Volume 14 Issue 6

Abstract: The development of advanced construction materials for typical boilers has been pivotal in enhancing the efficiency, reliability, and environmental sustainability of power generation systems. Modern boiler designs operate under extreme conditions, including elevated temperatures (up to 700°C) and high pressures, necessitating materials with exceptional mechanical properties and resistance to corrosion, oxidation, and creep. Key advancements include high-strength steels, nickel-based alloys, and advanced austenitic steels. High-strength chromium steels, such as HCM12, provide an optimal balance of weldability and resistance to creep and corrosion, making them suitable for pressure parts. Nickel-based alloys, such as Alloy 617 and Alloy 625, are integral for components exposed to the highest thermal stresses, offering superior creep strength and oxidation resistance. Austenitic steels, such as Sanicro 25, are widely adopted in superheater and reheater tubes due to their robust high-temperature performance. Research initiatives, such as the Advanced Ultra-Supercritical (A-USC) Boiler Consortium and the European AD700 program, have driven material innovation, focusing on achieving higher operational efficiencies while reducing greenhouse gas emissions. These projects emphasize material testing and qualification to ensure durability under prolonged high-temperature service. The ongoing evolution of boiler materials is essential for advancing ultra-supercritical and other next-generation power plants, enabling more efficient energy production with reduced environmental impact. This abstract summarizes the key materials and trends shaping the future of boiler construction.

Abstract: The PEMT (phosphatidylethanolamine N-methyltransferase) gene plays a critical role in the endogenous synthesis of phosphatidylcholine, an essential compound in lipid metabolism and liver health. The single nucleotide polymorphism (SNP) rs7946 (V175M) within the PEMT gene influences the enzymatic activity and structural stability, leading to altered choline metabolism. Individuals carrying Aallele exhibit reduced PEMT activity, predisposing themto non-alcoholic fatty liver disease (NAFLD), elevated homocysteine levels, and reproductive or cognitive risks under low choline diets. This impact is modulated by sex hormones, particularly oestrogen, rendering postmenopausal women more susceptible. This review summarises recent findings linking PEMT rs7946 variants with metabolic, hepatic, and psychological outcomes and explores gene diet interactions that informs genotype based nutritional recommendations. It also suggests tailored choline intake strategies based on genotype. We argue for the integration of PEMT screening in nutritional counselling, especially for vulnerable groups. Future nutrigenetics research is necessary to refine these guidelines and assess long-term impacts.

Abstract: This research paper explores the intricate relationship between media exposure and investor sentiment within the context of stock market investments. The stock market is a complex financial ecosystem where investor decisions are significantly influenced by various external factors, with media playing a pivotal role in shaping investor sentiment. This study aims to provide a comprehensive analysis of the multifaceted impact of media on investor sentiment, offering insights into its potential consequences for market behavior and stability. The study developed a conceptual framework that elucidates the mechanisms through which media channels, such as news outlets, social media platforms, and financial blogs, transmit information and shape investor attitudes. This research paper offers a holistic examination of the influence of media on investor sentiment towards stock market investments. It combines quantitative and qualitative methodologies to provide a nuanced understanding of the mechanisms at play and the potential consequences for financial markets.

Abstract: Context: - Cervicogenic headache (CGH) is a secondary headache arising from cervical spine dysfunction. Both manual traction (MT) and mechanical traction are widely used physiotherapeutic modalities for the management of CGH. Aims: - To compare the effectiveness of manual traction and mechanical traction in reducing pain intensity and improving functional outcomes in patients with CGH. Study design: - Experimental study (Pre-test, post-test matched pair design) Setting: - Outpatient Physiotherapy clinic. Material and Methodology: A randomized controlled trial was conducted with 60 patients diagnosed with CGH, randomly assigned into two groups (n = 30 each). Group A received manual traction, while Group B received mechanical traction. Both groups underwent intervention for 4 weeks (3 sessions/week). Pain intensity (Visual Analogue Scale - VAS), neck disability (Neck Disability Index - NDI), and cervical range of motion (CROM) were measured at baseline and post-intervention. Statistical analysis: The data was analyzed using SPSS 21.0. The software package and statistical test used are paired t-tests to compare the effectiveness of associated functional disability. A significant value of P <0.05 was set. Results: Both groups showed significant improvement post-intervention. However, Group A (manual traction) showed. Significantly greater reduction in VAS and NDI scores compared to Group B (mechanical traction) (p < 0.05). CROM improvement was also more pronounced in the manual traction group. Conclusion: Manual traction appears to be more effective than mechanical traction in reducing pain and improving function in patients with cervicogenic headaches.

Abstract: A growing interest has been noted in the concept of business management that consciously focuses not only on financial profits and economic matters but also on broadly defined social and ecological issues within our rapidly evolving market economy. The principles of environmental corporate responsibility are increasingly shaping the decision-making processes of entrepreneurs due to the rising necessity of this approach, referred to as sustainable development. The author of the study asserts that the human element must be integrated to implement the aforementioned business strategy. Eco-oriented management can only be effectively executed by employees who maintain a positive perspective on the environment, possess green competencies, and are accountable for the environmental impacts of their actions. Consequently, green human resource management—commonly known as HRM—plays a crucial role in enhancing environmental performance by fostering the adoption of ecological development principles and nurturing employees' environmentally friendly attitudes. The innovative approach to executing the HR function within organizations is termed "green human resource management," where environmental considerations form the foundation of all activities. This involves the establishment of HR policies aimed at promoting the sustainable utilization of business resources, safeguarding the environment, and enhancing employees' ecological awareness. This is due to the fact that the ecological consciousness of both managers and employees is vital for any business to function in alignment with the sustainability concept. The objective of the study is to illustrate the relevance of Green HRM to contemporary business practices and to propose actionable strategies that can be implemented in the field to enhance understanding of the subject and expand the application of the concept as a means of fostering sustainable enterprises. The theoretical segment of the study, which is based on a review of global literature, identifies various environmental practices applicable in the realm of human resource management.at every step of the hiring process, from the creation of "green" competencies and "green" working conditions to work design, employee selection, and work discipline shaping.

Abstract- Nature provides medicinal agents, where active components often combine with inactive substances. In recent years, the antibiotic resistance of human pathogenic microorganisms has been increased; which led to search for new antimicrobial substances from medicinal plants. In the present study, the methanol and ethanol extracts of four medicinal plants were evaluated against some selected bacteria such as Staphylococcus aureus., Bacillus subtilis, Bacillus megaterium, Escherichia coli, Klebsiella pneumoniae., Pseudomonas fluorescence and proteus vulgaris. The agar well diffusion method was used to perform the invitro anti-microbial activity.  The measurements of zone of inhibition were taken and compared.  The ethanolic extract exhibited greater antimicrobial activity compared to methanolic extracts. Plant extracts with antimicrobial properties, have potential therapeutic uses.

Abstract: A growing interest has been noted in the concept of business management that consciously focuses not only on financial profits and economic matters but also on broadly defined social and ecological issues within our rapidly evolving market economy. The principles of environmental corporate responsibility are increasingly shaping the decision-making processes of entrepreneurs due to the rising necessity of this approach, referred to as sustainable development. The author of the study asserts that the human element must be integrated to implement the aforementioned business strategy. Eco-oriented management can only be effectively executed by employees who maintain a positive perspective on the environment, possess green competencies, and are accountable for the environmental impacts of their actions. Consequently, green human resource management—commonly known as HRM—plays a crucial role in enhancing environmental performance by fostering the adoption of ecological development principles and nurturing employees' environmentally friendly attitudes. The innovative approach to executing the HR function within organizations is termed "green human resource management," where environmental considerations form the foundation of all activities. This involves the establishment of HR policies aimed at promoting the sustainable utilization of business resources, safeguarding the environment, and enhancing employees' ecological awareness. This is due to the fact that the ecological consciousness of both managers and employees is vital for any business to function in alignment with the sustainability concept. The objective of the study is to illustrate the relevance of Green HRM to contemporary business practices and to propose actionable strategies that can be implemented in the field to enhance understanding of the subject and expand the application of the concept as a means of fostering sustainable enterprises. The theoretical segment of the study, which is based on a review of global literature, identifies various environmental practices applicable in the realm of human resource management.at every step of the hiring process, from the creation of "green" competencies and "green" working conditions to work design, employee selection, and work discipline shaping.

Abstract: Lithium-ion battery systems (LIBS) have unique qualities like high efficiency, high capacity, better power, and low self-discharge. The fast development of the electric vehicle (EV) industry has created a huge market for LIBS to be used as an energy storage device and provide an eco-friendly transportation system. The high demands and market call for LIBS lag behind the safety concerns due to which the explosive incidents of LIBS take place. It is also seen that overcharge, deep discharge, lack of proper thermal management in two-wheelers, and poor selection of cell configuration are the key factors for the explosiveness of the LIBS. In this review, we have studied the reasons behind the occurrence of the above key factors and also analyzed the variation of electrical parameters like voltage (V), current (I), and internal resistance (R) with abnormal conditions. The aim of this study is to work for the betterment of the LIBS and contribute to uplifting the market's acceptability towards the enhanced EV industry.

Abstract - The integration of machine learning in investment matchmaking has the potential to democratize access to funding by efficiently connecting small-scale businesses with investors. Traditional investment processes rely heavily on manual networking and subjective decision-making, often excluding promising startups due to limited outreach or investor bias. Investify, a machine learning powered business investor matchmaking platform, seeks to bridge this gap. By leveraging AI-driven recommendation algorithms, the platform enables businesses to submit detailed proposals, while investors define preferences such as budget, industry, and risk appetite. The system then ranks and matches businesses with investors based on compatibility metrics, streamlining the funding process. Additionally, built-in communication and negotiation tools facilitate investor-business interactions, further enhancing decision-making. This literature survey explores existing research on AI-driven matchmaking systems, recommendation algorithms, and investment decision-making frameworks to provide insights into the effectiveness and potential of such a platform.

Abstract: Ransomware continues to be a pervasive and evolving cybersecurity threat, disrupting critical infrastructure, compromising data integrity, and causing substantial financial losses. This paper presents a comprehensive comparative analysis of common ransomware techniques alongside emerging anomalies observed in recent attacks. Traditional ransomware methods such as file encryption, network propagation, and ransom note delivery are explored to establish a foundational understanding. The study then shifts focus to newer, more sophisticated anomalies, including double extortion, fileless ransomware, and AI-driven obfuscation techniques. By examining attack vectors, payload behaviors, and evasion strategies, the research identifies patterns that aid in early detection and defense. The paper also evaluates real-world incidents and analyzes threat intelligence data to highlight trends in attacker behavior and targets. The findings aim to enhance awareness and preparedness among cybersecurity professionals, proposing proactive strategies and adaptive countermeasures for mitigating future ransomware threats in an increasingly complex digital landscape.

Abstract - Indonesia’s transition to renewable energy is critical for achieving national development targets and global climate commitments. However, competitiveness in this sector is shaped not only by cost and technology, but also by how firms and ecosystems navigate regulatory complexity, innovation imperatives, and multi-actor collaboration. This study proposes a conceptual model of dynamic competitiveness in Indonesia’s renewable energy sector by integrating three strategic theories: the Value Creation Model, the Sharing Economy Framework, and the Resource-Based View (RBV). Drawing on a synthesis of 27 academic and policy sources, the model identifies four strategic antecedents—innovation strategy, platform integration, community engagement, and policy alignment—that influence competitiveness through three mediating mechanisms: value creation positioning, participatory energy models, and strategic resource deployment. The model conceptualizes renewable energy competitiveness as the central outcome and highlights its practical manifestations in the form of technology adoption, expanded energy access, and an inclusive energy transition. This study offers a theory-driven, context-specific framework for analyzing competitive advantage in energy transitions. The model provides a strategic lens for renewable energy firms, investors, and policymakers to assess competitive positioning, ecosystem readiness, and the pathways toward socially inclusive and resilient clean energy systems

Abstract: MedMate Consult with AI is an innovative Android application designed to revolutionize the way users access healthcare services by providing a comprehensive platform for online medical consultancy. The app allows users to consult with qualified doctors through video calls, ensuring convenient and remote access to healthcare professionals. One of the standouts features of MedMate is its symptom-based doctor recommendation system, which      analyzes  user-provided symptoms and suggests the appropriate medical specialty for consultation, ensuring users connect with the right doctor for their needs. Additionally, the app offers a secure prescription management system, enabling users to save and access their medical prescriptions digitally, promoting better health record management. To maintain the highest standards of healthcare, MedMate incorporates an admin approval system for doctor registrations, where administrators meticulously verify and analyze the documents submitted by doctors before granting them access to the platform. This ensures that only verified and qualified medical professionals are available for consultations, enhancing user trust and safety. MedMate aims to bridge the gap between patients and doctors by providing a seamless, user- friendly, and secure platform for online medical consultations, prescription             management, and symptom-based                     doctor recommendations, ultimately improving accessibility to quality healthcare.

Abstract: The rise of e-commerce has significantly transformed retail, providing unmatched convenience, variety, and affordability. Platforms like Amazon, Myntra, and Flipkart have played key roles in reshaping shopping habits globally, with India emerging as a rapidly expanding market. This shift has been driven by digital advancements, widespread smartphone usage, and better internet accessibility, allowing consumers to browse, compare, and purchase products effortlessly. Kollam district in Kerala is experiencing a growing preference for online shopping. Traditionally known for its rich history and cultural diversity, Kollam is embracing digital retail due to urbanization, tech-savvy youth, and higher disposable incomes. Consumers rely on e-commerce for fashion, electronics, and daily necessities, influencing not only purchasing patterns but also lifestyle choices. This study examines the impact of online shopping on consumer behaviour in Kollam, focusing on Amazon, Myntra, and Flipkart. It explores key factors such as ease of use, product variety, pricing, and promotions that drive purchasing decisions. Additionally, the research investigates challenges like trust issues, product quality concerns, and delivery logistics. By analysing consumer preferences and perceptions, the study aims to provide insights for e-commerce businesses, policymakers, and researchers looking to understand digital retail trends in semi-urban areas. The findings will offer valuable perspectives on evolving shopping habits, helping businesses refine strategies and improve customer experiences in the growing digital marketplace.

