout, and overall experience. This system effectively eliminates impersonation, reduces lecturer workload, and encourages punctuality, thereby contributing to improved academic integrity and institutional efficiency.
🏷 Face Recognition, Attendance System, Haar Cascade Classifier, Web camera, Real-time Detection
Article 73 · pp. 875-879
Automating LCL Shipment Booking and Enhancing Customer Experience with AI - A Study on Process Efficiency and Digital Transformation in Logistics
Recent changes in the global economy and the increase in digital technology, plus the customer's demand for better service and communication from the logistics provider, have driven rapid changes in the logistics industry. In this context, there is a growing reliance on Less than Container Load (LCL) consolidation services as they provide an economical way for multiple customers to share a shipping container. However, operational inefficiencies such as delays in booking, lack of communication, reliance on manual coordination of operations and difficulty with documentation still negatively impact service quality and customer satisfaction.
The purpose of this research project was to determine the role of Automation (via Artificial Intelligence [AI]) in improving the efficiency of booking shipments for customers and ultimately improving the customer experience. The research involved an analysis of the various operational issues faced by logistics providers related to internal coordination and communication practices in the management of the consolidation-based logistics service process. The study involved administering a structured questionnaire to 50 respondents and using various statistical analysis methods such as percentage calculations, One-Way ANOVA, Independent samples t-tests and Chi-Square tests to evaluate the responses for the research purpose.
Manual processes and the time it takes to coordinate work are the main causes for inefficiency in operations. Employees surveyed indicated they are generally aware of and accept the use of AI-based systems, such as automated booking and documentation management. The results show AI-driven process automation can increase operational efficiencies, reduce the time it takes to book shipments, increase the accuracy of communications, and increase customer satisfaction for logistics operations.
🏷 Artificial Intelligence, LCL Consolidation, Logistics Automation, Shipment Booking, Customer Experience, Process Efficiency, Digital Transformation.
Article 74 · pp. 880-882
Reclaiming Numeracy: Evaluating the Impact of the Culturo -Techno-Contextual Approach (CTCA) on Mathematical Perception in Sierra Leonean Higher Education
Negative perception is a significant barrier to mathematical proficiency among tertiary students in West Africa. This study evaluates the impact of the Culturo-Techno-Contextual Approach (CTCA) on the cognitive and affective dispositions of 376 first-year students at the University of Makeni (UNIMAK), Sierra Leone. Findings from a quasi-experimental analysis demonstrate that CTCA significantly enhances student confidence, participation, and conceptual clarity. The study concludes that by centering instruction on the "Culturo-Techno" axis, educators can effectively dismantle "math anxiety" and improve academic performance.
🏷 CTCA, student perception, STEM education, culturally responsive pedagogy, math anxiety, ICT in education, Sierra Leone, pedagogical innovation, self-efficacy, learner engagement.×
Article 75 · pp. 883-896
Medicinal and Therapeutic Potential of Turmeric (Curcuma longa): A Comprehensive Review
Turmeric (Curcuma longa) has been a fundamental element of traditional medicine for ages, especially in Ayurvedic and Chinese practices. The bioactive molecule curcumin has attracted considerable interest for its medicinal potential. This review examines the therapeutic qualities of turmeric, emphasizing its anti-inflammatory, antioxidant, and antimicrobial actions. Turmeric's capacity to regulate many signaling pathways, including NF-kB, MAPK, and PI3K/Akt, enhances its therapeutic efficacy in the management of chronic conditions such as cancer, cardiovascular illnesses, and neurological disorders. Furthermore, turmeric's efficacy in wound healing, intestinal health, and metabolic regulation is examined. The review also examined the problems related to curcumin's bioavailability and methods to improve its medicinal efficacy. Turmeric's diverse medical characteristics render it a viable natural substance for both preventive and therapeutic applications. Additional research is necessary to thoroughly clarify its therapeutic potential and enhance its applications in contemporary medicine.
🏷 Turmeric, Curcuma longa, Curcumin, Anti-inflammatory, Antioxidant, Antimicrobial, Cancer, Cardiovascular disease
Article 76 · pp. 897-918
Assessment of Dumaran National High School Teachers’ Technological-Pedagogical Comspetence in Classroom Instructional Delivery
This study assessed the self-reported technological-pedagogical competence of teachers at Dumaran National High School in classroom instructional delivery. Specifically, it sought to determine the demographic profile of the respondents in terms of age, sex, teaching position, highest educational attainment, and years in teaching; assess their technological-pedagogical competence in terms of Technology Operations and Concepts and Pedagogy; and examine the relationship between their demographic profile and technological-pedagogical competence. The study was framed as a school-based local assessment intended to generate context-specific evidence for professional development planning at Dumaran National High School.
The study employed a descriptive-correlational research design using total enumeration. Thirty-three teachers participated in the study, excluding the researcher to avoid bias and conflict of interest. The research instrument was adapted from the National ICT Competency Standards for Teachers and was administered through Google Forms. Frequency counts, percentages, means, Cronbach’s alpha, Fisher’s Exact Test, cross-tabulations, Mann-Whitney U test, and Kruskal-Wallis test were used to analyze the data.
Findings revealed that most respondents were 25–30 years old, female, Teacher I, Bachelor’s Degree holders, and had 0–3 years of teaching experience. The teachers demonstrated a moderately high level of self-reported technological-pedagogical competence in both Technology Operations and Concepts and Pedagogy, with an overall grand mean of 2.99. Reliability analysis showed excellent internal consistency for the Technology Operations and Concepts subscale, Pedagogy subscale, and overall instrument. Although the overall competence level was moderately high, lower-rated areas were identified in the use of spreadsheets, online repositories, information and data management, and technology-supported assessment.
Using Fisher’s Exact Test, teaching position was found to have a significant association with categorical technological-pedagogical competence, while age, sex, highest educational attainment, and years in teaching were not significant. Follow-up cross-tabulations showed that teaching position was also associated with age and years in teaching, suggesting that the teaching-position result should be interpreted cautiously. Supplementary non-parametric tests using continuous mean scores showed no significant differences according to sex and highest educational attainment, but significant differences were found across age groups, teaching positions, and years-in-teaching groups.
The study concluded that teachers at Dumaran National High School generally perceived themselves as moderately competent in integrating technology into classroom instruction. However, continuous school-based professional development is needed, particularly in spreadsheets, digital repositories, data management, and technology-supported assessment. Since the study was conducted in a single school with 33 respondents and relied on self-reported data, the findings should be interpreted as context-specific and not broadly generalizable to all teachers.
🏷 Assessment, National High School, Technological
Article 77 · pp. 919-923
Comprehensive Sexuality Education: Reflections from Boudh, Odisha, India
Comprehensive sexuality education is seen as a key intervention to address the holistic development of adolescents with proper knowledge at the grassroots level. Lack of inclusion of the learners in the school curricular, discussions at community level, many a times imposes the superstitious practices those are restricting proper development. Breaking the Silence approach, is designed with a curriculum implementation was developed and tested in the district of Boudh. . This paper is a formative evaluation using case study method to capture the best practices.
Comprehensive sexuality education (CSE) is being implemented with a greater commitment to practice as a result of global networks and initiatives. The expertise in guidance, designing curricula, and strengthening implementation tools and techniques is coming up with a wide range of approaches. The present study suggests a behavior change approach through sharing best practices in the Boudh district of Disha, India, for CSE implementation.
This report provides an overview of the status of CSE implementation, drawing on specific information generated through analysis of the qualitative data collected from the field. The report examines the evidence base for CSE and, through a series of case studies, explores initiatives that are setting the standard and pioneering new practices in the delivery of CSE.
🏷 Comprehensive, Sexuality, Education, Reflections
Article 78 · pp. 924-936
Personalized AI Tutor An Intelligent Adaptive Learning System for Early Education
Personalized learning is increasingly gaining importance in the field of educational technology, particularly among young learners who require interactive tools and personalized content based on their learning speeds. Most learning platforms currently available offer identical content to all learners irrespective of differences in their comprehension, attention, and revision capabilities. This paper introduces an innovative AI Tutor designed for students in classes 1 to 5 by integrating adaptive learning, quizzes, speech communication, and analytical features in one application. The system uses HTML, CSS, JavaScript, Firebase Authentication, Firebase Cloud Firestore, and web browser speech recognition technologies. The learning content includes topic-specific lessons related to alphabets, numbers, shapes, colors, animals, fruits, transport, and objects. Student participation is monitored based on quiz scores, progress level, errors made, pronunciation practice, and completion status of topics. The system recommends relevant next topics for learning, modifies the practice flow, and facilitates smooth learning progression based on user data. Experimental observations indicate enhanced engagement, efficient topic tracking, and valuable personalization suggestions for early learners.
🏷 Personalized learning, artificial intelligence, adaptive learning, educational technology, Firebase, speech recognition
Article 79 · pp. 937-943
An Illuminative Analysis of College Students' Career Adaptability in Relation to Their Internship Experiences
There are a number of factors that contribute to the rising rate of unemployment among college graduates, one of which is a discrepancy between technological and economic advancements and workforce readiness. Individual career readiness is thought to assist businesses in locating such high-quality potential employees. It is anticipated that an internship program for college students will provide students with some preparations to enable them to successfully adjust to their workplace. Savickas (1997) defined career adaptability as an individual's capacity to adjust to a workplace environment. This study aims to investigate college students with internship experience's career adaptability. A quantitative descriptive method and a non-experimental design were used in this study. This study used a quota sampling method to select 64 college students who had completed an internship. The Career Adapt-Abilities Scale, developed by Savickas & Profelli (2012), contained a questionnaire whose reliability was 0.948. The study found that 76.6 percent of college students who had completed an internship exhibited high career adaptability. In addition, the profile of each aspect of career adaptability demonstrated that the majority of college students who have completed an internship also have a high profile in all aspects. It is possible to draw the conclusion that respondents already possess the capability of future workplace adaptation.
🏷 Flexibility in a career; Experiential Internship; Students at colleges.
Article 80 · pp. 944-949
Examination of the Relationship between Human Rights, Governance, and Emotions Within the Indian Legal System
The relationship between human rights, governance, and emotions within the Indian legal system cannot be overstated. The Indian Constitution provides for basic human rights while establishing democratic structures of governance for justice, freedom, equality, and dignity. Nonetheless, the realization of these principles is profoundly affected by various emotions, including fear, hatred, compassion, humiliation, empathy, and group feeling. The purpose of this research paper is to investigate the connection between human rights, governance, and emotions within the Indian legal system.
The methodological framework that will be used for this study will be a doctrinal approach in which the linkages between the different governing institutions, such as the judiciary, legislature, executive, NHRC, and NGOs, as well as emotions in human rights law, will be examined on the basis of constitutional documents, legislative acts, judicial decisions, literature, and policies. In particular, the cases relating to human rights violations, including custodial torture, gender discrimination, caste discrimination, freedom of expression, internet shutdown, and public interest litigation, will be considered in this research. This is because sometimes there are linkages between social violations and emotions, and people may use any means as a response to any kind of violation committed against them by other people.
🏷 Human Rights, Governance, Emotions, Indian Constitution and Social Justice.
Article 81 · pp. 950-957
Severity Prediction of Poliomyelitis Using Mathematical Model
The major challenge in severity detection of poliomyelitis is its stealthy nature. Hence in this research work Mathematical models have been developed to predict severity of poliomyelitis. presents a comparison of Mathematical models for Severity Prediction of Poliomyelitis. Three Mathematical models were developed viz. Ordinary Differential Equations (ODE), Partial Differential Equations (PDE), and Agent-Based Models (ABM) by using MATLAB IDE. These models show the disease progression in the body from different points of view. These models give a severity score from 0 to 5. It uses patient age and symptoms like fever temperature, muscle strength, reflex score, and breathing condition as input. We trained these mathematical models using records of 1,500 patient, it shows 92% average in predicting the severity level of Poliomyelitis. It supports the doctors to check the severity-level of Poliomyelitis.
🏷 Poliomyelitis, Mathematical Modeling, MATLAB, ODE, PDE, ABM
Article 82 · pp. 958-961
Environmental Impact Assessment on Rural Water Supply Scheme Under Jal Jeevan Mission.
The Jal Jeevan Mission (JJM), launched by the Government of India in 2019, aims to provide safe and adequate drinking water through Functional Household Tap Connections (FHTC) to all rural households. This research paper evaluates the environmental impacts of rural water supply schemes implemented under JJM. The study analyzes positive impacts such as improved water quality, reduction in waterborne diseases, and sustainable water management, as well as potential environmental challenges including groundwater depletion and infrastructure-related ecological disturbances. The findings suggest that while JJM significantly improves rural living standards, proper environmental management and sustainable water resource planning are essential for long-term success.
🏷 Environmental, Assessment, Rural Water Supply
Article 83 · pp. 962-970
VEGA: An AI-Based Software Framework for CT and X-Ray Luggage Threat Detection
The rapid growth of air travel and global logis- tics has intensified the need for efficient and reliable security screening systems. Conventional baggage inspection relies heavily on human interpretation of Computed Tomography (CT) and X-ray images, which is time-consuming and prone to fatigue- related errors. This paper presents VEGA, an AI-based software framework that enhances luggage threat detection by intelligently analyzing the output of existing CT and X-ray scanners. Instead of focusing on scanning hardware or radiation physics, VEGA applies deep learning techniques to pre-captured scan images to identify and classify objects within luggage. The system performs multi-dimensional image analysis, highlights suspicious regions, and assigns threat confidence scores to assist security operators. Experimental results demonstrate improved detection accuracy and reduced false alarms compared to manual screening. VEGA offers a scalable and cost-effective solution for integrating AI into modern airport, border, and logistics security workflows.
🏷 Artificial intelligence, baggage screening, com- puted tomography (CT) images, deep learning, luggage threat detection, object detection, security systems, X-ray imaging.
Article 84 · pp. 971-993
A Quantitative Analysis of Information Verification Habits and Cross-Checking Behaviors Toward AI Hallucinations Among College Students
This study investigated the information verification habits and cross-checking behaviors of Computer Studies students when encountering Generative Artificial Intelligence (AI) hallucinations. The primary objective was to determine how students' demographic and AI usage profiles influence their active verification methods, error detection success rates, and perceived reliability of AI tools. A quantitative descriptive-correlational design was adopted, utilizing a validated, scenario-based digital survey deployed via Google Forms to 200 Bachelor of Science in Information Technology (BSIT) and Bachelor of Science in Computer Science (BSCS) students at Quezon City University. The findings revealed that 84.5% of the respondents were highly active daily or weekly users of generative AI, primarily utilizing general web chatbots (58.5%) and dedicated Integrated Development Environment (IDE) assistants (41.5%) for debugging (45.5%) and code generation (33.5%). Students demonstrated a structured baseline of verification rigor (Composite Mean = 3.13, Agree), heavily prioritizing external validation such as manually confirming the existence of new libraries (Weighted Mean = 3.23) and cross-referencing official documentation (Weighted Mean = 3.20) rather than trusting the AI to self-verify its own outputs (Weighted Mean = 2.94). However, a significant cognitive vulnerability to automation bias was established, as students explicitly agreed that convenience and speed outweigh the risks of incorrect syntax (Weighted Mean = 3.11) and falsely perceived models as possessing deep contextual understanding (Weighted Mean = 3.20). Furthermore, a statistically significant negative correlation was found between the primary tool utilized and verification habits (r = -0.177, p = 0.012), proving that seamless inline code generation within dedicated IDE assistants suppresses manual auditing. Conversely, verification rigor remained entirely uniform across all academic year levels (F(3, 196) = 1.35, p = 0.261), overall usage frequencies (r = 0.053, p = 0.453), and task complexities (r = -0.097, p = 0.170). Ultimately, the study concludes that cognitive over-reliance is driven primarily by interface delivery friction rather than student seniority, designating an urgent institutional need to transition away from restrictive policies and proactively embed formal "AI Auditing" instruction, mandatory external citation protocols, and foundational mechanics of Large Language Models directly into the core programming curriculum.
🏷 AI Hallucinations, Automation Bias, Computer Studies, Generative AI, Information Verification
Article 85 · pp. 994-1005
Multiple Disease Prediction Using Machine Learning Techniques
The growing demand for early diagnosis and data-driven clinical support has positioned machine learning as a compelling tool in modern healthcare. Most existing disease prediction systems, however, are built around a single classification model that produces one deterministic output — an approach that inherently fails to reflect the uncertainty and symptomatic overlap commonly encountered in real-world clinical scenarios. This limitation restricts both patients and healthcare providers from meaningfully considering alternative probable conditions during preliminary assessment, and disproportionately affects individuals in underserved regions where timely access to professional medical consultation remains scarce. To address this gap, the proposed system employs a soft-voting ensemble framework that integrates three complementary classifiers — Decision Tree, Naive Bayes, and Random Forest — to generate more balanced and probabilistically informed predictions. Given symptom-based inputs, the system identifies and ranks the top four probable diseases, offering a broader diagnostic perspective than a single-model approach could provide. A real-time web interface allows users to select their symptoms and instantly view ranked predictions, supporting informed preliminary self-assessment and facilitating more meaningful consultations with healthcare professionals.
🏷 Machine Learning, Disease Prediction, Soft Voting Ensemble, Healthcare Decision Support.
Article 86 · pp. 1006-1017
A Study on Role of NEO Banks in Reducing Financial Stress and Improving Mental Well-being
Neo banks, also known as digital-only banks or challenger banks, are financial institutions that operate entirely online without traditional physical branches. The concept emerged in response to technological advancements, changing customer expectations, and the need for more efficient, user-friendly, and cost-effective banking services. In countries like India, the neo banking sector is expanding rapidly due to the increasing penetration of the internet and smartphones, supportive government initiatives for digital payments (like UPI), and a large unbanked or underbanked population. Overall, neo banks are transforming the financial services landscape by promoting financial inclusion, enhancing customer convenience, and driving innovation in the banking sector. The study is aimed at analysing the roles, convivence and satisfaction level on Neo- Banking Services. The study is exploratory and is based on primary data collected from 100 samples from Mysuru city of Karnataka. SPSS version 21 was used for data analysis and interpretation. The study found that there is a significance difference in factors with respect to roles, convivence and satisfaction level among respondents. The factors on roles, convivence and satisfaction level have been identified. Therefore, this study would assist students in acquiring the required knowledge to make savings and investment decisions in future in order to reach the desired economic growth.
🏷 Neo-banking, Roles, Challenges, Satisfaction Level, Investment Decision and Economic Growth.
Article 87 · pp. 1018-1030
Academic Task Overload and its Influence on the Productivity and Mental Health of Information Technology Students: A Basis for Curriculum Review
Academic task overload has become a growing concern among university students, particularly in demanding programs such as Information Technology, due to its potential effects on students’ productivity and mental health. This study aimed to determine the influence of academic task overload on the productivity and mental health of Information Technology students at Quezon City University and to provide a basis for curriculum review. Specifically, the study examined the respondents’ demographic profile, the level of academic task overload, productivity, and mental health, as well as the significant relationships and differences among the identified variables. The study employed a quantitative correlational research design utilizing a structured survey questionnaire distributed to 400 Information Technology students from first year to fourth year levels. The gathered data were analyzed using descriptive statistics, Pearson Product-Moment Correlation Coefficient, Independent Samples t-test, and One-Way Analysis of Variance (ANOVA). The findings revealed that the respondents experienced a high level of academic task overload, maintained a high level of academic productivity, and commonly experienced mental health concerns associated with academic demands. The study further revealed significant relationships between academic task overload and both productivity and mental health. In addition, significant differences were identified in selected variables when respondents were grouped according to gender, year level, and primary study habits. Overall, the study highlights the importance of managing academic workload and promoting strategies that support students’ productivity and mental well-being within Information Technology education programs.
🏷 academic productivity, academic task overload, mental health, quantitative correlational research, study habits
Article 88 · pp. 1031-1036
Role of Paralegals in Access to Justice
Strengthening the rule of law and promoting access to justice in developing countries have been longstanding international policy objectives. However, the standard policy tools, such as technical assistance and material aid, are routinely criticized for failing to achieve their objectives. The rare exception is paralegal aid, which is almost universally lauded by policymakers and scholars as effective in promoting the rule of law and access to justice. This belief, however, rests on a very limited empirical foundation regarding what paralegal programs accomplish and under what theory they operate. Equality in the administration of Justice is precisely a flowering stem to the Indian Constitution. Equality here is referred to as an equal access to the Court and of presenting the case before the Judiciary but access to the court is limped upon by the payment of sizeable court fees and the assistance of skilled lawyers. This is in the context where she/ he are denied equality in the opportunity to seek justice. Nobody in our Hindustan shall be adamantly denied her/ his rights at law for lack of means but to translate this into actuality and address the same it was/ is necessary to create a considerable apparatus both on paper and practice. The present paper addresses the issue of Legal Aid Clinics constituted under The Legal Services Authorities Act, 1987. It has been observed that Para-Legal Volunteers, who have been appointed to create Legal Awareness and to provide legal aid to the needy, is working efficiently to assist our developing country at par with the developed ones.
🏷 Indian Constitution, Legal Aid Clinic, Para-Legal Volunteer, SLSA, Free Legal Aid
Article 89 · pp. 1037-1142
Enhancing Tax Compliance and Citizen Trust: The Case for a Single Tax Jurisdiction in the European Union
This study aims to investigate the implications of establishing a Single Tax Jurisdiction (STJ) within the European Union, focusing on its potential to enhance tax compliance, efficiency, and citizen trust. Utilizing advanced AI analysis techniques, the research examines existing tax frameworks across member states, identifying inefficiencies and disparities that hinder effective tax administration. The methodology involves a comprehensive data analysis of tax compliance rates, administrative capacities, and stakeholder perceptions, supplemented by simulations of potential STJ implementation scenarios. Key findings reveal that a unified tax jurisdiction could significantly reduce compliance costs, streamline administrative processes, and improve revenue generation across diverse economic contexts. Furthermore, the study highlights the importance of transparency mechanisms, such as citizen panels and real-time data sharing, in fostering trust and engagement among taxpayers. Overall, the conclusions suggest that the establishment of an STJ presents a viable pathway for addressing the challenges of fragmented tax systems in the EU. By promoting a more equitable and efficient tax environment, the STJ has the potential to enhance fiscal legitimacy and support deeper economic integration within the Union. The findings underscore the necessity for collaborative efforts among member states to realize the benefits of a unified tax framework while ensuring that citizen concerns and expectations are adequately addressed. We submit a four-pronged approach will be necessary to move forward the EU as a Tax Polity: (1) An EU-MS Tax Partnership (2) A EU Common Tax base (3) The EU as a multilevel Tax administration (4) A Single EU Tax Jurisdiction. Finally, the EU doesn’t generate enough income per capita in comparison to GDP ratio that stands at €7,1 trillion in revenues compared to €24 trillion in GDP. This has a negative impact on the EU’s perceived and real abilities to accumulate wealth hence its potential to develop into a powerful polity.
🏷 Single Tax Jurisdiction, EU Taxation, Tax Compliance, Citizen Trust, Transparency, Tax Administration
Article 90 · pp. 1143-1178
Influence of AI Tools on the Information Literacy Skills of Grade 10 Students Within Pantay Integrated High School in Social Studies Subject
The rapid advancements in Artificial Intelligence (AI) have reshaped the way individuals engage with information, particularly in the educational context worldwide. AI technologies, which have evolved significantly in recent years (Moon et al., 2024) are transforming how students acquire, process, and evaluate information. Today’s generation of learners is increasingly interacting with AI tools, often without realizing their impact on learning outcomes. This global trend calls for a reevaluation of educational strategies to align with the digital competencies and critical thinking skills required in the modern world. As AI becomes deeply integrated into classrooms worldwide, it is vital to understand its influence on students’ ability to seek, assess, and utilize information, particularly in subjects like Social Studies, which demand analytical and evaluative skills.
This research investigates the influence of AI tools on the information literacy skills of Grade 10 students, with a focus on Social Studies. The study centers on Pantay Integrated High School in Tanauan City, Batangas, Philippines, chosen for its unique characteristics. This school offers Social Studies as a core subject and incorporates a curriculum that addresses contemporary issues, making it an ideal context for exploring the impact of AI on information literacy. Furthermore, the students’ access to various AI tools provides a relevant setting to analyze how these technologies support or challenge their ability to evaluate, retrieve, and synthesize information.
AI has shown immense potential to enhance educational efficiency, such as automating repetitive tasks and providing personalized learning experiences (Wen, 2024). These benefits improve how students interact with academic content. However, concerns persist regarding over-reliance on AI tools, (Hua, 2023) which could diminish students’ ability to critically assess the credibility of sources and foster ethical awareness (Murray, 2023).
The importance of AI in improving students’ information literacy is evident in its ability to assist in key areas such as information retrieval, evaluation, and synthesis. Studies indicate that AI-powered tools enhance students’ ability to critically assess sources and engage with diverse perspectives (D'Angelo et al., 2022). For example, AI can provide personalized learning experiences, facilitating targeted instruction based on individual progress and preferences (Bennani et al., 2021). In Social Studies, these capabilities enable students to gain deeper insights into complex topics and analyze issues effectively. However, (Ng, 2023) questions remain regarding how these tools shape students' cognitive and ethical engagement with information (Sebetci, 2019).
🏷 AI, Literacy, Social Studies
Article 91 · pp. 1179-1193
Predictive Maintenance Analytics and Fleet Downtime Reduction in U.S. Car Rental Operations
Fleet predictive maintenance analytics are reshaping vehicle maintenance management in U.S. car rental operations, enabling operators to predict component failures before they cause downtime. The independent variable in this analysis is predictive maintenance analytics adoption, defined as the deployment of IoT sensors, machine learning models, real-time alert systems, and explainable AI output interfaces across rental vehicle fleets. The dependent variable is fleet downtime reduction outcomes, operationalized as reductions in unplanned vehicle downtime, maintenance costs, safety incidents, and customer service failures attributable to vehicle mechanical failures. Drawing on Human Capital Theory and the Technology-Organization-Environment framework, the paper conducts a systematic narrative literature review across machine learning, vehicle maintenance, fleet management, cybersecurity, and operations management. It reviews ML and explainable AI techniques for vehicle fleet maintenance prediction, maps predictive maintenance demands by vehicle system, evaluates adoption barriers including cybersecurity and data privacy risks using the TOE framework, and proposes a four-stage implementation framework with verified cost ranges and ROI measurement approaches. The paper's principal finding is that barriers to effective predictive maintenance are concentrated in the organizational dimension: sensor infrastructure is increasingly available and ML models are deployable, but the capability of operations staff to interpret, evaluate, and act on predictive alerts is not being developed systematically. Braking and tire systems carry safety and legal compliance dimensions that make human oversight a regulatory requirement as well as an operational one. The paper contributes an integrated predictive maintenance framework specific to car rental fleet operations, the first vehicle system risk matrix calibrated to rental fleet oversight demands, a TOE-grounded barrier analysis incorporating cybersecurity risks, a four-stage implementation model with ROI measurement approach, and a proposed empirical validation design for primary data confirmation.
🏷 Predictive maintenance; Fleet management; Car rental operations; Machine learning; Human Capital Theory
Article 92 · pp. 1194-1207
Comparison of the Effective Adsorption Capacities of Phosphates Using Silica Molybdate and Nitrites Using Diazonium Silica with Raw Activated Carbon in Wetland Waters
Functions and the quality of wetlands have adversely been affected by factors such deforestation, fertilizers, and pesticides. Pollution caused by phosphates and nitrites affect aquatic life as these anions have serious side effects even at low levels. Wetlands are vital as they play a role to supply water used for domestic and farming. Pollution in wetlands leads to eutrophication, reduces dissolved oxygen among others. The objective of the study was to compare adsorption capacities of silica molybdate and diazonium silica with activated carbon. Silica molybdate was prepared by chlorination of raw silica then aminating using ethylenediamine (EDA) followed by addition of sodium molybdate to aminated silica. Diazonium silica was prepared by reacting aminated silica with sodium nitrite. FT-IR spectroscopy was used characterize the prepared adsorbents. The adsorption capacities of the adsorbents was compared with activated carbon. The adsorbents were used to remove PO43- and NO2- ions from water collected from wetlands. FT-IR results showed adsorption bands at 854cm-1 and 542cm-1 attributed to Mo=O and Mo-O stretching in molybdate. Diazonium silica showed a spectra band at 1587 cm-1 assign to open chain azo (N=N) group. Biosorption isotherm fitted well in Freundlich isotherm model for adsorption of phosphates. Biosorption isotherm model that fitted well for adsorption of nitrites was Langmuir model. For sorption of phosphates pseudo-first order kinetics model was best obeyed while for adsorption of nitrites pseudo-second order kinetic model was best obeyed. Adsorption capacity of phosphate was 194.53mg/g and 6.892 mg/g for nitrites as compared to activated carbon which was 8.241 mg/g for phosphates and 0.07985 mg/g for nitrites. For adsorption of nitrites biosorption isotherm fitted well in Langmuir model. For sorption of phosphates pseudo-first order kinetics model was best obeyed while for adsorption of nitrites pseudo-second order kinetic model was best obeyed. Adsorption efficiency of phosphates and nitrites in wetland waters, were 55.9% for phosphates and 44.7% for nitrites.Results obtained indicate that silica molybdate and diazonium silica are better adsorbents as compared to activated carbon.
🏷 Phosphates, Nitrites, Silica molybdate, Diazonium silica, Activated carbon, Wetland pollution.
Article 93 · pp. 1208-1221
Climate Change: Causes, Impacts, Mitigation and Sustainable Adaptation Strategies
Climate change has emerged as one of the most significant global environmental challenges of the twenty-first century. Anthropogenic activities such as fossil fuel combustion, industrialization, deforestation, urbanization, and unsustainable agricultural practices have intensified greenhouse gas emissions, leading to global warming and climate instability. The increasing concentration of greenhouse gases in the atmosphere has resulted in rising global temperatures, melting glaciers, sea-level rise, biodiversity loss, extreme weather events, and adverse impacts on human health, agriculture, water resources, and socio-economic systems. This research article provides a comprehensive overview of climate change, its scientific basis, causes, consequences, mitigation strategies, adaptation measures, and international policy responses. The study also discusses sustainable technological interventions, renewable energy systems, carbon sequestration, climate-smart agriculture, and the role of engineering and management approaches in combating climate change. Furthermore, the article highlights global agreements such as the Paris Agreement and the role of the Intergovernmental Panel on Climate Change (IPCC) in climate governance. The paper concludes that integrated global cooperation, technological innovation, environmental education, and sustainable development policies are essential to minimize climate risks and ensure a resilient future. Climate change is unequivocally caused by human activities, particularly greenhouse gas emissions, according to the latest IPCC synthesis findings.
🏷 Climate Change; Global Warming; Greenhouse Gases; Renewable Energy; Sustainability; Adaptation; Mitigation; Carbon Emissions; IPCC; Environmental Management
Article 94 · pp. 1222-1232
Bridging the Gap: Empowering Traditional Artisans and Artists Through Digital Marketplaces in Developing Economies
Traditional artisans and artists in developing economies continue to face limited access to wider markets despite growing interest in authentic and culturally rooted products. In Baguio City, Philippines, a UNESCO Creative City for Crafts and Folk Art, many artisans still depend on local exhibitions, tourism, community events, and informal selling networks. Digital marketplaces may help expand visibility and income opportunities, but participation remains shaped by issues of trust, accessibility, logistics, digital confidence, and cultural representation.
This study examines the perspectives, readiness, and support needs of artisans and artists in Baguio City regarding digital marketplace participation. A qualitative descriptive research design was used, supported by descriptive survey indicators and thematic analysis. Data were gathered from eighteen artisans and artists representing visual arts, jewelry, weaving, and other creative sectors. The findings show that participants generally have strong digital readiness and interest in online selling, especially when supported by training, secure payment systems, transparent logistics, and culturally responsive platform features.
Thematic analysis identified five major themes: digital readiness and willingness to adapt, trust and security concerns, market visibility and audience reach, cultural preservation through storytelling, and the need for institutional and community support. The study contributes to current discussions on digital inclusion and creative economies by showing that meaningful digital participation requires more than access to technology. It also requires trust-building, cultural sensitivity, practical support, and community-centered platform design.
The paper recommends the development of culturally responsive digital marketplace systems supported by local government, academic institutions, creative organizations, technology partners, and artisan communities. These systems should prioritize accessibility, transparency, digital literacy, authenticity protection, and ethical representation of traditional and indigenous crafts.
🏷 digital marketplace, artisans, creative economy, digital inclusion, cultural preservation, Baguio City, indigenous crafts
Article 95 · pp. 1233-1240
SkillSync: A Web-based System for Managing Trainers' Workload, Trainee's Progress and Training Programs of RMMC STVET
Technical Vocational Education and Training (TVET) institutions require an efficient system for managing trainer workloads, trainee progress, and training program information, but many continue to utilize manual or paper systems that result in disorganized records, limited monitoring capabilities, and delays in decision-making. Therefore, the purpose of this study was to design, develop, and evaluate a web-based training management system, SkillSync, to be implemented at Ramon Magsaysay Memorial Colleges, School of Technical Vocational Education and Training (RMMC-STVET). The research design was a combination of developmental and descriptive-evaluative research. SkillSync was developed through system development requiring data collection, testing, and piloting with administrators, trainers, and trainees using a survey, and the System Usability Scale (SUS) was utilized to evaluate the system. The data collected provided evidence that SkillSync is effective in supporting the management of training data and has improved the accessibility and organization of training data significantly. The SUS total score of 86.0 indicates an extremely usable system with a very high level of user satisfaction. Results also indicated that the users were highly accepting of the system by all stakeholders who were part of the evaluation process. In summary, SkillSync is a functional, user-friendly tool that can make a significant improvement in the efficiency of managing training and supporting decision-making for technical vocational education and training (TVET). SkillSync has demonstrated that web-based technology can enhance the administrative functionality of TVET institutions and promote the digital transformation of the administrative functions of TVET institutions.
🏷 training management system; technical vocational education; usability evaluation; web-based system; trainee progress tracking
Article 96 · pp. 1241-1258
A Low-Cost Real-Time Bus Stop Notification System for Rural Transportation in India
Public transportation in rural India is often the only means for many to access work, education, markets, and healthcare. However, service reliability is low: buses may arrive only once per hour, schedules are inconsistent, and passengers lack real-time information. This leads to lost time, reduced earnings, and increased dependence on expensive or unsafe private transport. We address these challenges with a low-cost, real-time bus stop notification system designed for rural India. The system integrates on-board GPS units, solar-powered displays at bus stops, and a cloud backend. To ensure data delivery despite unreliable mobile networks, we combine LoRa wireless, MQTT, and SMS as a fallback. Bus arrival times are predicted using a Random Forest model trained on actual bus movement data. Multilingual LED displays (Tamil, Hindi, English) address digital literacy constraints. The system buffers data during network outages, achieving 95% data retention. GPS noise is reduced using a Kalman filter, lowering position errors by 39.7%. Field tests on rural routes in Tamil Nadu show a mean arrival-time prediction error of 2 minutes, a 44% improvement over published schedules, and an 80% increase in prediction stability. A camera-based backup, using YOLOv8n, detects buses with 96.85% accuracy in areas with poor GPS coverage. The pilot, deployed on 8 routes and 12 stops serving approximately 2,500 daily passengers, reduced average passenger wait times by 15 minutes (47% improvement) and increased on- time arrivals by 20%. System uptime reached 98%. The solution reduces costs by over 90% compared to commercial alternatives, with hardware costs below 1,500 per bus and 10,200 per stop. Solar units operate for up to three days without sunlight, ensuring resilience during power outages. User feedback indicates 85% satisfaction with the displays, with 73% preferring information in Tamil. The system is cost-effective, reliable, robust to network disruptions, and accessible, providing a scalable model for rural transport information systems.
🏷 Intelligent Transportation Systems, rural mobility, bus tracking, Internet of Things, GPS, machine learning, arrival time prediction
Article 97 · pp. 1259-1268
AI-Driven Precison Agriculture and Crop Health Prediction System
Precision agriculture technologies have historically relied on expensive physical sensor networks and computationally prohibitive deep-learning architectures, creating severe financial and technical barriers for marginal farmers. To address this disparity, this paper presents the Smart Agro Advisor, a highly optimized, zero-hardware web ecosystem designed to democratize agronomic intelligence. Utilizing an asynchronous Python FastAPI architecture, the system integrates lightweight Scikit-Learn machine learning algorithms to perform real-time crop suitability and precise fertilizer dosage predictions. To bypass the extreme GPU requirements of traditional Convolutional Neural Networks (CNNs), this research introduces a novel, deterministic color-space (HSV) heuristic pipeline capable of instantaneous plant pathogen classification and soil type validation directly in the browser. Furthermore, the system entirely replaces physical IoT hardware by programmatically intercepting real-time telemetry from external cloud APIs, including OpenMeteo for hourly weather forecasting and Live Market APIs for economic intelligence. To ensure practical usability for demographics with limited digital literacy, the platform is encapsulated in a responsive Glassmorphic user interface fortified with Web Speech API integration, enabling full Voice-to-Text accessibility. Evaluation of the deployed architecture demonstrates robust algorithmic accuracy exceeding 92% across all models, alongside sub-second end-to-end inference latency, proving that accessible, API-driven software frameworks can successfully replace prohibitive hardware infrastructure in rural agriculture.
🏷 precision agriculture automation, heuristic image processing, zero-hardware web ecosystem, API driven telemetry, crop suitability prediction, speech-totext accessibility
Article 98 · pp. 1269-1288
From Decoction to Evidence: Modern Standardization of Ayurvedic Kwatha and Polyherbal Formulations
Standardization of Ayurvedic dosage forms have become a central priority for researchers, industry, and regulators because the clinical credibility of traditional medicines depends on reproducible composition, safety, and performance. Among classical dosage forms, Kwatha Kalpana (aqueous decoctions) represents a foundational extraction technology where heat and water mobilize primarily water-soluble phytochemicals from single or multiple herbs. Despite wide therapeutic use in classical practice, kwathas face practical barriers in contemporary care, labor-intensive preparation, short shelf life, batch-to-batch variability, microbial risk, and poor palatability. This review consolidates the historical rationale, formulation principles, preparation variables, and modernization opportunities for kwatha, while emphasizing polyherbalism as a systems-based strategy that can enhance efficacy through pharmacodynamic complementarity, improved bioavailability, and multi-target action. We synthesize current evidence on analytical standardization (organoleptic/physicochemical indices, microbial/heavy-metal limits, and chromatographic fingerprints) and highlight validated platforms such as HPTLC/HPLC marker quantification—including examples where andrographolide served as a robust marker for a classical decoction standardization workflow. We further outline practical, regulator-aligned pathways, process controls, preservative rationalization, and stability-guided formulation redesign to enable clinically relevant, scalable, and quality-assured kwatha products.
