Volume 15 Issue 4

Infrared thermography has emerged as a powerful non-contact technique for identifying temperature variations in silicon photovoltaic modules, which are often linked to performance degradation. This work presents an integrated approach that combines thermal imaging analysis with energy recovery using thermoelectric generators (TEGs). Temperature non-uniformities such as hotspots are detected through thermal imaging, indicating localized losses and inefficiencies within the PV panel. These temperature gradients are further utilized as a potential energy source by strategically placing TEGs to convert excess heat into electrical energy. Additionally, an ultra-low voltage boost converter is modelled to enhance the usability of the generated TEG output. The study not only evaluates the thermal behavior of PV modules under varying operating conditions but also demonstrates a method to improve overall system efficiency by recovering otherwise wasted thermal energy. This approach contributes to both performance monitoring and energy optimization in solar power systems.

This study investigates the role of dynamic capabilities, organizational agility, digital literacy, and leadership support in influencing digital transformation performance within the public sector. Drawing on the Dynamic Capability View (DCV), the research proposes an integrated model in which organizational agility mediates the relationship between dynamic capabilities and performance, while digital literacy and leadership support act as moderating variables. Data were collected from 123 civil servants at the Aceh Civil Service Agency (BKA) using a census approach and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results reveal that dynamic capabilities—comprising sensing, seizing, and reconfiguring—do not have a significant direct effect on digital transformation performance. However, they significantly enhance organizational agility, which in turn has a strong and significant impact on performance. Mediation analysis confirms that organizational agility fully mediates the relationship between dynamic capabilities and digital transformation performance, highlighting its central role as a translation mechanism. In contrast, the moderating effects of digital literacy and leadership support are found to be statistically insignificant, suggesting that these factors function more as enabling conditions than as direct amplifiers of performance relationships. These findings extend the Dynamic Capability View by emphasizing the importance of organizational-level mechanisms in realizing the value of strategic capabilities. Practically, the study underscores the need for public organizations to prioritize agility—through flexible structures, rapid decision-making, and process integration—over purely technological or skill-based interventions. Ultimately, the study demonstrates that successful digital transformation is fundamentally an organizational challenge rather than a technological one.

Background: Cervical radiculopathy is a common condition characterized by nerve root compression, leading to pain, disability, and functional limitations. Physiotherapy interventions such as Neural Mobilization (NM) and Positional Release Technique (PRT) are widely used; however, evidence comparing their combined effectiveness is limited.  Objective: To evaluate and compare the effectiveness of NM, PRT, and their combined application (NM + PRT) in individuals with cervical radiculopathy.  Methods: Forty-five participants were randomly allocated into three groups (n = 15 each): NM, PRT, and combined NM + PRT. Interventions were administered, and outcomes were assessed using the Numeric Pain Rating Scale (NPRS), Neck Disability Index (NDI), and hand grip strength. Data were analyzed using one-way ANOVA and Kruskal–Wallis test with significance set at p ≤ 0.05.  Results: All groups showed significant improvements in pain and disability. The combined NM + PRT group demonstrated the greatest reduction in NPRS (6.93 ± 1.62) and improvement in NDI (13.87 ± 8.55), indicating superior effectiveness. Grip strength improved in all groups; however, no statistically significant difference was observed between them (p > 0.05).  Conclusion: Both NM and PRT are effective in managing cervical radiculopathy, but their combined application provides superior outcomes in reducing pain and improving functional disability. This combined approach can be considered a safe and effective non-invasive treatment option. 

Soil moisture, defined as the presence or amount of water in the soil, is a critical element in hydrology, agriculture, and climate science, vital for understanding soil health, water cycle, and plant growth. Remote sensing, particularly using satellites like Sentinel-2 which is equipped with Multispectral instruments, offers a powerful tool for monitoring soil moisture across large areas. Therefore, this study was conducted to generate soil moisture maps of Solano Nueva Vizcaya through Geographic Information System by using Sentinel-2 Near InfraRed and Short Wavelength InfraRed bands for the years 2020 and 2025. It further aimed to determine the soil moisture level of the study area and compare soil moisture level differences between the years 2020 and 2025, and at the same time identify the minimum, maximum, and the mean soil moisture per barangay. The results revealed that the municipality of Solano has a minimum soil moisture level increased by 0.304678159, while the maximum decreased by 0.062563121. It was also revealed that most of the dry soil are located within the lowland areas and generally the results indicate a general increasing trend in soil moisture in Solano for the years 2020 and 2025.

This paper is about the development of SAFE, which stands for Silent Assistance for Emergencies. The SAFE system is a mobile-based emergency reporting system. It is designed to help the mute community in Alaminos City. Most emergency services need people to talk to them. This makes it hard for people who are deaf and mute to get help when they need it. The city is increasingly utilizing technology for its public services. This technology can really help make things more accessible and safer for everyone, the deaf and mute community, by using SAFE, the mobile-based emergency reporting system. The development of the SAFE application provides users with one-tap emergency buttons, GPS-based location sharing, and visual alerts for silent reporting. This study employed a developmental research approach using the Rapid Application Development (RAD) model to create a working mobile application suitable for emergency reporting. The evaluation results show that the system achieved an Excellent rating in terms of functionality, usability, reliability, and performance, indicating that SAFE is an effective and accessible emergency reporting tool for the deaf and mute community.

Access to reliable and clean energy remains a critical challenge in rural regions of developing countries, including India. A large proportion of rural households still depend on traditional fuels such as firewood, kerosene, and biomass for lighting and household energy needs. These conventional energy sources contribute to environmental degradation and expose households to harmful indoor air pollution, leading to various health risks. In this context, solar energy has emerged as a sustainable and decentralized solution to address energy poverty, reduce environmental impacts, and improve the overall quality of life in rural communities. This study examines the socio-economic and health impacts of solar energy adoption among rural households in Khandwa district of Madhya Pradesh, India. The research is based on primary data collected from approximately 300 respondents across 20 villages through structured household surveys. The study employs both descriptive and inferential statistical techniques, including mean analysis, frequency distribution, paired t-tests, and ANOVA, to evaluate the influence of solar energy adoption on household energy consumption patterns, income opportunities, educational outcomes, and health conditions.

This study investigates employee-employer conflict management and workplace effectiveness in FUGAZ banks in Delta State, Nigeria. Specifically, the study examines some proxies like the influence of conflict resolution strategies (CRS), communication of conflict mitigation (COM), and workplace policies and procedures (WPP) on workplace effectiveness (WE) connected to three (3) research question raised leading to the formulation of three (3) hypotheses. The study employed descriptive survey design. The population of the study was 576 employees while the sample size of 345 was determined using Yamane formula. The instrument used was structured questionnaire to draw information from the employee participant tested for a reliability alpha of 0.82 adjudged satisfactory. The instrument were administered out of which, a total of 224 returned, were found useful for data analysis. Data were analyzed using multiple regression, the results show that conflict resolution strategies have a significant positive influence on workplace effectiveness (β = -0.150, p < 0.009). Additionally, communication of conflict mitigation was found to have a significant impact on workplace effectiveness (β = 0.572, p < 0.001). Similarly, workplace policies and procedures concerning conflict management were found to have a significant positive effect on workplace effectiveness (β = 0.582, p < 0.001). Based on the finding recommendations were made that  the banks should adopt regular mediation sessions, grievance mechanisms, and training programs to improve employee relations and reduce workplace tensions, also foster a culture of feedback, use digital tools, and establish transparency policies to reduce misunderstandings and finally to ensure clear communication and consistent enforcement of conflict management policies to reduce conflict incidents and enhance workplace effectiveness.

Tomato cultivation contributes significantly to agricultural production, but it is highly prone to diseases such as Early Blight and Late Blight, which can severely affect crop yield if not identified at an early stage. These diseases spread quickly under favorable environmental conditions and can cause major losses to farmers. Traditional methods of disease identification depend on manual inspection, which is time-consuming, labor-intensive, and often unreliable, especially during the initial stages of infection. As a result, early symptoms are frequently overlooked, leading to reduced productivity. To address this problem, this paper presents TomLeafVision, a deep learning-based system designed for automated detection of tomato leaf diseases. The proposed approach classifies leaf images into three categories: Healthy, Early Blight, and Late Blight using a Convolutional Neural Network (CNN). To improve model performance, input images captured through mobile devices undergo preprocessing steps such as resizing, normalization, and data augmentation. Furthermore, transfer learning using the MobileNetV2 architecture is applied to enhance classification accuracy while reducing training time. The model is trained using the Adam optimizer with categorical cross-entropy as the loss function. Experimental results indicate that the system performs effectively on unseen data and achieves high accuracy. The proposed solution is user-friendly, cost-effective, and suitable for real-time deployment in agricultural environments.

Most automobile repair and maintenance apps utilize the rule base and case base reasoning methodologies in their implementations. These two methodologies have their strengths and limitations, some of these limitations can be overcome by the Convolutional Neural Networks (CNN), a specialized subset of deep learning that has excelled in image analysis due to its ability to learn hierarchical representations (Brito, 2023).  CNN extracts the pixel value of an image and create a feature map that it uses for processing through learning. In this study, 998 images of frequently occurring vehicle faults were captured from mechanic workshops operated within and by Akwa Ibom state Transport company. These images were subjected to 3 lightweight CNN models after pre-processing. The aim was to detect and classify faults in these damaged parts. Results obtained show that the 3 models demonstrated strong performance across the three key evaluation metrics: accuracy, precision, and recall with an accuracy of 92%, Precision of 91% and Recall of 90%. It is recommended that an integration of case-based reasoning, Fuzzy logic reasoning and CNN be undertaken to improve on the results and a high resolution camera be used to capture the images of the damaged parts for a better input to the CNN model.    

Contemporary network intrusion detection systems (NIDS) need to be able to classify various types of attacks in the high-dimensional, highly skewed network traffic. In this paper, a hybrid RF-PGNN framework will be presented, which combines a Random Forest (RF) and a Graph Attention Network (GAT) to utilize both feature-level discriminative patterns and sample-level relational structure. Similarities of RF leaf-assignments are utilized to create a proximity graph that indicates non-linear decision boundaries that are learned implicitly by the forest. This graph is then trained on to spread relational signals between neighbouring samples to the GAT. The standalone RF has an accuracy of 98.86 and GAT has an accuracy of 78.34 on a balanced seven-class subset of the CIC-IDS2017 benchmark. The ensemble with weight (RF weight 0.9, GAT weight 0.1) has an accuracy of 98.94 and macro F1-score of 0.9894 and ROC-AUC of 0.9986. This low standalone accuracy of the GAT can be attributed to the limiting nature of graph-scale to the use of edges, over-smoothing effects on the multi-layer passage of messages, as well as to the limitation of proximity-based edges to the encoding of directed flow semantics of fine-grained attack sub-types; however, the complementary relational signal that it provides can provide a consistent ensemble boost. The McNemar test shows that the RF baseline gain is significant (p < 0.05). Additional testing with an unequal class distribution suggests that RF-PGNN recovers macro F1 on classes that are attacked by minorities, implying that it can be used in practice even at suboptimal benchmarks like equal classes. The suggested framework provides a theoretically sound tool to integrate tree ensembles and graph-based learning to promote the further development of multi-class intrusion detection without losing interpretability.

This study examines the effect of financial technology (FinTech) adoption on the sustainability of deposit money banks in Nigeria over the period 2010–2024. Anchored on the Innovation–Stability Framework of Boot and Thakor (2019), the study investigates whether FinTech-driven channels improve bank sustainability directly and indirectly through operating efficiency and financial inclusion. Secondary data were obtained from the Central Bank of Nigeria, the Nigeria Inter-Bank Settlement System, the National Bureau of Statistics, and annual reports of selected banks. The study employs the Autoregressive Distributed Lag (ARDL) model to estimate short-run and long-run dynamics, while Structural Equation Modelling (SEM) is used to examine mediation effects. The findings confirm a long-run relationship between FinTech adoption and bank sustainability. Operating efficiency emerges as the strongest mediating channel and contributes positively to profitability and sustainability. By contrast, financial inclusion exerts a negative mediating effect, suggesting that rapid expansion without adequate risk management may strain bank resources. ATM and POS transactions show positive effects on efficiency and sustainability, whereas mobile banking produces mixed and largely negative outcomes. The study concludes that FinTech can support sustainable banking when supported by effective regulation, strong risk management, and adequate institutional capacity. It recommends policies aimed at improving digital literacy, strengthening cybersecurity, and deepening the regulatory framework for FinTech development in Nigeria.

Background: Higher education institutions are expanding online delivery and integrating generative artificial intelligence (GenAI), yet faculty readiness remains uneven, raising concerns about assessment validity, academic integrity, institutional legitimacy, and the quality of scalable online provision. Objective: This study develops the EPIQ-AI Readiness Framework, a multidimensional model that defines readiness for AI-supported teaching and online higher education across four aligned domains: epistemic, pedagogical, institutional, and quality-and-compliance readiness. Methods: Using an integrative secondary evidence synthesis, the study triangulates recent official statistics, large-scale faculty and institutional surveys, peer-reviewed studies, and policy frameworks published between 2020 and 2025. The analysis is organized across four readiness domains: epistemic, pedagogical, institutional, and quality-and-compliance readiness. Results: The evidence converges on four main findings. First, faculty adoption of AI is increasingly widespread, but confidence, pedagogical clarity, and depth of use remain limited. Second, institutional ambitions for online scale and AI integration are advancing faster than policy maturity, professional development, and support capacity. Third, assessment has become the central pressure point, with growing evidence that detection-centered academic integrity regimes are unreliable, potentially biased, and insufficient for high-stakes decisions. Fourth, faculty readiness is best understood not as an individual skills deficit but as a sociotechnical alignment problem shaped by governance, incentives, workload, literacy, course design support, and equity-sensitive implementation. Conclusions: The EPIQ-AI framework reframes readiness as a multidimensional condition for credible AI-enabled and online higher education by aligning epistemic judgment, pedagogical competence, institutional support, and quality-and-compliance safeguards. It offers a theoretically grounded and operationally actionable model for institutions seeking to strengthen AI literacy, redesign assessment, improve governance, and sustain epistemic integrity while advancing scalable, policy-compliant online delivery.

Persons with Disabilities in Alaminos City face significant challenges in securing employment due to limited access to job opportunities and reliance on manual, inefficient referral processes. This study developed a web-based job portal to bridge this gap, providing a centralized and accessible platform that directly connects Persons with Disabilities job seekers with potential employers. The system was designed to streamline the job search and hiring process, promoting inclusivity and empowering Persons with Disabilities to independently pursue suitable employment. The project utilized the Rapid Application Development methodology, emphasizing iterative prototyping and continuous user feedback to ensure the platform effectively addressed user needs. The developed portal features distinct user levels for Persons with Disabilities job seekers and PDAO administrators, facilitating profile creation, job posting, application management, and administrative oversight. Findings indicate that the portal successfully addresses the inefficiencies of the traditional employment referral system. User evaluations confirmed the system's high levels of usability, functionality, and reliability, establishing it as a very acceptable solution. The platform enhances the job-seeking experience for PWDs while providing employers with a streamlined tool for inclusive hiring. In conclusion, the web-based portal offers a sustainable digital solution that fosters greater employment equity for PWDs in Alaminos City. It is recommended for adoption by the PDAO to enhance their employment facilitation services. Future enhancements could include integrating assistive technologies and developing a mobile application to further expand accessibility and reach.

Accurate mapping of cropland in urbanizing areas is critical for sustainable land use planning and climate-resilient urban development. In this study, the suitability of two machine learning algorithms, namely Random Forest and Gradient Tree Boosting, was tested and compared in the context of land use/land cover classification in the Abuja Municipal Area Council in Nigeria. The evaluation was carried out with the integration of multi-sensor data obtained from Sentinel-1 Synthetic Aperture Radar and Sentinel-2 optical imagery in the Google Earth Engine platform. In order to improve the overall accuracy of the classification results, an extensive feature set was developed and incorporated into the machine learning algorithms. The feature set was developed based on the integration of spectral and phenological features with texture features. The overall accuracy of the classification results obtained with the two algorithms was found to be satisfactory, with values above 77%. However, the overall accuracy obtained with the Gradient Tree Boosting classifier was slightly better at 77.5%, with a Kappa coefficient of 0.719. However, when the class-specific accuracy was evaluated, the Random Forest classifier was found to be better in the context of classifying the cropland class. The overall accuracy of the Random Forest classifier in classifying the cropland class was found to be better in terms of precision at 78.9%, recall at 75.7%, and F1 score at 77.2%. Therefore, the Random Forest classifier was selected for the final classification of the study area. The classified image obtained with the Random Forest classifier estimated the total extent of the cropland class to be around 47,924 hectares in the study area in the year 2025. The estimated extent of the cropland class accounts for around 27.1% of the total area. The results obtained in this study demonstrate the suitability of the integration of multi-sensor data in improving the overall accuracy of the classification results. In addition, the results obtained in this study demonstrate the importance of class-specific accuracy in the selection of the machine learning algorithms for the purpose of classifying the land cover class.Top of Form

Exploration of living possibilities on different planets, such as the Moon and Mars, has become one of the primary focuses of current scientific and technological research. Humanity's quest to understand the universe, combined with the desire to explore the limits of nature, encourages the development of new approaches that could make life in space possible. In this context, a detailed examination of the physical and chemical properties of planets, an analysis of atmospheric conditions, and the design of sustainable life support systems are among the fundamental elements of these studies. Space missions, continuously conducted since the 1950s, have deepened humanity's knowledge of the universe, marking significant milestones in the exploration processes of this field. Initially limited to the exploration of Earth's orbit and the goal of reaching the Moon, these endeavors have taken on a much more comprehensive and sophisticated dimension with the rapid advancement of technology. Technological developments in spacecraft and observation systems, the widespread use of artificial intelligence-supported analytical methods, and increasing international cooperation have significantly accelerated progress in space research, allowing for the collection of more detailed and comprehensive data about the depths of the universe. Systematic research aimed at examining the conditions of space has led to large-scale innovations in the fields of science and engineering. In the context of expanding human habitation, Mars is predicted to be the next target. However, building a sustainable living environment on Mars requires the coordinated and integrated efforts of many disciplines beyond architecture, including engineering, biotechnology, material science, and others. This article will evaluate possible life scenarios and living conditions on Mars in line with predictions of living in space. It will also comprehensively analyze the architectural and engineering principles when designing human settlements and sustainable living spaces on Mars.

