Volume 14 Issue 5

Received: 30 April 2025; Accepted: 08 May 2025; Published: 28 May 2025 Abstract- The integration of wind energy into urban and rural energy systems presents a compelling pathway to sustainable energy generation. This study explores the design, optimization, and performance assessment of advanced wind turbine systems, emphasizing low wind speed applications and maximizing energy yield in varied geographical settings. Innovative methodologies such as computational fluid dynamics (CFD) and empirical performance testing underpin this research. The findings demonstrate enhanced energy efficiency through optimized blade designs and adaptive turbine configurations, addressing critical challenges such as turbulence and structural durability. This work contributes to advancing renewable energy technologies, offering scalable solutions for global energy sustainability.

Abstract: In this work we concern with identification Visible evidence that can only be observed with the naked eye and required for experiment of forensic scientist. the search details include many visible of physical properties that could be depend to explained many unknowns in crime scene which means critical truth for different cases. The details include appearance phenomena such color, smell, general shape, size, nature of material and predicted properties depending on general form.  The properties which mentions in the total work were reported here that could be enhance by using specific portable apparatus that could give very important information about the structure and nature with properties of evidence in crime scene.

Abstract: In modern communication networks, various routing protocols differ in architecture, adaptability, processing delays, and convergence capabilities. For real-time applications, where nodes are often distributed randomly, Open Shortest Path First (OSPF) stands out as a widely adopted dynamic routing protocol due to its efficient dissemination of network topology information. The performance of OSPF in such scenarios heavily depends on parameters like convergence time, bandwidth utilization, and scalability. One critical parameter, the Hello Interval timer, influences the speed and stability of OSPF neighbor discovery and maintenance. This paper evaluates the impact of modifying the Hello Interval timer on OSPF protocol performance in real-time application environments. Simulations are conducted using OPNET Modeler 14.5 to analyze how different Hello Interval settings affect network convergence and reliability.

Abstract: The Financial Technology (Fintech) sector is changing at a swift pace, as artificial intelligence (AI) is extending its influence. Greater complexity and global linkages are going to demand from fintech the power to rethink the integrity of its cybersecurity mechanisms and fraud tactics that have gotten intense up to a growing extent. The paper argues for the necessity of an "Algorithmic Fortress," an AI-driven cybernetic system incorporating all possible technologies targeted at securing digital financial networks against cyber-attacks and acts of financial fraud. The article delves into AI/ML, deep learning, anomaly detection through generative adversarial networks, etc., scope to predict battle, detect and fight problems. It does address adverse effects of AI risk, threatened system independence through synthetic identity fraud, application of AI for fraud detection in decentralized finance, DeFi, as well as the threat-hunting models that need to become autonomous. Supervised learning, unsupervised learning, and reinforcement learning are examination methodologies that are being applied in taking high recourse to the preservation of cybersecurity amongst their uncertainties. Our analysis will involve different experimentations of Python-based simulated attack scenarios to compare the two forms of cybersecurity. Also brought in are SmartArt visual representations revealed in multi-tier defensive architectures, combined with some strategic recommendations destined to protect future-facing fintech infrastructures from doing illicit deeds of algorithms. This study sketches possible solutions for securing the future-ready, trustworthy, and resilient fintech ecosystems once assisted by AI-enhanced, digital fortresses.

Abstract: This study investigates the application of Infrared (IR) sensors for accident detection and prevention on curved roads in hilly region of Kashmir. The area’s narrow, winding roads with poor visibility, especially at sharp curves, pose significant safety challenges. The proposed model leverages IR sensors to detect oncoming vehicles at blind curves, triggering visual alerts through light indicators to warn drivers in real- time. This proactive approach aims to minimize collisions, particularly in low- visibility conditions. The system is designed with sustainability in mind, operating on locally generated power to ensure functionality in remote areas. The research follows a qualitative methodology, analysing secondary data, including accident records from the Chadoora Police Station for. This study demonstrates how IR sensors, combined with visual warning systems, can enhance road safety by extending visibility and providing timely alerts to drivers. Implementation of this model requires adequate funding, technological resources, and administrative support.

Abstract- This paper explores the development and analysis of two methodologically aligned dashboards: the HR Analytics Dashboard and the Coffee Sales Dashboard. Both dashboards were developed to demonstrate the transformative power of data analytics. This improvs decision-making across diverse domains. The HR Analytics Dashboard focuses on workforce metrics for example attrition rates, recruitment efficiency, and employee performance etc. The Coffee Sales Dashboard focuses on sales performance, customer trends, and inventory management. This paper includes data collection methods, preprocessing techniques, analytical models, visualization strategies, insights derived, and the potential for cross-domain applications. This integrated approach highlights the versatility of data analytics tools, particularly Power BI, in managing and interpreting complex datasets.

Abstract: This paper explores a multidimensional approach to securing the India-Myanmar border in Mizoram through smart surveillance technologies, community-based engagement, tribal corridor management, and socioeconomic development. In light of strong local opposition to conventional fencing, we review global and national border management practices, analyze the unique context of Mizoram, and propose a sustainable and culturally sensitive strategy. We advocate for policy adaptations that align with the region's social fabric while strengthening national security. The study draws on comparative global experiences in managing borders inhabited by indigenous and tribal populations, offering a robust alternative to conventional fencing.

Abstract: Traditional agriculture practices often rely on generalized pesticide and fertilizer application recommendations, leading to inefficient resource use, increased costs, and environmental degradation. To address these challenges, we app an innovative solution that integrates precision agriculture techniques with ML to optimize farming practices. This work collects and processes data from multiple sources, including Soli Health Cards (SHC), weather patterns, and crop diagnostics such as the Leaf Colour Chart (LCC). By analysing these datasets using advanced algorithms, the system generates precise, actionable recommendations for pesticide and fertilizer application, ensuring sustainable and cost-effective farming. This app also adapts to real-time conditions. For instance, if a farmer's SHC indicates low nitrogen and phosphorus levels, an initial fertilizer recommendation is provided. During the growing season, the farmers can use LCC to monitor nitrogen levels and adjust applications accordingly. If weather forecasts predict heavy rainfall, the system advises delaying fertilizer application to prevent leaching., data-driven insights help farmers maximize crop yield while preserving soil health and minimizing environmental impact. Thus, our proposed Crop Precision Guard App empowers farmers with real-time, adaptive, and science-backed design-making tools, revolutionizing modern agriculture for greater sustainability and productivity

Abstract: This project focuses on predictive modeling of biogas production using machine learning to improve efficiency, reliability, and scalability. The dataset, sourced from Professor Jackson Milano’s research at Universidade Positivo, spans 14 months of continuous monitoring on a ranch in southern Brazil, using cow manure in four biodigester configurations. The data was cleaned and preprocessed, and machine learning algorithms such as Linear Regression, XGBoost, LightGBM, Random Forest, and TensorFlow were used to develop models for estimating biogas yield. The iterative training process ensured high predictive accuracy. A user-friendly web interface was developed to allow real-time interaction with the model, enabling users to input parameters and receive biogas output predictions. This project showcases the potential of machine learning in optimizing renewable energy systems, promoting sustainability, and smarter energy management.

Abstract: Learning Platforms generate huge data and play a vital role in the field of education as a nation's future is dependent upon the progress of the students. These platforms generate lots of data and, offer valuable opportunities to predict and categorize student performance. Machine Learning (ML) has become a prominent method for analyzing this data, providing meaningful insights into academic outcomes. This research proposes a deep learning-based approach for processing and classifying student performance using ML algorithms. ML classifiers like Support Vector Machines (SVM), Multi-Layer Perceptron (MLP), and Naïve Bayes are applied to preprocessed data. The models' effectiveness is measured using parameters like accuracy, precision, recall, F-score, and the time taken to train each model.

Abstract: Investment banking is a segment of the finance sector which builds a bridge between corporate clients and financial matters. Investment banking serves corporate entities with a plenty of financial affairs and does services such as capital raising, mergers and acquisitions and financial advising. Investment bankers are basically underwriters who intermediates security issuers and investors. They are big people in the financial sector, aiding in the financial growth of Corporates, government entities and startups researching on the dramatically developing, fast pacing financial industry, understanding the new trends and developments pitching for the greater development of the nation’s financial market as a whole. Investment banker is a highly paid professional with tremendous pressure and immense accountability and commitment towards the money matters of a company. This scholarly article does a doctrinal and non-empirical research about the role of investment banking in corporate governance. The researchers have referred to Indian and foreign authored books to draw inference and review in this article. The areas on the evolution, purpose and objectives of the concept, leading investment banks and the activities done by them have been analysed for a qualitative approach to research.   An in-depth examination of the role, functions, legal ramifications, regulatory structures, evolution of investment banking, and a concise critique of the investment banking concept.

Abstract: This paper presents the Runge-Kutta (RK4) method of order four for solving initial value problems of fourth-order ordinary differential equations. The proposed method is derived and is observed to be efficient and practically well-suited for handling high-order initial value problems. Two modal examples (linear and non-linear) are presented to demonstrate the reliability, accuracy and easy implementability of the method. The results obtained show a high level of accuracy when compared with the analytical solution.

Abstract: The article examines the application of Life Cycle Costing models for assessing and managing operational costs in large-scale construction projects. It analyzes the development of the Life Cycle Costing concept, its theoretical foundations, and practical applications. Special attention is given to the impact of Life Cycle Costing models on decision-making processes, as well as methods for accounting for risks and uncertainties. A comparative analysis of various models is conducted, highlighting their advantages and limitations. An original classification of LCC models is proposed based on their analytical complexity and suitability for different project contexts. A case-based simulation is presented to demonstrate how scenario analysis and uncertainty modeling affect long-term cost outcomes. The study results emphasize the importance of utilizing Life Cycle Costing to optimize costs and improve construction efficiency in the long term.

Abstract: This study presents a comparative analysis of menstrual health awareness, hygiene practices, and related challenges among adolescent girls in two socio-geographically distinct communities of Meghalaya, Mawlai (urban) and Nongsder (rural). The primary objectives are to assess menstrual hygiene management (MHM) practices, explore the influence of cultural and social taboos, and evaluate access to sanitary products and adequate facilities among adolescent girls aged 13–19. Adopting a cross-sectional design, data were collected using semi-structured questionnaires from 140 adolescent girls across six schools (four in Nongsder and two in Mawlai). The findings reveal notable rural–urban disparities in menstrual health awareness, hygiene practices, and access to menstrual products and sanitation facilities. Participants from Mawlai reported better access to sanitary pads, health education, and supportive infrastructure. At the same time, girls in Nongsder more frequently relied on traditional or reusable materials due to limited resources and awareness. The study also highlights a widespread lack of early menstrual education and limited family communication, especially in rural settings. Cultural taboos and silence around menstruation continue to shape adolescent girls’ menstrual experiences and often compromise their dignity and well-being. The study concludes that culturally sensitive, community-based interventions are essential to improve menstrual health outcomes. Recommendations include strengthening MHM education in schools, improving sanitation infrastructure, and fostering open dialogue within families and communities. Targeted efforts by government and non-government actors are necessary to promote menstrual equity and dignity for adolescent girls in both urban and rural Meghalaya.

Abstract: This study develops a machine learning model for predicting heart disease risk using patient data, including demographics, medical history, and clinical measurements. Various algorithms such as Decision Trees, Support Vector Machines (SVM), and Neural Networks are evaluated for their predictive accuracy. The aim is to assist clinicians in early diagnosis and intervention. The model is evaluated using accuracy, precision, recall, and F1-score, and focuses on building a robust tool for heart disease prevention

Abstract: Mushroom cultivation presents an enticing opportunity for small-scale farmers to diversify their operations and boost income with minimal capital and space requirements. In fact, it can be grown in relatively small spaces, including indoors or in controlled environments, which may provide a practical alternative for farmers. This qualitative multiple-case study aims to explore the mushroom cultivation and marketing strategies as well as the perceived opportunities in improving the cultivation and marketing strategies of small-scale farmers in Bansalan, Davao del Sur. This study collected data from three (3) small-scale mushroom farmers through in-depth interviews. Through within-case and cross-case analysis, the data gathered revealed that the cultivation practices of small-scale mushroom farmers include meticulous farming, temperature management, specific cultivation techniques, and maintaining a holistic environment. The marketing strategies consist of competitive pricing, marketing channel optimization, establishing transparent communication, achieving the best deals, and utilization of social media. Furthermore, the perceived opportunities for improvement comprise continuous learning, perception of technology, partnership and collaboration, government support, and market viability. The implications of the study suggest that mushroom cultivation offers small-scale farmers a pathway to diversify their operations and enhance income while emphasizing the importance of rigorous cultivation practices, continuous learning, effective marketing, and collaboration. Furthermore, the study underscores the importance of government intervention in addressing various barriers to mushroom cultivation, such as access to resources and infrastructure, while also emphasizing the necessity of establishing supportive facilities and regulatory frameworks conducive to the growth of the industry. Additionally, it encourages stakeholders' active involvement in supporting small-scale farmers through equitable practices, fostering partnerships between different actors in the agricultural value chain, and investing in research and development initiatives aimed at enhancing the sustainability and productivity of mushroom cultivation practices. Keywords: mushroom farming, small-scale farmers, cultivation practices, marketing strategies, opportunities, multiple-case study.

Abstract- The paper shows optimization of an inventory model with the considered assumptions that the demand rate is quadratic time-dependent and the deterioration rate is exponential. Further we have included the assumptions of the salvage value and partial backlogging Taking ideas by the limitations of prior research where constant or linear demand models are taken, the given inventory model considers scenarios in which the customer demand changes non linearly (quadratically)  and the item deterioration has an exponential rate pattern, commonly observed in the industries that deal with the decaying or technology driven items or products  A total expense function is formulated  and an optimal replenishment time interval is calculated to minimize overall inventory expenses. The convexity of the inventory expense function is analytically checked with the help of  to make sure that the unique optimal solution of this optimized inventory model exists  Also  a sensitivity analysis is performed using  to check the influence or impact of key inventory parameters on the optimal solutions, providing important results for decision-making. Further, the effects of the salvage value on the optimization are shown to reflect practical applicability in the modern inventory systems  Numerical examples  supported by graphical analysis and explanation, shows the significant applicability and efficiency of this given inventory model along with the dynamic market conditions  

Abstract: Artificial Intelligence (AI) has emerged as a transformative force in the modern business landscape, reshaping operations, customer experience and strategic decision- making across industries. This research paper explores the growing use of AI in various business functions ranging from Marketing and Finance to Human Resource Management and Logistics. It examines how AI technology is such as Machine Learning, Natural Language Processing, and Robotic Process Automation are being adopted to enhance efficiency, reduce costs and drive innovation. Further more, the paper identifies the major opportunities AI offers like data driven insights, personalization, and scalability. While also addressing key challenges such as Ethical concerns, Workforce disruption, high implementation cost, and lack of regulatory clarity. Through real world case studies and a comprehensive literature review, this paper highlights the dual nature of AI as both a catalyst for competitive advantage and a source of organisational complexity. It concludes with strategic recommendations for businesses to responsibly integrate AI into their operations and outlines future directions for AI in the global business ecosystem.