Abstract: This case study examines how artificial intelligence (AI) integrated into digital education for reshaping opportunities for empowering women in state like Uttar Pradesh, India’s most populous state by restricting barriers to access, education, skill development, learning quality, and employability. The integration of AI, digital education, and empowering women’s lining up with specialized areas like SDG 4: Quality Education and SDG 5: gender equality for making it highly pertinent for academic policies. This paper investigates the role of AI in education to empower women, particularly those from the marginalized societies. This study demonstrates how AI based platforms is enhancing education, fostering skill development, and economic independence by contributing to bridge the gender divide and social equality for women. A case study method will be applied by examining the real-life examples - Digital Empowerment Foundation’s Digital Literacy, HCLTech foundation’s Samuday, and AI Literacy for All, and some government initiatives like PMGDISHA and Mission Shakti. This approach will provide a comprehensive evaluation of AI use in empowering women through digital education and women’s multi-faceted assessment. The findings reveal that AI driven technologies enhanced the quality of education, vocational programs for women empowerment, and skill development, specifically in marginalized communities. This paper concludes that the intersection of AI in digital education has reshaped women’s by bridging the educational gap and promotes sustainability.

Abstract: One essential component of contemporary renewable energy solutions is wind energy systems. To maximize performance and reduce downtime, these systems must be efficiently controlled and monitored in real time. With an emphasis on control architectures, fault diagnostics, grid synchronization, and SCADA integration, this paper investigates the use of PLCs and automation technologies in wind energy systems. The suggested framework is extremely relevant for the deployment of industrial wind farms since it improves turbine efficiency, guarantees safe operation, and facilitates grid-friendly power delivery.

Abstract: Satellite-based internet constellations, and cybersecurity. Given the advancements in satellite technology and digital communications, which provide both opportunities and challenges. The international law that governs activities in space is known as "space law," and it is dynamic and ever-evolving. The primary concerns of space law have historically been the regulation of space-based weaponry, the cleanup of orbital debris, the launch protocols for satellites and spectrum bands for everyone. Legal positivism theory which can be justifies here as according to John Austin, “the existence of the law is one thing its merit or demerit is another. Whether it be or be not is one enquiry; whether it be or be not conformable to an assumed standard, is another enquiry.”

Abstract: This comprehensive study investigates the integration of Artificial Intelligence (AI) into organizational processes, with a particular emphasis on Human Resource Management (HRM) challenges in the Indian automobile industry. AI technologies such as machine learning, automation, and predictive analytics are transforming traditional HR functions into strategic, data-driven mechanisms. The research employs a mixed-method approach, drawing primary data from six automobile companies (two each from domestic, commercial, and two-wheeler sectors), involving 120 HR professionals. The findings reveal the strategic benefits of AI including enhanced recruitment, targeted employee training, improved performance appraisal systems, and predictive retention strategies. The study also highlights key barriers such as ethical dilemmas, skill deficiencies, and infrastructural challenges. Empirical data are presented through structured tables, and sectoral comparisons reveal the maturity and readiness for AI adoption. The paper offers strategic recommendations for organizations to ethically and effectively integrate AI in HRM, contributing to both academic understanding and industry practice.

Abstract: Happiness and well-being are increasingly recognized as critical components of human development and professional satisfaction. Despite their pivotal role in shaping future generations, college educators in Ernakulam district face challenges that adversely affect their happiness, with limited research exploring these specific issues. This study addresses this gap by examining the factors influencing the happiness of college educators, using both descriptive and analytical research methods. The research also examines the impact of stress and workload on educators' happiness levels. Findings reveal that educator happiness is shaped by a combination of financial and non-financial factors. Moderate salaries are associated with higher well-being, while excessive workloads and limited awareness of government support schemes reduce job satisfaction. The study highlights the need for supportive institutional environments and improved policy communication. By identifying the key influencers, this research provides actionable insights for policymakers and academic institutions to implement targeted strategies that enhance job satisfaction, reduce burnout, and ultimately foster a more motivated and effective teaching workforce. Promoting educator happiness not only benefits individual well-being but also improves instructional quality and student outcomes, contributing to a stronger educational ecosystem.

Abstract: Recognizing the essential for change and foreseeing organizations through change is the most challenging role for any leadership. Some changes can be alterable while others are not hence the risk involved in managing change. The study sought to determine the role of strategic culture on employee performance in public universities Upper Eastern Region, Kenya. Strategic culture refers to collective values, beliefs, norms and practices that define the character of a person in the work environment. The study was anchored on Kurt Lewis Change Management Theory. Descriptive survey design was adopted using quantitative methods. Employees of these universities were the study population, with a target population of 1296 employees and a sample size of 324 respondents. Proportional sampling technique was used in selecting Academic Staff and Support Staff while purposive sampling technique was used to select the university Management Staff, lastly stratified sampling was used to select the Administrative Staff. Data was obtained using questionnaires. The findings on correlation and regression analysis indicates that there was a significant positive relationship between strategic culture and employee performance in public universities in Upper Eastern, Kenya, where it was able to explain (β=0.495; p=0).

Abstract:   The objectives of this research are (1) To find out the amount of production costs. (2) To determine the amount of production, revenue and income from vegetable farming, the diversification pattern of spinach, kale and mustard greens in Nagori Purbasari, Tapian Dolok District, Simalungun Regency. The sampling technique used was a purposive sampling technique using the Slovin formula to find a sample size of 15 farmers. The data analysis method uses (1) production cost analysis and (2) farming analysis to calculate income and R/C ratio (farming feasibility). Research Results (1) The average production costs incurred by respondent farmers in this study was IDR 2,112,657 per farm. Where the highest costs are incurred for the use of labor production facilities. (2) The average income value is Rp. 1,312,257 per farmer in one month, with an R/C value of 1.6, which means that every farmer who spends Rp. 1 will get Rp. 1.6. Where the meaning of the R/C value = 1.6 is that the farming pattern of vegetable crop diversification of respondent farmers in Nagori Purbasari, Tapian Dolok District, Simalungun Regency is in a profitable condition but not yet suitable for development.

Abstract: Rituals serve as powerful tools in shaping organizational behavior and enhancing employee well-being by fostering consistency, emotional regulation, and cultural cohesion.  This article explores the multifaceted role of rituals in organizational contexts, highlighting their ability to reinforce core values, support employee engagement, and guide behavioral norms.  Drawing from neuroscience, organizational psychology, and empirical evidence, the study illustrates how rituals, ranging from daily check-ins and recognition ceremonies to mindfulness practices create predictable environments that promote stability, especially during periods of uncertainty or change.  Rituals activate neurological pathways associated with motivation, reward, and social bonding, thereby reinforcing desired behaviors and strengthening interpersonal relationships.  Moreover, case studies from organizations such as Google, Zappos, and Intel demonstrate that the implementation of structured rituals can enhance psychological safety, reduce stress, and improve team performance.  The paper also discusses how cultural and organizational variations influence the form and impact of rituals, emphasizing the need for context-sensitive applications in global and diverse work environments.  Leadership plays a critical role in embedding and modeling rituals that reflect organizational values, driving morale and cohesion.  However, the article cautions against the overuse or misalignment of rituals, which can lead to rigidity or employee disengagement.  Balanced and adaptive ritual practices, tailored to organizational needs can mitigate such risks and sustain long-term impact.  Ultimately, rituals contribute to organizational resilience, enhance job satisfaction, and support employee mental health, making them indispensable in modern workplace strategy.  By integrating rituals intentionally and authentically into organizational life, leaders can cultivate a high-performing, emotionally resilient, and value-driven workforce.

Abstract: The research studies how behavioral biases is affecting the teaching fraternity's investment choices. Many studies have been conducted in the area of investment decision-making, frequently to know the influence of behavioral biases on teaching people. Despite the unique traits of this group, little research particularly examines how these biases affects educators' investment decisions. The Investment decision has been depended on the two factors Emotional Biases and Cognitive biases. In this research cognitive biases frequently encourages teaching fields to adopt risky investment practices, while loss aversion makes them reluctant to sell up poor assets. It suggests that herding behavior is common, possibly caused by the social dynamics in educational institutions. A sample of 109 faculties across Bengaluru city is used in the research. The study aims to provide information regarding behavioral biases that the teaching fraternity's investment practices exhibit. It also seeks to spot decision-making trends that can result in investing problems. The results will add more knowledge on behavioral finance, especially in relation to a particular professional group. The findings can be used to develop focused educational interventions aimed at enhancing instructors' financial literacy and decision-making abilities.

Abstract: This paper explores the influence of LGBTQ+ (Lesbian, Gay, Bisexual, Transgender, Queer/Questioning, and others) representation in advertising on brand loyalty among Generation Z (Gen Z) consumers across various cultural contexts. Using a secondary research methodology, the study synthesizes insights from academic literature, marketing reports, and brand case studies to examine how inclusivity in brand messaging impacts Gen Z’s purchasing behavior and brand affiliation. Findings highlight that authentic representation fosters emotional engagement and long-term brand loyalty, while performative allyship often leads to backlash and reputational damage. The research also emphasizes the role of cultural sensitivity in shaping consumer perceptions, particularly in regions where LGBTQ+ topics remain contentious. Ultimately, this paper argues that inclusive branding, when implemented with sincerity and consistency, is not only ethically significant but also a strategic imperative for businesses seeking sustained relationships with Gen Z consumers worldwide.

Abstract- In the dynamic corporate environment, environmental sustainability has emerged as a pivotal factor influencing organizational success. Green Human Resource Management (GHRM) has been recognized as a strategic method to synchronize human resource policies with environmental goals, thereby improving organizational performance and valuation. This research examines the effects of GHRM practices on the valuation of Fast-Moving Consumer Goods (FMCG) companies, utilizing the Ability-Motivation-Opportunity (AMO) framework. A purposive sample of 14 FMCG companies listed in the Nifty 100 is analysed, with data spanning from 2019-2020 to 2023-2024, employing content analysis to evaluate the impact of GHRM components on firm valuation, as indicated by market capitalization. The research underscores the role of green HR practices, including green recruitment, training, and reward systems, in enhancing employee capabilities, fostering eco-friendly motivation, and creating opportunities for sustainable engagement. To address the objective the study has undertaken the wide usage of content analysis to quantify the aspects of the AMO framework from the company’s annual report and has executed the regression equation using the balanced panel framework. The results indicate a significant relationship between a few GHRM initiatives, namely recruitment & selection, pay, organizational culture, decision-making, and firm valuation, highlighting their strategic significance in tackling environmental issues, improving brand reputation, and promoting long-term competitiveness. This study enhances the comprehension of GHRM within the FMCG sector and provides practical recommendations for organizations aiming to incorporate sustainability into their human resource strategies.

Abstract: Forensic Accounting has emerged as vital method for detecting and mitigating corporate frauds and enhancing transparency in financial management of corporates. This study discusses the available theoretical framework of forensic accounting in India by probing the opinions of accounting professionals, particularly Chartered Accountants (CAs) in Karnataka. The study digs into key aspects like applicable legal framework and recently introduced Forensic Accounting and Investigation Standards (FAIS) 2023. With an extensive literature review and pragmatic analysis, the study highpoints the objectives of FAIS 2023 in fraud detection and mitigation. A survey conducted among accounting professionals reveals the perception about awareness of current framework and implementation challenges. The study ends with recommendations for strengthening the forensic accounting discipline to address corporate frauds more efficiently and effectively in India.

Abstract:  Amazon's e-commerce expansion in Sri Lanka may have an impact on local businesses, customer behavior, and market competitiveness, according to the survey. Sri Lanka's digital economy is going through significant changes as e-commerce expands internationally, posing both opportunities and challenges for local companies. The global e-commerce company Amazon has already established a significant position in several countries, serving a various customer base with cutting-edge technology, powerful logistical systems, and an extensive product selection. This study looks into the potential effects of these abilities on Sri Lankan market dynamics.

Abstract—This research investigates how social media us- age and study hours influence students’ academic performance and psychological well-being. Using multiple student-submitted datasets, we analyze behavioral patterns and academic outcomes through structured preprocessing, scoring, and visualization. The study finds that while social media correlates moderately with mental health indicators like anxiety and distraction, its direct relationship with grades remains weak and inconclusive due to self-reporting limitations. Our findings highlight key trends and offer insights for promoting balanced digital behavior.

Abstract: The Virtual Private Network (VPN) is a rapidly developing technology that has a crucial function in guaranteeing the secure transfer of data. A virtual linkage is created among geographically distant individuals and networks that are linked through a common or public network, such as the Internet. Despite the fact that data is transmitted via a public network, a Virtual Private Network (VPN) signifies that the data is conveyed through a secure and private connection. Virtual Private Network (VPN) technology enables secure communication between two hosts by creating virtual tunnels. Upon the establishment of a tunnel, it becomes possible to transmit data between the two servers. This paper provides a comprehensive analysis of Virtual Private Networks (VPNs), including their historical background, architectural design, employed protocols, and identified vulner- abilities. A Virtual Private Network (VPN) is implemented to secure the private network by utilising encryption and other security measures, thereby ensuring that solely authorised users are granted access to the system and preventing data interception. This survey comprehensively covers the topics of Virtual Private Networks (VPNs), their protocols, and the security measures implemented within VPNs. The present article has conducted an overview of the diverse protocols employed in the domain of Virtual Private Networking.

Abstract: Railway platform gaps present significant threats to passenger safety, convenience, and efficiency.  An automatic railway platform connecting bridge (ARPCB) is a promising solution that uses a mechanized bridge to cover the gap between the train and the platform.  This study provides a thorough examination of the design, implementation, and advantages of ARPCB systems.  Various existing methodologies, mechanisms, and control strategies are reviewed.  Additionally, the report identifies problems and potential research objectives for improving railway platform accessibility.

