Volume 14 Issue 10

Background: Accurate prediction of body weight (BW) in small ruminants is essential for effective herd management, breeding, and nutrition, particularly in resource-limited settings where weighing scales are unavailable. While linear body measurements (LBMs) are commonly used for BW estimation, the comparative performance of machine learning models in indigenous Zimbabwean small ruminants remains underexplored. Methods: This study evaluated the predictive performance of decision tree algorithms (CHAID, Exhaustive CHAID, CRT) and Automatic Linear Modeling (ALM) for estimating BW from four LBMs—heart girth (HG), body length (BL), withers height (WTH), and rump height (RH)—in 95 ewes and 126 does. Models were assessed using cross-validation, resubstitution error, and relative predictor importance. Results: Decision trees and ALM showed poor predictive performance in ewes, likely due to low variability in LBMs. In contrast, does exhibited strong predictive relationships: CHAID identified BL, Exhaustive CHAID highlighted HG, and CRT combined HG, BL, WTH, and RH, achieving lower cross-validation errors. ALM corroborated these findings, ranking HG and BL as the most informative traits. These results demonstrate that multivariate machine learning approaches can reliably estimate BW in does using simple, field-measurable traits. Conclusion: HG and BL consistently emerged as robust predictors of BW in does, while ewes require additional traits for accurate estimation. This study provides the first comparative evaluation of CHAID, Exhaustive CHAID, CRT, and ALM in indigenous Zimbabwean small ruminants, offering practical, cost-effective tools for livestock management and breeding programs in resource-limited settings.

Abstract: In many countries, a significant number of accidents occur at unmanned level crossings, where the lacks of human supervision and obstacles on the tracks have become serious daily concerns. Traditionally, railway gates are operated manually by gatekeepers. This paper proposes replacing manual operations with an automated railway gate system utilising Arduino, Sensors, Servo Motors, Buzzers and GSM modules to eliminate human intervention and reduce accident risk. The system detects trains and track objects using sensors positioned alongside the tracks. When a train approaches, the gate closes automatically and the system issues alerts to prevent accidents. Once the train departs, the gate reopens. Compared to manual operation, the automated gates are closed for a shorter duration. Obstacle detection is achieved in two ways: sensors at the front of the train sense objects on the track, and detected obstacles are communicated to nearby railway stations via GSM technology. The system employs infrared sensors to monitor train arrival and departure, ultrasonic sensors for obstacle detection, and Arduino to manage gate operation and messaging. The automated approach improves safety, efficiency and reliability at rail crossings.

Abstract: Overtime, scholars have come up with diverse opinion regarding the impact of foreign direct investment (FDI) on economic growth in host communities. Similarly, empirical findings arising from different contexts and time have equally been contradictory. Current effort to resolve these inconsistency narrows down to sectoral concentration of multinational firms and the capacity of host communities to absorb foreign technology. In addition to these factors, this study deemed it necessary to highlight the importance of government regulatory role. Analysis that defined this investigation was founded on the Solow’s labour-augmented production function, while Autoregressive Distributed Lag (ARDL) bound test approach was employed in the estimation that followed using data for the period 1991 - 2022. The analysis produced interesting findings: First, results show that FDI has a significant negative impact on the growth of the manufacturing sector. Second, the importance of absorptive capacity and government regulatory quality in determining the nature and magnitude of impact of FDI in the growth of the manufacturing sector were equally evident.

Abstract: Blood Alcohol Content (BAC) measurement is critical for individual safety, law enforcement, and public health, but conventional approaches such as breath analysers and blood tests are frequently invasive, periodic, and prone to error as a result of motion artifacts, temperature changes, and intersubject metabolic variations. To overcome these issues, the research in this paper suggests an AI-based wearable system that combines multi-sensor data fusion with machine learning (ML) algorithms to improve BAC estimation accuracy. The system utilizes Photoplethysmography (PPG), Transdermal Alcohol Sensing (TAS), pulse rate, Galvanic Skin Response (GSR), and Infrared (IR) temperature sensors, with real-time processing of data via a cloud-based framework. A dataset of varied sensor readings was gathered in a controlled setting to provide robust BAC estimation under different conditions. Sophisticated machine learning (ML) algorithms such as Multilayer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), and other algorithms were used for BAC prediction, utilizing multi-sensor data fusion to enhance resistance to external interferences. Results show that MLP had the best accuracy (98.99%), surpassing XGBoost and other traditional methods, with lower Root Mean Square Error (RMSE) (0.045), Mean Squared Error (MSE) (0.0020), Mean Absolute Error (MAE) (0.031), and Mean Absolute Percentage Error (MAPE) (3.2%), guaranteeing accurate BAC prediction. Pareto analysis shows Transdermal Alcohol Sensing (TAS) and pulse rate as the most significant parameters in BAC estimation. Competition analysis with prevalent models establishes the superiority of the system in reliability, accuracy, and real-time adaptation, setting it up for efficiency in alcohol monitoring, regulatory adherence, and health-related application.

Abstract: This research examines the regional effects of climate change in South Asia, Sub-Saharan Africa, and Latin America from 1991 to 2020, concentrating on temperature, precipitation, and socio-economic vulnerability. The study utilizes secondary datasets from ERA5, CRU, EM-DAT, FAO, World Bank, and ND-GAIN to implement the IPCC Risk Framework (Risk = Hazard × Exposure × Vulnerability) for the comparison of climate hazards across areas. The results show that South Asia saw the biggest rise in temperature (+1.1°C) and a 6% drop in precipitation. This caused more hot days and changes in the monsoon. Sub-Saharan Africa had the highest overall climate risk because of less rain (–10%), more droughts, and a low ability to adjust because of economic and infrastructure problems. Latin America experienced modest climate stress but gained advantages from comparatively greater adaptation capacity and institutional resilience. The comparative study highlights the imperative for region-specific adaptation solutions, including climate-smart agriculture, early warning systems, social support, and green infrastructure, to enhance long-term resilience.

Abstract: STEM education, an acronym for Science, Technology, Engineering, and Mathematics, emphasizes experiential learning through the integration of these subjects. It employs hands-on activities using specially designed kits, toys, or unconventional settings to foster a practical understanding of STEM concepts. This student-centered approach positions students at the forefront of learning, while teachers act as facilitators. The implementation of Atal Tinkering Labs (ATLs) in classrooms aims to create a stimulating environment, making education more engaging and supportive of innovation. Despite its potential, this pedagogy challenges the traditional education system, necessitating curriculum and teacher education reforms. Enhancing the STEM workforce is critical for addressing global competition and improving STEM literacy. While STEM education is globally recognized and adopted, including in countries like Australia, China, and the United Kingdom, India’s efforts, particularly through ATLs, are noteworthy. This paper explores the strategies employed by India to promote STEM education, focusing on the Atal Tinkering Labs initiative, and examines its effectiveness and challenges.

Abstract: This study explores the evolution and application of mathematical modelling to complex social issues such as poverty, crime, and terrorism. Traditionally rooted in epidemiology, compartmental models have been successfully adopted in criminology and public health to capture the dynamics of addiction, ideological radicalization, and recidivism. The model was derived from a five–compartment representation from which a set of five ordinary differential equations (ODEs) was developed to capture the dynamism of poverty, crime, and imprisonment in a deterministic SCJ₁J₂R compartmental model using the next generation matrix to obtain the reproduction number R₀. This is used to analyze the local stability of the crime-free equilibrium of the SCJ₁J₂R model. The crime-free equilibrium and the local stability of the endemic equilibrium show that R₀ is asymptotically stable if R₀ < 1. We use hypothetical data to simulate the sensitivity of the parameters of the basic reproduction number (R₀) so as to obtain R₀ < 1 for a crime-free society. The SCJ₁J₂R compartmental model differentiates incarceration based on criminal evidence and accounts for both natural and crime–induced mortality as well as reintegration processes. This review highlights the growing role of mathematical approaches in policy-relevant analysis of community safety, systemic intervention, and stability of the models.

Abstract: Tier III banks in Kenya have not been stable in their performance because they are exposed to system risks like the volatility of interests. These are banks that mainly reach small businesses and low-income earners, and they cannot take a lot of shocks in changes in credit expenses. This paper has investigated how interest rate volatility impacted the performance of Tier III banks in Kenya in the years 2004-2024. The key performance indicator used was Return on Assets (ROA). The sources of secondary data included Central Bank of Kenya (CBK), Kenya National Bureau of Statistics (KNBS) and the published financial statements of the 22 Tier III banks. Descriptive statistics revealed that the interest rates were between 6 and 18 with the mean of 9.46 and a standard deviation of 2.54, which revealed great volatility. During the same time, ROA was 1.1 on average, ranging between -0.41% and 2.37 with not all years showing positive returns. Correlation analysis showed that there was a weak positive relationship between interest rates and ROA (r = 0.191), which indicated that an increase in interest rates increased net interest margins temporarily. Regression analysis, however, revealed that ROA had a negative and slightly significant correlation with interest rates (= -0.003111, = 0.056), which meant that increased rates eventually caused the bank to become less profitable. The explanatory power of the model was R2 = 0.117 with a total significance of F = 4.12 (p = 0.056). The results showed that there could be short-term positive outcomes on lending rates as it could increase interest income, but the long-term impact of interest rate volatility was a negative outcome on the financial performance of Tier III banks in the form of higher loan defaults and decreased credit uptake. The research concluded that interest rate volatility has been a major risk issue that was weakening the stability of small banks in Kenya. It suggested that the CBK should take up the stable interest rate policies and Tier III banks must enhance credit risk management, diversify their income sources as well as the capital buffers to survive the volatility.

Abstract Background: Binocular single vision is the ability to maintain visual focus on an object with both eyes, resulting in a single visual image. Anomalies in this system can cause various visual symptoms and impact daily activities such as reading, driving, and using a computer. Despite the abundance of available data, there is a significant lack of clarity regarding precise definitional criteria, standardized methods for testing and diagnosis, and global prevalence studies. Aim: To report the frequency of binocular vision (BV) anomalies in symptomatic patients (e.g., blurring, headache, or eyestrain) and to evaluate the effectiveness of vision therapy (VT) Methodology: This was a retrospective observational study conducted at the B B Eye Foundation, Kolkata, India. The analysis included 45 healthy subjects, both male and female, aged 5 to 25 years. All patients underwent a comprehensive ophthalmic examination, including visual acuity, subjective/objective refraction, and assessment of accommodative and vergence facilities. A comprehensive eye examination with binocular vision (BV) evaluation was performed, followed by the application of vision therapy (VT). Statistical analysis was conducted on pre- and post-vision therapy (VT) parameters. Results: Among 45 patients’ binocular vision (BV) anomalies were found in 44 (97.7%). 35 (77.4%) had convergence insufficiency followed by fusional vergence dysfunction in 3 (6.7%), convergence excess in 3 (6.7%), divergence insufficiency in 1 (2.3%), accommodation insufficiency with convergence insufficiency in 1 (2.3%) and accommodative infacility in 1 (2.3%). Statistical analysis and data comparison were performed using the Mann-Whitney U test with SPSS v.25 statistical software. Descriptive statistics were used to interpret the results. Statistically significant differences were found in binocular vision (BV) parameters between pre- and post-vision therapy measurements Conclusion: Following vision therapy, the symptoms associated with binocular vision (BV) anomalies improved. In particular, significant improvements were noted in: accommodation insufficiency, near point of convergence, positive fusional vergence, and negative fusional vergence.

Abstract: The study employs a deep learning model using Convolutional Neural Network (CNN), specifically the ResNet34 model, for detecting common corn leaf diseases in the municipality of Polomolok and General Santos City, Philippines. Corn, a vital staple crop and key economic commodity, is highly susceptible to diseases that threaten productivity and food security, making accurate and efficient detection crucial for effective management and yield optimization. The dataset used in the study comprises 27,146 images categorized into five classifications: Brown Spot, Corn Rust, Leaf Blight, Maize Streak Virus, and Healthy leaves. To improve model generalization and address class imbalance, data augmentation techniques of Basic Image Manipulation such as rotation, scaling, flipping, shearing, and color transformation were applied. The proposed model achieved an overall classification accuracy of 98.67%, with consistently high precision, recall, and F1-scores across all categories, as further validated through confusion matrix analysis that confirmed its strong performance in distinguishing between disease classes. To extend practical utility, an initial version of a mobile application called LeafScan was developed, which allows users to take or upload corn leaf images for disease prediction. These findings demonstrate the effectiveness of deep learning in agriculture, offering a reliable and scalable tool for disease classification and proactive crop management. The study provides a foundation for implementing AI-driven agricultural solutions in the Philippines. Future work could focus on developing a fully functional and user tested mobile expert system application for real-time disease detection and on expanding the model to include additional corn diseases for broader applicability in precision farming.

Abstract. The heavy use of password-based authentication has created serious weaknesses in security systems for businesses. This has led to increasing threats from phishing, credential theft, and identity breaches. As organizations move online and adopt hybrid or remote work, they need secure, easy-to-use, and flexible authentication methods. This paper looks at how passwordless authentication systems can change how identities are managed in businesses. By examining different models, strategies to reduce threats, and user experiences, the study claims that passwordless authentication, which uses technologies like FIDO2, biometrics, and cryptographic tokens, provides better protection and efficiency than traditional credentials. The paper also offers a practical framework for implementation and a hypothetical case study to show how to adopt this approach in phases, outline technical issues, and highlight long-term benefits. Finally, the research talks about new trends, including decentralized identity and AI-based biometric systems, placing passwordless authentication as a key part of future-proof, zero-trust business security.

Abstract: Green finance has emerged as a transformative mechanism that aligns economic growth with environmental sustainability. In India, where rapid industrialization and urbanization are exerting pressure on natural resources, green finance plays a crucial role in promoting low-carbon and climate-resilient development. This paper examines the theoretical foundations and conceptual linkages between green finance and sustainable economic growth, with a focus on India’s key sectors, including energy, transport, manufacturing, and agriculture. Through a review of contemporary literature, the study highlights how financial instruments, such as green bonds, green credit, and sustainability-linked investments, contribute to achieving the Sustainable Development Goals (SDGs). The paper concludes that effective policy frameworks, regulatory support, and increased private participation are essential for expanding the green finance ecosystem in India.

Abstract: The built environment significantly contributes to global energy consumption, greenhouse gas emissions, and water usage, necessitating innovative sustainable architectural approaches in civil engineering. This study provides a comprehensive analysis of sustainable design strategies, advanced building materials, and their environmental performance to support the transition toward low-impact construction practices. By systematically reviewing recent literature and conducting simulation-based case studies, the research evaluates the effectiveness of passive design principles, low-carbon materials, and integrated building systems across diverse climatic contexts. Life cycle assessment (LCA) and life cycle cost analysis (LCCA) are employed to quantify the environmental and economic impacts of various architectural interventions over a building’s lifespan. Key findings highlight the critical role of passive design elements—such as optimized building orientation, high-performance envelopes, and natural ventilation—in reducing operational energy demand. Among materials, mass timber and geopolymer concrete emerge as promising low-embodied carbon alternatives with favourable durability profiles. Trade-offs between embodied carbon and operational savings are identified, underscoring the importance of holistic assessment frameworks. Additionally, daylighting strategies not only improve occupant comfort but also reduce reliance on artificial lighting, further cutting energy use. The study’s insights offer actionable recommendations for policymakers, practitioners, and rating bodies (e.g., ECBC, GRIHA, LEED) to refine codes and promote sustainable construction. Emphasizing integrated design and material choices is essential to achieve net-zero goals and advance resilient, eco-friendly civil infrastructure.

Abstract: This study presents a comparative evaluation of the phytochemical composition and health-promoting potential of seven medicinal plant extracts Azadirachta indica, Curcuma longa, Allium sativum, Ocimum gratissimum, Vernonia amygdalina, Aloe barbadensis, and Moringa oleifera on the health biomarkers of broiler chickens. By integrating phytochemical profiling, biochemical assessment, hematological indices, and antimicrobial testing, this research provides a multi-dimensional understanding of phytogenic addictive. Phytochemical screening confirmed the presence of phenols, tannins, alkaloids, flavonoids, saponins, and cardiac glycosides in varying concentrations. Among the extracts, Ocimum gratissimum and Azadirachta indica contained the highest flavonoid and saponin levels, respectively. Vernonia amygdalina and Moringa oleifera exhibited superior effects on liver and kidney biomarkers, enhancing total protein while reducing urea and creatinine levels. Allium sativum notably improved hematological parameters such as hemoglobin concentration, while Azadirachta indica increased total white blood cell counts. Antimicrobial screening demonstrated that Moringa oleifera and Vernonia amygdalina possessed the strongest inhibition against pathogenic bacteria, including Salmonella spp., E. coli, and Klebsiella spp. These findings reveal the synergistic potential of medicinal plants as phytogenic feed additives to improve organ health, immunity, and microbial safety in poultry production. The comparative approach enhances understanding of plant specific effects and supports development of natural alternative to antibiotics in broiler chicken.

Abstract: Customer satisfaction is a key performance indicator in the oil industry of Kenya which is determined by branding strategies and the quality of the service. Rebranding tools to increase customer experience and loyalty have become more and more popular among oil marketing companies. This paper has specifically looked at how brand refresh strategies have influenced customer satisfaction in the oil industry within Kenya. Brand Equity theory was used as the guiding theory. The research design adopted was descriptive research design that focused on station managers of Rubis Energy, Vivo Energy, and TotalEnergies in Kenya. There were 270 station managers which were the target population and a census approach was used. The major data collection tools were structured questionnaires. Pilot study was done on 27 station managers of Ola Energy and Be Energy to determine the validity and reliability of the research tool. Findings produced Cronbachs Alpha coefficients of more than 0.80 in all constructs with brand refresh yielding 0.84 and a total reliability coefficient of 0.86 indicating high internal consistency. Two hundred and one out of the 270 questionnaires distributed were returned and completed thus giving a response rate of 78.15. Data was examined through descriptive, as well as inferential statistics with the help of SPSS version 31. Results showed that brand refresh was strongly and statistically significant related to customer satisfaction (R = 0.624, 9 = 0.553, p < 0.05). The research found that regular brand refresh activities including new logos, professional branding images and better station surroundings can make a huge contribution to customer satisfaction. It advises that the oil marketing firms should always take initiative to create brand refresh in line with customer expectations in order to remain competitive, build loyalty and enhance customer retention. The study helps in policy formulation, managerial-decision making, and literature on branding, service quality, and customer satisfaction in the oil industry.

Abstract: Phytopharmaceuticals represent a promising fusion of contemporary scientific advancements and traditional plant-based knowledge. These products are rooted in the centuries-old herbal medicine systems of Ayurveda, Siddha, Unani, Sowa-Rigpa, and Naturopathy in India. The country’s extensive ethnobotanical knowledge draws from the distinct therapeutic frameworks and bioactive insights provided by each of these traditions. Despite this rich heritage, challenges remain in terms of clinical validation, standardization, quality control, and regulatory compliance. Unlike traditional AYUSH substances, India's regulatory framework for phytopharmaceuticals is evolving to ensure their efficacy, safety, and international recognition. As the global demand for plant-based medications continues to rise, phytopharmaceuticals are increasingly viewed as safer alternatives for existing and emerging medical conditions. Advancements in AI-driven drug discovery, nanofabrication, and extraction technologies have accelerated the development of this field. Furthermore, regulatory reforms and mounting clinical evidence have established phytomedicine as a legitimate, evidence-based treatment option. Emerging trends highlight a shift toward ethical supply chains, sustainable sourcing, and personalized preventive healthcare. With growing global markets and a critical role in addressing public health challenges, such as antimicrobial resistance (AMR), phytopharmaceuticals are poised for transformative growth. India, with its dual strengths in pharmaceutical innovation and traditional medicine, is uniquely positioned to spearhead this global movement.

Abstract—The rapid expansion of university campuses has led to frequent instances of misplaced personal belongings, creating inconvenience for students and staff. UniFind: Lost and Found Objects Management System for Campus using AI & Cloud Services is designed to provide a smart, efficient, and scalable solution to this challenge. The system leverages artificial intelligence for image recognition and natural language processing to accurately identify, classify, and match lost items with their rightful owners. By integrating with cloud services, UniFind ensures secure data storage, real-time accessibility, and seamless scalability for large user bases. The platform enables users to report lost or found items via a web or mobile interface, where AI-driven matching algorithms automatically suggest potential matches. Notifications and dashboards streamline communication between finders and owners, significantly reducing manual effort and delays. This project demonstrates how the fusion of AI and cloud technology can modernize campus management processes, enhance user convenience, and promote a responsible, collaborative environment within academic institutions.

Abstract: The fast gr‌owth o​f influencer marke​ting has grea‌tly influen⁠ced‍ how‍ cons‌um‌er⁠s b‌ehave in on‌line retail en‍viron‍ments. However, its impac⁠t on impulse buying—meaning unplanned or s⁠pontaneous purchas⁠es—is stil‍l not fully understood‌, par​ticula​rly when⁠ combine⁠d with‌ s‍oci⁠al and sit‌uation‌al⁠ fa‌ctors. This study aims to investigate the impac‌t of infl‍u‍en‍cer m‌ar‍keti⁠ng on‍ impu​l​se​ buying behavior amon‌g‍ online shoppe​r‍s. To provid‌e a stronger theore⁠tical foundation⁠, the rese‌arch integrates the Stimulus–Or‍ganism–R‍espo​nse (S-O-R) a⁠nd​ Elabor‍ation Likel‍i‌hood Model⁠ (EL‌M) frameworks to explain how psychologic‌al mechanisms drive impulsive behavior. We explore how social influence (peer pressure,‌ online reviews, and influen‌cer c⁠r‍edibility) and situational factor‌s (time pressure, discounts, limited off‌ers, and brow‍sing environment) interact to trigg⁠er impulse⁠ purchas‌es. The study also considers moderating c​onsu‌mer traits such a​s self-control, mater‍ialism, and trust,‌ as well as platform-s⁠pecific influen‌ces (⁠Instagram v​s. TikTok⁠). Finally, we d⁠i​scuss the e‌th​ical implica⁠tions of influenc⁠er marke⁠t‌ing in promoting res​ponsible and s​ustai‍nable d‌igi‌tal​ consumerism.

Abstract: The study investigates the relationship between interest rate fluctuations and stock market performance with a specific focus on private sector banks in India. Interest rates, being a crucial monetary policy instrument, directly influence capital costs, borrowing patterns, and investment decisions. Private sector banks, being highly sensitive to economic and financial changes, serve as an ideal sector to explore these dynamics. This research aims to examine both the short-term and long-term effects of interest rate changes on private sector bank stock prices, trading volumes, and investor sentiment. It also compares the responsiveness of private sector bank stocks with overall market indices. The study employs econometric techniques using secondary data on interest rates, stock prices, and market indices. Findings are expected to provide valuable insights for investors, policymakers, and financial institutions on managing risks and formulating strategies in the face of interest rate volatility.