This study investigates the role of digital educational technology in the service market and its impact on economic development. As economies transition to knowledge-based systems, digital learning platforms are crucial for building human capital and fostering innovation. Analyzing empirical data from 400 higher education students, this research identifies a critical paradox: while digital tool usage is high, self-assessed digital literacy remains low, indicating a gap between access and effective skill acquisition. Regression analysis reveals that a student's field of study is a more significant predictor of digital literacy than their level of higher education, highlighting a disciplinary divide that affects workforce preparedness. The study demonstrates that digital education acts as an economic catalyst by enhancing productivity, reducing costs, and creating new service-sector opportunities. However, challenges like technological inequality, uneven access between urban and rural areas, and insufficient policy frameworks hinder its potential. The findings underscore the necessity for integrated digital literacy curricula, tailored upskilling pathways, and robust public-private partnerships. It is concluded that strategic investment in a holistic digital learning ecosystem is imperative to harness the full economic potential of the service market and achieve sustainable, inclusive growth. According to the research, digital education technology is a key component of the service market, acting as a key enabler of economic transformation in the digital era and a transformation of conventional education delivery systems. This research underscores the need for policymakers and stakeholders to prioritise strategic investments in digital learning ecosystems to realise their full economic development potential.

This study examines the effect of inventory management strategies on project management performance in a Nigerian project consulting firm. Specifically, it evaluates the influence of safety stock on stock availability and just-in-time (JIT) on service reliability. A survey research design was adopted, covering a population of 69 staff using a census approach. Data were collected through a structured questionnaire, and reliability was confirmed using Cronbach’s alpha coefficient of 0.84. Regression analysis was used to test the hypotheses. The findings reveal that safety stock significantly improves stock availability (β = 0.492, p < 0.05), while JIT significantly enhances service reliability (β = 0.192, p < 0.05). The study concludes that inventory management strategies are critical drivers of project performance. It recommends the adoption of a hybrid inventory strategy that balances efficiency with risk mitigation to improve operational outcomes and customer satisfaction.

As the global demand for sustainable living intensifies, brands and consumers alike are turning to more conscious choices that align with environmental and social responsibility. In this shift, influencer marketing has emerged as a powerful digital tool, not just for promoting products, but also for spreading awareness and inspiring action toward sustainability. This study explores the evolving role of influencer marketing in promoting sustainability across industries, highlighting how digital creators have become powerful intermediaries in shaping consumer awareness, attitudes, and eco-conscious behaviours.  Using a qualitative and exploratory research approach, the study draws insights from secondary data, online sources, and of sustainability influencers across fashion, beauty, lifestyle, travel, home, and technology sectors. The findings show that authenticity, credibility, and transparency are central to effective sustainability communication, with micro- and nano-influencers often generating deeper trust and engagement.

Document forgery has become easier with the advancement of printing technologies and image editing software. Identifying the source printer of a printed document is an important task in forensic document analysis. Traditional approaches rely on handcrafted texture features such as Local Binary Pattern (LBP), Local Directional Pattern (LDP), and Local Optimal Oriented Pattern (LOOP). However, these methods require manual feature extraction and often fail to capture complex intrinsic printer signatures effectively. This research proposes a deep learning-based approach using Convolutional Neural Networks (CNN) to automatically identify laser printer models based on texture patterns observed in printed documents. The CNN model learns discriminative features from character-level images without requiring handcrafted descriptors. The dataset consists of scanned document images printed from ten different laser printers, and character-level segmentation is applied to extract the character ‘e’ images. The proposed CNN-based method achieves high classification accuracy and demonstrates superior performance compared to traditional machine learning approaches such as SVM with handcrafted features. The results show that CNN can effectively capture intrinsic printer signatures and improve document authentication systems in forensic applications.

The present paper suggests a Cat Swarm Optimization (CSO)-based model of optimal placement and parameter adjustment of a Unified Power Flow Controller (UPFC) to improve voltage stability in power systems under normal operating conditions as well as N-1 contingency operating conditions. The most flexible combined series-shunt FACTS controller, the UPFC, controls the magnitude of voltage, the line impedance and the phase angle simultaneously by coordinated series voltage injection and shunt reactive power compensation. The L-index of the partitioned bus admittance matrix is used to measure the voltage stability, and the worst-case contingency value L worst is used as the optimization fitness function. The CSO algorithm, which works based on dual seeking and tracing the behavioral modes, reduces L worst to UPFC placement and parameter constraints without the need to have the gradient information. The model has been confirmed in the IEEE 14-bus and in the IEEE 30-bus benchmark systems that are simulated in MATLAB/MATPOWER. Findings indicate that UPFC integration reduced the L-index by 0.26% in the IEEE 14-bus and 0.54% in the IEEE 30-bus system at normal and then CSO-optimized UPFC location leads to 4.28% reduction in the worst-case L-index at N-1 contingency on the IEEE 14-bus system and 24.85% on the IEEE 30-bus system. Optimization increased voltage profiles on all buses to the nominal 1.0 p.u. These results support the claim that the proposed CSO-UPFC framework is a computationally efficient and practically viable approach towards enhancing voltage security of the power system both when it is operating in normal conditions and when it is subjected to disturbances.

This study investigates the strategic interplay between Issue Composition (Proportion of Fresh Capital vs. Offer for Sale (OFS)), Market Sentiment ("hot" vs. "cold" markets), and the resulting Underpricing and Valuation of 236 Initial Public Offerings (IPOs) in the Indian capital market during 2009-2020. The analysis confirms a negative relationship between the proportion of fresh capital and underpricing, with full OFS issues (0% fresh) recording the highest average underpricing (20.61%) and 100% fresh issues recording the lowest (8.04%). Critically, a mean comparison shows that issuers opportunistically float a significantly lower proportion of fresh shares during hot market periods (51.62%) compared to cold periods (63.51%), suggesting the exploitation of market waves for existing shareholder exit. Furthermore, Ordinary Least Squares (OLS) regression analysis reveals that a lesser proportion of fresh issue is significantly associated with a higher Price-to-Book (P/B) Ratio, indicating more aggressive valuation when existing shareholders dominate the offering. The findings support the notion that issue structure acts as a signal to investors and that issuers strategically utilize market conditions to maximize proceeds for both the company and selling shareholders, reinforcing the relevance of prospect theory in the Indian IPO context.

This The Wireless Air Quality Monitoring System is designed to monitor environmental conditions in real time using IoT technology. The system detects harmful gases present in the air using gas sensors. It also measures temperature and humidity using appropriate sensors. An ESP8266 microcontroller is used to process the collected data efficiently. The system continuously compares gas levels with predefined safe limits. When the gas concentration exceeds the threshold, a buzzer is activated to alert users immediately. The processed data is transmitted wirelessly using Wi-Fi technology. Users can monitor real-time data through the Blynk IoT mobile application. The system is cost-effective, easy to install, and suitable for both indoor and outdoor environments. Overall, this project provides a reliable solution for improving safety and environmental monitoring.

This study aimed to determine how savings behavior predicts the financial satisfaction of selected employees in PHINMA Araullo University. Specifically, it examined the respondents’ demographic profile, level of financial satisfaction in terms of current consumption ability, wealth accumulation, precautionary savings, and future income expectations, as well as their savings behavior in terms of attitude toward saving, subjective norms, perceived behavioral control, and saving intention. A quantitative descriptive-correlational design was employed, involving 231 teaching employees selected through stratified random sampling. Data were gathered using a structured questionnaire and analyzed using frequency, percentage, weighted mean, Pearson correlation, ANOVA, and regression analysis. Findings revealed that respondents demonstrated a moderate level of financial satisfaction and a positive level of savings behavior. Monthly income showed a significant relationship with financial satisfaction, while civil status significantly influenced both financial satisfaction and savings behavior. Regression analysis confirmed that savings behavior significantly predicts financial satisfaction (R² = 0.506). The study concludes that strengthening savings behavior can improve employees’ financial well-being. It recommends implementing financial wellness programs, savings initiatives, and institutional support systems to enhance financial stability among employees.

Generative Artificial Intelligence (GenAI), which refers to models capable of creating original outputs such as text, images, audio, 3D content, and code, is transforming creative industries at a rapid pace. This paper examines the influence of key generative approaches—including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), autoregressive language models, diffusion models, and multimodal systems—on workflows in areas such as art, design, animation, music, marketing, and software development. It outlines the study’s objectives and scope, and discusses underlying architectures, operational processes, and the necessary hardware and software infrastructure. In addition, the paper explores practical applications, advantages, and major challenges, including technical limitations, ethical concerns, and economic implications. The study concludes by proposing practical strategies for individuals and organizations to adopt GenAI responsibly, ensuring that innovation is balanced with the preservation of human creativity and broader societal values.

Modern agriculture faces a convergence of critical challenges: acute labour shortages, escalating agrochemical costs, inefficient manual seed sowing, and persistent weed infestations that collectively reduce crop yields by 20 - 40% in smallholder farms. This paper presents a Multipurpose Agriculture Robot, a low-cost, farmer-configurable, IoT-controlled robotic platform designed to address these challenges through a unified modular architecture. The proposed system integrates three primary operational units - chemical spraying and irrigation unit, a precision seeding unit and a cutting unit -  mounted on a common ESP32-based chassis equipped with servo-actuated extensible folding-arm mechanisms. The arms dynamically adjust irrigation and pesticide spray coverage width from 30 cm to 90 cm per side in real time without halting field operations, a feature not available in any existing low-cost agricultural robot. Optional attachments including a field - leveling tool can be added or removed via a standardized quick-connect modular tool bay, enabling season-specific farmer configuration. A dedicated mobile application communicates with the robot over Bluetooth and Wi-Fi, providing real-time directional control, arm angle adjustment, spray activation and cutting unit. Mathematical models govern irrigation water calculation using soil moisture feedback and seeding error minimization using motor-speed adjustment.

India maintains its position as the central hub which has driven cryptocurrency from its initial experimental phase into a global financial revolution. India leads the world in blockchain adoption because it has 119 million crypto users, which makes it the top country for blockchain adoption. The nation enforces a 30 percent flat tax on Virtual Digital Asset earnings. This does not allow taxpayers to reduce their tax burden through loss deductions while it also requires a 1 percent Tax Deducted at Source. The paper analyzes how India has developed its regulatory framework and studies the Finance Act 2022 tax system impacts, and Digital Rupee expansion, and Web3 startup network, and decentralized finance potential for financial inclusion in India. The study shows that India allows about 60 percent of cryptocurrency transactions to occur outside its borders because of its current regulatory system, which is based on information from RBI publications and government policy documents, and Supreme Court rulings, and IMF and FATF reports, and Chainalysis and CoinSwitch industry data, and financial journalism until early 2026. The paper demonstrates that India requires a single regulatory framework, which provides fairness and clarity, and future-oriented guidance to achieve its digital asset economy potential.

The Milk is a widely consumed nutritional product, but its quality is often affected by contamination and harmful residues such as antibiotics and pesticides. Ensuring milk safety using traditional laboratory methods is time-consuming and not suitable for real-time monitoring. This project proposes a Milk Residue Limit Monitoring System using IoT and sensorbased technology for continuous quality assessment. The system utilizes sensors such as pH and temperature to monitor key parameters of milk. The collected data is processed using a microcontroller like ESP32 or Arduino and transmitted to a cloud platform for remote monitoring. The system analyzes the data by comparing it with predefined safe limits to detect contamination or spoilage. An alert mechanism is incorporated to notify users through buzzers and mobile notifications when abnormal conditions are detected. This approach reduces manual effort and enhances transparency in the dairy supply chain. The proposed system is cost-effective, reliable, and suitable for real-time applications. Overall, it ensures safe milk consumption and improves food safety standards.

E-Commerce Multivendor Marketplaces offer a variety of products to the consumer all sold on a single digital platform; while multivendor e-commerce marketplaces enhance accessibility, variety, and convenience, they also introduce significant complexity into the consumer decision-making process. The consumers often undergo heightened awareness, the benefits offered, many a times become a bane to completing the purchase. Consumers face issues such as information overload, quality validation, trust and most importantly the decision regarding vendor selection from among the many. For the sellers unless they understand and address these issues, acquiring and retention of customers looks grim. This paper brings to the fore an exploratory context regarding consumer preference in vendor selection when buying on e-commerce multivendor marketplaces.

As organisations scale, structural coordination failures produce what this paper conceptualises as Leadership Entropy — the progressive loss of decision clarity, authority alignment, and coordination effectiveness. Drawing on practitioner observation across Indian and emerging-economy organisations and the organisational design literature, this study introduces the Decentralised Mosaic Model. Four interdependent structural pillars — Shared Purpose, Transparent Intelligence, Distributed Authority, and Dynamic Alignment — enable semi-autonomous teams to maintain strategic coherence without hierarchical control. Testable research propositions and practitioner diagnostics are offered.

This research discusses the salinity cycle of irrigated and rain-fed soils in the Integral University Agricultural Farm, Lucknow, Uttar Pradesh, India. Sustainable agriculture is a significant challenge facing soil salinization especially in arid and semi-arid areas where irrigation activities, climatic fluctuations and poor drainage further leads to the soil piling up the soluble salts in the soil profile. The research will be used to compare the main salinity parameters in two opposite land-use systems, irrigation and rain-fed, in the same agroecological environment. A comparative cross-sectional design was used and systematic sampling of soils was done on the surface layer (0 15 cm). Laboratory tests were conducted in accordance with the standard procedures to establish physicochemical parameters, such as pH, electrical conductivity (EC), total dissolved solids (TDS), chloride (Cl -), carbonate (CO 3 2-), and bicarbonate (HCO3-). The values were compared to the internationally accepted FAO salinity classification standards. The results show that there is a significant variation in the salinity levels of rain-fed and irrigated soils. Irrigated soils have a greater salt content as a result of continuous irrigation with groundwater, high evapotranspiration and low drainage. In comparison, rain-fed soils are less salty, and this is affected by seasonal rainfall and natural processes of leaching. The findings indicate the importance of irrigation activities and water quality in influencing the dynamics of soil salinity. This work presents a scientific background of the salinity changes in alluvial soils of the Indo-Gangetic Plains and highlights the fact that site-specific management measures, such as better irrigation, drainage and constant monitoring of the soils are necessary. The results are useful to sustainable land and water management and policy implementation designed to reduce soil erosion and guarantee agricultural productivity in the long term.

A self-coupled motor–generator feedback system is sometimes proposed as a method to sustain continuous power generation by feeding the electrical output of a generator back into the motor that drives it. In theory, if the output power equals or exceeds the input power, the system could operate continuously without external energy input. However, due to inefficiencies inherent in electrical machines and transformers, the total efficiency of such a system is always less than unity. This paper presents a detailed theoretical analysis of a motor–generator feedback loop and investigates whether the introduction of a step-up transformer can increase the feedback power sufficiently to sustain operation. Using energy conservation principles and efficiency models of the motor, generator, and transformer, it is demonstrated that the system cannot achieve self-sustained operation because total system losses always exceed any apparent voltage gain provided by a transformer.

Objective: Rapid urbanization, infrastructural deficits, and environmental degradation continue to challenge the sustainability of Nigerian cities. Buildings, which account for a significant proportion of energy consumption and material use, play a critical role in shaping urban sustainability outcomes. In recent years, the Internet of Things (IoT) has emerged as a transformative technology capable of enhancing building performance through real-time monitoring, automation, and data-driven decision-making. Simultaneously, growing interest in sustainable construction materials, particularly those derived from local, agricultural, and industrial wastes, has gained traction as a means of reducing embodied energy and environmental impact. This paper examines the convergence of IoT technologies and sustainable construction materials within the Nigerian built environment, positioning smart buildings as foundational components of emerging smart city initiatives. Methodology: The study adopts an extensive literature review combined with a contextual analysis of Nigerian case studies. It explores existing research on IoT applications in buildings and sustainable material use, while assessing their relevance and applicability within the Nigerian built environment. Key Result: The study finds that IoT-enabled systems can significantly improve building performance by supporting energy efficiency, water management, safety, maintenance, and the performance validation of eco-friendly materials. It also reveals that sustainable materials derived from local, agricultural, and industrial wastes can reduce embodied energy and environmental impact when effectively integrated with smart technologies. Conclusion: The paper concludes that integrating IoT technologies with sustainable construction materials provides a holistic pathway toward developing resilient, resource-efficient, and context-responsive buildings in Nigeria. However, challenges such as cost, inadequate infrastructure, policy limitations, and technical capacity must be addressed to enable scalable implementation across different building sectors.

Hepatitis E virus (HEV) is an emerging cause of acute viral hepatitis, particularly in regions with poor sanitation. This study assessed the seroprevalence of HEV among patients attending the Federal Medical Centre (FMC), Mubi, Nigeria. Methods: A cross-sectional study involving 100 consenting participants was conducted. Blood samples were analyzed for HEV antibodies using standard serological methods. Demographic data and HEV seroprevalence were analyzed and presented in tables and figures. Results: Out of the 100 participants, 28 (28%) were male and 72 (72%) female, with a mean age of 36.40 ± 13.20 years. Overall, 6 (6%) tested positive for HEV antibodies, while 94 (94%) were negative. HEV seroprevalence among females was 4 (83%) and 1 (17%) among males. However, the difference was not statistically significant (p = 0.98). The majority of participants were HEV-negative, suggesting low-level circulation of the virus in this population. Conclusion: The study found a moderate HEV seroprevalence (6%) among patients at FMC, Mubi. These findings highlight the need for improved hygiene practices and routine screening for HEV, especially in healthcare settings. Further research is recommended to better understand HEV epidemiology in Nigeria.