Abstract — In an age where learning opportunities are abundant but time and attention are limited, personalized learning has become more important than ever. This paper introduces EduGenius, an AI-powered web platform designed to help learners achieve their goals through customized learning experiences. Users can input a topic they want to learn, along with their available time, language preference, and current skill level. The system then generates a personalized roadmap, complete with curated resources, schedules, and quizzes. Using generative AI, EduGenius dynamically adjusts the roadmap based on feedback and quiz performance to keep the learner on track. Progress is visualized through intuitive dashboards, and a gamified interface keeps users engaged with rewards like badges and points. EduGenius combines the power of AI with human- centered design to make learning more effective, enjoyable, and tailored to each individual.

Abstract— Emergency withdrawal plans are important in reality mapping of industrial threat areas and reducing the effects of disasters in high -risk areas. The project focuses on oil and gas refinery facilities across India, which suffers from incidents such as chemical leaks, fire and explosion. The proposed web application, ignition watch, integrates geophagical devices such as Leaflet.JS to imagine the risk areas through an interactive map. Live meteorological data - including air speed and UV radiation - is obtained through public API to assess environmental conditions affecting the spread of danger. The system calculates three concentrated danger areas (high, medium, low risk) with dynamic ready, which provides real -time estimates of potential danger areas. The smallest path for clearance is determined using the Dijkstra’s algorithm, which guides the populated population in the nearest predetermined safe areas. The system identifies and displays the nearest fire station or emergency response unit for each refinery. A search feature allows users to detect specific refineries and look at live danger updates and customized escape routes. Unlike traditional systems relying on static predictions, this device optimized for real-time environmental changes, providing accurate, location-specific statusable awareness. It aims to support public to improve preparations for emergency planners, first respondents and public, reduce response time and reduce the loss of life and infrastructure.

Abstract: This project presents a highly secure and intelligent Bank Locker Security System that integrates multiple layers of authentication, including face recognition, OTP verification, and traditional physical key access, to ensure maximum safety and reliability. The system aims to overcome the limitations of conventional bank locker mechanisms by introducing a multi-factor authentication model that minimizes the risk of unauthorized access and theft. The face recognition module, powered by AI, authenticates the customer using live camera input, while a one-time password (OTP) sent to the registered mobile number acts as a second layer of security. Only after successful verification of both digital steps is the customer allowed to use the physical key to access the locker, thereby creating a robust three-level authentication system. The solution also includes an admin dashboard for locker management, user access control, and real-time security logs. This modernized locker system enhances trust, improves security standards, and brings smart automation to traditional banking services.

Abstract: Environmental, Social, and Governance (ESG) frameworks are becoming the cornerstone of responsible decision-making in business and policy worldwide. In India, ESG is rapidly gaining momentum as both a regulatory expectation and a societal demand. This article evaluates India’s evolving ESG landscape, challenges, policy direction, and the role of public and private sectors in driving sustainability and inclusive growth. This article presents a systemic appraisal of India’s ESG landscape—tracing regulatory milestones, market signals, institutional bottlenecks, and firm‑level incentives. It concludes by proposing a calibrated roadmap that includes standardisation, enforcement, capacity‑building, and financial innovation to ensure that ESG not only aligns with global norms—but also advances India’s developmental and climate objectives.

Abstract: Microfinance has become a crucial mechanism for fostering rural development and promoting financial inclusion, particularly in developing countries like India. With a large proportion of India’s population residing in rural areas, access to formal financial services has historically been limited. Microfinance bridges this gap by providing small loans, savings accounts, and insurance products to underserved populations, particularly the poor and women, empowering them to start businesses, improve agricultural productivity, and enhance their standard of living. In rural India, microfinance institutions (MFIs) facilitate access to finance that traditional banks often overlook due to high transaction costs and perceived risks. By offering financial products tailored to the needs of low-income communities, microfinance enables individuals to move away from informal lenders with exploitative interest rates, fostering economic stability. Additionally, it promotes entrepreneurship and job creation, which are critical for local economic development.

Abstract: This study examined the extent to which perceived librarians’ service behavior and library anxiety are associated with library utilization among 295 senior high school students in a private university in Cagayan de Oro City. This descriptive-correlational study employed modified instruments and those designed by the researcher for data collection. The data were analysed using both descriptive and inferential statistics, with a particular focus on Spearman’s rho. The findings of the study revealed the following: the students viewed the librarians’ service behavior very positively, citing their professionalism, friendliness, and willingness to assist; the students experienced very low anxiety related to comfort in the library, moderate anxiety related to knowledge of library systems and mechanical aspects, and high anxiety pertaining to affective barriers; and the students had high utilization of the library’s print and electronic resources. Statistical analysis showed a significant positive correlation between perceived service behavior and library utilization. Additionally, a significant negative association was found between library anxiety and library utilization. These findings highlight the importance of librarians’ role in advancing service quality and in reducing library anxiety to promote full student utilization of library resources.

Abstract —Theemergenceofblockchaintechnologyhasrevolu-tionizeddigitalownershipthroughNon-Fungible Tokens (NFTs), enabling secure and verifiable asset transactions. However, con- ventional NFT marketplaces often lack interactive features that enhance user engagement. This research proposes an inno-vative NFT marketplaceintegratingareal-timechatsystem and Augmented Reality (AR) for 3D model augmentation. The platform enhances user experience by allowing seamless asset visualization,real-timecommunication, and securetradingwithin an immersive environment. The integration of AR provides a more intuitive interaction with NFTs, while the chat system fosters community engagement. Additionally, blockchain-based smart contracts ensure transaction transparency and security. This study explores the technical implementation, challenges,and impact of these integrations on the adoption of NFT marketplaces. The findings highlight the potential for a more interactive, engaging, and user-friendly digital asset ecosystem, paving the way for the next generation of NFT platforms. Overall the research goals are strong. However, consider simplifying sentence structure for clarity and replacing phrases like “enabling secure and verifiable asset transactions” with plainer language

Abstract: This study evaluates the skill sets of male and female BSBA Marketing Management graduates, focusing on the importance and proficiency of key competency areas. The findings show that male graduates tend to prioritize business and management competencies along with leadership skills. In contrast, female graduates place greater emphasis on interpersonal qualities and personal attitudes. Across all domains, males consistently rate their proficiency higher, with the largest gap observed in their knowledge and understanding of the job. These differences suggest distinct learning styles and career orientations between genders, highlighting the need for tailored strategies to enhance skills effectively. To address these needs, the study recommends specific interventions: leadership training, digital marketing certifications, and cross-functional projects for male students; and mentorship programs, confidence-building workshops, global marketing exposure, and communication training for female students. By implementing these customized skill development strategies, marketing education can cultivate balanced competencies and support a more inclusive and competitive workforce.

Abstract: This study examines capital structure and financial performance of quoted manufacturing companies in Nigeria, with a view to identifying the optimal mix of financing that enhances company profitability and market valuation. Employing an ex-post facto research design, the study analyzes secondary data from 38 manufacturing companies listed on the Nigerian Exchange Group (NGX) over the period 2012 to 2023. The investigation utilizes a panel Autoregressive Distributed Lag (ARDL) model—including error correction and bounds testing—to differentiate between short-run and long-run effects short-term debt, and long-term debt on key performance indicators such as Return on Assets (ROA), Return on Equity (ROE), and Tobin’s Q. The empirical findings reveal that long-term debt financing, exhibits a positive influence on both ROA and Tobin’s Q in the long run, indicating its role in financing growth and operational improvements through tax advantages and disciplined managerial oversight. Short-term debt, on the other hand, negatively affects financial performance in the short term due to liquidity risks and the burden of frequent repayments. Nevertheless, short term debt when used judiciously, can provide several notable advantages that complement the broader capital structure of a manufacturing company. One of the primary benefits of short-term debt is its inherent flexibility.

Abstract: Roads are built as infrastructure to facilitate mobility and accessibility of socio-economic activities in society. Highway is very essential in that it support the growth rate of the economy, agriculture, education and other sectors. Considering the benefits of road in economy growth, matters related to the development and maintenance of roads are a priority to be researched and developed in planning, implementing and maintaining them. One of the research targets is the paved roads within the Federal Polytechnic Ado Ekiti campus, which suffered significant defects, both minor and moderate damage. Although recently, most of these roads were rehabilitated but presently these roads are characterized by one defect or the other evidently noticed by some types of cracks on the surface of the pavements. The purpose of this study is to determine the type, extent and severity of these damages to the road surface, and then suggest measures for maintenance of these roads in order to optimize the durability of the roads. The application of the PCI is carried out by visual survey at the research location, determining the type and level of damage and measuring the damage dimensions which include length, width and depth, calculating the area of damage, analyzing the damage condition. The results of the analysis of the campus roads are classified with an average PCI value of 65. The appropriate improvement strategy suggested is routine maintenance.

Abstract: The microbial treatment methods are losing potentiality as pathogens are swiftly gaining resistance to antibiotics, which poses a threat to global health communities and necessitates searching for alternatives therapeutics from plants origin with limited side effects. This study aimed to investigate the phytochemical components and antimicrobial properties of extracts from Momordica balsamina leaves to scientifically justify the use of this plant as remedy for treating certain ailments. The ground powdered leaves of Momordica balsamina were percolated with Ethanol and fractioned into n–Hexane, Chloroform and Methanol fractions which were evaluated against Bacillus subtilis, Escherichia coli, Salmonella typhi and Staphylococcus aureus using Agar well diffusion method. The MIC and MBC were determined. Qualitative phytochemical screening of the plant extracts using standard method revealed the presence of alkaloids, flavonoids, glycosides, steroids, saponins and tannins. The antimicrobial analysis results revealed sensitivity with highest zone of inhibition of 29.0±2.0mm, 27.0±3.0mm, 19.0±1.0mm and 15.0±2.0mm for ethanol extract against Bacillus subtilis, Escherichia coli, Salmonella typhi and Staphylococcus aureus respectively. This study revealed extracts from Momordica balsamina leaves contained some bioactive compounds that could be used to treat ailments caused by these bacteria tested.

Abstract: This study determines risk mitigation strategies and practices in the retail gas sector of Nueva Ecija, utilized descriptive correlational research methods. Data gathering included distributing a researcher-made survey questionnaire to 150 respondents, consisting of 50 managers, employees, and customers across five cities within Nueva Ecija. The study findings reveal significant insights into the family-owned businesses in the retail gas sector of Nueva Ecija, highlighting their operational characteristics and risk management practices. Family-owned businesses, particularly those operating for 5 to 10 years with 5 to 10 employees, constitute a prominent segment. In this sector, emphasis is placed on adaptive capacity, environmental practices, and proactive measures to address emerging risks. Mitigating supply chain risks is crucial, with strategies like supplier diversification, inventory management adjustments, and leveraging digital technologies being key. The acceptance of the null hypothesis indicates that socio-demographic factors of respondents do not significantly influence risk mitigation practices. Building upon these findings, a proposed plan has been developed to enhance risk management within the retail gas industry. This plan encompasses activities tailored to address vulnerabilities, enhance resilience, and optimize operations, ultimately aiming to equip stakeholders with effective strategies for long-term success in a dynamic environment.

Abstract: This paper presents the creation of an economical, self-sufficient mobile robot intended for real-time obstacle avoidance and detection of uneven surfaces, ensuring safe and efficient navigation in unstructured settings. Constructed on the Arduino Uno platform, the system incorporates an HC-SR04 ultrasonic sensor for proximity-based obstacle detection and an MPU6050 accelerometer/gyroscope module to identify surface inclinations and irregular terrains. Additionally, the robot features an L298N motor driver that facilitates precise movement control, while a 16×2 LCD module offers ongoing feedback regarding system status and environmental conditions. The approach includes sensor fusion, modular hardware integration, and embedded software design, enabling robust decision-making and real-time adaptability. Experimental assessments reveal the system’s capability to navigate various terrains and avoid obstacles with minimal latency and high precision. The design's modularity, cost-effectiveness, and ease of deployment render it suitable for numerous applications, such as industrial automation, educational robotics, exploration, and disaster response. The findings highlight the potential of integrating obstacle avoidance with surface detection within a cohesive framework to improve autonomous robotic mobility in intricate real-world situations.

Abstract – Jeepneys have long served as a vital mode of public transportation in the Philippines. However, the traditional manual fare collection methods commonly used present challenges such as payment delays, human errors, and inefficiencies in route management. This study introduces an architectural design for an enhanced fare collection and tracking system that integrates automated fare processing with Global Positioning System technology. The system leverages Radio Frequency Identification to facilitate contactless fare collection, allowing passengers to pay electronically while enabling real-time tracking of jeepney locations. Targeting both jeepney operators and passengers, the system aims to optimize operations and improve the overall commuting experience. A purposive sampling technique was employed to select relevant participants, including jeepney operators, drivers, IT professionals, and passengers. Data were gathered through surveys, interviews, direct observations, and structured questionnaires. System development tools included Visual Studio Code for website implementation, Figma for user interface design, and Arduino IDE for hardware integration. Results indicate that the combination of RFID and GPS technologies significantly reduces fare-related errors, minimizes disputes, and eliminates the need for manual fare handling. Moreover, the automated system accelerates boarding processes and allows operators to manage routes and fare collection more efficiently. Overall, the study demonstrates that integrating modern technologies into jeepney operations can substantially enhance both operational efficiency and the commuting experience.

Abstract: Anomaly detection using Convolutional Neural Networks (CNNs) has emerged as a powerful tool for identifying criminal activities, including robberies, assaults, and homicides, within surveillance environments. This research presents a deep learning-based framework that leverages CNNs for spatial feature extraction and combines them with temporal modelling to recognize irregular behaviour in public safety contexts. By analysing surveillance footage and sensor-based data, the system detects anomalies in movement patterns, crowd density, and object interactions, thereby aiding in real-time threat assessment and crime prevention. The proposed method utilizes pre-trained CNN models for high-level visual representation and integrates hybrid approaches, such as CNN-LSTM and 3D CNNs, to capture spatiotemporal dynamics of suspicious activities. Our framework is tested on benchmark datasets like UCF-Crime and UAV surveillance feeds, achieving high accuracy in detecting abnormal behaviour. Furthermore, anomaly detection is enhanced using advanced feature extraction techniques and real-time classification mechanisms tailored for smart city surveillance systems. This study contributes to the evolving field of AI-driven public safety by integrating CNN architectures with context-aware, temporal anomaly detection techniques, enabling proactive criminal activity prediction and continuous learning from dynamic environments. The results demonstrate the potential of deep neural networks to support law enforcement agencies in real-time monitoring, ultimately reducing crime response times and enhancing urban security infrastructures.