Abstract: Distribution of fake news on social media is so fast that it endangers public trust and confidence in the political system and society. This paper introduces a powerful machine learning system for the fake news detection that applies the Input-Process-Output (IPO) model for the systematic research procedure. Thus, based on Natural Language Processing (NLP), statistical feature validation, and supervised learning models—Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) networks—we have successfully built a classification system that is not only accurate but also interpretable and consistent. Words from both fake and verified news sources were processed using sentiment analysis, TF-IDF vectorization, and syntactic feature extraction. Such statistical features as Chi-square tests, T-tests, and Pearson correlation coefficients singled out "Sentiment Polarity Variance" (Feature 100) as the most important one among a number of the features. The SVM model was found to be less effective than the LSTM model as the latter reached 94% for the overall accuracy along with precision (0.93), recall (0.94), and F1-score (0.93). This study strengthens the argument that there is a possibility of a combination of statistical and deep learning being used for the purpose of detection and reducing misinformation and confirming the appearance of safer digital information ecosystems.

Abstract- Effective coordination of construction documents among building industry professionals play a crucial role in successful project delivery. The study made a comprehensive exploration of the importance of coordination and communication in the construction industry, as well as its specific impact on the coordination of construction documents. The study combines the results of several studies and concludes with a focus group discussion with experts in the building sector. The vital significance of efficient coordination in improving project outcomes, lowering errors, and increasing stakeholder satisfaction is among the key lessons. The study emphasizes the significance of clear and concise documentation, timely information dissemination, and seamless collaboration among professionals. Additionally, it addresses the challenges faced in coordinating construction documents and their effects on project delivery. The research underscores that efficient coordination methods, open communication lines, and a collaborative culture are essential for mitigating the adverse effects of communication breakdowns and inadequate coordination. This comprehensive analysis contributes to a better understanding of current coordination practices in the construction industry, providing valuable insights for industry professionals and stakeholders alike.

Abstract: The rising incidence of fungal infections has highlighted the urgent need for treatments that are both effective and safe. Herbal antifungal creams have gained attention as a viable alternative to synthetic products due to their natural composition, reduced side effects, and eco-friendly nature. This research is cantered on the formulation and evaluation of a topical herbal antifungal cream that utilizes plant-derived active ingredients known for their antifungal efficacy. Specifically, neem, aloe vera, and clove oil were chosen for their well-documented activity against fungal pathogens. The cream was developed using a stable emulsion base to ensure smooth texture and ease of application. A series of evaluations—including pH stability, spread ability, and antifungal effectiveness—were carried out to assess the product's overall quality and therapeutic potential. The findings revealed notable antifungal activity, along with desirable physical and chemical characteristics, suggesting that the cream is a promising solution for managing fungal skin infections. This study reinforces the value of herbal-based formulations in dermatological care, while also supporting environmentally sustainable healthcare alternatives.

Abstract: The crux of the study is in the growing importance of CSR as sustainability and green innovation for IT supply chain operations created by the very endeavor of reducing carbon footprint. CSR has been a key strategy in the past few years for business performance maintenance, sustainable growth, and competitiveness. The current paper highlights the positive effect of CSR towards green innovation, specifically through the intersection of green transformational leadership and innovative strategies. Although there have been immense advancements, there remains a lacuna in sound CSR measuring mechanisms and reporting frameworks, particularly in the developing world. Leadership competencies, emotional intelligence, and governance framework are highlighted by the study as most important for effective CSR practice. Additionally, the impact of CSR on supply chain sustainability, i.e., environmental management and social equity, is also explained in terms of specific interest in green intellectual capital and renewable energy. The aim of this paper is to examine the role CSR and green innovation have in guaranteeing sustainability in IT supply chains and their role in business environmental objectives, specifically carbon emissions reduction. The research used a mixed-method design that incorporated case studies and qualitative interviews to research these relationships. Key conclusions indicate that CSR leads to competitive advantage via technological innovation, increases accountability, and facilitates the practice of sustainability. Implications are that businesses require more robust CSR structures and governmental imposed policies to meet environmental concerns and social fairness in supply chains.

Abstract: Every citizen of the nation has a fundamental right to education. With its many advantages, including improved skills, employment prospects, and financial security, education is essential for both individual and society growth. Additionally, it gives people the ability to make wise choices, get involved in their communities, and help create a more just and successful society. Since there is a concerning decline in the number of children enrolled in the research area, the article focuses on the enrollment status of children in the Government Kannada Lower Primary Schools in several blocks in Kalaburagi district. There is a need of government intervention to see that the reason behind the decrease in the enrollment as is because of the poverty or any other issues.

Abstract: The effect of static magnetic field on the pulsatile flow of blood with volume fraction of micro-organisms and gravity effect has been studied. The aim of the present investigation is to suggest how best the magnetic filed can be applied in the treatment of cardio-vascular diseases, in healing of fractures, infections etc. it has been observed that the increase in magnetic filed will decrease the velocities and in turn the flow-rate. In the absence of magnetic field, the results will coincide with the results of our earlier work and are in good agreement with theoretical and experimental results. As the magnetic effect increases, the velocities of blood and microorganisms decrease. Hence, the present model helps to control various types of cardio-vascular diseases with the application of magnetic field, which will help in deaccelerating the velocity of blood.

Abstract: This paper presents a comprehensive approach to enhancing web application security by mitigating two of the most prevalent and dangerous threats: SQL Injection (SQLi) and Cross-Site Scripting (XSS) attacks. Traditional defense mechanisms such as Web Application Firewalls (WAFs) and rule-based filtering often fall short due to their static nature and limited adaptability to novel or obfuscated attack vectors. To address these shortcomings, the proposed methodology integrates machine learning-based models trained on diverse datasets to accurately detect and classify malicious inputs. Extensive experiments were conducted in both controlled and real-time environments, evaluating the system’s performance using key metrics including accuracy, precision, recall, and F1 score. The results demonstrate that the machine learning model significantly outperforms traditional methods, achieving a detection accuracy of 96.4%, with high precision and recall values, thus offering both effectiveness and efficiency. The system also exhibits scalability and adaptability, making it suitable for deployment in live web applications. This research highlights the critical role of intelligent, data-driven systems in modern cybersecurity frameworks and establishes a strong foundation for future work focused on developing proactive and resilient web application defenses.

Abstract: This paper presents a comprehensive comparative analysis of Signature-Based and Anomaly-Based Intrusion Detection Systems (IDS) using key performance metrics such as detection accuracy, false positive rate, adaptability to new threats, computational overhead, maintenance effort, scalability, and real-time performance. By examining these metrics, the study highlights the strengths and limitations of each IDS approach in handling both known and emerging cybersecurity threats. Signature-Based IDS demonstrates high accuracy and low false positives but struggles with adaptability and maintenance demands, while Anomaly-Based IDS offers better adaptability and threat detection versatility at the cost of increased false positives and resource consumption. The analysis emphasizes that an optimal IDS solution should consider the specific security needs and operational context of the deployment environment. The findings suggest that a hybrid approach, leveraging the complementary advantages of both techniques, can provide a more robust and resilient defense against the growing complexity of cyberattacks in modern networks.

Abstract: AI is changing the way medical imaging and analysis are done, which is transforming healthcare. AI is improving the speed, accuracy, and efficiency of finding diseases, diagnosing them, and planning treatments by helping those analyze huge amounts of data. Deep learning and machine learning are both AI-powered technologies that are making radiology and imaging-based diagnostics better. They also make early disease identifying and personalized medicine possible. This paper talks about AI’s present and future potential role in medical imaging and evaluation, focusing on its uses, advantages, and difficulties. Radiology and pathology are being revolutionized by AI, from picture identification and analysis to automated image segmentation and categorization. AI is also making predictive analytics, finding new drugs, and virtual health helpers better. AI could completely change healthcare, but there are some problems that need to be fixed before it can be used widely. These include limited data, rule- based issues, moral concerns, and problems with integrating AI with other systems. As AI technology improves, it will continue to improve medical decisions and patient care around the world. This will make the healthcare system more efficient and improve patient results.

The Enhanced Security Electronic Voting Machine (EVM) embodies a significant advancement in the design and deployment of secure, scalable, and efficient electoral systems. Conventional paper-based voting and legacy EVMs are increasingly inadequate in mitigating vulnerabilities such as hardware tampering, unauthorized ballot casting, data integrity breaches, and logistical constraints. This project addresses these limitations through the integration of embedded systems engineering, secure communication protocols, and real-time data processing mechanisms. Central to the system architecture is the ESP32 microcontroller, selected for its dual-core Xtensa® LX6 processor, integrated IEEE 802.11 b/g/n Wi-Fi and Bluetooth 4.2 support, and extensive GPIO interface compatibility. The ESP32 operates as the system’s primary controller, managing input/output peripherals, executing cryptographic routines for data integrity, and facilitating secure wireless transmission of voting data. This design ensures end-to-end system reliability, enhanced tamper resistance, and real-time operability within mission-critical electoral environments.

Abstract: Rubber technical products are one of the main products with the widest range of uses today. In order to expand the scope of RTM use, it is necessary to increase its varieties. The most demanded rubber products are rubber products with high mechanical strength in contact with ozone. In this work, several polymer mixtures were used to obtain ozone-resistant rubber products. For this purpose, ethylene propylene terpolymer, polyvinyl chloride and butadiene-nitrile rubber were used. First, a binary system was created based on these polymers, their flow index was determined, and most importantly, their ratios to each other were determined. In order to prepare a composition based on these three polymers, an optimal recipe was selected using the mathematical modeling method, and a rubber mixture was prepared in laboratory cylinders based on this recipe. The mixing mode and the sequence of adding components were determined, and it was confirmed that the mixing process should be carried out at a temperature of 90 ° C and for 12 minutes. The vulcanization mode and time were also determined, and it was possible to obtain ozone-resistant rubber that could meet all operating conditions with a vulcanization temperature of 155 ° C and a period of 21 minutes. The main properties of the vulcanization are the strength limit in dispersion of 19. MPa (Standard 17.6), 71.1k/Hm (Standard 64.3) in ozone dispersion. Since the results were obtained using only the most modern analysis methods, it is possible to produce new rubber technical products based on the results obtained in this work in the production of ozone-resistant rubber products.

Abstract: The integration of intelligent technology into fitness equipment is transforming how individuals train, track progress, and prevent injuries. This paper examines FitTronix, a smart weight training system that delivers real-time feedback, personalized tracking, and advanced biomechanical monitoring. The study evaluates its impact on performance and injury reduction using data from user surveys, expert interviews, and system logs. Results show notable improvements in form accuracy, training consistency, and muscle engagement, with a marked decrease in injuries, particularly among beginners. FitTronix’s AI-driven analytics also support personalized fitness programming. The paper concludes by emphasizing its potential to boost training safety and efficiency, calling for further long-term studies and broader adoption of smart gym technologies.

Abstract: Health informatics focused on specific domain knowledge whereas Librarianship structured on generic subject areas. The health Science Librarians should acquire, exchange, organize and share knowledge. The skills required are acquire and preserve knowledge resources for future use and requires to understanding of both biomedicine and information science. The health Informatics specialists requires to have the domain knowledge in addition to health science librarianship. This paper highlights the role of Health science Librarians in Health informatics.

Abstract: Limestone is very useful for various purposes especially as chief raw material for manufacturing Portland cement which is the most common and extensively used adhesive in the construction industries. Hence, it is imperative in designing excavations in rocks to evaluate the mechanical properties and know the strength characteristics of the available rock materials so as to apply them to their appropriate uses. Thirty samples were collected from three different locations (ten from each) for analyses and their point load strength index, slake durability, natural moisture content, Schmidt hammer rebound values and their bulk density were determined as well as mineralogical composition. The limestone is found to be very durable as its slake durability after the first and second cycles ranges 99.68% and 99.34% respectively and falls within high strength rocks. It has low natural moisture content which accounts for the high strength of the rock. It was also observed that the uniaxial compressive strength (UCS) estimated with the aid of point load index has strong correlation than the UCS estimated with the aid of Schmidt rebound hammer values when compared with other parameters, this indicate that point load index is likely to be more accurate than that of the Schmidt rebound hammer. The average values of the point load test index of the samples from each of the three locations are 6.42, 5.91 and 5.67 respectively with Omi-Alayo phase 1 having the highest value. The average UCS ranges from 124.40 to 216.24Mpa, 93.06 to 228.48Mpa and 77.82 to 190.57Mpa respectively. Hence, the UCS for the three locations is 144.08Mpa. The average bulk density of the limestone is 2.64 which justify the high UCS values obtained from the Schmidt hammer rebound values which make the deposits at Omi-Alayo phase 1 and Phase 2 have a high UCS. It is observed that Omi-Alayo phase 1 has the lowest value of natural moisture content with 79% while the phase 2 has the highest with 99%. Result shows very low correlation and indirect relationship between the natural moisture content and the UCS values.

Abstract: This study presents a comprehensive analysis of Quality-of-Service (QoS) parameters in Kenyan mobile networks, emphasizing the consumer perspective between 2015 and 2017. The primary objective was to evaluate the performance of major Kenyan mobile service providers by examining key QoS dimensions such as network coverage, connection speed, service reliability, and customer satisfaction. Utilizing a mixed-methods research design, the study combined quantitative data from large-scale network measurements and consumer surveys with qualitative insights gathered through interviews and focus group discussions. Data collection encompassed diverse geographical locations and user demographics to ensure representativeness, and rigorous statistical techniques were employed to analyze the results. Findings revealed significant variability among providers with respect to service availability and quality, highlighting areas where improvements are critical. Notably, while network coverage was generally satisfactory in urban centers, rural and marginalized regions experienced considerable service gaps. Consumer feedback underscored challenges related to intermittent connectivity, slow data speeds, and perceived transparency in service delivery. The significance of this study lies in its empirical evidence-based approach, informing stakeholders — including policymakers, industry regulators, and service providers — about prevailing QoS conditions and consumer experiences in Kenya's evolving telecommunications landscape. By focusing on the consumer dimension, the research advances understanding of how technical performance metrics translate into real-world user satisfaction and offers actionable recommendations to enhance mobile network quality and inclusivity in Kenya.