The COVID19 pandemic accelerated remote and hybrid work adoption, exposing organizations to insider threats, data breaches, and advanced cyberattacks, which traditional perimeter-based models failed to address; in response, Zero Trust Architecture (ZTA) emerged, and its integration with Artificial Intelligence (AI) has become a cornerstone of cybersecurity strategies by enabling anomaly detection, automated policy enforcement, and rapid incident response. Guided by PRISMA methodology and Rapid review principles, this study systematically examined 25 publications from 2020–2030 across IEEE Xplore, ACM Digital Library, MDPI, SpringerLink, Elsevier, government repositories, and open access archives, applying strict eligibility criteria to ensure methodological transparency and relevance. Findings consistently show that AI-ZTA integration mitigates insider threats, prevents data breaches, and strengthens resilience against advanced cyberattacks, with chronological analysis revealing a progression from foundational frameworks (2020–2023), to risk-oriented literature (2024), applied deployments (2024–2025), and predictive analyses (2025–2026). The review concludes that AI-ZTA is positioned as a critical paradigm for securing decentralized environments, though its long-term success depends on safeguards, workforce training, regulatory compliance, and continuous evaluation mechanisms. This scope and format are consistent with established practices in cybersecurity research, where recent studies have also synthesized fewer than 25 papers through rapid review methods to deliver timely, rigorous, and actionable insights in emerging fields.

Bridge failures represent a critical challenge in transportation infrastructure, leading to structural damage, traffic disruption, and significant safety risks. Analysis of failure data indicates that many bridge failures occur during the service stage, primarily due to design deficiencies, overloading, and inadequate maintenance. These issues highlight the disparity between theoretical design assumptions and actual field conditions, necessitating comprehensive performance evaluation. In this study, a reinforced concrete bridge is analysed using advanced numerical modelling techniques to evaluate its structural response under realistic loading conditions. Key parameters such as bending moment, shear force, deflection, and stress distribution are examined to identify critical zones susceptible to distress. The analytical results are correlated with observed failure patterns, including cracking, spalling, and material deterioration, to validate the structural behaviour. Based on this integrated analytical and observational approach, appropriate mitigation and strengthening strategies are proposed to enhance structural performance. The study emphasizes that bridge safety cannot rely solely on design compliance but requires continuous monitoring, maintenance, and performance-based evaluation. The findings contribute to improved bridge safety assessment by establishing a link between numerical analysis, failure mechanisms, and real-world operational risks.

To make India self-reliant and for making India a global leader in manufacturing, Govt of India launched ‘Make in India’ initiative in 2014. The healthcare sector comprises of hospital, pharmaceuticals, health-tech online services, medical insurance, medical devices and equipment, and medical tourism. The healthcare sector contributes significantly to the economic development by creating millions of jobs and accounts for more than 3.3% of GDP. India’s dense population is over 140 Crore and about 10% of the population is over the age of 60 years. With such a huge population base and sedentary life style, the healthcare sector is growing very fast and it promises an optimistic picture in the future. The present paper aims to understand the impact of Make in India on the healthcare sector in India. The paper revealed that India’s healthcare sector is very much dependent on imports. However, make in India initiative reduces to some extent the dependency on import by manufacturing some of the products, medicines and medical devices, but much more is required to be done.  The Govt should invest its resources through Make in India initiative in healthcare sector for not only making India self-dependent but also for contributing in export promotion and employment generation. Govt should also make reforms to strengthen the healthcare sector.

This study focused on the design and development of a web-based Human Resource Management System (HRMS) for The Great Plebeian College. This study addressed limitations in the institution’s existing manual, paper-based human resource processes, which can be inefficient, time-consuming, and highly prone to data inaccuracies. These encountered difficulties made it clear that a reliable, efficient, and technology-driven HR management system solution was necessary. The developed system used the Rapid Application Development (RAD) model, which emphasized iterative development, rapid prototyping, and constant end-user involvement throughout the system development lifecycle. Using the Rapid Application Development (RAD) methodology enabled regular feedback and timely feature refinement by ensuring close alignment with actual institutional requirements. The developed HRMS included system features such as centralized employee information management, QR code–based attendance monitoring, leave requests processing, role-based access control, and report generation. Based on the ISO 25010 quality attributes, our evaluation found that the HRMS significantly improved the operational efficiency, data accuracy, security, and accessibility of HR records. Thus, the study documented a smooth transition from traditional manual HR management to a web-based solution that provides employees with an accessible digital platform, highlighting the role of information technology in improving management of an institution such as The Great Plebeian College.

Asthma is a lung disease that is a chronic respiratory disease in millions of people worldwide and, in most cases, leads to a lower quality of life and high healthcare expenditure. As early as possible, it is essential to ensure the management and prevention of serious exacerbations. The objective of the study is to come up with an explainable machine learning (ML) model, which exploits clinical and environmental data to forecast the risk of asthma in a person. The dataset combines patient-related clinical characteristics, such as age, symptoms, medical history, and results of spirometry, with the environmental variables of air pollution, humidity, and temperature. The approach will include training and testing various trained learning algorithms, such as Logistic Regression, Randome Forest, and XGBoost. SHAP and LIME are explainable AI methods that are used to achieve transparency, measure feature importance, and describe the explanation of individual predictions. The standard measurements of model performance such as accuracy, precision, recall, F1-score and ROC-AUC are used to evaluate model performance, ensuring predictive reliability and clinical relevance. Among the main results, it is possible to note that XGBoost gives the best predictive results in all measures, and the analysis of feature importance shows that the level of PM 2.5, humidity, wheezing, shortness of breath and the results of spirometry can be considered the most significant. Explainability analysis states that the predictions of the model are interpretable, which contributes to a better understanding of the model and clinical trust. Finally, the paper shows that a combination of clinical and environmental data with elucidatable machine learning offers a strong and clear framework to detect asthma at its initial stages. The method improves predictive power, enables informed medical decision-making, and provides a base of applied practice in healthcare systems, which ultimately increases patient outcomes and the adoption of explainable AI in respiratory medicine.

This study aims to analyze the influence of transformational leadership on employee performance and examine the role of employee engagement, work environment, and work culture as mediating variables at PT. Gading Kelola Sukses. The study used a quantitative explanatory approach with the Partial Least Squares–Structural Equation Modeling (PLS-SEM) method. The results show that transformational leadership has a positive and significant effect on employee performance, as well as increasing work engagement, work environment, and work culture. However, not all mediating variables showed strong statistical significance, so the mediating role that occurred was limited to partial. These findings confirm that improved performance depends not only on leadership style but also on the psychological, social, and cultural conditions established within the organization. Practically, this research provides recommendations for companies to strengthen cultural values, enhance a supportive work environment, and encourage employee engagement to optimize performance.

Paternity fraud, defined as the deliberate or inadvertent misattribution of biological fatherhood, has emerged as a critical social, legal, and psychological issue within contemporary Nigerian society. This study examines the causes of paternity fraud, the preventive strategies available, the counselling approaches and interventions required for affected individuals, and the mechanisms for victims’ compensation and legal redress in Nigeria. Adopting a descriptive and analytical approach, the study draws on existing literature, legal frameworks, and socio-cultural perspectives to explore the multifaceted dimensions of paternity fraud. The findings reveal that socio-economic pressures, weak legal enforcement, patriarchal family structures, stigma surrounding infertility, and limited access to affordable DNA testing significantly contribute to the occurrence of paternity fraud in Nigeria. Preventive strategies identified include public awareness campaigns, premarital and postnatal counselling, routine access to paternity testing, and strengthened legal and institutional safeguards. The study further highlights the importance of professional counselling in addressing the psychological trauma, identity disruption, and family instability experienced by victims. In addition, the paper examines gaps in Nigeria’s legal system concerning compensation and proposes policy reforms aimed at ensuring justice, restitution, and emotional rehabilitation for affected individuals. The study concludes that a coordinated response involving legal reform, counselling services, and public education is essential for addressing paternity fraud and safeguarding family integrity in Nigeria.

This study examines consumer awareness and usage of biodegradable bags with particular emphasis on environmental sustainability and behavioral intention. In recent years, the excessive use of plastic materials has resulted in severe environmental degradation, prompting the need for eco-friendly alternatives such as biodegradable bags. The study aims to assess the level of awareness, usage patterns, and key determinants influencing consumer adoption. A descriptive research design was employed, and data were collected from 175 respondents using a structured questionnaire. The study utilized percentage analysis, chi-square test, correlation, and regression analysis to interpret the data. Findings reveal that although consumers exhibit a relatively high level of awareness, consistent usage remains moderate due to constraints such as price sensitivity, limited availability, and perceived product quality. The results further indicate that environmental concern significantly influences usage behaviour, while price acts as a major barrier. The study highlights the need for strategic interventions, including awareness campaigns and policy support, to promote sustainable consumption practices.

With the rapid advancement of machine learning (ML), large-scale data collection has become essential for building accurate models. However, the use of sensitive data introduces significant privacy risks, including data leakage, unauthorized access, and inference attacks. Privacy-Preserving Machine Learning (PPML) has emerged as a crucial research area aimed at enabling data-driven learning while protecting individual privacy. This paper provides a comprehensive overview of major PPML techniques such as homomorphic encryption, differential privacy, secure multi-party computation, and federated learning. It also discusses key challenges including computational overhead, privacy-utility trade-offs, scalability issues, and regulatory concerns. Finally, future research directions are highlighted to guide the development of secure and efficient machine learning systems.

Human decision-making occupies a complex intersection between economic rationality and psychological reality. Classical economic theory posits that individuals select among alternatives in a manner that maximises utility; however, behavioural research consistently demonstrates that real-world choices are shaped by cognitive biases, emotional states, social influences, and the architecture of the choice environment. This paper examines the theoretical and empirical foundations of the psychology and economics of choice, with the objective of providing a comprehensive account of why individuals systematically deviate from purely rational behaviour. Drawing on Herbert Simon's concept of bounded rationality, Kahneman and Tversky's Prospect Theory, heuristic-based decision models, and Thaler and Sunstein's nudge theory, the study analyses the mechanisms through which deviations from rational choice can be predicted and, where appropriate, corrected. Key cognitive biases reviewed include anchoring, framing effects, loss aversion, the halo effect, the endowment effect, hyperbolic discounting, the decoy effect, confirmation bias, status quo bias, the bandwagon effect, availability bias, and overconfidence bias. The study further explores the paradox of choice—the counterintuitive finding that an excess of options diminishes rather than enhances decision satisfaction—and the practical applications of nudge theory across public health, personal finance, and public administration. The results of this conceptual analysis indicate that individual decision-making is not a product of deliberate rational computation but rather the outcome of a dynamic interaction between cognitive limitations, emotional states, and environmental framing. By integrating insights from behavioural economics and cognitive psychology, this paper argues that thoughtfully designed choice environments can guide individuals toward welfare-enhancing outcomes without undermining personal autonomy.

Curcumin, widely used as spice in Asia is found in rhizome of turmeric plant (Curcuma longa and Curcuma xanthorrhiza). It shows antibacterial, anti-inflammatory, hypoglycemic, antioxidant, antimicrobial activities and capable of treating fatal disease like cancer, Parkinson’s disease. It is highly unstable in aqueous solution specially in high pH condition makes it deceptively useful. In this this review we have shown several techniques and schemes that made curcumin a potent useful drug. Forming complex with proteins, lipids, lipoproteins, cyclodextrin and bio-mimicking molecules is effective to increase bioavailability of this hydrophobic molecule. Nature of interactions and binding mechanism with those molecules are discussed here. Most important aspect of this review is to deliver curcumin inside live cells and finding the effects on cell death (apoptosis) and proliferations. Different drug delivery systems have been developed which show excellent rate of internalisation. Curcumin has been delivered intravenously in live mouse model.

This study uses a qualitative case study methodology to investigate the investment methods used by High Net Worth Individuals (HNIs) in India. Understanding how HNIs allocate assets, manage risk, and make investment decisions has become increasingly important as India's wealth management landscape rapidly changes due to market volatility, digital innovation, and regulatory reforms. This study reveals important themes, such as asset diversification, advisor reliance, risk appetite calibration, and the impact of behavioral factors on investment choices, through an analysis of four illustrative case studies that represent various HNI profiles. The results imply that a complicated interaction between financial objectives, market information, and individual risk psychology shapes HNI investment strategies. This study adds to the expanding corpus of research on emerging market wealth management and behavioral finance.

The continuous advancement of digital technology has encouraged public healthcare institutions to adopt computerized systems to improve service delivery, efficiency, and patient satisfaction. This study focuses on the development and evaluation of a web-based queuing system for the Municipal Health Center of Mabini, which continues to rely on manual and paper-based queuing processes. These traditional methods often resulted in long waiting times, overcrowding, and difficulties in managing patient flow. Data for the study were gathered through structured survey questionnaires distributed to selected healthcare staff and patients of the health center to assess the performance and effectiveness of the developed system. The system was developed using the Rapid Application Development approach and designed following a three-tier architectural structure to ensure organized data processing, system reliability, and ease of maintenance. The web-based queuing system enables faster patient registration, systematic queue management, and real-time monitoring of queue status. The quality of the system was evaluated based on the software quality standards of the International Organization for Standardization. Healthcare personnel and patients served as respondents in the system evaluation. The results revealed an overall weighted mean of 4.43, interpreted as Excellent, with high ratings in efficiency, security, and acceptability. The findings indicate that the developed web-based queuing system effectively addresses issues related to long waiting times and unorganized patient flow. The study concludes that the system provides a reliable, secure, and acceptable digital solution that can enhance service delivery in community healthcare centers.

Sovereign digital money is becoming a policy reality in economies at every level of development, rather than just a theoretical idea. By comparing India's e-Rupee project with initiatives in China, the European Union, the United States, the Bahamas, Nigeria, and Sweden, this study examines the conceptual, technological, and macroeconomic aspects of Central Bank Digital Currencies (CBDCs). Based on an interdisciplinary evaluation of current research (Agur et al., 2022; Cunha et al., 2021; Kosse & Mattei, 2023), the study unifies regulatory ideologies, architectural decisions, and disparate national agendas into a single analytical framework. A sizable body of research warns against underestimating privacy erosion, disintermediation pressure on commercial banks, and growing cyber threats (Tronnier & Qiu, 2024; Wenker, 2022; Vucic & Luburic, 2022), despite the transformative promise of CBDCs encompassing monetary policy precision, payment system modernization, and financial inclusion (Yang & Zhou, 2022; Echarte Fernandez et al., 2021). With India serving as the main reference case, the study summarizes these conflicts and suggests a five-pillar governance architecture adapted to the circumstances of rising market countries.

The rapid integration of Generative Artificial Intelligence (GAI) into university ideological and political education has created new pedagogical opportunities while simultaneously introducing significant risks. While existing research has produced valuable macro level warnings concerning ideological security, discursive ethics, and teacher authority erosion, these warnings remain largely at the level of principle based alerts. They lack the granularity needed for frontline teachers to recognize and respond to specific risks in actual teaching scenarios. This study addresses this gap by shifting the analytical focus from macro warnings to micro risks. Employing a mixed methods approach that includes a survey of 500 ideological course instructors, semi structured interviews with teachers and students, and classroom observations across multiple universities in Jiangxi Province, the study systematically identifies risk manifestations across four teaching scenarios: lesson preparation, classroom instruction, interactive sessions, and assessment activities. The findings reveal concrete, observable, and intervenable micro risks, such as GAI generated case studies that subtly weaken the Party's leadership narrative, students uncritically copying AI generated answers, and automated grading systems that fail to detect ideologically problematic statements. A preliminary micro risk taxonomy is developed to make these risks visible, nameable, and actionable. The study further identifies three causal logics underlying these risks: technological bias embedded in training data, institutional absence of review mechanisms, and cognitive blind spots among teachers who mistakenly trust GAI neutrality. Theoretically, this study shifts the field from macro warnings to micro identification. Practically, the micro risk taxonomy provides a diagnostic tool that frontline teachers and administrators can use to recognize and respond to GAI risks in their daily work.

Database speed depends on how fast systems read and write data. Storage hardware plays a major role in this process. This study compares Hard Disk Drives and Solid-State Drives to see which handles database tasks better. HDDs use moving parts and spinning platters. These mechanical parts create delays when the system looks for data. SSDs use flash memory. This design allows for faster access and shorter wait times. This research used a descriptive method to gather information. The process involved running basic database tests and surveying IT lecturers. The tests measured query response times and data loading speeds. They also tracked disk write speeds. The survey asked lecturers about common problems they see in school computer labs. Results show SSDs perform better in every category. Query responses happen faster. System lag disappears. The findings prove storage type has a strong effect on database efficiency. Even simple systems show better results when using SSDs. IT lecturers reported fewer system crashes and faster boot times when using flash storage. Students finished their tasks faster when the lab computers used newer drives. HDDs struggle with random data access because the physical head must move to the correct spot on the disk. SSDs skip this step. Large data sets load in half the time when using an SSD. Database will handle more users at once without slowing down. This study helps students and schools understand why hardware choice matters. You get better performance by choosing the right drive. Proper hardware reduces frustration for developers and users. High speed storage makes database management easier for everyone. Your system stays stable even under heavy workloads. Investing in SSD technology improves the learning experience in technical courses. Hardware limitations should not stop your progress. Choose SSDs to ensure your database runs at its best speed. Better hardware leads to better results for your projects.

In the modern technology-oriented world, identifying medicinal plants has become very important for healthcare, biodiversity preservation, and the development of natural medicines. Traditional methods of plant identification mainly depend on expert knowledge and manual inspection, which makes the process slow and sometimes inaccurate. To address these challenges, Pso-CascadeNet presents an intelligent deep learning–based system that can recognize medicinal plants using images of their leaves. The system uses Convolutional Neural Networks (CNNs) to extract visual patterns from images, Particle Swarm Optimization (PSO) to automatically tune model parameters, and Support Vector Machines (SVM) to improve the accuracy of classification. A simple and interactive interface built with Streamlit enables users to upload leaf images and receive instant predictions, while FastAPI supports smooth backend communication and deployment. Performance evaluation using metrics such as accuracy, precision, recall, and F1-score demonstrates that the hybrid CNN–PSO–SVM model performs better than traditional classification techniques. Overall, the proposed framework offers a dependable, scalable, and user-friendly approach for digital identification of medicinal plants, benefiting research, learning, and sustainable use of herbal resources.