Abstract—Renewable-energy integration into power grids is constrained by the variable output of solar and wind resources. This paper proposes a Hybrid Energy Storage System (HESS) that couples lithium-ion batteries, supercapacitors, and flywheels and governs them with a Unified Mathematical Method (UMM) combining moving-average filtering with threshold-based cut-off logic. The architecture is modelled in HOMER Pro for the Grand Forks, ND (USA) resource profile and bench-marked against “Grid+Renewables” and “Grid+Renewables+Battery” baselines. The full three-storage configuration supplies 1 032 320 kWh yr−1 of useful energy—an increase of 77 % over the no- storage case—and eliminates 1.36 Mt CO2 yr−1 of emissions, a 245 % improvement relative to renewables alone. Valued at the Social Cost of Carbon (US$51 t−1) and the 45Q tax credit (US$85 t−1), the avoided emissions translate to annual economic benefits of US$69 000–US$116 000. The UMM reduces false cut-off events by more than 30 %, prolonging component life and enhancing overall system reliability. These results confirm that a tri-technology HESS managed by a unified control layer delivers superior technical performance, environmental gains, and financial returns compared with single-storage or no-storage configurations.

Abstract: In an era marked by hyperconnectivity and digital dependence, malicious websites have emerged as covert yet formidable threats to user privacy and data security. This paper critically examines the evolving architecture of spyware-laden websites, offering a structured typology of fifty distinct categories based on their operational logic, attack vectors, and user deception techniques. By integrating cybersecurity theory with real-time simulation, the study reveals how browser-level JavaScript functions are manipulated to implement surveillance tactics such as keylogging, clipboard hijacking, fingerprinting, and credential harvesting often without user awareness. Moving beyond surface-level classification, the paper explores the psychological dimensions of user behavior, including motivation, cognitive bias, and security fatigue, which often facilitate successful attacks. A live JavaScript demonstration is presented to illustrate the subtle but dangerous mechanisms employed by threat actors, grounding abstract concepts in practical reality. The paper further examines the role of emerging technologies such as artificial intelligence and machine learning in detecting and mitigating these threats. It also advocates for a proactive cybersecurity paradigm that emphasizes user empowerment, community engagement, and policy responsiveness. Through an interdisciplinary lens, the study argues that combating web-based spyware requires not only technical innovation but also behavioral insight and systemic reform. By exposing the invisible battlefield embedded within everyday browsing, this paper seeks to elevate digital literacy, inform security protocols, and inspire a new generation of defense strategies in the face of an increasingly deceptive online landscape.

Abstract: An increasing number of new businesses, technologies, and products are emerging, all undergoing rapid and continuous transformation. Collectively, these changes have created a new paradigm in global business, particularly intensifying competition within the engineering sector. In response to globalization and heightened market competition, companies have recognized the need for engineers at all levels who can work effectively in cross-functional and multicultural teams, possess leadership or managerial capabilities, and understand the broader business and societal implications of engineering decisions. Regardless of their area of specialization, engineers are finding that a successful career increasingly requires them to take on complex responsibilities that demand strong leadership skills. Their work now relies heavily on understanding and managing interactions across diverse domains—such as design, quality, sustainability, product planning, organizational behavior, people management, teamwork, and finance. The aim of this thesis is to explore the concept of engineering leadership, identify and understand the skills challenges engineers face in today’s highly competitive global environment—particularly as they transition into managerial roles—and propose solutions or guidance to help mitigate these challenges.

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Abstract – This study presents an accident prediction model for three different segments along Maharlika Highway, San Pablo City, Laguna, which utilizes an Artificial Neural Network (ANN) to analyze accident-related data from the area. The dataset included parameters such as vehicle type, driver’s age, license type, gender, month of occurrence, DUI of alcohol, time and day of accidents, traffic volume, and road characteristics that were used for feature selection. Before the study creation, data preprocessing was conducted to clean and organize the data. These data were trained to predict the likelihood of RTAs based on these features. The results showed that the most relevant features based on their contribution to accidents were vehicle type with 20%, age of driver with 16%, and license with 14%. The study also predicted accidents for 2024-2028 using the ANN-LSTM Prediction. The data were divided for testing, training, and validation. Segment 1 and Segment 3 showed the lowest MSE values of 1.92 and 1.95, respectively, with an epoch of 1100, while Segment 2 showed the lowest MSE of 1.81 with 600 epochs. The LSTM prediction demonstrated different accident trends for the three segments. For Segment 1 and Segment 2, there was an increase in accidents in 2025 before peaking in 2026 and 2027, respectively, followed by a decline in the following years. For Segment 3, there was a potential drop in 2025 before peaking in 2026 with 28 predicted accidents. The predicted trends implied a possible rise in accidents in the next few years, followed by a decline.

Abstract— In this paper, unmanned aerial vehicles (UAVs) for monitoring and surveillance within aquaculture farms situated in Iloilo, Philippines are investigated. The principal aim is to create a framework that can provide guidance on how to use UAVs for effective monitoring, data collection, and regulatory control within the aquaculture sector under the supervision of – Department of Agriculture in the Municipality of Barotac and Dumangas, Iloilo. The study examines the performance of UAVs in real-time surveillance mapping as well as geotagging and geo- referencing that would help in observing fish behavior and environmental settings. The research assesses the precision of UAVs in executing these assignments and compares various models based on their effectiveness in monitoring perimeters environmental data collecting and following changes in fish behavior. Findings reveal that UAVs especially those with high detection precision levels and advanced mapping capabilities may be good tools for improving management practices on aquaculture farms. The paper suggests some ways we may adopt UAV technology within aquaculture emphasizing its potential to minimize operational costs while at the same time increasing productivity. With the help of these findings, it is possible to develop initial guidelines for UAV use in the aquaculture industry that will serve as a basis for a regulatory framework ensuring the sustainable and efficient functioning of aquaculture farms. This framework seeks to support local government initiatives in enhancing farm monitoring, protecting the environment and managing resources by using advanced UAV technology.

Abstract— This paper examines the minimum initial capital an insurer needs to maintain to prevent insolvency, given that the probability of ruin does not surpass 1% and 5% of the discrete-time surplus process with an investment in a mutual fund. The simulation method is employed to determine the probability of ruin, while the regression technique is utilized for calculating the minimum initial capital.

Abstract: The textile industry is focusing on consumerization in the name of adaptation of innovation and advanced technology. To meet the demand for sustainability, the incorporation of natural textiles is necessary, and the adoption of natural and less harmful processes for the finishing process of the textiles, such as scouring, bleaching, and other wet processing treatments, so that the minimum use of chemicals leads to less hazardous conditions for human beings and the environment. This study provides a broad, comprehensive overview of existing research on scouring methods for cotton fabric.

Abstract: West Africa's e-commerce sector is expanding rapidly due to growing internet access, mobile adoption, and AfCFTA, yet remains constrained by infrastructure gaps, cybersecurity threats, and regulatory fragmentation. This pioneering mixed-methods study compares these barriers across six nations (Ghana, Nigeria, Senegal, Liberia, Sierra Leone, Guinea), integrating World Bank/ITU datasets with regulator interviews (n=12) and SME surveys (n=150). The analysis reveals severe urban-rural ICT disparities (>39 percentage points) limiting SME participation, Nigeria's disproportionate cybercrime burden (58% of regional losses, $500M annually), and costly regulatory misalignment (15-30% of revenues). These findings empirically validate van Dijk's (2020) digital divide theory and Williamson's (1981) transaction cost economics in West Africa's digital markets. Urgent interventions include targeted ICT investments to achieve 52% penetration thresholds, an ECOWAS Cybercrime Task Force (projected 22-28% fraud reduction), and AfCFTA protocol harmonization adopting Ghana's hybrid governance model ($140M annual compliance savings). These measures directly address implementation gaps in regional digital trade. The study calls for longitudinal tracking via a West African Digital Trade Index and AI-driven customs solutions to sustain progress. These evidence-based solutions balance immediate action with long-term innovation for inclusive digital growth.

Abstract—The rapid advancement of additive manufacturing has transformed prototyping and small-scale production by enabling the creation of complex geometries and customizable products through layer-by-layer fabrication. This paper presents the design, development, and evaluation of a cost-effective desktop 3D printer optimized for rapid additive manufacturing using Fused Deposition Modeling (FDM) technology. The printer combines a rigid aluminum extrusion frame, NEMA 17 stepper motors, a Bowden extruder with an all-metal hotend, and open- source electronics controlled by a 32-bit microcontroller running Marlin firmware. It supports a variety of thermoplastic materials such as PLA, ABS, PETG, and TPU, achieving print speeds up to 60 mm/s while maintaining dimensional accuracy within ±0.2 mm. Key features include automated mesh bed leveling, PID thermal control, and a user-friendly touchscreen interface. Iterative testing revealed improvements in filament handling and vibration control, confirming the printer’s suitability for edu- cational, research, and light industrial prototyping applications. This project highlights the potential of modular, customizable 3D printing platforms to democratize rapid additive manufacturing.

Abstract: Limitations more chaffy grains are collected in November and December. Skilled Paddy Yellowing of transplanted seedlings throughout winter season. The primary food crop of Tamil Nadu, paddy, is widely grown in the Vellore District over an area typically covering 42,900 hectares. The State Government has made numerous efforts over the years to deploy a wide range of advanced technology under different schemes in an effort to increase farm output. The Rice Intensification System (SRI) in Vellore District, a variety of technology techniques for water usage was pushed. The use of this innovative technology resulted in a 30% increase in productivity compared to the traditional farming approach. Farmers were introduced to this technique through demonstrations conducted as part of several Central and State government programs. Large numbers of middlemen, pricing fluctuations, payment delays, a lack of market information, and a lack of cooperative markets are examples of marketing issues. The bulk of rice farmers rely significantly on government marketing assistance, input subsidies, support prices, and useful rates of deduction for input costs.

Abstract - This study aims to identify the key factors influencing attitudes toward advertising on Instagram in young adults. With the platform's rapid growth as a primary medium for social networking sites, understanding user attitudes toward Instagram ads is essential for marketers seeking to optimize their campaigns. determinants such. Data was collected conveniently through surveys from Instagram users aged 18-30 years (young adults). The findings suggest that factors like as Overall attitude towards advertising, Consumerism, positive economic effect, Trust, and user demographics that shape individual attitudes form the attitudes of consumers, while overexposure and intrusive formats may lead to negative perceptions. The study concludes by offering actionable insights for brands to enhance user engagement and optimize ad strategies on Instagram. This research contributes to the growing body of knowledge on digital advertising and consumer behavior in social media environments.

Abstract: This study examined the moderating effect of firm size and age on the relationship between debt capital and financial performance of Listed Energy utility Firms in Nigeria for the period of five years (2019-2023). Short term Debt, Long Term debt and Total debt proxied debt capital while Return on Assets proxied financial performance. Firm size and age served as moderating variable. The study adopted ex-post facto research design and inferential statistics. Robust Fixed effect Models and Drisc/Kraay Standard Error were used in analyzing the data. Primary findings revealed that short-term debt, long-term debt and total debt have negative significant effect on return on assets. However, the secondary findings have revealed that moderated firm size and age on the total -term-debt has positive insignificant effect on return on assets while moderated-long-term-debt and moderated-total-debt have positive significant effect on return on assets. The moderation R-Squared was 0.901 while the primary R-Squared was 0.554. That means, firm size and age has moderated debt by 0.860. In view of the findings, this study concludes that firm size and age has great moderating effect on debt capital. Therefore, this study recommends that investors should consider firm size and age as an effective variable in making investment and financial decision. Secondly, management of companies should consider firm size and age in determining the debt to be collected.

Abstract: This study helps to explore and analyse the various factors influencing consumer purchasing behaviour towards Big Basket. Important factors examined include product variety, pricing strategies, delivery efficiency, promotional offers, digital user experience, and customer trust. A mixed-method approach was adopted, combining quantitative data from structured questionnaires and qualitative insights from consumer interviews. The findings reveal that convenience, competitive pricing, flexible delivery schedules, and trust in product quality are the most influential factors driving consumer preference. Additionally, digital literacy and previous online shopping experiences play a moderating role in shaping consumer choices. This study provides valuable inputs for e-grocery businesses, marketers, and stakeholders to better understand consumer expectations and to formulate strategies that enhance customer satisfaction and loyalty in a highly competitive market condition.

Abstract: This research shows that consumer brand loyalty and purchase behaviour towards One8, a celebrity-led lifestyle brand endorsed by Indian cricket icon Virat Kohli. The research aims to explore how factors such as product quality, brand image, celebrity endorsement, and emotional connection influence consumer decisions. With the rise of celebrity-endorsed brands in India, One8 has emerged as a key player in the lifestyle and athleisure market, offering a diverse product range including athleisure wear, fragrances, innerwear, footwear, and fitness services. The study utilized a descriptive research methodology with a sample size of 180 respondents, primarily from urban areas, aged between 18–30. Data was collected through structured questionnaires and analyzed using charts and graphs. Findings reveal that One8 enjoys strong consumer loyalty, particularly in athleisure and innerwear segments, driven by consistent product quality and the aspirational value attached to Virat Kohli’s endorsement. Most consumers prefer online shopping, and digital platforms like social media play a critical role in product discovery. Furthermore, sustainability and ethical practices emerged as influential factors in purchasing decisions. The results emphasize that brand loyalty is largely influenced by emotional attachment, perceived product value, and brand reputation. However, the study is limited by its geographic focus and sample scope. Future research should include comparative analysis with competing brands and a wider demographic range to gain deeper insights. The findings can guide One8’s marketing strategies to enhance customer retention, improve product offerings, and strengthen its brand position in the competitive lifestyle segment.