Abstract—After the pandemic situation in the world, a new era started where everything is going online like education, training, shopping, work, etc. by taking the advantages of AI technology and the online trend of doing YOGA at home, Bringing the idea, there is a chance to shift offline yoga training to online. This study introduces a novel method to detect real-time yoga poses using artificial intelligence (AI) and camera input and provide corrective feedback accordingly. The method uses a real-time pose prediction model, using the advantage of Media Pipe to capture human body landmarks from camera frames. These key points are then analyzed to identify different yoga poses. The system provides real-time corrective feedback on posture by analyzing it to help users make sure that it works smoothly. By combining AI-based pose recognition and feedback, this solution aims to improve yoga practice, especially for beginners.This research presents a deep learning-based approach for yoga pose detection using the YOLOv5 object detection framework. The data set, named YOLO Yoga Dataset.v1i.yolov5, consists of 1,013 annotated images in five yoga poses: Bridge, Downward Dog, Plank, Shoulderstand and Tree Pose. The data set was sourced and preprocessed through Roboflow and trained on YOLOv5 with data augmentation to improve generalization.

Abstract—Quantum dots (QDs) are semiconductor nanocrystals with exceptional optical and electronic properties, making them highly attractive in biomedical research [1], [6]. Their tunable fluorescence, broad excitation, and high photostability have positioned them as promising tools for cancer diagnostics [3], [7]. This review explores QD applications in in vitro diagnostics (IVD), point-of-care testing (POCT), bioimaging, and signal amplification strategies. Furthermore, emerging trends, safety considerations, and translational challenges are discussed, providing a roadmap toward clinical application [12].

Abstract: A Web-Based Gym Member Monitoring System Using Predictive Analytics and Decision Tree Algorithm focuses on enhancing gym operations and member management through data-driven automation. The system integrates QR code attendance tracking, automated payment processing, and predictive analytics to improve efficiency, accuracy, and decision-making at Almo Fitness Gym in Taguig City. The Linear Regression algorithm in the Almo Fitness Gym system can predict future attendance trends and revenue based on members’ historical usage patterns. Logistic Regression helps classify members as active or at risk of cancellation by analyzing their visit frequency and payment behavior. Meanwhile, Random Forest and K-Means Clustering enhance predictive analytics by improving the accuracy of retention forecasts and grouping members into segments for targeted fitness programs and personalized promotions. The platform’s web-based interface allows both administrators and members to access real-time information on attendance, payments, and progress tracking. The development process followed the Agile methodology, ensuring flexibility, user feedback integration, and iterative improvements across system modules. Evaluation was conducted using ISO 25010 software quality standards, focusing on functionality, reliability, usability, efficiency, and security. Results from 50 respondents comprising 30 gym users and 20 IT experts showed that the system performed excellently in reliability, efficiency, and security, confirming its effectiveness and user-friendliness. Users found the interface intuitive and responsive, while experts validated its compliance with standard software design principles. Findings demonstrated that predictive analytics and the Decision Tree algorithm are effective in optimizing gym operations, supporting data-informed decisions, and personalizing fitness management. Overall, FitTrack serves as both a technological and operational solution that transforms manual gym processes into an intelligent, automated system. The study recommends further development through mobile integration, offline functionality, and enhanced security measures to ensure scalability, accessibility, and long-term system sustainability.

Polyurethane (PU) foams are widely applied in cushioning, packaging, and construction, but their reliance on petroleum-based feedstocks and limited durability raises sustainability concerns. This study investigates the incorporation of ground tyre rubber (GTR) into flexible PU foams to enhance performance while promoting circular economy goals. Foams were synthesized via the free-rise method using polyethylene glycol (PEG 400) and isophorone diisocyanate (IPDI), with GTR fillers (≤0.4 mm) and silicone oil surfactant. Fourier-transform infrared spectroscopy confirmed successful urethane network formation, while scanning electron microscopy revealed that GTR disrupted cellular morphology, increasing porosity and heterogeneity. Mechanical analysis showed a substantial decline in compressive strength and modulus with increasing filler loading, attributed to poor filler matrix compatibility and uneven dispersion. Thermal analysis highlighted a dual effect where GTR suppressed the glass transition temperature, reflecting reduced microphase separation, yet improved thermal stability by delaying degradation onset and increasing residual char yield. Silicone oil partially mitigated structural collapse by refining cell morphology and enhancing compressive behavior. The results showed a trade-off between sustainability-driven waste rubber utilization and foam integrity. While GTR incorporation advances responsible resource recovery and contributes to Sustainable Development Goals (SDGs), unmodified fillers compromise mechanical reliability, limiting high-performance applications. Future work should emphasize interfacial engineering, including surface modification and compatibilizer integration, to reconcile environmental imperatives with performance requirements.

Bridge collapses are catastrophic infrastructure failures, leading to loss of life, disruption of mobility, and erosion of public trust. This paper analyses four major Indian bridge collapses (2013–2025) — Ultadanga, Talkies, Taratala (Majerhat), and Gambhira — using a Case–Cause–Lesson framework. A comparative study of Majerhat (2018) and Gambhira (2025) is presented to highlight recurring weaknesses in design, construction, maintenance, governance and have revealed severe deficiencies in structural health monitoring, inspection protocols, and disaster preparedness. This paper integrates case-based forensic analysis with international benchmarking to identify systemic gaps in bridge safety management. Using a mixed qualitative–quantitative framework, the study examines causes, consequences, and institutional responses to recent bridge failures. A comparative overview of international best practices (Japan, UK, USA) informs a roadmap for implementing IoT-based Bridge Health Monitoring (BHM) and Digital Twin frameworks in Indian conditions. Results highlight the urgent need for integrated governance, transparent maintenance systems, and community-focused disaster management protocols.

The growing demand for accessible, efficient, and data-driven healthcare systems has encouraged the development of digital solutions addressing maternal and infant health challenges. In response, the researchers developed WOMB: A Web-Based System for Maternity Support and Infant Health Tracking with Integrated Data Analytics and Smart Algorithms, a platform designed to assist mothers and healthcare professionals in monitoring health data, improving record accuracy, and enhancing communication. The WOMB system is a centralized, user-friendly web platform that strengthens the management of maternal and infant healthcare. It provides essential functions such as tracking infant growth, scheduling medical appointments, managing health records, and offering educational resources for both mothers and healthcare workers. Through data analytics and intelligent algorithms, the system generates predictive insights, automates reminders, and supports evidence-based healthcare decisions. Built using PHP, MySQL, HTML, CSS, and JavaScript, the system complies with ISO/IEC 25010 Software Quality Standards to ensure security, usability, functionality, and performance efficiency. Its beneficiaries include mothers, infants, and healthcare providers, as it promotes improved health tracking, organized digital record management, and better access to critical information. ISO/IEC 25010 is an international software quality standard that defines attributes such as functionality, reliability, and usability to evaluate software effectiveness. This study utilized an Applied Research Design and adopted the Waterfall Model of the System Development Life Cycle (SDLC) to guide system creation and evaluation. The model involved six key phases—requirement analysis, system design, implementation, testing, deployment, and maintenance—to ensure structured and high-quality development. Data were collected through surveys, interviews, and observations from selected healthcare professionals and mothers to identify their needs and challenges in managing maternal and infant healthcare records. The Software Development Life Cycle (SDLC) is a structured process for planning, creating, testing, and deploying an information system efficiently. A total of 60 respondents participated in the system evaluation, consisting of 40 user respondents (mothers and healthcare providers) and 20 technical respondents (IT specialists and system developers). They assessed the system using the ISO/IEC 25010 Software Quality Model, focusing on the attributes of functionality, reliability, usability, efficiency, maintainability, portability, and security. Statistical tools such as the weighted mean were applied to analyze the results, which showed overall average means of 3.50 for user respondents and 3.63 for technical respondents—both interpreted as Strongly Agree. ISO/IEC 25010 is an international software quality standard that defines attributes such as functionality, reliability, and usability to evaluate software effectiveness. The evaluation results show that respondents found the system functional, secure, and easy to use for managing maternal and infant data. Usability received the highest rating among users, while functionality ranked highest among technical respondents. Although reliability obtained slightly lower ratings, it remained positive, suggesting only minor areas for improvement. Overall, the findings affirm that WOMB effectively meets international software quality standards and fulfills its purpose of promoting digital innovation in maternal and infant healthcare. 

Abstract: This article focuses on the process of transferring Health Services to Municipalities in a context of Decentralization of Health Care to the State Representation Councils and Province Executive Councils, where it aims to identify the barriers to the transfer of Health Services for all Municipalities in Mozambique since its approval and entry into force. However, the qualitative methodology was used through the technique of bibliographical review of books, articles, dissertations, which allowed to highlight the need to transfer or create the financial, human and material resources necessary to guarantee the viability of the services, once assumed by the municipalities. Likewise, it justifies the need for the Central Government to stop prioritizing its coordination through the OLE (provincial and district governments) only through this route, privileging the municipalities. Finally, it emphasizes the need to sign more memoranda of a binding nature so that the process of transferring health services can take place.

Abstract: Femicide has increased in the last decade in the global index. In Kenya, femicide occurs under diverse age brackets but recently the trend is high among young women (16-35 years). In particular, the brutal murders of young women: emerging and with increasing trends and incidents in various places call for immediate solutions. The purpose of this study was to examine relationship between social dysfuctions and femicide cases among young women in Juja sub-county. The Feminist theory and Routine activity theory guided the study. The study was done at Juja sub-County in Kiambu County, Kenya. The study adopted a mixed–method research approach, utilizing the embedded design. The target population was 300,948 respondents. The accessible population was 200,510 respondents, out of which a sample of 278 was drawn through the Slovin's formula. The total sample was 288 respondents consisting of the 10 key informants included in the study. The researcher drew the sample using Stratified simple random sampling. Questionnaires and interview schedules were the main instruments of data collection where 245 questionaires were fully filled and returned and all the 10 interviews were conducted succesfully. Reliability was measured through Cronbach Alpha a statistic coefficient (a value between 0 and 1) used to rate the reliability of an instrument and was 0.813. Poisson regression analysis was conducted on the quantitative data with the help of Statistical Package for Social Sciences (SPSS) version 26.0 software. Qualitative data was analysed through thematic content analysis utilizing MAXQDA tool. The study established no statistically significant relationship between social factors and femicide with a p-value of .367, well above the .05 threshold, the model indicated that social variables do not have a direct, independent effect on the number of femicide cases. The study concluded that social dysfunctions alone are not the cause of femicide but is as a result of    a more complex, multi-faceted dynamic at play than a simple cause-and-effect and recommended that to reduce these risks, there is a need for strengthened social support systems. The study aimed to benefit the policymakers on formulating relevant social policies, inform the society on better societal practices, contribute to the academia theories and literature on femicide, as well as inform young women in adopting femicide prevention strategies.

Abstract: Fifteen vertical electrical soundings using the Schlumberger configuration with a current electrode spacing of AB/2 = 100m were conducted in Biu town, northeastern Nigeria. The field data were analyzed with IX1D inversion software to estimate porosity and identify porous zones. Results indicated four distinct layers with different thicknesses, resistivities, and weathering levels. The first layer is identified as alluvial topsoil, the second as weathered basalt, the third as fractured basalt, and the fourth as fresh basement. Resis­tivity values for the layers are: 2.16 to 149.00 Ωm for the topsoil, indicating porosity from secondary porosity or water content; 3.60 to 244.00 Ωm for weathered basalt; 16.50 to 372.20 Ωm for fractured basalt; and 55.04 to 449.75 Ωm for the fresh basement, reflecting high resistivity consistent with unaltered rock.

Abstract : The inefficiency often observed in oil extraction methods can be attributed to insufficient kinetic and thermodynamic data during the design phase. This study aimed to optimize the turmeric oil extraction process using 250 ml of n-hexane and 50 g of turmeric sample from Southern Kaduna. Six kinetic models and thermodynamic principles were applied to describe the extraction process. The physicochemical properties of the turmeric oil were analyzed following the methods recommended by the Association of Official Analytical Chemists. The results showed that the optimal conditions for turmeric oil extraction were temperature range of 343 to 353 K, an extraction time of 5 to 6 hours and a solvent-to-sample ratio of 1:5. The extraction followed pseudo-second-order kinetics, with an activation energy (Ea) of 18.8327 kj/molK. Thermodynamic analysis revealed an enthalpy change (∆H) of 19.6389 kj/mol, an entropy change (∆S) of 0.08710 kj/molK and positive Gibbs’ free energy change (∆G) at all tested temperatures. These thermodynamic results suggest that the extraction process is endothermic, requiring continuous energy input, exhibits high randomness and is non-spontaneous. Furthermore, the physicochemical analysis confirmed that the oil is safe for consumption and suitable for use in soap production, cosmetics and pharmaceuticals.

Abstract: Augmenting Automated Document Generation This paper introduces the Sandbox: Document Generating Engine, a novel, secure, and modular web application built with Python and Streamli (Achachlouei, A., Patil, M. A., Joshi, Q., Vair, T. & N. 2021). The primary research objective is to validate the feasibility and efficacy of augmenting Intelligent Document Processing (IDP) workflows by integrating Contemporary Large Language Models (LLMs) for semantic data-to-template mapping. Addressing the challenges of manual, time-consuming, and error-prone document creation, the system leverages Natural Language Processing (NLP) capabilities to analyze data uploaded in diverse formats (e.g., .csv, .xlsx, .txt) and automatically populate predefined document templates (Adhikari, P. R. 2018). The system features a robust secure authentication module utilizing bcrypt for password hashing and PostgreSQL for credential management. Our initial technical findings demonstrate high reliability, with Extraction Accuracy consistently over 95% across test documents. Furthermore, the system drastically reduced the time required for complex document creation, validating the capacity of LLM-enhanced IDP to yield substantial improvements in efficiency and productivity over simple rule-based methods. (Bitzenbauer, P. 2023).

Abstract: This paper examines colonialism and imperialism as interrelated systems that shaped India’s political, economic, social, and cultural landscapes from the seventeenth to the twentieth century. By analysing the British colonial enterprise and its underlying imperial ideology, the study explores how domination was institutionalized through governance structures, economic exploitation, and cultural hegemony. Drawing on historical records, postcolonial theory, and economic analyses, the paper argues that colonialism in India functioned not merely as political occupation but as a comprehensive system of control designed to sustain imperial power. It highlights the mechanisms through which colonial policies restructured India’s economy, disrupted indigenous industries, and redefined social hierarchies. Furthermore, the research investigates the lasting legacies of imperialism in contemporary Indian society, economy, and identity formation. Ultimately, this paper situates India’s colonial experience within the broader global context of imperial systems, offering insights into how resistance movements and nationalist discourses emerged as counter-systems challenging colonial domination.

Background: University administrators encounter multi-faceted decision-making problems including resource distribution, prediction of incoming enrollments and optimization of student success in the face of underlying uncertainty. The traditional deterministic models can hardly represent dynamic interdependence of educational systems. Methods: Authors present a holistic Bayesian statistical tool of university management, with hierarchical Bayesian and Bayesian optimization tools and Markov Chain Monte Carlo (MCMC) tools. Combining both the previous institutional knowledge and the observed data to give strong uncertainty quantification to administrative choices. Results: Simulation experiments and empirical research indicate that predictive performance is better than frequentist methods by 15-20% in the accuracy of enrollment prediction and a substantial increase in resource allocation efficiency. Bayesian model offers Confidential intervals that can be easily interpreted and high adaptability in decision making. Conclusions: Bayesian techniques provide a principled management tool to university administration, allowing data-driven decisions and clearly defining uncertainty. It helps in fair allocation of resources and enhance institutional strength in changing learning conditions.

Abstract: The conjugate gradient (CG) method is a one of the common approaches for solving unconstrained optimization problems, notably known for its suitability for large scale problems. Many recent studies show that this method is also useful for problems of smaller scale. One of the methods used for improving the performance of CG method is hybrid approach, where a CG method is combined with another method. In this study, the ARM CG method is combined with the SMR CG method and tested under exact line search. The resulting hybrid algorithm is globally convergent under exact line search and shown to perform well numerically in comparison to other tested CG methods.

Abstract: This research paper explores significant cybersecurity incidents that have impacted global firms and industries, pointing to weaknesses in digital infrastructure. Through the examination of high-profile case studies like Equifax, Facebook, JW Marriott, and Sony, the paper looks at causes, effects and operational disruptions that followed. The study focuses on how companies can enhance their security models, adopt proactive approaches, and reduce risks in an increasingly interdependent world. Finally, it emphasizes the need for strong defenses to deter further attacks.

Abstract: The Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS) stands as a cornerstone of India's social security architecture, aiming to enhance livelihood security in rural areas by providing at least 100 days of wage employment per household. This paper examines the utilisation of rural manpower under MGNREGS in Anantapur district of Andhra Pradesh, a region characterised by its semi-arid climate, recurrent droughts, and a high dependence on rain-fed agriculture. Through a qualitative analysis of secondary data from government reports and a review of existing literature, this study assesses the performance of the scheme in terms of employment generation, wage distribution, and asset creation. It finds that MGNREGS has been a critical lifeline for rural labour in Anantapur, significantly absorbing surplus agricultural labour, particularly during lean seasons and drought years. However, the study also identifies persistent challenges, including delays in wage payments, uneven distribution of person-days, and questions regarding the durability of created assets. The paper concludes that while MGNREGS has been effectively utilised as a safety net, its potential as a transformative tool for sustainable rural development in drought-prone regions like Anantapur requires strengthened implementation, greater focus on water conservation works, and better convergence with other developmental programmes.

Facial expressions are one of the most natural and universal ways of conveying human emotions, making their automatic recognition a critical component in affective computing and human–computer interaction. This paper presents a Convolutional Neural Network (CNN)-based approach for emotion recognition from facial images. The proposed model utilizes deep feature extraction to capture spatial hierarchies in facial regions, enabling accurate classification of emotions such as happiness, sadness, anger, surprise, fear, disgust, and neutrality. By training and evaluating the CNN on publicly available benchmark datasets, the model demonstrates robust performance and generalization across diverse facial variations. Experimental results highlight the efficiency of CNNs in recognizing subtle emotional cues without relying on handcrafted features. The proposed approach holds significant potential for applications in mental health monitoring, intelligent tutoring systems, adaptive user interfaces, and surveillance systems where understanding human emotions is essential.

The African Continental Free Trade Area (AfCFTA), inaugurated in 2018, represents a pivotal initiative toward regional economic integration, trade liberalization, and sustainable development across Africa. Central to its framework are objectives such as the elimination of trade barriers, a 90% reduction in tariffs, the facilitation of trade in goods and services, and the promotion of economic diversification and industrialization. Despite the official commencement of trading under AfCFTA on January 1, 2021, substantive intra-African trade activities under its provisions remain limited, signaling critical implementation bottlenecks. This study examines the supply chain inefficiencies that hinder the achievement of AfCFTA's objectives and assesses stakeholder perceptions regarding its effectiveness in addressing structural trade challenges. Employing a quantitative research design, primary data were collected through structured surveys administered to stakeholders actively engaged in cross-border supply chain operations within the AfCFTA framework. Findings indicate that persistent infrastructural deficits, non-harmonized trade policies, border inefficiencies, and low levels of technological integration significantly constrain intra-African trade. These challenges have curtailed seamless supply chain operations, undermining the anticipated benefits of the agreement. To address these systemic constraints, the study recommends a multifaceted strategy centered on regional policy harmonization, enhanced public-private collaboration, investment in trade-enabling infrastructure, and the adoption of digital and logistics technologies to foster a resilient and efficient continental supply chain network.

This study examines the impact of collaboration on employee performance, with innovation as a mediating variable among public sector employees in East Kalimantan, Indonesia. This study employs a quantitative methodology involving 172 respondents from various government institutions. Data analysis was conducted using Partial Least Squares structural equation modeling (PLS-SEM) via SmartPLS 4. The findings indicate that collaboration exerts a positive and significant influence on employee performance both directly and indirectly through innovation. Moreover, innovation plays a crucial mediating role, suggesting that collaborative work environments promote creative problem-solving and knowledge exchange, ultimately enhancing individual performance. These results contribute to the integration of Social Exchange Theory, Knowledge-Based View, and Dynamic Capability Theory in elucidating performance improvement within the public sector. Practically, this study highlights the importance of fostering collaborative cultures and innovation-driven management practices to improve bureaucratic efficiency and public service delivery in developing regions.

This study investigates how perceived organizational support (POS) influences job performance (JP) directly and indirectly through readiness to change (RTC) among nonacademic civil servants in Indonesian public universities. Drawing upon the Organizational Support Theory and the Change Readiness framework, this research analyzes responses from 180 administrative employees at Universitas Mulawarman undergoing institutional transformation. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), this study found that POS significantly enhanced both RTC and JP. Moreover, RTC positively influenced JP and mediated the relationship between POS and JP. These findings confirm that organizational support is a vital antecedent to fostering individual psychological readiness, which in turn promotes adaptive and productive behaviors during periods of change. The study contributes to the literature by integrating POS and RTC into a cohesive explanatory model of employee performance within public higher education institutions. The results highlight the need for policy strategies that emphasize inclusive change communication, supportive leadership, and long-term investment in human capital to improve organizational readiness and performance. The limitations of this study include the single-institution sample and cross-sectional design. Future research may explore comparative analyses across multiple institutions and adopt a longitudinal or mixed-method approach to capture the dynamic processes of organizational transformation.

Water pollution is an important environmental issue that affects human health, aquatic habitats, and the sustainability of natural resources. Reliable mathematical models that can forecast the behaviour of pollutants in three-dimension (3-D) water systems are needed to address this issue. In this study, a 3-D diffusion model is used to determine the periodic and location-based fluctuation of the pollutant concentration in water. Investigating the slow increase in pollution levels in a 3-D area and evaluating the precision of analytical and numerical approaches to diffusion-based pollution problems are the objectives of this work. Two approaches are used to accomplish this: the Adomian Decomposition Method (ADM), which is an analytical approach, and the Du Fort Frankel (DF) scheme, which is a numerical approach.  Initial and boundary conditions required for the modelling are provided by experimented data (Exp. data) from a 3-D cuboid tank filled with water and introduced with an iodized salt water solution as the pollutant.  Direct monitoring of pollution dispersion across time and space is made possible by this configuration, producing useful data for confirming the mathematical models. The results of the experiment verify that the levels of pollutants rise with time at each location in the 3-D region.  When comparing the outcomes of the DF approach with Exp. data and ADM, an insignificant difference, measured in parts per million (PPM), is observed, demonstrating the reliability and strength of the suggested model.  This study is important because it combines mathematical models and realistic observations to examine pollution of water in 3-D. The results show that the model is accurate and applicable to both controlled experiments and larger-scale water systems.  Additionally, this work advances the mathematical solutions for diffusion equations and offers useful information for sustainable resource use, pollution control, and water quality evaluation.