🏷 Kwatha (Kashaya), polyherbalism, synergy, standardization, HPTLC, HPLC, GC–MS, LC–MS/MS, quality control.
Article 99 · pp. 1289-1291
"Impact of Sub-Lethal Monosodium Glutamate Exposure on Hepatic Protein Profiles in Labeo Rohita"
This study evaluated the ecotoxicological impact of Monosodium Glutamate (MSG) on the freshwater fish Labeo rohita (Rohu). The experiment focused on alterations in liver total protein levels following chronic sub-lethal exposure. Labeo rohita were exposed to a constant sub-lethal dose of 10 mg/L of MSG across three progressive temporal intervals: 5, 10, and 15 days. A parallel control group was maintained in chemical-free freshwater. Quantitative estimation of total liver proteins was conducted using standard spectrophotometric methods.
The results demonstrated a highly significant, time-dependent decline in total liver protein concentration in all MSG-exposed groups compared to the control group. The baseline liver protein level in the control group was recorded at 65.4 ± 1.2 mg/g of wet tissue. Upon exposure to 10 mg/L of MSG, liver protein levels dropped to 52.1 ± 1.0 mg/g on Day 5, 41.8 ± 0.9 mg/g on Day 10, and reached a minimum value of 28.5 ± 0.7 mg/g on Day 15. This marked depletion highlights severe physiological stress, metabolic reallocation, and potential cellular degradation in the hepatic tissue. The study confirms that even low concentrations of agricultural or industrial MSG runoff pose a threat to aquatic organisms by disrupting essential protein synthesis pathways.
🏷 Monosodium Glutamate, Labeo rohita, Liver Protein, Ecotoxicity, Metabolic Stress.
Article 100 · pp. 1292-1301
Floatchat RAG: An AI-Powered Conversational System for Argo Oceanographic Data Exploration Using Retrieval-Augmented Generation
Oceanographic research involves massive volumes of heterogeneous data produced by autonomous profiling floats. The Argo program, one of the world's largest ocean observation efforts, generates datasets in NetCDF format containing temperature, salinity, and pressure measurements at varying ocean depths. However, accessing and querying this data requires specialized knowledge of scientific programming, data formats, and oceanographic conventions, creating barriers for non-technical users. This paper presents FloatChat RAG, an AI-powered conversational system that uses Retrieval-Augmented Generation (RAG) to enable natural language exploration of Argo float data. The system processes Argo NetCDF files streamed via OPeNDAP from NOAA's THREDDS servers into a SQLite relational database and generates semantic vector embeddings stored in ChromaDB using the all-MiniLM-L6-v2 sentence transformer model. A LangChain-based tool-calling agent, powered by Google's Gemini large language model, interprets user queries and autonomously selects from nine specialized tools spanning semantic search, structured SQL retrieval, geographic and temporal filtering, and interactive Plotly visualization generation. The system incorporates reliability mechanisms including API key rotation, deterministic fallback routes, and response caching. Evaluation on a proof-of-concept dataset from Indian Ocean Argo floats demonstrates 93.3% tool selection accuracy across 30 test queries, 100% factual correctness on deterministic queries, a semantic search precision@5 of 1.00, and a 0% hallucination rate. The system bridges the gap between raw oceanographic data and actionable insights through an intuitive Streamlit chat interface.
🏷 Argo Floats, Retrieval-Augmented Generation, Oceanographic Data Visualization, Natural Language Processing, Large Language Models, Vector Databases, Semantic Search, ChromaDB, LangChain
Article 101 · pp. 1302-1312
Zero-Based Grading System and Academic Performance among Quezon City University Students
The researchers analysed the student attitudes towards the Zero-Based Grading System (ZBGS) at Quezon City University and its effect on student academic performance. A descriptive quantitative approach was selected for the study. A total of 407 student participants completed a survey containing items regarding demographic profile, awareness of grading policies, understanding of criteria for academic outputs, perception of fairness in assessment, and impact on academic discipline. Data gathered revealed that the majority of participants were third-year students, from the College of Computer Studies and College of Engineering, with GWA values being in the range of either very good to good range, and a majority of the respondents had experienced ZBGS for one or two semesters. The study found that the majority of the respondents were aware of the grading policies which yielded the highest weighted mean. Moreover, the results of the analyses indicated that statistically significant differences in ZBGS assessments occurred based on year level and GWA of the respondents but not according to College/Department or length of time exposure in ZBGS. Furthermore, the correlation analysis revealed a significant relationship between ZBGS assessment and academic performance, indicating that more favorable perceptions of ZBGS were associated with better GWA. Overall, the results of this study provided evidence that students generally accepted ZBGS as a reasonable grading practice and that more favorable perceptions of the system were associated with better academic performance, supporting the clear and consistent implementation of grading policies.
🏷 academic performance, general weighted average, higher education institution, zero-based grading system, student perception
Article 102 · pp. 1313-1326
Factors Influencing AI Tool Adoption in Research Among Junior and Senior Students of QCU College of Computer Studies
The increasing integration of Artificial Intelligence (AI) in higher education has transformed academic research practices, particularly in literature review, data analysis, and research writing. This study investigated the factors influencing the use of AI tools in academic research among junior and senior students of the College of Computer Studies at Quezon City University. Specifically, it examined the respondents’ demographic profile, level of AI tool utilization, factors affecting AI adoption, barriers to AI usage, and the relationship between these factors and students’ actual AI usage behavior. The study employed a quantitative descriptive-correlational research design and utilized an online survey questionnaire administered to 130 students from the BS Information Technology, BS Information Systems, and BS Computer Science programs using stratified sampling. Descriptive statistics, Pearson Product-Moment Correlation, and Multiple Regression Analysis were used to analyze the collected data. Findings revealed that students demonstrated a moderate level of AI tool utilization in research activities, with perceived usefulness emerging as the most influential factor affecting AI adoption. Students commonly used AI tools for idea generation, information retrieval, and writing assistance; however, AI utilization remained limited due to concerns related to plagiarism, data privacy, ethical misuse, overdependence on AI-generated outputs, and insufficient institutional support. The study also found a significant relationship between the identified adoption factors and students’ actual AI usage behavior. Overall, the findings suggest that AI tools have strong potential to improve research productivity and efficiency, but educational institutions must establish clear policies, governance frameworks, and training programs to ensure the responsible, ethical, and effective integration of AI in academic research.
🏷 Academic Research Tools, AI-Assisted Research, AI Literacy, Educational Technology, Human-Computer Interaction
Article 103 · pp. 1327-1340
The Effect of Talent Development on the Health Sector performance in the County Government of Bungoma.
In today’s competitive and dynamic business environment, organizations must invest in talent development to remain innovative, productive, and adaptable to change. This involves activities such as training, coaching, mentoring, performance management, career planning, and leadership development. Many organizations face many challenges related to talent development increased job dissatisfaction, retention, and organizational growth. These challenges undermine employee performance in public health facilities in Bungoma County. The primary aim of this research was to establish the effect of Talent Development on Health Sector performance in the County Government of Bungoma, Kenya. The research was guided by the Theory on Talent Development which was conducted among the Health Sector staff in the County Government of Bungoma. This research employed a descriptive survey research design, analysing data through descriptive statistics, including mean, standard deviation, frequency, and percentage, as well as inferential statistics, comprising Pearson correlation and regression analysis, given in tabular form the target population of 240 respondents was drawn from the County Referral Hospital and 9 Sub-County Hospitals. Simple random sampling was used to select 15 Heads of Ward Sections while census method was adopted in selecting 94 respondents within the Ministry of Health to give a total of 109 respondents as the sample size. The research incorporated primary data sources collected using closed and open-ended questionnaires and interviews, which were pretested in Turkana County to evaluate validity and reliability. Quantitative data collected through questionnaires were analysed using SPSS, while qualitative data from open-ended questions and interviews were subjected to thematic analysis. The findings indicated that all aspects of Talent Development exhibited a positive and significant correlation with Health Sector Performance in Bungoma County, Kenya. The results posited that Health Sector Performance increases by 0.335 units for each unit rise in Talent Development (β2=0.335, p<0.05). This outcome suggests that the County Government of Bungoma should employ talent development as it plays a crucial role in shaping the efficacy of healthcare performance through enhanced competencies and expertise of healthcare professionals
🏷 Talent Development, Health Sector Performance, County Government
Article 104 · pp. 1341-1354
Time-Management Skills and Lecturer Productivity in Federal Colleges of Education in South-Western Nigeria.
Lecturer Productivity (LP), measured by Research Output (RO) and Teaching Effectiveness (TE), is crucial to the career progression of lecturers in tertiary institutions in Nigeria, including Colleges of Education (CoEs). However, literature indicate that LP is low across CoEs, particularly in Federal Colleges of Education (FCEs) in south-western Nigeria. Prior studies on LP have largely focused on demographic factors, self-esteem, psychological well-being, and hard skills among university and polytechnic lecturers, with little attention paid to the relationship between Time Management Skills (TMS) and LP in FCEs in south–western Nigeria. A descriptive survey design was employed in the study. The four first-generation FCEs (FCE (Special) Oyo, FCE Abeokuta, FCE Technical Akoka and Adeyemi College of Education (ACE) Ondo) were purposively selected. One hundred and nineteen Heads of Department (HoDs) were also enumerated. Proportionate-to-size sampling was used to select 10% of the lecturers and NCE III students, yielding sample sizes of 149 and 803, respectively. The instruments used were the TMSQ (0.78) and the LPQ (0.82) scales. The quantitative data were analysed using descriptive statistics and Pearson product-moment correlation. The findings of this study indicated that TMS (2.65) and LP (3.02) were high against the threshold of 2.50. Time management significantly correlated with LP in FCEs in south-western Nigeria. The study therefore recommended, among other things, that lecturer productivity could be increased by organising workshops and seminars for the lecturer on planning, organising, and time management.
🏷 Research, Teaching, Time management, Productivity
Article 105 · pp. 1355-1366
Entrepreneurial Networking and the Performance of Selected Micro Enterprises in Osun State, Nigeria
The persistent challenge of weak business performance among micro enterprises in Nigeria highlighted the need to examine how entrepreneurial networking affected enterprise performance, particularly in Osun State. Despite increasing participation in informal and formal business activities, limited integrated evidence existed on how different forms of entrepreneurial networking jointly affected the performance of micro enterprises, thereby necessitating this study. The study therefore examined the effect of entrepreneurial networking on the performance of micro enterprises in Osun State, Nigeria, with specific objectives focusing on personal networking, social networking, operational networking, and strategic networking. A quantitative research design was adopted. Data were collected through a structured questionnaire administered to 490 respondents selected from a population of 1,370,908 micro enterprise owners using multistage sampling technique. Data were analysed using descriptive statistics involving frequency and percentage, alongside inferential statistics involving multiple regression analysis. The findings indicated an overall entrepreneurial networking contribution of 74.6%, while personal networking (B = 0.619), social networking (B = 0.755), operational networking (B = 0.829), and strategic networking (B = 0.884) all exerted significant positive effect on micro performance. The study concluded that entrepreneurial networking significantly affected the performance of micro enterprises. It was recommended that micro enterprise owners should strengthen networking activities through improved relationship building and strategic collaboration. It was also recommended that policymakers should develop supportive frameworks that encourage networking platforms and strengthen enterprise linkages for improved sustainability and growth.
🏷 entrepreneurial networking, personal networking, operational networking, social networking, strategic networking
Article 106 · pp. 1367-1374
Growth and Yield of Carrot (Daucus Carota L) as Affected by Planting Methods in a Derived Savannah Zone of Nigeria.
Nigeria was conducted at the research farm of the Department of Crop Science and Horticulture, Nnamdi Azikiwe University, Awka, Nigeria Two planting methods were used (direct sowing, and transplanting) as treatments for the experiment, which was laid out in a Randomized Complete Block Design (RCBD) and replicated four times. Data was collected on the growth, yield, and root marketability parameters. All data collected were statistically analyzed using all outlined procedures. The results of the study showed no significant variation between the planting methods on the stem girth and leaf number at 4, 6, 8, 10, and 12 WAP and plant height at 12 WAP, while transplanting significantly varied (P<0.05) from direct sowing at 4, 6, 8 and 10 WAP. Planting methods did not influence the plant biomass, root length, number of roots harvested, fresh weight of leaves, fresh weight of roots, but were statistically similar. Transplanting influenced significantly the root diameter, while direct sowing also significantly influenced the harvest index and the root marketability parameters; root uniformity, marketable yield, and total marketable root yield%. From the results presented in the investigation, direct sowing had significant effect on root marketability, which is paramount in commercial carrot production. It is thereby recommended that for optimum carrot production and marketability, carrots should be directly sown.
🏷 Carrot, transplanting, direct sowing, growth and yield parameters
Article 107 · pp. 1375-1380
Physicochemical Quality Changes of Strawberries Stored in Active Packaging Incorporated with Ethylene, Humidity and Oxygen Scavengers at Ambient Temperature
Strawberry (Fragaria x ananassa) possesses high economic value but is highly perishable due to its soft texture and high moisture content. Active packaging (AP) technology serves as an innovative solution to extend the shelf life of horticultural products by controlling the internal conditions of the package. This study evaluates the effectiveness of AP containing KMnO4 as an ethylene scavenger, vitamin C as an oxygen scavenger, and silica gel as a humidity absorber on the physical and chemical quality of strawberries during storage at room temperature. The experimental design used a 2 x 2 x 3 factorial Completely Randomized Design with five replications. The treatment factors comprised KMnO4 (0 and 60 mg/mL), vitamin C (0 and 0.4 mg/mL), silica gel (0, 5, and 10 g/sachet). Visual observations indicated a significant decline in quality after the seventh day of storage, characterized by softened texture, darkening of the skin color, and the emergence of Botrytis cinereal mold due to high humidity. In the single-treatment test, the application of 5 g of silica gel was the most effective in minimizing the decrease in oBrix values of 4.58%. Meanwhile, the combination of 60 mg/mL KMnO4, 0 mg/mL vitamin C and 5 g of silica gel yielded the best results with a oBrix value of 4.90%, and 0 mg/mL KMnO4, 0 mg/mL vitamin C, and 5 g silica gel was capable of inhibiting the decline of vitamin C content during 7 days of storage. None of the treatments significantly influenced total sugar, free acid, sweetness levels, or vitamin C content, as the high rates of respiration and transpiration at room temperature triggered rapid fruit degradation.
🏷 active packaging, KMnO4, silica gel, strawberry, vitamin C.
Article 108 · pp. 1381-1387
Preparation and study of a composition based on butadiene nitrile polymer using Astragals
This paper is devoted to the topic "Preparation and Study of a Butadiene Nitrile Polymer-Based Composition Using Astragals."
Binary blends of butadiene nitrile rubber and astragals in various mass ratios were prepared, and the flow index of the alloy system was studied at temperatures of 130°C, 160°C, and 170°C and under loads of 12.0-21.0 kg. Our studies showed that at a temperature of 160°C and with an astragals content of 5 parts by weight in the binary blend, it acts as a modifier. As a result of the modification, the technological properties of NBR, primarily compatibility with other components and chemical stability, are increased by 1.5 times compared to standard modifying agents. An optimal formulation for producing rubbers based on butadiene nitrile and astragals was developed. A rubber compound based on an optimal formulation was prepared, and the vulcanization mode and time were determined. The resulting vulcanization temperature was 157°C, with a vulcanization time of 26 minutes. This work was conducted with the goal of acquiring a BNR-based rubber for cables that is chemically resistant to aggressive environments, more advanced, and fully capable of meeting operational specifications, exhibiting chemical resistance in saline areas, and suitable for use as a coating. The key parameters of the resulting mixture were determined using physical and mechanical methods and compared with standard rubbers, confirming that the proposed rubber's key parameters fully meet the standard requirements.
🏷 Astragals, nitrile butadiene rubber, modification, vulcanization, ecology
Article 109 · pp. 1388-1398
Adaptive Leadership, Knowledge Management, And Human Capital Development: A Conceptual Framework for Public Sector Leadership Training and Organizational Resilience in the Post-Pandemic Era
The post-pandemic environment has fundamentally transformed governance systems, public administration, and leadership practices worldwide. Public institutions increasingly confront complex adaptive challenges characterized by uncertainty, rapid technological change, evolving citizen expectations, and resource constraints. This thought paper examines how adaptive leadership, knowledge management, and human capital development collectively contribute to organizational resilience and public-sector effectiveness. Drawing upon adaptive leadership theory and contemporary public leadership scholarship, the paper argues that sustainable governance depends on leaders' capacities to mobilize learning, facilitate collaboration, and cultivate organizational adaptability. The paper advances an integrated conceptual framework positioning adaptive leadership as the strategic mechanism through which knowledge resources and human capital capabilities are transformed into institutional resilience. Furthermore, it highlights the implications of this framework for public-sector leadership training and development initiatives. By synthesizing insights from public leadership, knowledge management, and human capital literature, the paper contributes to emerging discussions on leadership development and organizational resilience in the new normal.
🏷 adaptive leadership; public leadership; leadership development; knowledge management; human capital; organizational resilience
Article 110 · pp. 1399-1410
Determinants and Socio-Economic Impacts of Nurse Migration: Evidence from Migrant Healthcare Professionals
The migration of nurses is a critical phenomenon with significant implications for both the source country and destination countries. This study explores the socio-economic conditions of migrant nurses and investigates the factor influencing their decision to migrate. The objective of the study is: To understand socio economic condition of migrant nurses and to find out the reason for preferring the destination and problem faced there. The study based on primary data collected from 50 nurses who working abroad. The research employs convenient sampling method by distributing a structured questionnaire through telephone. The findings highlight the primary challenges faced by migrant nurses, including cultural adjustment, professional integration and economic stability. Additionally, the study reveals that migration was preferred most of the nurses for career advancement and financial outcome and, more than one single push or pull factor is involved in migration. The analysis revels that better career opportunity, higher salary, and improved living conditions are the main drivers for migration. The study aims to provide a comprehensive understanding of the migration dynamics of nurses, contributing to policy discussions and managing and supporting migrant healthcare professionals. The study concluded that migration of nurses is necessary for professional growth of nurses and to achieve career advancement.
🏷 Immigration, multifaceted, Auxiliary Nurse Midwife (ANM), General Nurse Midwife, new economics of labour migration
Article 111 · pp. 1411-1422
Assessment of Housing Challenges in Higher Institutions in Nigeria: A Case Study of Federal Polytechnic, Ilaro
Access to adequate housing remains a persistent challenge for staff and students of higher education institutions in Nigeria. Growing student enrolment, insufficient on-campus residential facilities, and escalating private rents have widened the gap between housing demand and supply. This study examined housing challenges at Federal Polytechnic Ilaro (FPI), Ogun State, Nigeria. Data were collected through structured questionnaires administered to 83 staff and students, a physical condition survey of 100 housing units, and GPS mapping of residential locations and the road network. Geospatial analysis was performed in ArcGIS 10.6.1, and quantitative analysis combined frequency distributions, the Weighted Preference Index (WPI), the Relative Preference Ratio (RPR), binomial tests, two-proportion z-tests, goodness-of-fit chi-square tests, a Housing Condition Index (HCI), and a rent-to-income (RTI) ratio analysis. The results indicate that 44.58% of respondents reside in privately rented accommodation and 55.42% commute between 15 and 30 minutes daily. 52% of the sampled house unity have access, a proportion not significantly above 50% (p = 0.764). These suggest there is effective random access to electricity. Security fence were present in 85% of units (p < 0.001), significantlsy exceeding both water (z = 3.959, p < 0.001) and electricity access (z = 5.023, p < 0.001). The HCI found that 26% of units fall in poor or severely deprived categories. The modal rent band of ₦150,000–₦350,000 corresponds to a rent-to-income ratio of 29.8% of the annual minimum wage, approaching the 30% affordability threshold. Salary-deductible housing loans (WPI = 0.811) and a dedicated transport service (WPI = 0.803, RPR = 4.78) were the most preferred interventions. The study recommends salary-deductible loan schemes, a dedicated shuttle service, public-private partnerships for hostel construction, and engagement with local authorities on rental price guidelines.
🏷 Housing challenges; Rental Values, Higher Institutions; Housing Condition Index; Geospatial Analysis; Rent-To-Income Ratio; Federal Polytechnic Ilaro
Article 112 · pp. 1423-1450
Digital Environmental Reporting in Australia: Understanding Citizen Participation and Environmental Governance
Environmental apps and digital reporting tools are increasingly used to support environmental management in Australia, yet research remains fragmented across citizen science, digital government, technology adoption and environmental governance. This paper develops an integrated conceptual framework for digital environmental reporting in Australia through a systematic narrative review of 86 sources. The framework is conceptual and has not been empirically tested. It explains how four core constructs - digital tool characteristics, user behaviours, institutional settings and environmental outcomes - may be associated with the effectiveness of citizen-generated environmental reporting. The review identifies opportunities for improved monitoring coverage, transparency, operational efficiency, rapid detection of environmental harms and adaptive management. It also identifies challenges related to digital inequality, privacy, data governance, verification, institutional fragmentation, long-term maintenance, power, politics and Indigenous data sovereignty. Six propositions are advanced to guide future empirical research rather than to state causal findings. The paper contributes a governance-focused model that extends technology acceptance and digital government frameworks by situating environmental reporting within Australia's fragmented institutional context.
🏷 environmental apps; digital reporting; citizen-generated data; environmental governance; digital participation.
Article 113 · pp. 1451-1468
Mapping Review on AI, Employee Experience, and Work Performance: A Bibliometric Analysis
This bibliometric review examines the evolving relationship between artificial intelligence (AI), employee experience (EX), and work performance (WP) in organisational contexts from 2000 to 2025. As AI continues to transform human resource management practices, increasing scholarly attention has been directed toward understanding its impact on employee-centric outcomes and organisational effectiveness. The study analyses 373 peer-reviewed articles indexed in the Web of Science database, selected through the PRISMA screening process. Bibliometric and science mapping techniques were employed using Biblioshiny and VOSviewer to evaluate publication trends, leading authors and institutions, geographic contributions, keyword co-occurrence, and collaboration networks. The findings reveal a significant surge in research output after 2020, indicating heightened academic and practical interest in AI-driven HRM. Key themes identified include AI-enabled decision-making, employee engagement, performance enhancement, and organisational productivity. More recent studies increasingly focus on ethical considerations, transparency, employee well-being, and inclusivity in AI applications. This study offers a novel contribution by integrating AI, employee experience, and work performance into a single analytical framework, an area that has received limited systematic exploration. By mapping thematic evolution over 25 years, it highlights a clear shift toward human-centred and sustainability-oriented perspectives in AI research. Additionally, the study uncovers collaboration patterns and conceptual developments, providing valuable insights for future research directions in the field.
🏷 Artificial Intelligence (AI), Employee Experience (EX), Work Performance (WP), Human Resource Management (HRM), Bibliometric Review.
Article 114 · pp. 1469-1476
Agentic AI: Insurance Claim Processing System
Traditional insurance claim processing is a manual, labor-intensive function prone to human error and significant delays of 24–48 hours per claim. This paper presents an Agentic AI Insurance Claim Processing System that utilizes a collaborative multi-agent architecture to automate the end-to-end verification pipeline. Powered by the LLaMA 3.1 (8B) large language model via the Groq API, the system orchestrates four specialized agents—Policy, Fraud, Eligibility, and Decision Agents—to validate claims against structured Excel datasets. Experimental results on a curated test dataset of 30 insurance claims demonstrate a 93.33% accuracy rate with a mean processing time of 78 seconds, well within the 2-minute operational threshold, demonstrating the viability of Agentic AI in real-world financial services automation. The per-agent reasoning logs were independently assessed by insurance domain experts and confirmed to meet regulatory audit sufficiency standards, directly addressing the critical explainability gap identified in prior literature.
🏷 Agentic AI, insurance claim processing, multi-agent system, LLaMA 3.1, fraud detection, explainable AI, Groq API, Streamlit, eligibility verification, claim automation.
Article 115 · pp. 1477-1483
Comparative Study of CSR Livelihood Programs across Public and Private Sector Companies: An SEM Approach
This study aims to compare the CSR Livelihood Programs of Public and Private Sector Companies. This study is designed to compare the CSR Livelihood Program of Public and Private Sector Companies. This study aims to look into the effectiveness of Corporate Social Responsibility (CSR) livelihood programs of government and non-governmental organizations and how it influences the beneficiaries' socio-economic development. The research method is quantitative with the framework of SEM that aims to analyze the relationship between variables that are important in determining the outcomes of livelihoods. The study results show that the major constructs, which are latent, are CSR Program Design, Skill Development Initiatives, Resource Accessibility, Community Participation, Livelihood Improvement, and Beneficiary Satisfaction, where CSR Program Design, Skill Development Initiatives, Resource Accessibility, and Community Participation are independent variables and Livelihood Improvement and Beneficiary Satisfaction are dependent variables. Furthermore, the concept of the Sustainability of Livelihood is taken into account as a mediating variable to understand the long-term impacts. Structured questionnaires are used to gather primary data from beneficiaries of the selected CSR programs in both public and private sector companies. For statistical validity, a sample size of about 300-400 respondents is considered as valid. The hypothesized relationships and the structural paths between the two sectors are tested using the SEM model. The results will show striking differences in the implementation and impact of CSR livelihood approaches. It is hypothesized that the private sector companies will be more efficient and innovative in their delivery of the program, and the public sector companies will have wider outreach and inclusivity. The study also seeks to establish the factors that have been critical in contributing to sustainable livelihood development. The research builds on existing literature by conducting a comparative study on SEM, which allows for insights for policy makers, business strategists, and development practitioners to improve the efficiency and sustainability of CSR.
🏷 CSR, Livelihood Development, SEM, Public vs Private Sector
Article 116 · pp. 1484-1492
Impact of AI Dependency on Faculty Research Creativity and Critical Thinking in Higher Education Institutions
Artificial Intelligence has rapidly become an integral component of academic research and higher education practices. Faculty members increasingly rely on AI-assisted technologies for scholarly writing, literature exploration, data analysis, and idea development. Although these digital tools improve efficiency and accessibility in research activities, excessive dependence on AI-generated assistance may influence originality, reflective thinking, and independent academic judgment. The present study explores the relationship between AI dependency, research creativity, and critical thinking among faculty members in higher education institutions. A descriptive quantitative approach was adopted, and primary data were collected from 50 respondents through a bilingual Likert-scale questionnaire. Statistical techniques including correlation, regression, mean score analysis, and ANOVA were used to interpret the findings. The results indicate that AI technologies positively support research productivity and creative idea generation; however, overreliance on automated outputs may reduce deep analytical engagement and originality. The study highlights the need for ethical, balanced, and responsible integration of AI technologies in academic research environments.
🏷 Artificial Intelligence, Faculty Research, Creativity, Critical Thinking, Higher Education, AI Dependency, Academic Research, Innovation
Article 117 · pp. 1493-1498
“Skill Bridge: Bridging the AI Skill Gap in Rural MSMEs: Evaluating the Effectiveness of Government Led Digital Literacy and Training Programs”
Micro, Small and Medium Enterprises (MSMEs) in rural India are critical to employment generation and regional economic development, yet they remain largely excluded from the benefits of Artificial Intelligence (AI)‑driven transformation due to a persistent skill gap and low digital literacy. This study evaluates how government‑led digital‑literacy and training programmes—such as PM‑GKAN, PMGDISHA‑linked initiatives, and sector‑specific ICT schemes—have impacted rural MSMEs’ readiness to adopt AI tools in their production and marketing processes. Using a mixed‑methods approach, we find that basic digital‑literacy interventions significantly improve device and internet usage among rural entrepreneurs, but their effectiveness in translating to AI‑specific skills remains limited. The paper argues that targeted AI‑oriented upskilling, contextualised curricula, and stronger linkages between government programmes and local MSME clusters are necessary to bridge the AI skill divide and position rural MSMEs as competitive nodes in India’s digital economy.Artificial Intelligence (AI) is rapidly transforming business operations across the globe, including India’s Micro, Small and Medium Enterprises (MSMEs). However, rural MSMEs continue to face major barriers in adopting AI technologies due to inadequate digital literacy, poor technological infrastructure, financial limitations, and lack of specialized training. This research paper evaluates the effectiveness of government-led digital literacy and AI-oriented training initiatives in bridging the AI skill gap among rural MSMEs in India. The study focuses on schemes such as Pradhan Mantri Gramin Digital Saksharta Abhiyan (PMGDISHA), MSME Technology Centres, Digital India initiatives, and entrepreneurship development programs. A mixed-method research methodology was adopted using secondary data from government reports, policy documents, and recent MSME surveys. The findings reveal that while government programs have significantly improved basic digital awareness and internet usage among rural entrepreneurs, advanced AI adoption remains limited due to insufficient technical mentoring, inadequate infrastructure, and lack of industry-specific AI training. The paper concludes that a stronger integration of AI-focused curriculum, localized training models, public-private partnerships, and continuous support systems is necessary to build a sustainable AI-ready rural MSME ecosystem in India.
🏷 Bridging, AI, Digital Literacy
Article 118 · pp. 1499-1503
Impact of Artificial Intelligence on Corporate Governance and Board Decision-Making in the Digital Era
Business concerns are being forced by digital technology to update their current business strategies and create fresh ones that combine cutting-edge concepts in order to meet modern demands. Due to the popularity of the "Go digital" idea, businesses have transformed into inventive machines that focus more on the "Eco system" style of governance. In a networked world, innovative and nimble businesses are well-prepared and constantly adapting their plans to fit the needs of the moment, but established businesses encounter a significant disconnect between their governance strategy and their operational requirements. The businesses that have historically performed the best in the market face numerous problems as a result of fast-moving, technology-driven markets.
This conceptual paper aims to provide an overview of the potential available to companies in the digital era as well as the problems and difficulties that may arise. The paper's main goal is to highlight the opportunities and problems that Corporations and Companies face in the digital era and to identify the areas where their governance practices need to change. The theoretical information in the study is based on secondary data that was gleaned from a variety of sources, including text books, newspapers, journals, and online sources.
🏷 Department of Management Studies, Mailam Engineering College, Villupuram, Tamil Nadu, India
Article 119 · pp. 1504-1517
Effects of Plastic Storage Containers and Time on Potable Water
Water tanks are liquid storage containers that store water for human consumption. They are usually made of polyethylene (plastic), steel, clay, ceramics and fiber glass. The need to investigate the changes in water quality during storage in different types of water storage tanks or vessels is very crucial in establishing which tank contributes to deterioration or improvement of stored water during storage. Two sources of potable water (tap water and borehole water) were stored in three water storage tanks for a period of six weeks. The tanks include black plastic tank, blue plastic tank and green plastic tank. The water quality parameters examined were Temperature, Taste, Odour, Colour, Turbidity, Conductivity, pH and Total Heterotrophic Bacteria (THB). However, all parameters listed above were analyzed at a sampling frequency of seven days interval. The results showed that among the different coloured storage tanks used, black plastic tank was the best in terms of preserving water quality. The range in the following examined toxic parameters Total heterotrophic bacteria in tap water stored in black plastic tank, green plastic tank and blue plastic tank were 2×102CFU/100mL – 106×102CFU/100mL, 2×102CFU/100mL – 116×102CFU/100mL and 2×102CFU/100mL – 118×102CFU/100mL respectively. On the other hand, the range for the said parameters for borehole water stored in black plastic tank, green plastic tank and blue plastic tank were respectively 6×102CFU/100mL – 100×102CFU/100mL, 6×102CFU/100mL – 104×102CFU/100mL and 6×102CFU/100mL – 108×102CFU/100mL. Also, findings from the study recommends that, the maximum retention period for storing tap water or borehole water in plastic tanks to be at most 3weeks. From this work, it was established that, black plastic materials should be considered first when selecting a container material for storing water in large capacity.
🏷 Plastic Storage, Time on Potable Water, Containers
Article 120 · pp. 1518-1527
Palatability and Durability Test of Coumatetralyl Rodenticide with Various Flavors and Baits to Malayan Field Rats (Rattus tiomanicus Mill.)
Rats are wild animals that are often found in various habitats and become a nuisance in human life. Rats can cause damage and losses to crops, one of which is oil palm plantation. Therefore, control efforts are needed, one of which is chemical control using rodenticides. This study aimed to determine the level of attractiveness of Malayan field rats to various formulations of coumatetralyl-based rodenticides with cereal-based baits and with vegetable and animal flavorings. Study also conducted to measure the durability and effectiveness of these rodenticides after being exposed in the oil palm crop. The types of rodenticides used were coumatetralyl rodenticides in rice bait and rice plus wheat baits, with chocolate, pandanus, strawberry, and mealworm flavorings. This study consisted of three tests: Palatability, durability, and mealworm tests, each method lasts for three days pre-test, three days test, and fourteen days post-test. The methods used were choice test with eight treatments each for palatability and durability, and two treatments for mealworm test. Data were analyzed using a randomized block design, R Studio with Tukey's test at α = 5%. The results showed that the consumption of Malayan field rats on coumatetralyl rodenticides with flavored baits in the palatability and durability tests was not significantly different, while it was significantly different in the mealworm test. Coumatetralyl rodenticides with pandanus and chocolate flavors, with rice and rice-wheat baits, and positive control rodenticides with mealworms were more preferred by Malayan field rats than others. Further research is aimed at testing the coumatetralyl rodenticide with the best type of bait and flavoring agent for application in oil palm plantations to kill pest rats and reduce the damage they cause to oil palm fruit.
🏷 Effectiveness, chocolate, choice test, mealworm, pandanus, rice bait
Article 121 · pp. 1528-1536
AI-Based Deepfake Voice Detection Using MFCC Features and Random Forest Classification
The rapid proliferation of AI-generated audio poses a serious threat to digital forensics, voice-based authentication, and information integrity. This paper presents a deepfake voice detection system that combines Mel-Frequency Cepstral Coefficient (MFCC) feature extraction with a Random Forest ensemble classifier to distinguish real human speech from synthetically generated audio. The proposed system processes input audio files in WAV or MP3 format, extracts 40 MFCC coefficients as the feature representation, and classifies each sample as real or fake through a trained Random Forest model. The complete pipeline is deployed as a Flask-based web application, enabling browser-based access without requiring any specialist software.
Experimental evaluation was conducted on a balanced binary dataset comprising 300 real voice recordings and 300 AI-generated voice samples (total: 600 samples), split 80/20 for training and testing. The system was evaluated against two baseline classifiers under identical feature conditions. Results demonstrate that the proposed Random Forest model achieves an accuracy of 92.7%, precision of 91.9%, recall of 93.5%, and an F1-score of 92.7%, indicating strong effectiveness for practical deepfake audio detection. These results represent a substantial improvement over the SVM baseline (accuracy: 76.4%) and Decision Tree baseline (accuracy: 81.2%).
🏷 Deepfake Audio Detection, MFCC Feature Extraction, Random Forest Classifier, Voice Cloning, Flask Web Application, Audio Forensics, Fake Speech Detection, Machine Learning, ASVspoof
Article 122 · pp. 1537-1552
Women Entrepreneurship in India: Obstacles and Opportunities
Women Entrepreneurship (WE) in India has emerged as a significant contributor to economic growth and social development. This research highlights WE in India and also focuses on the challenges and opportunities faced by women entrepreneurs in India. The research discusses the current scenario of WE in India and reviews various literature to gain insights into the topic.
The research incorporated the primary quantitative and secondary qualitative data collection methods to examine the perception of various groups of individuals concerning WE and also determine the challenges and opportunities faced by women entrepreneurs. The primary data is analysed using statistical analysis methods, and the secondary data is analysed using thematic analysis.
The findings underscore the need for targeted interventions to empower women entrepreneurs, enabling them to overcome obstacles and leverage opportunities for sustainable growth and development. Based on these findings, the research has also provided actionable recommendations to boost women’s entrepreneurial activities in India.
🏷 Women Entrepreneurship, India, Gender Barriers, Financial Access, Socio-Cultural Challenges, Government Initiatives, Economic Growth, Gender Equality
Article 123 · pp. 1553-1566
Indian Neurotherapy in Obsessive–Compulsive Disorder (OCD) Management - A Case Report
by persistent intrusive thoughts and compulsive behaviours causing severe functional impairment. Despite pharmacological and psychotherapeutic advances, a substantial proportion of patients experience incomplete response, relapse, or limited access to evidence-based care. Indian Neurotherapy, a non-invasive naturopathic system employing targeted neuromuscular stimulation, offers a potentially viable complementary approach, yet remains undocumented in peer-reviewed clinical literature. Objective: To evaluate clinical outcomes of structured Indian Neurotherapy in a severe, treatment-refractory OCD case using the OCI-R as primary outcome measure, with 10-month follow-up. Methods: CARE-compliant observational single-case study at LMNTRTI, Punjab, India. Participant: 43-year-old female with 25-year OCD history, multiple comorbidities, and prior treatment failure across allopathic, Ayurvedic, and homoeopathic systems. OCI-R administered at baseline (14 June 2025), after 6 sessions (20 June 2025), post-intervention (22 August 2025), and followed up to 11 March 2026 (9 months). Results: OCI-R scores: 69/72 (severe) → 35/72 (49.3% reduction after 6 sessions) → 0/72 (complete remission at 10 weeks). At 9-month follow-up, the participant returned for general wellness goals with no OCD symptom recurrence. Conclusion: This CARE-compliant case report demonstrates complete, sustained remission of severe, long-standing OCD through Indian Neurotherapy, with mechanistic plausibility grounded in autonomic, vagal, serotonergic, and neurochemical regulatory pathways. Controlled trials are urgently warranted.
🏷 Obsessive–Compulsive Disorder; Indian Neurotherapy; OCI-R; Integrative Mental Health; Autonomic Nervous System; Vagal Tone Modulation; LMNTRTI; Case Report; Complementary Medicine
Article 124 · pp. 1567-1575
Influence of Short-Form Video Content on Impulse Buying Behaviour Among Gen Z Consumers in Puducherry Region
The rapid growth of short-form video platforms such as Instagram Reels, YouTube Shorts, and TikTok-style applications has significantly transformed consumer engagement and online shopping behaviour among Gen Z consumers. These platforms utilize entertaining, visually appealing, and highly personalized content that can strongly influence consumer emotions and purchase decisions. This study examines the influence of short-form video content on impulse buying behaviour among Gen Z consumers in the Puducherry region. The research focuses on key factors such as entertainment value, influencer credibility, Fear of Missing Out (FOMO), social media engagement, and perceived enjoyment in shaping impulsive purchasing decisions. A structured questionnaire-based survey method is proposed to collect primary data from Gen Z consumers who actively use short-form video platforms. The study aims to identify the extent to which exposure to short-form videos triggers unplanned purchases and alters consumer decision-making patterns. The findings are expected to provide valuable insights for digital marketers, e-commerce businesses, and social media advertisers in developing effective marketing strategies targeting young consumers. Furthermore, the study contributes to the growing body of literature on digital consumerism and emerging social media marketing trends in regional markets.