Abstract: It is no wonder that in today’s aggressive business environment, the challenges of sustaining in these competitive surroundings, preoccupies the minds of many Business leaders. Customers have many options to choose their telecom operator from competitive market. Furthermore, they often perceive that what they are purchasing is, for all practical purposes, a commodity that can be easily obtained from other companies, if need be. So, how does a corporation distinguish itself in a highly commoditized and competitive market? Today’s business is more dependent, than ever before, on their top performing product to be innovate and provide services that differentiate a company from its fierce competitors. In other words, corporations rely upon their product and human assets to survive and thrive. Airtel is one of the leading telecom firms of the country. Airtel is providing intermediate services to the people of India since 1995. The company is largest cellular service provider in India and third largest in the world in the field of various telecom services like Mobile network, wireless, landline, broadband, satellite television, etc. The company is famous for world presence, vast network coverage, excellent service and brand image managed by experts. This project was carried out for Bharti Airtel, Begumpet, Hyderabad, Andhra Pradesh. This is one of the leading centers for marketing and sale of Airtel product and services in southern region.

Abstract: This research investigates how sales promotions affect customer experience and satisfaction at Ratnadeep Supermarket, a rapidly growing grocery chain in India. In the current competitive and fast evolving retail landscape, customer expectations are rising prompting supermarkets to continually refresh their marketing and promotional tactics to draw in, engage and keep customers. Ratnadeep is recognized for its strong brand presence in numerous urban locations offering a diverse range of products well organized store layouts and frequent promotional deals. It is crucial to comprehend how these promotions influence customer satisfaction and purchasing behavior. Buy-One-Get-One (BOGO) offers, loyalty programs, festival promotions, and digital ads impact customer buying choices and satisfaction levels. the research also looks into how demographic factors such as age, income, and shopping habits affect the success of these promotions. These surveys assessed important aspects like promotional awareness, shopping frequency, perceived value of offers, and overall satisfaction. Furthermore, qualitative data was collected through semi-structured interviews with selected customer and Ratnadeep staff to gain deeper insights into consumer views and the company’s promotional tactics.

Abstract: This study analyzes the risk-return profiles of four Kotak Mahindra mutual fund schemes using Sharpe Ratio, Treynor Ratio, and Beta from 1st December 2024 to 20th January 2025, benchmarked against the NIFTY index. The Kotak Mahindra Midcap Balanced Fund-Growth showed the highest risk-adjusted returns (Sharpe Ratio), while the Prudential Balanced Fund-Growth ranked best by Treynor Ratio. The Blue Chip and Income Funds underperformed with low or negative risk-return metrics. The study highlights the need for informed investment decisions, greater investor awareness, portfolio diversification, and improved fund transparency.

Abstract: This research seeks to evaluate the financial stability and growth patterns of Bank of Baroda over the last five years. As a prominent public sector bank in India, Bank of Baroda holds a crucial position within the nation's banking and financial landscape. The analysis centres on essential financial metrics, including profitability, asset quality, capital adequacy, and management efficiency. Furthermore, the study examines the bank's dividend policy and its effects on shareholder value. By conducting a thorough review of annual reports, financial statements, and performance ratios, this research reveals trends in the bank's financial condition and growth path. The results aim to shed light on how well the bank has managed regulatory hurdles, market competition, and economic changes, ultimately providing recommendations for stakeholders and policymakers to improve the institution's long-term performance and sustainability.

Abstract: This Study investigates Human resource management is changing because of the incorporation of Artificial Intelligence (AI) into High Performance Work Systems (HPWS), especially in the innovative and dynamic IT industry. Examining how AI can improve HPWS efficiency, recognizing the difficulties and moral dilemmas associated with AI adoption, and contrasting conventional HPWS models with those improved by AI technologies are the three main goals of this project, which examines the complex effects of AI on HPWS. According to the survey, artificial intelligence (AI) solutions including predictive analytics, intelligent hiring systems, automated performance reviews, and personalized learning platforms are enhancing HR operations' speed, precision, and strategic alignment. The project illustrates how AI may greatly improve employee performance using case studies and empirical data.

Abstract: The rise of shared mobility services has revolutionized urban transportation, with self-driven rental bikes emerging as a popular alternative for daily commuting. This study focuses on understanding the users’ perception and the barriers associated with the use of self-driven rental bikes offered by Rapido. With the growing demand for convenient, affordable, and eco-friendly travel options, Rapido's bike rental service aims to meet the mobility needs of urban populations. However, adoption is influenced by several factors such as user awareness, safety concerns, pricing, app usability, and legal regulations. This research is conducted through a structured questionnaire distributed among existing and potential users of Rapido’s rental bikes. The objective is to analyze key determinants of user perception, including convenience, cost-effectiveness, ease of access, and satisfaction with the app interface. The study also highlights prominent barriers such as lack of parking infrastructure, fear of accidents, limited coverage areas, inadequate promotion, and concerns about vehicle condition and customer service. Findings reveal that while users appreciate the flexibility and affordability of self-driven rental bikes, hesitation persists due to safety issues and lack of awareness. The study suggests that strategic marketing, enhanced safety measures, and improved customer support can significantly increase user trust and adoption. This research provides valuable insights for Rapido and similar platforms to optimize their services, address user concerns, and expand their market reach.

Abstract: This project looks at how a report by Hindenburg Research affected the stock price of Adani Power Limited. The report, released in January 2023, accused the Adani Group of dishonest financial practices. After the report came out, many investors lost trust, and the stock price of Adani Power dropped by about 26%. This study checks how the stock price changed before and after the report to understand how such news can affect a company’s value. It also talks about how important honesty, good management, and clear communication are for keeping investors confident.

Abstract: The rapid growth of e-commerce in India has significantly transformed consumer buying behavior, particularly in the urban grocery segment. This study explores the impact of e-commerce on consumer purchasing patterns with specific reference to Zepto, a quick-commerce grocery delivery platform. The research aims to understand how convenience, price transparency, delivery speed, and user interface influence consumer preferences and loyalty. The study was conducted among a sample population of 120 consumers residing in urban areas of kompally, Telangana, who have made at least one purchase through the Zepto app in the past six months. A structured questionnaire was used to collect primary data, while secondary sources like journals, reports, and online articles were referenced to strengthen the analysis By analyzing both quantitative and qualitative data, this study contributes to a better understanding of how instant delivery platforms like Zepto are not just fulfilling consumer needs but actively shaping them. The insights aim to support strategic decisions for grocery e-retailers and inform broader discussions on the future of urban consumerism.

Abstract: This study explored the predictive role of cognitive flexibility and optimism on academic burnout and perceived stress among Bangladeshi medical students. A total of 250 participants (49.6% female, 50.4% male; M_age = 22.55, SD = 1.63) were recruited from various medical institutions using a purposive-incidental sampling method. Participants completed five standardized self-report instruments translated into Bangla: the Cognitive Flexibility Inventory (CFI), Maslach Burnout Inventory-Student Survey (MBI-SS), Life Orientation Test-Revised (LOT-R), Perceived Stress Scale-10 (PSS-10), and a demographic questionnaire. Data were analyzed using SPSS (v27). Pearson’s correlation showed that cognitive flexibility and optimism were significantly positively correlated (r = .294, p < .01), and both were significantly negatively correlated with burnout (r = –.442, r = –.369, respectively) and perceived stress (r = –.465, r = –.280, respectively). Hierarchical regression analysis revealed that cognitive flexibility and optimism significantly predicted both burnout (R² = .258) and perceived stress (R² = .239), with cognitive flexibility emerging as the stronger predictor in both models. Independent samples t-tests showed significant gender differences in cognitive flexibility and perceived stress, with males scoring higher in flexibility and females reporting more stress. No significant gender differences were found in optimism or burnout. These findings suggest that cognitive flexibility and optimism may act as protective psychological resources, buffering against stress and burnout among medical students. Interventions aimed at enhancing these traits could promote mental well-being and resilience in high-pressure academic environments. The study adds culturally relevant insights into the psychological mechanisms underlying student well-being in a South Asian medical education context.

Abstract: This research investigates the interplay between financial inclusion and cybersecurity in fostering sustainable digital banking adoption in India’s urban hubs, addressing a critical gap in balancing accessibility with system resilience. Through a qualitative methodology centered on case study analysis and secondary data, the study examines three pivotal cases: the Unified Payments Interface (UPI), the 2018 Cosmos Bank data breach, and the PMGDISHA digital literacy program. The UPI case highlights its transformative role in urban financial inclusion, achieving over 75% of retail digital payments by 2023, yet facing barriers like digital illiteracy and device affordability among marginalized groups. The Cosmos Bank breach exposes cybersecurity vulnerabilities, such as malware infiltration and inadequate monitoring, eroding user trust and necessitating robust technological and regulatory interventions. The PMGDISHA case underscores digital literacy’s role in bridging the urban digital divide, with a 40% increase in digital payment adoption in Delhi’s slums, though limited by infrastructure gaps. Findings reveal that UPI’s success stems from high smartphone penetration and affordable internet, while cybersecurity incidents deter adoption among low-income groups, and literacy programs require sustained engagement. The proposed framework integrates user-friendly platforms, AI-driven fraud detection, biometric authentication, data privacy laws, and expanded literacy initiatives to harmonize inclusion and security. Future research suggestions include longitudinal UPI adoption studies, blockchain integration, and comparative analyses with other emerging economies. Significance lies in providing policymakers and financial institutions with an actionable, evidence-based model to build inclusive, secure, and resilient digital banking ecosystems, aligning with India’s Digital India vision and global sustainable development goals.

Abstract: This study presents a cross-regional analysis of digital education awareness among secondary school administrators in Tabora, Tanzania, and Lucknow, India. With the rapid integration of digital tools in education systems globally, the role of administrators in facilitating, supporting, and sustaining digital education has become increasingly crucial. The research investigates the level of awareness, preparedness, and strategic engagement of administrators with digital education policies, platforms, and technologies. Using a mixed-methods approach, including structured surveys and semi-structured interviews, data were collected from 60 administrators across both regions. Findings reveal notable disparities between the two regions, with administrators in Lucknow demonstrating higher levels of digital awareness and infrastructure support compared to those in Tabora. However, both regions shared common challenges such as limited professional training, inadequate funding, and infrastructural gaps. The study underscores the importance of targeted capacity-building programs, policy support, and cross-regional collaboration to bridge the digital divide. It also highlights the potential of mobile-based platforms and low-cost digital solutions in overcoming infrastructure barriers in underserved regions. By comparing two distinct socio-economic and educational contexts, this research provides valuable insights into the evolving landscape of digital education in the Global South and offers policy recommendations to enhance the effectiveness and inclusivity of digital learning initiatives through improved administrative engagement.

Abstract: Microgrids represent a transformative paradigm in modern energy systems, enabling localized, efficient, and resilient energy management. With the growing urgency to decarbonize power systems and accommodate the increasing penetration of renewable energy sources, microgrids have emerged as a practical solution for integrating distributed energy resources (DERs), such as solar photovoltaics, wind turbines, and energy storage systems. Their ability to operate in grid-connected and islanded modes enhances energy reliability and autonomy, particularly in remote or disaster-prone areas. However, microgrids face significant operational challenges, including the intermittency of renewables, load uncertainty, and communication latency. To address these issues, artificial intelligence (AI) technologies have become increasingly central to microgrid optimization. This review critically examines the role of AI, including Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL), in enhancing key functions such as load forecasting, energy scheduling, fault detection, and cybersecurity. AI facilitates real-time decision-making and adaptive control through intelligent data-driven approaches, thereby improving microgrid efficiency and resilience. The paper also discusses microgrids' structural and functional design and highlights the need for interdisciplinary collaboration between power system engineers, data scientists, and control experts. It concludes by emphasizing the importance of translating AI models into practical applications to accelerate the deployment of innovative, low carbon microgrid infrastructures.

Abstract: The construction industry faces challenges like unsustainable supply chain practices and rising project costs, hindering environmental sustainability and economic efficiency. Despite global advancements, inefficiencies like resource waste and resistance to change exacerbate cost overruns and environmental degradation. This study investigated on Sustainable Supply Chain Management (SSCM) strategies and their influence on construction cost optimization for project delivery in South-South Nigeria. The study focuses on the level of adoption of SSCM strategies, barriers inhibiting the adoption of SSCM, construction cost optimization strategies and  the relationship between the level of adoption of SSCM strategies and construction cost optimization. The survey data was obtained through a structured multiple choice questionnaire administered to 495 construction professionals in South-South, Nigeria. Data collected were analyzed using mean item score, Kruskal-Wallis H-Test, and Ordinal regression. The findings of the study revealed the most significant factors for the adoption SSCM are: corporate social responsibility, training on SSCM, information sharing, managers’ commitment, sustainable construction, and clean program/production are the top five ranked in descending other. The study further shows that resistance to change, lack of financial incentives, limited sharing of information and knowledge, lack of coordination, limited teamwork, and lack of cooperation and communication among stakeholders are the most significant barriers inhibiting the adoption of SSCM. The study also revealed that construction cost optimization primarily focuses on value engineering, the use of pre-fabricated and modular components, project reviews, project scheduling, and multi-disciplinary team collaboration in that other. The study's revealed a positive relationship between SSCM and cost optimization strategies, with a Nagelkerke R² value of 0.981 suggesting a substantial portion of cost optimization variance can be attributed to how well the SSCM is adopted. The study concluded that the adoption of SSCM significantly leads to cost reduction. The study recommends an increase in awareness campaigns through workshops and seminars on the advantages of SSCM.