This project created a web-based system called ClusRoof for Nishin Metal Corporation, a company that offers commercial and industrial roofing services. The system helps the company manage customers and projects while also using the K-Means Algorithm to group customers based on their service requests. It includes an easy-to-use dashboard that shows important information such as project cost, duration, and type of service. The system also helps manage job orders, materials used, and service history. It was developed using Python, PHP, MySQL, JavaScript, and CSS, making it functional, secure, and simple to use. The system was tested and evaluated using the ISO 25010 standard, which checks quality in areas like functionality, reliability, usability, efficiency, and security. Based on feedback from both users and technical evaluators, ClusRoof was found to be effective, user-friendly, and reliable. It successfully met its goals by giving accurate customer grouping, clear data displays, and useful insights to help the company make better decisions. For future improvements, it is recommended to add new features like real-time payment options, automatic email notifications for inquiries, and an inventory module for materials. Continuous updates, security checks, and user support are also advised to keep the system running smoothly. Overall, this project shows that ClusRoof can help improve business processes, and customer service.

This study investigated the relationship between digital expertise, stress-coping skills, and technological adaptability among secondary school teachers in the Division of Bukidnon.  Utilizing a quantitative research design, 266 teachers participated in a survey that measured their levels of digital proficiency, coping strategies, and readiness to integrate technology into the classroom.  Results consistently showed high levels of digital expertise and stress-coping skills, with teachers demonstrating confidence in using digital tools, in adopting adaptive teaching methods, and in engaging in continuous learning.  Significant positive correlations were found between digital expertise and coping skills, on the one hand, and technological adaptability, on the other, indicating that teachers who possess strong technical competence and adaptive coping mechanisms are better equipped to manage technological change.  Regression analysis revealed that adaptability and continuous learning, along with problem-focused coping, were the strongest predictors of technological adaptability.  These findings underscore the importance of ongoing professional development, institutional support, and collaborative networks in maintaining teacher resilience and effectiveness in digitally enriched educational environments.

Abstract- Phase change materials store large amount of heat in the form of latent heat of fusion. For latent heat thermal energy storage systems, the comprehensive performance evaluation of phase change materials is very important task for the selection of the most suitable phase change material from among multiple alternative one. It is a typical multi-attribute decision making (MADM) problem. This work proposed a MADM approach to evaluate the comprehensive performance of the phase change materials using technique for order preference by similarity to ideal solution (TOPSIS), and applied it to evaluate the comprehensive performance of 9 alternative phase change materials for solar domestic hot water system. As the result, the comprehensive performance ranking of the phase change materials was n-eicosane, n-octadecane, n-nonadecane, RT 60, RT 30, calcium chloride hexa-hydrate, n-docosane, p116, and stearic acid. It could be actively applied to not only the phase change materials but also various materials comprehensive performance evaluation ones arising in practice.

Abstract: The Fourth Industrial Revolution (4IR) is transforming economies and labour markets across the world through automation with AI, robotics, blockchain and big data at its core. African states are home to over half of the global population aged under 25, and hence regionally youth employment faces both a huge potential resource in and a major threat from the Fourth Industrial Revolution: This research examines the influence of 4IR technologies on youth labor practices across growing African economies, shedding light on new possibilities as well as structural hurdles. DesignThis is a mixed-methods study and it seeks to use narrative review combined with comparative case analysis of Rwanda, Nigeria and Kenya synthesizing peer-reviewed literature, institutional reports and national policy documents from 2010 to 2025. Digital skill gaps, automation of jobs, and digital infrastructure disparities partially constrain youth inclusion even though 4IR enhances job creation in aspects such as digital entrepreneurship, remote work and gig economy. These gender disparities, coupled with rural-urban divides as well as the fragmented implementation of such policies further limit access to digital opportunities equitably. This study underscores the importance of coordinated and inclusive policy frameworks that support both digital infrastructure investment and help education systems to adapt to labour market needs, also empowering young people with appropriate skills needed in a digital economy.This research contributes to the broader discussion on inclusive development and future of work in Africa by providing context-specific insights as well as policy recommendations.

Abstract: India has a rich cultural heritage of traditional sports, physical training methods, and values that have been practiced for centuries. Performance in sports depends not only on physical skills but also on discipline, attitude, regularity, mutual respect, and gratitude towards coaches, teachers, and even training equipment. These qualities are deeply rooted in Indian culture, as reflected in traditions such as Guru Vandana, Guru Poornima celebrations, Ayudha Pooja, and the Gurukul system of learning. Integrating such cultural values with indigenous training practices like yoga, mallakhamb, kalaripayattu, and pehlwani provides a holistic approach to modern sports training. This article examines the significance of Indian traditions in shaping athletic performance, the benefits of traditional practices in contemporary sports science, and the future prospects of indigenization in sports training.

Abstract:  The task of facial emotion recognition is one of the widely used applications of image analysis as well as pattern recognition. In the biometric area of research, automatic face & facial expression recognition attracts researcher’s interest. For classifying facial expressions into different categories, it is necessary to extract important facial features that contribute to identifying proper and particular expressions. In this paper, a depression detection system is proposed. This paper will give an overview of the Depression Detection System using Facial Emotion Recognition techniques and datasets. Depression Detection systems based on facial gesture enable real-time analysis, tagging, and inference of cognitive affective states from a video recording of the face. It is assumed that facial expressions are triggered for a time period when an emotion is experienced, and so depression detection can be achieved by detecting the facial expression related to it. Out of all the major 6 emotions present, depression plays a vital role. Depression is classified as a mood disorder. It may be described as feelings of sadness, anger, or loss that interfere with a person’s everyday activities. Experimental results show that the scheme detects depression from real-time video capture from a camera with high accuracy.

Abstract: Faculty, are currently being pressurised to do multiple work with an eye on garnering higher grades in the accreditation race. It may be a good initiative to fire on all fronts but what is glaring is the lacunae in faculty skills, infrastructure and the servicescape. The pandemic has compounded this problem given the fact that the workplace has shifted from campuses to homes. The current study endeavours to examine the impact that the workplace ambience has on work efficacy and career-personal life equilibrium. The impression of marital contentment is also investigated. Purposive sampling led to 443 filled-in and complete responses which were analysed.

Abstract: The study investigates the unsteady, two-dimensional, laminar boundary-layer flow of a viscous, incompressible, electrically conducting, and heat-absorbing fluid past a semi-infinite vertical permeable moving plate. The flow is influenced by a uniform transverse magnetic field, along with thermal and concentration buoyancy effects. The plate moves at a constant velocity in the direction of the fluid motion, while the free-stream velocity follows an exponentially increasing small perturbation law. Time-dependent wall suction is applied at the permeable surface. The dimensionless governing equations are analytically solved using two-term harmonic and non-harmonic functions. The obtained solutions are shown to reduce to previously reported results under special limiting conditions. Numerical evaluation of the analytical expressions is carried out, and graphical illustrations of the velocity, temperature, and concentration distributions within the boundary layer are provided. Additionally, tabulated results for the skin-friction coefficient, Nusselt number, and Sherwood number are presented and discussed.

(eVTOL) capabilities. The primary objective is to explore the feasibility of incorporating VTOL functionality into a lightweight RC platform using accessible, low cost materials and components. The aircraft is constructed using foam board for its favorable weight to strength ratio and employs a straightforward elevon- based control mechanism for pitch and roll modulation. A brushless DC motor, electronic speed controller (ESC), and Li-Po battery constitute the propulsion system, while an Arduino Nano and IMU (MPU6050) support the eVTOL’s stability and control. The system is manually operated via a Flysky FS-i6 transmitter and receiver. The prototype demonstrated stable flight in fixed wing mode and basic lift-off and hover capabilities in VTOL mode; however, it encountered instability during mode transitions due to limitations in PID control tuning and power demands. The outcomes suggest that hybrid flight systems are achievable on a small scale, albeit with significant challenges in stability control and energy efficiency. This work lays a foundation for future investigations into hybrid UAV platforms and low-cost autonomous aerial mobility solutions.

Abstract: This article examines the effectiveness of the ARAL program in bridging educational gaps within Philippine public schools by drawing on Transformational Leadership Theory and Social Constructivist approaches. The study makes explicit the connection between transformational school leadership and measurable improvements in literacy achievement, learner engagement, and parental participation. By integrating social constructivist principles, ARAL actively encourages collaborative classroom practices, stakeholder involvement, and peer-supported learning, ensuring that theoretical frameworks are directly linked to daily instructional strategies. The research further addresses the challenges encountered during implementation, including resource disparities, policy constraints, and variations in local context, which often influence program outcomes. Case comparisons across diverse school regions and input from educators, learners, and community members enrich the analysis, revealing best practices and actionable recommendations. The findings affirm that the combination of visionary leadership, collaborative pedagogy, and context-driven solutions creates inclusive school environments where learners facing the greatest challenges can achieve sustained academic recovery and personal growth. The study provides practical insights for policymakers and practitioners committed to equitable, evidence-based reform, establishing ARAL as a replicable model for holistic educational transformation in the Philippines and similar contexts worldwide.

Abstract: Pre-scalar, this is considered as the most critical block in the design of frequency conflation plays veritably important part, whenever we talk about high frequency operation. These blocks are used generally in PLLs for frequency conflation. Rigorous trials have proved that the major donation for power dispersion in ADPLL is because of pre-scalar block. Hence it becomes veritably pivotal to design the pre-scalar block with minimal power dispersion. In this paper we borrow a fashion of using Power PC flip-flop to design a divide by 16/17pre-scalar, which consumes veritably minimal power. The propound circuit operates up to 1 GHz consuming veritably lower power of 0749mW. The affair frequency is in the range of 50 MHz to 60 MHz. Low power peak by 16/17pre-scalar using Power PC flip bomb is proposed at 0.12 µm CMOS technology using BSIM4 model at a force of 1.2 V. The circuit is schematically vindicated in DSCH 3.8. The layout verification was carried out in Microwind2. From the simulation it was observed that veritably minimal power dispersion was attained with a minimal face area of 1875.9 µm2.

Abstract: This study examined the influence of emotional burnout and digital dependency on pedagogical practices among basic education teachers in public elementary and secondary schools under the Department of Education, Division of Bukidnon, Philippines, for the academic year 2025 - 2026. Employing a quantitative research design, the study gathered data from a representative sample of teachers working in both urban and rural communities. The research utilized descriptive statistics, including mean, frequency distribution, percentage, and standard deviation, to summarize and interpret teachers’ responses regarding their levels of emotional burnout, digital dependency, and pedagogical practices. Correlational and regression analyses were likewise applied to identify the strength of relationships and determine which variable, alone or in combination, best predicted effective teaching practices. Results revealed that teachers experience heightened emotional demands and an increasing reliance on digital tools, both of which greatly influence their instructional strategies and learner interactions. Emotional burnout was found to weaken teacher motivation and engagement, while digital dependency reshaped their planning, communication, and assessment processes. Despite these challenges, teachers displayed resilience by employing adaptive approaches that sustain effective classroom instruction. The findings emphasize the need for continuous professional development, mental health support, and enhanced digital literacy programs that address the evolving demands of technology-driven education. These initiatives are essential for maintaining a supportive teaching environment and ensuring that educators remain equipped to deliver quality and meaningful learning experiences in Philippine basic education.

Abstract: The present study was undertaken to evolve food products containing millets in various combinations and find their acceptability and nutritive value of standard and most acceptable food products. Sensory evaluation of these recipes was done by a selected panel of 20 judges using a 5-point Hedonic Scale. Millet puff samples made by using a combination Jowar with rice grain and corn grain were also most acceptable as they scored 4.2 ± 0.26, 4.4 ± 0.29 and 4.7 ± 0.2 respectively.

Abstract. Corporate governance is the system that guides how a company operates and makes decisions, ensuring transparency, fairness, and accountability in its management and leadership practices. This case study investigates how Explainable Artificial Intelligence (XAI) is influencing corporate governance practices in healthcare Business Process Outsourcing (BPO) firms based in Bangalore, India. Transparent and interpretable AI systems are increasingly viewed as tools that promote fairness, accountability, and ethical conduct in decision-making (Adadi & Berrada, 2018). XAI enables managers to understand how automated decisions are reached, supporting compliance with internal policies and external regulations (Gunning et al., 2019). By focusing on the intersection of technology and governance, explainability emerges not merely as a technical feature but as a key governance mechanism that strengthens oversight and builds organizational trust (Doshi-Velez & Kim, 2017). The rapid expansion of Artificial Intelligence within healthcare BPOs has improved efficiency but has also introduced new challenges related to transparency, ethical responsibility, and regulatory compliance (Guidotti et al., 2019). In many organizations, AI systems operate as opaque “black boxes,” offering limited insight into how outputs are generated (Samek et al., 2021). This opacity can weaken internal control mechanisms and complicate audit processes, especially in healthcare environments that depend on confidentiality, data accuracy, and ethical accountability. Explainable Artificial Intelligence addresses these challenges by making algorithmic processes more interpretable for human users. Transparent models allow administrators, compliance officers, and clients to build confidence in automated decision systems (Miller, 2019). The central research question guiding this study is: In what ways can XAI enhance corporate governance mechanisms within healthcare BPO organizations in Bangalore? The study posits that explainability serves a dual purpose—enhancing the technical reliability of AI while reinforcing governance and ethical integrity. Through an analysis of real-world applications across selected healthcare BPOs, this paper identifies how explainable systems foster accountability, ensure data integrity, and strengthen stakeholder trust in AI-enabled environments.

Abstract: In Nigeria, the oil and gas sector is essential to global economic stability, powering industries, transportation, and trade. The oil and gas revenues make up a major share of government income and exports, but the sector has long suffered from weak regulation, corruption, and overlapping institutional mandates that discouraged investment and slowed growth. However, this review paper examines Nigeria’s regulatory reforms, with a focus on the Petroleum Industry Act (PIA) 2021, which streamlined agencies, improved fiscal transparency, and introduced Host Community Development Trusts to promote local benefits and reduce conflict. These measures have strengthened investor confidence, enhanced policy coordination, and encouraged new investment in the gas infrastructure, refining, and local content development.Hence, the study also draws lessons for United States energy policy, with emphasis on the importance of regulatory certainty, stakeholder engagement, and transparent governance in balancing energy security with climate goals. Furthermore, Nigeria’s experience shows that efficient regulation and strong government-business collaboration can speed up project delivery, reduce conflict, and turn natural resource wealth into a driver of sustainable economic growth.

Abstract: Countries all across the world have been placed under lockdown as a result of the unexpected emergence of the worldwide health disaster known as the Coronavirus Disease (or COVID-19). The crisis has also put the world's educational systems under strain, forcing academics to switch from face to face to online learning. Open and Distance Learning (ODL) method is one of the most suitable strategies that integrates virtual technology which consequently allows the continuation of the teaching and learning process. As an open university that already has ODL, learning methods and student service should be more robust. Quantitative research was conducted using a 34-item questionnaire to explore the challenges students face in the context of online learning; and to identify the student’s preferences for their convenience for the adoption of quality improvement in online learning. Data was collected from sixty-four (64) postgraduates students enrolled in the Faculty of Technology and Applied Sciences, Open University Malaysia. The study revealed that staying motivated and engaged in online classes, with a Relative Importance Index (RII) of 0.6188, stands out as the most significant challenge for the students. On a positive note, the availability of clear learning objectives, with the highest RII of 0.8188, emerged as the top preference among students for adopting quality improvement in online learning. These findings underscore the importance of addressing motivational factors and emphasising clear learning objectives to enhance the overall online learning experience.

Abstract: Azetidinone derivatives, known for their potent biological activities, have garnered significant attention in medicinal chemistry. Swiss ADME, a web-based computational tool, is used in the present study to assess the pharmacokinetics and drug-likeness profiles of certain azetidinone derivatives. A comprehensive in-silico analysis was conducted to predict key parameters such as absorption, distribution, metabolism, and excretion (ADME), alongside physicochemical properties, lipophilicity, water solubility, and bioavailability scores. The drug-likeness was assessed using Lipinski's Rule of Five and other medicinal chemistry filters. Results revealed that most of the azetidinone derivatives exhibit favourable pharmacokinetic profiles with high gastrointestinal absorption, moderate to high lipophilicity, and good oral bioavailability. Several compounds also complied with multiple drug-likeness filters, indicating promising lead-like characteristics. These results highlight the potential of Swiss ADME as a predictive tool in early-stage drug development and offer insightful information for the logical design and optimisation of azetidinone-based therapies.

Abstract: In modern distributed and cloud computing environments, achieving optimal load distribution while minimizing energy consumption remains a significant challenge. Traditional load balancing algorithms primarily focus on performance metrics such as response time and throughput, often neglecting energy efficiency and dynamic workload variations. This paper proposes a novel energy-efficient load balancing algorithm that integrates predictive analytics and real-time workload adaptation to enhance resource utilization and system sustainability. The proposed approach employs machine learning models to forecast incoming workloads and make proactive load distribution decisions based on energy metrics, including power consumption, CPU utilization, and task execution time. By continuously analyzing system behavior and adjusting task allocation dynamically, the algorithm ensures balanced workloads, reduced energy consumption, and improved overall system performance. Experimental evaluations demonstrate that the proposed method outperforms conventional algorithms in terms of energy efficiency, scalability, and adaptability, thereby contributing to the development of sustainable and intelligent resource management frameworks for next-generation computing environments.

Abstract. The Internet of Vehicles (IoV) is an emerging field with significant research and commercial potential. A central challenge in IoV is to aggregate the vast volumes of data generated by interconnected vehicles and transform it into actionable knowledge for intelligent decision-making, such as detecting vehicle collisions. While various classifiers have been developed for collision detection, there is a lack of rigorous research on selecting the most impactful features for these detections. Many existing studies rely on single algorithms and simple correlation coefficients for feature identification, which can be a limitation. To address this, this paper proposes a dynamic model that combines a Non-dominated Sorting Genetic Algorithm III (NSGA-III) with a deep Long Short-Term Memory (LSTM) network incorporating dropout to detect vehicle collisions in IoV environments. The model aims to simultaneously minimize the feature subset, reduce computational time, and maximize collision detection accuracy. NSGA-III evolutionary algorithm efficiently explores the feature space to optimize multiple objectives, while the deep LSTM is well-suited for capturing temporal dependencies in the sequential data generated by vehicles. The model was trained and evaluated using a dataset generated in a VISSIM traffic simulation environment, which recreated various urban driving scenarios. The experimental results demonstrate that the NSGA-III + Deep LSTM-RD model significantly outperforms baseline algorithms like deep LSTM, DRNN, GANN, and ANN, especially when using a reduced set of features. The model achieved high accuracy and lower error rates while maintaining fast execution times, highlighting the importance of combining advanced optimization techniques with deep learning for robust and adaptable predictive models.

Abstract: This study examines the influence of organizational culture on implementation of strategic plans in the Insurance Industry in Kenya, with a keen focus on adhocracy culture, based on the Competing Values Framework (CVF). Adhocracy culture embodies innovation, adaptability, and flexibility; characteristics necessary for entities operating in highly dynamic environments. While much of the existing literature opines that organizational culture influences implementation of strategic plans, hardly any empirical study has tested this interrelation in an African setting, especially in a highly regulated and dynamic environment such as the Kenyan Insurance Industry. Applying cress-sectional survey data collected from 30 insurance companies and analyzed using regression methods, the output reveals that adhocracy culture is a statistically significant predictor of strategy execution (). On the contrary, market, clan, and hierarchy cultures, though popular in the industry, had no statistically significant influence on the success of strategy execution. These findings underscore the vitality of adaptive and innovation-driven organizational set ups in translating strategic plans into actionable results, particularly in highly dynamic and evolving markets where conventional and structured approaches are insufficient. The study advances the discourse in areas of organizational culture and corporate strategy by presenting empirical evidence from a dynamic and highly regulated industry in an underexplored developing economy. The study advances the logic that adaptive organizational cultures driven by innovation are necessary for effectual strategy execution. The study presents practical lessons for policymakers and leaders charged with execution of strategic plans. The study reckons that dynamic cultural attributes hugely enhance strategy implementation in highly dynamic business environments. It makes a recommendation that firms institutionalize innovation processes, set up flexible decision-making systems, and encourage cross-functional collaboration to enhance effective strategy execution.            

Abstract: Weather forecasting plays a vital role in various domains, including agriculture, transportation, and disaster management. This paper presents the development of a lightweight weather forecasting application using JavaScript, designed to provide real-time weather information through a web-based interface. The application leverages publicly available weather APIs for data retrieval and integrates responsive design principles to ensure usability across multiple platforms. By utilizing JavaScript’s asynchronous capabilities and client-side rendering, the system delivers an interactive and efficient user experience without requiring heavy server-side computation. The proposed solution emphasizes simplicity, portability, and accessibility, making it suitable for users with limited computational resources. Experimental evaluation demonstrates that the application achieves fast response times and reliable accuracy in displaying weather conditions, thus offering a practical and cost-effective tool for real-time weather monitoring.

Abstract: This research was set to study the causes of vesico vaginal fistula (VVF) and its psychological and medical impacts on VVF patients in Obstetric Fistula Katsina and Kano centres Nigeria. Research’s evident showed that VVF arose from myriads of direct and remote factors. This is against the traditional claim of the cause of VVF in Northern Nigeria. Moreover, much emphasis is often paid on physical injuries of the patient while overlooking the patients’ psychological conditions. This is despite untold suffering the victims may be subjected. Thus, prompting this research where survey method was adopted using interview to capture needed information. Using purposive sampling technique, the total fifty eight (58) VVF/RVF patients were used. The data collected were analyzed using frequency table with simple percentages. The mean scores of psychological level of the patients were determined. Majority of the patients have small body size which underweight having < 18.5 BMI. Significant number of the new born baby recorded large body size. Most of the patients seek medical assistance after more than 2 days of labor. On the psychological stress of the patients under study, it revealed no emotional, physical, behavioral and social stress with recorded ≤ 1.49 mean scores. However, under cognitive and psychological conditions insignificant elements of memory problems and loss of interest were noticed in the patients’ represented by approximately 2 mean scores. Base on the research findings; to check the frequency of the obstetric fistula due to cephalo-pelvic disproportion, counselling can be employed to advice those with small stature to delaying either marriage or pregnancy. Moreover, pregnant women should be encouraged for regular anti-natal clinic during the pregnancy period.

Abstract—The rapid proliferation of Internet of Things (IoT) devices has raised significant security and privacy challenges, particularly in scenarios where mobile devices interact with IoT endpoints. Traditional authentication mechanisms often lack context-awareness and can be vulnerable to replay attacks, eavesdropping, and unauthorized access. This paper proposes a proximity-aware security mechanism that leverages one-time URLs (OT-URLs) to authenticate mobile devices with IoT devices in a secure and efficient manner. The proposed framework generates unique, time-bound URLs that are valid only within a specified proximity, ensuring that authentication is performed only by devices physically near the IoT endpoint. By combining proximity detection with one-time URL authentication, the system mitigates the risks of remote attacks while maintaining user convenience and scalability. Experimental evaluation demonstrates that the proposed approach achieves high security assurance with minimal computational overhead, making it suitable for resource-constrained IoT environments. This method can be effectively applied to smart homes, industrial IoT, and other mobile-IoT interaction scenarios.