Quadrotor unmanned aerial vehicles are nonlinear and underactuated systems whose control performance is strongly affected by model uncertainties and external disturbances. This paper presents an adaptive nonlinear control approach for quadrotor UAV trajectory tracking. The proposed strategy is developed from an adaptive fuzzy control framework for a general second-order nonlinear system, in which the control signal is generated through a Sugeno-type fuzzy structure and the controller parameters are updated online according to adaptive laws derived from the Lyapunov stability criterion. The resulting control architecture is then extended to the position and attitude subsystems of the quadrotor through six control channels with corresponding adaptive parameter update laws. Simulation results show that the quadrotor can follow the prescribed trajectory with stable position and attitude responses, while the tracking errors remain bounded and decrease toward a small neighborhood of zero. The results indicate that the proposed adaptive nonlinear control structure provides good adaptability and satisfactory tracking performance under the considered operating conditions. Therefore, the proposed approach is a feasible solution for quadrotor UAV control in the presence of uncertainties and disturbances.

This paper presents a structured review of flight control strategies for quadrotor unmanned aerial vehicles and identifies key research gaps affecting reliable operation under uncertain conditions. The study applies a systematic classification and analytical comparison of control approaches, including linear control, nonlinear Lyapunov-based methods, sliding mode control, adaptive and observer-based control, predictive control, and learning-enhanced strategies. Based on this review, a backstepping-based trajectory tracking controller is developed as a representative case study. The quadrotor dynamic model is established using Newton–Euler equations and organized into a cascade control structure with an outer-loop position controller and an inner-loop attitude controller. Stability of the closed-loop system is ensured using Lyapunov theory. Simulation results show that the proposed controller achieves accurate trajectory tracking with position and attitude errors converging to zero within approximately 5–8 seconds, while maintaining stable and feasible control inputs. The results confirm that backstepping provides strong theoretical stability and good tracking performance under nominal conditions; however, its robustness remains limited when disturbances and uncertainties are present. Therefore, integrating disturbance observers and adaptive mechanisms into backstepping control is identified as a promising direction for improving robustness and practical applicability in quadrotor control systems.

Cyber crime incidents in India have escalated in frequency and complexity, impacting individuals, organizations, and critical infrastructure. Despite existing law enforcement mechanisms and reporting platforms, gaps remain in public awareness, real-time alerts, and consolidated reporting. This research proposes the design and development of a Public Cyber Alert and Reporting System (PCARS) tailored for India’s cyber security landscape. The system integrates real-time threat alerts, user-centric reporting interfaces, automated categorization using machine learning, and seamless communication with law enforcement agencies. Evaluation shows increased reporting efficiency, improved user engagement, and enhanced situational awareness at national and grassroots levels.

Harithakarmasena (HKS) is a comprehensive women empowerment model developed for Solid Waste Management in rural and urban Kerala. It is managed  by Local Self Govts and Kudumbashree. Many women workers were got employment opportunities under this venture. Eventhough it is a successful opportunity for women labour participation many challenges are existing.  The present study focusing socio- economic status of HKS workers and their problems in this field. Kannur district of kerala selected as study area. 60 HKS workers of Kannur corporation were randomly selected for the study. Data collected through mailed questionnaire and in-depth interview also conducted with workers.   Simple statistical tools, percentage, average- mean, median, pie diagrams were used for analysis. Age, religion, education, maritial status, family type, remuneration, partner’ job, saving pattern, debt and repayment of debt, reason for preferring this job were analysed. The study revealed that there is substantial change in the Socio Economic conditions of Harithakarmasena workers. 35-45 is the median of age group and Plus two is the mean education of HKS in Kannur corporation.70% of the workers were married and most of their partners working informal sector. The salary of HKS ranges from 8000- 12000 rupees with the target of covering 55 households. Most of the workers engaged informal jobs before this venture where their salary ranges from 5000-6000 rupees. Most of the workers (38.33%) preferred this job expecting future benefits and 18.33% of workers were preferred for environmental concern. As HKS are connected with kudumbasree, all the workers have thrift behaviour. There is no regular investment behaviour among the HKS workers. 80% of the HKS workers have debt out of which 70% started repayment after got job in Harithakarmasena. Lack of corporation from some households, no restroom and toilet facilities make challenges for their working. Health issues like allergy, backpain and body pain are also hurdle for HKS in Kannur corporation.

Existing AI regulatory frameworks, including the EU AI Act, the General Data Protection Regulation (GDPR), and industry standards such as IEEE Ethically Aligned Design and ISO/IEC 42001, have demonstrated structural inadequacy in preventing ethical failures arising from AI-driven business decision-making. Responding to these documented deficiencies, this paper proposes and evaluates an Integrated Ethical AI Governance Framework (IEAGF) comprising four novel, complementary mechanisms: (1) Pre-Deployment AI Impact Assessments (AIIA), which mandate bias auditing, fairness evaluation, and stakeholder impact mapping before system deployment; (2) Explainable AI with Algorithmic Contracts (XAI-AC), which legally bind AI systems to defined behavioural parameters and transparency obligations; (3) Ethics-by-Design (EbD) Frameworks, which embed ethical principles, fairness constraints, and stakeholder inclusivity into AI development lifecycles; and (4) Algorithmic Sustainability Indices (ASI), which introduce standardised metrics for quantifying the energy consumption, socioeconomic impact, and renewable infrastructure usage of AI deployments. The IEAGF is evaluated against established practicability criteria across sectors including finance, healthcare, and logistics. Feasibility analysis demonstrates that the framework is implementable across organisational scales, aligns with existing ESG disclosure obligations, and provides regulators with enforceable technical benchmarks absent from current frameworks. The IEAGF represents a shift from reactive compliance to preventive ethical governance, grounded in both technical operationalisability and institutional accountability.

The aviation industry has increasingly leveraged artificial intelligence (AI) to enhance pilot performance, flight safety, and training efficiency. Among emerging AI technologies, Transformer-based models have shown exceptional capabilities in understanding complex sequences, processing large volumes of data, and generating predictive insights. In the context of pilot assistance and flight simulations, AI Transformers can analyze flight parameters, environmental conditions, and pilot behavior in real-time to provide intelligent decision support. They enable adaptive simulation scenarios, realistic virtual training environments, and predictive risk assessment, allowing pilots to practice emergency procedures and optimize decision-making under varied conditions. By integrating natural language processing, these models can also facilitate intuitive interaction between pilots and cockpit systems. This abstract explores the role of AI Transformers in modern aviation training and operational support, highlighting their potential to improve safety, efficiency, and the overall effectiveness of pilot training programs.

As electric vehicles gain traction across the globe, one persistent worry among drivers is whether their battery will last long enough to reach the next charging point, a concern commonly referred to as range anxiety. In this paper, we describe a practical route planning tool that tackles this problem head-on. At its core sits a Gradient Boosting Regressor trained on 20,000 synthetically generated trip records whose parameters are rooted in real-world physics. The model takes in the vehicle type, how much cargo is on board, the trip distance, driving speed, terrain changes, and outside temperature, and outputs an energy consumption estimate. On the server side, a FastAPI application pulls together driving directions from OSRM, live weather readings from OpenWeatherMap, elevation data from Open-Elevation, and nearby charger locations from OpenChargeMap. A step-by-step greedy algorithm then figures out where the driver should stop to recharge, while also factoring in how much the battery may have degraded over time. The accompanying mobile app, built with Flutter, shows the planned route on an interactive map and even works offline thanks to local caching. In our tests, the prediction model achieved an R² above 0.95 on unseen data.

This study examines the growing significance of social entrepreneurship as an emerging business paradigm that integrates economic objectives with social impact. In the Indian context where persistent challenges such as unemployment, socio-economic inequality, and the rural urban divide continue despite rapid advancements in digital and financial technologies social enterprises have evolved as innovative and sustainable solution providers. With particular reference to SELCO Foundation and the Chekutty Dolls Project, the study explores the role of social entrepreneurship in promoting sustainable community development in Kerala. The SELCO case highlights how decentralized renewable energy solutions can enhance livelihoods, improve access to healthcare and education, and foster financial inclusion among marginalized populations. In contrast, the Chekutty initiative illustrates how grassroots innovation and upcycling practices can facilitate post-disaster economic recovery, support traditional artisans, and contribute to environmental sustainability. The findings indicate that social enterprises play a pivotal role in addressing complex societal challenges while advancing social inclusion, ecological responsibility, and employment generation. Furthermore, the study underscores the importance of integrating experiential and research-driven social entrepreneurship education into university curricula to nurture future change-makers. Overall, social enterprises emerge as critical drivers of inclusive and sustainable development in India, with particular relevance to the socio-economic landscape of Kerala.

This study examines digital transformation and institutional pedagogical models at Citizens SS-Ibanda, a secondary school in Ibanda Municipality, Western Uganda. The research explores how artificial intelligence (AI), Information and Communication Technology (ICT) tools, e-learning, and blended learning reshape teaching, learning, and administration in a low-resource context. Guided by technology-enhanced learning, constructivist pedagogy, and institutional change theory, the article reports on infrastructural realities, teacher and student readiness, and leadership practices. Findings indicate that while digital tools enhance learner engagement and institutional efficiency, inconsistent power supply, limited internet access, and inadequate teacher training remain systemic constraints. The study concludes with policy and practice recommendations for embedding AI-supported blended pedagogy into Uganda’s secondary-school systems, particularly in urban and peri-urban secondary schools such as Citizens SS-Ibanda. As a single-school case study, findings should be interpreted with caution; future research should extend this design to multiple schools and incorporate direct student voice to strengthen generalisability and capture learning outcomes more fully.

Clustering is the task of grouping a set of objects in such a way that objects in the same group (cluster) are more similar  to each other than to those in other groups (clusters).  In this paper, a new procedure of linkage hierarchical clustering method (Variance-inter-quartile Range Hierarchical clustering method) was developed to reduce the effect of extreme values in the classification of groups into clusters. The new proposed method makes use of the variance to get the variance matrix and inter-quartile range for the construction of the dendogram which is used for the comparative analysis of the newly proposed method and the existing methods. A performance evaluation was carried out using the visual dendogram inspection and silhouette score on the newly proposed method VIQR (Variance-inter-quartile Range Hierarchical clustering method) and the existing methods (Single and Complete linkage). From the result, using visual dendogram inspection, It was observed that VIQR Hierarchical clustering method performs better than the existing methods for the classification of objects as it has similar dendogram with fewer steps in the classification procedure. Using Shilouette score as a method of validation of the clusters, from the result, the score for the proposed method is 0.53 which is higher than the scores of the existing methods; single linkage (0.23) and complete linkage linkage (0.30). In summary, VIQR is more efficient and robust classification method in terms of shorter algorithm, better control of extreme values, and less complexity. Therefore the VIQR should be adopted  as a better method for classification of observations or variables especially when dealing with problems that have to do with classification.

The rapid advancement of emerging technologies, particularly blockchain and artificial intelligence (AI), is transforming the traditional landscape of the accounting profession. This study explores the impact of these technologies on accounting practices, auditing processes, financial reporting, and accounting education. Blockchain, with its decentralized, immutable, and transparent ledger system, has the potential to enhance data reliability, reduce fraud, and enable real-time auditing. Simultaneously, AI-driven tools and automation are reshaping routine accounting tasks, improving efficiency, and supporting data-driven decision-making. This research adopts a descriptive qualitative approach, collecting primary data through structured questionnaires from Chartered Accountants and insights from professionals associated with leading audit firms, alongside secondary data from academic literature and industry reports. The study examines key hypotheses related to the influence of blockchain adoption on accounting curriculum, audit quality, accounting standards, and overall financial reporting practices. The findings suggest that the integration of blockchain and AI significantly improves audit quality, enhances transparency, and reduces operational costs, while also demanding a shift in the skillset of accounting professionals toward analytical and technological competencies. However, challenges such as scalability, interoperability, and potential job displacement remain critical concerns. The study concludes by recommending curriculum reform, professional re-skilling, and strategic adoption frameworks to facilitate the effective integration of these technologies into the accounting profession.

The reliability of joints in plastic assemblies depends strongly on the interaction between material behavior, joining technique, and loading conditions. In this study, Engineering components made from PMMA and PC-ABS were joined using two different methods: laser welding and adhesive joining. The objective is not a full comparative evaluation, but rather to understand the joint formation mechanisms and assess their mechanical response under typical service-level loading. This study primarily focuses on numerical simulation. However, experimental validation and fatigue behaviour analysis are recognised as scope for future research. Numerical simulations were carried out in LS-DYNA R12, where thermal and mechanical coupling were used to model the laser-welded interface, while a cohesive-zone approach represented the adhesive bond line. These models were used to predict pull strength and shear strength, capturing stress distribution, damage initiation, and failure progression for each joint type.

Privacy Preserving Data Publishing (PPDP) remains a critical challenge in the era of large-scale data sharing, where the need to balance data utility and individual privacy is inherently conflicting. Among existing models, k-anonymity continues to be widely adopted due to its simplicity and interpretability; however, traditional k-anonymization algorithms suffer from key limitations, including distribution-agnostic partitioning and inadequate handling of outliers, which lead to excessive information loss and reduced data utility. This paper proposes RAYDEN, a novel hybrid k-anonymization algorithm that integrates distribution-aware VP-tree partitioning with Connectivity-based Outlier Factor (COF) detection to address these limitations. The algorithm employs Gower distance to support mixed-type datasets and introduces a statistically adaptive threshold for robust outlier identification. Unlike existing approaches, RAYDEN incorporates a recursive outlier recovery mechanism that re-partitions detected outliers, maximizing data retention before applying suppression as a last resort. Experimental evaluation on the UCI Adult dataset demonstrates that RAYDEN consistently outperforms compared algorithms across key utility metrics utilized in the study. The outlier recovery mechanism achieves a mean recovery rate exceeding 90% across all k values, substantially reducing suppression-related information loss compared to Mondrian with COF. While incurring higher computational cost, the algorithm achieves practical execution times and significantly improves the privacy–utility trade-off, particularly at commonly used k values. These results establish RAYDEN as a robust and effective framework for privacy-preserving data publishing in mixed-type datasets.

Deaf and mute individuals in India and across the globe face a multidimensional set of challenges that transcend the limitations of hearing or speech impairments alone. These challenges encompass social, educational, psychological, economic, and intersectional dimensions that vary considerably across generational cohorts. Despite significant legislative advances, including India's landmark Rights of Persons with Disabilities (RPWDA) Act 2016, a persistent and critical gap remains in the effective empowerment of social workers to provide comprehensive, generation-specific, and culturally competent support to deaf and mute populations. This study explores the role of social workers in empowering deaf/mute individuals by integrating contemporary psychological principles with evidence-based social work practices. Particular emphasis is placed on understanding generational differences in coping mechanisms, access to resources, social integration pathways, and mental health outcomes. The research further investigates the transformative role played by the Indian Sign Language Research and Training Centre (ISLRTC) in standardizing and promoting Indian Sign Language (ISL) as a bridge between deaf communities and the broader hearing society. Psychological resilience emerges as a central unifying theme throughout this research. The study examines how personal traits, family dynamics, peer support systems, community networks, and professional interventions collectively shape the resilience capacities of deaf/mute individuals across generations. Special attention is directed to the factors that differentiate resilient from non-resilient adaptation trajectories, with a view to identifying leverage points for social work intervention. The research adopts a mixed-method design, combining quantitative surveys with qualitative interviews and focus group discussions. The sample encompasses social workers, deaf/mute individuals across three generational cohorts, and their family members drawn from both urban and rural settings across three Indian states. The study also critically examines the intersectionality of disability with gender, caste, religion, and socioeconomic status in the Indian context, offering perspectives that are largely absent from existing literature. The ultimate goal is to develop an evidence-based framework for training and deploying culturally competent, psychologically informed social workers who can serve as catalysts for the inclusion, resilience, and comprehensive well-being of deaf/mute individuals. The findings are expected to inform social work training curricula, disability policy formulation, ISLRTC program development, and community-based rehabilitation practice.

Ahead of the 2024 presidential election in the Republic of Indonesia, a number of names have begun to emerge from various political parties as potential presidential or vice presidential candidates. Several surveys have been conducted by various parties. Prabowo Subianto is one of the top three names from a survey of national leaders, he is a figure known to all people in this country. The Minister of Defense who is also the current chairman of the Gerinda Party. This study will photograph how student activists perceive Prabowo Subianto's personal branding as a candidate for President of the Republic of Indonesia on his Instagram, namely @prabowo. The source of this research comes from interviews with informants who are student activists as voter and documentation from Instagram @prabowo. This research uses descriptive qualitative research methods and aims to find out what factors determine followers' perceptions and how these perceptions are formed. The theory used in this study is the theory of perception and 8 characteristics of personal branding. The results of this research are the perceptions generated by the four informants who have responses that lead to the figure of Praboowo with firm, wise and nationalist leadership as a candidate for president of the Republic of Indonesia in 2024. They agree that there is potential in the next election with the work displayed by Prabowo, who is currently in office as Minister of Defense and saw that the influence of Prabowo Subianto's background which is a former military general has a power on the perception of informants, where from there one can see a promising attitude as a leader. Perceptions are formed through the interaction of experience, values, and media exposure. Informants assessed Prabowo's personal branding positively, particularly in terms of leadership, nationalism, closeness to the people, and competence.

Urban waste management remains a critical challenge in rapidly expanding Indian cities, especially South Mumbai, making it absolutely necessary to transition towards integrated sustainable practices due to increasing population density and limited disposal space. This review evaluates localized initiatives across educational institutions, places of worship, and local government as key catalysts of change, supported by indicative quantitative data and performance metrics Educational institutions, such as Jai Hind College, Siddharth College, KC College, act as institutional models to promote behavioral change through active waste segregation and management and on-site composting, serving as micro-models of sustainability. Similarly, places of worship like the Atmaram Buwa’s Kalaram Temple integrate spiritual traditions with ecological stewardship by composting floral offerings. Local government bodies, particularly in D ward, introduce policy-driven innovations like waste-to-manure conversion machines, promoting a circular urban metabolism. These efforts, supported by municipal representatives, reduce landfill burden. Emerging household-level innovations, such as domestic composting kits and clothing recycling, further decentralize waste management. Collectively, these multi-scalar examples underscore a significant transition in South Mumbai. The interplay of institutional, cultural, and governmental roles offers a comprehensive framework for how localized, stakeholder-driven initiatives can inform India’s broader urban sustainability agenda, despite challenges like inconsistent monitoring. Addressing these gaps through collaboration is essential for mainstreaming sustainable urban governance. (UN-Habitat, 2018)

This research paper communicates the investigations of  variation of Ultrasonic and thermoacoustic parameters with temperature of   a technically important Liquid crystal 8OCB (4′-octyloxy-4-cyanobiphenyl). Parameters like adiabatic compressibility, Molar sound velocity, Molar compressibility and intermolecular free length and also certain thermoacoustic parameters like., isochoric temperature coefficient of internal pressure (χ), isochoric temperature coefficient of volume expansivity (χ′), pressure co-efficient of bulk modulus (c₁), reduced compressibility (β̄), reduced volume (v̄) and Sharma constant (S₀) are also evaluated. The anomalous behaviour observed in ultrasonic and thermoacoustic parameters provide supporting evidence to the smectic A- nematic transitions at 66°C and nematic- isotropic transition at 78°C of the liquid crystal under study.