Abstract: This study seeks to investigate the incorporation of artificial intelligence (AI) in patient record management within private healthcare institutions in the Midlands Province of Zimbabwe. It examines the obstacles to adoption and techniques for successful implementation to improve patient care. A qualitative study methodology was utilized, incorporating semi-structured interviews with 37 healthcare practitioners from diverse private institutions. The interviews, ranging from 30 to 60 minutes, sought to elicit detailed insights into the problems and attitudes associated with AI integration. Thematic analysis was employed to discern reoccurring themes, with ethical approval and informed permission safeguarding participant confidentiality.The investigation identified significant obstacles to AI adoption, such as technological proficiency, worries around job security, and a preference for conventional record-keeping practices. Prominent topics encompassed insufficient training, significant implementation expenses, and concerns around data protection. In contrast, participants recognized the prospective advantages of AI, including enhanced accuracy, efficiency in recordkeeping, and accelerated access to patient information. These observations underscored the influence of existing practices on the quality of patient treatment. The results reveal substantial obstacles and prospects in the incorporation of AI into healthcare record management. Resolving challenges such as insufficient training, elevated expenses, and privacy issues is essential for fostering an atmosphere favorable to AI adoption. Through the implementation of targeted tactics, such as advanced training programs and partnerships with technology providers, healthcare facilities may fully leverage AI's capabilities, hence enhancing patient outcomes and operating efficiencies.

Abstract—This study investigates the effects of pho- tovoltaic (PV) system integration at different penetra- tion levels (0%, 10%, 30%, and 50%) on the perfor- mance of a three-phase distribution network modeled in Simulink. Using an adapted IEEE 9-Bus system, the research evaluates key system parameters such as total generation, total PQ load, total shunt impedance, total asynchronous machine count, and total system losses. The simulation model incorporates a detailed PV array design, pulse generation and control, and load flow analysis to capture dynamic behaviors un- der varying renewable energy contributions. Results indicate that while total generation slightly increases and shunt admittance varies with PV penetration, the overall impact on system losses and load remains minimal. The findings provide valuable insights into the feasibility of integrating solar PV systems into traditional power networks while maintaining system stability and efficiency.

Abstract— As the demand for secure data transmission continues to rise, steganography has become an essential technique in information security. Despite its importance, conventional steganographic approaches often lack flexibility and robust content protection. This paper introduces an innovative method that combines artificial intelligence with adaptive steganography and real-time content safeguarding. The proposed framework intelligently tailors steganographic strategies by analyzing the type of cover media and assessing potential risks. It also incorporates mechanisms for dynamic content modification, which respond to user interactions or predefined conditions. This work outlines the architecture, development, and performance assessment of the system. Experimental results highlight the benefits of AI-driven adaptability and dynamic content control in strengthening steganographic techniques. Additionally, the paper explores current challenges, future enhancements, and practical use cases of this forward-looking methodology.

Abstract: This study explores a green synthesis method for silver nanoparticles (AgNPs) using aqueous extracts of Tulasi (Ocimum sanctum) and Sandalwood (Santalum album). The extracts provide phytochemicals that act as reducing and stabilizing agents. The synthesized AgNPs were characterized using UV–Vis spectroscopy, FTIR, XRD, NMR, SEM, TEM, and zeta potential analysis. The AgNPs exhibited strong antimicrobial activity against Escherichia coli, Staphylococcus aureus, and Candida albicans, demonstrating the potential of these natural resources in nanomaterial development. This study uniquely integrates Tulasi and Sandalwood extracts for the first time in silver nanoparticle synthesis, with NMR characterization rarely reported in similar green synthesis approaches.

Abstract: India is one of the fastest growing economies of the world attracted a large no. of foreigners as well as domestic tourist.  After post pandemic this industry once again started reviving to reach its pre pandemic position. Tourism in recent years is not only to visit historical places; have many other motives also. This research objective is to cluster tourist arrivals in India using primary data collected from various tourist places covering historical monuments, religious places, research institutions and medical units and others. To understand people’s varied motive to visit in the country will support for making policy for tourist arrival in future. The findings of the study suggest that clusters of tourist arrivals are mainly to visit religious monuments, historical monuments, medical services, business and commercial activity, education and research work and some others miscellaneous works are primary concerns. The creation of more homogeneous sub groups on the basis of their travel motive will help us in formulation of policies and target marketing will enhance tourist arrival in the country. Three distinct market segments are identified— freedom and spiritual seekers, multi-purpose seekers, and fun and special interest in adventures seekers—based on a cluster analysis using k-means methodology.

Abstract: This paper investigated the implementation of a client/server system, (EnviroView) for management of green infrastructure to facilitate the development of a sustainable urban areas in Enugu State, Nigeria. The aim is to provide simple digital systems for environmental data gathering, grouping, and visualization. EnviroView will bring about a smart city planning and management in line with the Sustainable Development Goal 11 (SDG 11) on Sustainable Cities and Communities. The research adopted a case study approach in the collection of stakeholder responses using structured questionnaires from urban planners, City officials, and community members. The EnviroView system works well in capturing real time ecological data, maintaining data accuracy providing easy data access and visualization. The general feedback from user groups attested to the usefulness of the system in assisting sustainable urban policies. The research demonstrated that EnviroView if implemented shall advance the data-driven sustainability of cities and provides a stepping stone for innovative environmental management of urban areas, especially, in emerging economies, thus making contribution towards the realization of SDG 11.

Abstract: The surge in digital payments and fintech innovations has reshaped India’s financial ecosystem, influencing how monetary policy is transmitted across the economy. This article examines the role of digital payment systems, particularly the Unified Payments Interface (UPI), and fintech advancements in altering monetary policy mechanisms. By analyzing UPI’s dominance, fintech-driven financial inclusion, and their impact on liquidity, interest rates, and credit channels, the study highlights both opportunities and challenges. A literature review of scholarly works contextualizes these dynamics, emphasizing fintech’s transformative potential and regulatory hurdles. The discussion includes data tables and graphs, sourced from the Reserve Bank of India (RBI) and industry reports, illustrating trends in digital payment adoption and macroeconomic effects. Findings indicate that fintech enhances policy transmission efficiency but introduces risks like cybersecurity threats and regulatory gaps. The article concludes with recommendations for adaptive regulations, enhanced cybersecurity, and financial literacy initiatives to align fintech growth with monetary policy goals. This study underscores the need for a balanced approach to leverage fintech’s benefits while ensuring economic stability in India’s rapidly evolving financial landscape.

Abstract: An over the counter (OTC) drug can be purchased without any prescription. These drugs become popular due to their easy access and cost effectiveness and the self-medication practice is increasing day by day, especially among youth. People are taking many drugs without consultation or prescription for headache, cold & cough, pain etc. There are so many factors to study that what the consumers think while buying these OTC drugs. This paper investigates especially the effect of academic disciplines of students on the purchase and usage behaviour regarding over to counter (OTC) drugs. The study will assess a range of factors such as awareness level, frequency of use, sources of information and purchasing platforms with respect to the demographic and academic diversities. It explores the impact of academic backgrounds on self-medication practices, reliability on healthcare professionals, digital and social media. The study indicates the need to administered awareness programs and strategic initiatives to promote the safe use of OTC medications. Additionally, the study identifies behavioral differences in packaging engagement and purchasing channels across academic streams, contributing to safer usage of OTC drugs and supporting targeted awareness initiatives

Abstract: Earnings management in this study is based on Schipper's opinion which states that earnings management is management engineering carried out by management to deliberately intervene in the process of determining earnings or financial reporting by utilising accounting techniques and policies for specific purposes. This study aims to examine and analyse the perspective of prophetic values on earnings management practices. This study uses a qualitative descriptive method, data collection techniques and literature studies. The results showed that basically earnings management can be in accordance with Islamic values and principles. The company management should be responsible for maintaining the accountability of a company or organisation, so that no stakeholders are harmed by the existence of the organisation or company. Accountability is a reference to the implementation of the accounting process and the results of financial statements that describe the company's actual financial condition. Prophetic accountability is an important pillar driving civilisation towards essence.

Abstract -Rice husks, an abundant agricultural waste product, have emerged as a versatile and sustainable biosorbent for wastewater treatment applications. This systematic review comprehensively examined the effectiveness of rice husks and their derivatives in removing a wide range of pollutants, including heavy metals, organic compounds, inorganic anions, and emerging contaminants. Various modification strategies, such as chemical activation, thermal treatment, and nanostructuring, have enhanced the adsorption capacity and selectivity of rice husk adsorbents. This review highlights the potential of rice husks in addressing specific wastewater challenges, including BOD and COD reduction in dairy effluents, casein recovery, and nutrient removal. The adsorption process is influenced by several factors, such as pH, adsorbent dosage, contact time, and temperature, and conforms to well-established isotherm and kinetic models. Rice husk-based materials have demonstrated high removal efficiencies for heavy metals (e.g., Zn, Pb, Cu, and Ni), organic pollutants (e.g., phenols, dyes, and surfactants), and nutrients (e.g., ammonium and phosphate). Furthermore, the eco-friendly nature and cost-effectiveness of rice husks position them as sustainable alternatives to conventional adsorbents, aligning with the principles of a circular economy. However, despite significant progress has been made, further research is required to optimize modification techniques, explore new applications, and scale up these technologies for industrial use. This review underscores the promising role of rice husks in advancing sustainable wastewater treatment and resource recovery.

Abstract-Phishing remains a major global threat, exploiting human weaknesses to access sensitive data. Existing detection methods often struggle with high false positives and fail to adapt to evolving phishing tactics. This study proposes a phishing detection system that combines Support Vector Machine (SVM), Feed Forward Neural Network (FFNN), and Extreme Learning Machine (ELM) using a weighted voting approach. A cleaned and normalized dataset of URLs was used, with dimensionality reduction via PCA. The models were evaluated using metrics like accuracy, sensitivity, and detection time in MATLAB (R2023a). Results show that SVM achieves the best performance, with the lowest false positive rate (1.79%), highest precision (97.98%), and accuracy (97.75%). FFNN offers balanced performance, while ELM is the fastest but less accurate. The weighted voting mechanism consistently identifies phishing as the dominant class, enhancing detection accuracy. Overall, combining the three models improves robustness, with SVM emerging as the most effective classifier.

Abstract: Watershed preservation is essential for water sustainability and safety. The indigenous practices of the Obu-Manuvu living near the watershed play a crucial role in achieving this sustainable vision. The Pusaka Philosophy, which exemplifies their customary laws, governs them in protecting the environment. This study aimed to identify the underlying patterns of knowledge, attitudes, and practices among 200 Obu-Manuvu respondents regarding their efforts to protect the watershed, viewed through the lens of pusaka philosophy. The study employed a descriptive-quantitative research design to analyze the respondents` profiles and their level of knowledge, attitudes, and practices. The principal component analysis was used to extract the underlying latent variables that contributed to the observed patterns. The study's results revealed five (5) dimensions, each with an eigenvalue greater than one according to Kaiser's criterion. This result means that the knowledge, attitude, and practices of Obu-Manuvu in protecting the water source, based on Pusaka philosophy, are complicated and influenced by many factors, implying a collaborative effort to enhance water protection practices. Thus, this study suggests culturally appropriate actions, promoting community-led water management, encouraging local involvement in water decisions, and empowering community protectors.

Abstract: Nigeria needs to rethink how its cities grow by adopting building methods that are both eco-friendly and affordable. This review looks closely at how sustainable construction is being used in the Abuja Municipal Area Council (AMAC). It focuses on the combining local materials with modern technology to create smarter and greener buildings. Through the use of real-life examples, research studies, and government policies from Nigeria, the paper examines how green building methods can help reduce harm to the environment, reduce construction costs, and save energy. It also discusses the benefits and challenges of using these methods. Furthermore, the study adds to global conversations by showing how local creativity and the smart use of technology can help improve sustainable building in growing countries like Nigeria.

Abstract: Effective road infrastructure planning requires accurate, structured, easily accessible, and up-to-date data. However, in many regions such as Pematangsiantar City, data related to the road network is still scattered, lacks standardization, and is not regularly updated. This study aims to develop a WebGIS-based road database as a tool to support road infrastructure planning and management in an efficient and transparent manner. The methods employed include the collection of primary and secondary data, digitization of road maps, and the development of a WebGIS application using web-based programming technologies and spatial database management systems. The results show that the WebGIS system developed in this study is capable of presenting road information visually and interactively, including attributes such as street names, lengths, widths, and physical conditions. This system also enables regular data updates and can be accessed by various stakeholders, including local governments and the general public. These findings are consistent with previous studies which have shown that WebGIS is effective in supporting decision-making processes and enhancing transparency in public information related to infrastructure. Therefore, the development of a WebGIS-based road database in Pematangsiantar City is expected to serve as an implementation model that can be replicated in other regions.

Abstract— The global network infrastructure remains vulnerable and is susceptible to attacks from various sources. These attacks can take the form of denial-of-service (DoS) or other malicious threats. To safeguard such networks, Intrusion Detection and Prevention Systems (IDPS) are employed. These systems serve as critical security mechanisms designed to detect and prevent both internal and external threats.IDPS continuously monitor network traffic using a variety of techniques. When suspicious or malicious activity is detected, the system blocks the threat and generates alerts for further investigation. Intrusion remains a significant challenge, especially in hybrid computing environments.This paper explores key concepts including network intrusion, types of intrusions, intrusion detection systems, and a review of previous research related to IDPS. The software solutions implemented to counter such threats are referred to as intrusion prevention systems (IPS). Various types of prevention systems are discussed within this study.

Abstract- This paper presents the design and implementation of a multimodal, real-time Human-Computer Interaction (HCI) system called the Vocal Gaze Mouse Controller. The system integrates eye-gaze tracking, blink detection, and voice recognition to control mouse functions, providing a fully hands-free interaction mechanism. Designed especially for individuals with physical disabilities, the system enables cursor navigation and command execution using webcam-based eye tracking and microphone-enabled speech processing. We use Dlib for facial landmark detection, OpenCV for image processing, PyAutoGUI for mouse control, and Python’s Speech Recognition library for interpreting voice inputs. This solution is non-invasive, affordable, and highly accurate, making it suitable for real-world accessibility applications. Results from experimental evaluations show high accuracy in blink and voice recognition, as well as low latency and user satisfaction.

Abstract-- This paper presents the design and implementation of a five-fingered robotic arm that mimics human hand movements using Arduino Uno, servo motors, and potentiometers. The system translates human finger gestures into corresponding robotic finger movements via analog input signals from potentiometers. This project demonstrates the potential of low-cost, microcontroller-based robotic systems for applications in prosthetics, teleoperation, and human-machine interaction.