Abstract- The depletion of natural resources and need for advancements in industrial waste management have motivated researchers to focus on identifying innovative strategies to lower carbon emissions within the construction industry. Concrete’s inherent brittleness limits its ductility. To enhance this property, researchers have explored the incorporation of fibres in concrete. This research evaluates the durability of concrete containing Pyrolyzed Waste Tyre Wires (PWTW) under chemical erosion. Specimens with PWTW dosages of 0%, 0.5%, 1%, and 1.5% by weight were prepared and exposed to H2SO4 and MgSO4 solutions for up to 56 days. Compressive strength evolution and microstructural changes (SEM) were assessed. After 56 days, PWTW inclusion significantly mitigated strength loss due to chemical attack. Compared to the control (0% PWTW), the 1% PWTW mix showed the lowest strength loss in H2SO4 (11.65% vs 15.93%), while the 1.5% PWTW mix performed best in MgSO4 (20.9% vs 28.88% loss). SEM analysis revealed denser microstructures in PWTW-reinforced samples after chemical exposure. Findings demonstrate that PWTW enhances concrete's resistance to sulfuric acid and magnesium sulphate attack, supporting their use in durable and sustainable construction.

Abstract: This study investigated the determinants of poor academic performance in Mathematics at the West Africa Senior School Certificate Examination (WASSCE) in Kenema City, Sierra Leone. A sequential explanatory mixed-methods design was employed. The quantitative phase analyzed WASSCE results (2018-2022) from a stratified random sample of eight secondary schools (N=6,132 pupils). We employed multilevel logistic regression to model the odds of achieving a credit pass, controlling for pupil, teacher, and school-level factors. The qualitative phase involved validated surveys with 1,489 SSS3 pupils and 70 teachers, alongside interviews and observations. Quantitative results confirmed a systemic performance crisis, with significant yearly and between-school variance. Key predictors of failure included teacher qualification level (Odds Ratio [OR] for unqualified teachers = 0.38, p<.01), pupil attitude score (OR=1.24 per unit increase, p<.001), and socioeconomic status (OR for low-income=0.45, p<.01). A notable performance spike in 2021-2022 was partially explained by pandemic-related exam adjustments. Qualitatively, a high proportion (82.86%) of teachers were "trained but unqualified," fostering over-reliance on passive pedagogy, which exacerbated pupil disengagement. The study concludes that performance is shaped by a complex interaction of factors best understood through a Socio-Ecological lens. We propose a phased, three-year implementation plan for stakeholder-specific interventions targeting teacher upskilling, resource provision, and mindset change.

Abstract: This study investigates the impact of pollution load on the aquatic ecosystem of the Ona River in southwestern Nigeria, as well as the associated risks to the surrounding riverine communities. The Ona River, a vital freshwater resource traversing densely populated and industrialised zones, receives diverse effluents from industrial, agricultural, and domestic activities. A stratified sampling strategy was employed across nine sites, with water samples analysed for physicochemical and microbiological parameters, including heavy metals. The Pollution Load Index (PLI) was used to quantify the levels of contamination. Results revealed significant concentrations of heavy metals, notably lead (0.008–0.028 mg/L), iron (0.115–0.396 mg/L), arsenic (0.04 ± 0.02 mg/L), and chromium (0.06 ± 0.03 mg/L), several of which exceeded World Health Organisation (WHO) permissible limits. These contaminants pose substantial ecological risks by threatening aquatic biodiversity and disrupting ecosystem services, while also presenting severe public health concerns such as neurotoxicity, carcinogenesis, and organ dysfunction. Statistical analysis indicated uniform distribution of pollutants across sites, suggesting persistent pollution sources. The findings underscore the urgent need for regular monitoring, stringent pollution control, and targeted remediation strategies, including phytoremediation and the use of constructed wetlands. This research contributes to sustainable river management by providing empirical evidence to inform policy interventions that safeguard both ecological integrity and human health.

Abstract—Automatic license plate detection and recognition (ALPR) is a critical component of intelligent transportation systems, enabling applications such as traffic monitoring, parking management, and law enforcement. This paper presents an efficient Python-based approach that integrates image processing, computer vision, and optical character recognition (OCR) techniques for real-time license plate detection and recognition. The proposed framework employs pre-processing methods to enhance image quality, followed by contour detection and segmentation to accurately localize license plates. Subsequently, character recognition is performed using OCR to extract alphanumeric information from the detected plates. The system is evaluated on diverse datasets containing vehicles under varying lighting and environmental conditions. Experimental results demonstrate high detection accuracy, precision, recall, and F1-score while maintaining low computational overhead, making it suitable for deployment in real-world scenarios. The Python-based implementation ensures flexibility, scalability, and ease of integration with existing IoT and smart city infrastructures.

Abstract: Artificial intelligence (AI) is revolutionizing healthcare, particularly in the field of disease detection. AI-powered algorithms can analyze vast datasets of medical images, patient records, and genetic information to identify patterns and predict disease risks with unprecedented accuracy. In women's health, AI applications are proving particularly promising in areas such as breast cancer detection, early diagnosis of ovarian cancer, and prenatal risk assessment. AI-powered imaging analysis can significantly improve the accuracy and efficiency of mammograms and ultrasounds, leading to earlier detection and improved treatment outcomes. AI algorithms can also analyze a woman's medical history, lifestyle factors, and genetic predisposition to predict her risk of developing certain diseases and personalize preventive care strategies. As AI continues to evolve, it holds immense potential to transform women's healthcare by enabling earlier detection, more accurate diagnoses, and more personalized treatment plans, ultimately improving women's health outcomes and longevity.

Abstract— A novel autonomous proctoring software architecture that guarantees academic integrity throughout online tests. Strong, scalable, and minimally intrusive proctoring systems are more important than ever in the context of growing distant learning. Our method combines behavior analysis, biometric identification, and sophisticated machine learning algorithms to track candidate activity and identify anomalous trends instantly. According to experimental results, the suggested solution successfully balances privacy and usability while identifying possibly fraudulent actions. A thorough performance study and a detailed architectural design provide more details about the framework.

Abstract: There are several studies on decolorization and degradation of azo dyes by ozonation. However, the combined treatment of ozonation followed by aerobic biodegradation for mixed azo dyes has not been reported much. As prolonged ozonation alone is costly so aerobic degradation which is cheaper has been studied after ozonation to make overall treatment economic. The effect of ozonation on the degradation process of mixed azo dye solution, containing four azo dyes, CI Acid Red14 (AR14), Congo Red (CR), CI Reactive Black 5(RB5), CI Reactive Violet 5(RV5) each of 250 mgl-1 has been studied in the semi batch rector. The parameters such as pH, absorbance (at maximum wavelength), conductivity, COD, TOC and BOD5 were monitored during the ozonation process at different time intervals with influent ozone dose of 55.8 g, O3m-3. The results obtained indicate that ozonation is highly effective way to remove the color of synthetic dye solutions but still lots of organic contents remain at that time. Determination of coefficients of decay for individual dyes from ozonated samples of mixed dye by high performance thin layer chromatography (HPTLC) showed that decay rates of reactive dyes as well as di-azo dyes were less compared to other dyes and mono azo dyes.  BOD5 data revealed that partial oxidation of mixed azo dyes by ozonation increases the biodegradability (BOD5/COD ratio increases from .01 to 0.42) after 80 min ozonation. More then 90% reduction in COD and TOC takes place for mixed dye after 60 min ozonation followed by aerobic biodegradation. Results of integrated treatment indicate that treated wastewater may be disposed in water bodies or may be considered for reuse.

Abstract: Groundwater contamination is a growing concern for water quality. This study examined the concentration of trace elements in groundwater and their implications to human health in Southern Kaduna, Nigeria. A stratified random sampling technique was adopted in stratifying the twelve (12) local government areas into four strata, while the sampling locations were selected using a purposive sampling technique. Fourty groundwater samples were collected, that is, twenty (20) samples from hand-dug wells and twenty (20) samples from boreholes. Furthermore, the concentrations of lead (Pb), chromium (Cr), manganese (Mn), iron (Fe), and zinc (Zn) were determined by direct aspiration into an air-acetylene flame using a Buck Scientific 235 atomic absorption spectrometer (AAS) and compared with the national drinking water quality standards (NDWQS). On the other hand, the human health risk assessment model was utilised to evaluate the health implications. The results of the analysis reveals that most of the trace elements (measured in mg/l) were within permissible limits, except for Chikun Borehole 2 (0.062), Chikun Well 5 (0.080), Sanga Well 5 (0.080), Zango Kataf Well 4 (0.062), Jaba Well 1 (0.054), and Jaba Well 5 (0.120) for Mn and Chikun Borehole 2 (2.7), Zango Kataf Borehole 2 (0.450), Zango Kataf Borehole 5 (0.400), Jaba Borehole 3 (0.300), Jaba Well 3 (0.250) for Fe. Additionally, the human health risk assessment for Cr indicates a very low likelihood of 0.0002 for cancer development due to oral exposure. Lastly, the study recommends continuous monitoring of groundwater quality and mapping contamination hotspots for targeted intervention. 

Abstract: Conflict management is one of the key competencies that significantly affects the daily duties and employees’ performance. In organisations, conflict is inevitable in day-to-day operations. Because of external causes that force people to work in more unexpected situations, the study was conducted in Bangladesh. We have to piece it together from HR research, labour-management studies, and organisational behaviour reports. A total of 550 respondents participated in the study from different sectors (esp. in RMG, banking, telecom, SMEs, MNC, ICT). About 85% of the respondents have been working under the present employer for more than 3 years, which makes their evaluation of the company processes more reliable. Only 15% of the total respondents have managerial positions in the organisations. Its significance is heightened in emerging nations like Bangladesh. The main issues, trends, and understanding of conflict management are presented in this article, which is based on research done in Bangladesh also presents findings and makes suggestions on how to manage conflict to increase employee productivity effectively. The purpose of this study was to identify the primary obstacles to conflict management, assess the impact of these issues on employee and organisational productivity in Bangladesh, and provide recommendations to managers and staff on how to resolve conflicts and achieve positive outcomes. We also looked into other productivity-influencing aspects related to conflict management. The findings indicate that there is a lack of knowledge about conflict management in Bangladeshi organisations, both in terms of its positive and negative effects. The primary causes of conflict are inequitable roles and responsibilities. When resolving conflicts, employees prefer to use a collaborative manner.

Abstract: A literature review is a critical intellectual activity that highlights gaps in knowledge, places new research in the perspective of what is already known, and guides future study. The objective, technique, advantages, and disadvantages of the main categories of literature reviews—narrative, systematic, and critical reviews—are examined in this essay. While systematic reviews adhere to strict, repeatable protocols to reduce bias, narrative reviews offer comprehensive, descriptive summaries of findings. By combining quantitative results using statistical methods, meta-analyses expand on systematic reviews and improve the validity and generalisability of findings. Finding methodological or conceptual flaws in earlier research is the main goal of critical reviews. Along with discussing writing styles such as contextual, variable-based, and methodological organisation, the study highlights the significance of summarising local, national, and worldwide research to show its applicability.

Abstract: As data grows exponentially across various industries, the need for efficient data management and analysis becomes increasingly critical. Amazon S3 (Simple Storage Service) has established itself as a pivotal solution for data storage, appreciated for its scalability, durability, and cost-effectiveness. However, the real challenge lies in extracting valuable insights from the vast amounts of unstructured data stored in these S3 buckets. This paper explores the application of AI-powered techniques for automated data extraction, processing, and analysis, emphasizing their potential to enhance decision-making processes across industries. We delve into the methodologies, tools, and frameworks that enable seamless integration of AI into S3-based data environments, highlighting case studies and suggesting future directions.

Abstract: This research explored how secondary school teachers' motivation, involvement in Learning Action Cell activities, and instructional effectiveness are related.  Using a quantitative, descriptive-correlational approach, the study surveyed Valencia National High School-licensed teachers in the 2025–2026 academic year.  Extrinsic, social, and professional development, and intrinsic motivation were consistently high, with intrinsic motivation cited as the most important component of successful training.  Teachers regularly participated in Learning Action Cell activities, including professional development, group planning, and monitoring, which enhanced instruction delivery, as evidenced by student engagement, feedback, topic mastery, and evidence-based practices.  Regression analysis demonstrated that social motivation, reflective practice, professional development, and intrinsic motivation were all significant predictors of teaching effectiveness, and that there were significant positive relationships between motivation, collaborative practice, and instructional delivery.  The results emphasised the importance of promoting professional cooperation and personal fulfilment to improve teaching effectiveness.

Abstract: This study evaluated the marketing strategies in the food delivery industry in Nueva Ecija, with emphasis on consumer preferences and perceptions of service quality. Using a descriptive–quantitative design, data were gathered from 320 respondents who were active users of food delivery platforms such as GrabFood, Foodpanda, and ShopeeFood. Stratified–purposive sampling ensured representation across municipalities and user profiles. Data were collected through a structured questionnaire covering demographic profile, consumer preferences (price and affordability, convenience and accessibility, variety of choices, promotional strategies, and delivery speed and reliability), and service quality dimensions based on SERVQUAL (reliability, responsiveness, assurance, empathy, and tangibles). Results revealed that affordability, convenience, promotions, and reliable service delivery were the most influential factors shaping consumer preferences. Furthermore, service quality dimensions were rated positively overall, though respondents emphasized the need for improvements in timeliness, personalization, and digital updates. Correlation analysis indicated significant relationships between demographic characteristics—particularly age, sex, education, and income—and consumer preferences as well as perceptions of service quality, leading to the rejection of several null hypotheses. The findings highlight that marketing strategies and service quality are strongly shaped by demographic variations, emphasizing the importance of targeted promotions, improved delivery reliability, and continuous innovation in digital platforms.

Abstract: Despite AI and data analytics' growing importance for competitiveness, empirical evidence in Ghana is scarce. Organizations are urged to adopt digital transformation, but the link to performance is unclear. This study explores AI and analytics adoption in Ghanaian enterprises, assessing its impact on performance and identifying adoption barriers. A cross-sectional survey of 1,107 professionals, including HR managers, business leaders, and C-suite executives, was conducted. Adoption rates were analyzed using descriptive statistics, while correlation and multiple regression were employed to test the relationship between AI, data analytics, and organizational performance. Results show that 67% of enterprises have adopted AI, data analytics, or both, while 33% remain non-adopters. Among adopters, 30.3% integrate AI and analytics, 28.9% use analytics only, and 7.7% use AI only. Sectoral adoption varies, with Financial Services (85%) leading, while Retail and the Public Sector lag at 50%. Both AI and analytics significantly improve performance, with stronger results when integrated. Organizations should prioritize analytics as a foundation for AI, invest in workforce capability, and secure leadership commitment to scale adoption successfully. Wider adoption of AI and data analytics in Ghanaian enterprises has the potential to reshape work and service delivery across sectors contributing to national digital transformation. The findings advance understanding of how digital technologies influence performance in emerging market particularly Ghana. Its findings inform the design of strategies and policies that harness data-driven decision-making to drive organizational performance.

Abstract- It is important to evaluate scientifically stability of dam and embankment in large-scale hydropower construction, tideland construction and preventing natural disasters. The aim of paper is to establish a method of stability assessment of concrete-rockfill combination dam (CRCD) as a new dam form. In this paper, we investigated the step loads acting during construction, operation and seismic operation and their corresponding stability assessment methods for CRCDs. According to the results, the slope stability is also an important indicator for the stability evaluation of CRCDs and the stability assessment method of rockfill dams may be applied to the stability assessment of CRCDs.

Abstract—The most important part of property valuation is accurate property measurement. The more accurate measurement of all types of property, including residential buildings, could lead to an equal and fair value for the property. By comparing linear wall-to-wall measurements taken from point clouds with those taken from certified floor plans, this study evaluates the potential of three-dimensional (3D) point cloud data for use in property measurement. Dimensional measurements were obtained by processing 3D data of a residence in Kajang, Malaysia, using LiDAR-based scanning via PolyCam Pro on an iPhone 14 Pro Max. To assess the workflow’s generalizability, a sample dataset provided by PolyCam representing a landed residential unit was also tested using the same measurement procedure. The results indicate that consumer-grade point cloud data can achieve accuracy sufficient for expert valuation support, with deviations of approximately ±10 mm in the primary dataset and ±3.5 mm in the validation dataset. This shows how point cloud technology can improve transparency, reduce measurement errors caused by individuals, and help in the digital transformation of valuation workflows

Annotation: Youth unemployment remains a pressing socio-economic challenge in Azerbaijan, significantly affecting long-term economic development, social stability, and demographic potential. Despite steady macroeconomic growth, young people aged 15–29 continue to face disproportionately high unemployment rates (18.2% in 2024 vs. 6.9% national average). This study examines startup initiatives and entrepreneurship as strategic instruments to address youth unemployment, highlighting both opportunities and barriers. Using a qualitative desk-based methodology complemented by policy document analysis and international case studies, the research evaluates Azerbaijan’s entrepreneurial ecosystem, including government programs such as the State Program on Development of Entrepreneurship, the Ministry of Economy initiatives, and the Small and Medium Business Development Agency (SMBDA) interventions. The study also explores financial support mechanisms, including grants, concessional loans, and venture capital, as well as the role of educational institutions, incubators, and accelerators in equipping young people with the necessary skills for business creation and management. Key findings identify the critical success factors for fostering youth entrepreneurship, including effective policy support, access to finance, skills development, and innovation promotion. By synthesizing national data, comparative international experiences, and targeted recommendations, the study demonstrates how a strategically developed entrepreneurship ecosystem can enhance youth employment, promote economic diversification beyond the oil sector, and contribute to sustainable development in Azerbaijan. The research emphasizes the need for integrated approaches that link policy, finance, education, and innovation to fully harness the potential of young entrepreneurs.

Abstract: Comprehensive sedimentological investigations of the Awkuzu outcrops within the frontier part of the Anambra Basin, southeastern Nigeria, reveal two major facies associations: (i) fluvial/tidally influenced fluvial facies and (ii) estuarine facies. These facies correspond to the Eocene Nanka Formation, deposited in a tidally influenced fluvial–estuarine system characterized by alternating high- and low-energy depositional regimes. Integration of field observations, paleocurrent measurements, ichnological evidence, and pebble morphometric analyses provides insights into the facies architecture, depositional processes, and reservoir potential.The fluvial/tidally influenced fluvial facies comprises medium- to coarse-grained, moderately sorted sandstones and conglomeritic beds arranged in multistoried, fining-upward successions. Sedimentary structures such as planar and trough cross-bedding, erosional channel bases, and mud drapes suggest deposition under high-energy fluvial conditions intermittently modified by tidal currents. Trace fossils including Ophiomorpha, Planolites, and Arenicolites indicate episodic marine influence. Pebble morphometry shows Maximum Projection Sphericity (MPS) values of 0.76–0.85, Flatness Index (FI) of 58–67%, and OPI ranging from –0.12 to +0.18, denoting moderately high sphericity and rounding typical of sustained fluvial transport with limited tidal reworking. These features point to excellent reservoir potential, enhanced by high porosity, permeability, and lateral sand continuity.The estuarine facies comprises heterolithic and laminated fine- to medium-grained sandstones, siltstones, and claystones displaying flaser, wavy, and lenticular bedding indicative of tidal cyclicity. Skolithos and Planolites ichnofabrics reflect deposition in a well-oxygenated, shallow marine to inner estuarine setting. Pebble morphometry results (MPS: 0.68–0.77; FI: 70–80%; OPI: predominantly negative) indicate moderate sphericity and sub-rounded, oblate shapes, characteristic of low-energy, oscillatory tidal currents with limited abrasion. Although reservoir quality is reduced by mudstone intercalations, lateral sand continuity supports potential fluid connectivity. Overall, the facies architecture demonstrates that sedimentological controls and depositional energy variations fundamentally influence reservoir heterogeneity and quality within the tidally influenced fluvial–estuarine deposits of the Nanka Formation in the Frontier Anambra Basin.

Abstract: This study examines the relationship between Digital Financial Services (DFS), Digital Literacy, and Financial Inclusion in Nigeria, focusing on how DFS adoption enhances access to financial services and promotes economic inclusion. In emerging economies like Nigeria, where many remain unbanked, DFS offers opportunities for broader financial participation. Digital literacy is identified as a key factor moderating the relationship between DFS and financial inclusion. The study employs a quantitative approach, using survey data from 300 respondents across Nigeria. Descriptive statistics, correlation analysis, and regression were applied for data analysis. The findings revealed that DFS adoption significantly improves financial inclusion, with users of mobile banking and e-wallets experiencing enhanced access to credit and payment services. Digital Literacy was found to positively moderate this relationship, as individuals with higher digital skills benefit more from DFS adoption. The regression analysis supports these results, with an R² of 0.63, demonstrating that 63% of the variation in financial inclusion outcomes is explained by the model. The study concluded that DFS and digital literacy are essential for promoting financial inclusion in Nigeria, recommending policy interventions to improve digital literacy, enhance digital infrastructure, and support DFS adoption through regulatory measures.

Abstract—This study investigated work engagement and social support for 386 secondary school teachers in Hong Kong. It was driven by highly documented issues regarding their below-optimal physical and psychological well-being, which degrades personal well-being as well as the quality of education. Not only was it the aim of this research to quantitatively quantify these two variables, but also to establish a proactive approach to establish how to cultivate positive psychological strengths to maximise engagement. The goal was to provide a pathway for improving the quality of life and overall professional performance of teachers, creating a circle of virtuousness to the advantage of both students and teachers. Data was collected via well-designed web questionnaires and were processed using a stringent statistical approach. The key results offered a mixed and eye-opening picture: while a promising 63.7% of teachers demonstrated social support scores above the median, a much lower but disturbing percentage (43.3%) reflected high work engagement. This disconnection between support systems and engagement suggests that support systems per se are in place, yet perhaps not being maximally applied or to optimal targets. Most notably, the researchers have identified a moderate positive correlation, statistically confirming that greater perceived social support is associated in concrete terms with enhanced work engagement. This turns the finding from anecdotal observation to empirical fact. The relevance of the study transcends mere academic diagnosis to providing actionable intelligence for principals and policymakers alike. It emphasizes the sheer necessity to intentionally strengthen structural and affective support systems and, simultaneously, try to remove the underlying psychological health barriers facing teachers. These findings are highly relevant to teacher training colleges in course design, for school administration in setting supportive working cultures, and to the broader public that relies on a secure, motivated teaching profession.