It is basic mathematics which is the critical foundation that gives the predictability of the overall success of a student during his or her whole course of study. The delivery of knowledge in the contemporary educational setup entails the application of many different modalities of learning, ranging from simple printed handouts to complicated computerized programs. More importantly, the active learning process is not limited to the school environment, but it continuously goes on even at home. This paper provides a comprehensive developmental history and systematic analysis of BattleMath, which is a narrative-based mobile application designed as a universal learning tool in Grade 5 mathematics. The educational tool has been carefully created in consideration of the requirement of the national curriculum with reference to the Agile Development Model and the Mechanics-Dynamics-Aesthetics (MDA) model. The application directly tackles the psychological obstacle of math anxiety by creatively encoding the abstract division problems into a thrilling hero-villain experience. The player takes the adventurous role of Arlo, and he must successfully defeat the antagonist, Chronus, by being consistently accurate in his mathematical calculations. The researcher performed a detailed descriptive study to test the prototype, which involved gathering the necessary assessment data of Grade 5 students and their teachers at Maawi Elementary School, which served as the primary research site. It was found that the application has high technical quality, achieving an Excellent rating on Usability and a Very Good rating on Functional Suitability. Such highly beneficial acceptability scores indicate that BattleMath is acceptable and usable as a supplementary tool alongside conventional teaching strategies in various primary learning environments.

This study developed an asset integrity management strategy to ensure continuous safe operation of the 24in Trans-Niger pipeline case-study till its design end-of-life in 2025 and life-extension thereafter, despite low flow conditions and associated ongoing Microbiologically Induced Corrosion (MIC). The case-study for the work is a 24inches by 55km carbon steel oil pipeline which was commissioned in 1995 to evacuate production from four (4) flow-stations to an export terminal. In the course of the work, a critical analysis of the current pipeline integrity management system (PIMS) in place against industry best practices was carried out. A Corrosion Management System (CMS) for the pipeline asset was developed by defining a company-wide corrosion policy for this pipeline, conducting a corrosion risk assessment for the threat of low flow conditions, carrying out a Failure Mode and Effect Analysis for low flow condition, proposing an Integrity Management Team and developing a roadmap for the execution of the proposed Integrity Management System. Both quantitative & qualitative data were employed in carrying out the study scope of work. Quantitative data used in the study included: pipeline design/as-built data, inspection & monitoring data, historical production data, and pipeline integrity management system. While qualitative data was derived from interviews and discussions with pipeline integrity and operations personnel. From the analysis conducted, a goal of zero corrosion leaks on the 24in Trans-Niger pipeline would be achieved through the Corrosion Management Strategy managed by the Head of Pipeline Integrity. The output of the FMEA rated the criticality of failure mode as high due to the product of the susceptibility of failure and Asset, Environment, and reputation consequence. An essential component of the CMS is the CMS execution team composition and their interdependencies, as CMS execution requires team effort with clear responsibilities and accountabilities. This study concluded that Asset Integrity Management without following best practice or industry standard methodology is grossly ineffective and would lead to dire consequences as in the case of the 24in Trans-Niger pipeline case-study with its failure in 2018.

As India advances toward its 2047 development vision, the persistent underutilization of women’s economic potential remains a critical challenge. The female workforce participation is significantly lower than that of males. This study presents Hometrepreneurship as an innovative framework to promote inclusive economic growth. This research is developed as a concept note and initially presented as a poster at the Globalizing Indian Thought 2025 hosted by Indian Institute of Management Kozhikode, and subsequently refined based on expert feedback and suggestions. The primary objective of the study is to propose a comprehensive model for home based nano entrepreneurship that leverages women’s existing skills while preserving cultural integrity and family structures. The research adopts a conceptual and policy-oriented approach, drawing on secondary data from government and institutional sources. It examines structural barriers that limit women’s ability to monetize domestic competencies and evaluates emerging policy frameworks, especially for Kerala’s regulatory initiatives, as scalable models. The study also explores clustering strategies, digital integration and financial inclusion mechanisms to enhance enterprise sustainability and market access. Findings indicate that Hometrepreneurship can effectively transform underutilised household skills such as culinary practices, handicrafts and care services into viable income-generating activities. The model promotes flexible operations, community-based clustering, and efficient utilisation of domestic resources, with strong potential for formalising millions of women-led enterprises. The study concludes that Hometrepreneurship offers a practical and scalable pathway for women’s economic empowerment in India. By integrating traditional knowledge with modern market systems and supportive policy frameworks, it can convert invisible domestic contributions into measurable economic outputs, thereby supporting inclusive and sustainable national growth.

Technology has become the topmost agenda in the Indian banking sector for the last decade or so. The Unified Payments Interface launched in April 2016, and large public sector banks like State Bank of India developed technology-enabled banking platforms like YONO to reach customers. Similarly, large private sector banks like HDFC Bank migrated to AI and machine learning-enabled interfaces and offerings. The most surprising and interesting development has been the emergence of a large, ambitious, and rapidly growing financial technology (fintech) sector. Many of these fintech companies have grown to become large conglomerates in a short span of time. One such company is Paytm, which has grown from a small company accepting payments through mobile wallet to a company offering payments bank services to forty-five crore users. The Unified Payments Interface has grown exponentially and in calendar 2025, the interface averaged out twenty-one and a half billion monthly transactions worth thirty lakh crore rupees, the highest in the world, on a real-time basis. This paper attempts to examine the technology-driven changes in the businesses and operations of the banks. A comparative analysis of State Bank of India, HDFC Bank and Paytm Payments Bank / One97 Communications has been done to understand the way technology has impacted the banking businesses. Secondary sources like annual reports of the three banks, official data from the Reserve Bank of India, industry reports, and business journalism post 2026 have been used to study and analyse the subject. Observations reveal that public banks, private banks, and fintech banks are moving towards similar business models, driven by the increased importance of mobile-based interfaces, AI and machine learning, and the Unified Payments Interface. Each of the banks under study has also got unique strengths emanating from their history, customer base, and governing structures. The banks in India are currently experiencing convergence as well as divergence in their development a phenomenon relevant from the point of view of strategies, policies, regulations, and competition in the banking sector.

The hospitality industry is undergoing a deep transformation determined by rapid technological advancements and evolving customer expectations. Amongst these advancements, Artificial Intelligence (AI) has arisen as a critical strategic resource reshaping hospitality management practices. AI-driven systems are redefining service delivery, operational efficiency, decision-making processes, customer relationship management and fosters sustainable competitive advantage. This paper explores the strategic role of Artificial Intelligence in transforming hospitality management by examining its applications, benefits, challenges, and long-term implications. By means of an extensive review of existing literature, industry reports, and case studies, the study highlights how AI enhances personalization, automation, revenue optimization, sustainability, and competitive advantage. Nevertheless, the paper deliberates ethical concerns, workforce implications, and direction for future research work. The findings of the study recommended that AI is not simply a technological tool but a strategic enabler that redefines value creation in the hospitality sector.

Fingerprint-based authentication systems (FAS) play a crucial role in secure access control, including academic libraries. Conventional fingerprint recognition systems that rely on a single feature extraction technique often struggle to extract robust features, leading to high false positive rates and low accuracy. This research developed a feature-fusion authentication system for academic library access control using multi-feature extraction techniques. 324 university students fingerprint dataset from 81 subjects were captured. The acquired dataset was preprocessed (cropped, contrast adjustment, gray scale, binarization).  The Cross Number Algorithm (CNA) and Principal Component Analysis (PCA) were used for feature extraction. The Weighted Sum Rule was used to fuse extracted features from CNA and PCA, generating a unified feature vector. Random Forest Classifier was employed for classification. The results show that CNA–PCA based system achieved accuracy of 96.91%, CNA achieved accuracy of 94.14% and PCA produced accuracy (92.59%).

There is substantial scientific evidence that global climate change is intensifying environmental challenges, particularly in developing countries such as Nigeria, where rising temperatures, extreme rainfall, and heat stress increasingly affect the usability of outdoor environments. Despite growing discourse on sustainable design, a research gap remains in evaluating the flexibility and responsiveness of campus outdoor spaces to climate variability, especially within private university settings. This study aims to assess adaptive landscape design strategies by evaluating the flexibility of outdoor spaces in responding to climate change, using Caleb University as a case study. The objectives are to examine the current performance of outdoor spaces under changing climatic conditions, identify design limitations affecting user comfort and interaction, and propose adaptive landscape interventions that enhance resilience and usability. The study adopts a quantitative research methodology, utilizing structured online survey questionnaires administered to students, teaching staff, non-teaching staff, and visitors, with findings analyzed to determine the level of adaptability and inform climate-responsive landscape planning strategies.

Soil nutrient depletion is a major constraint on smallholder agricultural productivity in the Kenya highlands. This study evaluated soil nutrient status and pH variability in Borabu and North Mugirango constituencies of Nyamira County, Kenya, to develop site-specific fertilizer recommendations for smallholder farmers. Composite soil samples were collected from representative wards, air-dried, sieved through a 2 mm mesh, and digested using aqua regia. Potassium (K+) and calcium (Ca2+) were quantified by flame photometry; magnesium (Mg2+) by atomic absorption spectroscopy (AAS); phosphate (PO43-) and nitrate-nitrogen (NO3--N) by UV-Vis spectrophotometry using ammonium molybdate and Griess reagents respectively. Measured concentrations in both constituencies fell significantly below FAO crop-specific thresholds. K+ averaged 48.6 ppm in Borabu versus the FAO minimum of 120-200 mg/kg for maize; PO43- averaged 3.2 ppm versus the FAO minimum of 15-30 mg/kg. Soil pH averaged 5.8 (Borabu) and 6.0 (North Mugirango), suitable for maize and bananas but marginal for tea. Calibration models yielded R2 >= 0.97 for Ca2+, Mg2+, PO43-, and NO3-, confirming high analytical precision. Borabu soils showed uniform severe depletion; North Mugirango showed moderate spatial variability. GIS-driven, variable-rate fertilization integrating rock phosphate, DAP, dolomitic lime, and split nitrogen with leguminous cover crops is recommended to close nutrient gaps, improve yields by 30-50%, reduce input costs by 20-30%, and mitigate nutrient leaching into the Sondu-Miriu River Basin.  

Over the past decade, the mainstream adoption of digital currencies has also brought renewed attention to their foundational technology, blockchain. Blockchain is a distributed data structure that maintains a shared ledger across multiple participants without requiring a central authority. Its design includes features like being decentralized, keeping records in a specific order with timestamps, ensuring data security through cryptography, and requiring agreement from participants to validate. Together, these properties make ledger entries highly resistant to unauthorized modification and support transparent auditing across participating nodes. These features make blockchain a strong solution to many problems in traditional financial systems, such as having one point of failure, limited transparency, extra work for matching records, and needing trusted middlemen. Consequently, blockchain has attracted sustained interest from financial intermediaries, technology firms, and public sector institutions seeking more resilient and verifiable transaction and data-sharing mechanisms. This article provides a structured overview of blockchain fundamentals and its application landscape. It begins by defining blockchain and outlining its core operating principles, with particular emphasis on decentralization, immutability, and transparency.  Then, it looks at how blockchain has changed from early cryptocurrency-based systems to larger, multi-domain platforms that support decentralized, programmable applications. The main parts look at important parts of blockchain systems, the real-world challenges and limits they face, and examples of how they are used in different fields, as well as guidelines for deciding when to use blockchain as a solution. The study closes by outlining the current maturity of blockchain technologies, the principal technical and governance challenges that remain, and the research and engineering directions required to translate blockchain’s potential into scalable, trustworthy deployments.

The rise of digital communication has been accompanied by a significant increase in fraudulent and scam phone calls, causing both financial and emotional damage to individuals and organizations. Traditional rule-based detection systems have become insufficient in combating these evolving scams, which often rely on voice manipulation and social engineering techniques. This review paper explores recent research and technological advancements in artificial intelligence (AI), voice biometrics, and data mining methods for scam and fraud call detection. It surveys existing literature in the domain of telecommunication fraud prevention, highlighting methods such as machine learning–based classification, real-time voice recognition, and behavioral pattern analysis. The proposed system, ScamShield, integrates these ideas to create a lightweight Android-based application capable of identifying suspicious calls through voice and keyword analysis, storing call metadata, and providing scam-awareness alerts. The review aims to bridge existing research gaps by summarizing multiple approaches to telecommunication fraud detection and offering insights for future AI-powered solutions that ensure secure and trustworthy communication networks.

Voter education plays a significant role in promoting informed and responsible participation in democratic processes; however, many local communities face challenges in accessing reliable and structured voter education resources due to limited internet connectivity and reliance on informal information channels. This study presents a local–hybrid web-based voter education awareness platform designed to deliver accessible, multimedia-driven educational content for community use by integrating locally hosted infrastructure with cloud-mediated access services, enabling both local control and remote accessibility. The platform was developed using an iterative–incremental methodology and evaluated based on the ISO/IEC 25010 Software Product Quality Model. Evaluation results indicate that the system achieved high performance across key quality characteristics, including functional suitability, usability, performance efficiency, security, and maintainability. The findings demonstrate that hybrid deployment models can effectively address accessibility constraints while maintaining system quality and reliability, thereby offering a scalable and practical approach for delivering voter education in resource-constrained community environments.

Large-scale construction sites frequently require electrical power exceeding the capacity of a single 400A three-phase feeder. In such contexts, power utilities typically supply a small-capacity 11kV transformer (e.g., 200–300 kVA) housed within a purpose-built temporary transformer room. This paper examines the practical engineering considerations surrounding the construction of such temporary transformer rooms, with particular attention to design specifications for weatherproofing, oil containment, ventilation, and security. Beyond physical infrastructure, the paper addresses essential technical procedures including cable sizing in accordance with the EMSD Wiring Code, the role of the Registered Energy Assessor (REA) under Cap. 610, testing and commissioning (T&C) protocols, periodic inspection and testing (WR2), and the critical engineering task of circuit breaker discrimination. Drawing on authentic field experience from Hong Kong construction sites, this paper aims to share practical knowledge with industry peers, highlighting both technical requirements and common pitfalls that may lead to financial losses, regulatory non-compliance, or safety hazards. The paper concludes that early engagement of qualified professionals, rigorous adherence to testing protocols, and systematic verification of protective device coordination are essential to successful temporary electrical installations.

Water scarcity and declining soil fertility are major constraints to sustainable vegetable production in sub-Saharan Africa, particularly under rain-fed and poorly managed irrigation systems. This study examined how deficit irrigation, together with integrated fertilization methods, affected water-use efficiency (WUE) and nutrient-use efficiency (NUE) in cucumber (Cucumis sativus L.) plants. The study utilized a Randomized Complete Block Design (RCBD) with three replications to conduct experiments in Asaba, Delta State, Nigeria. The study applied three irrigation treatments, including full crop water supply at 100% and reduced supplies at 75% and 50% of crop water need, together with two NPK treatments at 60 and 120 kg/ha, two poultry manure treatments at 3 and 6 t/ha, and control plots. Fruit yield fluctuated drastically over the course of the experiments; it ranged from 2.26 t/ha with I50N0M0 to 4.78 t/ha with I100N6M6, an increase of 111.5%. Full irrigation (I100) consistently produced the highest yield, with yields reduced by 8.9% to 12.1% under I75, depending on nutrient management. Still, the application of nutrient-rich treatment (N6M6) at I75 achieved a yield of 4.20 t/ha, which was only 0.58 t/ha lower than full irrigation. Water and concordant nutrient management showed significant main and interaction effects on yield in a two-way ANOVA, with highly significant main and interaction effects on fruit yield. Variability is about 5.67%, reflecting high precision in the experiments. This shows that adequate interaction between water and nutrient availability strongly influences cucumber yield, differentiating production and must lead to effective water use; the adoption of such strategies should not substantially hamper production. The results demonstrated that irrigation at 50%, combined with high manure application at 6 t/ha and high NPK application at 120 kg/ha, produced the best irrigation water use efficiency of 19.49 kg/m³ and water use efficiency of 16.73 kg/m³. Under full irrigation conditions with low NPK and high manure application, the study observed the highest nutrient use efficiency, achieving a yield of 26.00 kg per kilogram of nutrient. Post-harvest soil analysis demonstrated that integrated nutrient applications enhanced soil pH, organic matter content, total nitrogen, and available phosphorus compared with unfertilized controls. The combination of organic amendments and deficit irrigation resulted in significant increases in water productivity while maintaining yield levels. The study establishes that South-South Nigerian sandy loam soils achieve optimal water and nutrient use efficiencies through integrated nutrient management, which combines deficit irrigation with 60 kg/ha NPK and 6 t/ha poultry manure for cucumber production.

Ground clutter remains a major challenge in radar systems, as low-frequency reflections from stationary objects can obscure high-frequency target signals, reducing detection accuracy. This study addresses this issue by designing and implementing four Infinite Impulse Response (IIR) filters, Chebyshev Type I, Chebyshev Type II, Elliptic, and Bessel, using Python. All filters were developed using the bilinear transformation method under identical specifications to ensure fair comparison. Performance evaluation based on frequency- and time-domain characteristics shows that the Elliptic filter achieved the best overall performance, with 43.5 dB clutter rejection, 94.2% noise reduction, and 9.4 dB target preservation, making it the most effective for clutter suppression. Chebyshev Type I also demonstrated strong attenuation and sharp transition characteristics, while Chebyshev Type II provided moderate suppression with a flat passband. Although offering lower attenuation, the Bessel filter preserved the signal waveform most effectively due to its superior phase linearity. However, the study is limited by the use of simulated signals under controlled conditions, which do not fully capture the complexities of real-world radar, such as dynamic clutter and environmental variability. In addition, implementation was restricted to a single platform. Overall, the results emphasize that filter selection should be application-dependent. Elliptic filters are recommended for maximum clutter suppression, whereas Bessel filters are better suited for phase-sensitive applications. Future work should focus on validating with real radar data, developing adaptive filtering methods, and implementing across multiple platforms to enhance practical applicability.