Abstract: Information on trends and changes in atmospheric conditions over a given region can enhance good policy making to develop strategies to curb heat waves and disaster risk management. The time series temperature data between 1971 and 2020 (50 years) of Sokoto Metropolis was used in this study to identify the trend and slope magnitude in the series. The trend was analyzed using the Mann-Kendal non-parametric test and the Sen’s slope estimator was used to estimate the degree or rate of increase or decrease. Linear regression was employed to evaluate the linear trend in the data and coefficient of variation, CV was used to estimate the level of variability. The decadal trend analysis shows an increasing trend as indicated by the Mann-Kendall’s tau and p-value of +0.48 and 7.8 x 10 -5 (<0.001) respectively. Also, the Sen’s slope estimator was positive and found to be +0.022 oC per year, representing the rate of increase in trend for yearly annual temperature. Furthermore, the results show that the analysis of monthly rainfall has a varied trend of temperature in the dry months. It also shows a positively increasing warming trend for the months of April, May and September while August and December show moderate warming. This study will help the government or policymakers to develop appropriate coping and mitigation strategies for the most vulnerable areas with regards to global warming and climate change.

Abstract— This paper presents an Elective Recommender System based on the Apriori Algorithm and Decision Tree Analysis for enhancing elective course selection in higher education. The system uses historical student performance data to recommend electives aligned with students' academic strengths and career goals. Association rule mining is used to recommend elective combinations based on past trends, while the Decision Tree algorithm is used in personalized recommendations with success likelihood. The system's performance was evaluated using the ISO/IEC  25010 Software Quality Model, yielding high scores in functional suitability, usability, and performance efficiency. The results show the system's potential in assisting both students and academic advisors in making data-driven elective course decisions.

Abstract: The study looks closely at the complicated, game-changing, and sometimes contradictory effects of Artificial Intelligence (AI) on modern service delivery. It looks at how AI changes the way businesses work, how customers interact with each other, and the moral limits of service ecosystems. Using a mixed-methods approach with multiple phases (interviews, experiments, case studies, and surveys), the results show that AI systems make tasks much more efficient, cutting transaction costs by up to 30% and response times by up to 40%. The paper makes the service quality seem better for standard tasks through calibrated anthropomorphic design (β=0.38, p<.001). But this "algorithmic hand" also causes serious social and emotional problems in complicated situations. Without real empathy and the ability to adapt to different situations, customers get frustrated and blame the company for AI failures; 82% of them blame the company for not being careful enough, which hurts trust more than anything else. The study shows that contingency factors—task complexity, customer demographics, interface transparency, and agent skill polarization—are important because they affect how well AI works. This leads to ethical issues like measurable algorithmic bias (0.75 SD lower satisfaction among elderly users; p < .01), growing privacy concerns, and widespread gaps in governance. We propose a new, multi-theoretical framework that integrates TAM, Social Presence Theory, and Attribution Theory. This framework explains how customers think and gives practical advice for hybrid human-AI systems. For professionals, its use means getting evidence-based advice on how to improve anthropomorphism, recover from failure, and reduce bias. It calls for regulatory frameworks that put fairness and human dignity at the top of the list. In the end, this work changes the definition of service innovation by saying that AI's real value is not in being able to work on its own, but in being ethically governed and sensitive to the situation. It also calls for more research into the new problems that generative AI is causing, with human flourishing as the main measure of progress.

Abstract: This study demonstrates an eco-friendly synthesis of silver nanoparticles (AgNPs) using aqueous extracts of Elettaria cardamomum (cardamom) and Cinnamomum verum (cinnamon). These extracts act synergistically as reducing and stabilizing agents. UV–Visible spectroscopy, FTIR, XRD, SEM, TEM, and zeta potential measurements were used for characterization. A UV–Vis absorption peak at ~420 nm confirmed nanoparticle formation. Electron microscopy revealed spherical nanoparticles of 15–30 nm size. The AgNPs showed dose-dependent antimicrobial activity against E. coli, S. aureus, and C. albicans. This green method holds promise for biomedical applications.

Abstract: The present study deals with the synthesis of an N-phenylbenzal-diphenylamine Schiff base derived from Benzaldehyde and Diphenylamine. This Schiff base is formed by the condensation of Benzaldehyde and diphenylamine. Schiff bases are known for their diverse biological and coordination properties due to the presence of an azomethine group. The Ni (II) complex of this SB was obtained through refluxing the SB with the metal salt in ethanol. Both the SB and complexes were confirmed via spectroscopic and physicochemical analyses. The UV-Vis spectra of Ni-bis-N-phenylbenzal-diphenylamine chloride complexes consistently showed absorption bands around 291.4 nm. These complexes exhibited strong antibacterial activity against Escherichia coli in disc diffusion tests. UV-Vis spectroscopy aided their synthesis and characterization. These results highlight the significant antimicrobial potential of Schiff base metal complexes.

Abstract—Brain neoplasms represent some of the most severe and life-threatening manifestations of cancer, with gliomas constituting the most prevalent subtype, which is further classified into Low-Grade Glioma (LGG) and High-Grade Glioma (HGG). The prompt identification, precise categorization, and accurate delineation of these tumors are imperative for effective diagnostic assessment, prognostic evaluation, and therapeutic strategy formulation. Nevertheless, the manual evaluation of magnetic resonance imaging (MRI) scans is labor-intensive and prone to inter-observer variability. In recent years, advancements in artificial intelligence (AI), particularly within the realms of machine learning (ML) and deep learning (DL) methodologies, have markedly enhanced the automation and precision of brain tumor identification and evaluation.This comprehensive review paper investigates contemporary developments in the automated classification and segmentation of brain tumors utilizing the BraTS dataset, which is widely recognized as a standard reference within the neuroimaging research community. We analyze a diverse array of methodologies, encompassing traditional machine learning algorithms, Convolutional Neural Networks (CNNs), UNet architectures, hybrid models, and ensemble-based strategies. The article underscores techniques that synergistically integrate handcrafted radiomic features with deep learning features to enhance both model robustness and interpretability. Documented accuracy rates in the studies reviewed vary from 66% to 99%, with Dice similarity coefficients employed to assess the segmentation efficacy of tumor sub-regions including tumor core (TC), whole tumor (WT), and enhancing tumor (ET). This scholarly endeavor aspires to furnish researchers and practitioners with valuable insights into cutting-edge methodologies for brain tumor analysis and to offer direction for forthcoming advancements in this domain.

Abstract: This paper explores the role of artificial intelligence (AI) and chatbots in social media marketing, focusing on their impact on customer engagement and experience. Drawing on established theories such as the Technology Acceptance Model, Uses and Gratifications Theory, Relationship Marketing, Social Presence, and Customer Experience theories, the study analyzes how AI-driven chatbots influence consumer behavior on social platforms. The theoretical framework provides a comprehensive understanding of chatbot adoption, personalization, efficiency, and their contribution to improved customer satisfaction. This study underscores the importance of integrating AI chatbots as strategic tools in social media marketing to foster stronger customer-brand relationships and enhance overall user experience.

Background: As digital adoption accelerates in India’s Non-Banking Financial Company (NBFC) sector, customer trust becomes a pivotal factor in ensuring continued usage of online financial services. Despite technological advancements in cybersecurity, trust deficits persist due to low user awareness and variable digital experience. This study investigates the role of perceived cybersecurity effectiveness (PCE) in building customer trust (CT), with attention to mediating and moderating factors such as awareness of cybersecurity features (ACF), prior digital experience (PDE), and demographics. Methods: A quantitative, cross-sectional research design was employed. Data were collected from 384 NBFC customers across urban, semi-urban, and rural regions in India using a structured questionnaire. Constructs were measured using validated Likert-scale items. Data were analysed using Structural Equation Modelling (SEM) via AMOS, with additional tests for mediation and moderation effects. Results: PCE had a significant positive effect on CT (β = 0.49, p < 0.001). ACF partially mediated the relationship between PCE and CT (β = 0.21, p < 0.01), while PDE positively moderated the PCE–CT relationship (β = 0.13, p < 0.05). Demographic factors, particularly digital literacy and education, significantly influenced how customers perceived cybersecurity and formed trust. Conclusion: The findings underscore that cybersecurity effectiveness must be matched by user awareness and tailored communication to build meaningful trust. NBFCs should prioritize not only technical security infrastructure but also user education and transparency strategies to foster trust in digital environments.

Purpose: This research examined how teacher motivation affects academic performance in public secondary schools within Masinga Sub-County, Kenya. The study developed a theoretical model to explain differences in stakeholder perceptions regarding motivation effectiveness. Methodology: The research utilized a convergent parallel mixed-methods approach involving 364 participants comprising 7 principals, 83 teachers, and 274 students from 25 public secondary schools. Stratified purposive sampling-maintained representation across different school categories. Data gathering employed validated questionnaires demonstrating strong reliability (Cronbach’s α = 0.903-0.907) alongside semi-structured interviews. Power analysis verified sample adequacy for detecting medium effect sizes (d = 0.5) with 80% statistical power. Data analysis incorporated descriptive statistics, Independent Samples t-tests with effect size calculations, multiple regression modeling, and thematic analysis achieving high inter-rater reliability (κ = 0.85). Results: Professional development demonstrated universal acceptance (73.5% teacher, 56.2% student consensus), whereas considerable perception disparities emerged for reward mechanisms and welfare initiatives. Teacher welfare exhibited the most substantial perception differences (teachers: M = 4.56, students: M = 3.92; t(91) = 4.54, p < 0.001, d = 1.02). Multiple regression demonstrated that motivation variables accounted for 34% of academic performance variation (R² = 0.34, F(3,359) = 62.1, p < 0.001), with professional development serving as the primary predictor (β = 0.42). Qualitative findings identified implementation inconsistencies and communication breakdowns as fundamental sources of perception differences. Conclusions and Recommendations: This investigation presents the “Stakeholder Perception Alignment Model,” illustrating how motivation transparency and communication quality affect stakeholder consensus. Results indicate that motivation approaches require effective execution combined with clear communication channels to maximize effectiveness. Recommendations encompass creating standardized motivation frameworks, improving stakeholder communication systems, and emphasizing universally appreciated professional development opportunities. This research advances Self-Determination Theory by showing how environmental factors influence motivation effectiveness within resource-limited settings.

Abstract- Artificial Intelligence (AI) is an overarching domain that integrates a variety of techniques, tools, and systems designed to enable machines to learn from data and perform predictive or decision-making tasks. Within this domain, computer vision stands out as a pivotal subfield, offering substantial contributions across multiple sectors, including agriculture. The integration of AI and computer vision has given rise to smart farming an advanced form of agriculture where traditional cultivation practices are optimized through intelligent technologies to enhance productivity, precision, and resource efficiency. A prominent application of computer vision in agriculture is the detection and classification of plant diseases through image classification and object detection techniques. These methods facilitate the automated identification of infected plant leaves by analyzing visual indicators such as lesions, spots, and discolorations. In this context, a specialized approach termed the Single Sample Computer Vision Recognition Algorithm (SSCVRA) has been employed to detect disease symptoms in eggplant leaves. The model was trained and validated using a dataset comprising 20,000 images, achieving an impressive classification accuracy of 99.47%. SSCVRA conducts disease identification by extracting and matching relevant image features, focusing on such parameters of color, shape, texture and comparing with consecutive region area of the leaf. The algorithm is capable of distinguishing among multiple plant diseases, including Powdery Mildew, Bacterial Leaf Spot, and Early Blight. Visualization outputs further support diagnosis by providing quantitative metrics on image similarity and disease likelihood based on color-based image analysis.

Abstract- The most prevalent kind of cancer worldwide is skin cancer. Early detection is crucial since failure to discover it in the primary stage could be fatal. Even though there are distinctions within the class and a lot of parallels between classes, it is too hard to tell with the naked eye. Due of the disease's widespread occurrence, several automated deep learning-based algorithms have been developed to date to assist physicians in spotting skin lesions early on. We trained VGG19 on the HAM10000 dataset by fine-tuning the Convolutional Neural Networks (CNNs) and using pre-trained ImageNet weights. With FT, the best performance was noted. With an overall accuracy of 82.4±1.9 percent, the developed model outperformed the one employed in transfer learning. This performance could save morbidity and treatment costs by providing a second opinion and bolstering the clinician's diagnosis.

Abstract: In recent years, the global trend toward vehicle electrification has emerged as a crucial effort to combat climate change and meet carbon emission reduction targets. At the center of this transformation, electric vehicle (EV) batteries play a decisive role from energy efficiency and range to operational safety. Although lithium-ion (Li-ion) technology currently dominates the market, various challenges such as the risk of thermal runaway, limited cycle life, and reduced durability at extreme temperatures are driving the search for alternative battery technologies. Solid-state batteries (SSB) have emerged as a cutting-edge solution by replacing flammable liquid electrolytes with solid electrolyte materials. This model is expected to offer higher energy density, superior thermal stability, and longer lifespan. This study conducted a systematic literature review (SLR) using the PRISMA framework to select and analyze 16 journal articles published between 2020 and 2025 that compared SSBs and Li-ion batteries based on two main dimensions: safety (RQ1) and vehicle performance (RQ2). The analysis results indicate that SSBs consistently exhibit better safety profiles, thanks to the presence of non-flammable solid electrolytes, structures that prevent the formation of lithium metal dendrites, and the ability to withstand high temperatures without significant degradation. In terms of performance, SSB also excels through high ionic conductivity enabling fast charging rates a cycle life exceeding 1,900 cycles at 60°C, and performance stability across extreme temperature ranges. Meanwhile, traditional Li-ion batteries still require material modifications such as the addition of graphene or lithium iron phosphate to enhance durability and charging speed. These findings collectively confirm that solid-state batteries not only address safety challenges but also enhance the durability and operational efficiency of electric vehicles. Therefore, accelerating the development and adoption of SSB technology is expected to be a strategic step toward achieving safer, more reliable, and sustainable electric mobility.