🏷 Short-form video content, Impulse buying behaviour, Gen Z consumers, Social media marketing, Digital consumerism
Article 125 · pp. 1576-1589
Deep Residual Convolutional Neural Networks for Robust Environmental Sound Classification Using Optimised Mel-Spectrogram Representations
Environmental sound classification (ESC) is a fundamental machine-audition problem in the context of smart-city sensing, industrial monitoring, healthcare, and consumer devices. In this study, a convolutional classifier is shown to be a powerful approach for single-channel Mel-spectrogram representations of the ESC-50 benchmark and is evaluated on it in a controlled empirical setting using ResNet-34 architecture. The contribution is not an architectural family, but rather an optimisation and reproducibility study that aims to highlight the influence of residual shortcuts, batch normalisation, dropout, Mel-filter resolution, masking/augmenting with `Spec Augment`-style, mixup, and learning rate scheduling on a consistent training pipeline. The model performance on the ESC-50 benchmark was 83.0% (five-fold CV) and 84.0% (best single-fold validated) at epoch 88. The revised analysis includes the computation cost estimation, per-class performance metrics, confusion matrix analysis, modern benchmark positioning, and confidence intervals. Results show that residual CNNs still provide a salient and interpretable baseline for small-data ESC, despite the current state of the art of large pre-trained transformer and attention base networks.
🏷 Environmental sound classification · residual networks · Mel spectrogram · convolutional neural networks · ESC-50 · data augmentation · deep learning
Article 126 · pp. 1590-1600
Effect of Drying Methods on Chemical Properties of Mango Paste
The influence of drying methods (hot-air, microwave, and sun drying) on the bioactive compounds and mineral composition of mango paste was investigated. Mango paste samples were processed under the different drying conditions and analyzed for vitamin C, beta-carotene, lycopene, total phenolic, flavonoids content, and selected minerals, including potassium (K), iron (Fe), magnesium (Mg), calcium (Ca), and phosphorus (P). Significant differences (p < 0.05) were observed among the drying methods for all parameters evaluated.
All the drying methods resulted in a reduction in vitamin C and phenolic compounds, while beta-carotene, lycopene, and most mineral elements increased significantly due to concentration effects associated with moisture removal. Fresh mango recorded the highest vitamin C content (64.08 mg/100 g), whereas hot-air drying caused the greatest reduction (25.80 mg/100 g). Sun-dried mango paste retained relatively higher vitamin C (61.62 mg/100 g) among the dried samples. Beta-carotene content increased from 5.82 mg/100 g in fresh mango to 10.92 mg/100 g in sun-dried mango paste, while lycopene content was highest in microwave-dried mango paste (2.37 mg/100 g). Total phenolic content decreased substantially after drying, with hot-air-dried mango paste recording the lowest value (0.65 mg/100 g). Conversely, microwave drying enhanced flavonoid retention, producing the highest flavonoid content (79.00 mg/100 g).
Mineral analysis revealed significant increases in potassium, iron, magnesium, and calcium contents following drying. Sun-dried mango paste exhibited the highest potassium concentration (501.45 mg/100 g), whereas microwave drying produced the highest calcium content (41.55 mg/100 g). In contrast, phosphorus content decreased in all dried samples compared with fresh mango.
Overall, microwave drying demonstrated superior retention of bioactive compounds and minerals, indicating its suitability for preserving the nutritional and functional quality of mango paste.
🏷 Mango, Drying, Bioactive Compounds and Mineral Content.
Article 127 · pp. 1601-1604
Extraction of Fe (III) Salicylates using Tris (2-Ethyl hexyl) Phosphate.
Iron is one of the most essential elements for life, playing a crucial role in both biological systems and industrial development.
From a biological perspective, iron is vital for the formation of haemoglobin, which is responsible for transporting oxygen in our body. Without adequate iron, the body cannot produce enough healthy red blood cells, leading to conditions such as Iron Deficiency Anaemia.
From an industrial point of view iron is the backbone of modern civilization. It is the primary component of steel, which is used extensively in construction, transportation, machinery, and infrastructure. From buildings and bridges to automobiles etc.
The Solvent extraction of Fe (III) from hydrochloric acid can be accomplished with various neutral organophosphorus compounds such as TBP, TOPO and other oxygen containing solvents such as alcohols, esters, ketones, dichloroether.
The paper describes the extractive determination of iron using sodium salicylate with TEHP as the extractant. Iron Fe³⁺ is a hard Lewis acid (high charge density) and TEHP has a phosphoryl oxygen (P=O) that acts as a strong donor ligand. The bulky 2-ethylhexyl groups in TEHP increase electron donation to the P=O group, enhancing its complexation ability with Fe³⁺. Compared to other organophosphorus extractants such as tributyl phosphate (TBP) and acidic extractants (e.g., D2EHPA), TEHP exhibits higher extraction efficiency and selectivity toward Fe³⁺, particularly under moderate acid conditions. Its enhanced hydrophobicity and branched structure contribute to improved phase separation, reduced third-phase formation, and higher metal loading capacity. These properties make TEHP more suitable for the present extraction system.
🏷 Extraction, Phosphate, Salicylates
Article 128 · pp. 1605-1610
IOT Based Patient Health Monitoring System
The rapid advancement of Internet of Things (IoT) technology has significantly transformed the healthcare sector by enabling continuous and remote patient monitoring. This paper presents an IoT-based smart patient health monitoring system using ESP32 that efficiently tracks vital health parameters such as body temperature, blood pressure, and glucose levels in real time. The system utilizes sensors to collect physiological data, which is processed by the ESP32 microcontroller and transmitted via Wi-Fi to a Node.js-based server. The collected data is stored in a database and visualized through a web-based dashboard, allowing caregivers and medical professionals to monitor patient health remotely. Additionally, the system generates alerts when abnormal health conditions are detected, ensuring timely medical intervention. The proposed solution reduces the need for manual monitoring, improves healthcare accessibility, and enhances patient safety. Due to its low cost, scalability, and real-time capabilities, the system is highly suitable for home-based and remote healthcare applications.
🏷 Internet of Things (IoT), Remote Health Monitoring, ESP32 Microcontroller
Article 129 · pp. 1611-1638
Moderating Effect of Corporate Governance on the Relationship Between Sustainability Reporting and Firm Value of Listed Oil and Gas Companies in Nigeria
The modern business environment necessitates continuous organizational adaptation to maintain competitive value in evolving markets. Firm valuation, conceptualized as the premium investors are willing to pay for corporate ownership, serves as a comprehensive indicator of both tangible assets and managerial efficacy in value creation (Massie et al., 2017; Setiadharma & Machali, 2017). Contemporary corporate reputation management extends beyond financial metrics to incorporate environmental, social, and governance (ESG) dimensions, with robust non-financial disclosure frameworks becoming increasingly critical for capital acquisition and shareholder value optimization.
This paradigm shift has occasioned the development of sustainability reporting (SR) as an institutionalized practice for disclosing organizational impacts across economic, environmental, and social domains (Global Reporting Initiative, 2017a). As a relatively recent innovation, SR has emerged as a strategic tool for ESG risk assessment, opportunity identification, and organizational transparency enhancement (Global Reporting Initiative, 2017). The implementation of SR frameworks facilitates improved strategic planning, operational adaptability, and business continuity in an era where sustainability considerations significantly influence corporate decision-making and stakeholder expectations.
Within the Nigerian context, SR adoption remains predominantly voluntary, with mandatory compliance limited to premium board-listed entities. Current motivations for SR implementation frequently emphasize philanthropic corporate social responsibility (CSR) initiatives and reputational enhancement, raising concerns about potential greenwashing practices (Okwuosa, 2024).
🏷 Moderating, Governance, Sustainability
Article 130 · pp. 1639-1652
Comparative Analysis of Web Design Performance: AI‑Assisted Tools Versus Traditional Figma‑Based Design
This study compared the performance of traditional Figma workflows and AI-assisted Figma workflows in developing high-fidelity UI/UX prototypes among BSIT students of Quezon City University during the Academic Year 2025–2026. Using a descriptive-comparative within-subjects design, the study evaluated both workflows in terms of Design Quality (Functional Suitability, Usability, and Performance Efficiency) and Creative Output (Creative Autonomy, Innovation, and Ease of Workflow). Data were collected from 60 purposively selected respondents through a researcher-developed questionnaire and analyzed using weighted mean, standard deviation, Wilcoxon Signed-Rank Test, and Spearman’s Rank-Order Correlation. Results showed that both workflows received positive evaluations; however, the traditional Figma workflow consistently obtained higher ratings. Significant differences were found in Functional Suitability, Usability, and Creative Autonomy, favoring the traditional workflow, while no significant differences were identified in Performance Efficiency, Innovation, and Ease of Workflow. Strong positive correlations were also observed between Design Quality and Creative Output in both workflows. The findings suggest that while AI-assisted tools improve workflow efficiency and convenience, traditional Figma workflows remain more effective in supporting usability, functional accuracy, and creative control. The study concludes that AI-assisted design tools are most effective when used as complementary technologies alongside human creativity and manual design expertise.
🏷 creative output, design quality, prototyping performance, usability, workflow efficiency, web designs, artificial intelligence, Figma
Article 131 · pp. 1653-1666
Level of Awareness and Susceptibility to Phishing Attacks Among Students of Quezon City University: A Stratified Survey Study
Phishing attacks continue to pose serious cybersecurity risks in educational institutions as students increasingly rely on digital platforms for academic and personal activities. This study aimed to determine the level of phishing awareness and phishing susceptibility among students of Quezon City University, particularly comparing IT and non-IT students. Using a descriptive-comparative quantitative research design, data were collected from 397 students through a stratified survey conducted using online and printed questionnaires. Statistical tools such as frequency and percentage, weighted mean, independent samples t-test, and Pearson Product-Moment Correlation Coefficient were used to analyze the data. The findings revealed that students generally demonstrated a high level of phishing awareness but showed a moderate level of phishing susceptibility. Results also indicated a significant difference in phishing awareness between IT and non-IT students, with IT students exhibiting higher awareness levels. However, no significant difference was found in phishing susceptibility between the two groups. Furthermore, a significant moderate negative relationship was identified between phishing awareness and phishing susceptibility, indicating that higher awareness is associated with lower vulnerability to phishing attacks. The study concludes that although students possess adequate knowledge regarding phishing threats, awareness alone does not completely prevent risky online behavior. The findings may contribute to the development of targeted cybersecurity awareness programs and safer digital practices among university students.
🏷 Cybersecurity Awareness, Phishing Attacks, Phishing Susceptibility, Stratified Survey, University Students, Quantitative Research
Article 132 · pp. 1667-1679
Performance Comparison of API Protocols: A Systematic Review of REST, GRPC and Graph QL
The correct choice of API protocols in the modern distributed systems defines the level of performance, scalability, and resource efficiency. The current paper contains a systematic literature review (SLR) of 20 empirical studies in total published in 2020-2025 by comparing the top three architectural styles, namely, REST, gRPC, and GraphQL. This review offers a review of the evidence-based framework of protocol selection by synthesizing data on latency, throughput, resource utilization, and scalability.
Our results suggest that gRPC provides the shortest latency with small message payloads since it uses HTTP/2 multiplexing and Protobuf serialization, thus this is best suited to inter-service communication. REST has the best raw throughput (1500-2000 RPS on average) but it has head-of-line blocking, and data over-fetching. On the other hand, GraphQL reduces memory usage (around 17 percent versus 20 percent with REST/gRPC) due to accurate field selection, but has high CPU cost in query parsing and resolution.
The review has been found to have significant research gaps, the most notable of which is absence of standardized three way benchmarking and performance analysis of the production scales. This paper ends with evidence-based suggestions of hybrid architecture solutions to provide an architectural and research base of selecting approaches to microservices in architectural projects.
🏷 API protocols, REST, gRPC, GraphQL, performance evaluation, distributed systems, protocol comparison, benchmarking
Article 133 · pp. 1680-1695
Predictors of COVID-19 Mortality: A Stratified 3 by 3 Factorial Correlation Analysis
In this paper the strength of association of some factors with COVID-19 mortality across countries is examined. This is essential for future pandemic preparedness. In this study, a 3 × 3 factorial design where 36 countries were stratified by GDP per capita and population density was used. Secondary data were sourced from the World Bank, United Nations, and Our World in Data repositories. A parsimonious linear regression model with four predictors: positivity rate, vaccination coverage, log (GDP per capita), and median age was fitted. Bootstrapping provided 95% confidence intervals. Low-GDP countries had higher positivity rates (10.2% vs. 3.0%) but lower reported deaths (167 vs. 1,186 per million) than high-GDP countries. The model explained 89.2% of variance (adjusted R² = 0.878, p < 0.001). Positivity rate was the strongest predictor of mortality (β = 10.51, p < 0.001), followed by GDP per capita (β = 1.39, p < 0.001). The positivity-deaths correlation was strongest in high-GDP countries (r = 0.898, p < 0.001) compared to low-GDP countries (r = 0.597, p = 0.019), suggesting that differential death reporting attenuates associations in low-resource settings. These findings suggest positivity rate as a high value predictor of COVID-19 mortality. Maintaining low positivity rates through accessible testing should guide future pandemic surveillance.
🏷 COVID -19, positivity rate, testing, vaccination, GDP
Article 134 · pp. 1696-1713
The Impact of Redistribution of Income on Nigeria Economic Growth and Stability
This study examines the impact of income redistribution on Nigeria’s economic growth and stability. Annual data covering the period 1990 to 2025 were sourced from the World Bank, National Bureau of Statistics, World Development Indicators and the Central Bank of Nigeria. The variables analysed include Real GDP, Personal Income Tax (PIT), the Gini coefficient, Inflation Rate, Health, and Hosing. Methodologically, the study employs Autoregressive Distributed Lag (ARDL) framework to analyze both short-run and long-run dynamics. The Bounds Test confirms the existence of a long-run cointegrating equilibrium, while the Error Correction Model (ECM) estimates approximately 88.8% speed of adjustment within a year. The empirical findings reveal a positive long-run relationship between economic growth and PIT, health expenditure, and housing. Specifically, PIT demonstrates a significant positive impact on economic growth in the long run but exhibits a negative effect in the short run. Conversely, inflation acts as the primary inhibitor of macroeconomic stability, depicts a persistent negative impact on economic growth. High inflation further worsens income disparity by widening the gap between the rich and the poor. This structural pressure emphasises the key role of Personal Income Tax as a stabilizing mechanism to foster economic growth in Nigeria. Based on these findings, the study offers three primary recommendations. First, policymakers must strengthen PIT administration by widening the tax base, mitigating evasion, and enforcing compliance within the informal sector. Second, public expenditure on health and housing must be expanded and efficiently managed. Targeted investments in healthcare infrastructure and affordable housing are vital to build human capital, enhance labour productivity, and stimulate long-term growth. Finally, inequality-reduction policies must prioritize vulnerable, low-income households to ensure inclusive growth and social cohesion.
🏷 Redistribution of Income, Progressive tax (as PIT), Economic Growth and Stability, GDP, Gini Co-efficient, ARDL, Health Expenditure, Housing, Inflation, Nigeria.
Article 135 · pp. 1714-1731
Level of AI Dependency Vis-à-Vis Study Habits Among College Students at Quezon City University
This study examined the level of Artificial Intelligence (AI) dependency among college students at Quezon City University (QCU) and its relationship with their study habits. Using a descriptive-correlational research design, data were gathered from 372 respondents across five colleges through a validated survey questionnaire. The study assessed AI dependency across four dimensions, frequency and duration of AI tool usage, reliance on AI for academic tasks, compulsive or habitual use of AI tools, and interference of AI usage with independent learning, and study habits across four dimensions: time management and study scheduling, consistency and regularity of review, use of study strategies and techniques, and level of focus and concentration during study sessions. Findings revealed that students demonstrated a moderate level of AI dependency and a moderate-to-agreeable level of study habits. Significant differences in AI dependency were observed when respondents were grouped by demographic profile. Notably, a significant relationship was found between AI dependency and study habits, suggesting that increasing AI dependency is associated with changes in students' approach to academic work. The study recommends institutional policies and instructional strategies to promote responsible and balanced AI use in higher education
🏷 Artificial Intelligence, AI Dependency, Study Habits, College Students, Quezon City University
Article 136 · pp. 1732-1748
Balancing Work – Life and Personal – Life Expectancies: Insights from Public and Private Sector Banks
Banking sector is strength for the economic development of any country and the women is playing an important role in functioning of the banking sector. At present, banks are creating more opportunities for women employees. Most of the women prefer jobs in banks because they are more attractive and comfortable. Currently, women employees are facing challenges in fulfilling the responsibilities both in professional life and work life. The present study is to know about the personal life expectations and work-life expectations of women employees working in the public and private sector banks in Visakhapatnam. A sample of 120 respondents was selected using simple random sampling. Data were collected through structured questionnaires based on a five-point Likert scale. Statistical tools including descriptive statistics, ANOVA, and multiple regression analysis were employed. The findings reveal significant differences in expectations across sectors and demographic categories. Regression analysis further indicates that age, marital status, work experience, and type of bank significantly influence work–life and personal–life expectations. The study provides managerial insights for designing gender-sensitive workplace policies.
🏷 Work-life balance, Banking sector, Women employees, Personal life expectations, Work-life expectations, Regression analysis, Self – management.
Article 137 · pp. 1749-1767
Virtual Sacred Spaces and AI-Augmented Pilgrimage Experiences in the Digital Era
Background
The convergence of artificial intelligence, virtual reality, and augmented reality technologies is fundamentally transforming religious practices and spiritual experiences. Traditional pilgrimage, historically requiring physical presence at sacred sites, is being reimagined through immersive digital environments that offer unprecedented accessibility while raising profound questions about authenticity, theological legitimacy, and the nature of sacred experience itself.
Objective
This study comprehensively examines the emergence of virtual sacred spaces and AI-augmented pilgrimage experiences, analyzing their technological foundations, spiritual implications, accessibility benefits, ethical challenges, and future trajectories. The research synthesizes current scholarship to understand how digital technologies are reshaping religious tourism, spiritual engagement, and the conceptualization of sacred space in contemporary society.
Methods
A systematic literature review was conducted across multiple scholarly databases, yielding 78 unique peer-reviewed publications. The top 30 most relevant studies were analyzed using thematic synthesis, focusing on technological implementations, user experiences, theological perspectives, ethical considerations, and cultural impacts.
Studies encompassed diverse religious traditions including Islam, Christianity, Hinduism, Buddhism, and Judaism, examining VR/AR/AI applications in pilgrimage simulation, sacred space reconstruction, and spiritual guidance.
Results
The analysis reveals that virtual sacred spaces successfully enhance accessibility for individuals facing physical, financial, or geopolitical barriers, with VR technologies demonstrating significant potential for pre-pilgrimage training and cultural preservation. AI-augmented companions provide real-time translation, historical context, and emotional support, deepening engagement with sacred sites.
However, critical limitations persist: virtual experiences cannot fully replicate the communal, ritualistic, and transformative elements of physical pilgrimage. Theological perspectives vary significantly across traditions, with concerns about sacramental validity, embodied presence, and the commodification of sacred experiences. User studies indicate positive responses to aesthetic quality and sense of presence, though multisensory integration remains technically limited.
Conclusion
Virtual sacred spaces and AI-augmented pilgrimage represent complementary rather than replacement technologies for traditional religious practices. They democratize access to sacred sites, preserve cultural heritage, and facilitate interfaith understanding, while simultaneously challenging established theological frameworks. Future development must prioritize ethical design, cultural sensitivity, theological consultation, and enhanced multisensory integration. The field requires interdisciplinary collaboration among technologists, religious scholars, ethicists, and faith communities to ensure these innovations serve spiritual authenticity rather than commercial exploitation.
🏷 Virtual reality; Artificial intelligence; Digital religion; Sacred spaces; Pilgrimage; Immersive technologies; Religious tourism; Spiritual experience; Theological ethics; Cultural preservation
Article 138 · pp. 1768-1778
Polyherbal Extracts and its Potential Impact on Dandruff Causative Agent of Malassezia globosa
At present, more than quasi of the worldwide people deal with the major skin problem with dandruff. The presence of the Malassezia fungus suggestively subsidizes this condition by potentially generating the production of cytokines in keratinocytes, the cells responsible for synthesizing keratin and activating provocative alleyways. Antifungal activity of selected Indian medicinal plant extracts against the dandruff causing pathogen. The present work assigned the four experimental plants named Eclipta alba, Hibiscus rosasinensis, Lawsonia inermis and Muraya koenigii and its polyherbal mixed composition of these four herbal extracts. The prepared aqueous poly herbal extracts were added to the wells of Malassezia globossa inoculated plates. From the present antifungal result was clearly expressed all the four individual plant extract showed minimum to maximum antidandruff activity among the four M.koeningi possessed maximum antifungal activity than other three extracts. Even though, a mixed composition of these four herbal extracts (aqueous) contains the maximum potential antidandruff effect than the individual extracts of E.alba, H. rosasinensis, L. inermis. Hence, the present experimental four herbal extract was definitely depicted for great potential against the dandruff and skin pathogenic fungal organism of M. globossa.
🏷 Herbal Extract, M.globossa, Eclipta alba, Hibiscus rosasinensis, Lawsonia inermis and Muraya koenigii.
Article 139 · pp. 1779-1790
AI - Based Crop Recommendation for Farmers
Agriculture is a significant factor in supporting the world's population, but sometimes, it becomes challenging for the farmers to decide on the crops to be grown considering the diverse nature of the soil, climate, and market demands, as well as a lack of proper guidance. A proposed AI-based crop recommendation system that helps the farmers take proper decisions in the field of agriculture. The proposed system uses machine learning algorithms to consider important factors like soil nutrients, temperature, humidity, rainfall, pH, and current market demands to recommend the best crop for farming. A multilingual web-based system helps the farmers of different linguistic backgrounds to input the environmental values and receive the recommendations in the desired language. The proposed system helps to increase the yield, optimize resources, and maximize the returns while reducing the risk of crop failure. The results of the experiments show the accuracy and efficiency of the proposed model.
🏷 Crop Recommendation System, Precision Agriculture, Soil Nutrients Analysis, Agriculture analytics
Article 140 · pp. 1791-1801
Corporate Sustainability Adoption under Industry 5.0: The Role of Regulatory, Market, and Stakeholder Institutions Across Economies
The world of business is entering a new phase called Industry 5.0. This shift is not only about using advanced technologies or smarter machines. It is also about making industries more human-centered, environmentally responsible, and resilient during times of crisis. In simple terms, Industry 5.0 encourages companies to care not just about profits, but also about people, society, and the planet. However, companies across the world are not adopting sustainability practices in the same way. A business operating in a developed country such as Sweden faces very different conditions compared to a company in an emerging economy like Bangladesh or India, even though both are influenced by the same global sustainability goals. This difference raises an important question: why do some companies strongly integrate sustainability into their operations while others struggle to do so?
The study argues that the answer lies in the institutional environment surrounding firms. Every company operates within a system shaped by government regulations, law enforcement, investor expectations, public awareness, and market conditions. These institutional factors strongly influence how seriously businesses adopt sustainability practices. The study focuses on four important elements: the strength of regulations, the effectiveness of enforcement, stakeholder pressure, and the maturity of sustainable markets.
Using secondary data and studies published between 2020 and 2025, the paper finds that companies in developed economies are more likely to adopt sustainability because strict laws, active investors, and informed consumers encourage responsible business behavior. In contrast, companies in developing economies often adopt sustainability mainly because international buyers, foreign investors, or export market requirements push them to do so. This unequal pattern of adoption is described in the paper as “institutional asymmetry.”
The study also proposes a conceptual model and four research hypotheses that can be tested in future research. Finally, it provides recommendations for policymakers and industry leaders to reduce these institutional gaps and create a more balanced and inclusive approach to sustainability under Industry 5.0.
🏷 Corporate Sustainability, Institutional Context, ESG Practices, Regulatory Enforcement, Market Maturity
Article 141 · pp. 1802-1813
“A Comparative Study of Perception towards Artificial Intelligence and Sustainable Development among Urban and Rural College Students.”
The current research was conducted to analyse the attitude of College students towards Artificial Intelligence (AI) and Sustainable Development and also to compare the perspective of urban and rural students as well as male and female students. Artificial Intelligence has emerged as a game changer technology affecting a variety of industries such as education, health, agriculture, environment management and government. At the same time sustainable development has become a worldwide goal for the balanced economic growth, environmental conservation and social well-being. A descriptive survey approach with a comparative research design was used in the study. A sample of 100 undergraduate college students including 50 urban and 50 rural students equally distributed between male and female students was recruited using stratified random selection from selected institutions of West Bengal. To gather data, the researcher prepared a self-structured instrument named as “Perception Scale on Artificial Intelligence and Sustainable Development” (PSAISD).
Data were analysed using statistical procedures including Mean, Standard Deviation and t - test. The results indicated that urban students had considerably more awareness levels towards AI and Sustainable Development than rural students owing to more exposure to technology and internet accessibility. The perception ratings of female students were marginally higher than those of male students with no statistically significant differences. The report also pointed to students’ understanding of AI applications in education, healthcare and environmental sustainability, and their worries around ethics, privacy and jobs. The report highlights the need of inclusive AI education, better digital infrastructure and awareness campaigns to encourage sustainable tech behaviours among students.
🏷 Artificial Intelligence, Sustainable Development, Perception, Urban Students, Rural Students, College Students
Article 142 · pp. 1814-1824
Analysis of solar panel characteristics
Reliable monitoring of photovoltaic (PV) modules is essential for assessing energy generation performance, diagnosing degradation, and ensuring long-term system reliability. Solar panels experience variations in output characteristics due to changing irradiance, temperature, environmental conditions, and aging effects. Conventional monitoring techniques rely on manual measurement or bulky instrumentation, which lack real-time visibility and are unsuitable for continuous data logging. This paper presents a data-driven Internet of Things (IoT)–based methodology for real-time solar panel characteristic monitoring and analytics.
The proposed system utilizes an ESP32 microcontroller interfaced with an INA219 voltage–current sensor to acquire live measurements of panel voltage, current, and instantaneous power. The data is timestamped using NTP synchronization and transmitted to a Firebase Real-Time Database for cloud storage. A Flutter-based Android application retrieves the data to provide live dashboards, historical charts, and CSV export functionality for one-hour intervals or the complete operational dataset. Time-series data collected from the system enables computation of analytical metrics such as daily energy generation, peak-power duration, stability under irradiance variation, and long-term performance trends. Experimental evaluation on a 11 W SLP011-12 solar module demonstrates accurate sensing, stable wireless data transfer, and effective visualization of more than 2,000+ recorded samples.
The contributions of this work include: (1) a low-cost, scalable IoT architecture for continuous PV monitoring, (2) automated cloud-synchronized data logging with precise timestamping, (3) an interactive mobile application for real-time analytics and dataset export, and (4) a foundation for future machine-learning–based performance prediction and fault diagnosis. This system provides an efficient research and industrial tool for solar panel condition assessment and long-term energy monitoring.
🏷 Solar energy monitoring, IoT, ESP32, INA219, Firebase, Flutter application, renewable energy analytics.
Article 143 · pp. 1825-1833
Block Chain-Enabled Secure E-Voting Framework with Facial Recognition for Voter Authentication
Democratic elections rely on trust, transparency, and tamper-resistance -- qualities that conventional and early electronic voting systems have consistently failed to guarantee. This paper presents a Blockchain-Enabled Secure E-Voting Framework with Facial Recognition for Voter Authentication, designed to address persistent vulnerabilities in existing electoral systems. The proposed system integrates a permissioned blockchain ledger with deep-learning-based facial biometric verification to ensure decentralized, immutable vote storage and strong identity assurance. A multi-layer security architecture combines homomorphic encryption, zero-knowledge proofs, and digital signatures to preserve voter anonymity while enabling end-to-end verifiability. Anti-spoofing and liveness detection mechanisms prevent impersonation via photographs, video replays, or deepfake-generated imagery. Smart contracts automate vote counting and result publication, eliminating human involvement in the tallying process. Experimental evaluation demonstrates a facial recognition authentication accuracy of 97.3% and an end-to-end voting transaction latency under 500 milliseconds, with blockchain confirmation averaging 2.4 seconds. The framework is scalable to national-scale elections and applicable to governmental, corporate, and institutional governance contexts.
🏷 blockchain; e-voting; facial recognition; homomorphic encryption; zero-knowledge proof; smart contracts; biometric authentication; electoral integrity
Article 144 · pp. 1834-1850
Theorizing Artificial Intelligence as an Organizational Actor: Insights from a Narrative Review
This narrative review examines how artificial intelligence (AI) is being theorized as an organizational actor within management and organization studies. The paper synthesizes fragmented theoretical perspectives to develop an integrated conceptual framework that identifies the antecedents, mechanisms, and contextual conditions shaping AI organizational actorhood. A systematic search of Web of Science was conducted, yielding 47 peer-reviewed articles published between 2020 and 2026. The selected literature spans management, business, and accounting disciplines. Through thematic analysis guided by institutional theory, agency theory, and sociomateriality, the review critically synthesizes scholars' conceptualizations of AI's organizational actorhood and proposes a testable conceptual framework. Four key themes emerge: (1) AI as an institutional actor subject to and generative of institutional pressures; (2) AI as an economic agent with principal-agent dynamics; (3) AI as a socio-material ensemble co-constituting organizational realities; and (4) AI's evolving autonomy from tool to quasi-autonomous actor. The review reveals that, while AI is increasingly theorized to possess agentic qualities, conceptualizations remain fragmented across theoretical silos. This review contributes a multi-dimensional framework for theorizing AI organizational actorhood that integrates institutional, economic, and socio-material perspectives. It identifies five antecedents, three moderators, three mediators, and four control variables that collectively shape the emergence of AI as an organizational actor. For managers and policymakers, recognizing AI as an organizational actor with emergent agency necessitates rethinking governance mechanisms, accountability structures, and legitimacy strategies. This is the first narrative review to propose a comprehensive, testable conceptual framework for AI organizational actorhood, synthesizing diverse theoretical traditions to advance a coherent research agenda.
🏷 Artificial intelligence, organizational actorhood, agency theory, institutional theory, narrative review
Article 145 · pp. 1851-1856
Technology Integration in teaching and learning in Zimbabwean Higher Education Institutions
The integration of digital technologies into higher education has become vital in improving teaching quality, student engagement and graduate employability. Despite supportive national policies such as Zimbabwe’s Education 5.0, technology in many universities remains underutilised for pedagogical transformation. In exploring the factors influencing the successful integration of technology into teaching and learning in higher education institutions in Zimbabwe, the study adopted the intepretivist paradigm, a qualitative research approach, as well as the case study design. Data were generated through semi-structured interviews with nine lecturers and focus group discussions with twelve undergraduate students. Data from interviews were analysed using thematic analysis, and initial coding was completed by label identification. Thematic analysis revealed that while both lecturers and students demonstrated generally positive attitudes towards digital technologies, effective integration was constrained by inadequate infrastructure, limited digital literacy, insufficient pedagogical training, high data costs, and inconsistent institutional support. Technology was largely confined to content delivery rather than interactive and student-centred learning. The study concludes that meaningful technology integration requires alignment between pedagogy, technology and content, supported by sustained professional development and institutional investment. The study also recommended that lecturers should integrate interactive and student-centred digital methods like simulations and collaborative tools rather than limiting them to content delivery. The study contributes context -specific insights into technology adoption challenges in Zimbabwean higher education.
🏷 technology integration, TAM, TPACK, perceived usefulness, perceived ease of use
Article 146 · pp. 1857-1862
Promoting the Use of Carved Bamboo Utensils as Sustainable Alternative at School: An Analysis of Mechanical Strength and Thermal Resistance
This study addresses the critical environmental challenge of single-use plastic-based utensils by evaluating carved bamboo utensils made from Bambusa blumeana as a substitute. Utilizing a quantitative mixed-method framework, the research subjected both bamboo utensils (experimental group) and conventional plastic-based utensils (control group) to stress profiling, thermal thresholds up to 99.1 ℃, and a 14-day environmental exposure, as well as the use of descriptive survey of twenty-five purposively selected consumers in Maigo, Lanao del Norte. The outcomes reveal that the fibrous structure of Bambusa blumeana contains a high tensile strength of approximately 23,500 psi, compared to the structure of the plastic-based utensils. The study utilized a comparative experimental design to evaluate mechanical strength, thermal resistance, and environmental durability, complemented by a descriptive survey involving twenty-five purposively selected consumers. While the survey results indicated a high level of consumer acceptance (GWM = 4.34), the findings should be interpreted within the context of the study's limited sample size. Overall, the results suggest that integrating regional bamboo resources into institutional supply chains may be a mechanically viable and environmentally sustainable strategy for reducing dependence on single-use plastic utensils.
🏷 Carved Bamboo Utensils, Mechanical Strength, Thermal Resistance, Sustainability, Plastic Pollution, Eco-Friendly Materials, Consumer Acceptance, Bambusa blumeana
Article 147 · pp. 1863-1870
Design and Development of a Solar-powered Water Purifier Prototype
Access to safe drinking water remains a persistent public health concern, particularly in educational institutions where students rely on shared water sources for daily consumption. Despite existing water management practices, microbial contamination and water quality issues continue to be reported, indicating the need for sustainable and accessible treatment systems. This study presents the design and development of a solar-powered water purifier prototype and evaluates its effectiveness in improving selected water quality indicators at Mindanao State University–Maigo College of Education, Science and Technology (MSU–MCEST).
An experimental-developmental research design was used. The prototype consisted of a solar panel, charge controller, rechargeable battery, power inverter, and a five-stage ultrafiltration system integrated with ultraviolet (UV) sterilization. Water samples were collected from four campus locations—the Senior High School (SHS) Building, Junior High School (JHS) Building, Administration Building, and Peace Park—and were subjected to pre-treatment and post-treatment analyses. Water quality assessment included selected heavy metal indicators using heavy metal test strips and microbiological analysis using bacterial testing kits.
Post-treatment results showed reductions in both microbiological contamination and selected chemical indicators. Cadmium (Cd), detected in several untreated samples, was not detected after treatment. Zinc (Zn) remained detectable in one post-treatment sample, indicating limited removal of certain dissolved constituents. All untreated samples tested positive for bacterial contamination, while all treated samples tested negative. These results indicate effective reduction of detectable bacterial contamination, attributed to the ultraviolet (UV) sterilization component under the conditions of the study.
Overall, the findings indicate improved microbiological and partial chemical water quality following treatment. However, limitations related to field-based testing methods, sample size, and duration constrain generalization. Further studies using laboratory-based analyses, expanded sampling, and long-term performance evaluation are recommended.
🏷 solar-powered water purifier, water purification, bacterial contamination, solar energy, water safety
Article 148 · pp. 1871-1879
Development of a Single-point Optical Scanning System for Teaching Transmission Electron Microscopy Principles
Transmission Electron Microscopy (TEM) is an important scientific imaging technique; however, students often find its underlying principles difficult to understand because of the abstract nature of electron optics and the limited availability of microscopy equipment in educational settings. This study developed and evaluated a low-cost Single-Point Optical Scanning Instructional System designed to demonstrate the fundamental principles of TEM through hands-on and visualization-based learning.
The prototype utilized a laser module, optical sensors, stepper motors, a turntable mechanism, an Arduino Uno microcontroller, and image reconstruction software to simulate scanning, signal detection, and image formation processes. A mixed-methods project-based research design was employed involving ten Grade 12 STEM students. Participants completed pre-test and post-test assessments to measure conceptual understanding before and after exposure to the instructional demonstration. Results showed an increase in mean scores from 8.20 to 12.40, representing a 51.22% improvement.
A paired-samples t-test indicated that the increase was statistically significant, t(9) = 8.20, p < 0.001. The findings suggest that the developed instructional system effectively improves students’ understanding of TEM principles while providing an affordable alternative to expensive microscopy equipment. The study highlights the potential of low-cost, interactive STEM instructional tools for enhancing science education in resource-limited learning environments.
🏷 Development, Single-point Optical, Scanning System
Article 149 · pp. 1880-1887
A Study on Factors Influencing the Choice of Motor Insurance Policies among Car Owners in Bangalore
A motor insurance policy provides an essential layer of financial security against the risks associated with a motor vehicle, including accidents, theft, natural hazards and liability risks to third parties. As the number of cars on the road has risen, technology has advanced, and the competition among insurance companies has grown, there has been greater interest in the factors that affect customer choice of motor insurance policy. The present study is an attempt to find out the important factors that affect the selection of Motor Insurance policies among vehicle owners in Bangalore. The research design used was a descriptive research design, and the data collected were primary data from 250 car owners who were given a structured questionnaire. A method called Exploratory Factor Analysis (EFA) was used to look for the dimensions that underlie policy choices. The Kaiser-Meyer-Olkin (KMO) index value was 0.894 with a significant Bartlett's Test of Sphericity, thus showing the data to be appropriate for factor analysis. The findings showed that there were five key determinants of policy selection: Service Quality and Claim Settlement, Premium and Cost Considerations, Company Reputation and Trust, Policy Coverage and Benefits, and Digital Convenience. A total of 72.84 per cent of the variance was accounted for by these five factors. Of these, the most significant factors impacting customer decisions were Service Quality and Claim Settlement. The results indicate that policies that boast quick claims settlement, low rates, full coverage, reliable insurance companies, and digital platforms are desirable to customers. The study offers valuable information for insurance companies to define their strategies towards customers and to enhance the attractiveness of their policies in a competitive market.
🏷 Motor Insurance, Insurance Policy Choice, Car Owners, Claim Settlement, Service Quality, Digital Insurance, Factor Analysis, Bangalore.