Online payment fraud detection is a critical problem in the financial sector due to the increasing volume of digital transactions. This paper proposes a machine learning-based fraud detection system using CatBoost, XGBoost, and a soft voting ensemble model. Principal Component Analysis (PCA) is applied for dimensionality reduction, and SMOTE is used to address class imbalance. The models are evaluated using precision, recall, F1score, and AUC. Experimental results show that the ensemble model outperforms individual models with improved accuracy and robustness. A real-time fraud detection system is also developed using Streamlit to support both single and batch predictions. The proposed system demonstrates high efficiency and scalability for practical applications.

Rapid urbanization in Bishnupur Municipality, Bankura District, has outpaced the capacity of conventional municipal solid waste (MSW) management systems, leading to environmental degradation and resource inefficiency. This study proposes a sustainable MSW management model integrating smart technologies, including waste segregation, recycling, and Waste-to-Energy (WTE) systems. A focused field study was conducted on cattle sheds (Khatals) to quantify organic waste generation and assess its potential for biogas production. The data analysis reveals that proper management of cattle waste alone could mitigate approximately 15 tons of methane annually, significantly reducing the municipality's carbon footprint. This research provides a strategic framework for adopting smart waste solutions in small-to-medium Indian municipalities to foster a circular economy.

An economical and sustainable method of soil improvement known as Microbial Induced Calcite Precipitation (MICP) has received significant attention in the past decade. The plasticity of lateritic soil bio-treated at stepped Brevibacillus brevis (B. brevis) suspension density and cementation reagent concentration using three mode of treatment (i.e. mixing, injection and spraying method). The B. brevis suspension densities and cementation reagents used to trigger the MICP process are 0, 0.5, 2.0, 4.0, 6.0 and 8.0 McFarland Standards (i.e., 0, 1.50 × 108, 6.0 × 108, 1.20 × 109, 1.80 × 109 and 2.40 × 109 cells/ml and 0.25, 0.5, 0.75 and 1.0 M, respectively.  The liquid limit value used to prepare the samples with three mix proportions of the bacteria and cementation reagent (viz: 25% bacteria-75% cementation reagent, 50% bacteria-50% cementation reagent and 75% bacteria-25% cementation reagent) is the one obtained from natural lateritic soil for all the three modes outlined. Atterberg limits and calcite content using the acid wash method tests were conducted on the treated specimens for all the three treatment modes. Results obtained show a general decrease in the Atterberg limits with higher B. brevis suspension density and cementation reagent concentration for mixing, injection and spraying method respectively. The best enhancement of plasticity index was obtained for lateritic soil sample treated with a mix ratio of 75 % B. brevis (2.40 × 109 cells/ml for mixing an injection as well as 1.8 × 109 cells/ml for spraying method of treatment ): 25 % cementation reagent (0.50M, 0.25 M and 0.75M for mixing, injection and spraying mode) with a corresponding peak calcite content  of 12.0 (2.40 × 109 cells/ml and 1.0M), 5.38 (2.40 × 109 cells/ml and 1.0M) and 3.50% (1.8 × 109 cells/ml and 1.0M) for mixing, injection and spraying treatment mode respectively.

Purpose: This study identifies critical workforce competencies required to accelerate Industrialised Building System (IBS) implementation in dilapidated rural school reconstruction projects in Sarawak, Malaysia, addressing a critical gap in educational infrastructure development. Design/Methodology/Approach: A comprehensive systematic review of 30 peer-reviewed studies was conducted, examining IBS implementation challenges, workforce competency requirements, and contextual factors specific to rural construction in Malaysia. Evidence was synthesized to develop a multi-tiered competency framework encompassing technical, managerial, digital, and context-specific capabilities. Findings: Successful IBS-based rural school reconstruction requires four critical competency domains: (1) technical competencies including prefabricated component handling, precision assembly, and quality control; (2) managerial competencies encompassing project coordination, supply chain management, and stakeholder engagement; (3) digital competencies involving Building Information Modeling (BIM), digital fabrication, and data-driven decision-making; and (4) context-specific competencies addressing rural logistics, local material integration, and community engagement. Major barriers include limited IBS knowledge among Sarawak contractors (78% reporting insufficient awareness), critical shortages of skilled installers, inadequate training infrastructure, and logistical challenges in remote areas. Research Limitations/Implications: The study is based on available literature and may not capture all emerging competency requirements. Future empirical research should validate the framework through field studies in active reconstruction projects. Practical Implications: The competency framework provides actionable guidance for government agencies, educational institutions, and construction firms to develop targeted training programs, establish industry-academia partnerships, and implement policy interventions that build sustainable local capacity for IBS-based school reconstruction. Originality/Value: This is the first comprehensive study to systematically identify workforce competencies specifically for IBS implementation in Sarawak rural school reconstruction, addressing the unique intersection of industrialized construction, educational infrastructure, and rural development contexts in Malaysia.

Disasters pose significant risks to communities, yet localized solutions for disaster preparedness are limited. Mobile technologies offer opportunities to develop an application like the DRRR focusing on the Disaster Risk Reduction Readiness (DRRR). This study addresses the need for a mobile application that enhances awareness and readiness for disasters. The study aims to develop a Disaster Risk Reduction Readiness (DRRR) mobile application that supports users in disaster preparedness through an interactive application. The Rapid Application Development (RAD) was used in this study particularly on requirement planning, user design, cutover, and construction. MIT App Inventor is used as a platform in the development of the DRRR Application. The use of 5-point Likert scale scoring in the evaluation of the development of the DRRR mobile application which derives from the ISO/IEC 25010 Systems and Software Quality Requirements and Evaluation (Square) Model. focusing on (1) Functional Suitability, (2) Performance Efficiency, (3) Usability (4) Reliability, (5) Security, (6) Maintainability, (7) Compatibility, (8) Portability and Overall Evaluation particularly on the different respondents like IT experts, DRRR Coordinator, LGU/Barangay Officials, Faculties, and Students. The DRRR mobile application provides hazard information, emergency kit checklists, evacuation maps, warning alerts, The results of the evaluation demonstrated improvements across all measures of the system development and become aware, ready during disasters thereby reducing the risk and severe impact of disasters.

Reliable electricity remains a major challenge in Nigeria, where frequent power outages disrupt homes, businesses, and institutions, leading to increased reliance on diesel generators that are costly and environmentally damaging. Solar photovoltaic (PV) systems provide a promising renewable energy solution due to Nigeria’s abundant solar resources, offering cleaner, quieter, and more cost-effective alternatives to conventional power supply. This review paper examines the design and optimization of solar PV systems with a focus on practical applications in Nigerian residential and commercial settings, using Ascenterra Homes Limited in Abuja as a case study. The study reviews essential components of solar PV systems, including solar panels, inverters, battery storage, and system integration, while highlighting strategies for optimization to meet specific energy demands efficiently. It discusses simulation and sizing techniques, such as HOMER and PVSyst, that help reduce energy costs and ensure reliability, taking into account local climate conditions and seasonal solar variations. Findings from recent literature shows that optimized solar PV systems can significantly reduce electricity outages, lower operating costs, and contribute to environmental sustainability. However, challenges such as high initial investment, lack of technical expertise, and policy gaps remain. The paper concludes that solar energy, when properly designed and optimized, offers a sustainable solution for reliable electricity supply and cost reduction in Nigerian homes and commercial complexes, and recommends supportive policies, energy audits, and professional capacity building to encourage wider adoption.

Fowl cholera, caused by Pasteurella multocida, remains one of the most economically devastating poultry diseases worldwide. Rapid and accurate diagnosis is critical for effective intervention, yet traditional methods often fall short in speed and predictive accuracy. This study presents a Big Data-driven data mining approach to diagnose fowl cholera in poultry, leveraging a dataset of 500 samples characterized by variables such as bird age, vaccination history, environmental conditions, clinical symptoms, and mortality rates. Machine learning algorithms including Logistic Regression, Random Forest, and Gradient Boosting were deployed to model disease prediction, with Random Forest achieving the highest accuracy at 94.6%. Data preprocessing techniques, feature selection, and cross-validation were applied to ensure robustness and scalability. The findings demonstrate that environmental factors, vaccination gaps, and bird age are among the most significant predictors. This research highlights the transformative potential of Big Data and advanced data mining in veterinary epidemiology, providing a scalable diagnostic framework for poultry health management.

GYMX Pro is an AI-powered fitness management platform designed to improve gym administration and personalized workout planning. The platform is a blend of artificial intelligence technology and traditional gym management services to create an efficient and data-driven fitness experience. It will simplify the process of running gyms and will make workouts more entertaining as it will give smart recommendations and live feedback to its members. The primary characteristic of the GymX Pro is an AI Trainer which is a virtual fitness assistant to a user. The AI Trainer examines more personal parameters like fitness objectives, body measurements, exercise history, and performance improvement in order to create personalized exercise programs. It is able to prescribe exercises and modify the intensity of training as well as provide feedback to the users to enable them to be consistent and achieve improved results. This smart module gives the members a chance to be guided just like a personal trainer in the absence of professional trainers. The application also offers an all-embracive gym administration functionality like member enrolment, enrolment tracking, attendance check, instructor booking, and automatic payment and renewal notifications. Moreover, the GymX Pro also offers progress tracking dashboards, which enables users to see how their strength, endurance, and fitness have improved with time. Trainers and administrators are also able to obtain analytics in order to improve the understanding of engagement of members and the maximization of training programs. GymX Pro allows to improve the efficiency of the gym    administration and motivate the fitness enthusiasts by means of AI-based personalization, an advantageous digital interface, and the efficiency of operations of the gym owners. The system can be seen as a progressive solution to the present state of the fitness industry, using AI, it provides smarter exercise, better communication with members, and an even more connected fitness ecosystem.

Mobile technology pushes groups, including businesses and other institutions to utilize digital tools to better manage and communicate in this era of digital advancement. This research is on work on a Church Mobile Application for NTCC (New Testament Christian Church). The Church Mobile Application is intended to disseminate information, keep members engaged, and improve Church communication. The primary objective is to develop an online application. A centralized mobile platform for all church leaders and members who regularly need announcements, events, devotional content, and regular church information. Using the RAD (Rapid Application Development) methodology, which is aimed at iterative development to facilitate constant interaction with the users and rapid prototyping. The Research Study also used Survey and Questionnaire to gather data that can be used in carrying out this study. The Church Pastor, Leaders as well as other Members of New Testament Christian Church (NTCC) were given the Survey Questionnaire. According to the results of the evaluation, the NTCC Church Mobile App reached a weighted mean of 4.67. That score is Strongly Agree. The NTCC Church Mobile App demonstrates user-level acceptance and satisfaction. The findings showed that the NTCC Church Mobile App solved problems with communication and manual information management. The NTCC Church Mobile App is a solution for church management and communication. Future works need to incorporate push notifications, multimedia feature integration, and expansion to NTCC branches. 

Legislative bodies worldwide are increasingly integrating digital technologies to enhance efficiency, transparency, and public engagement. In developing democracies such as Nigeria, however, the adoption of e‑legislature systems remains limited. This paper proceeds from the argument that analog lawmaking can be made substantially more efficient through the strategic application of technology to core legislative functions such as bill drafting, voting, and committee management. This paper examines the integration of digital technologies into the legislative processes of Nigeria's National Assembly. It identifies the barriers to e‑legislature adoption and analyses how these obstacles intersect with the constitutional and democratic functions of the legislature. Employing a qualitative case study design, the research triangulates primary legal instruments including the 1999 Constitution, the Standing Orders, and the Evidence Act with a thematic analysis of secondary literature, institutional reports, and comparative insights from e‑parliament initiatives across Africa. The study reveals that while digital tools offer the potential to improve bill drafting, voting efficiency, and citizen access, the Nigerian legislature confronts profound obstacles. These include an outdated legal framework that does not fully recognize electronic legislative acts, inadequate digital infrastructure, limited technical capacity among legislative staff, and an institutional culture that privileges informal, paper‑based traditions. The analysis further identifies a recurring tension between the speed of digital processes and the deliberative, consensus‑oriented character of lawmaking. Across legal, institutional, and infrastructural dimensions, the barriers tend to reinforce one another, sustaining a pattern of fragmented automation rather than systemic transformation.

The financial sector is one of the industries where credit card fraud detection is a critical issue because the number of legitimate transactions is by far outnumbered by the number of fraud transactions carried out. This paper performs an imbalance-sensitive analysis and hyperparameter optimization of three supervised machine learning (SML) models (Logistic Regression, Random Forest, and XGBoost) on the European credit card fraud dataset (n = 284,807; fraud rate = 0.172%). It embraced the CRISP-DM process model as the data lifecycle model to guide it. The training partition was only subjected to SMOTE after 80/20 stratified split to avoid data leaking and the hyperparameters are optimized using stratified 3-fold cross-validation. Each tuned model was further probability threshold tuned with probability threshold set to 0.70 to maximize Precision-Recall operating point. All the experiments were executed in Google Colaboratory on Python 3.10. Precision, Recall, F1-Score, ROC-AUC and the Area Under the Precision-Recall Curve (AUPRC) were used to evaluate model performance, and AUPRC was chosen as the ultimate measure due to extreme imbalance in the classes. XGBoost developed as the most effective model in general, having the highest AUPRC (0.817), ROC-AUC (0.970) and the perfect combination of Precision = Recall = F1 = 0.81, which was achieved by tuning the probability threshold to 0.70. Random Forest had the best Precision (0.93) with AUPRC of 0.805 and hence it is the most appropriate model in the minimum false positive. Logistic Regression achieved maximum Recall (0.86) but had low Precision (0.10) which restricted its feasibility of operation even with threshold modification. These results indicate that XGBoost, together with SMOTE, systematic hyperparameter optimization, and threshold calibration, offers the best and balanced fraud detection at extreme imbalance in the classes.

The adoption of Industrial Internet of Things (IIoT) technologies among manufacturing Micro, Small and Medium Enterprises (MSMEs) in India remains remarkably low despite a decade of government promotion. This paper reviews academic studies, government reports, and news articles published between 2018 and 2025 to understand the barriers to IIoT adoption in this sector. The review identifies four interconnected categories of barriers: technological (outdated machinery, poor connectivity), organizational (digital skill shortages, low awareness of government schemes), financial (credit gap of ₹80 lakh crore, high upfront costs), and environmental (underdeveloped vendor ecosystems, policy implementation gaps). Despite these barriers, documented success stories demonstrate that IIoT adoption is both feasible and beneficial. One study achieved 22.5% reduction in cycle time and 71.43% reduction in errors with an investment of INR 1.3 lakh. Industry reports document 20-25% improvements in efficiency and 35% reduction in defects among early adopters. The paper concludes that while the technology works and the costs are affordable for many medium enterprises, adoption remains low due to interacting barriers that no single policy or intervention can address alone.

Cassia fistula seed oil was extracted using three different methods: cold pressing, oven-assisted extraction, and Soxhlet extraction with n-hexane to evaluate and compare their efficiencies. The oil yield varied significantly across methods, with cold pressing producing the lowest average yield (16.87%), the oven method giving a moderate yield (18.76%), and Soxhlet extraction yielding the highest (28.15%). Optimization of the Soxhlet extraction process was carried out by varying key parameters, including solid-to-solvent ratio (3.63–10.36 g/g), temperature (54.88–80.11°C), and extraction time (1.31–4.60 h), to maximize oil recovery. The extracted oil was further characterized using Gas Chromatography Mass Spectrometry (GC–MS) and Fourier Transform Infrared Spectroscopy (FTIR) to determine its chemical composition and functional groups. The results demonstrate that while Soxhlet extraction with n-hexane provides superior oil yield, other methods may offer advantages in terms of simplicity and environmental considerations, highlighting the importance of selecting appropriate extraction techniques based on intended application.

Dividend policy plays an essential role in corporate finance. It helps investors understand a firm’s valuation. Dividend, an essential portion of a firm’s finances, is the amount of the profit paid back to the shareholders. This study focuses on understanding dividend policy decisions on the wealth of the shareholders. Furthermore, this study focuses on the five leading Indian public sector banks and includes State Bank of India (SBI), Punjab National Bank (PNB), Bank of Baroda (BoB), Canara Bank, and Union Bank. The analysis has been carried out for 5 years i.e. FY 2021-25 and focuses on two major attributes i.e. capital gains and dividend, that compose an integral part of the Total Shareholder Return (TSR). Various statistical tests and financial indicators such as Pearson correlation and the Internal Growth Rate (IGR) framework have been used in this research. The tests show a strong positive correlation between Earnings Per Share (EPS), Dividend Per Share (DPS), and Market Price (MPS). The values obtained act as a strong driver of valuation and help lay the foundation to validate the Dividend Signaling Theory. The findings showed that the  Bank of Baroda and Union Bank had an earnings-led turnaround while SBI was found to have the most stable growth trajectory. Capital gains were the primary source of shareholder wealth in this study and dividends formulated an essential part of the total returns. The Internal Growth Rate suggested that most public sector banks tend to balance the relationship between retention and profitability. Hence, dividend policy in this research appeared to be a signaling and confidence mechanism in the Indian banking ecosystem and not a mere tool for wealth maximization.

The combination between Artificial Intelligence (AI), Big Data and Business Networks has substantially transformed the system of performance of organizations and has enhanced inter-firm cooperation leading to innovation in various industries. This current bibliometric and scientific mapping research paper examines the intellectual environment of this interdisciplinary discipline in terms of the PRISMA model. The analysis is based on meta-data obtained on Web of Science (2015 to 2025). A package of visualizations (geographical distribution, annual publication patterns, author productivity, major journals, country output and keyword co-occurrence) are done by using RStudio and Vos viewer are used to map the central research topics and developing areas. Important groups draw the major themes and transformational research patterns. The ten most influential papers are put in the spotlight, which provides a selective reference to the background and breakthrough articles. The interpretation of the results on the collected data allows one to have a general picture of the evolution of the field among scholars. The findings indicate the constant growth of the academic contributions toward the entire world, with countries such as the United States, China and the United Kingdom taking the top table in terms of matters regarding research production. Key journals and authors are proposed, confirming the increased significance of AI and Big Data in optimization of the business networks in terms of its agility and efficiency. This work provides valuable lessons to artists and scholars due to its ability to capture the most up-to-date trends and highlight potential future directions of research that could benefit the resilience, scale and innovation in Networked Business context.