Abstract: Building Information Modeling (BIM) is fundamentally transforming the approach to interior design, moving practice beyond traditional, visually focused methods, such as mood boards, toward a comprehensive, data-driven, and collaborative digital workflow. Moreover, BIM technology offers a centralized platform that integrates non-graphical data, such as material properties and maintenance schedules, directly with the 3D model. This integration improves conceptualization, ensures detailed coordination, and enhances project lifecycle management, significantly lowering errors, costs, and project timelines. Furthermore, major areas of change include hyper-realistic visualization, real-time multidisciplinary clash detection, accurate quantity takeoff, and advanced physical environment analysis. This research presents how building information modeling (BIM) is transforming the interior design domain, positioning interior designers at the forefront of the Architecture, Engineering, and Construction (AEC) industry’s technological evolution.

Abstract: This study investigates the application of rock physics and Gassmann fluid substitution analysis for reservoir characterization in the KY Field of the Chad Basin, Northeastern Nigeria. The primary objective is to evaluate the impact of fluid saturation on seismic elastic properties, such as P-wave and S-wave velocities, and density, to better understand reservoir potential and fluid distribution. The research utilizes 3D seismic data and well log suites, including gamma ray, density neutron, resistivity, and sonic logs, from four wells (XY-1, XY-2, XY-3 and XY-4). Gassmann's equations are employed to model fluid effects on rock velocity and density, enabling the identification and quantification of fluid types and saturations within the reservoir. The study highlights the challenges of estimating water saturation from seismic data and emphasizes the importance of rock physics modeling in interpreting seismic amplitude anomalies. Results from crossplots of Acoustic Impedance (AI) versus Vp/Vs and LambdaRho versus MuRho reveal distinct lithological and fluid trends, with hydrocarbon-bearing sands showing significant deviations from brine-saturated sands. Fluid substitution analysis demonstrates that changes in fluid saturation, particularly gas content, significantly affect seismic responses, indicating partially gas-saturated reservoirs with patchy distributions. The findings provide valuable insights into the reservoir's elastic properties and fluid dynamics, offering a robust framework for future exploration and development in the KY Field. This research underscores the critical role of rock physics and fluid substitution in enhancing seismic interpretation and reservoir characterization in complex sedimentary basins.

Abstract: Companion diagnostics (CDx) are vital in personalized medicine because they help find patients who would benefit the most from specific treatments. The rules for these tests can be different depending on where you are, which affects how easily they can be used and developed. In the U.S., the FDA has a clear process for approving CDx on the same path as the therapies they relate to. Over in Europe, they recently made their regulations stricter with the In Vitro Diagnostic Regulation (IVDR), which means more requirements for proof and oversight. Even though there are efforts to make things more uniform, the differences in approval times, data needs, and who gets reimbursed can make it tricky to roll out CDx globally. This review looks at these varying rules and how they impact developing and using CDx. CDx has become essential in treating conditions like cancer, cystic fibrosis, and asthma. In cancer, for instance, CDx helps identify genetic issues that can lead to better treatment options. For cystic fibrosis, CDx is useful in picking the right modulators for improved patient results. In asthma, new CDx methods focus on biomarkers to customize treatments. But there are hurdles, like varying approval paths in the U.S., stricter regulations in Europe needing more proof and monitoring, and challenges in getting reimbursement. There’s also a need to consider genetic differences and acceptance of CDx outside of oncology. To really expand the benefits of CDx for different diseases, it’s important to work on more streamlined regulations, inclusive studies, and flexible approval paths.

Abstract – The integration of Artificial Intelligence (AI) into Higher Education Institutions (HEIs) promises significant pedagogical and administrative efficiencies, yet it concurrently introduces profound ethical dilemmas, particularly in data-rich environments. The study empirically investigates the prioritization of major AI ethical concerns—Data Privacy, Algorithmic Bias/Fairness, Transparency, and Accountability—among Nigerian postgraduate students. Utilizing a quantitative survey with 300 strategically selected postgraduate students across 2 federal universities and 2 state universities, data were collected using a structured questionnaire. The study, anchored in the Ethical Data Governance Framework (EDGF), addressed the question of what the major ethical concerns are and tested two null hypotheses on the significant differences in awareness and the prioritization of these concerns. Descriptive statistics, non-parametric Friedman Test, and Independent Samples t-test were employed for analyses. Although the findings revealed a high level of overall awareness regarding AI ethical implications (X̄ = 3.12), there is a significant difference in the awareness of ethical implications between students who have encountered AI applications (X̄ = 3.29) and those who have not (X̄ = 2.65). The Friedman Test and Independent Samples t-test unequivocally demonstrated a significant statistical difference in prioritization (Friedman, X2 = 12.34, p-value < 0.05; t-value of 4.10, p-value < 0.05), leading to the rejection of HO2. Data Privacy emerged as the overwhelmingly dominant ethical concern with a Weighted Mean Score (WMS) = 3.55, followed by Transparency (WMS = 3.38), reflecting a deep-seated trust deficit in institutional data stewardship and a strong student demand for Explainable AI (XAI). Students who have encountered AI applications demonstrated significantly higher awareness (X̄ = 3.29) and significantly higher overall prioritization (X̄ = 3.33) of ethical concerns compared to those who have not. This paper recommends that Nigerian HEIs must urgently adopt the EDGF principles by implementing stringent data privacy and transparency protocols with robust procedural tools to address data protection and enforce algorithmic accountability. This should be coupled with experiential AI training to foster trust and ensure the responsible adoption of AI in Nigerian higher education.

Abstract: This study explored the relationship between nutritional status and academic performance among elementary school learners classified as wasted/severely wasted based on their BMI in the Manay North District of Davao Oriental during the 2024–2025 school year. The objectives were to identify learners' profiles, causes of malnutrition, and how these factors impact academic achievement. Using a descriptive correlational research design, the study employed survey questionnaires, anthropometric measurements, and learners' report cards as primary data sources. A total of 219 learners identified as wasted/severely wasted participated in the survey. Data analysis involved frequency distributions, weighted means, Spearman's Rho correlation, standard deviations, percentages, and the Kruskal-Wallis test. The findings showed that most respondents came from economically disadvantaged families, mainly engaged in farming and informal labor, and had parents with low education levels. Poverty (M = 3.53) and nutritional illiteracy (M = 3.52) were identified as the main causes of malnutrition, followed by poor eating habits and health conditions. Academic performance among learners showed steady improvement across three grading periods, with mean scores of 81.50, 82.70, and 83.22— all categorized as satisfactory. This consistent progress suggests that learners gradually adapted to classroom routines, improved their study habits, and benefited from teachers' reinforcement and feedback. Correlation analysis revealed a positive relationship between weight and academic performance (r = 0.305, p < 0.01), while socio-economic status showed a negative correlation (r = -0.140, p < 0.05). Furthermore, poverty and poor eating habits were found to have weak but significant associations with students' grades. The study confirms malnutrition, mainly caused by poverty and lack of nutritional knowledge, harms students' academic performance. To improve nutrition and learning, schools should run feeding programs, educate parents, and support livelihoods. Collaboration among schools, local governments, and health agencies is vital to fight malnutrition and support student development.

Abstract: Surface water contamination by heavy metals poses significant ecological and public health challenges, particularly in rapidly urbanizing regions of developing nations. This study assessed the concentration, distribution, and associated ecological and health risks of potentially toxic elements in the Ona River, located within the Adeoyo region of Ibadan, Oyo State, Nigeria. Water samples were collected from three georeferenced sites and analyzed for Fe, Zn, Cu, Cr, As, Cd, Ni, Mn, and Mg using Atomic Absorption Spectrophotometry (AAS) following APHA (2022) standard protocols. Contamination indices including the Geo-accumulation Index (Igeo), Enrichment Factor (EF), Contamination Factor (CF), Pollution Load Index (PLI), and Ecological Risk Index (ERI) were employed to evaluate pollution intensity, while non-carcinogenic and carcinogenic health risks were computed using the United States Environmental Protection Agency (USEPA) model. Results revealed that Cr, Cd, As, Pb, and Fe concentrations were within permissible limits of WHO (2022) and Nigerian Standards for Drinking Water Quality (NSDWQ, 2015). In contrast, Ni and Mn exceeded recommended thresholds, indicating localized anthropogenic inputs, primarily from industrial and urban effluents. Nickel exhibited the highest CF (2.86–3.86) and EF (1735.29–3748.24), denoting considerable contamination and extreme enrichment, while PLI values below 1 suggested overall unpolluted status. The ERI values (15.87–21.78) indicated low ecological risk; however, Ni emerged as the most significant contributor to potential toxicity. Although the Hazard Index (HI < 1) implied minimal immediate health effects, long-term exposure may pose latent risks. The study concludes that while the Ona River water remains largely unpolluted, elevated Ni and Mn levels necessitate continuous monitoring, stricter effluent regulation, and sustainable watershed management to protect aquatic ecosystems and public health.

Abstract: We study cosmological dynamics in a five-dimensional Kaluza-Klein universe using modified  gravity. We choose a linear coupling between the Ricci scalar  and the trace of the energy-momentum tensor : , where  is negative. This form naturally yields a time-dependent cosmological constant influenced by the cosmic fluid. We apply a hybrid power-exponential volumetric expansion function, . For this scenario, we derive the complete field equations in the five-dimensional metric and calculate cosmological parameters: pressure, energy density, equation of state, deceleration parameter, and statefinder diagnostics. Our results demonstrate how modified gravity and an extra dimension influence expansion, shedding light on dark energy and higher-dimensional cosmology.

Abstract—The rapid evolution of electricity markets toward hybrid models—integrating both regulated and deregulated structures—has intensified the need for effective congestion management strategies to ensure system reliability, market efficiency, and fair access to transmission networks. This paper presents a comprehensive comparative study of various congestion management techniques employed in hybrid electricity markets, including redispatching, transmission pricing, optimal power flow (OPF)-based methods, and market-based mechanisms such as nodal and zonal pricing. The study evaluates these approaches based on key performance indicators such as cost efficiency, computational complexity, transparency, and market fairness. A simulation-based analysis using standard test systems is conducted to highlight the operational impacts and economic implications of each method under varying load and generation conditions. The results demonstrate that hybrid congestion management models, which combine technical optimization with market-based incentives, offer a more adaptive and economically balanced solution compared to traditional approaches. The study concludes with insights into the practical challenges, policy implications, and future research directions for implementing robust congestion management frameworks in evolving hybrid electricity markets.

Abstract—The growing complexity of modern power systems, coupled with the increasing integration of renewable energy sources, demands advanced control strategies to ensure enhanced power quality and system reliability. This study presents the implementation of novel control methodologies designed to mitigate power quality issues such as voltage fluctuations, harmonic distortions, and reactive power imbalances. The proposed control framework employs adaptive and intelligent algorithms, including model predictive control and artificial intelligence-based optimization, to dynamically stabilize system performance under varying load and generation conditions. Simulation and experimental results demonstrate significant improvements in voltage stability, reduced total harmonic distortion (THD), and enhanced overall system reliability. The findings highlight the potential of these innovative control strategies to support the development of robust, sustainable, and efficient power systems for future energy networks.

Abstract: "Artificial Intelligence (AI) has emerged as a transformative instrument in biological sciences, facilitating the analysis of complex biological datasets, predicting molecular interactions, and automating laboratory and field operations.". This paper explores key AI strategies—machine learning, deep learning, computer vision, and natural language processing (NLP)—and their applications across genomics, proteomics, taxonomy, ecology, and medical diagnostics. Recent advancements in artificial intelligence include the utilization of models such as convolutional neural networks (CNNs) and support vector machines (SVMs). The integration of AI with biological databases accelerates drug discovery, species classification, and environmental assessment. The study highlights future directions and ethical considerations in implementing AI responsibly for sustainable biological research.

Abstract:  Concrete is very low in ductility; to improve this property, the use of steel fibres spread in all its sections to reinforce it, has been studied by many researchers. Steel fibres are short metallic materials of small diameter, manufactured from steel ore, in the absence of adequate manufacturing steel factories in a third world country like Nigeria, the utilization of steel fibres derived from recycled waste tyre wire as reinforcement in concrete has been investigated in this study, focusing on its impact on the mechanical properties of concrete after exposure to elevated temperatures. Concrete specimens with varying fibre dosages (0%, 0.5%, 1%, and 1.5%) were evaluated for compressive, split tensile, and flexural strengths after 28days curing, at ambient temperature and high temperatures. Furthermore, concrete reinforced with steel fibres exhibited improved compressive strength retention after exposure to temperatures ranging from 200°C to 800°C compared to control concrete. However, there was no significant strength retention of split tensile strength at elevated temperatures. There was a direct correlation between flexural strength loss after elevated temperature exposure, and increase in fibre dosage. Microstructural analysis revealed differences in the morphology of concrete specimens before and after exposure to high temperatures. Overall, this study underscores the beneficial effects of incorporating recycled waste tyre wire as fibre reinforcement in concrete, offering a sustainable solution for enhancing the mechanical properties and resilience of concrete structures in both normal and high-temperature environments.

Abstract: This study investigates the spatio-temporal variability and long-term rainfall trends in the Eleyele Dam Basin, southwestern Nigeria, using daily ERA5/ERA5-Land datasets covering 1981–2023. Preprocessing ensured homogeneity, persistence control, and serial correlation adjustment. Trend analysis was performed using the non-parametric Mann–Kendall test and Sen’s slope estimator, with trend-free prewhitening applied to mitigate autocorrelation effects. Spatial rainfall distribution was generated through Inverse Distance Weighting (IDW), optimized via leave-one-out cross-validation. Results indicate a persistent north–south gradient, with annual totals ranging from ~1,266 mm in the north to ~1,539 mm in the south. The basin exhibits a distinct bi-peak regime, with maxima in June and September, and 86% of rainfall concentrated between April and October. Statistically significant negative trends were identified in March, April, May, and December (Sen’s slope −0.48 to −1.62 mm yr⁻¹), pointing to a delayed onset and weakened early wet-season contribution. Annual rainfall shows a significant decline (−5.26 mm yr⁻¹), and dry-season totals also decreased (−2.91 mm yr⁻¹). Coefficients of variation highlight increased interannual variability, especially in dry months, underscoring unreliability of inflows. Spatial maxima occur in the southern sub-catchment, where high rainfall intensity coincides with greater erosion and sediment delivery risks. These shifts imply reduced inflows, prolonged dry-season deficits, and heightened supply shortfalls. The findings underscore the need for adaptive reservoir rule-curve updates, catchment erosion mitigation, and climate-resilient water resource planning to safeguard Eleyele’s multipurpose role in Ibadan.

Abstract: The production of rechargeable batteries, particularly lithium-ion batteries, can have significant environmental impacts. These are recycled by grinding them, neutralizing the acid, and separating the polymers from the lead. The recovered materials are used in a variety of applications, including new batteries. The lead in a lead–acid battery can be recycled. These include the environmental cost of mining lithium and other materials, the energy-intensive production process, and the challenges associated with recycling. Recycling provides many benefits to our environment. By recycling our materials, we create a healthier planet for ourselves and future generations. Conserve natural resources: Recycling reduces the need to extract resources such as timber, water, and minerals for new products. This article will primarily describes the impact of battery recycling and the benefits of battery recycling.

Abstract: Effects of a green globe is felt even far away from the point of greening. Biodiversity protection or otherwise has far reaching effects even beyond the source zone. This has fueled the need for this research, gauging contribution of external actors on park protection in Old Oyo National Park (OONP). Two sets of questionnaire were administered to park officers and Support Zone Communities (SZCs) assisted with organized Focused Group Discussions (FGDs) in each of the six ranges of the park. Secondary data augments survey from records obtained from the park head office at Isokun. A total of 70 PMs were surveyed purposively at the Park headquarters and at the six ranges. From a sampling frame of 30%, a total of 40 villages were randomly and unevenly pooled from all six range stations depending on number of villages per range. Five households were randomly selected each and the household heads were purposively sampled. This was aided with FGD sessions. Most (74%) of the SZCs engage in farming, some (22.5%) practice both hunting and herding with overlap in each case. Average annual PCI for the SZCs was lesser than that of the PMs. External actors engage with the SZCs in economic empowerment, park-community synergy and more. These activities were agreed by PMs (42.86%) to be in concord with sustainable conservation, while SZCs agreed that park-community collaboration benefit them (66.5%). Community empowerment and partnership among others are inevitable to attain social acceptability, economic viability and ecological sustainability of park resources.

Purpose: This study examines retail investors' awareness, perceptions, and willingness to invest in green bonds within India's evolving sustainable finance landscape. It seeks to bridge the critical knowledge gap between policy-driven green finance initiatives and actual retail market participation by identifying key barriers and enablers influencing investment behaviour. The research provides empirical insights into how demographic factors, financial literacy, and market structures shape sustainable investment decisions in emerging economies. Methodology: The study employs a mixed-methods approach, combining quantitative analysis of survey responses from 100 retail investors with qualitative thematic analysis of 20 in-depth interviews. Data collection focused on four dimensions: awareness levels, risk-return perceptions, investment willingness, and structural barriers. Statistical analysis included descriptive metrics and correlation studies, while interview transcripts were coded using NVivo to identify recurring themes. The sample was stratified across age groups, education levels, and occupational categories to ensure representative insights. Findings: The results reveal moderate but uneven awareness, with higher recognition among older (46+ years) and more educated investors. Key findings include: (1) significant conceptual confusion regarding green bonds' environmental purpose, with many associating them incorrectly with infrastructure (27.6%) and education (25.0%); (2) equal weighting of financial returns (20.5%) and environmental impact (20.5%) in investment decisions; (3) pervasive trust deficits, with 43.6% expressing scepticism due to greenwashing concerns; and (4) structural barriers including limited accessibility and preference for traditional instruments. Notably, 41.6% of respondents demonstrated positive reception, suggesting untapped market potential conditional on policy interventions. Originality: This study contributes novel insights into sustainable finance behaviour in three key dimensions. First, it provides the first comprehensive assessment of India's retail green bond market, filling a critical gap in emerging market research. Second, it introduces an integrated framework analysing both cognitive barriers (trust deficits, knowledge gaps) and structural constraints (market access, liquidity). Third, the findings challenge conventional assumptions by demonstrating how Indian investors equally weigh financial and ethical considerations—a departure from dominant Western models emphasizing environmental values. The research offers evidence-based policy recommendations for accelerating retail participation in sustainable finance transitions.

Abstract Background: Osteoarthritis (OA) represents a chronic degenerative disorder of synovial joints leading to pain, stiffness, and restricted motion. Limitations of conventional pharmacotherapy and the chronic nature of OA have encouraged exploration of complementary systems such as homoeopathy for safer symptomatic control. Objectives: To comprehensively evaluate available clinical and experimental evidence regarding the therapeutic utility of homoeopathic medicines in OA, highlighting fifteen key remedies and their characteristic indications. Methods: A narrative review of fifty publications retrieved from PubMed, Scopus, AYUSH Portal, and homoeopathic journals (1999 – 2024) was conducted. Included studies comprised randomized controlled trials (RCTs), observational and mechanistic studies evaluating homoeopathic interventions in OA. Data were synthesized descriptively according to PRISMA-adapted methodology. Results: Across studies, individualized and complex homoeopathic prescriptions demonstrated meaningful improvement in pain, stiffness, and function. Remedies such as Rhus toxicodendron, Bryonia alba, Calcarea fluorica, and Sulphur were most frequently indicated. Mechanistic reports suggest modulation of inflammatory and oxidative pathways. Evidence quality remains limited by small samples and heterogeneity, yet safety outcomes were uniformly favorable. Conclusion: Homoeopathy may serve as an effective and well-tolerated adjunct in OA management. Further large-scale, well-designed trials with standardized outcome measures are essential to establish efficacy and clarify mechanisms.

Abstract: The subjects for this study were 50 boys and girls were selected. These subjects between ages of 5 to 7 years were studying in primary school of district pulwama from the school. All subjects were randomly selected for the equal ratio of 5 to7 years of age group. From the attendance register of the students the age was calculated separately and Subjects were divided into two groups i.e. experimental (N1=25) and Control group (N2=25). In the present Study, following Physical Variables were selected: Shuttle run and Standing broad jump. Shuttle run measures ability and speed. Standing broad jump-it measures explosive leg strength. In this study paired t – test was employed to know the impact of movement education programme in elementary schools. In this study level of significance was set at 0.05. Results describes a significant difference in the experimental group and an insignificant difference in control groups in both variables at level of significance 0.05

Abstract: A simple spectrophotometric method for the determination of titanium is developed using 3-hydroxy-2-(2'-thienyl)-4H-chromen-4-one as a complexing agent for the metal ion in slightly alkaline solution (pH 8.0-8.5). The resulting complex bears metal to ligand ratio of 1:1 whose absorption maximum lies at 430nm. The method obeys Beer's law in the range 0.0-5.0 μg Ti ml-1 with molar absorbtivity and Sandell's sensitivity values of 1.40´104 l mol-1 cm-1and 0.0034 μg Ti cm-2 respectively. The limit of detection, of the method is 0.0086 ppm. The complex is stable for 24 hours. It is free from interference of a large number of cations and anions. Utilizing the proposed procedure, the analysis of various synthetic samples, soil, water and pharmaceutical sample has been carried out to a greater degree of accuracy.

Abstract: In general entrepreneurship is the ability of the person covering his/her skill of employing innovation creation, imitation risk-bearing, leadership in the organized way. It provides opportunity to a person means of earning by giving self- employment and in providing employment to others. In the present scenario there is earnest need for attracting the educated youth towards in entering the of entrepreneurship instead of running behind the government jobs. In this reference the attitude towards entrepreneurship should be cared and studied empirically. Thinking on these lines in present investigation a comparison of male and female undergraduate students on their degree of attitude of towards entrepreneurship has been studied in relation to the level of academic –achievement on the sample of 1048 undergraduate students of Shri Rawatpura Sarkar University Raipur in the state of Chhatisgarh.The attitude of subject towards entrepreneurship was assisted was using self-developed tool. The studied that: Male high academic achiever for better than to those with low academic achiever. Whereas the finding are reversed in case female students. However the female students were found to have better attitude towards entrepreneurship to their male counter parts either high or low academic achievement.

Abstract: This study investigates how perceived organizational support (POS) influences job performance (JP) directly and indirectly through readiness to change (RTC) among nonacademic civil servants in Indonesian public universities. Drawing upon the Organizational Support Theory and the Change Readiness framework, this research analyzes responses from 180 administrative employees at Universitas Mulawarman undergoing institutional transformation. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), this study found that POS significantly enhanced both RTC and JP. Moreover, RTC positively influenced JP and mediated the relationship between POS and JP. These findings confirm that organizational support is a vital antecedent to fostering individual psychological readiness, which in turn promotes adaptive and productive behaviors during periods of change. The study contributes to the literature by integrating POS and RTC into a cohesive explanatory model of employee performance within public higher education institutions. The results highlight the need for policy strategies that emphasize inclusive change communication, supportive leadership, and long-term investment in human capital to improve organizational readiness and performance. The limitations of this study include the single-institution sample and cross-sectional design. Future research may explore comparative analyses across multiple institutions and adopt a longitudinal or mixed-method approach to capture the dynamic processes of organizational transformation.