Abstract: Predicting mental health-related challenges, such as programming anxiety, is essential for delivering timely support and improving academic outcomes among students in computer-related disciplines. This study examined the use of Automated Machine Learning (AutoML) to predict programming anxiety levels using a dataset composed of demographic, academic, and behavioral attributes collected from students at a public university in the Philippines. Through the Altair AI Studio Educational 2024.0.0, eight classification algorithms were employed to automatically generate predictive models, which were subsequently evaluated to determine their performance. Among the models produced, logistic regression emerged as the best-performing, achieving the highest accuracy (97.8%), F-measure (98.3%), and recall (99.7%), while also offering advantages in computational efficiency and interpretability. Feature importance analysis identified working student status, previous semester general weighted average, multimodal learning preferences, and access to multiple ICT devices as key predictors of programming anxiety. These results underscore the practical utility of AutoML in educational contexts and highlight its potential for enabling early identification and intervention to support students’ mental well-being and academic performance in computing disciplines.

Abstract: AI Adoption in Ghanaian HR This study investigated how HR professionals in Ghana perceive and plan to use Artificial Intelligence (AI) in their roles.  Employing a mixed-methods approach, researchers surveyed 200 HR professionals across various industries (banking, telecom, education) and conducted follow-up interviews.

Abstract: Understanding the causes of death is essential for effective health policy planning and intervention, this study investigates mortality patterns in Ekiti State University Teaching Hospital (EKSUTH), Ado-Ekiti, Southwest, Nigeria, to identify the leading causes of death and provide evidence-based recommendations for their prevention. Secondary data were collected from the Information Management and Medical Records Department of EKSUTH, which covers all recorded deaths, including stillbirths, over eleven years (2004–2014). The dataset included primary causes of death, age, sex, and time of death. Descriptive statistics, graphical analyses, and inferential tests, including t-test, Chi-square test, Kruskal-Wallis test, and Jonckheere’s trend test, were performed using IBM SPSS Statistics Version 20. A total of 1,954 death records were analyzed. Infections were the leading cause of death, accounting for 397 (20.3%) of all cases, followed closely by cardiovascular diseases, 382 (19.5%) deaths. Males accounted for 1,090 (55.8%) deaths while females accounted for 864 (44.2%) deaths. The age group 0–10 years recorded the highest number of death cases, accounting for 483 (24.7%) deaths, indicating a need for urgent public health attention. To further reduce the mortality rate, the study recommends targeted interventions for high-risk groups, enhanced public health campaigns focusing on disease prevention, nutrition, and road safety, and increased allocation for pediatric and emergency care units.

Abstract: Entrepreneurship has gained much prominence in both developed and developing countries and has created higher demand for entrepreneurship education. Education lay a significant role in job creation, influencing politicians to recognize and support entrepreneurial start-up activity due to its positive contribution to the economy. Entrepreneurship education has the mandate to train the youth with functional knowledge and skill to build up their character, attitude and vision. It has vital role in job creation. This paper explores the initiatives in entrepreneurship education in various parts of the world. This necessitates pro-active policy interventions in favour of entrepreneurship education, this paper tries to conceptualise the phenomenon of entrepreneurship education, starting form genesis of term 'entrepreneur', its definition, nature and new role of teachers and teacher training institutions in encourage young entrepreneurs. It also tries to facilitate an understanding about 'entrepreneurial teacher and training institution' and emphasizes the active role of teacher as a 'facilitator'. It deals with the urgency of policy interventions in India in Entrepreneurial education. In conclusion, observation of changes in the trend of Entrepreneurial education has been discussed.

Abstract: The Smart Cooler is an innovative cooling system designed to enhance energy efficiency, user convenience, and environmental adaptability by integrating Internet of Things (IoT) technologies. Traditional air coolers rely on manual operation and offer limited control over cooling efficiency. In contrast, the Smart Cooler automatically monitors and adjusts temperature, humidity, and fan speed using sensors and microcontrollers. Users can control and monitor the system remotely via a mobile application or voice commands, ensuring personalized comfort and optimized energy consumption. This paper presents the design, development, and implementation of a Smart Cooler that demonstrates improved performance, energy savings, and smarter user interaction compared to conventional cooling systems.

Abstract: Geodetic receivers are more demanding in cost and operation than low-cost devices, especially smartphone receivers. Low-cost devices usually have lower positioning accuracy and thus limited to Navigation and GIS applications. With the coming of dual frequency (L1/L5) GNSS smartphones, it is important to diagnose whether their accuracy could meet topographic expectations. Xiaomi 12t smartphone is chosen here with the main objective to measure its accuracy in absolute positioning. By so doing, two geodetic pillars were surveyed one after the other with the smartphone in question. The methodology entails 4h of GNSS observation in PPP static mode at both the control points CP1 and CP2 and the raw data logged by the phone was post-processed online at NRCan/CSRS PPP to obtain the phone’s coordinates. The accuracies were evaluated as absolute discrepancies in phone’s coordinates in planimetric dimension and also using the statistical model from CSRS in evaluating the 95% confidence interval. Apart from the east direction at CP1 that showed a severe discrepancy of 7.954m, the average planimetric accuracy observed in the north and up directions at the two control points using both models in error analysis were found to occur within 2m to 3m interval. The general conclusion was that Xiaomi 12t smartphone is not suitable for topometric and geodetic surveys but very suitable for navigation and GIS applications. The following recommendations were proposed; smartphone accuracy analysis in PPP static using a much longer observation time (at least 10h), determination of precise location of smartphone’s Antenna Reference Point, update of Cameroon Geodetic Network to the ITRF20 realization, similar research with two or more smartphones placed in different directions with use of augmentation systems such as SBAS and NTRIP, similar research with post processing done with a software that enables the treatment of L1 and L5 carrier frequencies of the smartphone and also use of a smartphone that captures signals from both the L1 and L2 carrier wave.

Abstract: Hydroponic farming is a widely used method that utilizes nutrient-rich water instead of soil to grow plants. Several studies showed that new technologies have been implemented in hydroponic farming, particularly to improve its irrigation system. Thus, this study incorporated the Node MCU and Arduino Uno module. The system will use the system hardware and the Blynk IoT application, in which real-time readings of the sensors will automatically be displayed on the LED display on the panel and the phone. This research used an experimental design. It utilized the descriptive-comparative design in interpreting the data to determine the differences in the average activation time between the system hardware and the Blynk IoT application. The system has been programmed using the Node MCU and Arduino Uno module, and sensors have been installed to determine the temperature and soil moisture in hydroponics. Four hundred trials have been conducted to test the different functions of hydroponics. The results indicate that in terms of the function in water and AI mode, utilizing Blynk IoT and system hardware vary significantly. The system activates at an average of 7.41 with a standard deviation of 1.40 seconds. The water mode and AI mode operations are at an average of .32 and 1.80 and have a standard deviation of 0.14 and 0.22 seconds, respectively. Furthermore, the p-value is 0.000, less than 0.05, rejecting the null hypothesis.

Abstract: Mining activities on Homonhon Island, Philippines, have increasingly contributed to coastal water pollution and siltation, particularly during extreme weather events. This study investigates the influence of climatological factors—specifically precipitation—on mining-induced siltation using satellite-based remote sensing. Sentinel-2A imagery was employed to monitor and quantify changes in water turbidity and sediment deposition before and after typhoon events. Two spectral indices, the Normalized Difference Water Index (NDWI) and Normalized Difference Turbidity Index (NDTI), were applied to assess the extent of siltation across different time periods. Results revealed a notable increase in siltation areas following typhoons, with the year 2023 showing a surge of approximately 11 million square meters of affected areas, despite receiving only 23.9 mm of rainfall. This indicates that cumulative land degradation from mining operations may have heightened siltation susceptibility even under moderate precipitation. These findings highlight the critical role of satellite-based monitoring in supporting sustainable land use and coastal resource management in mining-affected regions.

Abstract: Paddy is one of the main crops throughout the world. It plays the role of important food crops in most parts of Asia (Chen et. al.). Paddy is prone to a lot of diseases so there is a need of proper monitoring system to monitor the paddy crop during its production. There are three stages of production of paddy i.e.Vegetation, reproductive and ripening. The field of IoT helped a lot in the development of monitoring system to detect the growth as well as diseases of rice crops. This article solely focused on the review or study of different articles which proposed different monitoring and detection system for monitoring of rice crop. To conduct the study different papers have been reviewed and found that there are a lot of devices present to detect the crop production but none of them are 100 percent accurate.

Abstract  Tree smuggling, especially of high-value species like sandalwood and teak, poses a significant threat to biodiversity and forest ecosystems. These trees are highly sought after for their commercial value, making them frequent targets of illegal logging operations. This illicit activity not only depletes valuable natural resources but also contributes to deforestation and environmental degradation. In response to this growing concern, an IoT-based monitoring system has been developed to detect and prevent such activities. At the core of the system is a Node MCU microcontroller, integrated with multiple sensors such as accelerometers to detect tree tilting, fire sensors for identifying potential fire hazards, ultrasonic sensors for motion detection, and pH sensors to monitor soil health. These sensors provide real-time data, accessible via an Android smartphone application, enabling immediate awareness and action. Supporting components like buzzers, water pumps, and relays offer responsive measures such as alarms and fire suppression. This innovative solution aims to safeguard endangered tree species, mitigate illegal activities, and promote ecological sustainability through smart, technology-driven forest management.

Abstract: The global transition toward sustainable energy has accelerated interest in alternative fuels such as biodiesel, valued for its renewability and environmental advantages. This study investigates the performance and emission characteristics of biodiesel in comparison to conventional diesel, emphasizing the application of Artificial Intelligence (AI) for predictive modeling. Machine learning techniques—including Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forests—are utilized to model key engine parameters such as brake thermal efficiency (BTE), brake-specific fuel consumption (BSFC), and emissions including carbon monoxide (CO), and nitrogen oxides (NOx). The findings reveal that while biodiesel blends tend to produce slightly lower power output, they achieve notable reductions in CO and hydrocarbon emissions, with a corresponding increase in NOx levels. The AI models demonstrate strong predictive capabilities across diverse operating conditions, facilitating the optimization of fuel blends and engine settings. This integration of AI into biodiesel research presents a promising pathway for enhancing the environmental and operational performance of internal combustion engines.

Abstract: This study evaluates the effect of corporate report determinants on the quality of corporate reporting by Nigerian deposit money banks (2013-2022). Data was extracted via content analysis of financial statements and related reports from 12 banks listed on the Nigerian Stock Exchange. Using multiple regression analysis (OLS), the study finds that ESG disclosures have the strongest relationship with corporate reporting quality (t-value = 1.670), but their marginal statistical significance (p-value = 0.0985) indicates a weak impact. Other variables, except for the intercept, are not significant predictors. The findings suggest that quality disclosure depends more on the intrinsic content of the information rather than its volume, emphasising the importance of meaningful reporting over mere disclosure quantity.

Abstract: Herbal hair oils have become increasingly popular in the personal care sector due to their natural composition, therapeutic effects, and reduced risk of adverse reactions. This research aims to develop and evaluate a herbal hair oil formulated with plant-derived ingredients known to support hair health. Selected herbs such as Amla (Emblica officinalis), Neem (Azadirachta indica), Brahmi (Bacopa monnieri), and Aloe Vera (Aloe barbadensis) were incorporated for their nourishing, antimicrobial, and scalp-revitalizing properties. The formulation process focused on optimizing the ratios of these herbal extracts within a suitable carrier oil to achieve both product stability and enhanced efficacy. Key physicochemical parameters—including pH, viscosity, and emulsion stability—were measured to ensure formulation quality. In vitro tests were carried out to assess the oil’s ability to minimize hair loss, promote scalp wellness, and improve hair texture. Additionally, sensory evaluation offered valuable feedback on attributes such as fragrance, consistency, and user experience. The outcomes of this study could contribute to advancements in the development of herbal cosmetic products.

Abstract: The study examines the nexus between infrastructure provision and poverty alleviation in rural communities of Akwa Ibom State, Nigeria, focusing on how the availability and ease of use of social infrastructural facilities as well as the extent to which these facilities have enhanced the wellbeing of the rural dwellers. The objective of this study was to idiographically ascertain the influence of infrastructure provision and accessibility on livelihood improvement in rural communities. Using Taro Yamane statistical technique, 400 households in 30 randomly sampled rural communities drawn from 19 out of 31 LGAs of the State were selected for oral interview using structured questionnaire while the secondary data generated from the Akwa Ibom State Statistical Year Book were used to authenticate the quality of data collected from the questionnaire survey and complement the data obtained through direct field observation of available infrastructure in the sampled settlements. The study reveals that considerable increase in poverty incidence in the rural communities is associated with poor and inadequate infrastructure provision which negatively impact living conditions across the rural communities with attendant policy implications. The study highlights the need for government at all levels in the State and Nigeria in general to undertake a strategic and long-term perspective through increase in her budgetary allocations and radical investment in key infrastructural provision in order to improve the living conditions in the rural communities and lessen poverty levels in the communities.

Abstract: Vehicles such as cars and trucks use a lot of energy because of friction when they pass over speed limiters. Nonetheless, it is possible to transform this energy into electrical power. Innovative techniques for utilizing renewable energy have been developed in response to the pressing need for sustainable energy solutions. As a novel method of producing electricity from vehicle motion, this project explores the design and construction of a mechanical speed breaker in the shape of a dome. In four distinct load scenarios—5 kg, 7.5 kg, 10 kg, and 15 kg—we contrasted the experimental findings with theoretical projections. When a 15 kg weight was applied, the maximum power produced was 0.335W. We have included a chain sprocket for effective energy transfer in order to enable power generation with lighter vehicles going over the speed breaker.

Abstract: This article presents an investigation into the exact solution of the damped forced Burger equation with constant coefficients, utilizing a homogenization technique. By introducing a suitable change of dependent variable, the partial differential equation is transformed into a homogeneous equation of a specific degree. Subsequently, shock waves are computed employing a perturbation-like scheme that leverages linear and nonlinear operators in a finite number of steps. The study further illustrates the behavior of 2-shock and 3-shock waves under different forcing terms, providing valuable insights into the dynamics of the system. This approach enables a deeper understanding of the complex interactions between nonlinearity, damping, and external forcing in the Burger equation.