InsurTech, which stands for "insurance technology," refers to technology and innovation with the primary purpose of providing better services and technology to the insurance business in current progressive technological environment and industrial revolution. That industry is increasingly relies on technology to gain a competitive advantage, and it helps decision-makers make sound decisions. New technologies are integrating and strongly influencing people's work and life, and they have emerged as a key driver of the insurance industry's constant innovation. The application of InsurTech has gained widespread attention in the industry, and it is essential to conduct an in-depth deconstruction and analysis of its impact on insurance business innovation. This study aims to analyse the recent trends and patterns of research in the field of Insurtech using bibliometric methods. For this, 668 articles in total were retrieved from the Scopus and Web of Science databases after removing the duplicates. The data was analysed using Bibliometric R-package. The results identified the leading countries, institutions, authors, journals, most occured keywords, scientific publications on the topic and shed light on current research trends. Some of the results demonstrate that the most cited country is the United Kingdom followed by China and Germany, the most productive institution is University of Vaasa, the most productive author is Zhang J followed by Giudici P, Grobys K, Okoli T and Wang J, and the most productive journal is Journal of Risk and Financial Management followed by IEEE Access.

Digital transformation is fundamentally re-engineering global trade, with its application in port logistics being critical for enhancing supply chain performance. This study examined the effect of digital transformation specifically Port Community Systems Implementation (PCSI), Automation of Cargo Handling (AOCH), Digital Customs Clearance System (DCCS), and Use of IoT for Tracking and Monitoring (IOTM) on Supply Chain Performance (PFSC) at Apapa Port, Lagos State. In this study, supply chain performance is evaluated using three metrics: average cargo dwell time, port throughput and berth productivity, and supply chain reliability. Using a cross-sectional survey design, data were collected from 153 key stakeholders across four critical departments via structured questionnaires administered through Google Forms. Partial Least Squares Structural Equation Modelling (PLS-SEM) was employed to analyze the data, revealing that Use of IoT for Tracking and Monitoring (β = 0.394, p < 0.001) and Digital Customs Clearance System (β = 0.191, p = 0.038) significantly affect supply chain performance, while Port Community Systems Implementation (β = 0.118, p = 0.190) and Automation of Cargo Handling (β = 0.055, p = 0.573) had no significant effect. Collectively, these digital transformation variables explain 68.5% of the variance in supply chain performance (R² = 0.685). While PCSI and AOCH were perceived positively, they did not translate into measurable performance gains. This study concluded that real-time visibility and process automation are pivotal to reducing dwell time, enhancing throughput, and ensuring delivery reliability during port operations. Recommendations for Apapa Port include prioritizing IoT deployment and digital customs integration, strengthening stakeholder collaboration for PCS, and upgrading automation infrastructure with reliable power and maintenance systems to bolster overall supply chain resilience.

Image Processing offers solutions to a broad range of real-world challenges. Security issues and theft have been on the rise for several decades. There has consistently been an absence of adequate security systems to ensure safety for both commercial and residential properties. Consequently, real-time surveillance has become essential. However, this necessitates high-resolution cameras and extensive storage systems to record and access the footage of the captured videos. In this study, an effort has been made utilizing a digital image processing approach that incorporates motion detection and face recognition techniques to minimize memory storage without compromising the integrity of the original image. This system aims to achieve surveillance without relying on high-end components and devices. The work is divided into three primary components: motion detection, face detection and ultimately face recognition. The reliability and efficiency of the system can be enhanced by improving its accuracy and speed. This system can be utilized by consumer markets for the surveillance of their properties. The industrial sector can adopt this method to bolster security and to ascertain whether the detected individual is an employee. This approach can be applied in apartments, home automation systems, R&D test units, restaurants and various other commercial environments.

Rapid urbanization has intensified pressure on housing markets in fast-growing cities such as Lagos, leading to rising demand for residential accommodation and unstable rental values. Property management practices are increasingly recognized as critical determinants of rental housing market performance and stability. However, inconsistent management approaches, poor maintenance, and ineffective tenant management often contribute to rental price disparities across urban residential areas. This study examines the influence of property management practices on the stability of residential rental values in Lagos. It investigates commonly used management methods, the role of maintenance practices, tenant management strategies, and the contribution of professional property management. The findings show that regular property inspections (30%) and routine maintenance and repairs (25%) are among the most widely used management practices. Maintenance activities contribute 35% toward maintaining stable rental values, while proper tenant screening accounts for 32.5% in ensuring consistent rental income. Professional property managers also enhance operational efficiency and maintenance performance by 30%. The study concludes that effective maintenance, structured tenant management, and professional property management are essential for sustaining stable residential rental values in urban housing markets.

Traditional Hospital Management Systems (HMS) primarily serve administrative and record-keeping functions but lack any form of autonomous clinical intelligence. With the growing complexity of patient data, healthcare institutions increasingly require systems capable of proactive decision support, early disease detection, and dynamic patient monitoring. This paper presents IntelliHMS 2.0, an innovative hospital management ecosystem enhanced with Agentic AI, a new paradigm where autonomous, goal-driven AI agents operate collaboratively to perform multi-step reasoning, analyze multi-modal patient data, and provide real-time disease prediction. Unlike traditional Machine Learning models that perform static onetime predictions, Agentic AI systems autonomously retrieve data, interpret clinical signals, reason over medical guidelines, generate insights, and trigger appropriate actions. IntelliHMS 2.0 integrates multiple specialized AI agents—including a Data Retrieval Agent, Disease Prediction Agent, Clinical Reasoning Agent, Monitoring Agent, and Explainability Agent—to create a fully autonomous predictive workflow. Using cloud-based microservices and secure API-driven architecture, the system ensures scalability, reliability, and continuous adaptation to patient conditions. By transforming disease prediction from a single-step model into a selfdirected, autonomous diagnostic pipeline, this system significantly improves early risk detection, enhances clinical decision-making, and streamlines overall hospital operations. This research highlights the potential of Agentic AI to revolutionize modern healthcare systems, enabling proactive, intelligent, and adaptive care delivery.

Stroke remains one of the leading causes of mortality and long-term disability worldwide, posing a significant burden on healthcare systems and society. Early identification of individuals at high risk of stroke is crucial for implementing preventive strategies and reducing fatal outcomes. This research proposes an intelligent stroke prediction system based on advanced machine learning techniques that analyse clinical and demographic data to assess stroke risk with high accuracy. The proposed framework utilizes a structured healthcare dataset comprising key attributes such as age, hypertension, heart disease status, body mass index (BMI), average glucose level, smoking habits, and lifestyle factors. A comprehensive data preprocessing pipeline is implemented, including missing value imputation, categorical encoding, feature scaling, and class imbalance handling using resampling techniques. Multiple supervised learning algorithms, including Logistic Regression, Decision Tree, and Random Forest, are employed and comparatively evaluated to identify the most effective predictive model. Experimental results demonstrate that ensemble-based models, particularly Random Forest, outperform other classifiers in terms of accuracy, precision, recall, and F1-score, achieving robust and reliable predictions. The model also incorporates feature importance analysis to interpret the contribution of critical risk factors, thereby enhancing transparency and clinical relevance. The proposed system offers a scalable, cost-effective, and efficient solution for early stroke risk detection and can be integrated into modern healthcare infrastructures, including electronic health record systems and mobile health applications. Furthermore, this study highlights the potential of machine learning-driven predictive analytics in transforming preventive healthcare by enabling data-driven decision-making and personalized risk assessment.

GestureSync Pro is a real-time hand gesture recognition system designed to bridge the communication gap between sign language users and the general public. The system utilizes computer vision and deep learning techniques to recognize American Sign Language (ASL) gestures and convert them into meaningful text and speech output. A webcam is used to capture live video input, and MediaPipe is employed to extract hand landmarks for efficient feature representation. A Convolutional Neural Network (CNN) model is trained on a large dataset of hand gestures to accurately classify ASL alphabets. The system further integrates heuristic logic and a hold-to-confirm mechanism to improve prediction stability and reduce false detections. To enhance usability, the recognized gestures are processed using AI-based sentence generation to produce grammatically correct outputs, which are then converted into speech using a real-time speech synthesis module. The model is deployed using TensorFlow.js, enabling fast and efficient inference directly in the browser. GestureSync Pro provides an accessible, cost-effective, and real-time solution for sign language communication, with potential applications in education, healthcare, and human-computer interaction.

The acceleration of digital transformation has fundamentally reshaped work structures within the Information Technology (IT) sector, particularly in emerging economies such as India. While this transformation has enhanced productivity and global competitiveness, it has simultaneously intensified workplace demands, resulting in growing concerns about employee well-being. This study reconceptualizes occupational stress as a systemic outcome arising from the interaction between job demands, workforce attributes, and organizational conditions. Using a descriptive and analytical research design, data were collected from 655 IT professionals employed in two major Hyderabad-based organizations. Statistical techniques including Chi-square tests, Z-tests, correlation analysis, and multiple regression were employed to examine relationships between occupational stress and personal strain.The findings indicate that workforce attributes such as age, income, educational qualification, work experience, marital status, and organizational role significantly influence stress perception, whereas gender does not. A strong positive relationship between occupational stress and personal strain (r = 0.733) was observed, with stress variables explaining 74.1% of the variance in strain outcomes. Furthermore, no significant differences were found between organizations, suggesting that stress is driven by broader industry-level dynamics rather than firm-specific factors. The study contributes to existing literature by shifting the focus from individual coping mechanisms to systemic organizational design, emphasizing the need for strategic interventions aimed at sustainable workforce management.

Land use land cover change is the major concern, an indicator of deterioration and misuse of natural resources. This report briefly showed paradigm study carried out in Maze national parks in Gamo zone, Ethiopia a couple of years ago. The study's objective was to investigate the land use land cover changes and the plant in Maze National Park. The study was carried out on 150 plots on 10 transects from randomly selected areas. In addition, 50 quadrates for grass species sampling and 25 quadrates for soil sampling were randomly selected. According to land satellite image acquired from 1975 to 2015 there has been a considerable land use land change occurred in the park.  Eight plant communities identified with savanna grasses type take the largest proportion. According to the study, 81 woody plant species belonging to 41 families were identified. Nineteen grass species with 24 other different forbs were also part of floristic composition of the park. Fabaceae is the most abundant family, and Combertum adenogonium is the dominant species. The soil seed bank diversity was found to be higher than the standing vegetation. Immediate intervention is required to conserve endangered species of plants.

The accelerated spread of Artificial Intelligence (AI) within higher education institutions such as universities signifies a profound technological advancement with dual implications for sustainable development. While AI promises unique opportunities for youth empowerment, its application demands a critical examination of its effect on student wellbeing.  This study investigates the influence of AI-mediated educational processes on university students’ mental health through the lens of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Using a convergent parallel mixed-methods design at a selected public university in Zimbabwe, the study combined quantitative data (n=303 students) with qualitative insights from focus group discussions with students and lecturers. Findings showed a inflexible "Hardware Hierarchy," where 85.5% of students recognise the laptop as an vital "academic station" for critical AI confirmation, while mobile-only users experience a "technologically hollowed-out" state. Even though AI is highly cherished for its usefulness among students using tools like ChatGPT AND Google Gemini as "always-on" tutors, it is somewhat linked with adverse mental health outcomes. These manifest as "Turnitin Anxiety," "Temporal Anxieties" connected to computer laboratory access, and ethical panic emanating from a "legal vacuum" in institutional AI policy. Furthermore, qualitative narratives demonstrate "Techno-Exhaustion" among faculty, particularly female lecturers  endeavouring to balance domestic work with the rigours of AI-output verification. Overall, the study concludes that the digital divide has evolved from a matter of connectivity to a "Divide in Wellness." It recommends institutional innovation beyond simple technological application and proposes application of robust ethical AI policies and subsidised hardware and WIFI data support to ease psychological risks while promoting resilient human capital development within the Education 5.0 framework.

Accidental deaths represent a significant public health and socio-economic concern in India. This study presents a comprehensive statistical analysis of accidental deaths categorized into natural causes and unnatural causes over a period of seven years (2019–2025). The data used in this study is secondary in nature and collected from official records including police reports, hospital data, and government publications. The study applies descriptive statistics, ABC analysis, t-test, ANOVA, and Tukey’s post hoc test to identify patterns, variations, and significant differences among accident categories. The results indicate that certain causes such as lightning, exposure to cold, and other causes contribute significantly to total accidental deaths.Further, ANOVA results confirm the presence of statistically significant differences among accident categories across years, and Tukey’s test identifies specific group differences. The findings provide insights for policymakers to implement targeted safety measures and reduce accidental deaths.

This article examines the renewed strategic importance of gold and silver in the period 2000–2025, arguing that their contemporary relevance is best understood at the intersection of reserve strategy, industrial transformation, and geopolitical risk. Using data from the World Gold Council, IMF COFER, the IEA, USGS, S&P Global, and the Silver Institute, the study documents a structural shift in official-sector behaviour toward gold accumulation amid declining U.S. dollar reserve share and expanding use of alternative settlement arrangements. Empirical evidence further characterizes gold’s macro‑financial role as state‑dependent: weakly correlated with average inflation but more responsive during tail inflation and conflict-driven risk episodes, consistent with a crisis‑hedge function in sovereign balance sheets. For silver, the analysis traces the post‑photography demand reallocation toward electronics and solar photovoltaics, highlighting rapid PV‑linked usage growth alongside supply constraints associated with by‑product production, multi‑year market deficits, and the moderating influence of recycling and technological substitution. The paper also incorporates market‑microstructure considerations—ETF tracking error, liquidity mismatches, and margin‑amplified volatility—to clarify transmission channels that can intensify short‑run price dynamics, particularly in silver. A regional perspective, with emphasis on India’s dual role as a major gold‑holding society and an expanding manufacturing and renewables hub, connects these findings to policy choices in reserve diversification and critical‑materials planning. The study concludes by outlining implications for policymakers, central banks, and industrial 

This paper presents the development and pilot evaluation of the DICT Employee Attendance and Accomplishment System (DICT-EAAS), a web-based platform for improving attendance-related Daily Time Record (DTR) and accomplishment-document workflows. In the participating Department of Information and Communications Technology (DICT) Region 2 offices in Santiago, Cauayan, and Nueva Vizcaya, attendance is already captured through a biometric scanner that exports a PDF file. However, employees still prepare official DTRs, accomplishment reports, adjustment slips, and supporting files through separate applications. This fragmented workflow may cause delays, encoding errors, formatting problems, and makes submission tracking harder to manage. DICT-EAAS was developed to bring these tasks into one process through biometric attendance PDF upload, automated DTR generation in the required format, accomplishment report creation, adjustment slip preparation, PDF compilation, reviewer routing, and administrator monitoring. The main feature of the prototype is the DTR generator, which converts the biometric attendance PDF into the required employee DTR format. The system was developed using a layered web architecture with a React + Vite frontend, Laravel backend, Python-based document processing module, relational database, and file/template storage. A closed-ended pilot survey was completed by six DICT employee users: three from Cauayan, one from Santiago, and two from Nueva Vizcaya. These respondents used employee accounts to test the DTR-generation and related document-preparation workflow. During the employee-account try-out, the reviewer and administrator processes were explained to them, and the related dashboards were also demonstrated during the DICT office presentation. Using an adapted ISO/IEC 25010-based five-point survey instrument, the pilot obtained an overall mean of 4.78 (SD = 0.35) and Cronbach’s alpha of 0.972. Functional suitability received the highest mean (4.94), followed by performance efficiency (4.83). The findings suggest that DICT-EAAS is useful for organizing attendance-related document handling, especially official-format DTR preparation, but larger role-based and longer-term validation is still needed.

This study investigates the statistical properties, volatility dynamics, and forecasting performance of crude oil returns using a comprehensive time series modeling approach. Descriptive analyses reveal that returns are centered around zero with small negative averages, exhibiting pronounced volatility clustering and episodes of extreme deviations driven by geopolitical and macroeconomic shocks. Diagnostic tests confirm the presence of nonlinear dependence and heteroskedasticity, making models such as GARCH suitable for capturing the persistent volatility patterns. Stationarity of the return series supports the application of ARIMA and GARCH-based models, which effectively accommodate the complex features observed in the data. Model selection across varying sample sizes consistently favors parsimonious ARIMA(0,0,q) structures with no autoregressive terms, while GARCH(1,1) captures the high volatility persistence evident in the market. Forecasting evaluations demonstrate that model accuracy improves with larger datasets, with combined ARIMA-GARCH models, especially those with higher-order ARIMA specifications, outperforming simpler models in larger samples. The findings underscore the importance of incorporating volatility modeling and selecting appropriate model complexity based on data availability to enhance forecast precision. Overall, the results provide valuable insights into the dynamics of crude oil prices and offer robust guidance for modeling and forecasting in volatile energy markets.

A Learning Management System provides a centralized platform for course management, content delivery, and student engagement. This study is entitled Utilization of Learning Management System (LMS) and Teachers’ Perceived Performance in the Online Learning Modality at President Ramon Magsaysay State University North Campuses. It is descriptive research with a questionnaire as the main instrument in gathering  data. The sampling technique was population sampling, hence the fifty-seven (57) teachers of the North Campus at President Ramon Magsaysay State University. The statistical tools used were frequency distribution, percentage, mean, analysis of variance (ANOVA), and Likert scale. A study involving an early-adult female teacher aged 21-25, with an MA, found that accessibility features were highly rated. The study found no significant correlation between faculty performance in online learning and their use of Learning Management Systems (LMS). The reliability of LMS usage was not significantly influenced by factors such as age, gender, educational attainment, number of subjects handled per semester, or trained attendees. The study suggests that implementing a training program can help educators design dynamic virtual learning environments, leading to better learning results. Funding such programs can also advance online learning.