Abstract: This research aimed to investigate the effect of Gamified Quizizz and Google Classroom Platforms on students' retention in algebra. Three research questions and three hypotheses guided the study. A quasi-experimental design was employed, involving 251 Junior Secondary School III Mathematics students (JSS III) from intact classes in three selected private junior secondary schools in the Port Harcourt Metropolis. Data were collected using the Algebra Performance Test (APT), which was further turned Algebra Retention Test (ART), which was validated by three experts and had a reliability coefficient 0.92. Data analysis was performed using SPSS software, with a significance level set at 0.05. Data were analysed using mean, standard deviation, and analysis of covariance (ANCOVA) at the 0.05 significance level. Findings revealed that the retention level of the students taught Algebra concepts using GQP was higher than students taught Algebra concepts using GCP, followed by students taught Algebra concepts using FFM. Also, findings showed that the joint effect of GQP, GCP and FFM and gender in students’ retention of the knowledge of Algebra concepts. The study concluded that gamified learning platforms, particularly Quizizz (GQP), significantly improve Algebra retention compared to Google Classroom (GCP) and traditional face-to-face (FFM) methods. It was, however, recommended that curriculum planners should integrate gamified learning approaches into the educational framework for secondary schools in Rivers State to improve the retention of mathematical concepts among students.

Abstract— Biomedical waste management (BMWM) has emerged as a significant global concern due to its growing volume and potential hazards to human health and the environment. Biomedical waste includes infectious, toxic, and radioactive materials generated from hospitals, clinics, research centres, and laboratories. This review examines the classification, sources, and treatment methods of biomedical waste, with a focus on healthcare institutions in India. It also examines international and national regulations, the challenges of implementation, and the pressing need for awareness, effective policy enforcement, and sustainable waste management technologies. Future directions emphasise the importance of green technology, training, and the 5R approach (Refuse, Reduce, Reuse, Repurpose, Recycle) to achieve a zero-waste healthcare ecosystem.

Abstract: The present study explores the shifting trends in cropping patterns in the state of Haryana from the post-Green Revolution period to the present day. Haryana, a primarily agrarian state, has witnessed considerable alterations in its agricultural environment, chiefly driven by technology innovation, government laws, irrigation development, and market demands. Historically, the cropping pattern was more diversified, comprising cereals, pulses, oilseeds, cotton, and sugarcane. However, with the introduction of the Green Revolution in the late 1960s, there was a noticeable shift toward the cultivation of high-yielding kinds of wheat and rice. This transformation was supported by the assured procurement system, minimum support prices (MSP), and canal and tubewell irrigation infrastructure. While this resulted to food security and money creation in the near term, it also contributed to regional imbalances, soil degradation, and groundwater depletion due to monoculture practices. In recent decades, there has been a progressive trend toward diversification, with farmers demonstrating increasing interest in horticulture, oilseeds, and organic farming, especially in response to environmental concerns and sustainability issues. Government efforts supporting crop diversification and natural farming are playing a crucial role in this development.

Abstract—The growing integration of renewable energy sources, nonlinear loads, and distributed generation has posed significant challenges to maintaining power quality in modern distribution networks. Conventional transformers, though reliable, lack the flexibility to mitigate issues such as voltage sag/swell, harmonics, unbalance, and reactive power disturbances. This paper presents a comprehensive study on the application of Advanced Power Electronic Transformer (PET) topologies for enhancing power quality in distribution systems. The proposed PET architecture incorporates multi-level converter stages with bidirectional power flow control, enabling dynamic voltage regulation, harmonic compensation, and load balancing. Simulation models are developed in MATLAB/Simulink to evaluate the performance of modular PET configurations under varying load and grid conditions. Comparative analysis with traditional transformer systems demonstrates significant improvements in voltage profile, total harmonic distortion (THD), and system efficiency. The study highlights the potential of PET-based solutions as a next-generation approach for intelligent, reliable, and sustainable distribution network operation.

Abstract—In modern smart grids, maintaining high power quality is essential for ensuring efficient, reliable, and stable operation of electrical systems. The proliferation of nonlinear and sensitive loads has increased issues such as voltage sag, swell, harmonics, and reactive power imbalance, which adversely affect overall power performance. This paper presents a detailed modeling and simulation study of a Unified Power Quality Conditioner (UPQC) designed to mitigate power quality problems in smart grid environments. The UPQC integrates both series and shunt active power filters within a single framework to simultaneously compensate for voltage and current-related disturbances. The system is modeled and simulated using MATLAB/Simulink to evaluate its dynamic response under various operating conditions, including load variations and fault disturbances. Simulation results demonstrate that the proposed UPQC effectively mitigates harmonics, restores voltage profiles, and maintains unity power factor, thereby improving overall system performance and reliability. The findings validate the UPQC as a robust and efficient solution for enhancing power quality in next-generation smart grids.

Abstract: Over 1.6 billion people worldwide suffer from anaemia, which is still a major public health concern. Low-resource environments bear a disproportionately high burden of this condition. Although conventional blood tests necessitate invasive procedures, trained personnel, and laboratory infrastructure, early detection is essential to preventing serious clinical outcomes. Using a Raspberry Pi platform and Internet of Things (IoT)-enabled remote monitoring, this work introduces AcuSense, a portable, non-invasive anaemia detection system that combines photoplethysmography (PPG) and conjunctiva image analysis. The system uses deep learning-based conjunctiva imaging and real-time PPG signal acquisition to provide highly accurate haemoglobin level estimates. IoT integration improves accessibility for underserved and rural communities by enabling automated data storage, cloud analytics, and remote healthcare provider alerts. Strong correlation between experimental evaluation and conventional haemoglobin measurement techniques highlights AcuSense's potential as an affordable, quick, and non-invasive diagnostic tool.

Abstract Purpose: The purpose of this article is to explore the users' knowledge about artificial intelligence in an automated scenario of central library of Banaras Hindu University. According to the study, user awareness is still moderate even though AI is improving cataloguing, resource access, and user services at the Central Library. Scope: This study explores the users’ awareness of Central Library of Banaras Hindu University in intelligent environment as well as the automation of this academic library utilizing AI. Methodology: For this study, a total of 100 questionnaires were distributed, and 85 BHU users answered. The data was analysed using Microsoft Excel to understand user perspectives and experiences, as well as to investigate the impact of AI on library automation. Findings: Of those surveyed, just 45.88% were aware of chatbots and other AI tools. 56.47% of them visited daily, despite this. According to a survey, 64.71% of Central Library patrons are aware of automation. The report highlights how artificial intelligence (AI) is transforming traditional library operations by enhancing services and user engagement through intelligent technology. Results demonstrate that AI improves productivity and customization, satisfying the changing requirements of scholars, teachers, and students.

Abstract: This study aims to develop a systematic review of the influence of agricultural financing awareness and access to microfinance credit on financial challenges among agricultural entrepreneurs from both global and Philippine perspectives. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework was used as a guideline to ensure methodological rigor and transparency in the systematic literature review process. Scholarly articles published between 2015 and 2025 were examined, and 20 empirical studies that met the inclusion criteria were selected for analysis. The findings reveal that both agricultural financing awareness and access to microfinance credit play significant roles in reducing financial challenges, enhancing financial resilience, and improving decision-making among agricultural entrepreneurs. However, the review also identified inconsistencies in the strength of these relationships across different contexts, as well as a limited number of studies exploring their combined effects. These gaps highlight the need for further research to validate and extend existing findings across diverse agricultural settings. Research examining the combined effects of agricultural financing awareness and access to microfinance credit on financial challenges remains limited, particularly in the context of developing countries like the Philippines. This study contributes by synthesizing recent empirical evidence and proposing a more comprehensive conceptual understanding of how financial literacy and credit access jointly influence the financial sustainability of agricultural enterprises.

Abstract: In this paper we introduce a new class of b-generalized closed sets, b- generalized open sets in topological spaces, and study some of its basic properties. 

Abstract - Food adulteration has become one of the most pressing issues in public health, as it compromises food safety, nutrition, and consumer trust. Various real-time examples highlight how adulterants such as synthetic colors, chemical preservatives, and harmful substitutes are intentionally added to food products for profit, posing serious health risks. To address this challenge, advanced computational techniques, including Natural Language Processing (NLP) and Deep Learning, have been increasingly explored for the detection, prediction, and prevention of food adulteration. By leveraging machine learning models, datasets related to food quality and adulteration patterns can be analyzed to identify hidden correlations and risk indicators. The study further incorporates supervised learning algorithms and confusion matrix evaluation to measure the classification performance in predicting adulterated versus non-adulterated samples. The presented confusion matrix illustrates the predictive accuracy and misclassifications across multiple classes, providing a clear performance insight into the model. Overall, this research emphasizes how Artificial Intelligence (AI) and Machine Learning (ML) techniques can significantly strengthen food adulteration detection, thereby ensure public safety and contribute to a healthier society.

Abstract— The paradigm of web applications has shifted from static content delivery to dynamic, real-time interactive systems. This paper presents a comprehensive study on the design, implementation, and performance evaluation of "Sphere Strike," a full-stack, browser-based abstract strategy game. While the core technologies utilized Flask, Socket.IO, and heuristic-based AI are well-established, the novelty of this work lies in their specific synthesis to address a discernible gap in existing digital versions of the game "Chain Reaction." We detail a client-server system architected on a Python Flask backend, utilizing the Socket.IO protocol over WebSockets to facilitate low-latency, event-driven communication. A primary contribution is the engineering of a multi-strategy, heuristic-based Artificial Intelligence (AI) agent with tiered difficulty levels. The agent's intelligence is derived from a detailed heuristic evaluation function, and its most advanced tier employs a 1-ply lookahead search to identify and exploit game-winning tactical opportunities. The system was containerized using Docker and deployed on a cloud platform (Render.com) to validate its production-readiness. Empirical performance evaluation confirms that the system maintains a mean round-trip network latency of under 200ms and an AI move calculation time consistently under 100ms, delivering a seamless user experience.

Abstract: As industrial processes grow in complexity and demand greater precision, traditional control systems though fast and reliable remain largely reactive, responding only to real-time sensor inputs. This latency, though minimized with advanced processors and high-speed communication protocols, poses significant limitations in highly sensitive or dynamic environments where proactive control is essential. This paper explores the integration of Machine Learning (ML) into instrumentation and control systems as a transformative approach toward achieving predictive and autonomous automation. By analyzing the architecture of a PLC-driven motor control system with real-time sensor feedback, this study illustrates how ML algorithms can be employed to anticipate system behavior, replicate sensor inputs, and enable self-adaptive responses in real-time. The research highlights the potential of ML to enhance traditional control frameworks by learning environmental patterns, such as wave-induced motion or system oscillations, and generating predictive control outputs that minimize delays and improve system responsiveness. The paper concludes that with further research and deployment, ML-enhanced control systems can transition from reactive automation to intelligent, self-governing platforms, redefining the future of industrial process control.

Abstract: Using digital innovations such as cloud computing, blockchain, big data analytics, mobile banking, and artificial intelligence to deliver financial services is known as Financial Technology (FinTech). FinTech prioritizes speed, accessibility, and user-centric design in contrast to traditional systems. This study examines the impact of Financial, Technology (FinTech) adoption on the financial performance of 32 public and private sector banks listed on the NSE in India from 2015–2016 to 2024–2025. FinTech tools such as mobile banking, credit/debit cards, UPI, AI, and blockchain have transformed India’s banking system by enhancing efficiency, accessibility, and customer experience, while also posing challenges in cybersecurity, regulation, and restructuring. The research uses a quantitative, descriptive design with secondary data from RBI, the World Bank, and banks’ annual reports, applying descriptive statistics, correlation, and regression analysis. FinTech adoption is measured through transaction-based indicators (mobile, credit, and debit usage), with bank size, GDP, and inflation as control variables. Findings indicate that FinTech adoption significantly improves financial performance. Mobile banking enhances capital adequacy and earnings through efficiency gains, while card usage strengthens liquidity and profitability via increased transactions and fee income. GDP growth further boosts bank performance, whereas inflation has mixed effects on asset quality and liquidity. Private banks, being more technologically agile, show faster adoption and better results compared to public banks, which face legacy infrastructure and administrative hurdles.

Abstract: This study examined the factors driving the adoption of Climate Smart Agricultural (CSA) practices in Northern Ghana. In nine selected communities, 318 smallholder farmers were interviewed, comprising 115 adopters and 203 non-adopters of CSA practices. Descriptive statistics were used to analyse the most common CSA practices in the area. Chi-square analysis and binary logistic regression were performed to examine which factors most likely influenced the use of the selected CSA practices. The study found that age, educational level, household size, access to extension and farmer-based organisations (FBO) membership of all smallholder farmers surveyed directly influenced smallholder farmers’ utilisation of CSA practices. As revealed in the study, up to 90% of the smallholder farmers in the study area were aware of the various CSA practices being examined. The study also showed that smallholder farmers frequently used on-farm composting, with the Chi-square test showing that access to extension services, household size, and educational attainment all had an impact on smallholder farmers' usage of on-farm composting. The study recommends that the Ministry of Food and Agriculture (MoFA) intensify extension service delivery on CSA practices to promote their adoption by smallholder farmers in the face of climate variability and change.

Abstract: The main objective of the present study was to determine the concentrations of the radioisotopes potassium-39K, thorium-232Th, and uranium-238U in soil samples collected from selected areas around El-Obeid City, Sudan. Inductively Coupled Plasma Mass Spectroscopy (ICP-MS) analysis was employed to quantify these isotopes. Soil samples were obtained from five locations surrounding El-Obeid, specifically from two regions in the southern and western parts of the city within North Kordofan State, in areas located away from residential zones. High correlation coefficients between 232Th and 238U indicate that these isotopes likely originate from the same source. Principal component and factor analyses further confirm that 232Th and 238U share common sources in the soil samples collected from various locations around El-Obeid City. In addition, the measured concentrations of 238U and 39K are in reasonable agreement with values reported in the literature. The concentration of 232Th observed in this study is relatively high compared to values reported from other regions worldwide, suggesting possible local geological or anthropogenic influences.

Abstract: Illegal mining / “galamsey” is a major source of soil pollution in Ghana, but ecological risk levels at local sites remain poorly quantified. This study assessed heavy metal contamination in soils from three illicit mining sites at Noyem and Nyafoman in the Birim North District, using pristine soils as controls. A total of 40 samples were collected and analyzed for Cu, Zn, Fe, Hg, and Cd concentrations using atomic absorption spectrometry. Results were summarized in tables and a bar graph, and contamination was evaluated through geoaccumulation index (Igeo), contamination factor (CF), pollution load index (PLI), enrichment factor (EF), and ecological risk index (RI). The mining sites recorded markedly higher concentrations of Zn (≈40 ppm), Fe (≈5 ppm), Hg (≈0.04 ppm), and Cd (≈0.07 ppm) compared with the control (Zn: 2.4 ppm; Fe: 0.3 ppm; Hg: 0.008 ppm; Cd: 0.0004 ppm). Conversely, Cu was slightly higher in the control soils (0.70 ppm) than in the mining sites (≈0.15–0.21 ppm). Igeo values indicated extreme Cd contamination (>6), strong Zn and Fe contamination (>3), and moderate Hg contamination, while Cu showed no enrichment. PLI values exceeding unity (8–10) confirmed overall soil pollution. Cd exhibited the highest CF (43–66) and Eri (4200–6100), followed by Hg (175–210), resulting in composite RI values (4400–6400) that signify very high ecological risk across all sites. These findings demonstrate that illicit mining in Noyem and Nyafoman has significantly increased soil contamination, with Cd and Hg posing the greatest ecological threats. Stronger regulation of mining practices, mercury phase-out, and remediation strategies such as phytoremediation and soil amendments are recommended to mitigate risks and protect community health.

Abstract: This study investigates the effect of financial inclusion on performance of Small and Medium Scale Enterprises in Ondo State. A quantitative survey research design was employed. The population for the study comprised of all registered SMEs in Ondo State, Nigeria. The sample size used for this study was determined using the Taro Yamane (1967) formula for finite populations. The study utilized 300 SMEs as sample size across Ondo State using a combination of stratified random sampling and purposive sampling was used.  Data were collected using a structured questionnaire and were coded and analyzed using the Statistical Package for the Social Sciences (SPSS v30). Multiple regression analysis was used to test the study’s hypotheses. The result shows that financial inclusion, measured through access to formal finance and usage of financial services, does not significantly predict SME performance. The study also found that business environment (β = 0.145, p = 0.016) and education level of the entrepreneur (β = 0.150, p = 0.010) significantly influence SME performance. Similarly, owners or managers with higher education levels may better navigate financial management, marketing, and operational decisions, enhancing performance. These findings reinforce the multidimensional nature of SME success, highlighting that human capital and environmental factors can moderate or enhance the effects of financial inclusion. The study therefore recommends that Training programs targeting SME owners, especially those with lower educational backgrounds, can enhance managerial and financial decision-making skills, thereby improving business outcomes

Abstract: Once upon a time, jute was the golden fiber of Bangladesh. The country used to earn the highest amount of foreign currency by exporting jute and jute goods. However, starting in the 1980s, due to lack of proper policy and declining global demand, export earnings from jute fell significantly. Synthetic substitutes were one of the major threats. But currently the situation is totally different because of serious concerns about environmental pollution. Secretary of the Ministry of Textiles and Jute, Lokman Hossain Mia said that the interest of jute product is increasing because people of Western countries including Europe are increasingly using the natural fibers in different industries and in day-to-day life. At present jute is the second largest export earning sector next to RMG of Bangladesh. The sector has an average contribution of 3% in our export earnings and 1% in GDP. Moreover, though RMG sector is the highest contributor in our economy in terms of export earnings, it is to be remembered that 1 Dollar of export earnings from Jute sector is tantamount to that of 4 Dollar from RMG sector since each 100 Dollar export of RMG item requires 55-60 Dollar import of raw materials. Different tables and charts have been used to represent the findings of the study. The data sources have been reported in each table and chart. This study was conducted in the months of April, May and June 2025. In this study I used secondary data published in both printed and electronic sources. This study is descriptive, conceptual and depends on historical facts and figures. Finally, this paper suggests some policies to achieve our ancient fame of golden fiber.

Abstract—Voltage stability is a critical aspect of maintaining the reliable operation of modern power systems, particularly with the increasing integration of renewable energy sources, dynamic loads, and complex grid configurations. This paper presents a comprehensive analysis of voltage stability assessment techniques aimed at enhancing the overall stability and resilience of power systems. Various methodologies, including continuation power flow, modal analysis, and time-domain simulation, are explored to evaluate system performance under different operating conditions. The study emphasizes the identification of weak buses, critical voltage margins, and potential collapse points to aid in preventive control strategies. Furthermore, the role of advanced computational intelligence methods such as Artificial Neural Networks (ANN), Fuzzy Logic, and Machine Learning algorithms in improving predictive accuracy and real-time monitoring is discussed. Comparative results demonstrate the efficiency of hybrid assessment models in detecting instability precursors and optimizing reactive power compensation. The findings contribute to the development of more robust voltage stability frameworks, ensuring secure and efficient power system operation in the evolving energy landscape.

Abstract—The evolution of major powers in the global system has always been intricately tied to technological advancement and innovation. This paper explores the interplay between technological progress and shifts in global power dynamics, emphasizing how emerging technologies—such as Artificial Intelligence, quantum computing, biotechnology, and cyber capabilities—are redefining geopolitical hierarchies and strategic influence. The study examines historical transitions in power distribution, analyzes contemporary trends in techno-political competition, and evaluates how technological superiority shapes national security, economic growth, and international diplomacy. By employing a comparative analytical framework, the paper highlights the growing influence of digital sovereignty, data dominance, and innovation ecosystems as the new determinants of global power. The findings underscore that the 21st century’s geopolitical landscape is increasingly characterized by “techno-hegemonic” rivalry, where nations capable of leading in technological domains hold a decisive strategic edge. The paper concludes with insights into potential future trajectories of power transitions and the importance of international cooperation to ensure ethical, equitable, and sustainable technological governance.

Abstract: The article evaluates significant Indian IT companies' liquidity from 2005 to 2015. The essay starts by describing the investigation's scope, then briefly reviews accounting and liquidity performance literature. Next, it describes the study's aims, data, and methods. After that, it analyzes the results and concludes.

Abstrak. Maintenance is a combination of several management methods without changing the basic features and functions of building structures and service systems. Effective maintenance planning ensures optimal use of the structure and reduces operating costs. Building inspection is one of the methods in maintenance management for assessment of building physical condition. Moreover, this method is a preliminary analysis to gather information, assess and identify damages to building structures. This paper discussed variety of structural inspection methods including inspections for pitched roof and fade. Literature was collected from journals, conference proceedings, standards and books. These methods were compared and classified according to defect. The results demonstrated that there are weaknesses in the method of building structure inspection. For example, there are varieties of information in the verification of damage causing inconsistencies in the inspection. Hence, causing inconsistent final report on maintenance performed. Besides, the assessments also depend on the discretion and experience of the inspector resulting in subjective evaluations. For instance, the final reports were interpreted differently from individual perspectives. In conclusion, building structure inspection requires a guide to standardize the inspection process. In addition, specific strategies are required to ensure the documents provided can be reduced consistently by various inspectors.

Abstract: This study presents an original empirical investigation of barriers and constraints against women’s presence in the accounting profession in Bangladesh from both organizational and socio-cultural perspectives. While the accounting literature has explored the global context of gender issues, there is limited research contextualized in the institutional and cultural configuration of Bangladesh. The research objectives are to identify the key determinants of women’s entry and progress into the accounting profession, investigate organizational and socio-cultural constraints, and provide recommendations to foster gender inclusivity and equality in the accounting field. A quantitative descriptive design was chosen for this study. The data were collected among 123 female accounting professionals and students in different parts of Bangladesh using a structured questionnaire. Statistical analyses involved SPSS and included descriptive statistics, reliability tests, and correlation analysis for key variables. Findings indicate that organizational constraints lie in discrimination in promotion, with a mean of 3.15, glass ceiling with a mean of 3.02, and job stress with a mean of 3.01. Socio-cultural factors are revealed in stereotypical attitudes with a mean of 3.28 and family responsibilities with a mean of 3.13. The organizational and socio-cultural constraints were related positively with a strong correlation. However, despite the presented constraints, women feel encouraged by their professional interest for 40.7% and by following the education for 19.5%. The findings also address the necessity of HR policies, training programs, flexible work arrangements, and social media campaigns to encourage women’s leadership roles in accounting.