Article 150 · pp. 1888-1897
Geospatial Assessment of Limestone Mining-Induced Land Use Change in Okpella, Edo State, Nigeria
This study assessed limestone mining-induced land use and land cover change and its implications for agricultural land loss in Okpella, Edo State, Nigeria. A mixed-methods approach was adopted, integrating remote sensing, Geographic Information System (GIS) analysis, questionnaire surveys, and focus group discussions. Multi-temporal satellite images for 2002, 2014, and 2024 were classified using a supervised Random Forest algorithm to quantify land use changes, while descriptive and inferential statistics were used to analyze socio-economic data obtained from 400 respondents. Results indicate substantial land transformation over the study period. Agricultural land declined from 229.17 km² in 2002 to 104.29 km² in 2024, while mining areas increased from 0.36 km² to 9.52 km². Built-up areas also expanded gradually, while vegetation cover decreased consistently. Classification accuracy assessment showed strong reliability, with overall accuracies ranging from 88.89% to 92.86% and Kappa coefficients between 0.846 and 0.899. Regression analysis revealed a statistically significant relationship between limestone mining and land use change (R² = 0.387, p < 0.05), indicating mining as a key driver of spatial transformation in the study area. Respondents’ perceptions further confirmed that mining activities negatively affect soil quality, crop yields, and agricultural practices. The study concludes that limestone mining has significantly contributed to agricultural land loss and landscape transformation in Okpella. Sustainable land management strategies, stricter environmental regulation, and reclamation policies are recommended to balance mineral extraction with agricultural sustainability.
🏷 Limestone mining; land use and land cover change; GIS and remote sensing; agricultural land loss; Okpella, Nigeria
Article 151 · pp. 1898-1908
Assessment of Limestone Mining and Its Effects on Crop Production in Okpella, Nigeria
This study examined the environmental and agricultural impacts of limestone mining in Okpella, Edo State, Nigeria, using a survey research design. Data were collected from 400 respondents and analyzed using descriptive statistics, correlation, and regression techniques. The findings revealed that a majority of respondents perceived limestone mining as having severe environmental consequences, with 65% rating land degradation and 60% rating water contamination as extremely negative. Similarly, 52.5% of respondents reported extremely negative impacts on biodiversity, while 50% indicated severe vegetation loss. Regarding agricultural production, 55% of respondents strongly believed that mining has negatively affected soil quality, while 62.5% strongly believed that it has reduced crop yields. In addition, 50% strongly believed that mining disrupts agricultural practices. Descriptive statistics showed low mean scores (soil quality = 1.875; crop yield = 1.725), indicating strong agreement on the negative impacts. The correlation analysis revealed strong positive relationships among soil degradation, reduced crop yields, and disrupted farming practices (r = 0.694–0.782). Regression results indicated that limestone mining significantly affects crop production (R² = 0.612, p < 0.05), with a negative coefficient (B = -0.683), suggesting that increased mining intensity leads to declining agricultural output. The study concludes that limestone mining has significant adverse effects on environmental sustainability and agricultural productivity in Okpella. It recommends improved environmental management practices and further longitudinal and empirical research to support sustainable coexistence between mining and agriculture.
🏷 Limestone mining; environmental impact; crop production; soil quality; agricultural productivity; Okpella.
Article 152 · pp. 1909-1947
Design for Assessing the Quality of Internet-Based Governance of Cooperative Legal Entities
Corporate governance is a crucial factor in preventing corruption while simultaneously enhancing organisational performance and sustainability. In the context of cooperatives as member-based economic entities, implementing good governance continues to encounter various challenges, particularly those related to the limitations of measurement models that are both comprehensive and adaptable to the development of digital technology. This study aims to identify and design a more comprehensive internet-based cooperative governance quality measurement model by employing a qualitative approach through documentation techniques, unstructured interviews, and focus group discussions (FGDs) involving stakeholders such as regulators, cooperative practitioners, and academics. The findings indicate that existing governance measurement models have not yet fully accommodated the complexity of cooperative characteristics and the demands of digitalisation. Consequently, this study develops a new model by integrating five principal aspects, namely stakeholders, transparency, responsibility, accountability, and technical accessibility, which are further elaborated into 167 indicators. The novelty of this research lies in the utilisation of the internet as a governance measurement medium, as well as the incorporation of stakeholder and technical accessibility dimensions as the main components of governance evaluation. The resulting model is expected to serve as a more systematic, adaptive, and relevant instrument for assessing the quality of cooperative governance, while simultaneously supporting greater transparency, accountability, and public trust. Nevertheless, this study still has limitations regarding the number of informants and the absence of extensive empirical testing; therefore, future research is recommended to examine the validity and implementation of the model across various cooperative contexts in Indonesia.
🏷 Cooperative governance, good corporate governance, digitalisation, stakeholders, transparency, accountability.
Article 153 · pp. 1948-1957
Transfer of Training in Malaysian TVET Institutions: The Role of Attitude, Organisational Commitment, Perceived Importance and Gender Differences
Technical and Vocational Education and Training (TVET) is a central pillar of Malaysia’s human capital development strategy, particularly in producing industry-ready graduates aligned with Industry 4.0 requirements. The effectiveness of this system is highly dependent on TVET lecturers’ ability to transfer training outcomes into actual teaching practice. However, despite substantial investment in professional development, training transfer among lecturers remains inconsistent and insufficiently examined. This conceptual paper proposes a study involving TVET lecturers in Malaysian higher learning institutions as respondents to investigate transfer of training. The study is guided by three objectives: (i) to assess the level of training transfer, (ii) to examine gender differences, and (iii) to determine the influence of attitude, organisational commitment, and perceived importance on training transfer. The study is underpinned by the Theory of Planned Behaviour (TPB), which explains how attitude, organisational commitment, and perceived importance shape behavioural intention and subsequently influence training transfer. Gender Role Theory is integrated to account for possible gender-based differences, particularly in male-dominated technical fields such as automotive and mechanical engineering. A quantitative cross-sectional survey design is proposed, with data analysed using descriptive statistics, independent sample t-tests, and multiple regression analysis via SPSS. This study is expected to extend the literature by integrating TPB and Gender Role Theory in explaining training transfer among TVET lecturers in Malaysia, while providing practical implications for enhancing the effectiveness of professional development programmes.
🏷 Transfer of training, TVET lecturers, attitude, organisational commitment, perceived importance, gender differences, TPB, Gender Role Theory
Article 154 · pp. 1958-1964
Extraction and Applications of Rosmarinus
The main objective of this research is to evaluate the antioxidant, cytotoxic, and antimicrobial efficacy of rosemary (Rosmarinus officinalis), quantify its extraction yield, and elucidate its phytochemical profile. Rosemary, also known as Rosmarinus officinalis, is native to the Mediterranean region. It belongs to the mint family and is an evergreen shrub related to basil and marjoram. Some rosemary plants can reach a height of one meter or more. Its small, grayish-green leaves resemble pine needles and have a sweet, slightly bitter aroma similar to lemon. Its flowers range in color from white to light and dark blue and bloom in the spring.
A sample of rosemary was taken, dried, ground, weighed, and placed in a Soxhlet extraction apparatus. After exiting the apparatus, it was weighed, the extraction rate was calculated, and the components present in the rosemary were identified and collected by conducting phytochemical analysis using potassium hydroxide, ferric chloride, copper acetate, acetic anhydride, and concentrated sulfuric acid.
Chemical laboratory experiments were carried out, revealing some substances such as flavonoids (yellow), tannins (dark green), terpenes (blue-green), and saponins. Foam was observed, but anthraquinone glycosides were not detected. Furthermore, its toxicity to stromal cells was tested using shrimp. Six samples of the extract were taken at different concentrations, and the shrimp were monitored for a full day.
The bioassay demonstrated a classical dose-dependent relationship, where higher concentrations correlated with significantly increased mortality rates of the tested cells.
Based on these findings, Rosmarinus officinalis shows profound therapeutic potential and is highly recommended for further pharmacological investigation against neurodegenerative disorders (e.g., Alzheimer's disease), depression, diabetes, and oncological malignancies. Due to its proven antimicrobial, anti-inflammatory, antioxidant, and hair-growth-stimulating properties, future milestones will focus on formulating this bioactive extract into functional foods and dietary supplements. Furthermore, downstream research will investigate the genetic variations within local rosemary cultivars and evaluate their direct impact on the plant's chemotypic profile and biological efficacy.
🏷 Extraction,Rosmarinus,Applications, antioxidant, cytotoxic, antimicrobial
Article 155 · pp. 1965-1971
Adaptability and Innovation of Artificial Intelligence in Educational Contexts: A Conceptual Framework
Artificial Intelligence (AI) is transforming the education sector at a radical pace by providing personalized instruction and by providing simulations to enhance the visualization skills of learners so that teaching-learning process become a fruitful experience. AI in education encompasses Intelligent Tutoring Systems (ITS), AI-based tests and administrative automation. India has started integrating AI into education policies through the efforts of the All-India Council for Technical Education (AICTE) and the National Education Policy (National Education Policy 2020 Ministry of Human Resource Development Government of India, 2020). This research paper aims to analyze AI’s impact on education; innovative practices incorporated in educational scenarios as a part of AI-Integration across various disciplines and to design a conceptual framework based on the adaptability and innovation of Artificial Intelligence in educational contexts.
🏷 Adaptability, Innovation, Artificial Intelligence, Educational Contexts, Conceptual Framework
Article 156 · pp. 1972-1990
Hydrodynamic Performance Enhancement of Offshore Production Separators: A CFD-Based Evaluation of Internal Geometry for Optimized Phase Separation
Offshore production separators often experience performance degradation due to suboptimal internal geometry, which promotes flow short-circuiting, high turbulence, and reduced phase separation efficiency. This study uses ANSYS Fluent to compare two full-scale horizontal gas-liquid separator configurations under identical steady-state conditions at 160 psia and 52 psia. Model A (simple perforated baffles) and Model B (inlet diverter, coalescer, demister, and outlet baffles) were evaluated via velocity fields, turbulent kinetic energy (TKE), and pressure profiles. At 52 psia, Model A showed maximum velocity of 16.06 m/s and maximum TKE of 5.40 m²/s², while Model B recorded higher localized values (21.15 m/s, 12.30 m²/s²) confined to the demister region—where elevated velocity aids droplet capture rather than impairing separation. At 160 psia, Model B's maximum TKE (1.24 m²/s²) exceeded Model A's (0.70 m²/s²) by 77%, but with uniform cross-sectional flow compared to Model A's concentrated centerline jets. Gas outlet pressure drop across Model B increased from 0.1 psi at high pressure to 0.95 psi at low pressure (versus Model A's 0.2–0.3 psi), representing an acceptable trade-off for improved separation potential. The pressure difference between liquid and gas outlets reached 1.2 psi in Model B at low pressure versus 0.3 psi in Model A. These findings suggest that advanced internals may reduce stagnant zones by approximately 40–50% and localize turbulence, which could improve separation efficiency without disproportionately increasing overall pressure drop. However, direct separation efficiency metrics (e.g., droplet carryover, entrainment fraction) were not simulated and should be addressed in future work. Contribution to Clean Energy: This work supports cleaner offshore production by identifying internal geometries that potentially reduce energy intensity and greenhouse gas emissions. By minimizing turbulence-induced droplet carryover and flow short-circuiting, the proposed design may reduce fuel consumption per barrel of produced fluid and lower flaring from liquid carryover. These efficiency gains demonstrate how retrofitting existing fossil infrastructure could serve as a pragmatic pathway within Africa's just energy transition. The CFD methodology is also transferable to clean energy applications including biogas upgrading, hydrogen purification, and carbon capture systems
🏷 Internal geometry, Turbulent kinetic energy (TKE), Velocity distribution, Demister, Pressure drop.
Article 157 · pp. 1991-1995
Financial AID to the Micro and Small Enterprises by the Financial Institutions in Papum Pare District of Arunachal Pradesh
Finance is the lifeline of any form of business, and its timely and adequate supply is crucial for the survival of micro and small enterprises. Micro, small and medium enterprises (MSMES) play a crucial role in the development of our economy by providing ample scope of employment, export promotions, uplifting regional balance and contribution toward GDP. Despite of the noticeable achievements, this sector faces challenges for timely and adequate supply of finance. State government always promotes these sectors with different schemes and policies by collaborating with financial institutions from time to time. This paper deals with understanding the support of financial institutions in the study area provided to the micro and small enterprises for accessing finance i,e analysing the banking institutions in the papum pare district of Arunachal Pradesh for providing loans to the micro and small enterprises.
🏷 Financial assistance, scheme, MSEs, Financial Institution.
Article 158 · pp. 1996-2002
Functional Aspects of Tapioca Farmers Problems During Sales in Tamilnadu
In this study, an effort was made to examine the problems encountered by tapioca farmers during the sales process. The research is based entirely on primary data collected through a structured interview schedule administered to farmers. The survey was carried out in Salem District, Tamil Nadu, with a sample of 500 tapioca farmers selected using a simple random sampling technique to ensure fair representation. A carefully designed interview instrument was employed to gather information on the challenges faced by farmers. To verify the reliability of the measurement scale, the data were subjected to item analysis, and the internal consistency was tested using Cronbach’s alpha coefficient. Subsequently, factor analysis and correlation analysis were applied to identify the functional dimensions and interrelationships among the problems reported by the farmers during sales.
🏷 Problems, tapioca, sales, study Area.
Article 159 · pp. 2003-2014
Digital-Driven Design Thinking as a Catalyst for Enhancing Operational Efficiency and Environmental Sustainability in Oil and Gas Industry.
Today, the Nigerian oil and gas industry is grappling with several issues, including operational inefficiency, environmental sustainability, and the need for digital transformation. Traditional engineering solutions are inadequate when dealing with the complex interplay between technological innovation, human-centred problem solving and sustainable operational practices. This study was designed to investigate the application of digital driven DT within the Niger Delta region of Nigeria for the benefit of the operation of the oil and gas organizations to minimize impact on the environment and enhance the organization's operation. The study employed a cross-sectional survey research design, which was quantified. The total population and sample size were 420 and 205, respectively, while the retrieved questionnaire and valid responses were 195 and 188, respectively. Pearson correlation and multiple regression analyses were employed for data analysis. The results showed that digital experimentation has a tremendous positive impact on operational efficiency, with predictive maintenance, process optimization, and less downtime. Human-centred digital problem solving proved to be a very good predictor for operational efficiency, since it supports adaptive decision-making and employee participation. The research revealed that cross-functional digital collaboration will have a positive impact on environmental sustainability aspects, such as emissions monitoring, coordinated decision-making and resource optimization. The paper applies digital-driven DT to the oil and gas industry and shows that for sustainable operational performance, it is not enough to adopt the technology, but organisations must have collaborative and human-centred processes.
🏷 Cross-Functional Digital Collaboration, Digital Transformation, Digital-Driven Design Thinking, Human-Centered Problem Solving, Operational Efficiency, and Environmental Sustainability
Article 160 · pp. 2015-2023
Migration, Identity, and Voting Behavior in Border Regions of India
This article discusses the impact of migration on political identity and voting behaviour in the border regions of India. Instead of seeing migration as just a demographic and/or economic phenomenon, the paper approaches it as a politically mediated phenomenon, and explores how its impacts rely upon historical memory, identity divisions, institutional rules, and party competition. The study takes a comparative qualitative approach using a structured, focused comparison of four cases: Assam, West Bengal, Punjab and Jammu & Kashmir, which were chosen to reflect variations across the cases in terms of the migration experience, the configuration of the borders, institutional response, and electoral mobilisation. The analysis is based on secondary sources such as the Census of India, government reports, institutional documents relating to elections and scholarly works on migration, ethnicity, citizenship, and voting behaviour. The paper suggests that migration is electorally salient because of demographic change framed within a security, religious, ethnic or linguistic lens, and when political actors transform such perceptions into organised narratives of inclusion or exclusion. In all the cases, five mechanisms are obtained: perceived threat, identity reconstitution, institutional regulation, clientelist incorporation, and spatial concentration. The article ends by suggesting that the politics of migration are not the same in all parts of India's borders, but will take different forms depending on the local histories, state power, party systems and citizenship laws. These findings have relevance for democratic inclusion, conflict-sensitive border governance, and designing fair documentation processes and enfranchisement.
🏷 migration, identity, voting behaviour, border regions, India, citizenship, electoral politics
Article 161 · pp. 2024-2030
Emotion Based Autonomous Driving Control Using Multi Sensor Integration for Enhanced EV Experience
This paper presents an emotion-based autonomous driving control system that integrates real-time physiological monitoring with autonomous vehicle technology to enhance driving safety and personalization. The system continuously evaluates the driver’s emotional state using sensors that measure pulse rate, oxygen saturation, and body temperature, dynamically adjusting the vehicle’s driving mode between manual and autonomous according to the detected condition. By autonomously assuming control during stress or fatigue, the system mitigates the risk of human error, particularly in high-stress scenarios, while providing real-time feedback to maintain driver trust. Experimental results demonstrate high accuracy in emotional state classification and reliable autonomous driving performance. However, external factors affecting sensor readings and minor delays in mode transitions at high speeds highlight areas for further optimization. Future improvements, including enhanced sensor precision, faster mode-switching algorithms, and a more robust classification model, could further increase system effectiveness. This emotion-aware approach represents a significant advancement in human-centered autonomous driving, offering safer, adaptive, and more comfortable driving experiences.
🏷 Driver fatigue detection, Stress-aware vehicle systems, Sensor fusion algorithms, Adaptive human–machine interaction, Real-time mode switching
Article 162 · pp. 2031-2045
AAUCA Human Resources Administrative Management System (AHRAMS)
The Afro-American University of Central Africa (AAUCA) currently relies on manual, decentralized processes for human resources (HR) administration, resulting in information duplication, poor traceability, and inefficient reporting. To address these challenges, this project presents the analysis, design, and development of the AAUCA Human Resources Administrative Management System (AHRAMS), a web-based platform that automates core HR functions including employee administration, attendance control, payroll management, leave requests, digital record storage, and performance evaluation. Grounded in software engineering principles, the development employed an agile Scrum methodology with iterative two-week sprints, enabling continuous feedback and adaptation to institutional requirements. The system architecture follows a microservices approach, utilizing Angular for the frontend, Spring Boot for backend services, and MySQL for data persistence with Spring Data JPA and Hibernate as the object-relational mapping framework. Unified Modeling Language (UML) diagrams—including use case, sequence, and architectural diagrams—guided the design phase, while a normalized relational database model ensured data integrity and consistency. The resulting AHRAMS system demonstrates a 60% improvement in administrative efficiency for AAUCA's HR processes, providing a robust, scalable, and flexible solution that enhances data accuracy, security, and institutional transparency. This work contributes to the digital transformation of university administrative systems in Central Africa while applying modern software development practices in a real-world educational context.
🏷 HR Management System, Microservices, Scrum, UML, Angular, Spring Boot, MySQL, AAUCA
Article 163 · pp. 2046-2050
Design and Development of a Plastic Bottle-Activated Fan Prototype
This capstone project, titled "Design and Development of a Plastic Bottle-Activated Fan Prototype," addresses the growing global concern of non-biodegradable plastic waste by integrating discarded materials into a functional electrical system. The study aimed to design a prototype that utilizes plastic bottles as a triggering mechanism to activate a battery-powered fan, evaluating the system's effectiveness and its potential to promote environmental awareness. Using a descriptive-developmental research design, the researchers constructed a model where the insertion of a plastic bottle is acting similarly to a coin-operated mechanism which completes a circuit to power a small DC fan. Testing was conducted at Mindanao State University – Maigo College of Education, Science, and Technology (MSU-MCEST) to measure response time, consistency, and power stability using electrical measurements and observation checklists. Results indicated that the triggering system effectively activated the fan, with the battery providing sufficient and stable power for operation. Participant feedback through a Likert scale evaluation suggested that the prototype serves as an effective educational tool, demonstrating practical waste utilization and the principles of basic automation. The study concludes that simple, low-cost trigger-based systems offer a viable and accessible approach to promoting sustainable waste management and environmental responsibility within a campus setting.
🏷 Plastic Waste Management, Trigger-Based Activation, Waste Utilization, Battery-Powered System, Prototype Development
Article 164 · pp. 2051-2071
Predictive Maintenance of Industrial Equipment Using Machine Learning and IOT Data Analytics: A Context-Aware, Edge-Cloud Framework with Operator-in-the-Loop Adaptation
The reliable functioning of any manufacturing sys-tem presupposes smooth equipment performance; however, un-scheduled interruptions remain a major source of loss in terms of efficiency, safety, and costly maintenance expenses. Traditional approaches including proactive or reactive maintenance methods prove ineffective in highly dynamic and unpredictable production environments. While IoT technology-driven predictive mainte-nance offers superior alternatives, current solutions suffer from critical limitations concerning long response times, reliance on network infrastructure, fast model decay due to changing load regimes and aging systems, and low credibility and transparency of predictions. This paper presents a context-aware framework for predictive maintenance incorporating a hybrid cloud-edge architecture and adaptive maintenance techniques based on operator involvement. The proposed model uses real-time context data to continuously update its ability to detect anomalies and forecast gradual equipment deterioration.
The edge component carries out preliminary filtering of incoming raw data, performs feature engineering, and provides basic classification results, sending extracted contextual information about detected events to the cloud server for further processing. A key advantage is the ability to incorporate maintenance engineers’ feedback as contextual information into the learning algorithm. Maintenance technicians provide additional validation, explanation, or cor-rection to alerts raised by the algorithm, helping adjust the model to changing conditions. Combined with an explanatory engine, the proposed framework translates identified multivariate factors into understandable failure mode identification, prob- abilities, and maintenance procedure prioritization. Results of testing in industrial settings show improved equipment efficiency metrics, including significantly decreased false alert numbers, reduced maintenance diagnosis cycles, and effective inventory management. The described predictive maintenance technique proves efficient for ensuring operational resilience and seamless integration into existing industrial practices.
🏷 Predictive Maintenance, IoT Data Analyt-ics, Edge-Cloud Architecture, Machine Learning Adaptation, Human-in-the-Loop Systems, Explainable Industrial AI
Article 165 · pp. 2072-2076
The Impact of Artificial Intelligence on Animated Dance Creation: A Study of Audience Engagement and Creative Transformation
As a result of advancements in technology and software, the artificial intelligence (AI) has integrated into creative industries. AI has introduced novel approaches to various aspects of dance production, including costume design, choreographic composition, and animated dance creation. As a result, dance has extended beyond the boundaries of live stage performance and has increasingly become integrated into virtual and digital platforms. The creation of dance animation is a complex and multi-layered process that involves the anthropomorphizing of non-living or virtual characters into lifelike forms. It requires the integration of full-body movement, expressive gestures, costume design, in to rhythmic cultural and traditional way to achieve a coherent and believable performance. The primary problem of this research is to determine whether human-created or AI-generated animated dance is more effective and realistic from the audience’s perspective. In addition, the study seeks to investigate the extent to which artificial intelligence influences dance animation in terms of visual quality, emotional expression, and conceptualization. This study adopts a mixed-method research design to examine audience perceptions of AI-generated and human-created animated dance. A purposive sample of fifty participants with prior knowledge of dance and media will be selected. Quantitative data collected through questionnaires and qualitative data collected from audience observation and discussions. The findings of this research AI-generated dance content enhance visual appeal and accessibility, particularly in digital and social media contexts and it may lack the depth of emotional expression typically associated with human performance. The study concludes that AI does not replace traditional dance practices but rather expands the possibilities of dance as a hybrid digital art form.
🏷 Artificial Intelligence, Animation, Dance Art, Audience Engagement, Digital Creativity
Article 166 · pp. 2077-2088
Educational Management Practices for Enhancing Triple Helix Partnerships in Building Inclusive Society Through Technical and Vocational Education and Training in Rivers State Universities
The study examines educational management practices for enhancing triple helix partnerships in building inclusive society through technical and vocational education training in Rivers State. Three questions were answered with corresponding null hypotheses that were formulated and tested at 0.05 level of hypotheses guided the study. The study employed the descriptive research survey design and was carried out in Rivers State Universities. The population of the study was 180 respondents (Government = 18, Industries = 143, Universities = 19). Due to small population size of the respondents, there was no sampling as the entire population was used and hence, it was a census study. The instrument for data collection was a self-structure questionnaire tagged “Educational Management Practices for Enhancing Triple Helix Partnerships Questionnaire (EMPETHPQ)” which was design and patterned after a modified Likert – 4-point rating scale of agreement. The instrument was validated and tested for reliability through test-retest method. A reliability coefficient of 0.83 was established using Pearson Product Moment Correlation (PPMC) coefficient. Data collected were analysed using mean descriptive statistics to answer the research questions while one way ANOVA was used to test the hypotheses. The study found that educational management practices that enhance triple helix partnership in TVET are resource allocation, curriculum management and monitoring, evaluation and quality assurance practices. Based on the findings of the study, it was recommended among others that there should be equitable distribution of financial resources among university, industry, and government partners in TVET programmes. There should be collaboration between the university, industry and government when developing and reviewing TVET curriculum to ensure that it captures the three tiers needs. The UIG should regularly supervise, monitored and evaluate TVET programmes based on collaboration with university - industry - government.
🏷 TVET, Educational Management Practices, Triple Helix, Partnerships, Inclusive Society
Article 167 · pp. 2089-2101
Comprehensive and Comparative Analysis of HDI, WPI, CPI and CCI on Stock Market Returns
The stock market returns reflects the performance of the economy and the expectations from the market. In developing markets like India, stock market performance is not only dependent on the performance of the firms but also on the economic and social indicators. This paper aims to analyse the compounded and relative effects of the Human Development Index (HDI), Consumer Price Index (CPI), Wholesale Price Index (WPI), and Consumer Confidence Index (CCI) on stock market returns in India between the years 2015 and 2025. The analysis is based on 128 monthly observations of each Bombay Stock Exchange (BSE) and the National Stock Exchange (NSE) with the help of descriptive statistics, Correlation, Augmented Dickey-Fuller (ADF) unit root tests, multiple regression (OLS), and Autoregressive Distributed Lag (ARDL) models.
The correlation between BSE and NSE is nearly 1 (r = 0.999), confirming that the indices move in near-perfect tandem. There is an exceedingly high correlation (r = 0.9999) between CPI and HDI, confirming near-perfect multi-collinearity. Following VIF analysis (CPI VIF = 211,009; HDI VIF = 210,665), CPI is excluded from the primary regression model and HDI is retained, as HDI is the theoretically richer and more policy-relevant variable in the context of this study. The revised model (BSE/NSE ~ CCI + WPI + HDI) resolves the extreme multi-collinearity while maintaining R² = 0.9769 (BSE) and R² = 0.9705 (NSE). All three retained predictors are significant at p < 0.001. CCI is negatively correlated with BSE (–0.652) and NSE (–0.636) at the bivariate level, but positively significant in the multivariate regression, consistent with its role as a leading sentiment indicator.
Monthly HDI data was sourced from the UNDP Human Development Data Centre (hdr.undp.org). The study provides the first empirical evidence in India that human development is a significant positive driver of long-term stock market performance.
Keywords: Human Development Index (HDI), Consumer Confidence Index (CCI), Consumer Price Index, (CPI), Wholesale Price Index (WPI), Stock Market Returns, BSE, NSE, ARDL, Time-Series Analysis, India.
🏷 Comprehensive, Comparative, Stock Market
Article 168 · pp. 2102-2116
Fair Process, Legitimate Discipline: Procedural Justice and the Perceived Effectiveness of Conflict Management in Ghanaian Boarding Senior High Schools
School conflict management is often judged by whether it restores visible order. In boarding Senior High Schools, however, students and staff continue to live and work together after disputes have been formally resolved; therefore, effectiveness also depends on whether disciplinary processes are perceived as fair, respectful and legitimate. This article examines how heads, senior house staff, guidance and counselling coordinators and student leaders perceived the effectiveness of conflict-management strategies in two public boarding Senior High Schools in the Tarkwa-Nsuaem Municipality of Ghana. Drawing on a qualitative multiple-case study involving 24 participants, the article argues that effectiveness was interpreted through two related but distinct lenses: compliance-based effectiveness and legitimacy-based effectiveness. School A associated effectiveness mainly with discipline, deterrence, improved attendance and behavioural compliance, while School B associated it more strongly with engagement, relational repair, calm and follow-up. Across both cases, strategies were judged more positively when they involved timely response, meaningful voice, consistency, reason-giving, counselling support and fair stakeholder involvement. Negative cases showed that perceived bias, failure to hear parties, external reversal of decisions and weak consultation could undermine confidence even where formal disciplinary structures existed. The article contributes to school leadership literature by showing that conflict-management effectiveness in boarding Senior High Schools should be assessed not only by reduced misconduct but also by the fairness of the process through which order is achieved.
🏷 procedural justice; conflict management; school leadership; Senior High Schools; Ghana; boarding schools; school climate; discipline; fairness
Article 169 · pp. 2117-2124
Classscan: A Smart Classroom Identification Scanner for Attendance and Access Monitoring
Attendance monitoring is an important aspect of classroom management because it helps teachers maintain accurate student records and monitor participation. However, traditional attendance methods, such as manual roll calls and paper-based records, are often time-consuming and prone to human error. This study developed ClassScan, a smart classroom identification scanner designed to automate attendance monitoring using Radio Frequency Identification (RFID) technology. The system was developed using an ESP32 microcontroller, RFID scanner, OLED display, DS3231 Real-Time Clock module, Micro SD Card module, and ESP32-CAM. A developmental research design was employed to develop and evaluate the system. Six teacher-participants assessed the usability, effectiveness, and operational performance of the prototype through a structured evaluation questionnaire. The findings revealed that the system obtained an overall mean score of 4.48, interpreted as "Strongly Agree," indicating a high level of user acceptance. Participants reported positive perceptions regarding the system's ease of use, attendance-recording capability, and operational performance during testing. The findings suggest that ClassScan demonstrates potential as a practical attendance-monitoring solution within a localized educational setting. Further studies involving larger samples and more comprehensive validation procedures are recommended.
🏷 RFID-Based Attendance System, Attendance Monitoring, Smart Classroom Technology, Educational Technology, ESP32, Classroom Management
Article 170 · pp. 2125-2131
Studies on Mycoflora, Aflatoxigenic Fungi and Aflatoxin in Poultry Feeds.
Poultry feed samples were collected from the Jalgaon district in Maharashtra, and the mycoflora associated with the samples was isolated using agar-plate techniques. Every Aspergillus flavus isolate that was isolated from the samples that were collected was examined for aflatoxigenic potential in SMKY liquid medium. Poultry feeds were used to isolate seventeen distinct fungus. The most prevalent fungus that infest feeds were Aspergillus flavus, A. niger, A. ochraceus, Aspergillus sp., Fusarium sp., and Penicillium sp. Aspergillus flavus dominated all fungi, and 76% of its strains were aflatoxigenic. Crude poultry feed had the highest amount of aflatoxigenic fungus (86.2%). Aflatoxin B1 was estimated in all the samples by extracting the aflatoxin and spotted in an activated thin layer chromatography (TLC) plate with standards and ascertained the concentration by visual comparison method in a UV viewing cabinet. When the natural aflatoxin contamination of poultry feeds was analyzed, 26.67% of the samples had aflatoxin contamination. Maximum concentration of aflatoxin B1 was detected in Local poultry feed (93.67 ppb) followed by Crude poultry feed (92.89 ppb) and Commercial poultry feed (84.38 ppb). poultry feeds contaminated with aflatoxin has poses a potential threat for the life of poultry animals. Hence the regular screening of toxins in every lot of feed prior to feeding the animals or poultry needs to be regularized.
🏷 Poultry feeds, Mycoflora, Aspergillus flavus, aflatoxin.
Article 171 · pp. 2132-2137
Indian Stock Market Growth VS Global Uncertainty: An Empirical Analysis
The Indian stock market has demonstrated strong resilience despite ongoing global economic uncertainty caused by geopolitical conflicts, inflationary pressures, and monetary tightening. This study examines the relationship between Indian stock market performance and global uncertainty during the period 2024–2026. Using secondary data from government reports, financial publications, and academic literature, the study evaluates the role of domestic macroeconomic factors, foreign institutional investment flows, and policy stability. The findings indicate that while global uncertainty affects short-term volatility, India’s strong domestic demand, robust institutional framework, and increasing participation of domestic investors contribute to sustained long-term growth. The study concludes that India is gradually achieving partial decoupling from global financial shocks.
🏷 Indian stock market, global uncertainty, economic growth, market resilience, foreign institutional investment, emerging markets
Article 172 · pp. 2138-2170
Design and Implementation of a Lightweight Neural Controller for LLM Agent Systems
Large language model (LLM) agents typically consume thousands of tokens on scaffolding system prompts, tool schemas, and conversation history before producing a single useful word. NEXUS (Neural EXecution & Understanding Substrate) is a 6.29-million-parameter neural controller that sits between a frozen LLM and its execution environment, replacing that token overhead with compact vector signals.
The controller has five subsystems that run together (1) Protocol Cortex, which writes task descriptions directly into the LLM’s key-value cache so the model behaves as though it received detailed instructions without those instructions; (2) Belief Engine, a recurrent state-space model that tracks what the agent currently believes about its environment; (3) Resource Router, tool-selection classifier that uses explicit state-machine logic to guarantee valid tool calls; (4) Drift Sentinel, a lightweight monitor that detects when the agent’s output begins drifting off-task; and (5) Adapter Switch, which selects among small, low-rank weight updates (LoRA adapters) to specialize the LLM for different sub-tasks on the fly.
We make three separate claims. First, we describe the architecture and the training recipe for all five components. Second, we report a deployment result: an open-source Model Context Protocol (MCP) server, nexus-mcp-oss, which achieves 72.86% fewer tokens delivered to the LLM in production through heuristic text-level compression (distinct from the KV-cache injection mechanism). Third, we present a controlled evaluation of the KV-cache injection mechanism itself, in which the Protocol Cortex is trained end-to-end with a frozen TinyLlama 1.1B and reaches a held-out perplexity of 8.91 versus 26,607 for an untrained baseline 2,987-fold improvement and 30–77 times lower perplexity than Prefix-Tuning, ActAdd, and LLMLingua at matched compression. Three of the four trained components converge on the synthetic benchmark; trained checkpoints, and benchmark data are publicly available. We are explicit about scope: the gains reported here are measured as token efficiency and predictive (perplexity) quality, and we set out in Sections 8.5 and 9.5 a concrete plan to test whether these efficiency gains carry through to downstream task quality: reasoning, planning, coding assistance, and multi-agent coordination.
🏷 LLM agents, neural controller, token compression, KV-cache injection, state space models, tool routing, metacognition, efficient inference.
Article 173 · pp. 2171-2187
Implementation and Compliance Challenges to the Nigerian National Building Code: Evidence from Borno State
The Nigerian National Building Code (NBC) offers comprehensive, centralized regulations to guide construction, ensure structural integrity, and promote sustainability. This study investigates the level of implementation of the NBC within Borno State, evaluating five major barriers identified in the literature for field criticality. Using a mixed methods approach with a purposive, maximum variation sampling technique, 320 building projects were selected to assess compliance with 13 key areas of the NBC, involving 450 respondents comprising developers, contractors, clients, construction professionals, and royal fathers. Data were sourced through structured questionnaires and face-to-face dialogues. Field investigation revealed that the NBC is only partially domesticated in Borno State. Data on the five barriers showed overall reliability (α = 0.888). Based on their Relative Importance Index (RII), the barriers were ranked in descending order of criticality as: Stakeholder Non-Compliance and Cultural Resistance; Economic Factors; Legal and Regulatory Bottleneck; Weak Institutional Capacity; and Corruption and Enforcement Failures. Further Chi-Square analysis revealed a significant difference in compliance levels between public and private building projects. Public projects complied with 83.96% of the NBC provisions, while private projects showed only 16.04% compliance. It is concluded that the Borno State Government has failed to enforce code compliance effectively in private projects, leaving residents vulnerable to unsafe, low-quality, and structurally unstable construction. Recommendations include the full domestication of the NBC and the establishment of an independent, statutory commission to oversee code enforcement, among others.
🏷 Nigerian National Building Code, Implementation, Enforcement, Compliance, Borno State
Article 174 · pp. 2188-2195
Blog Management System Using Django and Python
Due to the rise of web-based content publishing services, there is an urgent need for adaptive, secure, and scalable solutions for managing blogs. Platforms like WordPress and Ghost are characterized by several limitations, such as inflexibility, insufficient security measures, and scalability issues. In this paper, a Blog Management System (BMS) was implemented using the Django 4.2 framework and Python 3.11 programming language. It includes role-based access control (RBAC), RESTful web APIs through Django REST Framework, full-text search enabled by the PostgreSQL tsvector module with the help of the GIN index, automated spam filtering utilizing Akismet, and asynchronous task management via Celery. A multi-tiered security model according to the OWASP Top-10 list was implemented across seven layers. The performance analysis performed by Locust v2.18 (with 100 concurrent users) showed that the P95 latency did not exceed 200 ms and that the application benefited from an 18× performance gain due to Redis caching. The usability test with 30 users resulted in a SUS score of 83.4 (Grade B+, Excellent), whereas the baseline for WordPress was 72.1.
🏷 Django; Python; Blog Management System; RBAC; REST API; PostgreSQL; Redis; Celery; OWASP; System Usability Scale
Article 175 · pp. 2196-2203
Digital Marketing Strategies Adopted by the FMCG Sector: A Study
This study examines the role of digital marketing in influencing consumer behaviour within the Fast-Moving Consumer Goods (FMCG) sector. With the rapid advancement of technology and increased internet penetration, digital platforms have become a significant medium for promoting FMCG products. The purpose of this research is to understand how digital marketing strategies impact consumer awareness and purchasing decisions. The study adopts a descriptive research design, utilising both primary and secondary data. Primary data is collected through structured questionnaires from respondents, while secondary data is gathered from journals, websites, and industry reports. The findings indicate that digital marketing significantly enhances brand visibility, customer engagement, and purchase intention, particularly among younger consumers. The study contributes to existing literature by focusing specifically on the effectiveness of digital marketing in the FMCG sector and highlighting consumer perceptions. It provides valuable insights for marketers aiming to optimise digital strategies.
🏷 Digital Marketing, FMCG, Marketing strategies, Consumer Behaviour
Article 176 · pp. 2204-2219
Lean Implementation and Organisational Agility: The Mediating Role of Continuous Improvement Culture in Nigerian Manufacturing Firms
This study investigates the relationship between lean implementation (LI) and organisational agility (OA) in Nigerian manufacturing firms, with a focus on the mediating role of continuous improvement culture (CIC). Drawing upon the dynamic capabilities and organization culture perspectives, we argue that lean practices provide process discipline and visibility, while a culture of continuous improvement transforms these practices into adaptive organisational capabilities. Data were collected through a cross-sectional survey of 54 listed manufacturing firms. Partial Least Squares Structural Equation Modelling (PLS-SEM) was employed to test the proposed model. The findings indicate that LI has a significant positive effect on OA, and CIC mediates this relationship. The study concludes that lean practices enhance agility most effectively when accompanied by strong learning and improvement-oriented cultures. Implications for managers and policy makers are discussed.