Abstract: Sugarcane holds importance as a cash crop with the sugar industry playing a crucial role, in various economies globally. It is essential to manage the supply chain to ensure a flow of sugarcane from farms to sugar mills and ultimately to consumers. This review paper explores key challenges to manage sugarcane supply chain in the field of seasonal variability, quality control, procurement and pricing, logistics and transportation, sustainability and environmental concerns, supply chain coordination and price fluctuations faced by the sugarcane supply chain Management, highlighting the complexities and obstacles that impact its efficiency and sustainability. The examination covers challenges related to sourcing, transportation, processing and distribution, within the supply chain. Furthermore, it explores solutions and strategies aimed at tackling these obstacles to enhance management of the sugarcane supply chain.

Abstract: High Hydrostatic Pressure (HHP) processing is a promising non-thermal food preservation technique that enhances microbial safety and shelf-life while maintaining the nutritional and sensory quality of products. In the dairy industry, HHP significantly influences the structure and functionality of milk proteins. The application of high pressure (typically 100–600 MPa) induces conformational changes, affecting protein solubility, denaturation, and aggregation. Caseins remain relatively stable under pressure, whereas whey proteins like β-lactoglobulin and α-lactalbumin undergo notable structural alterations, which can modify their functional and nutritional properties. These changes may lead to improved digestibility, emulsification, foaming, and even reduced allergenicity. HHP processing thus offers potential advantages in developing value-added and hypoallergenic dairy products. However, optimal pressure conditions must be carefully tailored to preserve desired qualities while minimizing negative impacts. This review explores the mechanisms and implications of HHP on milk proteins, highlighting its relevance in modern dairy processing.

Abstract: Catalysts nanoparticle sulfated titania aerogel have been prepared through one-step synthesis by the sol-gel method using sulfuric acid as catalyst followed by the one-step CO2 supercritical extraction.  The catalysts were tested in reaction of oleic acid with glycerol to produce glycerol mono oleate (GMO). Thermal evolution of the gels was evaluated by TGA-DTA, N2 adsorption, TEM and XRD, and the IR absorption spectra measurements were made to discuss the structure of sulfated titania.  The anatase phase is stable after calcination at temperatures up to 700oC, and the specific surface area, total pore volume and average pore diameter of anatase phase do not change significantly after calcination at 600oC.  Thermally stable and highly acidic sulfated titania aerogel is attractive as catalyst.  The aerogels calcined at 500, 700 and 750oC have similar activities for synthesis of glycerol mono oleate surfactant.  However, the activity of the aerogel calcined at 600oC is low. At 500oC, a relatively large amount of sulfate remains, and the aerogel has high activity.

Abstract: The search for natural bioactive compounds that can serve as alternatives to synthetic preservatives has also double-paced. This study investigates the preservative efficacy of Syzygium guineense leaf extracts on the proximate and mineral composition of Tomato fruits. The proximate and mineral analyses of tomato fruits preserved by S. guineense leaf extracts were determined. A significant decrease in the Potassium, Magnesium and Sodium contents of the preserved tomatoes at 40C were also observed, while the percentage of Calcium increased. Preserved tomato shows a lower considerable loss in the Moisture (from 90.99 to 87.86%), Crude fibre (from 0.39 to 0.32) and Carbohydrate contents (from 3.81 to 2.55). Preservative study revealed that ethanolic extract of S. guineense extends the shelf of tomato at 250C and 40C beyond 14 and 21days respectively while the control samples showed a rapid deterioration in texture, colour and weight within 7 and 16days respectively. There was significant difference in both the proximate and mineral composition of the treated and control tomato samples. Hence, the crude or fractionated components of S. guineense leaf extracts may serve as alternative preservative agents to synthetic preservatives in extending the shelf life of tomato fruits.

Abstract: As cloud and edge computing evolve into foundational elements of national digital infrastructure, security concerns remain at the forefront, particularly in emerging markets like Cameroon. This study examines the security challenges within Camtel’s cloud-edge ecosystem, identifying critical vulnerabilities, evaluating mitigation strategies, and proposing a multi-layered security framework. We integrate AI-enhanced Intrusion Detection Systems (AI-IDS), Trusted Execution Environments (TEEs), and Blockchain-based audit logging to strengthen authentication, data integrity, and threat detection. Through STRIDE-based threat modeling and experimental validation, we assess security-performance trade-offs, quantifying latency, throughput, and power consumption impacts across various configurations. Results indicate that while TEEs and AI-IDS significantly enhance system resilience, they introduce resource overhead that requires careful optimization. The study provides strategic recommendations for secure cloud-edge adoption, contributing to Cameroon’s national cybersecurity framework while offering insights applicable to broader edge computing deployments. The study also highlights current threats, evaluates Camtel’s evolving infrastructure, and proposes actionable security frameworks to ensure resilience, data sovereignty, and trust in public services.

Abstract: Unprecedented difficulties brought on by the COVID-19 pandemic have accelerated the pace of digital disruption in a number of industries, requiring organizations to swiftly adjust and come up with fresh, creative solutions. This study looks at how resilience-building in post-pandemic businesses is impacted by organizational readiness for digital transformation, leadership adaptability, and strategic agility. 150 respondents from a variety of industries participated in a quantitative survey, and data was analyzed using multiple regression, partial least squares structural equation modeling (PLS-SEM), correlation analysis, and reliability tests. Research shows that organizational resilience is significantly and favorably impacted by strategic agility, leadership adaptability, and digital readiness. This highlights the critical role that flexible strategies and proactive leadership play in preserving competitive advantage in the face of ongoing digital disruption.

Abstract: Karsts are the result of complex interactions between geology, climate, hydrology, and biological factors over long periods of time. These landscapes comprise a variety of surface and subterranean landforms formed primarily through the dissolution of soluble rocks, especially limestone. The dissolution process creates unique surface features and intricate networks of underground cavities, while the deposition of calcium carbonate (CaCO₃) forms secondary mineral deposits known as speleothems. Sri Lanka is home to several limestone caves, with the Waulpane limestone cave in the Ratnapura District being one of the most significant. This study focuses on identifying the diversity and distribution of micro karst landforms within the Waulpane cave. Both primary and secondary sources were used in the study; primary sources included direct observations, interviews, and sample collections, while secondary sources, such as literary materials, provided additional context and information. Research findings revealed a variety of speleothems such as stalactites, stalagmites, drapery-like formations, and karren features present on the walls, ceiling, and floor of the cave. These formations vary widely in size and morphology. Some stalactites resemble bulbs, cones, leaves, and pillars, while others take shapes akin to pencils, wall lamps, arches, hourglasses, and flower petals. Most of these features are concentrated in the central part of the cave, where environmental conditions are most favourable for their development. The formation of these features is influenced by several interrelated factors, including the calcium carbonate-rich spring water, local climate, topography, vegetation cover, hydrological patterns, and the spatial distribution of limestone.

Abstract: This research aims to determine the effect and the appropriate dosage of MKP organic fertilizer on the growth and production of red onion plants (Allium ascalonicium L). The research was conducted from November 2023 to January 2024 in Huta I Sirube-rube, Dolok Pardamean District, Simalungun Regency, at an altitude of 1400 m above sea level. The research used a non-factorial Completely Randomized Design (CRD). The factor of MKP fertilizer application consists of 5 treatment levels, namely: M0=Without treatment (control) M1=100Kg/ha, (20g/plot) M2=150Kg/ha, (30g/plot) M3=200Kg/ha, (40g/plot) and M4=250Kg/ha, (50g/plot). The parameters observed in this study include: Plant Height (cm), Number of Leaves (strands), Fresh Tubers Weight per Plant (g), Fresh Tubers Weight per Plot (Kg), and Dry Tubers Weight per Plot (Kg). The observation results were analyzed using analysis of variance at a 5% confidence level. The research results show that the application of MKP organic fertilizer has a significant effect on plant height at 2 and 6 MST, fresh tuber weight per plant, fresh tuber weight per plot, and dry tuber weight per plot, but has no significant effect on plant height at 4 MST and the number of leaves. The treatment of MKP organic fertilizer M4 250 kg/ha (50 g/plot) showed the highest plant heights at 2, 4, and 6 MST, which were 10.75 cm, 20.25 cm, and 32.50 cm, respectively. It also resulted in the highest number of leaves at 2, 4, and 6 MST, which were 16.67 leaves, 22.67 leaves, and 28.75 leaves, respectively. The heaviest fresh tuber weight per plant was 77.89 g, the heaviest fresh tuber weight per plot was 2.69 kg, and the heaviest dry tuber weight per plot was 2.50 kg.

Abstract: The present study is on “Study Involvement of Senior Secondary School Students of Sikkim”. Quantitative research method was employed. A sample of 400 senior secondary school students was selected from East Sikkim. Out of 400 students, 200 students were selected from government schools and 200 students from private schools in which 100 male and 100 female from both the institutions. The sample was collected by way of Random Sampling Techniques. The findings of the study suggested that mean value of arts, science and commerce do not coincide, which indicate the fact that the commerce students study involvement is slightly better as compare to arts and science. It was also found that mean value of General, OBC, ST and SC students do not coincide, which indicate the fact that the ST students study involvement is slightly better as compare to General, OBC and SC students.

Abstract: Infertility, a global health concern affecting millions, is progressively connected to oxidative stress, an imbalance between the production of reactive oxygen species (ROS) and the susceptibility of the antioxidant defense system. Male is solely responsible for about 20% and a causative factor in about 30% to 40% of cases. But women decline steadily with age, especially at aged 35 years or older after 6 months of unprotected sex. Antioxidant enzymes, like catalase (CAT), superoxide dismutase (SOD), peroxiredoxins (PRXs), glutathione peroxidase (GPx), glutathione reductase (GR), and the four enzymes of the ascorbate–glutathione pathway, which  symbolizes an essential endogenous defense mechanism against ROS-induced cellular impairment in the reproductive system of both males and females. This review scrutinized current studies by explaining the crucial role of these enzymes in the pathogenesis of infertility. In males, compromised antioxidant enzyme activity in seminal plasma and spermatozoa contributes to elevated oxidative stress, leading to dysfunctional sperm motility, viability, DNA fragmentation, and finally diminished fertilization possibilities. Likewise, in females, dysregulation of antioxidant enzyme levels in follicular fluid and the reproductive tract can negatively affect the quality of oocytes, endometrial receptivity, and contribute to conditions like polycystic ovary syndrome (PCOS) and endometriosis, which are associated with infertility. Conversely, it is recommended that interventions aimed at improving antioxidant enzyme activity, either by direct supplementation of enzymatic cofactors or indirectly through wide-range of antioxidant therapies and lifestyle modifications, may give opportunities for improving reproductive resolution in both sexes. Finally, this review particularly inform the critical role of antioxidant enzymes in maintaining redox homeostasis within the reproductive system and evaluate their potential as therapeutic targets in solving infertility issues.

Abstract: The advent of quantum computing poses significant threats to classical cryptographic systems, notably RSA encryption [1]. This paper explores the simulation of quantum attacks on RSA using Qiskit, focusing on the implementation of Shor's algorithm [1], [2]. Through detailed case studies, circuit analyses, and performance evaluations, the feasibility and implications of quantum-based RSA attacks are demonstrated. The study underscores the urgency for transitioning to quantum-resistant cryptographic schemes [7].

Abstract: This paper explores the evolving maturity and dynamism of welding and fabrication in modern industry, emphasizing the critical role of quality management and standardization in welded construction. As a primary method for joining materials, welding requires strict adherence to international standards to ensure safety and performance. These standards vary globally, necessitating the expertise of a Responsible Welding Coordinator to ensure compliance, particularly in high-responsibility projects. The proposed Integrated Weld Quality Assurance (IWQA) framework incorporates standardized audits, compliance assessments, and proactive implementation strategies to enhance operational efficiency, minimize defects, and support cost-effective production. The model also recommends the appointment of a Welding Health and Safety Lead to ensure the integration of Quality (ISO 9001), Occupational Health and Safety (ISO 45001), Environmental (ISO 14001), and Weld Quality Management Systems (ISO 3834). Aligned with the human-centric vision of Industry 5.0, the IWQA framework leverages emerging technologies such as Artificial Intelligence (AI), Internet of Things (IoT), and augmented reality for welder training, enabling real-time quality monitoring and continuous improvement. Furthermore, the framework embeds circular economy principles, emphasizing energy efficiency, eco-friendly materials, and waste reduction to promote long-term sustainability. This integrated approach not only supports environmental responsibility but also strengthens workplace safety, operational reliability, and global compliance. Overall, this research presents a scalable, standards-aligned model that supports high-quality, sustainable, and socially responsible welding operations, contributing meaningfully to the future of smart manufacturing under Industry 5.0

Abstract: This study explores the relationship between librarians’ knowledge of library materials preservation and their compliance with preservation standards. Utilizing a descriptive-correlational research design, the study employed validated questionnaires to gather quantitative data from 109 professional librarians in Cagayan de Oro City, Philippines. The findings revealed a high level of knowledge among librarians about preservation practices, with a mean score of 4.18. Correspondingly, a high level of compliance with preservation standards was observed, with a mean score of 4.23. A significant relationship between knowledge and compliance was established using Pearson correlation analysis. The results highlight the importance of professional development in enhancing preservation practices and ensuring adherence to standards. This research underscores the need for continued education and training to maintain the integrity and accessibility of library collections.

Abstract: Soil stabilization is an essential process in improving the engineering behavior of sub grade soil for construction activities. This study investigates the potential of using glass powder, an industrial waste product, as a sustainable soil stabilizer. Various percentages of glass powder (2%, 4%, 6%, 8%, and 10%) were added to the native soil collected from a site. Laboratory tests including Atterberg’s limits, Standard Proctor test, and California Bearing Ratio (CBR) were performed to evaluate the impact of glass powder in addition on soil properties. The results demonstrate significant improvement in CBR values and density, indicating enhanced strength and suitability for use in subgrade applications. The study concludes that glass powder can be effectively utilized in soil stabilization, promoting both engineering performance and environmental sustainability.