Abstract: Attaining the prompts and cost-effective execution of a project presents remarkable challenge, which is the key factors of adequate planning and scheduling of the project. This research categorically focus on finding a balance between the cost of project and the minimizing expected time needed for the conclusion of a construction project. Data were collected from the Building Practice Ltd., a construction firm situated in Lagos State, Nigeria. The finding adopted both the Programme Evaluation and Review Techniques (PERT) and Critical Path Method (CPM). Analysis was carried out, the possible routes of the activities involved in the building project, location of critical path and expected duration for every activity in the building project was determined. The probability of completing the project within the specific time was evaluated and the result indicated that there is a 50% chance for the projected to be executed within the specify period. The project was delayed due to inadequate equipment, loss of weeks as a result of changing in plan and insufficient manpower. The initial completion and actual completion time was estimated to be two months difference which actually reduce the time and increase cost efficiency of the project.

Abstract: This research examines community perspectives on climate change in Ampesekrom, near Nkawkaw, situated in Ghana's Eastern Region. The study employs a combined methodology that integrates meteorological data evaluation with community questionnaires (involving 100 participants) to explore trends in climate variability and adaptation strategies within forest-reliant communities. The meteorological evaluation identifies clear decade-by-decade temperature and precipitation trends across the three-decade timeframe. Peak temperatures show a general upward trajectory with significant variations, whereas precipitation patterns exhibit early decreases followed by more recent increases. A comparative evaluation with Abetifi, a neighbouring high-altitude station (positioned at 601m elevation compared to Nkawkaw's 275m), demonstrates varying climate effects associated with elevation and land use modifications. Precipitation patterns exhibited considerable variation, with significant decreases from the initial to the middle decade (roughly 15-25% reduction) before experiencing modest recovery during the final decade. Survey results indicated extensive recognition of climate change (98.5% confirmed that the climate is changing), accompanied by nuanced comprehension of the anthropogenic factors contributing to environmental transformation. Participants pinpointed forest clearing, farming intensification, vegetation burning, and urban development as key factors driving environmental transformation, revealing their understanding of connections between human actions and climatic conditions. The study recommends implementing community-focused educational programs that clarify climate change origins, indicators, and adaptation approaches through understandable terminology and contextually relevant illustrations. Additionally, it suggests enhancing agricultural advisory services that deliver technical guidance for climate adaptation, guaranteeing consistent interaction between agricultural advisors and farming communities, while improving climate information accessibility to support better agricultural planning and risk mitigation decisions.  

Abstract:  Acacia liquid honey can be processed into powdered honey using vacuum drying. The aim of this research is to obtain optimal conditions for powdered honey formulation consisting of the percentage of liquid honey and operational conditions, namely drying temperature. To obtain this, chemical tests are carried out, namely reducing sugar, diastase enzyme, and IC50. This research uses the Design Expert 12 application with the RSM (Response Surface Method) method, namely CCD (Central Composite Design) with 2 factors and 3 responses. The results of processing powdered honey obtained optimal results at a liquid honey percentage of 70% compared to maltodextrin of 30% and a drying temperature of 55oC. Chemical testing obtained optimal results for reducing sugar of 68%, diastase enzyme of 3.35, and IC50 of 18.88 mg/ml.

Abstract Background: Academic underachievement in Botswana's Tswapong Region has persisted despite substantial government investment in education infrastructure and teacher development programs. The region's five-year mean performance declined from 42.70% (2018) to 31.55% (2022), restricting students' access to tertiary education and diminishing their vocational prospects. While previous investigations have examined social and pedagogical factors, the psychological determinants, particularly psychological capital and self-handicapping behaviors, remain inadequately explored within this educational context. Objective: This study examined how psychological capital and self-handicapping independently and collectively predict academic achievement among Form 2 students in Tswapong Region. The investigation further explored gender differences in these psychological constructs and their differential relationships with academic performance. Methods: An exploratory correlational design was employed among 372 Form 2 students (162 males, 210 females) from six purposively selected secondary schools. Data were collected using adapted instruments: the Academic Psychological Capital Questionnaire (APCQ) measuring hope, efficacy, resilience, and optimism; the Academic Self-Handicapping Questionnaire (ASHQ) assessing behavioral and claimed handicapping; and end-of-term summative assessment scores reflecting academic achievement. Standard multiple regression analysis determined the collective predictive capacity of psychological capital and self-handicapping, while bivariate correlations and independent samples t-tests explored individual relationships and gender differences. Results: Psychological capital demonstrated a weak positive correlation with academic achievement (r = .27, p < .001), while self-handicapping showed a moderate negative association (r = -.28, p < .001). Gender analysis revealed no significant differences in psychological capital (t = -1.36, p > .05), though males reported substantially higher self-handicapping behaviors (t = 7.70, p < .001). The regression model was statistically significant, F(2, 369) = 16.19, p < .001, with both predictors collectively explaining 7.6% of achievement variance. Self-handicapping emerged as the stronger predictor (β = -.28, p < .001), while psychological capital's contribution was non-significant (β = .25, p > .05) when considered alongside self-handicapping. Conclusions: Self-handicapping represents a more potent determinant of achievement than psychological capital among Tswapong secondary students. The modest explained variance suggests additional unmeasured factors substantially influence academic performance in this context. Gender-specific patterns in self-handicapping behaviors necessitate differentiated intervention approaches, particularly for male students who demonstrated elevated handicapping tendencies across behavioral and claimed dimensions.

Abstract: The study is set to examine the Technical and Vocational College (TVC) Principal’s perception on democratic governance in public tertiary institutions. The study utilized cross sectional descriptive survey strategy that employed mixed methods approach of inquiry in a sequential procedure. The target population of the study was 54 Principals, trainers, parents and 244 trainees of public technical and vocational institutions in Bungoma, County, Kenya. Data was collected and analyzed using descriptive statistics such as means and frequencies to summarize data and inferential statistics: regression model, ANOVA and the test were used to test difference between means scores of variables in the study hypothesis. The findings of the study indicated that; there is still need for TVC Principal to be enlightened on how to lead tertiary institutions democratically, opportunities for trainee’s participation on tertiary institutions governance. The study recommends that Principals should engender parents’ participation in TVC governance. it is anticipated that the findings of the study will inform the ministry of education on training needs of TVC Principals and board of governors with regard to democratic management of tertiary institutions and how to create a democratic culture in their tertiary institutions.

Abstract: The geohydrological status of the Indian landmass at the regional level for drinking water is generally complex due to differentiated hydrogeological conformations with significant lithospheric and sequential variations, complex and varied textures, climatological peculiarities, and diverse hydrochemical conditions. Ponders carried out over time have uncovered that Sandstone, Shales, and Limestones of the Vindhyan period undoubtedly rise above the unconformity boundaries. Cosmically, two bunches of gemstone conformations have been connected depending on characteristically diverse power through pressure of groundwater, viz., permeable conformations, and Fissured conformations. This investigation paper is arranged to measure the groundwater danger of the geohydrological strata of the Peninsular level, centred on the Deccan Level by CGWB and other geohydrology information from colourful offices to portray the changes in ground drinking water through diverse seasons. These geohydrological variations have resulted in a complex mosaic of groundwater availability and quality across the Peninsular region. The interplay between lithological diversity and structural features influences the storage, movement, and recharge potential of aquifers. Additionally, the region's dependence on monsoonal rainfall and variable recharge rates further complicates the sustainable management of underground drinking water sources. The characteristic conditions influencing groundwater management include climatic parameters such as precipitation, evapotranspiration, and waterways. This investigation paper encourages considering the inconvenience in picking up sensible data on the groundwater conditions in the Deccan Level locale through agent checking wells and other geographical bed structures. The critical traits of groundwater administration checking are groundwater recharge danger position and its effect on biology. The issue of the investigation paper centres on the suggested measures to diminish the danger of the geohydrological circumstance of drinking water position in the ponder zone and advance suggested measures to have an economic impact on the environment.Keywords: Deccan Level, Geography, Geohydrology, Biology, Drinking water.

Abstract: Language is the effort of the mind and feelings to convey meaning to others which is used as a means of communication. Audio-visual media is a medium that can develop children's language skills. The purpose of this study is to find out how to develop children's language through audio-visual media in Ad-Dahlan Kindergarten, Sucinaraja District, Garut Regency. This research is a qualitative descriptive research involving 2 teachers and 20 children in class B1, data is collected through observation, interviews, analysis documents, data is analyzed qualitatively using data reduction, data display , and conclusion drawn. The results show that the teacher makes a learning implementation plan, the teacher studies the manual for the use of media, the teacher prepares and arranges the media equipment to be used, the teacher ensures that the media and all equipment are complete and ready to be used, the teacher explains the goals to be achieved, the teacher explains the material to the students before the activity takes place, the teacher conducts an evaluation. So all indicators of language development achievement can develop according to the level of development. The seven steps in the use of audio-visual media have been implemented by teachers at Ad-Dahlan Kindergarten, Sucinaraja District, Garut Regency and can be an alternative to develop early childhood language skills.

Abstract—The development of Automated Guided Vehicles (AGVs) has revolutionized material handling in industrial and commercial settings. This project focuses on designing, devel-oping, and testing a prototype AGV for store and warehouse automation. The AGV utilizes an ESP32 microcontroller, infrared (IR) sensors for line-following, and an ultrasonic sensor for obstacle detection. The vehicle is designed to follow a fixed path marked by a black line on the floor while avoiding obstacles in real-time. Built using cost-effective components, this prototype serves as a scalable and adaptable solution for educational and industrial applications. The system’s performance was validated through a series of controlled tests, demonstrating successful navigation, obstacle avoidance, and payload transport capabil-ities. The project aims to address the limitations of manual material handling, such as inefficiency, safety risks, and high labor costs, offering a reliable and cost-effective alternative for indoor logistics. Future enhancements could include dynamic navigation, wireless communication, and advanced control al-gorithms to further optimize the AGV’s performance in more complex environments.

Abstract: Latrines play a critical role in maintaining public health but can also act as reservoirs for microbial contamination, particularly in low-resource settings. This cross-sectional study assessed hygiene risks by quantifying microbial loads on high-touch surfaces of public and household latrines in Ghana. A total of 200 surface swabs were collected with 80 from public latrines and 120 from household toilets. Samples were analyzed for Escherichia coli, Staphylococcus aureus, and total coliforms using standard culture methods. Results were expressed as log₁₀ colony-forming units per square centimeter (CFU/cm²). Mean microbial loads were significantly higher on surfaces of public latrines than on household toilets (p < 0.05). Door handles and flush levers showed the greatest contamination, with E. coli reaching 3.82 ± 0.41 log₁₀ CFU/cm² in public latrines compared with 1.61 ± 0.32 log₁₀ CFU/cm² in household toilets. Cleaning frequency and disinfectant use were inversely associated with surface contamination. These findings demonstrate that communal sanitation facilities may pose greater hygiene risks than private toilet facilites due to inadequate cleaning and overcrowding. Strengthening sanitation management through regular disinfection, adequate maintenance, and user hygiene education is essential to reduce potential pathogen exposure and improve overall environmental health.

Abstract: Urban mobility in Abuja continues to suffer from prolonged passenger waiting times and unpredictable departure schedules due to the dominance of occupancy-based dispatch systems in the informal transport sector. These inefficiencies impose high opportunity costs on commuters, create unstable revenue patterns for drivers, and limit the development of reliable urban transport services. This study introduces the Transport Fare Insurance Scheme (TFIS) as a financial–institutional framework designed to redistribute demand risk, enable timely departures, and stabilize driver income. Using a mixed-methods approach combining commuter surveys, operational cost assessments, simulation modeling, and stakeholder interviews, the research evaluates how fare-insurance mechanisms can reshape departure behavior and improve service reliability. The simulation results indicate substantial reductions in average waiting time and greater income predictability for operators under insured-departure scenarios. Behavioral and institutional insights further reveal that policy coordination, union sensitization, and digital fare governance are essential conditions for successful TFIS adoption. Although the simulation relies on model-based parameters requiring future pilot validation, the findings demonstrate that TFIS offers a practical, scalable pathway for enhancing transport efficiency in Abuja and similar Sub-Saharan African cities. The study contributes to emerging research on integrating financial instruments into public transport operations and aligns with global development targets under SDG 9 (Industry, Innovation and Infrastructure) and SDG 11 (Sustainable Cities and Communities).

Abstract Background: Nowadays, business organization implementing the Sustainable Development Gold (SDG) in their business operations and company policy as their support towards sustainability. Sustainable logistic is supporting the United Nations Sustainable Development Goals (SDGs) aim to improve economic, social, and environmental outcomes through increased efficiency, lower emissions, and improved resource management. It helps to achieve goals such as sustainable energy (SDG 7), responsible production and consumption (SDG 12), infrastructure (SDG 9), and fewer traffic accidents (SDG 3). Key factors include developing green energy applications, streamlining supply chains, supporting sustainable procurement, and utilizing technologies such as artificial intelligence and blockchain to improve supply chain sustainability and performance. This study aims to understand the trend of sustainable logistic studies, key theme, publication trend and citation, authors, and co-word related to sustainable logistic. This study will provide an insight to for future study on sustainable logistic. Method: A bibliometric analysis is use in this study by extracting data from Dimension.Ai database. By using three keyword which are ‘Sustainable’ AND ‘Logistic’ AND ‘Malaysia’. Performance analysis, science mapping network analysis use in this study. Result: Publication trend show that the post-peak trend suggests that while the initial fervour may have subsided, the topic continues to be a subject of ongoing, albeit less intensely focused, academic inquiry. The frequent number of authors are 3 to 5 people. Co- words analysis show how the topic being choose and discuss within the industry. This topic become matured and more analysis needed for the future study Conclusion: Base on the bibliometric analysis, we can conclude that the field of telematic system acceptance is a dynamic and deeply interconnected research area. The densest clusters demonstrate that the study has expanded beyond the individual user to encompass the broader system performance, transport efficiency, and industry-level integration.

Abstract: This paper presents a thematic emulsion of over thirty empirical studies (2020 – 2025) examining Innovative Work Behaviour (IWB) in healthcare. It integrates the tone-determination proposition and job institutional issues. The review identifies harmonious drivers, including transformational and green transformational leadership, structural commission, diversity climate, job casting, and supportive invention climates, which together promote idea generation, creation, and consummation.  Pivotal walls are also synthesized resource constraints, hierarchical societies, shy leadership support, limited invention knowledge, staff resistance, and lack of forums for hand-driven invention — challenges that are amplified in low-resource surroundings. Empirical validation suggests that IWB improves care quality, process effectiveness, organizational rigidity, demand productivity, and staff engagement when supported by enabling structures and incentives. Notwithstanding, most primary studies are cross-sectional and concentrated in high- and middle-income countries, which limits their transferability to settings like Nigeria, where high case-to-staff ratios and fragile structures prevail. Thematic emulsion is shown to be a rigorous system for integrating behavioural invention validation and exposing methodological gaps. Practical recommendations include fostering inclusive, low-bureaucracy societies, leadership training, commissioning resources and decision-making authority, invention forums, and policy initiatives.  Future research should prioritize longitudinal and mixed-style studies in low-resource settings to unpack terrain-specific mechanisms and issues. By sticking IWB within an integrated theoretical lens, the paper offers a practical roadmap for directors, policymakers, and researchers seeking to translate hand-led invention into advancements in patient care, safety, and institutional sustainability.

Abstract: In this study, Box-Behnken’s Response Surface Methodology (RSM) was applied to study the esterification reaction effectiveness of zinc chloride activated Ngbo clay catalyst. The XRF result showed that both raw and activated clay contain contaminations of oxides and other impurities; however, the clay mineral compositions are not significantly affected by zinc chloride treatments. Additionally, it indicates a high content of silicon and aluminium oxides compared to other oxides.XRD pattern results showed several characteristic peaks due to the mineral compositions present. The analysis of the peaks showed sharp peaks with low intensity at 2θ = 11.300, which corresponds to the main peak used in the identification of kaolinite clay, as reported in the literature.The esterification was monitored based on the process conditions of temperature, time duration, amount of reactant, catalyst weight and particle size. The Box -Behnken’s Response Surface Methodology indicates that the zinc chloride clay-catalysed esterification reactions proceed through dual mechanisms: an Acid-complex and an Alcohol-complex mechanism, with the Alcohol mechanism dominating. The esterification efficiencies of acetic acid and ethanol by zinc chloride activated Ngbo clay catalyst, optimized using RSM models indicated the estimated esterification percentage of ˃95%. The predicted and experimental values obtained under the same conditions showed a difference of less than 5%, indicating that the Box-Behnken design approach is an efficient, effective, and reliable method for the esterification of acetic acid with ethanol. The produced catalyst was optimized using A-One way ANOVA modelling, which indicated a correlation coefficient of the regression of 0.9551, which implies that 95.51% of the total variation in the esterification reaction was attributed to the experimental variables. The result obtained indicated that the process could be applied in the esterification of acetic acid to avoid the drawbacks of corrosion, loss of catalyst and environmental problems. 

Abstract: This study examines the impact of female board participation on earnings management in Nigerian manufacturing firms between 2014 and 2024. Drawing from annual reports of sixteen listed companies, female CEO power and female board size were used as measures of board gender participation, while earnings variability served as a proxy for earnings management. Employing a static panel regression under an ex-post facto research design, the results indicate that female CEO power has a negative but statistically insignificant association with earnings management, whereas female board size shows a positive and marginally significant effect at the 10% level. These findings suggest that while the presence of women in top leadership positions may not independently deter earnings manipulation, larger female representation on boards could be linked to increased earnings management activities, potentially reflecting contextual and governance factors in the Nigerian corporate environment. The study recommends appointing more qualified women as CEOs, enhancing their ownership stakes to strengthen influence, and ensuring that female board members are empowered to contribute effectively. These insights contribute to corporate governance debates and offer guidance for policy reforms aimed at improving board diversity and financial reporting integrity.

Abstract: The study explored the perceived solutions to the sanitation challenges in George Compound in Lusaka, Zambia. The study objective was to assess the socio-economic situation of the beneficiaries of Household Sanitation Project in George Compound. The researcher adopted the social constructivist ontology, knowledge was co-constructed with participants through an interpretive phenomenological approach, and reflexive thematic analysis. The sample was selected purposively and determined using data saturation, ensuring rich, contextual insights from relevant stakeholders. The study revealed that poor sanitation affects men and women differently and it was further observed that women are more affected due to their biological nature, which limit their ability to use public spaces for relief, unlike men who can use open areas. The construction of toilets under the project helped address gender-specific concerns related to sanitation. Moreover, most study participants noted that in the past, cultural norms discriminated against women and favoured men. Similarly, the researcher disclosed that most study participants revealed that toilets constructed by LWSC significantly reduced the disease burden for both men and women. Furthermore, the study revealed that the company offered flexible payment options, making it easier for beneficiaries to access sanitation products. Theoretically, it corroborates systems theory by illustrating the interrelated roles of stakeholders in sustainable sanitation. For policy, the study emphasizes integrated, multi-stakeholder interventions informed by practical experiences. Practically, it highlights the need for community sensitization and engagement to ensure sustainability of sanitation innovations. Knowledge-wise, the study provides contextual evidence specific to George Compound, documenting challenges, innovations, and operational insights that can inform similar peri-urban settings. In conclusion, addressing sanitation challenges in peri-urban Lusaka requires a holistic, systems-oriented approach that integrates infrastructure, regulation, education, cultural socialization, and community participation to ensure equitable, sustainable, and effective on-site sanitation services.

Abstract: Online crime victimisation harms not only individuals but also the wider community by weakening trust and safety. This study looked at how parental emotional attachment influences the chances of online crime victimisation among university students at Chuka University in Tharaka-Nithi County, Kenya. The target population was 19650 students pursuing different courses in Chuka university. Participants were randomly sampled from the population after the pilot study which was conducted in Tharaka University. During the pilot study, the reliability of research instruments was measured (Cronbach Alpha of .87) and deemed valid to collect actual data. This study relied on the principles of Routine Activity Theory and Social Learning Theory. After seeking permission from relevant authorities such as Tharaka University Ethics Committee, NACOSTI and Chuka University management the researcher proceeded to collect the actual data from 155 students using questionnaires by physically visiting the lecture hall. After inferential data analysis the results showed strong links between weak parental bonds and higher vulnerability to online victimisation. Maternal attachment (ρ = .554, p < 0.01) and paternal attachment (ρ = .483, p < 0.01) both played important roles in shaping how safe or at-risk youths were online. In today’s world, digital technologies bring both opportunities and risks. The findings show that parental emotional support remains a strong shield even in an age where artificial intelligence, social media, and online platforms shape young people’s lives. By using technology in education, counselling, and law, society can build new ways to protect and guide youths. The study concludes that strengthening parental support, building emotional resilience, and encouraging self-control can lower the chances of online victimisation. At the same time, digital tools such as online safety training, mental health support apps, and technology-informed laws can help create safer spaces. Working together, parents, teachers, and policymakers can use these tools to protect young people and support sustainable development in line with the wider goal of building safer and more inclusive societies.

Abstract - Egg quality in terms of freshness for unfertilized free range chicken eggs was investigated in the study using a portable near-infrared spectrometer operating at 900 nm to 1700 nm. The egg freshness is commonly quantified based on the Haugh unit which is time consuming and destructive. Near-infrared spectroscopy can be used to assess the quality of eggs at the various stages of the supply chain. The portable near infrared spectrometer operating in reflectance mode was used to scan the egg sample in vertical and horizontal position. The result showed that the highest classification accuracy was from the free-range chicken egg obtained from the BPSU farm with a cross-validation accuracy of 68.20% and a test accuracy of 63.60 %. Position 1 (vertical) gives the highest accuracy and it is attributed to the changes in air cell height during ageing which affects the spectral data obtained from the egg samples. The spectra were pretreated using Savitzky Golay  1st derivative with a smoothing point =7 and analyzed using stepwise discriminant analysis. The result showed the potential of the portable near infrared spectrometer to predict the freshness of free-range chicken egg real time. It can be implemented in online grading and can be a nondestructive technique for detecting egg freshness.

Abstract: About 27% of the air pollution in India is contributed by vehicles. The continuous increase in the vehicular number, the traffic congestion, and adulteration of fuels in vehicles and improper management of traffic is causing increased air pollution due to vehicular movement. The present study was carried out at 5 locations in Srinagar city with 4 locations being located in high traffic areas of the city and one location situated in the outskirts of the city where traffic movement was less during a period of 1 year from June 2019 to May 2020. PM1 and PM2.5 particulate matter was monitored at every fortnight on the said locations for a period of 1 year using Aerosol Mass Monitor AEROCET-831. The monitoring period coincided with the COVID-19 period too. The readings showed significant differences seasonally and also due to changes in traffic flow due to COVID 19 pandemic. The observed data for concentration of particulate matter was mapped using IDW mapping to see the changes in concentration of pollutants with the change in location.