Urban wetlands face growing threats from microplastic pollution, yet their function as sinks and conduits for microplastics in sub‑Saharan Africa is poorly characterised. This study examined the spatial and vertical distribution, morphological features, polymer composition, and likely source pathways of microplastics in Lubigi Wetland, Kampala, Uganda. Surface water and sediment samples were taken at nine sites across upstream, midstream and downstream zones, with vertical stratification into surface, middle, bottom and sediment layers. Microplastics were isolated by density separation and oxidative digestion, identified by stereomicroscopy, and chemically characterised by Fourier Transform Infrared Spectroscopy (FTIR). Six morphological categories were recorded: fibres, filaments, films, fragments, microbeads and pellets. Microbeads were dominant (58.7%), especially in sediments and bottom waters, while fibres and fragments were relatively more common in surface layers. Transparent particles were the most frequent, and particles smaller than 300 μm made up 61% of all counts. Polyethylene terephthalate (PET) and polypropylene (PP) were prevalent in water samples, whereas polyethylene (PE), polystyrene (PS) and polyvinyl chloride (PVC) were more abundant in sediments. Sediments held significantly higher microplastic loads than water (p < 0.001), with midstream sites (Namungona, Nabweru) acting as depositional hotspots. Spatial patterns reflected inputs from urban runoff and wastewater and were shaped by wetland topography. Overall, the wetland shows moderate contamination, with microplastic distribution governed by hydrological and anthropogenic factors. We recommend improved waste management, routine monitoring, and the integration of microplastic control measures into wetland restoration and urban planning.

When a single aspirin molecule dissolves in water, roughly 1022 surrounding water molecules must rearrange themselves. How does that invisible molecular choreography produce the single solubility number printed on a pharmaceutical data sheet? This paper answers that question by constructing a statistical field-theoretic framework that bridges quantum-chemical detail and macroscopic thermodynamics in a principled way. We define a solubility field φ(r, x) — a coarse-grained order parameter whose uniform saddle-point value equals macroscopic solubility in well-mixed systems, and whose spatial variations encode mesoscopic heterogeneity near interfaces and critical points. The equilibrium configuration of this field minimises a Landau–Ginzburg free-energy functional whose coefficients are constrained by established limiting laws: Henderson–Hasselbalch for pH-dependence and Debye–Hückel for ionic-strength effects. The framework proposes — as a testable prediction, not an established fact — that solubility can exhibit universal critical features under specific symmetry-breaking conditions. Aspirin serves as the primary working example, yielding crossover behaviour with β ≈ 0.48 near pKa (mean-field regime) and ν ≈ 0.61 (consistent with 3D Ising universality at longer scales), with 15% scatter in scaling collapse that motivates further study. Preliminary analyses of ibuprofen and naproxen show compatible scaling, strengthening the universality claim. We clarify why pH functions as an effective conjugate field through ionisation equilibrium, why Debye screening renders Coulomb interactions effectively short-range, and how existing models — COSMO-RS, SMx, and UNIFAC — emerge as successive approximations to the exact partition function. This revision adds a concrete computational protocol for extracting Landau coefficients from quantum-chemical calculations, discusses kinetic extensions via time-dependent Ginzburg–Landau theory, and provides detailed experimental guidance for validating the framework’s predictions.

Quality management is an essential aspect of ensuring that construction projects are completed successfully. Conventional approaches to quality control tend to be reactive since defects are detected after completion, which necessitates rework, wasted materials, higher costs, and delayed schedules. To address such problems, the construction sector is gradually embracing proactive quality management techniques that aim at preventing defects and enhancing performance continuously. The present study examines the effects of proactive quality management techniques on the performance of construction projects. Specifically, the study addresses preventative inspections, method statements and quality checklists, and worker and engineer training programs. In this research, a mixed-method design was utilized, comprising questionnaires, interviews, site visits, and analysis of Non-Conformance Reports (NCRs). The gathered data was analyzed using percentage analysis, Relative Importance Index (RII), and ranking techniques. The results show that preventive inspections play a crucial role in recognizing any flaws in the early stages of development, whereas the use of method statements and quality checklists brings about consistency in construction operations. However, it was determined that training and skill development programs had the most significant contribution to productivity levels, quality of workmanship, and eliminating recurrent flaws. This study suggests that quality management approaches play an essential role in improving construction performance by decreasing the occurrence of defects, minimizing rework, reducing the cost of poor quality (CoPQ), and increasing productivity.

The dairy industry is one of the major agro-based sectors contributing significantly to global wastewater generation. Dairy effluent is characterized by high concentrations of organic matter, suspended solids, fats, and nutrients, resulting in elevated biochemical oxygen demand (BOD) and chemical oxygen demand (COD). Conventional treatment methods using chemical coagulants such as alum and ferric salts are widely employed; however, these methods are associated with drawbacks including high sludge production, residual toxicity, and increased operational costs. In recent years, natural coagulants derived from plant-based materials, biopolymers, and agro-wastes have emerged as sustainable alternatives due to their biodegradability, low toxicity, and economic feasibility. This review critically examines the characteristics of dairy wastewater, mechanisms of coagulation, and the performance of various natural coagulants. Furthermore, their integration into multistage treatment systems and future prospects for large-scale applications are discussed, highlighting their potential for sustainable wastewater management.

Businesses now, because of the amount of data available, are using business analytics more and more to work better, improve how things are done, and help them make big plans. This research looks at how business analytics helps CME Laboratories Bharat Pvt. Ltd. in Chennai, India (a condition monitoring lab that is officially ISO/IEC 17025 accredited) to be more efficient in their daily work. The research is descriptive and a questionnaire with specific questions was used to gather information from 110 employees in different sections of the company. It considers five main areas: how well things work (operational efficiency), the issues with running things, how key performance indicators are watched, using business analytics, and how analytics generally affects the lab’s work. To be sure the answers were consistent, and to understand how different things relate to each other, the research used statistical methods, specifically Cronbach’s Alpha and Pearson correlation. The outcome of the research is that businesses which do use analytics and see improvements in how well they’re running are very strongly and demonstrably connected. Effectively using analytics in daily work and consistently following KPI’s leads to being more efficient, getting things done faster, and making better choices. This supports the idea that analytics is becoming a really important part of a lab’s strategy.

Small-scale kakanin businesses play a vital role in local food entrepreneurship yet often face challenges in managing fluctuating ingredient prices and limited financial literacy. This study explored the costing management practices of eight kakanin vendors in Pagadian City using a qualitative case study approach. Guided by Transaction Cost Theory, data were collected through semi-structured interviews, observations, and business record reviews. Results revealed that direct costs were tracked through itemized expenses, estimation, manual recording, and occasional digital tools, while indirect costs were allocated using simple methods like percentage mark-ups. Pricing decisions relied on cost breakdowns, market trends, and competitor comparisons. Vendors minimized expenses through portion control, bulk buying, and recipe adjustments to address inflation and raw material shortages. These findings indicate that despite informal costing systems, vendors’ practices align with cost-minimization and uncertainty-reduction principles. The study suggests that improving financial literacy and introducing basic digital tools can enhance cost accuracy and business sustainability.

This phenomenological research explored the lived realities of rural school heads as they navigated the intricacies of educational leadership in resource-limited settings. Through in-depth interviews and thematic analysis, the study uncovered the core of rural school heads’ leadership experiences, highlighting the challenges they faced, opportunities encountered, and the meanings they attributed to their roles. We navigated their different experiences with school leaders in some remote areas of Davao del Norte and Davao de Oro, and how they addressed difficulties, including scarce resources, remote locations, and minimal exposure to new practices. As leaders, they employ adaptive strategies including community engagement, innovative problem-solving, and collaborative decision-making. Through partnership with the community, rural school heads can enhance educational outcomes, promote student success, and contribute to community development.

Startups operate under conditions of high uncertainty, limited resources, and intense competitive pressure. It makes internal organizational factors crucial for survival and growth. Among these factors, organizational culture plays a central yet often underestimated role in shaping entrepreneurial behaviour and performance outcomes. This study examines how organizational culture influences startup success in Bengaluru, one of India’s most prominent startup ecosystems. Drawing on the culture–capability–performance framework, the paper proposes that locally embedded cultural practices. The practices such as innovation, autonomy, collaboration, ethical orientation, and learning. These practices enhance intrapreneurship, employee creativity, and entrepreneurial orientation, which in turn drive both financial and non-financial performance outcomes. Based on an integrative review of entrepreneurship and organizational behaviour literature, the study tries to develop a context-specific conceptual model and a set of testable hypotheses for Bengaluru-based startups. The paper further argues that startup age, growth stage, and founders’ prior organizational experiences moderate the strength of the culture and performance relationship. By evaluating organizational culture of Bengaluru’s startup ecosystem, this research contributes to the growing literature on startup management and offers practical insights for founders seeking sustainable growth beyond short-term scaling imperatives.

Reinforcement Learning (RL) has become an important branch of artificial intelligence for solving sequential decision-making problems in uncertain and changing environments. Unlike supervised learning, RL allows an agent to learn optimal actions through interaction with its surroundings by maximizing long-term rewards. Recent progress in deep learning, computing power, and data availability has significantly expanded the use of RL in healthcare, robotics, finance, transportation, and smart systems. This paper presents a structured review of RL for intelligent decision-making, covering theoretical foundations, modern algorithms, methodologies, applications, benefits, and future opportunities. Special attention is given to safe RL, explainable RL, multi-agent systems, and real-time adaptive intelligence. The study concludes that RL is expected to play a major role in next-generation autonomous and human-centered AI systems.

Existing cloud storage auditing mechanisms rely on third-party auditors (TPAs) or centralized verification, introducing single points of failure and trust assumptions. While blockchain-based approaches have been proposed, they suffer from high on-chain storage overhead, linear verification complexity, and lack of dynamic auditor reputation. This paper introduces ZK-PoR-DR — a novel Zero-Knowledge Proof of Retrievability integrated with a Dynamic Reputation Consensus mechanism. Unlike prior work, ZK-PoR-DR enables: (1) constant-size proofs regardless of file size, (2) off-chain proof generation with on-chain verification using zk-SNARKs, (3) a reputation-based auditor selection protocol that penalizes malicious or lazy auditors via slashing and reward distribution, and (4) post-quantum security via lattice-based commitments. We provide a full algorithm, system architecture, security proofs against adaptive adversaries, and experimental evaluation showing 90% reduction in on-chain gas costs and 3.2x faster verification compared to baseline schemes (Proofs of Replication, Filecoin). No prior work has combined these four properties simultaneously. The protocol is ready for deployment but has not yet been adopted by any major cloud or blockchain platform.

Industrial wastewater containing heavy metals poses significant environmental challenges due to its impact on water quality parameters, particularly pH and contaminant concentration. The presence of copper ions can increase the acidity of wastewater, affecting both treatment efficiency and environmental safety. Therefore, there is a need for sustainable and cost-effective treatment methods that not only remove contaminants but also stabilize pH levels. This study aims to evaluate the pH behaviour and adsorption performance of a coconut husk adsorbent in industrial wastewater treatment. The adsorbent was prepared from coconut husk through washing, chemical activation using sodium hydroxide (NaOH), and low-temperature carbonization. Synthetic wastewater was formulated using kaolin and different concentrations of copper. Batch adsorption experiments were conducted to assess changes in pH and absorbance before and after treatment. The results showed that the initial pH decreased with increasing copper concentration, with values of 4.85, 4.66, and 4.01 recorded for Sample 1, Sample 2, and Sample 3, respectively. After treatment, the pH increased to 5.12, 4.99, and 4.60, indicating improved pH stabilization. In terms of adsorption performance, absorbance values decreased from 2.137 to 1.956 and from 2.494 to 2.357 for Sample 2 and Sample 3, respectively, demonstrating effective contaminant removal. However, a slight increase in absorbance from 1.292 to 1.404 was observed for Sample 1, suggesting reduced adsorption efficiency at lower contaminant concentration. In conclusion, the coconut husk adsorbent demonstrated promising potential as a low-cost and sustainable material for industrial wastewater treatment. Its ability to improve both pH stability and adsorption performance highlights its applicability as an alternative to conventional adsorbents, particularly under moderate to higher contaminant loading conditions.

This work is devoted to the topic "Obtaining and studying a composition based on butadiene nitrile polymer using Astragals". Binary mixtures of butadiene nitrile rubber with astragals in different mass ratios were prepared and the flow index of the system alloy was studied at temperatures of 130, 160, 170 ℃ and under the influence of loads of 12.0-21.0 kg. Our studies have shown that at a temperature of 160 ℃ and when astragals has 5 mass parts in the binary mixture, it acts as a modifier. As a result of the modification, the technological properties of BNK, mainly compatibility with other components and chemical stability, increase by 1.5 times compared to standard resins. We have developed an optimal recipe for obtaining rubbers based on butadiene nitrile rubber and astragals. A rubber mixture was prepared based on the optimal recipe, the vulcanization mode and time were determined, which determined the vulcanization temperature to be 157C and the vulcanization time to be 26 minutest-his work was carried out on the purchase of BNR-based rubber for cables that is chemically resistant to aggressive environments, more perfect and fully capable of meeting operational characteristics, has chemical resistance in saline areas, and is suitable for use as a coating. The main indicators of the obtained regime were determined by physical-mechanical methods and compared with standard rubbers, and it was confirmed that the main indicators of the proposed rubber fully meet the requirements of the standard

Air pollution is one of the most significant health concerns on earth, and the World Health Organization believes that 7 million premature deaths happen annually due to air quality. In this paper, the author is going to provide an elaborate, deploy-able system architecture that incorporates IoT sensor networks, real-time data processing, and machine learning advanced algorithms to monitor and predict air quality. It is made of distributed low-cost sensor nodes, 5G/4G cellular communication infrastructure, cloud-based data processing pipelines, and LSTM-GRU hybrid neural networks to predict AQI. 24 months of performance analysis of 47 urban monitoring stations indicates the probability of making 24-hour AQI predictions with accuracy of 91.3 percent with RMSE of 12.8µg/m3 for PM2.5 concentration. Compared to classical ARIMA approaches, it is demonstrated that it has a 18% improvement and 12% improved compared to single LSTM models.  Some of the features of the system include real-time alerts, health advisory services, and regulatory compliance reporting. Scalability analysis aids the confirmation of linear increase of costs (O(n)) with density of sensor network which allows cost-effective deployment over geographical areas. The work is useful in modernizing environmental monitoring infrastructure, and in evidence-based policy formulation of air quality management.

This study examines the influence of digital engagement on interpersonal engagement among higher secondary students. Data were collected from 470 students in Kozhikode district through a survey method. Data were collected by using the tools Questionnaire on Digital Engagement and Interpersonal Engagement Scale (Arya & Muneer, 2024). The data were analyzed using percentage analysis and one-way ANOVA. The study found that all the students studied used digital devices and most of them (66.4%) were for academic purposes. The ANOVA results show that excessive use of social media and digital games (more than 3 hours) significantly reduced students' interpersonal engagement (p < .01). The use of technology for academic purposes does not adversely affect social relationships. This study highlights the need to maintain a balance between technology and direct social interactions.

Blockchain technology and cryptocurrency have emerged as two of the most consequential financial innovations of the past two decades, yet the gap between their theoretical potential and real-world adoption within mainstream financial services remains conspicuously wide. This paper investigates that gap through a mixed-methods approach, combining a systematic review of thirty peer-reviewed academic sources with primary survey data drawn from 102 respondents representing young, digitally literate demographics. The study finds that while awareness of blockchain and cryptocurrency is relatively widespread, deep comprehension, active usage, and genuine user trust remain limited. Survey respondents show cautious optimism rather than firm conviction — the majority are open to engaging with blockchain-based financial services but are held back by concerns over security, regulatory legitimacy, and a general unfamiliarity with how these technologies actually function. The research identifies four interconnected barriers to adoption: trust deficits, regulatory fragmentation, scalability constraints, and the persistent gap between surface-level awareness and functional understanding. The study concludes that blockchain and cryptocurrency are not questions of 'if' but of 'when' and 'how' — and that realising their potential will require coordinated effort from regulators, financial institutions, technology developers, and educators acting simultaneously rather than sequentially.

The proposed decision framework adopts a deliberately modular structure in which criterion weighting, spatial modelling, and alternative ranking are treated as independent analytical stages, thereby eliminating functional redundancy and reducing computational uncertainty. Uncertainty in expert judgment is addressed solely during the weighting phase through the application of the Fuzzy Analytic Hierarchy Process, ensuring that ambiguity does not propagate into subsequent spatial analyses. Spatial suitability is then modeled using a constrained Weighted Linear Combination approach that produces a continuous surface reflecting ecological and planning limitations. Rather than ranking individual grid cells, final prioritization is conducted at the decision-support level using the Technique for Order Preference by Similarity to Ideal Solution, where candidate locations are assessed as discrete, implementable planning entities. To safeguard the integrity of the results, sensitivity testing is intentionally restricted to the final ranking stage, preventing distortion of the spatial evaluation process. The framework is demonstrated using municipality-scale data, confirming its potential as a transparent, replicable, and technology-oriented decision support approach for solid waste management planning. A case-based implementation using municipal-scale spatial data is included to demonstrate the practical applicability and robustness of the proposed framework.

Rapid urbanization in Kenya triggers explosive demand for precast concrete products. Conventional production relies heavily on natural river sand. Over-exploitation causes scarcity and environmental destruction. Simultaneously, uncontrolled plastic waste accumulation worsens environmental health. This study investigates developing eco-friendly composite materials to produce interlocking paving blocks, partition blocks, and landscaping tiles. We replace natural fine aggregates with postconsumer recycled Polyethylene Terephthalate (PET) waste. We replace river sand with shredded PET bottles at volumetric mass equivalent levels of 0, 5, 10, 15, and 20 percent in standard cementitious mixes. We evaluated physical and mechanical properties including workability, density, water absorption, and compressive strength per standard BS EN and ASTM protocols. Results indicate systematic reduction in density and workability with increasing PET content. Compressive strength decreases progressively due to stiffness incompatibility and hydrophobic nature of the interfacial transition zone. The 5 percent PET replacement mix achieved a 28-day compressive strength of 14.49 MPa. This formulation satisfies requirements for pedestrian walkways, cycle paths, and non-load-bearing applications. This research demonstrates the technical viability of diverting municipal plastic waste into sustainable urban infrastructure.