Abstract: This paper analyzes the internal factors that determine the survival and success of Small and Medium Enterprises (SMEs) in Mozambique, between 2005-2023. It focuses on strategic cost management, environmental adaptability, deep knowledge implementation, organizational sustainability, and results-based management. Given the significant role of SMEs in Mozambique’s economy - accounting for substantial employment and innovation despite facing financing constraints, infrastructure deficiencies, and a complex regulatory environment - this study aims to evaluate how internal management practices impact their sustainability. The methodology comprises a systematic literature review and empirical data analysis based on surveys and business reports collected between 2005 and 2023. Statistical validation techniques ensure the robustness of findings. The results provide valuable insights for policymakers and business leaders seeking to enhance SMEs’ competitiveness and long-term sustainability.

Abstract: Recent satellite imagery analysis in the Eastern Leyte province indicates significant growth in mangrove forests following the destruction of approximately 86% of these forests during Typhoon Yolanda (Haiyan) in 2013. However, there are no studies that explore the process of mangrove expansion in the region and the factors influencing its growth. This study aims to investigate and analyze the climatological and anthropogenic factors contributing to the expansion of mangrove forests in the area. Using the mangrove index derived from Sentinel-2A satellite imagery, a percentile-based threshold segmentation was applied to classify and identify mangrove extent. Annual mangrove area data from 2017 to 2023 were calculated and correlated with climatological variables and anthropogenic factors. The result of the analysis shows a strong positive correlation between mangrove coverage and sea level with a variance of r2=0.90 and α<0.001 which is statistically significant. The relationship of the population to the mangrove coverage shows the model has a variance of r2=0.64 and α=0.030. In contrast, the rainfall shows a very weak and has no significant relationship with the mangrove coverage with a variance of r2= 0.022 and α=0.05. The findings reveal the significant role of sea level and population in the mangrove expansion. This study provides significant insights into accurate rehabilitation plans and strategies for mangrove preservation and conservation in the province.

Abstract: The electric vehicle (EV) market in India has seen rapid growth, with major players like Tata Motors and MG Motors competing to capture the evolving consumer base. This study explores how brand positioning strategies influence customer perception in the Indian EV industry. Through primary data collected via surveys and secondary data from industry reports, this comparative study analyzes the branding dimensions that shape consumer trust, preference, and purchase decisions. The findings indicate that while Tata Motors leverages its domestic brand value and affordability, MG Motors emphasizes technological innovation and a premium experience. The study offers insights for marketers to align branding strategies with customer expectations in the growing EV segment.

Abstract: This case study from Parul University explores AI and data science applications in FinTech. Using a mixed-methods approach, we analyse real-world implementations in fraud detection, credit scoring, and customer engagement. Results show a 25% improvement in credit scoring accuracy, 40% faster fraud detection, and 30% higher customer satisfaction. The study demonstrates how multidisciplinary approaches enhance operational efficiency and financial inclusion while underscoring the need for ethical frameworks and institutional support. Findings offer a strategic blueprint for educational and industrial adoption.

Abstract: This case study from Parul University explores AI and data science applications in FinTech. Using a mixed-methods approach, we analyse real-world implementations in fraud detection, credit scoring, and customer engagement. Results show a 25% improvement in credit scoring accuracy, 40% faster fraud detection, and 30% higher customer satisfaction. The study demonstrates how multidisciplinary approaches enhance operational efficiency and financial inclusion while underscoring the need for ethical frameworks and institutional support. Findings offer a strategic blueprint for educational and industrial adoption.

Abstract: The application of queuing models in university libraries is essential for enhancing service efficiency and improving overall user experience. These models help manage user flow, minimize waiting times, and optimize resource allocation by analyzing key factors such as arrival rates and service durations. Specifically, this study applied the M/M/1 queuing model, which assumes a single-server system with exponentially distributed inter-arrival and service times. The model was used to determine waiting times, queue lengths, and resource utilization in university library facilities. The analysis was conducted using QM (Quality Management) software for Windows. Deterministic numerical results were compared with software-made outputs to validate the model. Additionally, the study included graphical representations of probabilities and cumulative probabilities against the number of users in the system. Various plots were created to analyze utilization factors between the fixed parameters μ₁ and μ₂ under varied arrival rates (λ). A regression equation was developed to examine the relationship between waiting time in the queue and utilization factors. Finally, the correlation between these factors was established to aid decision-making. By analyzing these parameters, library administrators can make informed decisions about staffing and service strategies to better meet user demand. In conclusion, implementing this mathematical queuing model significantly improves operational efficiency, user satisfaction, and the effective use of resources in university libraries.

Abstract: This study investigates the development of a personalized multimedia learning system designed to overcome the limitations of traditional content delivery methods, which often utilize a generic, one-size-fits-all approach. By tailoring educational materials to align with user preferences, such as content format and information density, as well as learning styles—like visual, auditory, and kinesthetic—this research seeks to enhance user engagement and improve knowledge retention. Recent studies by Feng and Yang (2023) and López-Morales and Rosado-Muñoz (2023) suggest that personalized approaches significantly increase user satisfaction and retention. Utilizing advancements in learning management systems (LMS) and user behavior analytics, this research gathers user preferences and learning style data to facilitate dynamic content adaptation. This customization promotes deeper engagement and highlights the importance of accessibility, inclusivity, and ethical considerations surrounding user data management (Papadopoulos & Tsoukalas, 2022; Hwang & Chang, 2021). The findings provide actionable insights for educators and content creators, advocating for the responsible development of multimedia platforms that empower users and optimize their learning experiences.

Abstract: Davao City, one of the leading urban centers in the Philippines, has positioned itself as a pioneer in integrating sustainability into local tourism. This study evaluates the eco-friendly initiatives undertaken by the city and assesses their impact on tourism, local communities, and environmental management. By utilizing a mixed-methods approach combining surveys, interviews, and field observations, this research explores the implementation, effectiveness, and challenges of programs such as ecological waste management, community-based tourism, and sustainable accommodations. The findings reveal a high level of awareness among tourists and a strong commitment among stakeholders, yet point to persistent challenges in infrastructure, policy enforcement, and community engagement. This paper concludes with recommendations to reinforce eco-tourism strategies in Davao City and offers insights for other urban destinations in the Global South.

Abstract: Speech Emotion Recognition (SER) is an essential component of affective computing and human-computer interaction, yet its deployment in acoustically adverse environments remains challenging. This paper critically examines the transition of SER systems from controlled laboratory settings to real-world scenarios characterized by diverse noise profiles—such as urban public spaces, vehicular interiors, and call center infrastructures. Key challenges addressed include acoustic feature degradation under noise, the domain shift problem, variability in language and speaker characteristics, and the demand for low-latency processing. We analyze context-specific constraints across multiple applications, including social robotics, driver assistance systems, and contact center analytics. Performance evaluation is discussed through a multi-metric lens, incorporating recognition accuracy (e.g., UAR, WER) and intelligibility scores (e.g., STOI, PESQ). A comparative synthesis of existing models—ranging from traditional classifiers to deep learning architectures such as CNNs, LSTMs, and Transformers—is presented. Particular attention is paid to methods enhancing robustness via data augmentation, speech enhancement, and domain adaptation. Drawing upon literature from 2020 to 2025 and foundational studies, this work offers a structured review and identifies future directions for resilient and scalable SER system design.

Abstract: Effective management of energy and water resources is crucial for achieving sustainable development. This report examines the difficulties encountered by utility organizations in Sri Lanka, specifically the National Water Supply and Drainage Board and the Ceylon Electricity Board. These institutions initially adopted analog meters, which result in elevated operational costs due to their reliance on monthly readings and billing cycles. In response, the Ceylon Electricity Board is shifting towards digital metering, while the National Water Supply and Drainage Board is beginning to implement digital solutions aimed at enhancing efficiency. The primary objectives include lowering peak power generation costs, decreasing consumption levels, and fostering improved consumer engagement. A proposal has been put forth for an IoT-based embedded device system designed to facilitate data collection and exchange, enabling two-way communication for monitoring and control from base stations. The proposed system encompasses hardware components, cloud architecture, and features that enable real-time data access, thereby offering a comprehensive overview of smart metering solutions necessary for optimal resource management in Sri Lanka. Consumers will have access to both a mobile app and a web application that provide real-time insights into their usage patterns, tariffs, reports, and overall consumption trends while easing some of the administrative burdens faced by governing institutions. Equally, these institutions will utilize a web application to oversee the entire system.

Abstract: Abiotic stress factors—including drought, salinity, extreme temperatures, and heavy metal exposure—pose serious threats to global agricultural productivity and food security. In response, plants have developed complex physiological and molecular systems to detect and counteract these environmental challenges. Key components include dynamic signaling pathways, efficient reactive oxygen species (ROS) detoxification mechanisms, regulation of gene expression by specialized transcription factors, and accumulation of osmoprotectants to maintain cellular balance. Breakthroughs in omics-based technologies and precise gene-editing platforms such as CRISPR/Cas have accelerated the identification and functional analysis of genes linked to stress resistance. Moreover, emerging insights into epigenetic modulation and stress memory suggest additional layers of adaptive regulation. This review consolidates recent findings on the molecular frameworks of abiotic stress resilience in plants and evaluates current biotechnological strategies to enhance crop tolerance. We also highlight future directions that integrate synthetic biology, nanotechnology, and systems-level approaches to address agricultural challenges under climate variability.

Abstract: This literature review examines the role of digital transformation in accelerating the achievement of the United Nations Sustainable Development Goals (SDGs) from 2015 to 2025. By synthesizing scholarly research, policy documents, and case-based evidence, the review explores how emerging technologies—such as artificial intelligence, big data, blockchain, and the Internet of Things—have contributed to progress across key sectors including health, education, governance, economic inclusion, innovation, and climate action. The analysis highlights how digital innovation has enhanced service delivery, improved efficiency and transparency, and created new pathways for inclusive growth and environmental sustainability. However, it also identifies persistent challenges, including unequal access to digital infrastructure, regulatory and institutional gaps, and ethical concerns surrounding data governance and digital equity. The findings offer actionable insights for academics, policymakers, and development practitioners by outlining both the opportunities and constraints of digital transformation in diverse contexts. The review concludes with recommendations for leveraging digital innovations, enhancing inclusive digital ecosystems, strengthening cross-sector collaboration, and aligning technology-driven initiatives with sustainable development priorities, ensuring that digital progress contributes meaningfully to leaving no one behind in the digital age.

Abstract: The Patternized Secret Knock Detecting Door Lock is an advanced security solution that combines simplicity and innovation by using acoustic signal recognition for access control. This system captures vibrations from knocks using a piezoelectric sensor, processes them through a microcontroller, and compares the detected pattern with a pre-programmed secret knock. Only when the knock sequence matches is the lock mechanism activated, ensuring secure and personalized access. The design is user-friendly, allowing customization of knock patterns and providing an alternative to traditional locks or keypads. It prioritizes energy efficiency, ease of installation, and resistance to tampering or false triggers. By eliminating the need for physical keys or cards, this solution minimizes the risk of unauthorized duplication and offers a discreet, reliable locking mechanism suitable for homes, offices, and restricted areas. The work highlights the practical application of embedded systems and signal processing in modern access control technologies.

Abstract: Several mango varieties, including local ones, were collected from the different vendors in the Parbhani fruit market and studied to measure the incidence of spongy tissue disorder. Incidence was measured using a disease incidence formula. Among these, Alphonso, Badam, Kesar, Parbhani Hapus, and Local mango fruits showed spongy incidence. It was observed that the incidence of spongy tissue in different varieties sampled was contrasting. The highest incidence was found in Alphonso fruits, whereas the lowest was found in Kesar fruits.

Abstract: Mobile Ad Hoc Networks (MANETs) represent a critical advancement in wireless communication, enabling dynamic, infrastructure-less networking suitable for various applications, including military, disaster recovery, and remote sensing. However, their decentralized architecture, dynamic topology, and constrained resources expose them to a wide range of security challenges. This research paper presents a comprehensive evaluation of existing security mechanisms designed to protect MANETs, encompassing cryptographic protocols, intrusion detection systems, secure routing techniques, and trust-based frameworks. By systematically analyzing their effectiveness, limitations, and adaptability, the study highlights gaps in current approaches. Additionally, this work identifies and characterizes MANET-specific security threats, including wormhole, black hole, sybil, and denial-of-service attacks. Through this dual-focused investigation, the paper not only maps the current landscape of MANET security solutions but also provides a structured threat taxonomy, offering insights into the evolving threat model. The findings aim to guide the development of more robust, adaptive, and context-aware security frameworks tailored for MANET environments.

Abstract: Multi-agent artificial intelligence systems (MAS) is transforming the healthcare sector by enabling intelligent decision-making, robotic interventions, and critical care management. Despite growing interest, a comprehensive synthesis of their real-world applications, performance, and limitations is lacking. This systematic review aims to evaluate the deployment of MAS in healthcare across three key domains: Clinical Decision Support Systems (CDSS), Robotic Interventions, and Critical Care Monitoring. It also addresses ethical and legal considerations, offers a comparative framework, and identifies research gaps. A systematic search of PubMed, IEEE Xplore, Scopus, and Web of Science was conducted following PRISMA guidelines. A total of 150 records were retrieved. After duplicate removal and screening, 32 studies met inclusion criteria. The Joanna Briggs Institute (JBI) Critical Appraisal Checklist guided quality assessment. Data were extracted using a structured form and organized by topic. Among the 32 studies included, 12 focused on MAS-based CDSS, 11 on robotic interventions, and 9 on MAS in critical care. Most MAS demonstrated improved diagnostic accuracy, aided real-time decision support, and enhanced patient monitoring. However, over 60% involved practical models and lacked clinical validation. Ethical concerns, particularly around autonomy, data privacy, transparency, and legal accountability were sometimes overlooked. Only 7 studies discussed ethical or legal implications in depth. A comparative framework was developed, comparing MAS tools based on scalability, clinical validation, autonomy, human-AI interaction, and data transparency. No doubt, MAS hold transformative potential across healthcare domains. However, real-world deployment remains unclear. Greater emphasis on clinical validation, ethical design, and regulatory frameworks is needed. This review provides suggestions for future research and responsible adoption of MAS in healthcare.