Abstract: Oral film Phrasings have surfaced as an innovative and patient-friendly medicine delivery system designed to ameliorate remedial efficacity, convenience, and patient compliance. These flicks are thin, flexible lozenge forms that snappily disintegrate or dissolve when placed on the lingo or in the buccal depression, allowing the medicine to be absorbed either locally or systemically through the oral mucosa. This mode of delivery offers distinct advantages over conventional lozenge forms similar as tablets and capsules, including ease of administration without the need for water, rapid-fire onset of action, and enhanced bioavailability due to the avoidance of first- pass hepatic metabolism. Depending on their intended purpose and point of action, oral flicks are generally classified into three main orders presto- dissolving flicks, which disintegrate fleetly in the oral depression; buccal flicks, which cleave to the buccal mucosa for controlled medicine release; and mucoadhesive flicks, which insure prolonged contact with the mucosal face for sustained remedial goods. The expression of oral flicks requires careful selection of applicable film- forming polymers (similar as hydroxypropyl methylcellulose or pullulan), plasticizers, active pharmaceutical constituents, and other excipients to gain the asked mechanical strength, inflexibility, decomposition profile, and stability. Critical quality evaluation parameters include film consistence, folding abidance, tensile strength, face pH, decomposition time, and medicine content uniformity, which insure harmonious performance and patient safety. With growing interest in case- centric medicine delivery systems and nonstop advancements in polymer wisdom, nanotechnology, and manufacturing ways similar as solvent casting and 3D printing, oral film phrasings are gaining elevation as an effective,non-invasive, and protean platform for both systemic and original medicine delivery in different remedial operations.

Abstract: CCTV cameras are widely used to monitor traffic, but they often come with limitations that still force officials to check traffic flow manually. In this work, we introduce a simple and automatic method to measure traffic volume and vehicle speed by analyzing pixel patterns from CCTV video. Our approach begins by marking vertical and horizontal reference lines on each lane and collecting pixel information from these lines in every video frame. When a vehicle crosses these lines, the change in pixel brightness clearly reveals its movement. By studying these brightness changes, we can automatically detect vehicles and calculate key traffic parameters accurately.

Abstract: Porcelain veneers represent a minimally invasive yet highly aesthetic treatment option for anterior teeth. Advances in ceramic materials, adhesive protocols, and digital workflows have significantly improved longevity and predictability. This narrative review outlines the indications, contraindications, material choices, preparation designs, adhesive cementation protocols, and complication management related to veneer therapy. Particular emphasis is placed on evidence-based decision-making and clinical nuances that influence long-term success, including case selection, occlusal planning, and periodontal considerations. The review also highlights methodological limitations in the current literature—such as heterogeneous follow-up periods, variable preparation designs, and differing adhesive strategies—that may affect interpretation of survival data. Comparative insights from long-term clinical trials and systematic reviews are integrated to provide balanced, clinically relevant guidelines.

Abstract: Social media has emerged as a significant force in shaping public opinion, offering a unique platform for information dissemination, political mobilization, and social movement facilitation. This literature review explores the role of social media in forming public opinion, highlighting its distinctive attributes, such as interactivity, immediacy, and user-generated content, which set it apart from traditional media. The review examines the influence of social media on public sentiment, where it has been shown to impact morale and support for government policies. However, the rise of echo chambers and filter bubbles on social media poses challenges, as these phenomena can distort public perception and contribute to polarization. The review also discusses the potential of social media for real-time public opinion monitoring and its implications for policy-making. While social media enhances civic engagement and democratizes information, it also presents risks, such as the spread of misinformation and conspiracy theories. The review emphasizes the need for critical literacy among users to navigate the complexities of social media and mitigate its negative effects. Future trends and implications, including the impact of emerging technologies and evolving user behaviors, are also considered. Overall, this literature review provides a comprehensive overview of the role of social media in shaping public opinion, highlighting both its positive and negative aspects.

Abstract: The research reviewed the establishment of War and Economic Crimes Courts as a crucial mechanism to address persisting human security challenges and abuses in Liberia. The aftermath of conflicts left Liberia grappling with numerous challenges, ranging from economic instability to the absence of justice for victims of war crimes and economic crimes. In pursuit of sustainable peace and human security, the establishment of War and Economic Crimes Courts (WECC) for Liberia has been proposed as a mechanism to address the legacies of violence and impunity. This paper reviewed the necessity, feasibility, and potential impact of implementing a WECC in Liberia. This article provides insights into the potential of WECC mechanisms as a catalyst for long-term peace, stability and human security in Liberia. The findings showed that the establishment of War and Economic Crimes Courts for Liberia have the potential to contribute significantly to the country's efforts to achieve enhanced peace,  justice, reconciliation, and sustainable development. However, it would require political will, financial resources, and robust institutional support to effectively be implemented. The Government will have to seek support, including funding, technical assistance, and expertise, from the international community to assist with the establishment and functioning of the War and Economic Crimes Courts separately. Also, the Government should collaborate with civil society organizations, international partners, and the public to build consensus on and support for the establishment of the courts for Economic Crimes and War Crimes. This involves conducting awareness campaigns, hosting public forums, and facilitating nationally inclusive dialogue to address concerns and garner backing for the process.

Abstract: This study presents a comprehensive numerical investigation into the boundary layer behavior of an electrically conducting fluid influenced by simultaneous magnetic and radiative effects over an exponentially stretching surface. The analysis focuses on the impacts of magnetic field intensity, thermal radiation, and Prandtl number on the velocity, temperature distribution, skin friction coefficient, and heat transfer rate. A system of coupled nonlinear differential equations governing the momentum and energy transport is formulated and solved using MATLAB’s bvp4c solver. Results reveal that increasing magnetic field strength significantly reduces fluid velocity due to the Lorentz force, while enhancing the temperature profile through magnetic damping. Similarly, stronger thermal radiation increases fluid temperature and thickens the thermal boundary layer, though it reduces the Nusselt number by diminishing surface temperature gradients. In contrast, higher Prandtl numbers lead to lower temperatures and enhanced heat transfer without substantially affecting skin friction. These findings highlight the critical interplay of magnetic and radiative phenomena in modifying boundary layer characteristics, offering practical insights for the design and optimization of advanced thermal systems in materials processing, energy harvesting, and magnetic insulation technologies.

Abstract: The integration of technology into the operations of micro enterprises has become a critical factor in enhancing business performance, especially in the context of business growth. However, the empirical effect of AI-driven technological integration on micro enterprises in Osun State, Nigeria, remains underexplored. This study therefore examined how AI-enabled technological tools affect micro enterprise performance within the state. A survey research design was employed, covering 30 Local Government Areas (LGAs) and the Modakeke-Ife Area Office, with a study population of 288,780 micro business owners across various sectors who have created paid jobs for others (NBS, 2017). A sample size of 400 was determined using Taro Yamane's formula. Data were collected through a structured questionnaire and analysed using SPSS version 25, utilising both descriptive statistics (frequencies and percentages) and multiple regression analyses. The findings indicate that digital skills (0.795), digital infrastructure (0.724), and e-commerce platforms (0.692), when powered by AI technologies, significantly affect business growth among micro entrepreneurs. Collectively, these factors account for 78.4% of the variance in business growth. The study concludes that AI-driven technological integration is a key driver of micro enterprise performance and employment. It is recommended that policymakers and stakeholders focus on improving AI-enabled digital infrastructure, enhancing digital literacy, and supporting intelligent e-commerce adoption to boost micro enterprise growth and job creation in Osun State.

Abstract: Organisational culture shapes employee attitudes, motivation, and overall job performance, especially in public universities where non-academic staff play crucial administrative roles. However, limited empirical studies have examined the combined effect of organisational culture, such as leadership style, communication patterns, and shared values, on employee performance across all six South-West states of Nigeria. This study therefore investigated the effect of organisational culture on employee performance among non-academic staff of public universities in South-West Nigeria. The objectives of the study are to examine the effect of leadership style, communication patterns, and shared values on employee performance. A descriptive survey research design was adopted. The study was conducted across 12 public universities (federal and state) within Lagos, Ogun, Oyo, Osun, Ondo, and Ekiti States. Data were collected from 395 respondents using a structured questionnaire, selected through a multistage sampling technique from a population of 34,880 non-academic staff. Descriptive statistics (frequencies and percentages) and multiple regression analysis were performed using SPSS version 25. The results showed that leadership style (B = 1.548), communication patterns (B = 1.465), and shared values (B = 1.436) all significantly affected job commitment (p = .000). The study concluded that organisational culture components positively affected employee performance. The study recommended that university administrators should adopt transformational leadership, promote internal communication, and embed institutional values.

Abstract—Low-frequency oscillations (LFOs) pose a significant threat to the dynamic stability and reliable operation of large interconnected power systems. Traditional damping controllers often face challenges in adapting to nonlinear system behavior, varying load conditions, and network topology changes. This paper presents a machine learning-assisted framework for the monitoring and damping control of low-frequency oscillations in power systems. The proposed approach integrates advanced data-driven techniques to identify oscillatory modes, predict instability patterns, and optimize control parameters in real time. A hybrid model combining signal processing for feature extraction and supervised learning for oscillation prediction is developed to enhance situational awareness and decision-making. Simulation studies on standard IEEE test systems validate the proposed method’s ability to improve damping performance, reduce oscillation amplitude, and enhance overall system stability under dynamic operating conditions. The results demonstrate that machine learning-based adaptive control offers a robust and scalable solution for mitigating low-frequency oscillations in next-generation smart power grids.

Abstract: The rapid evolution of malware poses a significant cybersecurity challenge, as attackers increasingly employ sophisticated obfuscation techniques to evade detection. Polymorphic and metamorphic malware utilise different obfuscation techniques such as packing, encryption, and code mutation to evade traditional signature-based detection models. Conventional static and dynamic analysis tools frequently struggle to de-obfuscate these threats. This paper proposes a novel system that integrates reverse engineering techniques with ensemble learning models containing Gradient Boosting Machines, Convolutional Neural Networks, and Gated Recurrent Units, to address these challenges. Reverse engineering enables in-depth structural and behavioural analysis of malicious binaries, exposing hidden payloads and execution patterns. Ensemble learning enhances detection by combining the strengths of multiple algorithms to improve accuracy and adaptability. The proposed system not only identifies obfuscated malware with high precision but also predicts emerging variants, offering resilience against evasion tactics. By uniting explainable reverse engineering with advanced ensemble learning, the system provides scalable, real-time protection against evolving malware threats.

Abstract: The rapid expansion of the Internet of Things (IoT) has enabled pervasive sensing, automation, and data-driven decision-making. However, privacy and security challenges remain critical in Wireless Sensor Networks (WSNs), where limited computational and energy resources render traditional routing protocols vulnerable to traffic analysis, identity spoofing, and data manipulation. Existing routing schemes emphasize performance or energy efficiency but lack adaptive, privacy-aware mechanisms capable of responding to dynamic threats. This paper presents Dynamic Privacy-Aware Routing (DyPAR), an adaptive probabilistic routing protocol that balances privacy preservation, energy efficiency, and computational feasibility for large-scale IoT networks. DyPAR incorporates entropy-based relay selection, dynamic adjustment of forwarding probabilities, and context-aware weighting to reduce adversarial traceability while maintaining efficient routing. The protocol integrates lightweight privacy-preserving components, including Efficient Key Management (EfKM), Privacy-Aware Data Aggregation (PrADA), and an Adaptive Privacy Parameter Change Mechanism (A2PCM) for real-time adjustment based on network conditions and data sensitivity. Extensive simulations across heterogeneous network sizes and attack models show that DyPAR achieves high privacy compliance, strong resilience against Sybil, eavesdropping, and data-tampering attacks, and improved packet delivery performance relative to established privacy-aware routing baselines. While DyPAR maintains low energy consumption in benign scenarios, computational and energy overhead increase under multi-vector adversarial conditions, highlighting the need for further optimization in ultra–resource-constrained environments. Future work will explore (i) lightweight cryptographic integration to reduce energy cost, (ii) federated learning–based adaptive routing to enhance real-time privacy decisions, (iii) real world and energy-efficient clustering for large-scale deployments, and (iv) blockchain-enabled distributed trust frameworks to mitigate identity spoofing and coordinated attacks. Overall, DyPAR offers a scalable, adaptive, and privacy-preserving routing solution for next-generation IoT systems, providing a strong foundation for secure and resilient sensor network communication.

Abstract: This study presents a comprehensive computational analysis of blood flow resistance in a symmetrically stenosed artery under the influence of an external magnetic field—a scenario of growing clinical relevance due to the rising interest in magnetically assisted therapies. Modeling blood as a viscous, incompressible, and electrically conducting fluid with radially variable viscosity, the problem incorporates both geometric non-uniformity and magnetohydrodynamic effects through the inclusion of a transverse magnetic field. The governing equations, derived in cylindrical coordinates and non-dimensionalized using characteristic parameters, are solved using the Finite Difference Method, offering robust insights into velocity distributions and flow resistance. The results reveal that stenosis height alone significantly elevates resistance, while the presence of a magnetic field amplifies this effect nonlinearly, with higher field strengths causing pronounced suppression of axial velocity. Velocity profiles flatten and shear rates near the arterial wall intensify with increasing stenosis and magnetic influence, underscoring the synergistic impact of these parameters. This work not only advances our understanding of MHD- modulated hemodynamics but also provides a theoretical foundation for future biomedical applications such as targeted drug delivery, vascular diagnostics, and therapeutic flow control.

Abstract: This paper surveys recent advancements in artificial intelligence (AI) that have directly improved the capability of systems to solve real-world problems. We review progress in foundation models and multimodal systems, generative models (diffusion and transformer families), human-in-the-loop alignment (RLHF), and privacy/resilience techniques for deploying AI at the edge (federated/TinyML). Building on the literature, we identify important gaps in robustness, evaluation, and societal alignment, then propose a methodology combining multimodal pretraining, task-specific fine-tuning with human feedback, and privacy-preserving edge deployments to address practical tasks in healthcare triage, environmental monitoring, and robotics. Experimental designs, datasets, metrics, and ethical safeguards are provided to enable reproducible, responsible research.

Abstract: This theoretical paper examines how disciplined and repetitive thought patterns can induce lasting behavioural change through the mechanism of neuroplasticity. It introduces the concept of “neuroplastic mutation”—a metaphorical transformation in the brain’s wiring triggered by mental discipline. Integrating insights from neuroscience, cognitive psychology, and philosophy of mind, the framework argues that rigorous thought acts as a catalyst for rewiring identity, habit, and response. The paper positions neuroplastic mutation as a bridge between unconscious automaticity and conscious behavioural evolution. 

Abstract: This systematic review examines how Institutional Theory has been applied to understand governance and administration in educational contexts, with a specific focus on policy enactment. Guided by PRISMA principles, the review analyzed empirical and theoretical studies published over the past two decades to identify dominant themes, methodological trends, and gaps in the existing literature. Findings reveal that Institutional Theory provides valuable insights into how schools, higher education institutions, and educational systems negotiate external pressures, internal norms, and organizational routines when enacting policy. The review highlights three major themes: (1) the influence of institutional environments on decision-making and administrative practices, (2) the role of organizational actors in shaping and mediating policy implementation, and (3) tensions between formal policy expectations and actual practices within educational settings. Despite its growing use, the review notes limited research in developing-country contexts and a need for integrated frameworks that combine Institutional Theory with complementary perspectives such as policy sociology and organizational change. The study contributes to a deeper understanding of policy enactment processes and recommends future research directions that emphasize multi-level analyses, contextual diversity, and the dynamic interplay between institutions and educational actors.

Abstract: The contemporary workplace demands continuous learning, agility, and behavioral adaptability, shifting the foundations of what is traditionally understood as “talent.” While ability—encompassing skills, competencies, and domain expertise—remains an important predictor of performance, emerging organizational behavior research highlights attitude as the central catalyst influencing how ability is developed, applied, and sustained. This paper examines the attitude–ability nexus and explores how mindset, motivation, and emotional orientation reshape talent in dynamic work environments. Integrating psychological theories, behavioral frameworks, and empirical insights, the study proposes a conceptual model illustrating how attitude accelerates mastery by amplifying learning agility, adaptability, and interpersonal effectiveness. The paper concludes with implications for managers, HR practitioners, and researchers, emphasizing the critical need to prioritize attitudinal competencies in talent strategies.

Abstract: Authentication enables individuals to be automatically recognized based on their behavioral or physiological traits. Biometrics is extensively utilized in many commercial and official identifying systems to facilitate automated access control. This research presents a model for multimodal biometric recognition that utilizes a feature level fusion method. The suggested method encompasses a series of five processes, namely pre-processing, feature extraction from all attributes, feature level fusion, feature space reduction, and recognition via machine learning techniques. The initial stage involves the pre-processing of three distinct modalities, namely iris, pamprint, and fingerprint. Next, the process of feature extraction is conducted for each modality in order to extract the features. Following this, the features extracted from three modalities were combined at the feature level. The utilization of feature level fusion in integrating multiple biometric data presents several advantages in comparison to alternative fusion procedures, but accompanied by the notable limitation of creating feature vectors of substantial dimensions. The main objective of this study is to analyze the difficulties related to the management of high-dimensional data and investigate several methods of feature reduction that can be applied to multimodal biometric systems. This study presents a novel approach that employs Cuckoo Search (CS) optimization technique for the purpose of feature selection. The objective is to address the challenges related to integrating the Iris, palmprint, and fingerprint feature spaces at the feature level. Normalization is applied to bring all the feature spaces into same domain during integration of features at feature level. Machine Learning approaches are utilized to assess the effectiveness of feature selection based on Cuckoo Search Algorithm (CSA) and feature space reduction using Principal Component Analysis (PCA) on the CASIA, IITD, and FVC databases. Additionally, matching is performed using the Euclidean distance. The trials undertaken in this study indicated a significant reduction in the feature space when iris, palmprint, and fingerprint characteristics were merged at the feature level. Specifically, the use of CS resulted in a greater reduction compared to PCA. The decrease in size led to an improvement in the accuracy of recognition.

Abstract: A dramatic evolve from conventional broken systems to a straightforward, patientfocused digital solution is required because of the increasing demands of attentive healthcare. MedConnect, a innovative web-based healthcare platform created to improve and sped up access to healthcare services, is presented in this paper. Secure and comprehensive patient medical history and its management, easy online appointment bookings and face to face online consultations through Google Meet integration are few of the essential features that MedConnect provides. By giving physicians access to a patient database and powerful management tools, the platform empowering better diagnosis. MedConnect helps lower traditional barriers to healthcare by offering a unified platform where patients can schedule appointments, view medical records, and join virtual consultations. Built with HTML, CSS, and JavaScript on the front end, and powered by PHP and MySQL on the back end, the system emphasizes security, privacy, and scalability. This paper describes MedConnect's architecture, implementation, and potential, emphasizing how it can act as a catalyst for Indian healthcare that is it being more effective and accessible. This system is a major step towards transforming the healthcare sector through technology, innovation, along with the goals of cutting wait times, improving doctor-patient interaction. Thus, offering a seamless healthcare experience.

Abstract - Over the last decade, Automatic Speech Recognition (ASR)—have advanced substantially. The field has undergone a fundamental transformation with the introduction of end-to-end models, and its development has been further accelerated by recent developments in attention-based techniques and transfer learning on large- This paper compares state-of-the-art techniques in detail and provides a thorough review of research done in Hindi ASR since 2010. It examines modern methods for both monolingual and multilingual systems with an emphasis on deep learning models. This study examines multiple models on publicly available speech datasets to evaluate their performance for practical implementation. It also discusses open-source ASR research findings, challenges, and future directions, especially in mitigating data dependency, improving generalizability across low-resource languages, handling speaker variability, and operating in noisy conditions.

Abstract: In the present era of rapid technological transformation, technical proficiency and adaptive capabilities have become crucial determinants of organizational success. Despite widespread access to training resources, many firms continue to struggle with persistent talent shortages and widening skill gaps. These gaps impede employee performance, reduce organizational efficiency, and ultimately affect economic progress. In current era of technological growth, individuals who possess proficient technical skills can easily elevate themselves to higher positions within organisations, enterprises, and society.   It is a widely accepted fact that despite having access to numerous resources, many firms are still grappling with the fundamental challenges posed by the talent gap.   The presence of skill gap issues not only hampers the performance of employees and companies, but also has a detrimental effect on the economic growth.   Every organisation has the responsibility to detect skill gaps among personnel and implement effective ways to address these fundamental challenges.   The primary objective of the study was to evaluate the discrepancy in skills and establish the correlation between skills and performance of the personnel employed in IT companies situated at Infopark cochin.   A sample of 50 IT companies was chosen from the Infopark for the study.   The necessary data for the analysis were gathered from the HR managers of the individual IT organisations. A well-designed questionnaire was used as the method of data collection.   The study was done by analysing the employees' diverse skill sets, identifying skill gaps, assessing their influence on employee performance, and examining the strategies implemented by firms to address these issues. A strong correlation has been shown between the fundamental challenges of the skills gap and the performance of firms. 

Abstract: This study examines the role of training programmes, handloom expos, and tourism in empowering women artisans in Assam. Using secondary data from 2011 to 2023, correlation and trend analyses are employed to evaluate the impact of policy, employment outcomes, and market access. Results show little correlation between training and stable employment, but a strong positive relationship (r=0.99) between exposure and sales revenue, emphasising their economic significance. Tourism demonstrated limited influence on handloom demand. The findings underscore the need for integrated strategies that combine skill development, market access, and tourism to support sustainable livelihoods for women weavers and preserve indigenous textile traditions.

Abstract: In contemporary corporate environments, work-life balance and physical health have emerged as crucial factors influencing job performance. This study looks into how these elements affect workers' output in an Indian workplace. Descriptive statistics, reliability testing, correlation, and multiple regressions were among the statistical analyses carried out in SPSS using a structured questionnaire and data from 100 respondents. The results showed that while physical well-being and work-life balance are correlated, they do not directly affect each other statistically significantly. In contrast, work-life balance significantly and favorably predicts job performance. About 37% of the variance in job performance was explained by the regression model, and Cronbach's alpha values showed that the scales were reasonably reliable. In order to improve employee productivity, the study emphasizes how important it is for Indian businesses to invest in health-supportive policies and create flexible work environments. In order to direct future research in this developing field, limitations and implications are also examined.

Abstract: This paper explores how investment scams in India have evolved from traditional schemes to modern online frauds utilizing digital platforms and social media. It compares the operational methods of past and current investment scams and explains the reasons behind this shift, including digital expansion and easier access to online investment opportunities. Based on secondary data from academic journals, reputable news sources, and government reports, the study uses a case-based comparative approach. It emphasizes the need for stronger regulation, better financial literacy, and increased investor awareness to safeguard individuals from financial fraud in the digital age. Overall, the analysis demonstrates that although scam methods have changed, ongoing gaps in regulation and investor awareness still facilitate investment scams.