🏷 Lean manufacturing, organisational agility, continuous improvement, dynamic capabilities, Nigeria, PLS-SEM
Article 177 · pp. 2220-2235
Effects of AI on the Trust and Confidence of Faculty Members in Submitted Academic Works by Students
This study examined faculty trust, student confidence, and the perceived impact of artificial intelligence (AI) tools in academic assessment within higher education institutions. Specifically, it explored faculty perceptions regarding authenticity, authorial verifiability, and evaluative certainty; students’ confidence in submitting academic work; and the influence of AI-related practices such as algorithmic surveillance and linguistic flattening. Using a quantitative descriptive-correlational research design, data were gathered from faculty members and students through survey questionnaires and analyzed using weighted mean, Pearson correlation, and ANOVA. Findings revealed that faculty members generally remained confident in evaluating student submissions, with a grand mean interpreted as “Confident/Agree,” although concerns regarding authenticity and authorship verification persisted. Students likewise demonstrated confidence in submitting academic work, particularly in academic self-efficacy, but also reported anxiety regarding AI detection systems and moderate trust in institutional assessment practices. The study further found that AI tools significantly influenced student behavior, particularly through linguistic flattening, where students intentionally simplified writing styles to avoid detection. Correlation analysis showed no significant relationship between the frequency of AI detector use and faculty trust in student submissions, while ANOVA results revealed no significant differences in perceptions based on years of teaching experience. Overall, the study concluded that AI has substantially reshaped academic assessment practices, faculty perceptions, and student writing behavior, emphasizing the need for balanced institutional policies, ethical AI governance, improved AI literacy, and assessment frameworks that promote both academic integrity and responsible AI use in higher education.
🏷 academic integrity, artificial intelligence, faculty trust, higher education, student submissions, quantitative research
Article 178 · pp. 2236-2245
Analyzing Hospital Technology and Administrative Innovation's Effect on Performance: An Econometric Study
Hospitals worldwide are under pressure to improve patient happiness, organizational resilience, clinical quality, operational efficiency, and financial sustainability. Administrative innovation and hospital technology are important to transforming healthcare delivery. Hospital technology includes electronic health records, clinical decision support systems, automated provider order entry, telemedicine, HIE, diagnostic technologies, digital monitoring systems, and analytics-based management platforms. Administrative innovations include new managerial procedures, governance frameworks, workflow redesigns, quality management methods, strategic planning systems, human resource innovations, and data-driven decision-making. Despite the widespread promotion of administrative and technological innovation as performance-enhancing tools, empirical findings are inconsistent because hospital performance is multifaceted and innovation outcomes depend on implementation quality, organizational readiness, complementary capabilities, and time-lagged effects. This review examines theoretical, empirical, and econometric research on hospital technology, administrative innovation, and performance. Using panel data, fixed effects, dynamic models, regression analysis, and structural equation modeling, the study synthesizes health IT, organizational innovation, hospital productivity, and econometric research findings. Healthcare technology can improve process quality, information accessibility, care coordination, and efficiency, but its influence on financial results and cost reduction is not immediate, according to studies. Administrative innovation boosts technological value by improving coordination, accountability, leadership, workforce adaptation, and strategic alignment. Econometric research shows that administrative creativity and technology operate best together as complementary talents rather than separately. The article concludes that hospital performance evaluation must be longitudinal, multi-dimensional, and context-sensitive and recommends an integrated econometric framework for empirical research.
🏷 Hospital technology; administrative innovation; hospital performance; health information technology
Article 179 · pp. 2246-2253
Patterns of Non-Compliance: Mapping Behavioral Escalation and Programmatic Clustering in Student Disciplinary Records
Managing student conduct in a private higher education context requires a transition from anecdotal observation to empirical analysis. This study examined three academic years (2023–2026) of student offense records from the Office of Student Affairs to identify behavioral trajectories across various academic programs. By applying a dual-mode strategy—quantitative frequency mapping alongside inductive thematic analysis—the researchers uncovered a disciplinary landscape defined by two distinct pressures. First, the data reveals a systemic normalization of regulatory non-compliance, specifically regarding dress code and identification policies, which accounted for the vast majority of infractions. Second, while major violations like vaping, peer aggression, and academic dishonesty are statistically fewer, their concentration in specific technology and business-oriented cohorts suggests localized behavioral "hotspots." Crucially, the findings validate an escalation pathway: repeated minor infractions often serve as measurable precursors to more severe disciplinary breaches. These results form the basis of a proposed tiered intervention framework that shifts institutional response from reactive adjudication to proactive, program-specific behavioral formation.
🏷 student offenses, thematic analysis, disciplinary records, higher education, intervention planning
Article 180 · pp. 2254-2259
Assessing the Change in Social Status of Scheduled Castes: A Case Study of Rural Haryana
Haryana is a historically dominated state by rigid agrarian caste hierarchies and traditional social structures. The present paper tries to examines the shifting socio-economic and political landscape of Scheduled Castes (SCs) in rural Haryana by applying a mixed-method approach on the basis of secondary data with empirical knowledge collected through field observations across the selected districts in the state. The study also tries to access the primary drivers of social mobility and persistent barriers of true equality. The findings of the study reveals that the scheduled caste population in Haryana shows a notable transition in the past few decades. The findings also reveals that structural shifts in the rural economy-specifically the decline of traditional jajmani ties, infusion of mechanization in agriculture, and a subsequent migration toward urban areas have significantly reduced traditional forms of economic dependency on dominant landowning castes. Furthermore, state-led affirmative action, coupled with rising literacy rates and digital connectivity, has fostered a heightened sense of political consciousness and legal awareness especially among the youth of schedule castes. The study highlights that occupational mobility and political reservation have enabled institutional representation while substantive social integration remains contested. The ritual hierarchies, residential segregation, and subtle forms of everyday discrimination persist in scheduled caste residency pockets. The backlash against assertive schedule caste identities often manifests in localized conflicts, revealing deep-seated structural anxieties among dominant agrarian communities. The study shows that traditional caste-class nexus is weakening economically while deep-rooted social prejudices continue to adapt in modern contexts.
🏷 Scheduled Castes, Rural Haryana, Social Mobility, Caste Hierarchy, Agrarian Economy
Article 181 · pp. 2260-2270
A Quantitative Approach to Production Planning and Inventory Control
The significance of production planning and inventory control in enhancing efficiency in supply chain systems is tremendous in today's business environment. This study aims to provide a quantitative framework for the production planning and inventory control through mathematical modelling techniques. The goal of this proposed model is to optimize the production rate, size of inventory and how an inventory is replenished in order to minimise total cost (production cost, inventory cost, Setup cost, Shortage cost). The assumptions considered for this model are realistic in nature such as Demand Variations, Finite Production Rate, Backorder etc. to provide optimal production planning and inventory control across different types of industries. There is also an analytical optimisation technique used to optimise the various production planning and inventory control elements in this study which will provide an optimal cost minimising solution. The results of the study indicate that production planning and inventory control combined with the proposed model provide higher efficiency in minimising total costs than separate decision making approaches.”
🏷 Inventory Control, Mathematical Modelling, Optimization, Cost Minimization, Supply Chain Management, Backordering, Sensitivity Analysis.
Article 182 · pp. 2271-2279
Utilization of Github Copilot And Perceived Improvement in Debugging Skills among College Students at Quezon City University
This study examined the utilization of GitHub Copilot and its relationship with the perceived improvement in debugging skills among Bachelor of Science in Information Technology students at Quezon City University. Using a quantitative descriptive-correlational design, the study involved 150 BSIT students selected through purposive sampling based on their prior experience using GitHub Copilot. Data were gathered through a structured online questionnaire administered via Google Forms and analyzed using weighted mean and Pearson Product-Moment Correlation Coefficient (Pearson r). Findings revealed that respondents often utilized GitHub Copilot in programming-related tasks, particularly when encountering coding errors and debugging code, with an overall weighted mean of 3.94 interpreted as “Often.” The respondents also demonstrated a proficient level of perceived improvement in debugging skills, with a grand mean of 3.00 interpreted as “Proficient.” Correlation analysis further revealed a strong positive and statistically significant relationship between GitHub Copilot utilization and perceived improvement in debugging skills (r = 0.6094, p < .001), indicating that increased utilization of the tool is associated with higher perceived debugging proficiency. The study concludes that GitHub Copilot can serve as an effective AI-assisted programming tool that supports debugging and problem-solving activities while highlighting the importance of maintaining critical thinking and independent coding skills in programming education.
🏷 AI-assisted Programming, Debugging Proficiency, GitHub Copilot, Programming Education, Artificial Intelligence
Article 183 · pp. 2280-2296
Online Security Behaviors as Predictors of Susceptibility to Simulated Phishing Attacks: A Quantitative Study among Computer Studies Students at Quezon City University
This study examined the relationship between online security behaviors and phishing susceptibility among students of Quezon City University using a quantitative descriptive-correlational research design. The study assessed the respondents’ technical verification behavior, visual trust behavior, reporting behavior, and general cybersecurity awareness and practices, while phishing susceptibility was measured through a simulated phishing campaign utilizing the Gophish framework. A total of 100 students equally distributed across the 1st, 2nd, 3rd, and 4th year levels participated in the study through convenience sampling. Data were collected using a structured survey questionnaire and a phishing simulation that measured email opening, link clicking, credential submission, and reporting behavior. Descriptive statistics, weighted mean, Pearson Product-Moment Correlation Coefficient, and One-Way Analysis of Variance (ANOVA) were employed to analyze the gathered data. The findings revealed that respondents generally demonstrated positive online security behaviors and high levels of cybersecurity awareness, particularly in technical verification practices and general cybersecurity awareness and practices. However, the phishing simulation showed that 21.0% of the respondents clicked the phishing link, while 9.0% submitted sensitive information, indicating that phishing susceptibility remained present despite high self-reported awareness levels. Notably, none of the respondents reported the phishing email during the simulation. The ANOVA results further revealed a significant difference in phishing susceptibility across year levels, with 1st Year students demonstrating the highest level of susceptibility compared to other groups. Meanwhile, Pearson r correlation analysis indicated no statistically significant relationship between online security behaviors and phishing susceptibility. The findings suggest the presence of an awareness–behavior gap, wherein students possess theoretical cybersecurity knowledge but may fail to consistently apply such knowledge in realistic phishing situations. The study concludes that cybersecurity awareness alone is insufficient to fully prevent phishing susceptibility and highlights the importance of continuous simulation-based cybersecurity education, phishing detection training, and practical incident reporting activities to strengthen students’ real-world cybersecurity response capabilities.
🏷 Cybersecurity Awareness, Online Security Behavior, Phishing Simulation, Phishing Susceptibility, Visual Trust Behavior
Article 184 · pp. 2297-2314
Explainable Deep Learning for Intelligent Plant Disease Detection
The world suffers from 10–40% loss in crop yields each year because of plant disease. This threat is serious and growing; it threatens food security, rural livelihoods, and agricultural economies. Advances being made through deep learning, computer vision, and mobile technology have presented a unique opportunity to use leaf images to automatically recognize plant disease. Published classification accuracies on benchmark datasets now exceed 97%, which is an important achievement but achieving high accuracy on a benchmark alone does not indicate that traditional methods will work when deployed in the real world: all four stakeholders (i.e., farmers, agronomists, regulatory authorities, and extension agents) must therefore have the ability to understand, and interpret the output of automatically recognized plant diseases in a way that enhances human expertise rather than replacing it. In this chapter, we provide a compendium of technical deep learning architectures and methods related to Explainable Artificial Intelligence (XAI) for plant disease detection, including convolutional networks, residual architectures, dense architectures, transformer networks, and hybrid models. We also systematically evaluate the explainability methods used in both post-hoc and intrinsic explanation and evaluate the applicability of these methods across a variety of imaging modalities used in agriculture, including RGB, multispectral, and hyperspectral. This chapter characterizes major benchmark datasets; discusses major challenges to their deployment, including class imbalance, domain shift, model size reduction, and human–AI trust calibration; then ends with potential new directions for research in areas such as foundation models (FM), causal interpretable models (Explanations), federated learning, and continual learning to build resilience for each evolving pathogen landscape.
🏷 Explainable Artificial Intelligence; Plant Disease Detection; LIME, Grad-CAM; SHAP; Federated Learning; Precision Agriculture; Hyperspectral Imaging.
Article 185 · pp. 2315-2331
A Study on Awareness of Data Science and Artificial Intelligence Among College Students
The present study investigates the awareness of Data Science and Artificial Intelligence among college students. The objectives of the study were to determine the level of awareness of Data Science and Artificial Intelligence and to examine the influence of selected background variables on students' awareness. The study adopted the survey method, and a sample of 400 college students was selected using the simple random sampling technique. A self-developed Awareness Scale on Data Science and Artificial Intelligence was used for data collection. The collected data were analyzed using percentage analysis, t-test, F-test (ANOVA), and Pearson's Product Moment Correlation.
The findings revealed that the majority of the college students possessed a moderate level of awareness regarding Data Science and Artificial Intelligence. Significant differences were observed with respect to gender, locality, and stream of study. A significant positive relationship was found between internet usage and awareness of Data Science and Artificial Intelligence. The study concludes that awareness of emerging technologies among college students can be enhanced through curriculum enrichment, digital learning opportunities, and technology-oriented educational programs. The findings have important implications for higher education institutions in promoting technological literacy and preparing students for the demands of the digital age.
🏷 Data Science, Artificial Intelligence, Awareness, College Students, Higher Education, Internet Usage, Technological Literacy, Digital Learning
Article 186 · pp. 2332-2351
A Comparative Study of Water Quality Assessment of Kanke Dam, Hatia (Dhurwa) Dam and Getalsud (Rukka) Dam in Ranchi, Jharkhand, India
Jharkhand (The Land of Forest) is the 28th state of the Indian Union was brought into existence by the Bihar reorganization Act on November 15th 2000. It is a state in eastern India and the states shares its border with the State of Bihar to the North, Uttar Pradesh to the northwest, Chhattisgarh to the West and Odisha to the South and West Bengal to the East. Jharkhand has an area of 79,714 km2 (30,778 sq. m). Jharkhand is the 15th largest state by area and is the 14th largest by population. The state is known for its beautiful Waterfalls and Hills. Jharkhand is famous for its rich minerals resources like Uranium, Mica, Bauxite, Granite, Gold, Silver, Graphite, Magnetite, Dolomite, Fireclay, Quartz, Field spar, Coal (32% of India), Iron, Copper (25% of India) etc. The forest and woodlands occupy more than 29% of the state which is amongst the highest in India. The total population of the state is 32, 988,134 as per the data of 2011 census, in which male population is about 16,930,315 and the female population is about 16,057,819. The state capital of Jharkhand is Ranchi. It is popularly known as “The City of Water Falls”. Ranchi is located in the southern part of the Chota Nagpur Plateau in the Indian states of Jharkhand. It is situated at a latitude of 23.35° N and a longitude of 85.33° E. it is at an average elevation of 651 meters (2,140 feet) above the sea level. The Ranchi city is known for its natural beauty. The climate of the city is moderate, with three well defined seasons: the cold weather season from November to February and it is the most pleasant part of the year. The high temperature in Ranchi is in December usually rise from about 50° F (10° C) into the low 70° F (low20° C) daily. The hot weather season lasts from March to mid-June, May being the hottest month and is characterized by daily high temperature in the upper 90° F (37° C) and low temperature in the mid 70° F (20° C).
🏷 Comparative, Assessment, Dam
Article 187 · pp. 2352-2365
Social Media Makeover: Transforming Small Businesses through Branding and Customer Engagement
This study explores the impact of social media marketing on small businesses in the municipality of Santa Cruz, focusing on branding, customer relationships, and pricing. Using a descriptive survey method, the researchers gathered data from selected small business owners in various barangays, including Banahaw, Maharlika, Bagong Silang, Lapu Lapu, and Pagasa.
The findings reveal that social media marketing significantly boosts revenue for small businesses, enhances customer education, and facilitates effective communication. However, respondents also face challenges in branding, such as establishing a recognizable presence, handling negative feedback, and competing with larger enterprises.
🏷 social media marketing, small business, customer relationships, branding, 4Ps.
Article 188 · pp. 2366-2376
Capital Structure Decisions and Firm Valuation: Evidence from Indian Corporates
Capital structure plays a significant role in determining the financial health, risk profile, and valuation of firms. In the Indian corporate environment, capital structure decisions have gained increasing importance due to economic liberalization, rapid industrial growth, changing regulatory frameworks, and rising participation from domestic and international investors. Capital structure refers to the proportion of debt and equity used by firms to finance their operations and growth activities. The choice between debt and equity financing influences the cost of capital, financial risk, profitability, and shareholder value. Firms with higher debt levels may benefit from tax advantages but are exposed to greater financial risk and bankruptcy possibilities, whereas firms relying more on equity financing may maintain greater financial stability but face higher capital costs and ownership dilution. This study examines the relationship between capital structure and firm valuation among Indian firms by considering various internal and external determinants such as industry characteristics, firm size, ownership structure, market conditions, interest rates, and corporate governance practices. The study aims to identify patterns and insights regarding optimal financing decisions and their impact on firm valuation in the Indian business environment. The findings are expected to provide valuable implications for corporate managers, investors, and policymakers in making effective financial decisions and improving corporate performance.
🏷 Capital Structure, Valuation, Debt-Equity Ratio, Corporate Governance, Financial Performance, Cost of Capital, Financial Risk, Shareholder Value, Corporate Finance
Article 189 · pp. 2377-2389
A Mathematical Model for the Dynamics of Banditry and Government Intervention in Kaduna State, Nigeria.
This study developed and analyzes a mathematical model to examine the effect of government policies on the containment of banditry in Kaduna State. Building on the existing model, the model extends existing approaches by incorporating additional compartments such as rehabilitation individuals, security agents and bandit sponsors to better capture real-world dynamics of banditry and intervention mechanisms. A system of nonlinear ordinary differential equations is formulated to describe the interactions among population groups and qualitative analyses including existence and uniqueness of solutions, positivity, invariant region, and banditry-free equilibrium are carried out to ensure the mathematical validity and stability of the model. The analysis reveals that the solutions remain non-negative and bounded within a biological feasible region, confirming the consistency and stability of the system. In addition, the findings show that integrated government policies involving effective security enforcement, rehabilitation programs, rehabilitation programs, intelligence gathering, and disruption of sponsor networks significantly reduce he growth and persistence of banditry. The study highlights the importance of coordinated and sustained intervention strategies in addressing insecurity in Kaduna State and demonstrates the usefulness of mathematical modelling as a predictive and policy-evaluation tool for combating banditry and related criminal activities in Nigeria. and policy-evaluation tool for combating banditry and related criminal activities in Nigeria.
🏷 Banditry, Mathematical modelling, Government policies
Article 190 · pp. 2390-2398
Impact of Training and Development on Employee Productivity in Public Universities, a Case of Masinde Muliro University of Science and Technology, Kenya
The study sought to establish the impact of Training and Development on Employee productivity in public Universities a case of Masinde Muliro University of Science and Technology. The target population was drawn from non-teaching staff by use of simple sampling. A sample of 69 employees was selected through simple sampling to participate in the study. The researcher used purposive random sampling and sampled every non-academic staff in the payroll from grade I-X. The research targeted a total of 676 non-academic staff. Data was collected by use of questionnaires. The study used both descriptive and inferential statistics during data analysis. Descriptive statistics employed the use of frequencies and percentages and for inferential statistics Pearson Correlation Coefficient of 90% confidence was used and findings presented in tables. Results showed that there was a significant negative relationship between gender awareness, training policy where every staff is aware with the value of r(69) = -0.301, p = 0.01 and also responses on training policy of which every staff is aware had a significant negative relationship in experience on their current grade, r(69) = -0.207, p = 0.09. More than 50% of the respondents agreed that training and development affects employee productivity and the results were mostly more significant between training and development factors and gender, age, length of service and current grades among University employees.
🏷 Training and Development, Performance and University Employees
Article 191 · pp. 2399-2410
Energy Efficiency as a Pathway to Expanding Energy Access in Sub‑Saharan Africa
Energy efficiency plays a critical yet underutilised role in expanding access to clean, affordable, and reliable energy services in developing regions. In Sub‑Saharan Africa, energy policy interventions have historically prioritised supply expansion, often overlooking the significant potential of demand‑side energy efficiency measures. This paper evaluates the economic and systemic effectiveness of reducing energy demand as a strategy for improving energy access while enhancing sustainability. It examines key technical, financial, institutional, and informational barriers that constrain the adoption of energy‑efficient technologies and practices across the region. The analysis demonstrates that targeted policy interventions including regulatory frameworks, financial incentives, and capacity‑building initiatives, can accelerate the uptake of energy‑efficient buildings, appliances, and industrial systems. Strengthening incentives for households, utilities, and industries can support long‑term investments in cost‑effective energy efficiency measures. The study concludes that integrating energy efficiency into national energy access strategies represents a scalable and economically sound pathway toward achieving universal energy access and sustainable development in Sub‑Saharan Africa.
🏷 Energy efficiency, Energy access, Sub-Saharan Africa, Policy barriers, Cost-effectiveness
Article 192 · pp. 2411-2417
Research Repository System for Greater Research Accessibility for Senior High School Students in Alaminos City Division
Limited access to completed research outputs remains a common challenge among senior high school students, especially in public school divisions where research papers are often stored manually, kept in separate schools, or made available only through limited physical archives. This study aimed to design, develop, and evaluate a web-based Research Repository System that would promote greater research accessibility within the Alaminos City Division. Specifically, the study sought to provide a centralized and secure digital platform where approved research papers could be uploaded, organized, searched, and retrieved by authorized users such as students, teachers, and research coordinators.
The study employed a descriptive-developmental research design. The developmental aspect focused on the planning, design, coding, testing, and deployment of the repository system, while the descriptive aspect involved evaluating the quality and acceptability of the system based on the ISO/IEC 25010 software quality model. The system was assessed in terms of functionality, performance efficiency, usability, reliability, and security. Data were gathered from selected evaluators who examined the system’s features, ease of use, response time, dependability, and protection of stored research documents.
Results showed that the developed Research Repository System received excellent ratings across the evaluated software quality criteria. The system was found to be functional, user-friendly, efficient, reliable, and secure. These findings indicate that the proposed system can effectively address the problem of limited research access by providing a structured, accessible, and sustainable digital archive for completed senior high school research outputs.
In conclusion, the web-based Research Repository System supports improved research engagement by making previous studies easier to locate and use as references. It also helps strengthen research culture, knowledge sharing, and academic collaboration within the Alaminos City Division.
🏷 Research Repository System, Research Accessibility, Senior High School, Web-Based System, ISO/IEC 25010
Article 193 · pp. 2418-2427
Writing Resistance, Reclaiming Agency: Gender Justice and Social Transformation in Contemporary Indian Women’s Fiction
The discourse of gender justice in Indian English fiction has emerged as a significant literary and socio-political intervention through the writings of Indian women novelists. Their narratives challenge patriarchal structures, expose socio-cultural inequalities, and foreground women’s agency within contexts shaped by caste, class, religion, sexuality, and colonial legacies. This paper examines how select Indian women writers—Arundhati Roy, Anita Nair, Bama, and Meena Kandasamy—reclaim female subjectivity and articulate possibilities of social transformation. Drawing upon feminist literary criticism, postcolonial feminism, intersectionality, subaltern studies, and gynocriticism, the study argues that these writers resist hegemonic narratives by constructing women and marginalized communities as active subjects rather than passive victims.
The paper analyses The God of Small Things, Ladies Coupé, Karukku, and The Gypsy Goddess to explore the relationship between gendered oppression, caste hierarchy, institutional violence, and resistance. It further examines how family, religion, caste structures, and state power regulate women’s identities and lived experiences. Through fragmented narration, dialogic storytelling, autobiographical testimony, metafiction, and vernacular expression, these writers create alternative narrative spaces that foreground silenced voices. The study also situates these texts within contemporary feminist discussions concerning digital activism, anti-caste movements, and evolving debates on gender justice. It concludes that Indian women’s fiction continues to reshape public discourse by reclaiming agency, challenging structures of inequality, and redefining literature as a site of feminist intervention and social critique.
🏷 Gender justice, agency, Indian women’s fiction, intersectionality, patriarchy, postcolonial feminism, Dalit feminism
Article 194 · pp. 2428-2439
A Dual-Phase Hyperparameter Tuning Approach for Emotion Detection Using Boosting-Based Machine Learning Algorithms
The purpose of this study is to develop and evaluate a dual-phase hyperparameter tuning approach for enhancing the performance of emotion detection systems using boosting-based machine learning algorithms. The methodology involves data collection and preprocessing, feature engineering, model definition, training, and evaluation. Specifically, the study applies a two-stage optimization process in initial coarse tuning with RandomizedSearchCV followed by fine-tuning with GridSearchCV on models including XGBoost, LightGBM, CatBoost and GradientBoosting. The results showed that LightGBM achieved the highest overall accuracy of 92.20%, followed by XGBoost with 91.47%, GradientBoosting with 91.19%, and CatBoost with 88.23%. Confusion matrix analysis revealed that LightGBM and XGBoost produced more balanced and accurate classifications across the six emotion classes, while CatBoost exhibited higher misclassification rates in challenging classes. In terms of computational efficiency, LightGBM provided the best balance between accuracy and training speed, whereas XGBoost demonstrated the lowest memory usage. GradientBoosting achieved competitive performance but required significantly higher computational resources, while CatBoost achieved the fastest prediction time. Based on the findings, LightGBM was identified as the most suitable boosting algorithm for emotion classification due to its superior balance of predictive performance, efficiency, and reliability. Future studies are recommended to explore hybrid and deep learning approaches, larger datasets, and real-time implementation strategies to further improve emotion classification systems.
🏷 Emotion Detection, Hyperparameter Tuning, Boosting Algorithms, Text Classification, Natural Language Processing.
Article 195 · pp. 2440-2448
Weapon Detection and Alert System Using Yolo Deep Learning Technique
Gun-related violence poses a major threat to public safety and well-being worldwide. Traditional surveillance systems rely on human monitoring, which is prone to fatigue and delayed response. This paper proposes a smart surveillance system for real-time weapon detection using deep learning techniques. The system employs the YOLOv3 algorithm to detect guns, rifles, and fire from video streams with high accuracy. It also captures the location of incidents and stores data for further analysis. A multi-system architecture is implemented using socket programming to simulate real-world integration. The proposed model ensures fast detection with reduced computational cost. It enhances response time and minimizes human dependency in surveillance tasks. The system can be extended to autonomous security and robotic applications. Experimental results demonstrate its effectiveness in improving public safety and security systems.
🏷 YOLO, Deep Learning, Weapon Detection, Computer Vision, Surveillance System
Article 196 · pp. 2449-2466
AI-Driven Welfare Scheme Recommendation Using Random Forest and RAG
We propose a scalable framework that integrates a multi-output Random Forest classifier with a retrieval-augmented generation module to recommend government welfare schemes to citizens based on their demographic profiles. The system first applies a preprocessing pipeline that normalizes raw input features—such as age, income, occupation, and caste—using a fuzzy matching algorithm to resolve lexical inconsistencies in categorical variables. A multi-label Random Forest ensemble, comprising hundreds of decision trees, then predicts eligibility probabilities across all available schemes simultaneously, and a calibrated confidence threshold selects a candidate subset of schemes. To ensure factual accuracy in the natural language explanations delivered to users, we incorporate a retrieval-augmented generation component. This module embeds verified scheme descriptions into a high-dimensional vector space, retrieves the most relevant document chunks for the candidate schemes using cosine similarity, and feeds both the retrieved context and the user’s original query into an instruction-tuned large language model. The classification stage thus acts as a computational filter that narrows the retrieval search space, thereby improving both system efficiency and response precision. The primary contribution of this work lies in the novel coupling of an ensemble-based eligibility predictor with a retrieval-constrained generative model, which prevents hallucinated outputs while remaining adaptable to large-scale, heterogeneous citizen data. Experimental evaluations on synthetic datasets, designed to mimic real-world public records, demonstrate that the framework achieves high precision in eligibility prediction and generates coherent, evidence-backed recommendations. This approach has significant implications for making complex social welfare systems more accessible to underserved populations.
🏷 Artificial Intelligence, Government Schemes, Eligibility Prediction, Machine Learning, Random Forest, Decision Tree, RAG Chatbot,
Article 197 · pp. 2467-2477
Knowledge, Attitude, and Digital Literacy of Agriculture Students
This study was conducted at Pangantucan Bukidnon Community College (PBCC) in the Philippines. The objective of the study is to determine the students' knowledge, attitudes, and digital literacy on digital mass media. The data were collected personally by the researcher from the 2nd-year and 3rd year students of Pangantucan, Bukidnon Community College. A total of 108 students served as respondents in the study. Frequency counts, Likert scales, and ranks were employed. Findings show that the primary devices used by the participants were phones(97.22%) with secondary devices being laptops. The average screen time spent by students in using mass media is four hours. The most accessed digital platforms are Facebook, YouTube, and TikTok, while the webpage from the state and universities is moderately utilized. Digital mass media were perceived by Students positively with a positive attitude on digital tools with high level of knowledge. The level of usage was moderate (overall mean = 2.97), with Facebook groups, YouTube tutorials and TikTok content being used the most for agricultural information. The sample was overwhelmingly male (53.21%) as are the demographics of the agricultural workforce nationally.Digital mass media was considered very useful in learning about agriculture by the students (with overall mean score 4.18) because it is relevant, convenient and can make learning more easy for complex topics. The attitudes expressed were overwhelmingly positive, with a strongly agree rating with an overall mean of 4.68, which means that digital media helps to increase involvement, motivation and independent learning. The overall mean of the knowledge level was also rated as high (3.88), which indicates students' ability to use digital tools for academic and agricultural activities, as well as evaluate information retrieved from the internet. The frequency of digital mass media exposure was also high (overall mean 3.48), with numerous uses across multiple platforms and seeking out agricultural information. Challenges faced by the students include rampant fake news, intermittent internet connection, and limited research on agriculture students’ digital self-efficacy.
🏷 Digital Mass Media, Agricultural Education, Digital Literacy, Student Perception
Article 198 · pp. 2478-2495
Lightweight Dual-View Feature Fusion for Hand-Object Interaction Recognition
Wrist-worn hand-object interaction (HOI) recog- nition is a critical capability for wearable rehabil- itation systems, assistive technologies,augmented reality and human-computer interaction applica- tions. Compared with fixed external cameras, wrist-worn wearable devices provide user-centered observations that are more suitable for contin- uous real-world interaction monitoring. How- ever, many existing wrist-worn HOI recognition systems still face important challenges, includ- ing incomplete interaction representation caused by single-view observations, viewpoint ambiguity, self-occlusion and the high computational com- plexity of recent deep learning approaches. To address these limitations, this paper proposes a lightweight dual-view framework for hand-object interaction recognition using synchronized palm- view and back-view RGB images acquired from a wrist-worn dual-camera device. The proposed framework employs a shared MobileNetV2 back- bone combined with multi-level feature extraction to jointly capture fine-grained spatial details and high-level semantic representations. To effectively integrate complementary information from differ- ent network depths, a view-specific adaptive fusion mechanism is introduced to dynamically balance intermediate and deep feature representations for each visual stream. The fused dual-view represen- tation is subsequently used for interaction classifi- cation. Experimental evaluation under the Leave- One-Participant-Out (LOPO) cross-subject pro- tocol demonstrates that the proposed framework achieves a mean accuracy of 82.36% and a mean F1-score of 81.65% while maintaining low compu- tational complexity suitable for real-time wearable applications. Ablation studies further confirm the effectiveness of the proposed multi-level feature extraction and adaptive fusion strategy. The pro- posed approach provides an effective balance be- tween recognition performance and computational efficiency for lightweight wearable HOI recognition systems.
🏷 Hand-object interaction recognition; Dual-view learning; Adaptive feature fusion; Lightweight deep learning; Wrist-worn systems
Article 199 · pp. 2496-2507
“Artificial Intelligence and Its Influence on Human Resource Practices in the IT Industry.”
The Research case "A Study on AI Impact towards Human Resource with Reference to IT Company" examines how artificial intelligence (AI) is revolutionizing the Human Resource Management (HR) industry, with a particular emphasis on the tactics and contributions of IT Company, a world leader in technology and consulting services. This Study explores the revolutionary potential and related constraints of artificial intelligence's role in HR. In particular, it encompasses difficulties such as operational challenges in integrating AI into legacy HR, ethical and regulatory considerations related to AI, data privacy concern, ethical & legal issues and automation task and enabling data driven decision-making across various HR functions concerns resulting from reliance on massive datasets. The results are intended to provide light on how artificial intelligence (AI) might further disrupt the various HR sector and the strategic role IT Company plays in influencing this change and benefiting the sector. What an artificial intelligence (AI) is revolutionizing the Human Resource Management (HR) industry, with a particular emphasis on the tactics and contributions of IT Company, a world leader in technology and consulting services. This Study explores the revolutionary potential and related constraints of artificial intelligence's role in HR. In particular, it encompasses difficulties such as operational challenges in integrating AI into legacy HR, ethical and regulatory considerations related to AI, and automation task and enabling data driven decision- making across various HR functions.
🏷 Artificial Intelligence, Human Resource, IT Industry
Article 200 · pp. 2508-2516
Resonix - Environmental Sound Classification and Haptic Alert System for Hearing-Impaired Assistance
Hearing-impaired individuals often face difficulty in recognizing important environmental sounds such as sirens, alarms, and warning signals, which can affect their safety and situational awareness in daily life. This paper presents Resonix, an AI-based wearable assistive system designed to identify critical environmental sounds and provide real-time haptic alerts through vibration patterns. Environmental sound datasets including ESC-50 and UrbanSound8K were utilized for model development. Five relevant sound classes — siren, dog bark, alarm, baby cry, and glass break — were selected for classification. Audio preprocessing techniques such as normalization and noise reduction were performed before training the models. Multiple machine learning models including KNN, SVM, Random Forest, and a Convolutional Neural Network (CNN) were evaluated for environmental sound classification. Experimental results showed that the proposed CNN model achieved the highest accuracy of 93.0% compared to other models. The trained model was integrated with an ESP32-based wearable setup consisting of three microphone modules, two vibration motors, and an emergency push button for real-time haptic alert generation. The proposed system aims to improve environmental awareness and assist hearing-impaired individuals through a simple and low-cost wearable solution.
🏷 Convolutional Neural Network (CNN), Wearable Assistive System, Haptic Alert System, ESP32, Machine Learning, Audio Classification
Article 201 · pp. 2517-2522
Extent Difficulty of Cataloging Books as Perceivedby Library and Information Science Students
Cataloging is essential for efficient information retrieval in library science. This mixed-methods study investigates the book cataloging challenges faced by Bachelor of Library and Information Science (BLIS) students at Tagoloan Community College, utilizing surveys and in-depth interviews. The results reveal that while most students showed satisfactory proficiency, they experienced mild difficulties in identifying access points, assigning classification numbers, and selecting subject headings. Crucially, a strong correlation emerged between students' perceived difficulties and their cataloging outcomes, proving these challenges directly impact academic performance. Qualitative data highlighted a critical need for more practical exercises, better access to cataloging tools, and collaborative learning spaces. Based on these findings, the study recommends enhancing instructional strategies and updating learning resources to prepare students for real-world professional demands.
🏷 Cataloging, cataloging books, cataloging difficulties, access point, classification, subject heading, accuracy, reliability, accessibility.
Article 202 · pp. 2523-2535
Sustainability Reporting and Operational Performance of Selected Environmentally Sensitive Firms in Nigeria
This study investigated the impact of sustainability reporting on the operational performance of environmentally sensitive firms listed on the Nigerian Exchange Group (NGX). Employing an ex post facto research design, the study utilized secondary data derived from the annual and sustainability reports of eight firms between 2019 and 2024. Environmental, social, and governance (ESG) disclosure indices were developed via content analysis, utilizing a binary scoring methodology aligned with the Global Reporting Initiative (GRI) framework. Analytical procedures included descriptive statistics, correlation analysis, panel least squares regression, heteroskedasticity testing, and the Hausman specification test. The results indicate that while environmental and governance reporting have positive but statistically insignificant effects on operational performance, social reporting exerts a negative and statistically insignificant influence. Conversely, firm age exerted a significant negative impact on operational performance. The Hausman test supported the selection of the random effects model as the preferred estimator. The findings indicate that while sustainability reporting enhances transparency, accountability, and stakeholder confidence, its influence on the operational performance of environmentally sensitive firms in Nigeria remains limited. The study recommends that firms move beyond mere disclosure compliance, strengthen governance frameworks, and align social investment strategies with core operational objectives.
🏷 Environment Reporting, Social Reporting, Governance Reporting, Operating Margin Ratio, GRI, Nigeria
Article 203 · pp. 2536-2544
The use of Artificial Intelligence as a Motivational Factor in Enhancing Teachers’ Job Performance in Senior Secondary Schools in Ikpopa-Okha Local Government area, EDO State.
The pace at which teachers are motivated for greater service is deteriorating in secondary schools is alarming. The fall in performance and productivity of the secondary teachers is of great concern to the stakeholders. Therefore, it examines the practical application of AI in lesson delivery, administrative duties, and student’s assessments, as well as the challenges faced by educators in integrating these technologies in senior secondary schools, Ikpoba-Okha Local Government Area, Edo State. The survey research design was adopted. In collecting the data for the research, the study made used of simple percentages parameters to draw up the population sample size for the study which was collected from both primary and secondary source. Research questionnaire was designed for the secondary school teachers in Ikpoba-Okha Local Government, Edo state. The finding of the study revealed that there was a positive and significant relationship between artificial intelligence usage as a motivational factor in enhancing teachers assessment/grading of students, virtual teaching and teacher’s performance in Government senior secondary schools. Based on the findings, it was recommended among others that Government through training should encourage teachers to utilise AI- Powered assessment and automated grading systems to navigate and adjust the level of difficulty and workload they face in assessing /grading of students in order to improve individual teacher performance.