Abstract Purpose The research aimed to determine the impact of training programs on staff performance and institutional effectiveness in a challenging healthcare environment. Methods A quantitative, explanatory research design was employed. Questionnaires were distributed to 245 randomly selected permanent staff members from a larger population of 630 employees. Statistical analysis (using SPSS) was used to assess the relationship between training and performance through descriptive statistics and multiple regression analysis. Findings The study found a significant positive correlation between training and employee performance. Training programs, particularly those focused on content delivery, facilitation, and evaluation, have a positive influence on key performance indicators such as work quality, initiative, time management, and teamwork. Well-structured training preparation also improved employee readiness and engagement, leading to better overall performance outcomes. Originality The study provides empirical data from a Ghanaian public hospital, contributing to the limited existing literature on training and performance in African healthcare settings. The findings highlight the importance of aligning training initiatives with institutional goals to improve healthcare delivery through human capital development, offering practical implications for hospital administrators, HR professionals, and policymakers.

Abstract: To develop an efficient operations organizing optimization of internet servers that will certainly bring great concessions to satisfy the operations job hold-up prerequisite and also efficiency demand. Techniques: This research provides a delay aware as well as performance-efficient power optimization (DAPEEO) strategy for operations implementation in a diverse atmosphere (i.e., edge-cloud setting). This method offers an operations implementation version which satisfies the application hold-up requirement together with efficiency need. Searching for: Our design has actually been developed to lower the power usage, rise throughput, lower computational price together with computational time to give a delay-aware along with performance-efficient operations design for the internet servers in crossbreed cloud computer. Our design Delay Aware together with Performance Efficient Workflow Scheduling of Web Servers in Hybrid Cloud Computing Environment (DAPEEO) has actually minimized the power intake by 4.217%, raised the throughput by 19.51%, as well as minimize the computational price by 62.38% when contrasted with the existing”” Deadline-Constrained Cost Optimization for Hybrid Clouds (DCOH)”” designs. Even more, the typical power intake revealed a decrease of 40.993% along with 90.384% when contrasted with the DCOH along with Self- Configuring and also Self-Healing of Cloud-based Resources (RADAR) work version specifically. Experiment end result reveals the DAPEEO strategy accomplishes much superior power effectiveness, throughput along with computation set you back decrease when contrasted with the existing operations implementation design. Uniqueness: Existing version fell short to stabilize decreasing expense, as well as conference operations implementation target dates under a varied atmosphere. Beyond, the DAPEEO works in bringing trade-offs in minimizing power dissipation and also conference job target dates with minimized expense under the edge-cloud computer design.

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Abstract: This study investigates the impact of job security on unethical pro-organizational behavior in selected commercial banks within the Abuja metropolis, Nigeria, with a focus on the mediating role of obsessive work passion. Drawing upon Social Exchange Theory and Self-Determination Theory, the research aims to understand how the perception of job security affects employees' passion for their work, which subsequently influences their ethical decision-making processes. Utilizing an explanatory research design, this study adopted a quantitative methodology to collect data via a cross-sectional descriptive survey. The sample comprised 354 employees from five selected banks in Abuja. To test the research hypotheses, Structural Equation Modeling Partial Least Squares (PLS) was employed using SMART PLS 4.1 software. The results indicate a significant negative relationship between job security and unethical pro-organizational behavior, suggesting that higher levels of perceived job security are associated with lower incidences of unethical actions intended to benefit the organization. Conversely, the study found no significant relationship between job security and obsessive work passion, implying that job security does not necessarily drive an unhealthy fixation on work. However, a significant correlation was identified between obsessive work passion and unethical pro-organizational behavior, indicating that a strong, albeit unhealthy, attachment to work can lead to unethical practices aimed at benefiting the organization. Additionally, it was revealed that obsessive work passion does not mediate the relationship between job security and unethical pro-organizational behavior. These findings underscore the vital role job security plays in shaping employees' attitudes and behaviors at work, highlighting the importance of fostering a secure work environment to promote ethical conduct and positive work passions.

Abstract: Businesses dedicate extensive resources to social media platforms because they serve as popular advertising and marketing platforms. Nevertheless, it is never simple for companies to create social media advertising that draws customers and persuades them to purchase their goods. This paper is an attempt to critically review the body of related knowledge and examine how social media advertisements affect user(s) attitudes and intentions to purchase. This study seeks to pinpoint crucial elements influencing user(s) inclination to make future purchases, by exploring the connection between exposure to social media advertisements and purchase intentions. This review analyses studies published between 2013 and 2023 that investigate the relationship between social media advertisement effects on user attitudes and purchasing intentions. Through synthesizing diverse research findings, this review paper underscores key insights and outlines potential avenues for future research in this domain. The data for this review were sourced from the Scopus database. The research demonstrates that social media advertising has positive impacts on user attitudes and purchasing intentions. A well-executed advertising campaign on social media platforms, and engaging content, can effectively stimulate the attitudes and purchase intentions of users. This review investigates multiple factors that modify how social media advertising influences consumer attitudes and purchase intentions. Factors such as the credibility, informativeness, and entertainment of social media advertisements, and advertisement value all play significant roles in shaping user(s) attitudes and purchase intentions.

Abstract: The occurrence and distribution of soils in nature varies from location to location. The type of soil depends on the rock type, its mineral constituents and the climatic regime of the area. Soils are used as construction materials or the civil engineering structures are founded in or on the surface of the earth. Geotechnical properties of soils influence the stability of civil engineering structures. As soil is the ultimate load bearer so it’s stability is a necessity and hence many efforts have been done for its stabilization. The performance of any structure depends upon the behavior of underlying soil mass and on the bearing capacity of soil. Expansive soil or clay is considered to be one of the more problematic soils and it causes damage to various civil engineering structures because of its swelling and shrinking potential when it comes into contact with water. Expansive soils behave differently from other normal soils due to their tendency to swell and shrink. In a well-organized environment, disposal of waste poses a great threat as regards where and how to effectively dispose the waste material without any harmful effect to society. In the recent times, utilization of solid waste materials in soil stabilization has gained eminence as an effective means to manage wastes generated from various industries.  In this paper, a review is given on utilization of different solid waste materials which have  been used to stabilize soft soils. Though, there are lots of methods and techniques are available to stabilize these soil. This study provides how waste materials can be used to stabilize the soft soil using alkali activation technology.

Abstract: Lottery is one of the most popular forms of gambling which offers gamblers opportunity to win large cash prize for a relatively low cost. Research on lottery gambling could examine the effect of mediators in the relationships between independent variables and gambling behaviour. We consider the case of premier lotto and selected six (6) different players at random over a number of years. The methods used in these researches are Markov Chain and Stationarity of the state process. For every player in the system, we obtain the long run probability or likelihood of winning if every player continues playing indefinitely. We obtain the inter state transition probabilities indicating the transition of each player from one year to other.

Abstract: The demand for green and sustainable energy has grown significantly in recent years. Traditional energy sources, such as fossil fuels and nuclear power, pose serious environmental risks, driving the adoption of renewable alternatives. [1] While solar energy has seen substantial advancements, other technologies—including piezoelectric, radio frequency (RF) harvesting, and gyroscopic energy harvesting—remain underdeveloped. Among these, RF energy harvesting holds immense potential to revolutionize low-power applications, particularly for Internet of Things (IoT) devices such as sensors, wearables, smartphones, and unmanned aerial vehicles (UAVs). However, current RF harvesters suffer from inefficiencies in energy synthesis, resulting in low voltage output and limited practical use. For instance, Powercast’s state-of-the-art solution achieves 75% efficiency but only within a 24-meter range. [2] To address these challenges, this paper introduces novel filter bank architecture for RF energy harvesting, leveraging microstrip, passive, and active filters to achieve wider bandwidth and improved efficiency. Filters play a critical role in signal processing, and while modern digital filters excel in reconfigurability, analog filters remain essential in RF harvesting to mitigate noise and enhance voltage stability. Our proposed design integrates four specialized filters, rigorously tested through MATLAB and AWR simulations. The results demonstrate significant improvements in energy synthesis, paving the way for self-powering devices that could eventually eliminate the need for traditional chargers and charging ports. [2] [3]

Abstract: This Paper Outlines Malaysia's Approach To Evaluating Public Infrastructure Projects, With A Particular Focus On The Housing Sector. The Most Significant Difficulty In Assessing Affordable Housing Is Measuring Social Benefits Or Drawbacks, Which Are Often Considered 'Intangible' And Do Not Have A Market Value. Much Of The Cultural, Social, And Environmental Impacts Are Sometimes Overlooked. Three Central Aspects Guide The Author In Formulating Its Main Research Question: What Are The Primary Project Appraisal Strategies For Public And Social Housing Practised In Malaysia, And How Can They Be Improved. This Paper Proposes A Framework For The Cost-Benefit Analysis Of Public Housing In Malaysia That Can Be Enhanced By Strengthening The Program's Non-Market Impact Evaluation In Social Housing Initiatives.

Abstract: This research addresses the critical gap in integrating AI-driven data systems—specifically Machine Learning (ML), Natural Language Processing (NLP), Internet of Things (IoT), blockchain, streaming, and security—for enhanced educational applications. Current implementations of these technologies operate in silos, lacking a cohesive strategy to unify their capabilities. We propose a novel integrated framework that bridges these domains, enabling synergistic data management, personalized learning, and administrative efficiency. Through a mixed-methods approach combining qualitative case studies and quantitative performance metrics, we demonstrate that this framework significantly improves educational outcomes, data interoperability, and security. Our findings reveal that the unified model not only streamlines educational processes but also offers scalable solutions for sectors like healthcare facing similar integration challenges. This work advocates for a paradigm shift toward collaborative, cross-technology AI systems to solve complex data-driven problems across industries.

Abstract: This study was conducted to determine the suitability of bast and core fibre derived from S. Officinarum bagasse (an agricultural waste) for pulp and paper making. The bagasse was chipped and macerated in a 1:1 mixture of acetic acid and hydrogen peroxide for three weeks to release the fibres. The fibre dimensions were measured under the microscope and morphological indices calculated. Physicochemical properties (ash and moisture content, cold water, hot water and 1 % NaOH solubility) were also determined using American Society for Testing and Materials (ASTM) method. The bast fibre was digested with 2 and 4 M NaOH in ratios 1:10, 1:20 and 1:30 (w/v) at pulp time of 30, 60 and 90 minutes, respectively. The mean values obtained for fibre length, fibre diameter, lumen width and cell wall thickness for both core and bast fibre were in the range 0.736 mm to 1.542 mm, 0.0237 mm to 0.0242 mm, 0.0178 mm, and 0.0025 mm to 0.0029 mm respectively. The morphological indices for core and bast fibres were; 10.54 to 12.58, 0.35 to 5.81, 32.21 to 67.43 and 78.83 to 78.90 for coefficient rigidity, runkel ratio, felting power, and coefficient flexibility respectively. It was observed that the best pulp yield was obtained with 1:10 (w/v) solid to liquid ratio of 2 M NaOH solution at digestion time of 90 minutes. The papers produced were examined using Scanning Electronic Microscope (SEM). The finding of this study revealed that bast fibre of S. Officinarum plant holds greater promise as a sustainable alternative source for papermaking than the core fibre.

Abstract: This study explores the role of TikTok’s content marketing strategy in building customer engagement for Indonesian SMEs through interviews with 23 SME Owners from various businesses. The result identifies key strategies that contribute to successful engagement on the platform. Findings indicate that TikTok’s interactive features, such as duets, challenges, and comments, significantly enhance customer engagement. The Circular Model of Some provides a structured approach for businesses to optimize their content strategy, emphasizing sharing, optimization, management, and engagement. Challenges include the unpredictability of TikTok’s algorithm, rapidly changing trends, and limited digital access in certain regions. The study concludes that leveraging TikTok’s interactive features and adapting to dynamic trends are crucial for businesses, content creators, and digital marketers in formulating effective social media strategies.

Abstract: Considering how students learn with educational technologies is a necessity in improving learning outcomes in personalized and adaptive learning systems. This research looks into the implementation of a twin predictive model to categorize and predict students’ learning styles within the context of a serious educational game. In particular, the study exploits the Gregory Learning Style Model consisting of Concrete Sequential, Abstract Sequential, Abstract Random, and Concrete Random learners to identify cognitive preference. This study used interaction data from Jo Wilder and the Capitol Case educational game alongside a hybrid machine learning model which employed K-Means Clustering for unsupervised classification and behaved as a Decision Tree Classifier for supervised classification. Behavioural features that included session activity, game progress, and time spent enabled the model to accurately categorize learners into correct Gregory learning style groups. Assessment of the model’s performance showed high precision, with the decision tree classifier attaining 92.4% accuracy and over 90% F1-score in the majority of learning style classes. The analysis showed also that the total time spent in the game 49.60% and session level 26.77% were the strongest predictors of students’ learning styles. This study helps advance educational data mining, adaptive learning, and serious game design by showing an accurate model for learning style prediction. These findings are critical for personalized instructional design, individualized real-time support, and the integration of behaviour analytics into instructional systems. This work also supports the automated learner model design and the adaptive content delivery within digital learning environments.

Abstract: The banking sector in Nigeria plays an important role in the nation's financial ecosystem and is a major driver of economic growth and development. However, despite this essential role, Nigerian banks are exposed to a wide range of risks that can significantly impact their financial stability and overall performance. This study investigated the effect of credit risk management on the financial performance of deposit money banks in Nigeria, using panel data from five selected banks. Employing both Random and Fixed Effects models, the research evaluated how credit risk influences financial performance, with Return on Assets (ROA) serving as the key performance indicator. The findings revealed a positive and significant relationship between credit risk and ROA, indicating that higher credit risk levels are associated with improved financial outcomes. This underscores the importance of robust credit risk management and well-structured governance frameworks in driving financial performance. The study concluded that credit risk can positively impact market-based performance, as investors may perceive higher risk as a signal of greater potential returns. It emphasized the importance of robust risk management practices and governance mechanisms in sustaining long-term financial health. Consequently, the study recommended that banks strengthen their credit risk management frameworks, promote a culture of transparency and ethical behavior, and conduct regular reviews of governance structures to ensure alignment with evolving regulatory and market conditions.