🏷 Artificial Intelligence, Teacher Performance, Motivational Factor, AI- Powered assessment, Job Performance
Article 204 · pp. 2545-2557
A Hybrid mT5-Based Machine Translation System for Kanuri–English Educational Translation in a Low-Resource Setting
Kanuri is a morphologically rich, low-resource language spoken primarily in northeastern Nigeria and the Lake Chad basin, yet it remains almost entirely absent from modern natural language processing (NLP) and machine translation (MT) research. This paper presents a hybrid AI-based Kanuri–English machine translation system designed for primary school educational use. The proposed system integrates the multilingual Text-to-Text Transfer Transformer (mT5) with a deterministic dictionary-based lookup module to address the critical challenge of data scarcity inherent to low-resource language translation. A domain-specific parallel corpus of 522 bilingual entries—comprising classroom instructions, greetings, and basic educational vocabulary—was manually compiled and used to fine-tune the mT5 model, while a structured Kanuri–English lexicon was developed to supplement neural translation outputs. The hybrid architecture exploits the complementary strengths of learned neural representations and rule-based lexical mappings, substantially improving translation accuracy and semantic reliability within the educational domain. An interactive web-based interface incorporating text input, browser-based speech recognition, automatic translation, and text-to-speech output was implemented to support multimodal engagement for young learners and educators. Evaluation using accuracy, precision, recall, and F1-score demonstrates that the hybrid system achieves 100% accuracy on the in-domain evaluation set, compared with 34.67% for the standalone mT5 model. Error analysis confirms that hybrid integration mitigates the generalization weaknesses of purely neural approaches in low-resource conditions. The findings provide a replicable framework for inclusive NLP tool development for other underrepresented African languages and contribute to the broader goals of educational equity and linguistic inclusion for Kanuri-speaking primary school students.
🏷 Kanuri language, low-resource machine translation, mT5, hybrid translation, multilingual NLP, African language technology, educational NLP, transformer fine-tuning.
Article 205 · pp. 2558-2566
Number Plate Detection System Using Computer Vision and Automatic Toll Collection
The Number Plate Detection and Automatic Toll Collection System is a computer vision-based project designed to automate vehicle identification and toll payment at toll plazas. The system uses Raspberry Pi 4B (4GB RAM) as the central processing unit, powered by a 5V 3A adapter and integrated with an Entry IR Sensor, an Exit IR Sensor, a Logitech C270 USB Camera (1280×720 resolution), a Servo Motor, an I2C 16x2 LCD Display, and an LED Indicator. When a vehicle approaches, the Entry IR Sensor triggers the USB Camera to capture the vehicle’s front image. OpenCV is used for number plate localisation and image pre-processing, and Tesseract OCR (configured with Page Segmentation Mode 8 and the LSTM engine) extracts the vehicle’s registration number from the cropped plate region. The detected number plate is first displayed on the web interface before any gate action is taken. The system then queries a three-table MySQL/SQLite database for owner details and applies the Haversine formula to GPS coordinates to determine if the owner resides within 10 km of the toll plaza; if so, the toll is waived, otherwise it is automatically deducted. The Servo Motor lifts the gate barrier, the I2C 16x2 LCD displays the transaction status, and the LED Indicator and Buzzer provide real-time feedback. The Exit IR Sensor detects vehicle departure and triggers gate closure. The system achieves Tesseract OCR precision of 90.81% and recall of 83.97%, with end-to-end operation completing in under 2 seconds per vehicle, significantly reducing manual intervention and enabling smart contactless toll operations.
🏷 Number Plate Detection, Automatic Toll Collection, Raspberry Pi 4B, Tesseract OCR, OpenCV, IR Sensor, Servo Motor
Article 206 · pp. 2567-2575
Performance Analysis of Low-Computational Computer Vision Techniques for Recyclable Waste Identification.
Waste management on the low-resource setting is also a serious issue because of the shortage of infrastructure, and the lack of access to modern technologies, and the use of manual sorting of waste leads to environmental pollution and ineffective recycling. Although deep learning-based image classification has demonstrated the potential of automating waste, the majority of studies emphasize high-computation models, which cannot be used in resource-constrained environments. The research gap is a significant lack of systematic comparisons on both classification and computational efficiency of low-computational convolutional neural network (CNN) models. This study aim to examine the appropriateness of lightweight CNN models in the identification of recyclable wastes with a tradeoff between accuracy and resource consumption. Three low-computational CNN models, namely EfficientNet-Lite, MobileNetV2, and SqueezeNet, were trained and tested using publicly available datasets, such as TrashNet. Accuracy and F1-score were used to measure the classification performance, and latency, memory usage, and model size were used to measure computational efficiency. Findings show that EfficientNet-Lite had the best accuracy (92.4) and F1-score (92.0) but used more memory and inference time whereas MobileNetV2 provided a reasonable compromise between performance and efficiency. SqueezeNet was the lightest and the fastest model but with a lesser classification reliability. These results give practical recommendations on the design of AI-powered waste management in low-resource settings. This study helps to advance sustainable recycling efforts because it emphasizes the existence of the accuracy-latency-memory trade-offs of lightweight CNNs and informs practitioners and researchers on the need to use the right model whenever deploying lightweight CNNs to mobile, edge, or embedded applications in developing regions.
🏷 Recyclable Waste Identification, Low-Computational CNN, Computer Vision, Resource-Constrained Environments.
Article 207 · pp. 2576-2591
Dream-Based Classification of Driver Behavioural Patterns in Nigerian Road Traffic Accidents
Road traffic accidents remain a leading cause of mortality and morbidity in Nigeria, yet structured analysis of the behavioral patterns underlying crash causation is largely absent from the literature. This study applies the Driver Reliability and Error Analysis Method (DREAM) to classify driver behavioral patterns from Nigerian road traffic accident records sourced from three national newspaper outlets. A total of 65 accident records were extracted, spanning all geopolitical zones and involving five vehicle categories: trucks (41.5%), buses (38.5%), cars (32.3%), three-wheeled vehicles (12.3%), and two-wheeled vehicles (3.1%). Each record was independently coded by two analysts using the DREAM taxonomy, identifying the observable driver critical events and its underlying causal behavioral and situational contributors. Cumulative DREAM charts were constructed for each critical event by accumulating causal pathways across the dataset. Too High Speed was the most prevalent, accounting for 41.5% of records, followed by Insufficient Force, 20.0%; Wrong Direction, 18.5%; No Action, 15.4% and Too Late Action 4.6%. Across all 65 records, 32 distinct genotype codes were identified. The dominant genotypes were Misjudgment of situation, n = 27; Habitually stretching rules, n = 26; Equipment failure, n = 25; and Inadequate vehicle maintenance, n = 24. 58.5% involved collisions with motorized counterparts and 10 (15.4%) involved non-motorized road users. The findings reveal that Nigerian road crashes are driven by a convergence of deliberate norm violation, cognitive misjudgment, driver skill deficits, and systemic vehicle maintenance failures, providing an evidence base for targeted road safety interventions in Nigeria and comparable low- and middle-income country contexts.
🏷 driver behavior, road traffic accidents, DREAM methodology, accident phenotypes, Nigeria
Article 208 · pp. 2592-2607
Institutional Reforms and Capital Market Deepening in Frontier Economies: A Comparative Policy Analysis.
In the current study, the correlation between institutional reforms and capital market deepening is examined using the methodology of comparative case study and a policy-focused analytical approach. Many frontier markets continue to be shallow, marked by low liquidity and low levels of participation. In light of these considerations, the following research questions are stated: Do institutional reforms lead to capital market deepening? Why do similar reforms have different effects in various countries? Under what circumstances are institutional reforms most effective? Based on IMF, World Bank, and financial sector data of the respective countries, an analysis of institutional, macroeconomic and financial sector factors will be conducted in the context of a structured analytical approach.
Results show that institutional reforms foster the development of capital markets primarily if they are credible, enforceable, and are made under macroeconomic stability. Investor protection and market transparency reforms tend to be more effective than general governance reforms. However, weak enforceability, sequencing issues and macroeconomic instability greatly limit the positive influence of reforms. Financial sector liberalization measures in turn produce inconsistent results when not complemented by institutions. Cross-national analysis reveals that the varying enforcement capabilities, reform sequencing, and the structure of domestic financial sector explain a considerable part of the differences in results. The significance of the study consists in its focus not on institutional variables but on the implementation of policies. From this perspective, the study provides implications for practitioners on how reform measures should be formulated in order to ensure effective capital market deepening in frontier economies.
🏷 Institutional reforms, Capital market deepening, Frontier economies, Financial liberalization, Investor protection, Macroeconomic stability
Article 209 · pp. 2608-2619
Comparative Studies on Lipase Production and Optimization using Bacillus Subtilis and Pseudomonas Aeruginosa
The presents study deals with screening of lipase producers from the lipase rich environment like petrol bank, Auto mobile shops from soil sample. The micro organisms have the ability to produce the lot amount of lipase. The sample was collected from (petrol bank, Auto mobile shops). Tributyrin agar medium was prepared 14 strains were isolated from that 2 strains able to produce lipase. The bacterial strain confirmed by biochemical method. The isolated bacterial strain are Bacillus sp. and Pseudomonas sp. The bacterial strains need temperature around 350 c, PH 7, olive oil as a carbon source and Incubation for 48hr. If the temperature is higher the enzyme productivity is also high. Thin layer chromatography of lipids revealed that bacillus sp has higher lipase activity than pseudomonas sp.
The present study revealed that extracellular lipase production by Bacillus sp. and Pseudomonas sp. isolated from soil samples was found to be accelerated at optimized culture conditions such as medium pit, temperature and carbon source. From the result, it could be concluded that the medium pH of 7.0 and temperature 350c were optimum for maximizing lipase production by Bacillus sp. and Pseudomonas sp. Inferred that the optimum contain based on this study lipase activity is higher in Bacillus sp. and Pseudomonas sp. than compare other 12 strains.
🏷 Lipase,Enzyme productivity,Optimization,Pseudomonas sp
Article 210 · pp. 2620-2628
Wildlife Conservation Awareness Through Convergence of Media and Performing Arts
Wildlife conservation is a critical global issue, demanding innovative approaches to raise awareness and inspire action. This abstract explores the synergistic potential of media convergence and performing arts in fostering wildlife conservation awareness. By integrating various media platforms—such as social media, documentaries, and digital campaigns—with the dynamic and emotive power of performing arts—including theater, dance, and music—this approach leverages the strengths of both fields to create a compelling and immersive educational experience. Media convergence allows for the broad dissemination of information, reaching diverse audiences and facilitating interactive engagement. Social media campaigns, virtual reality experiences, and online documentaries can vividly depict the beauty and plight of wildlife, while providing accessible educational resources and real-time updates on conservation efforts. Meanwhile, performing arts offer a unique, visceral connection to these issues. Through emotionally charged performances, artists can evoke empathy, highlight the cultural significance of wildlife, and dramatize the urgent need for conservation.
🏷 wildlife, wildlife conservation, performing arts, media
Article 211 · pp. 2629-2636
Enhancing Library User Experience through Queuing Theory: A Performance Analysis of OPAC Systems
Libraries have evolved from traditional repositories of books into complex service-oriented systems that cater to diverse user needs. With the growth of digital catalogues and electronic services, ensuring efficiency in user interactions has become increasingly important. Queuing theory, a mathematical approach to analyzing waiting lines and service congestion, provides a valuable framework for understanding and optimizing library processes. This paper examines the application of queuing theory in library management systems, with a special focus on Online Public Access Catalogue (OPAC) searching. By exploring theoretical models such as M/M/1 queues, Little’s Law, and system utilization measures, the research highlights how performance indices like average waiting time, queue length, and service rate can be leveraged to improve the user experience. Through case-based illustrations, this study demonstrates how queuing models can guide decision-making in resource allocation, technological upgrades, and service delivery. The findings suggest that a systematic application of queuing principles can reduce congestion, improve user satisfaction, and ensure sustainable service delivery in modern libraries.
🏷 Queuing theory, Library management systems, OPAC searching, Service optimization, Performance analysis
Article 212 · pp. 2637-2644
Consumer Confidence and Its Influence on Sustainable Transportation Practices: A Study on the Mediating Effect of Electric Vehicle Acceptance
This study examines the relationship between consumer confidence and sustainable transportation practices, focusing on the mediating role of electric vehicle (EV) acceptance. As environmental concerns and carbon reduction goals gain importance, understanding factors influencing sustainable mobility becomes essential. Consumer confidence, reflecting individuals’ economic optimism and financial security, may significantly affect their willingness to adopt innovative technologies such as EVs. Using a quantitative research design, data will be collected through structured questionnaires and analysed using mediation analysis techniques. The study aims to provide insights for policymakers and industry stakeholders to promote electric vehicle adoption and encourage sustainable transportation behaviour.
🏷 Consumer Confidence, Sustainable Transportation, Electric Vehicle Acceptance
Article 213 · pp. 2645-2655
Linking Waste Disposal Practices (WDP) to Carbon Footprint (CF): A Mediation Analysis of Public Environmental Awareness (PEA)
The increasing environmental challenges associated with improper waste management have intensified global concerns regarding carbon emissions and climate change. This study examines the relationship between Waste Disposal Practices (WDP) and Carbon Footprint (CF), with Public Environmental Awareness (PEA) serving as a mediating variable. Data were collected from households, local residents, and environmentally conscious citizens across various municipalities in Thrissur district, Kerala. A total of 540 valid responses were analyzed using Structural Equation Modeling (SEM). The findings indicate that responsible waste disposal practices significantly reduce carbon footprint by promoting recycling, waste segregation, composting, and sustainable disposal behaviors. The study further reveals that public environmental awareness positively strengthens environmentally responsible practices and mediates the relationship between waste disposal practices and carbon footprint. The research highlights the importance of awareness campaigns, environmental education, and policy-driven waste management initiatives in achieving long-term environmental sustainability and carbon reduction goals.
🏷 Waste Disposal Practices, Carbon Footprint, Public Environmental Awareness
Article 214 · pp. 2656-2664
Development of a Hybrid Energy Harvesting Electric Travel Bag for Portable Electronic Charging
Your reliance on mobile phones, laptops and other handheld devices is becoming something of a matter of fact as you get intrinsic need to charge there things while traveling and having no access to grid electricity. The following study designed and investigated a hybrid energy harvesting electric travel bag integrating a polycrystalline solar panel, piezoelectric elements, battery, inverter and charging outputs. The qualitative case study was augmented by descriptive prototype measures. Methods included interviews with five electrical engineering informants, direct observations while interacting with a prototype, documentation of the design and testing process, and records from controlled experiments involving piezoelectric loading (up to 417 N), solar-panel battery charging (0-10A charger current), laptop charging (up to 61W load per laptop placed directly on top of the platform), and inverter loading (150 VA inverter driving resistive loads). Based on the interviews, you discovered that users want an easy-to-transport energy source that is reliable, safe and portable. It was observed and documented that energy inputs of solar and mechanical could be incorporated together in a transportable system. A technical record indicated the battery state of charge was comprised of the following where 18 V solar panel increased it from 0% to full over five test days & 5 V Increase a smaller battery (a kiosk) from 0% to full in two test days. The piezoelectric subsystem generated greater voltage as the mechanical load increased, at 5 kg the average value reached 3.814 V for the parallel configuration. Testing was carried out with a laptop, which resulted in approximately a 19.8% state of charge boost after 20 minutes, and during inverter testing the metal-air power packs operated lower load over a longer period of time. Consequently, this research establishes that the Electric Travel Bag appears to be a viable additional portable charging system for use with renewable energy sources; however, various improvements in charging efficiency, power regulation, arrangement of components for prolonged usage without over-weight and electrical safety measures have potentiality as future works.
🏷 Electric Travel Bag; Solar Panel; Piezoelectric Energy Harvesting; Portable Charging System; Renewable Energy
Article 215 · pp. 2665-2673
Inclusive Workplace Practices and Organisational Commitment: The Mediating Influence of Teamwork
This study examines the relationship between Employee Inclusion and Organisational Commitment, with a specific focus on the mediating role of Teamwork among employees working in public and private sector organisations in Kerala. Inclusive workplace practices have become increasingly important in modern organisations as they foster employee participation, belongingness, fairness, and collaboration. The study aims to understand how inclusive organisational environments strengthen employees’ commitment toward their organisation through effective teamwork processes. A structured questionnaire was administered to employees across various organisations, resulting in 382 valid responses for analysis. Structural Equation Modelling (SEM) was employed to examine the proposed relationships among the study variables. The findings reveal that Employee Inclusion significantly and positively influences Teamwork and Organisational Commitment. Teamwork was also found to positively affect Organisational Commitment and partially mediate the relationship between Employee Inclusion and Organisational Commitment. The study highlights the importance of fostering inclusive and collaborative workplace environments to strengthen employee loyalty, cooperation, and long-term organisational commitment.
🏷 Employee inclusion; Organisational commitment; Teamwork; Inclusive workplace; Collaboration
Article 216 · pp. 2674-2685
Effect of Institutional Support Mechanisms on Academic Productivity: The Moderating Role of Social Isolation in Universities of Kerala
This study examines the effect of Institutional Support Mechanisms on Academic Productivity among university students in Kerala, with particular emphasis on the moderating role of Social Isolation. The study was conducted among undergraduate and postgraduate students from the University of Kerala, University of Calicut, Cochin University of Science and Technology (CUSAT), Mahatma Gandhi (MG) University, and Kannur University. A pilot study involving 100 respondents was initially conducted to validate the questionnaire, followed by the distribution of 620 structured questionnaires through online platforms and academic networks. A total of 548 responses were received, out of which 502 valid responses were used for final analysis. The findings reveal that Institutional Support Mechanisms significantly and positively influence Academic Productivity, while Social Isolation negatively affects students’ academic engagement, motivation, and performance. The moderation analysis further confirms that Social Isolation weakens the positive relationship between institutional support and academic productivity. The study highlights the importance of strengthening institutional support systems alongside promoting social connectedness within universities to enhance students’ academic performance and well-being.
🏷 Institutional Support Mechanisms, Academic Productivity, Social Isolation
Article 217 · pp. 2686-2696
Gender Independence and Career Advancement of Women Employees: The Mediating Influence of Workplace Gender Equality Policies
This study examines the relationship between Gender Independence and Career Advancement of Women Employees, with Workplace Gender Equality Policies acting as a mediating variable. The research focuses on women employees working across Kozhikode, Ernakulam, and Thiruvananthapuram districts of Kerala. Following a pilot study involving 80 respondents, the final structured questionnaire generated 320 valid responses. Structural Equation Modeling (SEM) was employed to examine the proposed relationships among the study variables. The findings revealed that Gender Independence significantly influences Career Advancement among women employees. Workplace Gender Equality Policies were also found to positively affect career advancement and partially mediate the relationship between Gender Independence and Career Advancement. The study highlights the importance of organizational equality policies, gender-inclusive work environments, leadership opportunities, and institutional support systems in promoting women’s professional growth and career development.
🏷 Gender Independence, Career Advancement, Workplace Gender equality Policies
Article 218 · pp. 2697-2705
Ethical Work Environment and Employee Retention: The Role of HR Practices in Fostering Employee Wellbeing
This study examines the relationship between Ethical Work Environment and Employee Retention, with HR Practices acting as a moderating variable. The research was conducted among employees working in Technopark (Thiruvananthapuram), Infopark (Kochi), and Cyberpark (Kozhikode), Kerala. Following a pilot study involving 100 respondents, the final survey generated 492 valid responses. Structural Equation Modeling (SEM) was employed to analyze the relationships among the study variables. The findings revealed that Ethical Work Environment significantly influences Employee Retention. HR Practices significantly moderated the relationship between Ethical Work Environment and Employee Retention by strengthening organizational commitment and workplace satisfaction.
🏷 Ethical Work Environment, Employee Retention, HR Practices, Employee Wellbeing
Article 219 · pp. 2706-2715
The Silent Signal: Investigating The Impact of Greenhushing on Employee Turnover Intention Through the Mediating Role of Perceived Organizational Hypocrisy
In recent years, organizations have increasingly adopted sustainability initiatives; however, many deliberately limit the communication of such efforts, a phenomenon referred to as greenhushing. This study investigates the impact of greenhushing on employee turnover intention, with perceived organizational hypocrisy acting as a mediating variable. Drawing on data collected from employees across organizations in Kerala, the study employs a quantitative, descriptive research design using primary data gathered through a structured questionnaire. Established and validated measurement scales were adapted to assess greenhushing, perceived organizational hypocrisy, and employee turnover intention. The data were analyzed using appropriate statistical techniques to examine direct and indirect relationships among the study variables. The findings reveal that greenhushing significantly influences employees’ perceptions of organizational hypocrisy, which in turn increases turnover intention. The research contributes to the emerging greenhushing literature by empirically demonstrating its internal organizational consequences and offers practical insights for managers to adopt transparent and authentic sustainability communication practices to reduce employee turnover.
🏷 Greenhushing; Organizational Hypocrisy; Employee Turnover Intention; Sustainability Communication; Green Human Resource Management
Article 220 · pp. 2716-2729
“Comparative Analysis of AI Models and Traditional SEO Techniques Using Real-World Survey and Performance Data”
This study presents a comparative analysis between Artificial Intelligence (AI)-based models and traditional Search Engine Optimization (SEO) techniques using real-world survey and performance data. The research integrates user behavior insights and technical SEO metrics to evaluate the effectiveness of predictive AI models in improving search engine visibility and website performance. A dataset derived from survey responses (n ≈ 130 analyzed; ~1230 collected) and SEO performance indicators is utilized. Machine learning models such as Random Forest and Regression are compared with traditional heuristic SEO methods. The results demonstrate that AI-driven approaches significantly outperform traditional techniques in predicting ranking outcomes, optimizing page performance, and enhancing user engagement. The study contributes to the development of intelligent SEO systems and highlights the practical applicability of AI in modern digital ecosystems.
🏷 AI in SEO, Machine Learning, Website Optimization, SEO Prediction, Comparative Analysis, User Behavior Analytics
Article 221 · pp. 2730-2738
Leveraging Financial Data for Research: An Empirical Demonstration of Statistical Tools on Key Variables
The paper has made an attempt to understand the application of financial variables on research. The paper has identified some key variables in accounting and finance and the way the ratios show the relationship among the variables. The paper has brought forth statistical application of accounting variables for selected companies during the period. By applying the technique of ANOVA, it is understood that the selected companies employ different amount of debt during the period by showing the significant difference in terms of leverage ratio. The application of correlation and regression techniques show the relationship between Non-Performing Asset (NPA) and Return on Equity (ROE)and the extent to which ROE is affected by NPA for the selected period by taking into account of various banks. With the help of Altman Z score the discriminant analysis is carried out to identify the financial health of the firm for the selected period.
🏷 Leverage Ratio, Return on Equity, Non-Performing Assets, ANOVA, Z score.
Article 222 · pp. 2739-2752
Enhancing Customer Satisfaction: Analyzing Influential Factors in Mobile Money Services
This study investigated the factors affecting customer satisfaction with mobile money services in Santa Cruz, Marinduque. The goal was to identify elements related to convenience, transaction costs, transaction speed, security trust, and rewards.
Using a descriptive survey method, the researchers surveyed 50 randomly selected mobile money users in Poblacion, Santa Cruz, to assess their demographic profiles, including gender, preferred services, and length of usage. The findings revealed a majority of female respondents using GCash, with most having used mobile money services for over a year but less than five. While they reported satisfaction, it was not at a high level. Therefore, the researchers concluded that mobile money providers like GCash or Maya need to improve their services to enhance customer satisfaction.
🏷 customer satisfaction, mobile money services, convenience, transaction cost, transaction speed, security & trust, rewards & vouchers.
Article 223 · pp. 2753-2762
Seasonal Variation in Primary Productivity in Relation to Physico-Chemical Parameters of Lower Tiger Hills Reservoirs, Nilgiris District, Tamil Nadu, India
The present study investigated the variation in primary productivity in the Upper and Lower Tiger Hills Reservoirs located in Udhagamandalam. The relationship between primary productivity and selected physico-chemical parameters, including air temperature, water temperature, conductivity, and dissolved oxygen, was also evaluated. Water samples were collected from four stations in each reservoir at fortnightly intervals from November 2025 to April 2026. Primary productivity was estimated using the dark bottle method. The results revealed a significant relationship between primary productivity and the measured physico-chemical parameters. Both reservoirs exhibited comparatively high productivity, supporting the development and sustenance of plankton communities. The findings indicate that physico-chemical characteristics play an important role in regulating primary productivity and maintaining ecological balance in these freshwater ecosystems.
🏷 Primary productivity, Tiger Hills Reservoir dissolved oxygen, conductivity, temperature, plankton
Article 224 · pp. 2763-2769
Deepfake Detection System Using Hybrid CNN–VIT Architecture
The increasing use of deepfake media is a serious threat because highly realistic fake images and videos can be generated and posted online, leading to scams, identity theft, and misinformation. Because of how realistic these images and videos are, it is becoming increasingly difficult to distinguish them manually. Our project provides a softwarse application that can help determine whether an uploaded image or video is real or fake. It checks facial characteristics and the overall facial structure to identify characteristic differences between real and fake media. Additionally, it performs frequency analysis to identify characteristic artefacts that are usually created when digital processing of images occurs. To ensure that the results are interpretable, the application provides visual feedback about the facial areas that contributed to the determination, providing users with a clear understanding of why a particular image or video is real or fake. The project aims to develop a useful and easy to-use application that can be used for cybersecurity, digital forensics, and online media verification
🏷 Detection, Architecture, Hybrid
Article 225 · pp. 2770-2774
The impact of Generative AI on the Efficiency and accuracy of drug discovery
Drug discovery traditionally is a long and expensive process. It requires huge financial investment and can possibly take more than a decade to develop a single drug. In this paper, It has been explored how generative AI is changing this process. By using advanced models, generative AI can design new drug molecules, predict their properties, improve efficiency by reducing both time and cost required for research to get precise and accurate data. These models can generate drug candidates with better binding affinity and drug like properties, making the selection process more reliable. It is also being used across different stages of drug discovery, from identifying targets to monitoring drug safety. Although generative AI has great potential to transform drug discovery by making it faster, more efficient and accurate, there are still some challenges like the need for high quality data, lack of model transparency, regulatory concerns and more real world testing and comparison. These improvements are still needed for its full adoption.
🏷 Generative AI, Drug Discovery, Molecular Design, Time Optimization, Cost Reduction, Predictive Modeling.
Article 226 · pp. 2775-2780
Optimal Choice of mother wavelet for satellite obtained Very Low Frequency radio wave feature selection
Very low frequency (VLF) radio wave signals, frequency range 2 kHz to 20 kHz play imperative role for probing of ionosphere and magnetosphere. There are three main VLF signals whistlers, chorus and hiss. They all have specific characteristics with respect time and frequency. Wavelet analysis is a widely used time-frequency analysis method. To propose a more competent analysis of the VLF signal, the wavelet analysis is deliberated. In wavelet analysis, the best mother wavelet is chosen for evaluating the signals, as many mother wavelets applied to the signal may yield diverse results. In this study, we used VLF signals obtained from the DEMETER satellite for wavelet analysis. For the “optimal" choice of the mother wavelet for such signals, we applied an energy-based quantitative approach at different frequency levels of VLF signals, which were obtained by Discrete Wavelet Transform (DWT). The results show that Haar and Bior3.5 are the optimum mother wavelets for the analysis of VLF signals.
🏷 Radio wave signals, very low frequency, Mother wavelet
Article 227 · pp. 2781-2792
Development of Laboratory Manual as Supplemental Materials in Teaching Technical Vocational Education Program
This study aimed to develop laboratory manual as supplemental materials in teaching technical vocational education program in the College of Education, Technical Vocational Education Department for the academic year 2024-2025. This study employed a mixed-method approach using descriptive, developmental, and experimental designs guided by the ADDIE model (Analysis, Design, Development, Implementation, Evaluation). It involved diagnostic testing, bibliometric review, and quasi-experimental procedures to develop and validate a TESDA-aligned laboratory manual for Civil Technology. Data was gathered from students, teachers, curriculum experts, and academic heads using validated instruments, with results analyzed through average weighted mean and standard deviation. The results indicate that the study identified least learned competencies in Civil Technology II and used these to develop a TESDA-aligned laboratory manual. Bibliometric insights shaped its structure, and experimental results confirmed its effectiveness in improving student learning, earning strong support from students, teachers, and academic heads. It is strongly recommended to may consider adopting and integrate the TESDA-aligned laboratory manual into the Industrial Arts curriculum to strengthen hands-on learning and improve student competency.
🏷 Development, laboratory manual, supplemental material, descriptive, innovation, experimental
Article 228 · pp. 2793-2803
Students’ Perceptions of Digital Tools in Teaching and Learning Fine and Applied Arts
This study investigates the perceptions of undergraduate students regarding the use of digital tools in the teaching and learning of Fine and Applied Arts at Adeyemi Federal University of Education, Ondo. Against the backdrop of the post-COVID-19 pandemic, it examines how studio-based art students integrate such tools in their coursework. A descriptive survey design was employed, using an online questionnaire administered via Google Forms to a convenience sample of 107 undergraduate students from 100-level to 400-level. The instrument captured the students' familiarity with digital tools, their frequency and purpose of use, perceived benefits for creativity and understanding, and the various challenges associated with access and instructional support. Descriptive statistics were used to summarise patterns of device ownership, tool usage, and perceptions across academic levels. The findings indicate that most of the respondents use personal digital devices and show readiness for greater digital integration, particularly where such tools support visualisation, experimentation, and flexible access to learning resources. However, limited infrastructure, unstable internet connectivity, and varied lecturer competence constrain effective adoption. The study recommends strategic ICT infrastructure investment, targeted staff development, and curriculum review as crucial interventions in the alignment of Fine and Applied Arts education in Nigeria with contemporary digital practices and the enhancement of students’ learning experiences.
🏷 Art curriculum development, digital tools, fine arts education, student perceptions, Nigeria.
Article 229 · pp. 2804-2811
Effects of School-Based Psychosocial and Health Support Interventions on Educational Outcomes Among Children from Disrupted Family Backgrounds in Sri Lanka: A Mixed-Methods Study
Background: Children from disrupted family environments frequently experience emotional distress, reduced supervision, poor school attendance, limited educational support, and adverse health outcomes. These factors negatively affect academic achievement, classroom engagement, and psychosocial development. Although school-based psychosocial and health support interventions have demonstrated effectiveness internationally, evidence from Sri Lanka remains limited.
Objective: To examine the effects of school-based psychosocial and health support interventions on educational outcomes among primary school children from disrupted family backgrounds in Sri Lanka.
Methods: A mixed-methods study was conducted among 150 students enrolled in Grades 3–5 from five schools in the Rajagiriya Education Zone, Sri Lanka. Participants were identified through welfare records, counselling referrals, teacher recommendations, and school administrative records. Quantitative data were collected using academic records, attendance reports, and structured questionnaires assessing motivation, confidence, engagement, and perceived support. Qualitative data were obtained through semi-structured interviews with teachers and school administrators and through classroom observations. Quantitative findings were analysed using descriptive statistics, while qualitative data were analysed thematically.
Results: The study included 150 children from disrupted family environments. Female students consistently demonstrated higher academic performance, attendance, classroom participation, and confidence than male students. Academic outcomes improved progressively from Grade 3 to Grade 5. Major barriers affecting educational achievement included emotional distress, poor concentration, absenteeism, low motivation, inadequate parental support, and socioeconomic hardship.
Students who received school-based health support—including nutritional assistance, routine health screening, counselling referrals, and teacher-led psychosocial support—demonstrated better attendance, participation, and educational engagement. Teachers were identified as key protective factors through individualized support, positive relationships, and emotionally supportive classroom environments. However, limited formal training in trauma-informed education and inadequate counselling resources constrained intervention effectiveness.
Conclusion: School-based psychosocial and health support interventions contribute substantially to improved educational outcomes among children from disrupted family backgrounds. Strengthening trauma-informed teaching, counselling services, school health programmes, and multi-sectoral collaboration between education, health, and child protection sectors is essential to improve educational equity and child well-being in Sri Lanka.
🏷 Disrupted family backgrounds, psychosocial support, school health services, trauma-informed education, educational outcomes, child well-being, Sri Lanka
Article 230 · pp. 2812-2823
Socio-Economic and Spatial Determinants of Housholds’ Water Accessibility in Lokoja, Nigeria
Access to adequate water is a major challenge facing many households in Nigeria especially In Lokoja, which has the largest burden of morbidity and mortality. Previous studies on water accessibility focused on quality, without considering the pattern of urban households’ access to water based on geographic location and household socio-economic characteristics. Therefore, this study investigated socio-economic and spatial determinants of households’ access to safe water in Lokoja, Nigeria with a view to developing practical and policy guidelines that will enhance access to safe drinking water. Cross-sectional design was adopted. A semi-structured questionnaire was administered to 350 household heads in the six residential zones across Lokoja as identified in the Lokoja Urban Master Plan. This was obtained using Taro Yamane sample size calculator based on the 2025 population projection of 58,219 households. The data were analyesed using descriptive and inferential statistics at p ≤ 0.05. Male-head households constituted 57.6%. Households sourced water from boreholes (64.5%), tube-well (11.5%) hand-dug well (28.9%), rain harvesting (2.7%) and stream (0.10%). About 59% households had optimal access to water, 26.9% intermediate access and 14.4% has basic access, while pipe-borne water was not functional. There is association between Socio-economic characteristics of households and access to safe water R2=0.54. The results obtained indicate that households has varied degree of water accessibility across residential zones in Lokoja. To achieve equitable access to safe water, restoration of public water reservoir system, provision of motorised and hand-pumped boreholes especially low ad medium density area were recommended among others.
🏷 Access to safe water, Accessibility, Lokoja Water Supply
Article 231 · pp. 2824-2839
Determinants of Electronic Payment System Adoption in Nigeria: An Integrated Tam and Delone & Mclean Model Approach.
This study develops an integrated theoretical framework combining the Technology Acceptance Model (TAM) and the DeLone and McLean's Information Systems Success Model is utilized to analyze the factors influencing the acceptance of electronic payment systems (EPS) in Nigeria .This model includes elements such as system quality, service quality, perceived usefulness, perceived ease of use, perceived risk, trust, and infrastructure challenges are identified as contextual obstacles impacting adoption results in a developing country, On the other hand, users' views and behavioral intentions about EPS utilization are found to be predicted by company reputation. 3,450 valid responses were gathered from clients of particular commercial banks in Southwestern Nigeria using a quantitative survey design. Data were analyzed using multiple regression and principal component analysis.
Findings reveal that system quality (β = 0.237, p < 0.001), service quality (β = 0.410, p = 0.002), perceived usefulness (β = 0.119, p = 0.004), perceived ease of use (β = 0.306, p = 0.001), perceived risk (β = 0.237, p < 0.001), trust (β = 0.010, p < 0.001), and organizational reputation (β = 0.267, p < 0.001) significantly influence users’ attitudes toward EPS adoption. Furthermore, attitude significantly predicts behavioral intention to use EPS.
Infrastructural constraints such as erratic electricity supply, poor internet connectivity, and high operational costs were identified as major barriers to adoption. The study concludes that EPS adoption in Nigeria is shaped by a combined influence of technological, behavioral, trust-based, and infrastructural factors.
The study extends TAM and DeLone–McLean models by introducing infrastructural constraints as contextual determinants in developing economies. The findings offer implications for policymakers, financial institutions, and digital payment service providers in strengthening digital payment ecosystems.
🏷 Electronic Payment System, Trust, System and service quality, Perceived risk, Organizational Reputation, Attitude, TAM, DeLone & McLean, Adoption, Infrastructure, Nigeria.
Article 232 · pp. 2840-2856
Transformer-Based Model for Named Entity Recognition in Hausa Language Texts
Named Entity Recognition (NER) is a fundamental problem in Natural Language Processing (NLP) that focuses on identifying and classifying textual entities such as people, places, organizations, dates, and numerical expressions. Although transformer-based architectures have greatly improved NER performance across major global languages, there has been little gain for low-resource African languages like Hausa. The lack of annotated corpora, language variety, orthographic irregularities, and limited computational resources continue to impede the development of strong Hausa NLP systems.
This paper presents a transformer-based framework for Named Entity Recognition in Hausa language texts that uses recent deep learning architectures for low-resource language processing. The proposed framework combines Hausa textual data collection, preprocessing, manual annotation, transformer fine-tuning, and model evaluation. Multiple transformer models, such as Multilingual Bidirectional Encoder Representations from Transformers (mBERT), XLM-RoBERTa, and AfriBERTa, are being investigated for their effectiveness in recognizing named entities in Hausa texts sourced from news articles, social media platforms, educational materials, and online repositories.
The study uses entity annotation schemas, standardized NLP preprocessing methods, and performance evaluation metrics like Precision, Recall, Accuracy, and F1-Score in a supervised learning approach. The study intends to provide a transformer-based entity recognition system, a reusable Hausa NER dataset, and empirical insights into deep learning applications for native African languages.
The findings demonstrated the feasibility of transformer architectures in improving Hausa language entity extraction and advancing the broader ecosystem of low-resource language technologies.
🏷 Named Entity Recognition, Hausa Language, Natural Language Processing, Transformers, Deep Learning, Low-Resource Languages, Machine Learning, Artificial Intelligence.
Article 233 · pp. 2857-2870
The Effect of Service Quality and Trust on Customer Satisfaction and Customer Loyalty
This study aims to analyze the effect of service quality and trust on customer satisfaction and customer loyalty at PT Qolbu Amanah Perdana Travel, a travel agent/companies in Banjarmasin. This study is included in quantitative research. The population consists of the company’s customers who have used the company’s services at least three times, therefore the sampling method uses total sampling. The regression analysis uses multiple linear regression. The results of this study can be concluded that service quality has a significant effect on Customer Satisfaction loyalty at travel companies in Banjarmasin. Customer trust has a significant effect on Customer Satisfaction at loyalty at travel companies in Banjarmasin. Service quality has an effect on Customer Loyalty loyalty at travel companies. Customer satisfaction has a significant effect on Customer Loyalty loyalty at travel companies. Furthermore, trust has a significant effect on Customer Loyalty loyalty at travel companies.
🏷 service quality, trust, customer satisfaction, and customer loyalty.
Article 234 · pp. 2871-2881
Impact of E- Accounting Software Adoption on Operational Efficiency of SMEs in Bangladesh
In the modern digital economy, mobile accounting software is revolutionizing SMEs' financial management. This study analyses the impact of mobile accounting software on operational efficiency and evaluates the associated challenges faced by Bangladeshi SMEs. The study employs a qualitative research approach and secondary data from academic literature, industry publications, and supplier documentation on accounting software. The findings indicate that mobile accounting software enhances operational efficiency by minimizing manual accounting tasks and optimizing financial operations. It improves decision-making by providing fast, trustworthy, and accurate financial information. It contributes to cost reduction by lowering administrative expenses and boosting resource utilization. The paper identifies several obstacles and challenges affecting adoption in Bangladeshi SMEs. Despite these challenges, mobile accounting software presents significant opportunities to improve financial management practices and organizational growth.
🏷 E-Accounting, Mobile Accounting, Cloud Accounting, SME, Operational Efficiency, Bangladesh.
Article 235 · pp. 2890-2907
Bio-Cultural Regenerative Food and Beverages System among the Indige-nous Communities in Assam Vis-à-vis South-East Asian Nations
This study investigates the bio-cultural regenerative food and beverage systems of indigenous communities in Assam, comparing them with those of Southeast Asian nations, utilising a secondary, evidence-based meta-synthesis framework. By integrating ethnobotanical, microbiological, and socio-ecological datasets, the research analyses traditional fermentation systems, including Xaj-pani, Jou, Judima, Apong, Tapai, Tapuy, and Look-pang, as dynamic regenerative models rooted in local ecological knowledge systems. The results indicate substantial evolutionary convergence across the Assam–Southeast Asia bio-cultural corridor, particularly in the prevalence of functional microbial phyla, such as Firmicutes, and in the incorporation of wild edible plants into fermentation starter cultures. These systems enhance food security, microbial diversity, nutraceutical health, and community resilience, while maintaining local agrobiodiversity through low-energy traditional biotechnology. Nevertheless, rapid marketisation, monocultural agriculture, environmental change, and the decline of Traditional Ecological Knowledge (TEK) pose significant threats to their persistence. The study contends that indigenous fermentation systems constitute decentralised, regenerative sustainability models with considerable relevance for post-2030 ecological restoration, food sovereignty, and community-driven bioeconomy.
🏷 Bio-cultural regeneration, Indigenous fermentation, Traditional Ecological Knowledge (TEK), Regenerative sus-tainability, Ethnobotany, Microbial diversity, Food sovereignty, Traditional biotechnology
Article 236 · pp. 2908-2940
Climate-Resilient Rural Water Supply Systems in India: Integration of Sustainability, Smart Infrastructure, and Water Security under Jal Jeevan Mission
With the launch of Jal Jeevan Mission (JJM), there is a major shift in the approach of rural water supply schemes in India, from infrastructure to integrated climate-resilient water security solutions. The focus of the traditional water supply projects in rural areas has been on simple infrastructure like hand pumps, bore wells, pipelines and limited distribution networks. But the escalating demands of groundwater, climate variability, water quality degradation and increasing demand for water use in rural areas have led to the development of sustainable, technology-based and environmentally friendly water management systems. This paper critically examines the developing framework of the rural water supply infrastructure in India by incorporating the concepts of engineering systems, recharge of water resources, planning climate resilience, smart water management technologies, development principles based on ESG and circular water economy concepts. The study uses qualitative analytical review, which is based on government documents, technical manuals, policy documents and published literature on Jal Jeevan Mission, Integrated Water Resource Management (IWRM), and sustainable rural infrastructure development. The review emphasizes the need of sustainability of the sources, decentralized governance, smart monitoring systems and recharge structures of the groundwater to guarantee long-term rural water security. The case studies from Gujarat, Telangana, Rajasthan and Maharashtra prove that the use of integrated water infrastructure models is successful in enhancing service reliability and resilience to climate change. The paper also explores implementation issues like over-exploitation of groundwater, lack of operation and maintenance, financial sustainability problems and institutional fragmentation. The study identified key factors that future-ready water systems in rural areas need to incorporate to achieve equitable, resilient rural water security and meet the Sustainable Development Goals (SDGs): engineering infrastructure, environmental sustainability, digital monitoring, climate adaptation, and community participation.
The paper proposes a conceptual performance evaluation framework incorporating measurable indicators related to service reliability, groundwater sustainability, water quality improvement, energy efficiency, governance effectiveness, and community participation. Future research should integrate field-based empirical investigations, quantitative performance assessments, and longitudinal monitoring to strengthen evidence-based rural water infrastructure planning and policy formulation.
🏷 Rural water supply, Jal Jeevan Mission, climate resilience, groundwater recharge, water security, ESG infrastructure
Article 237 · pp. 2941-2951
Functional properties and sensory evaluation of powdered akamu fortified with edible palm weevil (RHYCHOPHORUS PHOENICIS)
This study evaluated the functional properties and sensory acceptability of powdered akamu (fermented maize porridge) fortified with edible palm weevil (Rhynchophorus phoenicis) larvae powder as a potential complementary food. Yellow maize and fresh R. phoenicis larvae were processed into fine powders and blended at varying ratios (100:0, 95:5, 90:10, 85:15, 80:20) to formulate complementary food samples. Functional properties, including bulk density, oil absorption capacity, swelling index, gelatinization temperature and viscosity, were determined, while sensory evaluation assessed appearance, mouthfeel, aroma, taste and overall acceptability using a 9-point hedonic scale by 25 trained panelists. Results showed that fortification significantly influenced functional properties: bulk density decreased from 0.64 g/ml (control) to 0.40 g/ml (80:20 blend), indicating suitability for nutrient-dense complementary foods; oil absorption capacity increased from 2.35 g/ml to 2.92 g/ml, enhancing flavour retention and mouthfeel; swelling index, gelatinization temperature and viscosity were also significantly affected (p < 0.05). Sensory evaluation revealed that higher levels of fortification slightly reduced scores, with overall acceptability ranging from 6.80 (control) to 4.50 (80:20 blend), reflecting familiarity preference for unfortified akamu. The study demonstrates that R. phoenicis larvae can be successfully incorporated into powdered akamu to improve protein content and functional properties, offering a sustainable and nutritionally enriched complementary food option, though sensory optimization may be needed for higher fortification levels.
🏷 powdered akamu, Edible palm weevil, Functional properties, Sensory evaluation, Complementary foods.
Article 238 · pp. 2952-2965
Effect of calcination temperature on biodiesel production from palm kernel oil using a bifunctional catalyst derived from calcium carbide residue (CCR) and anthill clay (AC)
This study aims at investigating the effect of calcination temperature on biodiesel production from palm kernel oil using a bifunctional catalyst derived from calcium carbide residue (CCR) and anthill clay (AC). The specific objectives include to synthesize a ZnO-doped CaO/Anthill clay composite using CCR and anthill clay as precursors, to evaluate the effect of various calcination temperatures (650 °C to 850 °C) on the catalyst’s chemical and structural properties, to identify the optimal temperature that maximizes the density of active sites while preventing structural degradation, and to test the catalyst's effectiveness by converting palm kernel oil (PKO) into biodiesel and evaluating the final yield. The CCR and AC precursors were prepared and characterized using X-ray diffraction, scanning electron microscopy, Fourier Transform Infrared spectroscopy, X-ray fluorescence, and Brunauer-Emmett-Teller. Composites of the prepared CCR and AC were formulated in ratios 1:4, 2:3, 1:1, 3:2, and 4:1 of CCR:AC and doped with 1.5 M zinc nitrate, by wet impregnation and calcined at 3 different temperatures (650 oC, 750 oC, and 850 oC), resulting in 15 samples (A1 – E3) which were used for the production. Ratio 2:3 catalyst (D2) calcined at 750 oC gave the highest yield (92.73%) and reusability studies gave an overall reduction of 27.69 % in yield across 6 cycles. Its XRF analysis gave compositions of 52.1% CAO, 10.4% SiO2, and 7.4% Al2O3. Its surface area, pore diameter and pore volume were 226.9m2/g, 2.9nm and 0.2cc/g respectively. The PKO characterization results showed the PKO’s suitability for the biodiesel production. The produced biodiesel properties aligned with ASTM D6571 and EN 14214 standards upon characterization. This study successfully synthesized a high-performance ZnO-doped CaO/Anthill clay composite from sustainable materials, identifying calcination temperature as the key factor influencing catalyst activation and structural stability and also contributes to the promotion of renewable and sustainable energy.
🏷 Palm kernel oil, Calcium Carbide Residue, Anthill clay, Calcination Temperature, Biodiesel
Article 239 · pp. 2966-2977
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 240 · pp. 2978-2987
Urbanization and Flood Risk in Nairobi: A Historical Analysis of Settlement Patterns and Disruption of Riparian Systems
Urban flooding has become a persistent challenge in rapidly growing cities, particularly in developing countries. Before urbanization and even before independence, Nairobi was defined by a dense network of rivers, streams, springs and wetlands that shaped its natural landscape. These natural systems facilitated infiltration, regulated water flow and minimized surface runoff, thereby making the early Nairobi landscape resilient to flooding. This study aimed to examine how urbanization has influenced flood risk in Nairobi through a historical analysis of settlement patterns and the disruption of riparian systems. This study employed a desk-based literature synthesis methodology utilizing secondary data analysis to examine the relationship between urbanization, riparian encroachment and flood risk in Nairobi's river corridors.Pre-1963 Nairobi maintained a highly efficient natural hydrological system comprising three primary perennial rivers, the Nairobi River (main stem), Ngong River and Mathare River. Landsat time-series analysis revealing built-up areas expanded 372% from 4.6% to 21.7% of riparian land coverage between 1992 and 2022. Vegetation cover declined 77% from 8.4% to 1.9% while water surfaces contracted 8.1% during the same period demonstrating clear causal progression from historical floodplain attenuation capacity through urbanization drivers to settlement encroachment, riparian disruption and flood amplification. Nairobi's recurrent urban flooding results directly from systematic riparian encroachment spanning 1963-2025.
🏷 Urbanization, Urban Flooding, Riparian Systems
Article 241 · pp. 2988-2994
Toxicity of Decamethrin on haematology profile of climbing perch Anabas testudineus (Bloch.)
The present investigation was undertaken to study the chronic toxic effects of a sublethal concentration (0.08 ppm) of decamethrin on the haematological profile of Anabas testudineus over an exposure period of 20 days. The treated fish exhibited several behavioural responses, including loss of natural coloration, turning almost reddish in colour, loss of equilibrium, and jerky movements before death.
The treated group also showed significant alterations in the haematological profile, with decreases observed in haemoglobin (Hb), red blood cell (RBC), and white blood cell (WBC) counts. In contrast, differential leukocyte count (DLC) parameters such as neutrophils, monocytes, and esinophils showed increased values.
These findings indicate that decamethrin exerts adverse chronic toxic effects on fish health. Therefore, it is suggested that maintaining decamethrin concentration below 0.08 mg/L in water is more suitable for the culture of Anabas testudineus to ensure optimum growth performance and survival rate compared to higher concentrations.
🏷 Anabas testudineus, decamethrin, insecticide, haematology profile, pyrethroid.
Article 242 · pp. 2995-3013
Application of Cost Engineering as a Tool For Budgetary Forecasting and Control in Municipal Public Works: (Case Study, Municipal Administration of Cacuaco 2023-2026)
This research analyzes the application of Cost Engineering as a tool for forecasting and budgetary control in municipal public works, using the Municipal Administration of Cacuaco as a case study between 2023 and 2026. The study aimed to understand how Cost Engineering contributes to financial efficiency, mitigation of budget deviations, and improvement of transparency in municipal public management. The research is framed within the pragmatic paradigm, adopting a mixed-methods approach (qualitative and quantitative) with descriptive and explanatory characteristics. Data collection was conducted through document analysis, semi-structured interviews, and participatory observation, involving a sample of 30 participants composed of managers, financial technicians, and officials responsible for planning and executing public works. Quantitative data were analyzed using descriptive statistics, while qualitative data were treated through thematic content analysis. The findings reveal that 90% of participants acknowledge the application of Cost Engineering within the Municipal Administration of Cacuaco, while 97% consider this tool relevant for forecasting and budgetary control in public works. The main benefits identified include financial risk mitigation, accurate cost estimation, improved budget planning, and reinforcement of administrative efficiency. The results further demonstrate that Cost Engineering contributes to reducing waste, improving decision-making, and strengthening accountability in municipal management. The study concludes that Cost Engineering is a strategic instrument for the modernization of municipal public administration, promoting better resource allocation, financial sustainability, and transparency in public works execution. However, challenges remain regarding technical staff training, digitalization of control systems, and consolidation of institutional monitoring practices.
🏷 Cost Engineering, budget control, public works, municipal management, Cacuaco
Article 243 · pp. 3014-3026
Penetration Testing for Websites using LLM
Despite growing interest in applying Large Lan-guage Models (LLMs) to security assessment, existing prototype systems rarely provide controlled benchmarks, reproducible ar-tifacts, significance testing, or structured human evaluation. We introduce a reproducible, safety-bounded LLM-augmented web assessment framework that layers an LLM reasoning module atop conventional scanners to perform context-aware triage, con-strained payload proposal, evidence-grounded response analysis, false-alarm suppression, and actionable fix guidance. Empirical evaluation across WebGoat v2023.4, DVWA v1.9, bWAPP v2.2, and five authorized staging replicas of disclosed targets — spanning 1,247 endpoints and 462 labeled ground-truth flaws shows that the proposed approach raises micro-averaged F1 from 0.58 to 0.82 over scanner-only baselines, lowers the false-alarm rate from 23.4% to 7.2% (McNemar p<0.001), and shortens analyst review time by 43%, from 47 to 27 minutes per engagement (Wilcoxon p=0.008). Over 2,500 test executions, the safety layer blocked every destructive candidate payload with zero unsafe executions. A structured 10-person evaluation with five security practitioners and five application developers con-firmed statistically significant improvements in comprehension speed, report clarity, and remediation actionability (p<0.01).
🏷 Large Language Models, web penetration test-ing, vulnerability assessment, OWASP, CWE, reproducibility, statistical evaluation
Article 244 · pp. 3027-3036
Economic Implications of the USA–Iran Conflict on India: Evidence from Oil Price Shocks, Trade Dynamics and Financial Market Responses
The conflict between the United States and Iran has become one of the most important geopolitical issues influencing the global economy. Due to its strategic location in the Middle East and its impact on international energy markets, any escalation in tensions between these two countries has far-reaching economic consequences for both developed and developing nations. India, being one of the world's largest importers of crude oil and a rapidly growing economy integrated with global trade and financial systems, is particularly sensitive to such geopolitical developments.
This study examines the economic implications of the USA–Iran conflict on India, with special emphasis on energy security, trade balance, inflation, exchange rate movements, and financial market performance. The research is based on secondary data collected from various sources, including reports published by the Reserve Bank of India (RBI), the Ministry of Petroleum and Natural Gas, the International Monetary Fund (IMF), the World Bank, and the Ministry of Commerce and Industry. The study analyzes how disruptions in oil supply, sanctions, and uncertainties in the Middle East influence India's macroeconomic environment.
The findings indicate that geopolitical tensions between the United States and Iran significantly affect global crude oil prices, leading to an increase in India's import bill and putting pressure on the country's trade balance and current account position. Rising fuel prices contribute to inflationary pressures, while increased demand for foreign exchange affects the value of the Indian rupee. The study also finds that periods of heightened conflict create uncertainty in financial markets, resulting in fluctuations in stock prices and investor sentiment.
The paper concludes that although India has taken several measures to strengthen its economic resilience, including the development of strategic petroleum reserves and diversification of energy sources, sustained geopolitical instability in the region continues to pose significant risks. Therefore, greater emphasis on energy diversification, renewable energy development, and strategic economic planning is essential to reduce India's vulnerability to external shocks.
🏷 USA–Iran Conflict, Indian Economy, Crude Oil Prices, Inflation, Trade Balance, Financial Markets, Energy Security.
Article 245 · pp. 3037-3047
Pharmacological Evidence for Traditional Antispasmodic Herbs in Siddha Medicine: A Comprehensive Review
Background: Traditional Siddha medicine has utilized antispasmodic herbs for centuries in Sri Lanka yet lacks comprehensive scientific validation. This gap between clinical efficacy and evidence-based documentation impedes acceptance within modern healthcare systems.
Objective: This systematic review synthesizes ethnobotanical evidence from classical Siddha texts with contemporary pharmacological research to establish the scientific basis for ten commonly prescribed antispasmodic herbs.
Methods: A cross-disciplinary approach integrated traditional Siddha Materia Medica classifications with peer-reviewed pharmacological studies employing ex-vivo tissue assays and in-vivo animal models using extraction methods congruent with traditional preparations.
Results: Ten herbs Justicia adhatoda, Ruta angustifolia, Datura species (D. niger, D. metel, D. stramonium, D. innoxia), Hyoscyamus niger, Trachyspermum ammi, Papaver somniferum, Ferula asafoetida, Syzygium aromaticum, Moringa oleifera, and Sphaeranthus indicus demonstrated antispasmodic activity through multiple mechanisms: calcium channel blockade, muscarinic receptor antagonism, and direct smooth muscle relaxation. Trachyspermum ammi, Justicia adhatoda, and Datura species exhibited high-quality experimental evidence with activity comparable to standard pharmaceuticals. Others demonstrated moderate pharmacological support or traditional ethnobotanical validation. Critical safety contraindications identified include narrow therapeutic windows for tropane alkaloid-containing species, cardiovascular risks with Cannabis sativa, and haemostatic interactions requiring perioperative management.
Conclusion: Substantial alignment exists between traditional Siddha practice and modern experimental validation. This evidence supports integration of standardized antispasmodic herbal extracts into evidence-based healthcare, with priority given to clinical safety profiles, quality standardization, and investigation of synergistic multi-herb formulations characteristic of Siddha pharmacology.
🏷 Antispasmodic; Siddha; Justicia adhatoda; Trachyspermum ammi; Datura species; Calcium channel blockade; Muscarinic receptor antagonism
Article 246 · pp. 3048-3051
Election Jitters and Indian Stock Market Performance-A conceptual study
The stock market is highly sensitive to political developments, particularly elections, which create uncertainty regarding future economic policies and governance. In emerging economies like India, elections significantly influence investor sentiment, market volatility, and sectoral performance. This paper examines the impact of election jitters on Indian stock market performance with special reference to the 2014, 2019, and 2024 general elections. The study highlights how political uncertainty, investor psychology, and expectations regarding policy continuity shape stock market trends. The paper also reviews previous literature on election-induced volatility and identifies research gaps in understanding the long-term effects of political events on financial markets. The findings suggest that stable and reform-oriented governments generally improve investor confidence and market performance, whereas uncertain mandates increase volatility and risk aversion. The study provides insights for investors, policymakers, and researchers regarding the relationship between political events and stock market behavior.
🏷 Election Jitters, Stock Market Volatility, Political Uncertainty, Investor Sentiment, Nifty 50, Sensex, Indian Elections.
Article 247 · pp. 3052-3060
India's Economic Resilience Amid Global Geopolitical Uncertainties: Structural Foundations, Policy Responses, and the Road Ahead
This paper examines the structural and policy determinants of India's macroeconomic resilience during the period 2024–2026, a phase characterised by acute global geopolitical stress, including escalating West Asian conflict, US trade tariff imposition, and sustained post-pandemic supply chain fragmentation. Drawing on data from the Reserve Bank of India (RBI) Annual Report 2025–26, the Economic Survey 2025–26, IMF World Economic Outlook projections, and UN WESP 2026, the study identifies three interacting pillars of resilience: (i) robust domestic demand anchored by rising rural consumption and public capital expenditure; (ii) structural diversification of manufacturing through policy-driven frameworks such as the Production-Linked Incentive (PLI) scheme; and (iii) strengthened external buffers comprising record foreign exchange reserves, diversified foreign direct investment inflows, and expanded trade partnerships. India recorded GDP growth of 7.6% in FY2025–26 — up from 7.1% the prior year — maintaining its position as the world's fastest-growing major economy with an average annual growth of 7.8% across the five-year period 2021–2026. The paper further analyses key structural vulnerabilities — including currency depreciation, shallow manufacturing depth, and limited global value chain integration — and evaluates the policy recalibrations required to sustain and deepen this growth trajectory. The findings have implications for the broader literature on economic resilience in large developing economies navigating an era of geopolitical multipolarity.
🏷 Economic resilience, India, geopolitical uncertainty, GDP growth, Production-Linked Incentive, trade diversification, foreign direct investment, macroeconomic stability
Article 248 · pp. 3061-3067
Leachate Characterization, Pollution Load Assessment, and Waste Management Infrastructure Policy for an Unregulated Open Dumpsite in a Tropical Peri-Urban Environment: Evidence from Benin City, Nigeria
The leachate resulting from improperly managed and unregulated open dumpsites acts as a proximate and immediate link between waste accumulation and resultant groundwater and soil contamination; however, it has received relatively little attention in terms of analytical studies within the Nigerian environmental literature compared to the resultant endpoints of contamination. This study seeks to provide a comprehensive characterization and pollution load assessment of the leachate from the Upper Ekehuan/Asoro open dumpsite in Ovia North-East LGA, Edo State, Nigeria, with a focus on two leachate samples obtained from active zones of seepage within the site, employing the Pollution Load Index and Nemerow Pollution Index models. The results showed that the leachate was of extreme pollution potential with Iron concentrations of 66.65–103.85 mg/L or 67–104 times over the WHO limits; Nickel concentrations of 4.73–7.37 mg/L or 237–369 times over the WHO limits; Cadmium concentrations of 0.87–0.93 mg/L or 174–186 times over the WHO limits; and Hexavalent Chromium concentrations of 1.98–3.08 mg/L or 40–62 times over the WHO limits. The PLI value ranged from 62.4 to 94.7, indicating that both leachate samples fall within the 'Extremely Polluted' category. Comparatively, this study found that the leachate from this site compares favorably with the most polluted leachate from landfill sites across Africa. A conceptual model of a Source-Pathway-Receptor relationship was employed to illustrate the pathway of contamination resulting from the leachate. This study concludes that no investment in soil and groundwater remediation will be effective if the pathway of leachate contamination and infiltration is not first addressed and halted. This study also offers policy recommendations to NESREA, the Federal Ministry of Environment, and the SWMA of Edo State.
🏷 leachate characterization, pollution load index, Nemerow pollution index, source-pathway-receptor model, waste management hierarchy, dumpsite remediation, open dumpsite, Nigeria
Article 249 · pp. 3068-3076
Hydrogeochemical Suitability of Groundwater for Irrigation and Agriculture: A Sodium Adsorption Ratio, Kelley Index and Irrigation Water Quality Index Analysis of Sedimentary and Basement Aquifers in Southwestern Nigeria
Agricultural water resource management in sub-Saharan Africa increasingly relies on groundwater resources that have seldom been hydrogeochemically characterized for their suitability in irrigation water supply. In Sobe community in Edo State and Elegbeka community in Ondo State, southwestern Nigeria, groundwater from hand-dug well water supplies both domestic and agricultural water uses. However, their suitability for irrigation water supply has never been evaluated with hydrogeochemical indices of irrigation water quality. This study evaluated the Sodium Adsorption Ratio (SAR), Kelley Index (KI), Permeability Index (PI), Magnesium Hazard (MH), and a composite Irrigation Water Quality Index (IWQI) on groundwater from 25 sampling points across two geological terrains: Imo shale geological series and Basement Complex migmatite. The results showed that although concentrations of potassium ion are 10 to 18 times higher than permissible limits for domestic water supply by NAFDAC, they are a major agronomic benefit to low-fertility soils. This poses a domestic/agricultural water trade-off with policy implications. Although SAR values classify all groundwater from these two study areas as low to medium salinity hazard, chloride and sulphate ion concentrations in some of the water samples from Elegbeka community result in a high salinity hazard classification for water from Wells 6 and 10. Magnesium ion concentrations greater than 50% classify water from these two wells in Elegbeka community as unfit for irrigation water supply. From the Kelley Index calculation results, 79% of Sobe community water wells are suitable for irrigation water supply compared to 60% of water wells in Elegbeka community. The hardness-dominated water chemistry of water from Elegbeka community poses a precipitation hazard to soil permeability in fine-textured soil plots. This study provides a first-time characterization of water suitability for these two communities and can be a model for promoting safe water use for agriculture and protecting food security.
🏷 irrigation water quality; sodium adsorption ratio; Kelley index; magnesium hazard; Hydrogeochemistry; Nigeria; food security; groundwater agriculture
Article 250 · pp. 3077-3089
Unified AI-Based SaaS Platform Delivering Comprehensive Integrated Services
In the modern digital era, users frequently rely on different applications for tasks such as content writing, plagiarism detection, text summarization, interview preparation, and image editing. Managing multiple platforms for these activities often interrupts workflow, increases effort, and affects overall productivity. To simplify this process, this paper introduces Quick.ai, an AI-powered Software-as-a-Service (SaaS) platform that combines several intelligent tools into a single web application. The platform is developed using the PERN stack, which includes PostgreSQL, Express.js, React.js, and Node.js, allowing the system to remain scalable, responsive, and efficient. Quick.ai provides features such as Post Creator, Prompt Generator, Text Summarizer, Plagiarism Checker, Interview Question Generator, Repurpose Engine, Background Removal, and Object Removal. Testing and evaluation of the platform showed improved workflow management and stable performance across all modules. The system achieved a content quality score of 4.3 out of 5, text summarization accuracy of 87%, and image processing accuracy ranging from 70% to 80%.
🏷 Artificial Intelligence, SaaS Platform, PERN Stack, Text Summarization, Image Processing, Content Generation
Article 251 · pp. 3090-3099
AI-Powered SQL Bug Analyzer and Auto Fix Assistant
This work describes the development of a system based on AI and chat technology that allows users to communicate with several databases by posing questions in natural language. Standard databases require knowledge of SQL and technical skills from their users; therefore, they are difficult to use for individuals who lack expertise in computer science and programming. In this work, we incorporate MERN stack technology and integrate AI technology within it to translate user commands into valid queries for databases. Our proposed system is capable of processing queries and communicating with different types of databases, including MongoDB, MySQL, SQLite, and BigQuery..
🏷 Artificial Intelligence,Text-to-SQL, SQLFixAgent, Large Language Models, SQL error correction, Multi- agent framework, Semantic accuracy
Article 252 · pp. 3100-3110
Remote, Hybrid, or In-Office? Comparing Productivity and Well-being Outcomes Across Work Arrangements in India's IT Industry
Most research on remote work treats it as a binary choice -- either you work from home or you do not. But India's IT workforce in 2026 operates across three meaningfully different arrangements: fully remote, hybrid, and traditional in-office. This paper compares all three simultaneously, examining their effects on employee productivity and well-being, with work-life balance (WLB) as a mediating variable. We surveyed 378 IT professionals across Bengaluru, Hyderabad, Pune, and Chennai, grounding the study in Job Demands-Resources (JD-R) theory and Boundary Theory. Structural equation modeling (CFI = .961, RMSEA = .046) showed that both remote and hybrid workers reported significantly higher productivity than their in-office colleagues (standardised beta = +0.38 and +0.29 respectively, p < .001). The well-being picture was more complicated. Hybrid workers gained more on that dimension (beta = +0.26, p < .01) than fully remote workers (beta = +0.21, p < .05), and we think this has a lot to do with technostress -- remote workers reported the highest technostress scores in the sample (M = 3.58), which appeared to partly offset the gains from avoiding the daily commute. Hybrid workers also reported the strongest work-life balance overall (M = 3.47, versus 3.29 for remote and 3.01 for in-office). WLB mediated outcomes meaningfully in both the remote and hybrid groups. Taken together, the findings suggest that hybrid work -- rather than full remote -- may be the more sustainable option for IT professionals trying to maintain both performance and personal wellbeing over time. We close by discussing what this means for managers navigating return-to-office decisions, and where future research needs to go.
🏷 comparative work arrangements, employee productivity, employee well-being, hybrid work, India, IT industry, Job Demands-Resources theory, remote work
Article 253 · pp. 3111-3127
Comparative Analysis of Organizational DNA Dimensions and Their Influence on Social Sustainability Performance: Evidence from the Egyptian Steel Industry
The growing emphasis on corporate sustainability has intensified the need to understand the internal organizational mechanisms that drive social sustainability outcomes. While previous studies have examined Organizational DNA as a holistic construct, limited attention has been devoted to investigating the relative influence of its individual dimensions on Social Sustainability Performance (SSP). This study addresses this gap by examining and comparing the effects of four Organizational DNA dimensions—Decision Rights, Information, Motivators, and Organizational Structure—within the context of the Egyptian private steel industry.
Using data collected from 553 employees across major private steel companies in Egypt, the study employs Structural Equation Modeling (PLS-SEM) to evaluate the direct and indirect effects of Organizational DNA dimensions on SSP. Green Training is incorporated as a complementary organizational mechanism to better understand how internal organizational capabilities are translated into sustainability outcomes.
The findings reveal substantial differences among Organizational DNA dimensions. Organizational Structure emerged as the strongest predictor of Social Sustainability Performance, followed by Motivators and Decision Rights. In contrast, Information demonstrated no significant direct effect on SSP but exhibited a significant indirect effect through Green Training. The results suggest that sustainability performance is influenced not only by organizational resources and policies but also by the configuration of internal organizational systems that enable employee engagement, knowledge application, and sustainability-oriented behaviors.
This study contributes to organizational behavior and sustainability literature by providing a comparative ranking of Organizational DNA dimensions and identifying the most influential organizational levers for enhancing social sustainability performance in industrial organizations. The findings offer practical guidance for managers and policymakers seeking to accelerate sustainability transformation through organizational design and human resource development initiatives.
🏷 Organizational DNA; Social Sustainability Performance; Green Training; Organizational Structure; Sustainability Management; Steel Industry; Egypt
Article 254 · pp. 3128-3133
SualPoint: A Sual Task Monitoring System
The Department of Interior and Local Government of the Municipality of Sual monitors and supervises its activities and tasks manually, but sometimes the reliance on manual processes leads to inefficiencies and delays in completing tasks. Hence, to resolve this issue, we designed and developed a web and mobile-based task monitoring system named SualPoint – A Sual Task Monitoring System. It has been created by making use of the best technologies related to the World Wide Web and mobile platforms to support task assignment, task management, and task reporting in real-time. The Agile methodology was adopted to support iterative development, continuous stakeholder feedback, and user-centered design. The tools used to develop this web application were HTML, Laravel, CSS, JavaScript, Tailwind CSS, PHP, MySQL, Flutter, XAMPP, and Visual Code. These features are provided in a way that the system allows user authentication, task creation, task assignment, progress monitoring, sending notifications automatically, and generating reports. These features were evaluated under the ISO/IEC 25010 standard for software quality, which involved functionality, usability, reliability, efficiency, and security. Evaluators of the features included DILG personnel and information technology experts. The results showed that the developed system is helpful in improving the monitoring of tasks, reducing manual record maintenance, improving personnel communications, and increasing transparency in the management of tasks. In general, the developed system was rated very well in terms of its acceptability and appropriateness for use in the DILG office.
🏷 SualPoint, Web and Mobile-Based, DILG, ISO/IEC 25010.
Article 255 · pp. 3134-3142
Impact of Biodiesel on Environment and Health: A Comprehensive Analysis
The change have spurred a worldwide movement toward sustainable energy sources, of which biodiesel is increasingly important for transportation. Including the whole lifetime from feedstock development through manufacturing to end-use consumption, this thorough research study offers a complete review of biodiesel's many effects on environmental systems and human health. By means of thorough analysis of production techniques, emission characteristics, and health impact assessments, this study synthesizes results from several fields to offer a complete knowledge of biodiesel's contribution in sustainable development. The study combines information from many geographical areas and climate zones' worth of field research, laboratory experiments, and epidemiological investigations. Depending on feedstock choice and production techniques, our results show notable correlations between biodiesel usage and lowered greenhouse gas emissions; observed reductions range from 50% to 80% compared to traditional diesel fuels. Presenting fresh insights on the intricate relationships between biodiesel generating systems and local ecosystems, the study also addresses important questions about land use changes, water resource management, and food security consequences. Moreover, this study investigates the public health effects of switching to biodiesel by means of thorough air quality assessments and population exposure studies, therefore examining both direct and indirect health impacts. The study ends with evidence-based suggestions for production techniques and regulatory frameworks that maximize environmental benefits while lowering any negative consequences on ecosystem stability and human health.
🏷 Biodiesel Production Systems, Environmental Impact Assessment, Public Health Implications, Emission Characterization
Article 256 · pp. 3143-3148
Service Marketing Strategies in Multi-Specialty Hospitals
The modern healthcare sector is experiencing hyper-competition driven by rapid technological advancements, evolving patient expectations, and the aggressive expansion of private corporate hospital networks. To maintain market viability and institutional growth, multi-specialty hospitals are shifting from traditional operational methodologies to strategic, patient-centric service marketing frameworks.
This paper evaluates the implementation of service marketing strategies in multi-specialty healthcare settings, focusing on the extended 7Ps marketing mix, digital marketing adoption, patient relationship management (PRM), service quality (SERVQUAL) parameters, and technology-driven healthcare delivery.
Utilising a descriptive and analytical research design based on contemporary secondary data spanning up to 2026, the study reveals that an integrated approach combining digital transformation with physical service optimization significantly enhances patient satisfaction, brand equity, and institutional sustainability. The paper concludes that continuous process engineering and ethical, transparent marketing practices are vital for securing a sustainable competitive advantage.
🏷 Service Marketing, Multi-Specialty Hospitals, Patient Satisfaction, Healthcare Marketing, 7Ps Marketing Mix, Hospital Branding, Digital Transformation.
Article 257 · pp. 3149-3160
Digital Fashion Photography and Generative AI: Understanding Student Perceptions, Learning Experiences, and Creative Development at NIFT Bengaluru
This qualitative research study investigates how students at the National Institute of Fashion Technology (NIFT), Bengaluru engage with Generative Artificial Intelligence (AI) in the context of digital fashion photography education. As AI tools such as Midjourney, DALL-E 3, Adobe Firefly, and Stable Diffusion are increasingly integrated into fashion media learning environments, critical questions arise around how students perceive these technologies, how they influence creative development, and what pedagogical and ethical tensions emerge in the classroom.
The study employed a qualitative research design, drawing on in-depth semi-structured interviews and open-ended questionnaires administered to a purposively selected sample of 42 students enrolled in undergraduate and postgraduate fashion communication and photography programmes at NIFT Bengaluru. Data were collected over a ten-week period (February–April 2026) and analysed using thematic analysis guided by Braun and Clarke’s (2006) six-phase framework.
Four principal themes emerged from the data: (i) AI as a creative scaffold—students described generative AI primarily as a tool for concept visualisation and ideation rather than final output generation; (ii) tension between technological enthusiasm and creative authenticity—many participants expressed ambivalence about AI’s impact on their identity as photographers; (iii) pedagogical gaps in AI literacy—students reported a lack of structured institutional guidance on the ethical and technical dimensions of AI use; and (iv) cultural and representational concerns—participants frequently highlighted the inadequacy of AI-generated imagery in reflecting South Indian and broader Indian aesthetic identities.
The findings underscore the need for AI-integrated, critically reflective fashion photography curricula at NIFT Bengaluru, and contribute original qualitative evidence to the growing scholarly discourse on AI-mediated creativity in fashion education.
🏷 Generative AI, Digital Fashion Photography, Fashion Education, ,Student Perceptions, Creative Development, AI Literacy, Fashion Communication, Visual Aesthetics
Article 258 · pp. 3161-3177
Topological and Fractal Modelling of Nagara Temple Architecture: A Comparative Mathematical Study of Kandariya Mahadeva and Konark Sun Temples
North Indian Nagara temple architecture exhibits remarkable geometric sophistication through recursive tower clustering, curvilinear verticality, radial organization, axial alignment, cyclic ordering, and hierarchical spatial planning. While these temples have been extensively studied from historical, archaeological, iconographic, and cultural perspectives, their mathematical interpretation using topology, graph theory, and fractal geometry remains comparatively underexplored.
This study proposes a reproducible mathematical framework for analysing selected architectural features of Nagara temple architecture using graph-theoretic abstraction, normalized connectivity measures, fractal-inspired modelling, symmetry analysis, and spatial topology. Architectural variables including shikhara clustering, spatial connectivity, and radial organization were identified from published architectural documentation and encoded into measurable parameters. The framework is applied to two representative North Indian temples: Kandariya Mahadeva Temple, Khajuraho, and Konark Sun Temple, Odisha. Kandariya Mahadeva Temple is analysed as an example of recursive vertical clustering, whereas Konark Sun Temple is examined for its radial geometry, cyclic ordering, and astronomical orientation.
A composite parameter termed the Nagara Temple Geometric Index (NTGI) is introduced to integrate recursive geometry, spatial topology, and radial symmetry within a normalized comparative framework. The resulting values are intended as comparative mathematical indicators derived from defined architectural features rather than measures of cultural, artistic, or spiritual significance.
The proposed framework contributes to interdisciplinary research linking mathematics, architecture, computational heritage studies, and Indian Knowledge Systems. A validation pathway based on architectural plans, photogrammetry, GIS analysis, image processing, and computational feature extraction is also outlined to support future refinement and empirical verification of the model.
🏷 Nagara Temple Architecture; Fractal Geometry; Graph Theory; Topology; Sacred Geometry; Radial Symmetry; Computational Heritage; Indian Knowledge Systems.
Article 259 · pp. 3178-3187
TeleRehab AI: An AI-Powered Mobile Physiotherapy Assistant
This paper introduces TeleRehab AI, an intelligent mobile-based physiotherapy system designed to assist patients in performing rehabilitation exercises independently. The system combines conversational AI with real-time pose analysis to deliver personalized and interactive guidance. It integrates four major components: a fine-tuned LLaMA 3.2 3B language model for physiotherapy consultation, a structured multi-layer message processing pipeline, an on-device pose estimation module using Google ML Kit BlazePose, and a lightweight exercise classification model deployed via TensorFlow Lite. The system evaluates squat performance using biomechanical parameters such as knee angle, torso inclination, and movement depth. A Support Vector Machine model achieves an accuracy of 84.8%, and is further compressed into a compact neural network using model distillation, achieving 99.7% agreement while maintaining a model size of only 4.9 KB. Unlike traditional solutions, the proposed system operates directly on mobile devices without continuous internet access, making it suitable for large-scale deployment in resource-constrained environments.
🏷 Telerehabilitation, pose estimation, LLM fine-tuning, on-device inference, TFLite, exercise classification, physiotherapy AI, mobile health, biomechanical analysis
Article 260 · pp. 3188-3199
Reframing Hospital Digital Marketing: Moving from Search Intent to Consultation Intent
Hospital digital marketing is frequently evaluated using visibility-oriented indicators, including clicks, reach, and impressions; however, these metrics provide limited explanatory power regarding patient decision-making, consultation uptake, treatment conversion, and organisational performance. This narrative review reconceptualises hospital digital marketing as a strategic clinical intelligence system that links digital behaviour to consultation intent, service utilisation, patient retention, and institutional reputation. Guided by narrative review reporting standards, the study synthesised literature from healthcare management, digital marketing, digital health, patient experience, health communication, and behavioural science to examine how online search behaviour is shaped by uncertainty, perceived risk, affordability, family influence, and reassurance-seeking. The proposed framework differentiates search intent from consultation intent and identifies multiple frontline touchpoints, including the consultation room, call centre, counselling desk, nursing station, and billing counter, as sources of actionable intelligence for detecting hesitation, dropout, and trust breakdowns. The review further advances a multi-level KPI architecture and balanced scorecard that connect awareness, conversion, trust, and business outcomes more analytically than campaign metrics alone. Overall, hospital digital marketing should be understood as an integrated management function rather than a promotional activity. When aligned with patient experience and operational design, it can strengthen consultation pathways, improve trust formation, and support strategic performance.