Abstract: It studies the specific context of Hyderabad market in evaluating the influence of influencers on structural to brand image. Influencer marketing is one of the most potent strategies of digital marketing that brands can use for reaching social media users by focusing on specific segments. The credibility that trusted influencers build with their audience has enormous power to sway public perception of brands. In the context of Hyderabad's unique marketplace, the study examines the impact of influencer marketing on the brand image along with changes in the consumer behavior process leading to the formation of brand loyalty. Adopting an exploratory practices-based approach enhances our understanding of the dynamism of influencer marketing by applying both quantitative and qualitative data collection methods. A survey mediated investigation examined 300 people for their relationships with influencers and their changes in brand preference in Hyderabad. The research tool consisted of detailed inquiries regarding consumer perceptions of influencer trustworthiness and their engagement with influencers, along with their assessments of influencer promotion trustworthiness agreement. In addition to interviewing social media influencers, the research included twenty marketing professionals to identify their strategies to create favorable impressions of the brand using influencer marketing campaigns. From Hyderabad specifically, research illustrates a potent way in which influencer advertising changes for brands outside their very own comfort zone because youthful users of the service go for Instagram over as well as YouTube both within Hyderabad. Trust placed in influencers, ultimately, depends on their authenticity that aligned with the needs and interests of consumers themselves. Influencers create relatable entertainment-based content that has a significant direct effect on a brand’s image, especially within the fashion and beauty industries. Studies show that micro-influencers are better at offering stronger purchase intention effects for consumers because they develop familiarity with their audience members, through actual supportive and real interaction. The brand perception strengthening is driven by a close fit between a promoter’s personal brand with the company they promote. In the case of Hyderabad, individuals responded more favourably to brands that were endorsed by influencers because they related with them and their personal values and lifestyles. This way, brands boost their visibility and also acquire credibility through these digital engagement metrics (likes, comments, shares, etc.), both contributing to shaping brand image. This study provides new information on Indian influencer marketing as it relates to the thriving market of Hyderabad. Brands can create their best brand image while affecting consumer behavior by collaborating with the help of influencers through developing real relationships mainly based on trust and shared buyer values. Insights revealed empower marketers to tailor influencer marketing strategies to the nuances of consumer preferences in Hyderabad.

Abstract:  Crop loss due to wild animals and birds remains a persistent challenge for farmers, especially in rural and semi-rural areas. Traditional methods such as scarecrows, fencing, and chemical deterrents often prove either ineffective in the long term or environmentally harmful. This research presents an eco-friendly and sustainable approach to crop protection using solar-powered sound sensors. The proposed system utilizes non-conventional solar energy to power sound-emitting devices triggered by motion or sound detection. When an intruder—such as a bird or animal—is detected in the field, the device emits high-frequency or predator-like sounds that naturally repel them without causing harm. This technology reduces dependence on electricity and chemical repellents, offering a cost-effective solution for farmers. Field tests showed a notable reduction in crop damage while maintaining ecological balance. The design is simple, requires minimal maintenance, and can be adapted to different crop environments. This study illustrates that integrating solar energy with sound-based deterrence is a viable strategy for modern, sustainable agriculture.

Abstract: This study investigates the emission levels of Volatile Organic Compounds (VOCs) across different tillage operations and tractor speeds in agricultural practices. A field experiment was conducted on a 14.4-hectare farmland using four tractor models (MF new, MF old, NH new, NH old) at three different speeds (15 km/h, 20 km/h, and 24 km/h). The tillage operations included 1st plough, 2nd plough, harrowing, and ridging, with a total of 432 experimental runs. VOC emissions were monitored using a Multireal Pro Handheld Gas Analyzer. The results indicate that the highest VOC emissions occurred at 20 km/h, while at 24 km/h, emissions remained relatively stable across all tillage operations. Among the tillage operations, ridging and 1st ploughing exhibited the highest VOC emissions, while harrowing consistently produced the lowest. These findings emphasize the need for speed regulation and optimized tillage operations to mitigate environmental impact. Future research should explore sustainable tillage practices and emission control technologies to promote environmentally friendly farming.

Abstract: This research examines the impact of social media marketing (SMM) on brand recognition, consumer preferences, and buying intentions among young adults in Bengaluru, India. The study explores how SMM tactics affect millennials' interactions with durable goods in the context of increasing digitization and evolving marketing strategies. The investigation employed a mixed-methods approach, gathering primary data from 400 social media-active young consumers (18-45 years old) via structured surveys, complemented by secondary data from scholarly sources. The relationships between SMM and key consumer behavior variables were analysed using statistical techniques, including correlation analysis, regression models, and hypothesis testing. The results indicate a Strong Positive Correlation between SMM and purchasing decisions (r = 0.804, p < 0.05), suggesting that targeted advertisements, influencer promotions, and interactive content significantly influence buying behavior. However, the link between SMM and brand awareness was found to be considerably weaker (*r = 0.048*), implying a limited direct effect on brand recall. Brand preference showed a moderate positive relationship with SMM (r = 0.292, p < 0.05), emphasizing the importance of tailored marketing and customer experience. Regression analysis confirmed that SMM, brand awareness, and brand preference together account for 73% of the variance in purchase decisions (R² = 0.730), highlighting SMM's crucial role in consumer decision-making processes. The research concludes that while SMM is an effective tool for influencing purchase intentions, its capacity to build brand awareness may require additional strategies. Practical suggestions include utilizing visual content, collaborating with micro-influencers, and striking a balance between digital campaigns and traditional media. The study's limitations, such as its focus on Bengaluru and age-specific sampling, point to opportunities for future research across diverse demographics and regions. These findings provide actionable insights for marketers seeking to optimize SMM strategies in the dynamic digital marketplace.

Abstract: This article provides a novel viewpoint on how contentment could be incorporated in corporate philosophy for being spiritual while functioning in a materialistic world. Spirituality has been viewed in this article as joyfulness or a state of true happiness arising out of contentment. A discussion on how this meaning of spirituality could go along with various perspectives of spirituality in businesses and how it could do away with various caveats of bringing spirituality into a business as laid out in existing literature has been done. It has been concluded that spirituality can coexist with materiality in business organizations by changing the perspective of the corporate mind from maximization to sufficiency where sufficiency in the present time may be determined on the basis of future requirements of sustainability. People toil day and night in search of happiness but are seldom happy. A moderation in viewpoint from maximization to sufficiency or contentment both in personal and professional lives could result in joyfulness and make life easy for everyone in the society.

Abstract: The present study deals with the synergistic influence of gyrotactic microorganisms and bimolecular reactions on the bidirectional flow of tangent hyperbolic fluids under Nield boundary conditions. Further, the flow characteristic of the non-Newtonian fluid is enhanced by incorporating the impact of thermal radiation, heat sources, Brownian motion, and thermophoresis. The presentation of these phenomena is vital for an extensive range of applications, including industrial processes, biomedical engineering, and environmental management. The analysis employs advanced mathematical modelling which needs suitable transformation rules to get the non-dimensional form and further numerical simulation is presented with the assistance of the “shooting-based fourth-order Runge-Kutta technique”. The results are depicted for the several contributing factors via the built-in in-house function bvp4c in “MATLAB”. The authentication of the study with the prior research is a benchmark to precede further research in this direction.

Abstract: This study presents a high-fidelity Computational Fluid Dynamics (CFD)-based comparative analysis of passive and active winglet configurations for narrow-body aircraft, focusing on aerodynamic performance, fuel efficiency, and structural trade-offs. While passive winglets are widely implemented due to their simplicity and drag-reduction benefits, active winglets offer adaptive geometry modulation, enhancing performance across various flight phases. Using an Airbus A320-style model, CFD simulations were conducted under standardized cruise conditions to quantify lift (Cl), drag (Cd), and the lift-to-drag ratio (L/D), complemented by scoring for structural complexity and maintenance. The results revealed that the active winglet outperformed the passive configuration, yielding a 10.5% L/D improvement and up to a 6.11% drag reduction during cruise, which translates to fuel savings of 3.87–6.11% across takeoff, cruise, and descent. However, the trade-off analysis highlighted significantly increased structural, actuation, and maintenance demands in active systems. As a solution, a hybrid winglet design—combining passive-flex tips with low-degree-of-freedom actuators—was proposed to balance aerodynamic gains with integration feasibility. The study contributes novel, CFD-driven, regionally contextualized data to sustainable aircraft design, particularly in the context of Southeast Asia’s aviation sector. Limitations include the lack of wind tunnel validation and simplified actuator modeling. Future research should focus on prototyping, aeroelastic simulation, and the integration of AI-based real-time control. The findings offer practical insights for fleet retrofitting and next-generation aerodynamic optimization.

Abstract: The overall performance and functionality of the textile fabric depend on the geometrical properties of the fabrics. This article explores key geometrical parameters such as yarn density, fabric thickness, fabric structure, GSM of the fabric, texture, etc. Understanding these structural characteristics is essential for the development of innovative fabric design and so influences the end use. The study provides valuable insights for the textile designers and researchers in the selection or development of textile fabrics for the particular functionality.

Abstract: As we get further into this period of fast paced innovation, the domain of learning and development (L&D) is undergoing significant change. Technologies like Artificial Intelligence, ML, VR, and adaptive learning systems are breaking new ground in training and enabling companies to build a more flexible and reliable workforce. This paper delves into the essential part of these technologies in reshaping L&D practices, focusing on their impact on workforce skills and competencies. By examining both theoretical frameworks and practical applications, the study explores how digital tools are attracting employee engagement, personalization of learning experiences, and the expansion of critical assistances required in a dynamic business environment. The difficulties of integrating these technologies are also covered in the paper, with concerns about data confidentiality, accessibility, and the digital divide. Practical recommendations are offered to help organizations strategically leverage emerging technologies to adoptive a philosophy of continuous learning and adaptability. The study concludes by identifying future trends and research opportunities in the evolving field of digital learning and development.

Abstract: Knowledge graphs (KGs) have emerged as fundamental structures for organizing interconnected data across diverse domains in the semantic web ecosystem. However, most real-world KGs remain incomplete, limiting their effectiveness in downstream applications. This paper presents a novel neural-symbolic framework that integrates Graph Neural Network (GNN) distillation with Abstract Probabilistic Interaction Modeling (APIM) to address critical challenges in knowledge graph completion (KGC). Our approach tackles the over-smoothing problem in deep GNNs through iterative message-feature filtering while incorporating semantic web technologies for enhanced knowledge representation. The proposed framework introduces a unified architecture that combines symbolic reasoning with deep learning to leverage complementary benefits from both paradigms. We evaluate our methodology on standard benchmarks including WN18RR and FB15K-237 datasets, achieving significant performance improvements over baseline models. Experimental results demonstrate a 10.9% improvement in Hits@1 metric compared to state-of-the-art approaches with Mean Reciprocal Rank (MRR) scores of 0.523 on FB15K-237 and 0.440 on WN18RR. The framework effectively addresses semantic similarity challenges while maintaining computational efficiency through knowledge graph embeddings that preserve hierarchical relationships [4][5]. Our contributions include the introduction of automatic embedding dimension learning for hierarchical entities, novel semantic enrichment techniques for information retrieval and comprehensive evaluation protocols that ensure fair comparison across different model architectures. The research bridges the gap between semantic web technologies and machine learning communities, providing practical solutions for real-world knowledge graph applications with validated experimental results and reproducible methodologies.

Abstract: Healthy soil forms the foundation on which sustainable agriculture is established. The Green Revolution technologies have more than doubled the yield potential of cereals, especially rice and wheat, in India. However, conventional chemical farming practices, involving the excessive use of chemical fertilizers and pesticides, damage beneficial soil microorganisms, alter soil properties, increase crop production costs, and are not sustainable over the long term. The future food security and economic independence of the country depend on enhancing the productivity of bio-physical resources through sustainable production methods, improving crop tolerance to adverse environmental conditions, and minimizing crop and post-harvest losses. Traditional agricultural practices such as growing competitive crop varieties, modifying sowing and planting techniques, adjusting sowing times and directions, mulching with organic residues, using green and brown manures, and adopting reduced or zero tillage create unfavorable conditions for weed seed germination and growth. These methods can play a crucial role in designing sustainable and eco-friendly agricultural systems, increasing the chances that rural communities will accept, develop, and maintain these innovations. In this regard, green cultivation practices of crop production are considered environmentally safe, selective, biodegradable, economical, and renewable alternatives suitable for organic farming systems. Bio-intensive or organic agriculture aims to achieve the highest yields from the smallest land area while simultaneously enhancing and maintaining soil fertility by maximizing biomass production per unit area. Therefore, environment friendly agricultural technologies that ensure food safety and do not harm nature are essential for food security, improving human health, and conserving and rehabilitating the environment for the well-being of future generations. This paper reviews the principles of natural farming, highlighting its eco-friendly nature and sustainability.

Abstract: `Seasonally, this study investigates the relationship between atmospheric parameters (temperature, relative humidity and oxygen) levels of greenhouse gases (GHGs: CH4 and CO2), criteria air pollutants (CAPs: PM2.5, PM10, O3, CO, SO2, and NO2), and other gaseous pollutants (HCHO, NO, NH3, and VOC) at selected dumpsites in metropolitan cities of southwestern Nigeria. The city’s dumping sites (State) were Olusosun Ojota (Lagos), Saje Abeokuta (Ogun), Agodi Ibadan (Oyo), Akure (Ondo), Ado Ekiti (Ekiti), and Osogbo (Osun). The portable meteorological station (Kestrel model: 3000 NV, Boothwyn, USA) measured atmospheric parameters, while the Multimeter BOSEAN T Z01 and AEROQUAL 500 series air quality meter measured the pollutants’ levels. Utilizing correlation heat maps, the analysis revealed significant associations between meteorological parameters and pollutant emissions. During the dry season, methane (CH₄) and carbon dioxide (CO₂) exhibited strong positive correlations with temperature (r = 0.99 and 0.65, respectively), driven by microbial activity under anaerobic conditions. Ammonia (NH₃) and nitric oxide (NO) showed close linkages with CH₄ and CO₂ (r = 0.80–0.88), suggesting shared nitrogen transformation pathways. In the wet season, temperature similarly influenced CH₄ (r = 0.99) and CO₂ (r = 0.81), while combustion related pollutants like nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) displayed near perfect correlations (r = 1.00). Ozone (O₃) exhibited strong inverse relationships with precursors (CO, NO) due to photochemical dynamics. Findings show temperature, microbial processes, and combustion drive pollutant emissions, stressing the need for region-specific waste strategies to mitigate air quality and climate impacts in tropical urban areas.

Summary The aim of this study was to evaluate the performance of procalcitonin and C-reactive protein in the diagnosis of early neonatal infection at Brazzaville University Hospital. This is a cross-sectional analytical study of 43 patients followed up in the Neonatology Department of the Brazzaville University Hospital Centre between July and November 2021. Procalcitonin, C-reactive protein and blood cultures were positive in 90.7%, 37.2% and 60% of cases respectively. The p-value was significant for procalcitonin (p = 0.001). The performance criteria for the biomarkers studied evolved in the opposite direction. However, the sensitivities of procalcitonin and C-reactive protein were 100% and 42.3% respectively, and the specificities of these biomarkers were 70.6% and 23.5% respectively. Procalcitonin cannot be used as the sole marker of neonatal infection ; it must always be combined with C-reactive protein.