🏷 Hospital Digital Marketing; Patient Journey; Consultation Intent; Trust; Behavioural Economics; Digital Health; Hospital Management; Service-Dominant Logic; Conversion Science; Healthcare Strategy.
Article 261 · pp. 3200-3203
A Study on Digital Competency Development among Life Insurance Advisors for Enhancing Sales Performance
The rapid growth of digital technologies has transformed the insurance industry, particularly in the field of life insurance advisory services. Life insurance advisors are increasingly required to adopt digital tools and develop technological competencies to remain competitive and improve sales performance. This study examines the role of digital competency development among life insurance advisors and its impact on sales enhancement. The research focuses on identifying essential digital skills, technology adoption practices, and training mechanisms that contribute to advisor efficiency and customer engagement. The study adopts a quantitative research approach using structured questionnaires distributed among life insurance advisors. The findings are expected to reveal that digital competency significantly influences customer relationship management, communication effectiveness, lead generation, and sales productivity. The study also highlights the importance of continuous digital training and organizational support in improving advisor performance. The research contributes to the existing literature by providing practical insights for insurance companies to strengthen digital capabilities among advisors for sustainable business growth.
🏷 Digital Competency, Life Insurance Advisors, Sales Performance, Technology Adoption, Insurance Industry, Digital Transformation.
Article 262 · pp. 3204-3223
The Application of Electrocoagulation in Textile Wastewater Treatment- A Scoping Review of Recent Trends (2021-2025)
Nowadays, electrochemical wastewater treatment methods are receiving increasing interest among researchers to overcome the limitations of conventional chemical and biological treatment methods. Electrocoagulation (EC) is one of the methods that generates in-situ coagulants via electrically oxidizing a sacrificial anode. Through a scoping review, this study aims to identify and analyse the state-of-the-art in EC application for the treatment of synthetic textile dye effluents. This study was conducted following the PRISMA protocol on the Scopus database from 2021 to 2025. This work mapped the research trend on EC, its application mechanism, and advancements for improved efficiency. It further investigated the modelling techniques and hybrid technologies integrated with electrocoagulation and identifies the research gap from the literature. The results show a steep increase in publications over the years, with most originating in Asia, demonstrating to be effective in treating a wide range of dyes. Several factors directly controls the electrochemical reactions and some indirectly affects the performance. Overall, while EC treatment demonstrates strong performance at lab scale, most literature reviewed in this study were conducted under controlled conditions with limited evidence for industrial scale feasibility for long-term and continuous flow management. For optimization, RSM, with Central Composite, and Box-Behnken Designs, is the most widely used modelling technique, and integration of advanced techniques such as ozonation, the Fenton process, catalyst addition, and sonication shows the potential to achieve enhanced wastewater treatment efficiency. Further integration of AI shows future direction towards enhance process control in EC.
🏷 Electrocoagulation; Scoping review; Textile dyes; Textile wastewater; Wastewater treatment
Article 263 · pp. 3224-3236
Acadlib Tracker: Development of a Python-Based Student Gradebook, GPA Tracking, and Library Management System using Object-Oriented Programming
Managing student grades, GPA calculations, and library records in academic institutions is often done manually, leading to errors, inefficiencies, and delayed reporting. This study presents AcadLib Tracker, a Python-based system that integrates student gradebook management, GPA computation, and library management into a unified platform using object-oriented programming (OOP).
The system allows teachers and administrators to input and update student grades, automatically compute GPA, monitor library book availability, and track borrowing and return activities. Developed using Python and OOP principles, with SQLite for database management and a graphical interface for ease of use, AcadLib Tracker ensures accurate record-keeping, reduces manual workload, and provides quick access to academic and library data.
Functional testing demonstrated that the system successfully performs its intended operations, including grade entry, GPA calculation, library transaction management, and report generation. Overall, the system provides a reliable and user-friendly solution for academic record management and library monitoring in educational settings.
🏷 AcadLib Tracker, Student Gradebook, GPA Tracking, Library Management System, Python, Object-Oriented Programming, SQLite
Article 264 · pp. 3237-3250
A Multi-Theoretical Framework for Corporate Social Responsibility and Corporate Financial Sustainability: Towards an Integrated Conceptual Model
The relationship between corporate social responsibility (CSR) and corporate financial sustainability (CFS) remains theoretically fragmented. This paper addresses that gap by proposing a multi-theoretical conceptual framework integrating stakeholder theory, agency theory, the resource-based view, signaling theory, and institutional theory to explain how five CSR dimensions affect CFS through specified mediating and moderating pathways. Drawing on a critical synthesis of seminal theoretical works and contemporary empirical literature, the framework encompasses one dependent variable (CFS), five independent variables (environmental CSR, social CSR, governance quality, CSR disclosure transparency, and CSR strategic integration), three mediators (corporate reputation, cost of equity capital, and stakeholder trust), three moderators (board independence, institutional ownership, and national regulatory environment), and four control variables. Eleven falsifiable propositions are advanced. The CSR–CFS relationship operates through distinct, theoretically grounded pathways: corporate reputation and stakeholder trust transmit value-creation effects, while reduced cost of equity capital constitutes the capital-market channel. Board independence, institutional ownership, and regulatory stringency function as amplifying boundary conditions. As a conceptual paper, empirical validation via longitudinal, multi-country panel data is required. The framework’s originality lies in its theoretically pluralist, variable-explicit architecture that rejects single-theory reductionism and specifies testable transmission mechanisms for CSR–CFS scholarship.
🏷 Theoretical Framework
Article 265 · pp. 3251-3258
Android Quiz Game for Computer Fundamentals, A Fun and Interactive Learning Tool
In today’s technology-driven learning environment, the integration of mobile and interactive applications has become essential in enhancing student engagement and comprehension, particularly in computer- related courses. Within the Bachelor of Science in Information Technology (BSIT) program, students often face difficulties in mastering computer fundamentals due to limited interactive learning tools and reliance on traditional instructional methods. To address these challenges, this study proposed the development of an Android-based quiz game entitled Android Quiz Game for Computer Fundamentals: A Fun and Interactive Learning Tool designed to provide a fun, engaging, and effective supplementary learning tool for Computer Fundamentals. The application incorporates gamification elements such as points, levels, leaderboards, multiple game modes, and interactive visuals to promote motivation, active participation, and knowledge retention. The study employed a developmental and descriptive research design and followed the Agile development model to ensure flexibility, continuous improvement, and user-centered design. Data were gathered through interviews and survey questionnaires, with purposive sampling applied to select BSIT students and an IT expert as respondents.
Evaluation results indicate that the Android quiz game effectively enhances learning engagement and provides an interactive alternative to traditional study methods. The system achieved a high level of acceptability and usability, demonstrating its potential to support efficient academic learning. Overall, the study concludes that the proposed application contributes to a more organized, engaging, and technology-responsive learning environment for BSIT students.
🏷 Android application, quiz game, computer fundamentals, gamification.
Article 266 · pp. 3259-3281
“Content Networking Framework for AI Enhanced Biomedical Imaging Systems”
The rapidly advancing biomedical imaging systems characterized by AI algorithms are transforming diagnostic processes, clinical decision‑making, and data handling. However, despite all of this progress, most healthcare environments struggle with fragmentation of imaging data, limited interoperability between PACS, VNA, and AI engines, and inefficient content routing across PACS, VNA, and AI engines. In this paper, we present a content‑networked framework to bring together the visual data, maximize content streams in the metadata for AI‑enhanced biomedical imaging systems by unifying and optimizing mapping, linking content between content and services, to have the full processing information for metadata and integrate various AI‑driven visualization tools for data to the workflow process. The framework sets up a structured content‑networking layer to support improved data access, retrieval efficiency, and intelligent triage. A consensus among expert validation from radiology, biomedical engineering, and health informatics professionals validated five constructs — competency, readiness, capability, performance, and organizational support — and have Cronbach’s alpha values of ~0.79–0.88. The findings validate that a framework like this one is feasible, scalable, and aligned with digital health transformation priorities, particularly within high‑demand clinical environments. This work lays the groundwork for future AI‑enabled imaging ecosystems and informs the establishment of interoperable, efficient, and patient‑focused diagnostic workflows.
🏷 AI Enhanced Imaging, Content Networking, Biomedical Imaging Systems, Interoperability, PACS/VNA Integration
Article 267 · pp. 3282-3304
Housing Crisis and Informal Settlements: Urbanization, Infrastructure and Planning Challenges in Kabul City, Afghanistan
Kabul city the capital of Afghanistan is one of the most neglected and rapidly expanding cities in South Asia. In addition to decades of violent war, forced relocation, and institutional failure, it has a serious housing shortage. Based on five criteria—tenure security, water availability, sanitation, durable materials, and adequate living space—86% of Afghanistan's urban housing stock can be classified as "slum" (UN-Habitat, 2017). The difficulties of urbanization, the expansion of informal settlements, inadequate infrastructure, and poor planning in Kabul are all covered in detail in this article. The study uses data from international agencies, urban planning studies, and peer-reviewed literature to monitor Kabul's population expansion from 500,000 to an estimated 4.6 to 6 million people, most of whom reside in unplanned settlements without basic amenities. According to compiled data, there was no appreciable development in the infrastructure of peripheral informal settlements prior to 2021. The main causes of the issue are examined in this study, including internal displacement, migration from rural to urban areas, unstable land tenure, corruption, and ongoing underfunding of local governments. Evidence-based policy recommendations for community-driven infrastructure, tenure regularization, integrated metropolitan governance, and participatory upgrading are presented in the paper's conclusion. The findings have broader ramifications for comprehending urbanization affected by war and sustainable development in fragile states.
🏷 Kabul, Afghanistan, housing crisis, informal settlements, urbanization, infrastructure, Urban planning, internal displacement, land tenure, fragile states.etc.
Article 268 · pp. 3305-3317
Rural Health Unit-Bani Talaan System: A Digital Record Keeping Solution for Barangay Health Workers
This study presents the development of the RHU Bani Talaan System, a digital information management system designed to improve record-keeping and service delivery at the Rural Health Unit (RHU) of Bani. As healthcare facilities increasingly adopt digital solutions to enhance efficiency and accuracy, the RHU Bani faces challenges related to manual data handling, delayed retrieval of patient records, and limited access to health information. The RHU Bani Talaan System was developed to address these challenges by providing a centralized and computerized platform for managing patient records, health services, and reports. This study employed a developmental and descriptive research approach to design, develop, and evaluate the system. An analysis of the existing processes and problems was conducted to determine the system requirements and ensure alignment with the operational needs of the health unit. The results indicate that the implementation of the RHU Bani Talaan System improves data accessibility, reduces processing time, and enhances the accuracy and security of health records. The introduction of the system supports more efficient healthcare service delivery and strengthens information management within the RHU.
🏷 Rural Health Unit, Health Information System, Digital Records Management, Healthcare Information System, RHU Bani Talaan System
Article 269 · pp. 3318-3329
AgriConnect: A Mobile Application Linking Farmers and Businesses in Western Pangasinan
This study focused on the development of AgriConnect, a mobile application designed to connect farmers directly with businesses in Western Pangasinan. The application was developed to address common challenges faced by farmers, including limited market access, dependence on middlemen, lack of direct communication with buyers, and reduced income opportunities. These issues often result in lower profit margins, inefficient transactions, and difficulties in establishing reliable market connections. The study aimed to provide a digital solution that would improve agricultural trading processes and strengthen relationships between farmers and businesses.
A descriptive–developmental research approach was utilized in the study. Data were gathered through survey questionnaires and interviews conducted with farmers, business owners, and representatives from local agricultural offices to identify existing trading practices, challenges, and essential system requirements. The Agile methodology was employed in the development process, allowing iterative improvements and continuous refinement of the application based on user feedback and evaluation. The system was developed using modern mobile application technologies and integrated features designed to support efficient agricultural transactions.
AgriConnect includes functionalities such as secure user authentication, product listings, direct messaging, notifications, transaction history, sales computation, and performance analytics. These features enable users to communicate effectively, manage transactions efficiently, maintain organized records, and monitor trading activities through a centralized platform. The application also provides farmers with greater product visibility and direct access to potential buyers, reducing reliance on intermediaries.
The study concludes that AgriConnect effectively addresses key problems in the existing agricultural trading system by providing a transparent, accessible, and reliable digital platform. The evaluation results indicated that the application is usable, acceptable, and beneficial for both farmers and businesses. The findings suggest that AgriConnect has the potential to improve communication, transaction management, market accessibility, and overall agricultural trading efficiency in Western Pangasinan.
🏷 agriculture, mobile application, agricultural trading, farmers, businesses, market access.
Article 270 · pp. 3330-3354
Design and Functional Evaluation of a Gui-Based Expense Tracker System Using Python, Tkinter, and MYSQL
Manual expense tracking often results in disorganized records, inaccurate calculations, and difficulty in monitoring financial activities. To address these problems, this study developed a GUI-based Expense Tracker System designed to help users record, organize, update, delete, and monitor expenses efficiently. The system was developed using Python as the main programming language, Tkinter for the graphical user interface, and MySQL as the database management system for permanent record storage. The application includes essential expense management features such as Add Expense, View Expenses, Edit or Update Record, Delete Record, Clear All Records, and Report or Analytics Window. It also provides category filtering, automatic summary computation, database connectivity, and light/dark mode interface options to improve usability. Functional testing was conducted to determine whether the system performs correctly under valid and invalid input conditions. The testing covered major operations such as adding records, validating empty fields, detecting non-numeric amounts, checking date formats, filtering categories, updating records, deleting records, generating reports, and verifying database connectivity. The results showed that the system successfully performed all tested functions and maintained accurate synchronization between the graphical interface and the MySQL database. Overall, the Expense Tracker System provides a simple, organized, and user-friendly solution for managing expense records while demonstrating the practical application of object-oriented programming, GUI development, and database integration.
🏷 Cooperative governance, good corporate governance, digitalisation, stakeholders, transparency, accountability.
Article 271 · pp. 3355-3359
Pectin: A multifunctional plant polymer
Pectin is a structurally complex polysaccharide that is present in primary cell walls of higher plants and is famous for causing fruit ripening as well as the creation of gels in food items like jams and jellies. In the last twenty years, there is a lot of new knowledge in the field of polymer chemistry and plant biology science and biomaterials science that has broadened the scope of knowledge regarding pectin structure, biosynthesis, and applications. In addition to its conventional food applications, pectin is of significant use as biomaterials in drug delivery systems, tissue engineering scaffolds, biodegradable packaging and environmental remediation technology. This paper gives the molecular structure of pectin, chemical principles of gel formation, the biological applications of pectin in growth and development of plants and modern interdisciplinary applications. This article links plant cell wall biology, materials science, and biotechnology by proving how a typical plant polysaccharide has been turned into a primary material in sustainable technological innovations.
🏷 Multifunctional, Polymer, Smart Biomaterials
Article 272 · pp. 3360-3369
Enhancing IOT Energy Efficiency Using Distributed Edge Computing Mechanisms
The rapid expansion of the Internet of Things (IOT) has significantly increased the number of interconnected smart devices across healthcare, agriculture, smart cities, industrial automation, and transportation systems. However, the continuous operation and data transmission of IoT devices result in high energy consumption, limited battery lifespan, increased network congestion, and reduced system efficiency. Traditional cloud-centric architectures often introduce higher latency and excessive energy utilization due to long-distance communication and centralized data processing. To address these challenges, this paper presents a distributed edge computing mechanism for enhancing energy efficiency in IoT environments. The proposed framework utilizes decentralized edge nodes to process, filter, and manage data closer to IoT devices, thereby reducing communication overhead, minimizing latency, and optimizing computational resource allocation. The framework integrates intelligent task scheduling, adaptive load balancing, and energy-aware data processing techniques to improve overall system performance while conserving device energy. Experimental analysis demonstrates that the proposed approach significantly reduces energy consumption, improves response time, enhances network reliability, and extends the operational lifetime of IoT devices compared with conventional cloud-based models. The findings indicate that distributed edge computing provides an effective and scalable solution for achieving sustainable and energy-efficient IoT ecosystems in next-generation smart applications.
🏷 Internet of Things (IoT), Edge Computing, Energy Efficiency, Distributed Computing, Smart Devices, Energy Optimization, Task Scheduling, Load Balancing, Sustainable IoT, Network Performance.
Article 273 · pp. 3370-3379
An Intelligent Blockchain Framework for Secure Communication in IOT Environments
IOT (Internet of Things) is rapidly expanding with the creation of smart environments around agriculture, healthcare, industrial automation and transportation. However, as the number of sensitive data exchanges between different IoT devices increases, the number of privacy and security challenges is also growing. These challenges include unauthorized access to, or tampering of, data; identity spoofing; and cyberattacks. Most traditional security models are centralized and are either too slow due to their inherent scalability limitations or possess a single point of failure which makes them unsuitable for IoT ecosystems of a large-scale and dynamic nature. This intelligent framework will allow for decentralized blockchain architectures coupled with intelligent security mechanisms to provide for secure, transparent, and tamper-proof communications between devices on the Internet of Things as well as those on IoT networks. The framework will use ledger-like technology, smart contracts, lightweight encryption techniques, and intelligent authentication to increase the integrity of data, preserve the privacy of that data, and create trust management options among all devices connected by their respective IoT networks. Edge-enabled blockchain processing will also be included within the framework to reduce the computational overhead in processing transactions and to increase the efficiency of real-time communications. The experimental analysis of the intelligent blockchain framework will demonstrate how it can substantially improve security for communication over other forms of conventional IoT security models and reduce risk of unauthorized access to information, increase reliability in the transaction process, and decrease vulnerabilities to each network compared to other traditional models of security in the Internet of Things.
🏷 Internet of Things (IoT), Blockchain Technology, Secure Communications, Smart Contracts, Edge Computing, Cybersecurity, Distributed Ledger, Data Privacy, Authentication.
Article 274 · pp. 3380-3387
Comparative Assessment of Enzyme inhibiting Properties of Selected Ethnomedicinal Cereals (Panicum sumatrense and Eleusine coracana) used in The Management of Diabetes Mellitus
This study comparatively evaluated the inhibitory effects of two ethnomedicinal cereals, Panicum sumatrense and Eleusine coracana, on carbohydrate-hydrolyzing enzymes associated with postprandial hyperglycemia. Aqueous extracts of the cereals were prepared and tested for their inhibitory activities against α-amylase and α-glucosidase enzymes at concentrations ranging from 1–5 mg/ml. Acarbose served as the standard reference drug. The results showed that both extracts exhibited inhibitory activities against α-amylase and α-glucosidase enzymes in a concentration-dependent manner. For α-amylase inhibition, Panicum sumatrense and Eleusine coracana showed IC₅₀ values of 3.25 mg/ml and 5.00 mg/ml respectively, while acarbose exhibited a lower IC₅₀ of 1.28 mg/ml. In the α-glucosidase assay, Eleusine coracana demonstrated stronger inhibitory activity (IC₅₀ = 3.87 mg/ml) compared to Panicum sumatrense (IC₅₀ = 4.30 mg/ml), although both were less potent than acarbose (IC₅₀ = 0.30 mg/ml). The findings suggest that these ethnomedicinal cereals possess significant enzyme inhibitory activities which may contribute to their traditional use in the management of diabetes mellitus.
🏷 Ethnomedicinal cereals, Aqueous extracts, Enzyme inhibitory, Diabetes mellitus
Article 275 · pp. 3388-3394
Light-Fidelity Technology
Light Fidelity (Li-Fi) is an emerging wireless communica-tion technology that utilizes visible light high-speed data transmission, offering an efficient alternative to conventional radio frequency systems. This research presents the design and implementation of a Li-Fi-based audio communication system using both LED and Laser as optical transmitters. [1] The study focuses on a comparative analysis of these two light sources in terms of transmission range, signal strength, direc-tionality, and overall performance. The system converts audio signals into modulated light, which is transmitted through LED and Laser sources and received by a solar cell, then amplified and reproduced via a speaker. Experimental results demonstrate that LEDs provide wider coverage with moderate signal strength, whereas lasers offer long-distance, focused transmission with improved directionality.
🏷 Li-Fi, Visible Light Communication (VLC), Au-dio Transmission, Photodiode, Wireless Technology
Article 276 · pp. 3395-3407
Developing and Evaluating a New OJT Feedback Form: Evidence from a Mixed-Methods Assessment of Internship Quality and Assessment Continuity
A Philippine university recently transitioned from a 10-item OJT feedback instrument to a newly developed multidimensional OJT evaluation form designed to enhance internship assessment and quality assurance. The last cohort of student-trainees evaluated with the old instrument: 25 Computer Studies student-trainees, 8 BSCS, 17 BSIT deployed to 10 host organizations in Region XII in Summer 2025, serves as a baseline to assess the quality of the OJT program and the implications of this instrument change. The study provides a quantitative benchmark and structural gaps in the new form using descriptive statistics, independent samples t-testing, Cronbach's alpha reliability analysis, and reflexive thematic analysis of the open-ended responses. The pre-existing instrument produced a grand mean of M = 4.63 (SD = 0.61), with scores of Supervision & Mentorship and Workplace Environment obtaining the highest scores (both M = 4.72). The item in which training duration adequacy was rated the lowest (M = 4.24, SD = 0.97) and the only one showing a statistically significant program difference was rated significantly lower by the BSCS students compared to the BSIT students (M = 3.50 vs. 4.59; t(23) = −3.03, p = .006, d = −1.30, a large effect). This finding was further reinforced by four qualitative themes: unfamiliar workplace technologies, non-existent deployment planning, delays to resource access, and organizational constraints.
Most importantly, the revised New OJT Feedback Form instrument does not include training duration adequacy, which was the construct with the greatest policy relevance, and does not have a holistic single item structure as did the old instrument that allowed direct ten dimension profiling. The study sets a baseline for future assessment and shows that the New OJT Feedback Form represents a major institutional innovation, provides greater construct coverage, provides better qualitative feedback mechanisms and provides better support for quality assurance and program evaluation. A key contribution of the study is the development of a revised multidimensional OJT Feedback Form designed to support internship evaluation, quality assurance, and evidence-based program improvement in computing education. The study suggests that the training duration subscale be restored, that the anchors of the program comparability subscale remain the same, and that a pilot study be conducted using the revised instrument to see how well it performs against the baseline before fully implementing the instrument in institutions.
🏷 OJT assessment, internship evaluation, work-integrated learning, computing education, feedback instrument development
Article 277 · pp. 3408-3425
Design and Development of an Autonomous Fire Extinguishing Robot Using Arduino Mega and Dry Powder Extinguisher for Class B Fires
Firefighting is one of the most dangerous occupations, with firefighters frequently facing extreme heat, toxic smoke, explosions, and structural collapse. These adverse conditions lead to numerous injuries and fatalities each year. Although robotic systems have been proposed to reduce human exposure, many existing designs rely on remote control, use water or fans that are ineffective for flammable‑liquid fires, or suffer from limited detection ranges and slow response times. This study addresses these gaps by designing and fabricating an autonomous fire-extinguishing‑ robot specifically targeting Class B fires (flammable liquids and gases). The robot employs an Arduino Mega 2560 microcontroller as the central processing unit, three flame sensors (left, forward, right) for fire localization within a range of 10 cm to 90 cm, four ultrasonic distance sensors for obstacle detection and avoidance, and a 1 kg dry‑powder extinguisher actuated by a 12 V solenoid valve. A servo motor sweeps the nozzle from 50° to 130° and back to distribute the extinguishing agent over a calculated area of 113.1 cm². The drive system consists of four 9 V DC gear motors controlled via an H‑bridge driver, with speed ramped linearly from 0 to a maximum PWM value of 180 (achieving a measured maximum velocity of 0.030 m/s). The obstacle avoidance algorithm compares left and right distances; if an obstacle is closer than 25 cm in front, the robot turns toward the clearer side. Testing on a 1 m² test area with controlled alcohol‑based fires demonstrated that the robot reliably detects fire at up to 90 cm, navigates around obstacles, and completely suppresses the fire within a single continuous run. The flame sensor output decays nonlinearly with distance, following an inverse‑square trend, which allows approximate distance estimation but limits resolution beyond 70 cm. Across five repeated trials, mean fire detection time was 2.1 ± 0.3 s and mean extinguishing time was 11.8 ± 0.8 s, with a 100% extinguishing success rate. Results confirm that the autonomous system performs consistently without human intervention, offering a scalable, low-cost solution to reduce firefighter casualties. Future improvements should focus on larger chassis, onboard battery integration, and autonomous extinguisher replacement.
🏷 Autonomous firefighting robot; Arduino Mega 2560; flame sensor; ultrasonic obstacle avoidance; Class B fire; dry powder extinguisher; servo nozzle sweep; PWM motor control.
Article 278 · pp. 3426-3438
Neuromarketing at the Crossroads: Current Trends and Future Perspectives- Systematic Review
In today's competitive business world, it has become increasingly difficult to capture consumers’ attention and outshine competitors. Today's business is continuously tracking what consumers buy, and it has always been difficult to understand why they buy and how their thought process works in making such decisions. Using only traditional methods would not provide an edge over competitors; to sustain and survive in the competitive market, researchers began to work with a combination of traditional and modern marketing strategies, i.e., neuromarketing. Neuromarketing is the study of consumers’ emotional and cognitive responses to media and marketing Stimuli. The new techniques of neuromarketing Insights into consumer decisions and actions that are unconscious and not taken into account by mere traditional marketing research methodologies, the study aims to explore the concept deeper and highlight the critiques of the field along with its ethical considerations Another important aspect of neuromarketing, that is growing at a faster pace is the sensory marketing (visual, auditory and kinesthetics) used in modern day marketing. This study highlights the importance of sensory neuromarketing in uncovering consumer psychology and detecting brain activity during consumer engagement with different products. The study employs a conceptual approach with insights drawn from existing literature and industry examples to critically examine the risks and opportunities inherent in sensory marketing for the coming generations. The findings suggest that although neuromarketing can improve the methods of identifying consumer decision making not free from limitations, and there exists a need for ethical considerations and regulatory attention to mitigate its potential adverse effects. The paper advances insights into how sensory marketing shapes the behavioural outcomes of consumers.
🏷 Neuromarketing, Neuroimaging Techniques, Sensory Marketing, Consumer Psychology.
Article 279 · pp. 3439-3451
Personalized Therapeutic Healthcare Assistant
The Therapeutic Optimization for the Analysis of Personalized Health Measurements aims to develop an intelligent system that analyzes individual health parameters to deliver optimized therapeutic inter- ventions tailored to the unique physiological and lifestyle profile of each user. The system harnesses real-time health metrics, including vitals such as heart rate, blood pressure, sleep cycles, glucose levels, physical activity, and dietary habits, collected through wearable IoT devices and mobile health apps. By leveraging advanced data analytics and machine learning models, the project identifies trends, anomalies, and correlations within this multidimensional dataset.
The therapeutic engine evaluates the effectiveness of treat- ments using dynamic health score mod- eling and adjusts regimens using adaptive algorithms. It integrates guidelines from modern medicine, alternative therapies, and patient preferences to ensure personalized, evidence-based outcomes. A fourth model predicts goal achievement timelines by analyzing features such as caloric balance and hydration efficiency, providing users with actionable feedback using rule based algorithm.The integration of these AI-driven components into a scalable digital platform demonstrates the potential of machine learning in transforming health management. Future enhancements include improving model accuracy, enabling real-time feedback, and deploying the system as an accessible mobile application.
🏷 Biomarker Analysis, Electronic health records (EHRs).
Article 280 · pp. 3452-3458
A Software-Driven AI Approach to Logistics Process Automation
This paper presents the design and development of an AI-enabled, software-based system for automating logistics op- erations, aiming to upgrade conventional supply chain processes. The solution includes two dedicated mobile applications—one for customers and the other for delivery personnel—along with a comprehensive, web-based admin dashboard. All components are integrated to function cohesively, ensuring efficient management and real-time synchronization across the logistics chain.
The system provides complete lifecycle support for shipments, from initial booking to final delivery confirmation. Key features include user-friendly shipment request forms, automatic identi- fication of pickup and drop-off points via the Google Maps API, and optimized routing through Dijkstra’s algorithm to reduce travel time. Real-time location tracking, powered by continuous GPS updates, enables both clients and administrators to monitor deliveries with accuracy and transparency.
For administrative users, the platform offers a powerful control panel to manage shipment approvals, assign delivery fees, allocate drivers, and oversee real-time progress. To minimize manual intervention, an AI-driven chatbot is integrated to re- spond to common customer queries, including delivery tracking, estimated arrival times, and issue reporting.
A built-in analytics module further enhances decision-making by visualizing key metrics such as delivery volume by location, commonly used routes, cost breakdowns, and preliminary rev- enue estimates. By merging automation, live data processing, and AI-driven insights, the platform delivers a scalable, efficient solution for optimizing logistics operations and modernizing traditional supply chain systems.
🏷 Logistics automation, Firebase, Google Maps API, React Native, AI chatbot, route optimization, data analytics, Dijkstra algorithm
Article 281 · pp. 3459-3477
Employee Expectations, Actuals, and Workplace Harmony: A Deep Empirical and Theoretical Investigation into the Emergency Management Sector in Nigeria
Employee expectations constitute a foundational determinant of organisational behaviour, workforce morale, and workplace harmony. In the emergency management sector—where operational effectiveness is contingent upon teamwork, psychological safety, and coordinated crisis response—the alignment or misalignment between employee expectations and organisational realities carries profound implications for institutional performance and disaster response outcomes. This study presents a deep empirical and theoretical investigation into the relationship between employee expectations, organisational actuals, and workplace harmony within Nigeria’s emergency management sector. Anchored in psychological contract theory (Rousseau, 1989, 1995), equity theory (Adams, 1963, 1965), expectancy theory (Vroom, 1964), and organisational justice theory (Greenberg, 1987; Colquitt, 2001), the study employs a mixed-methods explanatory research design integrating a structured survey instrument administered to 320 employees across the National Emergency Management Agency (NEMA) and selected State Emergency Management Agencies (SEMAs). Quantitative data were subjected to descriptive statistical analysis, Pearson’s correlation analysis, multiple regression modelling, and exploratory factor analysis (EFA). Qualitative data derived from semi-structured interviews with 28 purposively sampled senior staff were analysed using thematic analysis. Findings reveal that expectation gaps relating to welfare provisions, hazard allowances, career progression, professional development, and leadership transparency exert statistically significant negative effects on workplace harmony (R² = 0.68, p < 0.01). Leadership transparency emerged as the most influential mediating variable. Institutional analysis of NEMA’s HR governance framework identifies systemic policy-implementation gaps as structural antecedents of expectation-actuality discrepancies. The study contributes original theoretical synthesis through a proposed Expectation-Actuality-Harmony (EAH) integrative model and advances targeted policy recommendations for HR governance reform within Nigeria’s emergency management architecture.
🏷 employee expectations, workplace harmony, emergency management, psychological contract, equity theory, organisational justice, Nigeria, human resource management, NEMA
Article 282 · pp. 3478-3488
Motion-Activated Garage Door System with Parking Detection and IOT Notification
Traffic has been a prevalent issue since the introduction of automobiles, and many countries face it; one of its root causes is car parking. Improper parking of vehicles more often than not causes traffic congestion in urban areas, as it is usually parked on sidewalks that obstruct not only pedestrian walkways but also the road that vehicles use. Parking garages should be required for every vehicle owner, whether it be a motorcycle or, most importantly, an automobile.
This research paper discusses the process and methods used to develop a motion-activated garage door system with parking detection and IoT notification, with a focus on improving convenience, safety, and accessibility in homes. The design uses an ESP32 microcontroller, an ultrasonic sensor to enable the door to respond to motion, another ultrasonic sensor to detect whether a parked car is inside the garage, a buzzer for sound cues, and a servo motor.
🏷 Automatic Garage Door System, Arduino Uno, ESP32, Ultrasonic Sensor, Parking Detection, IoT, Blynk, Buzzer, Motion Detection, Embedded Systems, Automation
Article 283 · pp. 3489-3511
Teaching Competence and Student Achievement Based on Philippine Professional Standards for Teachers
Teaching competence is a fundamental determinant of instructional quality and student learning outcomes. This study assessed the teaching competence of educators and the academic achievement of students in the District of Calbiga II, Schools Division, during the School Year 2025–2026, aligned with the Philippine Professional Standards for Teachers of the Department of Education. Utilizing a quantitative descriptive research design, the study employed comparative and correlational analyses to examine differences in teacher and administrator assessments and to determine relationships among key variables. Findings revealed that teachers generally demonstrated competence across all PPST domains, particularly in content knowledge, pedagogy, assessment practices, ICT integration, learning environment, and professional development. Student performance showed that most learners achieved mean grades ranging from 85 to 89, reflecting satisfactory academic achievement. Significant differences emerged between teacher self-ratings and administrator evaluations, highlighting the need for clearer evaluation standards and collaborative alignment. Positive attitudes toward the PPST and participation in relevant in-service trainings were associated with higher teaching competence, while age and experience showed slight negative correlations. A weak but significant relationship was found between teaching competence and student achievement, with heavy workload identified as a primary implementation challenge.
🏷 Teaching Competence, Philippine Professional Standards for Teachers (PPST), Student Academic Achievement, Teacher Professional Development, Teacher Assessment
Article 284 · pp. 3512-3526
FitDrive Management System: A Python-Based Gym Membership System Using OOP Principles
The FitDrive Management System is a desktop-based application developed in Python with SQLite database integration to efficiently manage gym member records. The system automates administrative tasks including adding, updating, deleting, and searching member information, significantly reducing the need for manual record-keeping. A reporting feature allows staff to view total registered members and estimated monthly revenue, improving data accessibility and organization. Input validation ensures that incomplete or invalid entries cannot be saved, enhancing accuracy. Through a structured database design and user-friendly interface, the system improves operational efficiency and minimizes errors in managing member information. The project demonstrates the practical application of Python programming, Object-Oriented Programming (OOP), and database management to create a functional management system for small to medium-sized gym facilities.
🏷 Gym Management System, Python, SQLite, OOP, Membership Management, GUI, Database Integration
Article 285 · pp. 3527-3537
Integrated Biotechnological Approaches Involving Plastic-Degrading Microbes, Microalgae-Derived Bioplastics, And Bioremediation for Plastic Pollution Control
Plastic pollution has become a dominant environmental issue in the 21st century as plastic production and consumption has increased exponentially throughout the world. Plastics have transformed many industries since their widescale adoption in the mid-20th century thanks to affordability, durability and light-weight. But these same properties have also enabled their environmental persistence, with extensive accumulation in terrestrial, aquatic and marine ecosystems. Millions of tons of plastic waste are estimated to pour into the environment every year; a fraction then breaks down into myriad microplastics and nanoplastics that pose serious ecological and human health risks.
Biotechnology has adapted itself to this need and is slowly but steadily proving to be a field that showcases new solutions through sustainable practices, especially when it comes to plastic pollution. Biotechnological approaches provide eco-friendly strategies for plastic degradation, recycling and replacement by utilizing the metabolic capacity of microorganisms, enzymatic reactions, and photosynthetic pathways. Microbial degradation, microalgae-based bioplastic production and bioremediation technologies stand out among these as they can establish a closed loop or integrated circular system for plastic waste management.
Microbial degradation of plastics involves the use of bacteria, fungi, and other microorganisms capable of breaking down complex polymer structures into simpler compounds. This process is primarily mediated by extracellular and intracellular enzymes such as hydrolases, esterases, and oxygenases, which cleave polymer chains through hydrolysis and oxidation reactions (Wei & Zimmermann, 2017). Over the years, several microbial species have been identified with the ability to degrade commonly used plastics such as polyethylene, polypropylene, and polyethylene terephthalate (PET). However, the efficiency of natural microbial degradation is often limited by factors such as polymer crystallinity, hydrophobicity, and environmental conditions (Restrepo-Flórez et al., 2014).
🏷 Biotechnological, Plastic-Degrading, Microalgae-Derived
Article 286 · pp. 3538-3543
Hire Ready AI: An AI-Powered Resume Builder and Mock Interview Preparation System
Preparing for job interviews can be an arduous task. This is due to the lack of structured, timely, and personalized support, which could help candidates to understand potential areas of improvement. To overcome this, we propose Hire Ready AI, an intelligent platform for Resume enhancement and Interview preparation. The system takes as input the candidate's profile and resume and dynamically generates interview questions. As the candidate responds, Hire Ready AI evaluates the candidate's answer across multiple dimensions such as content, grammar, organization, communication, confidence, resume consistency and natural language processing. A voice response is expected from the interviewee and will be transcribed using speech recognition. Then the text is also used as another source of input for the system to analyze the candidate's confidence, personality, and tone of job interviews. We perform simulation experiments on multiple mock interview sessions and evaluate the system performance. The results show that our proposed framework generates reliable responses, timely feedback, and personalized interview questions.
🏷 Hire Ready AI, NLP, ML, Voice Analysis, IQG, Response Evaluation Model, Confidence Analysis, Real-time Feed-back System.
Article 287 · pp. 3544-3565
The Impact of Cost–Benefit Analysis on the Development of Residential Construction Projects in Sub-Saharan African Countries: A PRISMA-Guided Systematic Empirical Review (2010 – 2025)
The use of cost-benefit analysis (CBA) in residential construction development across Sub-Saharan Africa (SSA) is an area of empirical inquiry that is important yet not cohesively synthesized. With rapid urbanization, housing deficits estimated at over 51 million units, and growing infrastructure investment needs, the evidence on CBA's value as a decision-support tool in SSA's residential sector remains fragmented and locally bounded. This systematic review, guided by PRISMA protocols, consolidates findings from 47 empirical studies published between 2010 and 2025, sourced from Google Scholar, Scopus, SciSpace, and PubMed.
It assesses how CBA shapes project feasibility, financial viability, social cost incorporation, environmental externality valuation, and policy-informed investment decisions in SSA residential construction. The findings indicate that CBA adoption enhances investment efficiency and risk management, yet significant methodological obstacles remain, particularly data scarcity, informal land tenure, challenges with shadow pricing, and systematic undervaluation of social and environmental costs.
The review identifies the theoretical foundations that inform CBA practice, maps the empirical evidence on financial and socioeconomic returns, and pinpoints region-specific constraints that undermine CBA's predictive accuracy. It concludes with policy recommendations and directions for future research, emphasizing the need for context-sensitive CBA frameworks, professional capacity development among built environment practitioners, and the incorporation of sustainability cost metrics into standard appraisal practice across SSA.
🏷 cost–benefit analysis, residential construction, Sub-Saharan Africa, housing development, project appraisal, urban housing deficit, PRISMA systematic review, construction economics, affordable housing
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