Abstract: Deep infiltrating endometriosis (DIE) and adenomyosis are common causes of pelvic pain and infertility. Accurate preoperative diagnosis is critical for management. Transvaginal ultrasound (TVUS), increasingly augmented with elastography, and magnetic resonance imaging (MRI) are the leading noninvasive modalities.

Abstract: Driver drowsiness is a serious safety issue that contributes significantly to road accidents. This paper presents a hardware-based system for detecting early signs of fatigue in drivers using a low-cost setup. The system’s objective is to issue alerts to drivers showing signs of inattention or drowsiness. It utilizes an infrared eye blink sensor integrated with an Arduino microcontroller, a buzzer, a relay, and a DC motor. The eye blink sensor tracks eyelid movements, and when prolonged eye closure is detected, the system triggers an audible alarm and deactivates the motor to simulate a vehicle stop. The system operates without the use of artificial intelligence or camera-based image processing, relying instead on time-based thresholds. Experimental testing showed consistent activation during simulated drowsy states, though formal metrics such as accuracy and false positive rates were not recorded. While the current model is basic and non-adaptive, it provides a practical foundation for low-resource applications and can be expanded with intelligent features for better real-world usability.

Abstract: Understanding how customers react to emerging technologies, particularly artificial intelligence (AI), is crucial for researchers and retailers alike, especially in light of the increased interest in fashion and digital advancements. Examining consumer perceptions and buying intentions about AI devices was the aim of the study. A conceptual model pertaining to consumers' attitudes and purchasing intentions toward an AI device—Echo Look—was developed and evaluated by modifying the technological acceptance model. In all, 626 participants (61% female) in the top 05 Indian cities between the ages of 20 and 60 took part in the study. The findings showed that customers' attitudes about AI were significantly influenced by perceived usefulness, perceived simplicity of use, and perceived performance risk. Purchase intention was positively impacted by good sentiments concerning technology. Implications for theory and practice are examined in light of these findings.

Abstract:  This study examines the risk-return profile of Cryptocurrencies compared to Indian stock Indices, BSE Sensex and Nifty 50, to identify their unique investment characteristics. By analysing data from ten major Cryptocurrencies alongside Sensex and Nifty 50, the study applies statistical measures, including mean, variance, skewness, kurtosis, Jarque-Bera (JB) test, two-sample t-test, ANOVA test and Tukey HSD test. Findings reveal that Cryptocurrencies offer higher returns but are significantly more volatile, while Indian Stock Indices provide moderate, stable returns with implications for risk-seeking versus conservative investors. The Study reveals that Cryptocurrencies are suitable for investors who can tolerate high volatility for potentially higher returns, whereas Traditional Stock Indices are better suited for those prioritising stability and consistent growth.

Abstract: This study explores the role of portfolio rebalancing as a vital, yet underappreciated, component of investment strategy within emerging markets—particularly India. While rebalancing is not intended to maximize returns, it serves as a disciplined risk-control mechanism that ensures long-term asset allocation aligns with investor goals and tolerance. In environments marked by volatility, sentiment-driven retail behaviour, and limited institutional infrastructure, the practice of rebalancing remains inconsistent and often misunderstood. The research identifies four central challenges: behavioural biases like return chasing and loss aversion; operational frictions such as tax implications, transaction costs, and lack of automation; gaps in financial literacy, especially outside metropolitan areas; and weak data transparency from advisors and platforms. These impediments collectively erode the effectiveness of strategic portfolio management among retail investors. In response, the study highlights recent innovations (2017–2025) that have begun bridging these gaps. FinTech platforms like Smallcase, Kuvera, and INDmoney offer algorithmic rebalancing, drift alerts, and goal-based frameworks that democratize access to disciplined investing. Regulatory nudges from SEBI and AMFI have promoted cost transparency and financial education, while product innovations such as Balanced Advantage Funds and ESG-linked hybrid portfolios offer built-in rebalancing dynamics. Through case studies—ranging from structured mutual funds like HDFC Balanced Advantage to lifecycle-based pension models and DIY investor experiences—the study illustrates both the feasibility and evolving ecosystem of rebalancing tools in India. Finally, it recommends policy measures including tax incentives, regulatory disclosures, and collaborative digital literacy programs to embed rebalancing more firmly in investment practice. In summary, this research advocates a systemic shift: from viewing rebalancing as an optional tactic to recognizing it as a fundamental pillar for sustainable, long-term investing in emerging economies.

Abstract:  The era of dynamic development demands faster, more cost-effective website development. This cloud-based B2B SaaS platform helps agencies build and manage websites quickly and affordably using a simple drag-and-drop website builder with integrated domain hosting. The platform allows users to customize website elements, preview designs in real time, manage media, and track projects with a to-do board. It supports secure authentication, role-based access, and seamless Stripe payment integration. De- signed for speed, ease of use, and compatibility with major browsers, the platform ensures smooth performance even during peak use while being energy efficient. This solution enables agencies to work faster, take on more clients, and deliver better results, improving both their efficiency and customer satisfaction.

Abstract: The paper investigated how leveraging Artificial Intelligence (AI) can support more inclusive mathematics education for students with learning disabilities, a critical concern within broader science education. Learning disabilities such as dyslexia, attention-deficit/hyperactivity disorder (ADHD), and dyscalculia frequently impede students’ access to and success in mathematics classrooms due to challenges in processing, retention, and engagement with abstract concepts. Drawing on current research and classroom practice, this study examines two central AI applications: adaptive learning systems that modify instructional content in real time based on student performance, and personalized support tools that deliver differentiated feedback aligned with individual cognitive and learning profiles. The discussion is organized under the following subheadings: The Transformative Role of AI in Inclusive Mathematics Education; Understanding Learning Disabilities and the Imperative for Inclusion; Leveraging AI for Adaptive Mathematics Instruction; Personalized Support and Feedback in AI-Mediated Learning; Longitudinal Monitoring through Learning Analytics; Ethical and Practical Considerations; and Envisioning the Future of Inclusive AI in Science and Mathematics Education. The paper argues that, when implemented thoughtfully and ethically, AI offers significant promise in dismantling structural barriers, fostering learner autonomy, and promoting equitable participation in mathematics education—advancing the goals of inclusive science education.

Abstract: Liver cirrhosis, a chronic disease characterized by fibrosis and impaired liver function, poses significant diagnostic challenges. Early prediction is crucial for patient prognosis and timely intervention. This paper explores the applications of Random Forest, a robust ensemble learning technique, for predicting the stage of liver cirrhosis using a publicly available dataset. The process includes data cleaning, feature selection, model training, and performance analysis. The results show that Random Forest offers improved prediction accuracy compared to several traditional models, indicating its viability for real-world diagnostic use, outperforming several baseline models, thus validating its applicability in real-world clinical scenarios. 

Abstract: Background: Basic Life Support (BLS) is a vital component of emergency medical care. Early and effective BLS can drastically improve survival rates following cardiac arrest or respiratory failure. This study evaluates the level of knowledge, attitude, and practice regarding BLS among MBBS interns, who serve as the first responders in many clinical emergencies.

Abstract- The frantic carrying out of operations in a distributed database management system has to meet the joint contingent properties of an ACID (especially, consistency and isolation) in order that the present simultaneous transactions can fail or pass in any way. Techniques such as Two-Phase Locking, Timestamp Ordering, Optimistic Concurrency Control, and Multi-Version Concurrency Control (MVCC) have something different with advantages and disadvantages under various high contention or skewed workloads. This paper proposes a hybrid concurrency control model, which can intelligently select among these protocols based on real-time transaction attributes and workload conditions. In case of another transaction being blocked by an active one, a transaction classifier and protocol allocator is set to switch back and forth dynamically between strategies. With the help of PostgreSQL and Python, simulating and evaluating the system using metrics such as throughput, latency, abort rate, and occurrence of deadlock performance. Overall, hybrid models can exhibit performance improvements, especially with read-intensive and mixed workloads, by - reducing transaction aborts and giving a faster response time. Hybrid models will offer modern scalable DDBMS a good alternative.

Abstract: The integration of Artificial Intelligence (AI) in higher education has significantly transformed traditional teaching and learning models. By enabling adaptive learning environments, AI technologies offer personalized and student-centric experiences. This study explores the opportunities and challenges associated with implementing AI in online higher education platforms, with a focus on enhancing student engagement and performance. A descriptive research design and statistical tools like percentage analysis and chi-square test were used to analyze data from 125 students. The findings provide insights into the key factors affecting AI-driven education, and the study proposes recommendations to optimize its benefits.

Abstract: This paper presents the design and implementation of an air quality monitoring system based on an MQ135 gas sensor and an Arduino Uno microcontroller. The MQ135 sensor detects common pollutants (e.g. CO₂, NH₃, benzene, smoke) by measuring changes in air conductivity, and its analogy output is processed by the Arduino board. Sensor readings are calibrated against known conditions, and then converted to parts-per-million (PPM) levels. The system displays real-time air quality readings on an LCD and can trigger alerts when thresholds are exceeded. Data collection and analysis are demonstrated through sample readings in different environments. The system’s accuracy, limitations, and potential improvements (such as IOT connectivity, mobile apps, and machine learning) are discussed. This project shows that a low-cost MQ135/Arduino platform can provide continuous air quality data for environmental monitoring.

Abstract: Load balancing is a critical component in distributed systems, ensuring efficient resource utilization, minimized response time, and improved system performance. Despite numerous algorithms proposed in the literature—ranging from static to dynamic and heuristic to AI-based approaches—many still face challenges concerning energy optimization, adaptability to dynamic workloads, and scalability in large-scale environments. This paper presents a comprehensive review and comparative analysis of existing load balancing techniques across cloud, edge, and distributed computing environments. The study systematically evaluates each algorithm’s operational principles, strengths, and limitations. Key performance parameters such as energy consumption, load adaptability, fault tolerance, and scalability are examined. Through this analysis, the paper identifies significant research gaps and proposes essential criteria that a next-generation load balancing algorithm must fulfill. The findings lay the groundwork for developing a more robust, adaptive, and energy-efficient load balancing solution tailored for future computing paradigms.

Abstract: This study investigates the satisfaction levels of women passengers utilizing the Vidiyal Scheme in Villupuram District. The Vidiyal Scheme, an initiative aimed at enhancing transportation accessibility for women, provides affordable and secure travel options. The research focuses on understanding the perceptions, experiences, and challenges faced by women passengers who rely on this service. A structured questionnaire was used to collect data from a representative sample of women who have used the Vidiyal Scheme in the district. The analysis reveals key factors influencing satisfaction, including the affordability, safety, punctuality, and comfort of the service. Additionally, the study highlights areas for improvement in service delivery, particularly in terms of frequency and infrastructure. The findings suggest that while the scheme has been beneficial in providing a secure mode of transport, there is room for enhancing the overall service quality to further meet the needs and expectations of women passengers. This study aims to contribute to policy recommendations for optimizing the Vidiyal Scheme and similar initiatives aimed at empowering women through better transportation systems.

Abstract: In this study, a novel ultra-wideband (UWB) radar-based method for early, non-invasive breast cancer diagnosis is presented. The system uses a curved seventh derivative Gaussian UWB pulse that is filtered by a sharp transition bandpass FIR filter in both monostatic and bistatic radar configurations. The pulse shape enhances radiation power, spectrum efficiency, and FCC-compliant safety. Tumors are located and detected using Specific Absorption Rate (SAR) measurement and backscattered signal processing. Results from experiments and simulations confirm that the system can accurately identify cancers.

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Abstract: Water is a critical yet often overlooked resource in the efficient operation of hospitals. Healthcare facilities consume vast quantities of water for sanitation, medical procedures, cooling systems, and landscape maintenance. Given increasing global water scarcity and the rising cost of utilities, optimizing water consumption has become a strategic imperative for sustainable hospital operations. This paper explores the application of Artificial Intelligence (AI) technologies in monitoring, managing, and reducing water usage across three key areas: sanitation systems, HVAC (Heating, Ventilation, and Air Conditioning) cooling processes, and hospital gardens. The study proposes a comprehensive AI-based framework that integrates real-time data collection through IoT-enabled sensors, predictive analytics, and machine learning algorithms to achieve water efficiency without compromising hygiene, safety, or environmental aesthetics. In the domain of sanitation, AI systems can identify peak usage patterns, detect leaks, and recommend optimized flush schedules and fixture upgrades to minimize water wastage. For instance, AI can predict toilet and sink usage trends in patient and staff areas to enable dynamic water allocation and maintenance scheduling. Furthermore, anomaly detection algorithms can alert facility managers to leaks or inefficiencies in plumbing infrastructure. In cooling systems, particularly those that rely on water-cooled chillers and evaporative cooling towers, AI can analyse weather conditions, hospital occupancy rates, and thermal load patterns to adjust water flow and reuse rates. Machine learning models trained on historical data can optimize operational cycles, thereby reducing both water and energy consumption. Additionally, AI can facilitate the integration of greywater reuse systems by predicting safe recycling cycles based on water quality metrics and system demands. Hospital gardens and green areas, while beneficial for patient recovery and employee wellbeing, often suffer from inefficient irrigation practices. AI-powered irrigation systems use satellite imagery, soil moisture sensors, and local weather forecasts to determine precise watering schedules and amounts. By shifting from time-based to need-based watering, hospitals can achieve substantial reductions in water usage while maintaining healthy landscapes. This paper includes a review of current AI technologies applicable to water management, a survey of case studies from hospitals that have implemented smart water systems, and a simulation model demonstrating potential water savings of up to 35% through integrated AI solutions. Challenges such as upfront costs, data integration, and staff training are also discussed, along with strategies to overcome them. In conclusion, leveraging AI for water usage monitoring and reduction in hospitals represents a promising convergence of healthcare, environmental stewardship, and smart technology. By adopting AI-driven approaches, hospitals can not only reduce operational costs but also contribute to broader sustainability goals, including compliance with national green building standards and global climate targets. Future research can further refine these technologies, incorporating advanced AI techniques such as deep learning and reinforcement learning for autonomous water management. The implementation of AI in hospital water management marks a critical step toward intelligent and sustainable healthcare infrastructure.