Abstract: This research determines the competitive advantage of the service sector in Nueva Ecija through the application of Porter’s Five Forces, employing descriptive correlational research. Data collection involved a researcher made survey questionnaire administered to 150 respondents, comprises of 50 managers, employees, and customers across three cities in Nueva Ecija. The results show that majority of surveyed businesses have been operating for 11 to 16 years, typically family-owned, and employ 5 to 10 staff members. They primarily offer full-service dining experiences and exhibit a preference for adopting Point of Sale (POS) systems for their technological infrastructure. Likewise, respondents highlight essential strategies for maintaining competitiveness, including fostering strong partnerships, understanding competitors' pricing tactics, and managing buyer power and supplier relationships effectively. They also emphasize the importance of countering substitution threats by offering locally sourced unique ingredients. There is a consensus on proactive measures to sustain competitiveness and overcome industry challenges. Moreover, respondents’ express confidence in the industry's resilience against new entrants, stressing the significance of continuous innovation, adapting to consumer preferences, and leveraging economies of scale and operational efficiencies. The analysis of demographic profiles unveils nuanced relationships between various factors and competitiveness, emphasizing the intricate dynamics shaping the sector's landscape. Lastly, the researcher proposes actionable activities based on the study's findings, highlighting the importance of understanding these dynamics for businesses to formulate effective strategies and enhance competitive positions in Nueva Ecija's dynamic market.

Abstract: Radio-over-Free-Space Optics (RoFSO) systems have emerged as a promising solution to address the growing demand for high-capacity and flexible wireless communications in smart city environments. RIS-assisted RoFSO systems offer a compelling solution to the communication challenges inherent in smart city applications. By leveraging the reflective and reconfigurable capabilities of intelligent surfaces, these systems can overcome the traditional LOS limitations and significantly mitigate the adverse effects of atmospheric turbulence, as accurately modeled by the Málaga-M distribution. The demonstrated improvements in outage probability, channel capacity, and bit error rate highlight their potential to provide reliable, high-speed, and flexible wireless connectivity in dense urban environments. However, the performance of RoFSO links is significantly affected by atmospheric turbulence. The Málaga-M distribution has gained prominence as a comprehensive model to capture a wide range of turbulence conditions. Recent advancements in Reconfigurable Intelligent Surfaces (RISs) offer a new dimension to mitigating the limitations of FSO channels. This paper provides a comprehensive literature review on the integration of RIS with RoFSO systems over Málaga-M fading, focusing on their applicability in smart city deployments.

Abstract: The rapid urbanization of the 21st century has introduced a variety of stressors that significantly impact mental health. This study explores these stressors and their impact on anxiety and depression within the context of Ilishan-Remo, a rapidly developing sub-urban area in Ogun State, Nigeria. The survey research design was adopted. A sample of 427 adult residents of Ilishan-Remo was selected through the multi-stage sampling technique. Instruments used for data collection were the Demographic Data Inventory (DDI), Environmental Stressor Questionnaire (ESQ), Social Readjustment Rating Scale (SRRS), Economic Hardship Questionnaire (EHQ) and Hospital Anxiety and Depression Scale (HADS). Three hypotheses were formulated and tested by means of the simple linear regression analysis at the .05 level of significance. Results revealed significant impacts of environmental stressors (such as noise pollution and overcrowding) (β = .280, t = 10.638, p < .0005), social stressors (such as social isolation and weakened community ties) (β = .263, t = 8.807, p < .0005) and economic stressors (β = .317, t = 13.914, p < .0005) on anxiety and depression among the residents. It was subsequently recommended, among other things, that governments should develop and enforce urban planning and environmental policies aimed at reducing noise pollution and managing overcrowding to create a healthier living environment for residents.

Abstract:  Advancements in proteomic and genomic technologies have transformed molecular biology by enabling comprehensive analysis of biological systems at the molecular level. This literature review explores the evolution, methodologies, and practical applications of key proteomic and genomic techniques. In proteomics, tools such as two-dimensional electrophoresis, mass spectrometry, Western blotting, Edman degradation, and functional protein microarrays have facilitated high-throughput protein identification, post-translational modification analysis, and biomarker discovery. Similarly, genomic methodologies like PCR, recombinant DNA technology, gel electrophoresis, and Southern blotting have revolutionized gene detection, manipulation, and expression profiling. The review also highlights the interdisciplinary impact of these technologies across clinical diagnostics, oncology, autoimmune disorders, infectious disease surveillance, cardiovascular research, and personalized nutrition. Integrative approaches combining proteomics and genomics are enabling the discovery of novel therapeutic targets, improving disease classification, and advancing precision medicine. Despite current limitations, such as the absence of amplification techniques for proteins and challenges in data interpretation, ongoing innovations promise to bridge these gaps. This synthesis underscores the pivotal role of molecular techniques in deepening our understanding of human biology and accelerating biomedical advancements for improved healthcare outcomes.

Abstract: The tracking and managing of the on-shelf products and stock in an automated store is performed manually in regular periods of time. So, more resources are required, which increases the nances. Automated stores are also plagued by the major issue of shoplifting. The proposed smart shelf system solves these issues. Each shelf is equipped with load cells and RFID modules integrated with stm32 bluepill to monitor shelves in real-time. Communicating via Ether- net, they connect to a central Raspberry Pi unit, facilitating man- agerial oversight. This innovation tackles ine ciencies of manual tracking and security vulnerabilities, o ering automated solutions. By streamlining operations and enhancing stock prediction, these systems optimize customer experience while mitigating shoplift- ing risks. This integration of technology rede nes retail, providing adaptive solutions to meet contemporary market demands.

Abstract: The increasing adoption of Internet of Things (IoT) devices in Cameroon presents significant security challenges, particularly concerning regulatory compliance. Ensuring secure and adaptive management of IoT systems is critical to mitigating cyber risks while aligning with national regulations. This study investigates the use of Reinforcement Learning (RL) for enhancing IoT security management in Cameroon, with a particular focus on compliance with national cybersecurity regulations (e.g., Law No. 2010/012). Using a Markov Decision Process (MDP), the research defines regulatory-compliant state and action spaces, and trains a Q-learning agent within a simulated IoT environment (CyberBattleSim).

Abstract: Periodontal disease, a chronic inflammatory condition affecting the supporting structures of the teeth, is a leading cause of tooth loss and is closely associated with systemic conditions such as diabetes and cardiovascular disease. Early diagnosis and accurate prediction of disease progression are critical for effective treatment and prevention. In recent years, artificial intelligence (AI) has emerged as a powerful tool in healthcare, demonstrating potential in enhancing diagnostic precision and predictive capabilities in periodontal care. This article provides a comprehensive overview of AI applications in predicting periodontal disease, focusing on machine learning (ML), deep learning (DL), and neural networks. It explores various datasets, clinical parameters, and imaging modalities used to train AI models, such as radiographs, periodontal probing depths, and microbial profiles. The article also reviews recent advancements in AI-based prediction models, their performance metrics (accuracy, sensitivity, specificity), and clinical applicability. Furthermore, it discusses challenges including data standardization, ethical considerations, model interpretability, and integration into existing dental practice workflows. The findings suggest that AI, when properly validated and integrated, can significantly improve early detection, risk assessment, and personalized treatment planning in periodontal care, ultimately leading to better patient outcomes and resource optimization.

Abstract: Era of society 5.0, which is not easy to face and own the challenge for education, especially Christian religious education. In addition to having quite a tough challenge for Christian religious education, era Society 5.0 also presents good opportunities that can help Christian religious education to develop towards a better and more advanced direction. Based on the development of educational technology, Christian religious education is needed to adapt biblical principles to the current state of Christian society. A significant challenge is how to apply Christian theology in Christian religious education using technology without violating the doctrines that are fundamental to the Christian faith. TransformNation is a program that operates in the field of education, specifically supporting Christian schools in rural areas in their efforts to empower marginalized communities.   To address this problem, one of the methods researchers employ in this study is the implementation of an application, Kambiumgo, in Christian religious education, particularly in the field of discipleship. Based on these conditions, the following problems are formulated: how does the implementation of the Kambiumgo application form the character of Christ through discipleship in the TransformNation program in the era of society 5.0? The objectives to be achieved in the study are to know the implementation Kambiumgo application, which forms the character-building Christ in the program TransformNation through discipleship in the era of society 5.0. The method used in this study is a qualitative approach with a descriptive case study method. The results of this study found that the Kambiumgo application is very useful for helping students from the TransformNation program to study God's Word and discuss it anywhere and anytime. Due to its flexible, communicative, and easily accessible nature, this helps the formation of Christ's character in the student discipleship TransformNation program conducted through small groups. Based on this research, it is suggested that in addition to using the kambiumgo application, small group is also expected to hold meetings routinely and provide feedback among small group members to find out the spiritual growth of each member so that they have a real relationship with Christ, not just carrying out tasks or routines.

Abstract: Primarily in India from Ancient times, Mantras/Maha Mantras originates as sacred phrases or sounds that are believed to have spiritual and transformative power used for various purposes including meditation and healing. The aim and objective of this article is to explore the hidden meaning of chanting mantras and the effect on human brain and the science behind it. Mantras are rooted from primordial sound known as “OM” which is subject to creation and they are a system of vibrations discovered and practiced by Maharshis and sages through spiritual insight. When it put in to practice, it helps to remove all obstacles, rids of all miseries, raises a sādhaka or practitioner in wisdom, and catalyzes the process of realizing and achieving the goals set forth for and by every Spiritual being in a human form which is believed to science. By understanding the symbolism behind different mantras, one can appreciate their power and integrate them into lives for greater harmony and enlightenment. Tracing from history of mantras thousands of years back to ancient civilizations and spiritual traditions across the world. The origins of mantras can be found in Hinduism, Jainism, Buddhism, Sikhism as well as other indigenous cultures. From Vedic Period, mantras are an integral part of Vedic literature, which dates back to around 1500 BCE. The Vedas, the oldest sacred texts in Hinduism, contain hymns and chants that were recited by ancient sages during rituals and ceremonies. According to the texts, these vedic mantras have divine origins as well as they invoke the blessings of the gods. Also, they maintain cosmic order and achieve spiritual enlightenment. The universe is composed of vibrating energy. Each sound with its specific frequency carries a particular vibration that can affect our physical, mental, and spiritual well-being. The rhythmic practice of repeating mantras will focus on mind and mind becomes absorbed in vibrations and transcends ordinary thoughts which becomes a gateway to deeper state of consciousness and spiritual growth which means a power (Sakti) to the devotee in the form of formulated and expressed thought. In scientific research, the effect of mantras noticed that, “chanting OM has increased mental awareness and reduction in heart rate.”  Therefore this article highlights about the practical use of Mantra recitation in this today’s environment which impacts on human body, brain and soul which leads to a balanced life.

Abstract—The personalization of virtual fashion recommendations remains hindered by limited integration of chromatic and anthropometric factors, especially skin tone compatibility. This study addresses a critical research gap by proposing a voice-enabled 3D fashion recommendation system that incorporates deep learning-based skin tone classification and adaptive garment suggestion. The primary objective is to enhance aesthetic compatibility and user satisfaction through real-time, personalized recommendations. Utilizing a custom-designed Deep Convolutional Neural Network (DCNN) and reinforcement learning algorithms, the system classifies user skin tones with 89.14% accuracy and adapts recommendations based on user feedback, reducing outfit resets by 54%. A curated dataset encompassing five skin tone categories and a multi-stage image preprocessing pipeline ensures inclusive and robust performance. The results demonstrate significant improvements in recommendation relevance and user engagement, with 88% satisfaction and 93.7% dominant tone detection accuracy. These findings underscore the system's potential to set new benchmarks in personalized fashion retail while promoting inclusivity and sustainable consumer practices.

Abstract: Examinations are central to student evaluation in schools, and the reliability of teacher-made tests plays a crucial role in ensuring fair assessment. This study investigates the internal consistency and reliability evidence associated with mathematics teacher-made tests in selected senior high schools in the Techiman Municipal of Ghana. Focusing on Cronbach’s alpha, inter-item correlation, corrected item-total correlation, and split-half reliability coefficients, the study assessed terminal examination results over three academic terms. Findings revealed moderate to high levels of internal consistency across terms, with Cronbach’s alpha ranging from 0.815 to 0.953. These results indicate a generally acceptable level of reliability in school-based assessments (SBA), though variations suggest areas for standardization and improvement. Implications for improving assessment practices and educational policy are discussed.

Abstract: Zimbabwe's Technical and Vocational Education and Training (TVET) sector faces unprecedented challenges in the digital age, requiring innovative approaches to both technological integration and sustainable financing. This study examines the interplay between digital transformation and funding models in Zimbabwe's vocational skills training landscape, analysing current challenges and identifying emerging opportunities. Through a comprehensive literature review and comparative analysis with regional counterparts, this research reveals significant gaps in digital infrastructure, policy frameworks, and funding mechanisms that hinder TVET effectiveness. The study finds that Zimbabwe's youth unemployment rate exceeds 47%, while only 15% of students complete upper secondary education, highlighting critical skills mismatches. Digital transformation initiatives remain fragmented, with inadequate public-private partnerships limiting sustainable funding. Comparative analysis with South Africa and Botswana demonstrates successful models of integrated digital-funding strategies. The research identifies five key opportunity areas: enhanced digital infrastructure development, formalized public-private partnership frameworks, innovative financing mechanisms, industry-aligned curriculum development, and informal sector integration. Recommendations include establishing a National TVET Digital Transformation Fund, developing regulatory frameworks for public-private partnerships in skills development, and implementing competency-based training programs aligned with Fourth Industrial Revolution requirements. This study contributes to understanding how developing nations can leverage digital transformation and sustainable funding models to address skills gaps and youth unemployment challenges.

Abstract—Excessive and improper use of fertilizers leads to soil degradation and reduced agricultural productivity, negatively impacting farmers’ income. Description: Create a data-driven solution that recommends optimal fertilizer types and quantities based on soil health, crop type, and weather patterns, ensuring sustainable agricultural practices. Expected Solution: An application that analyzes soil data and provides tailored fertilizer recommendations, promoting sustainable farming while enhancing crop yield and farmer income. The implemented model achieves high accuracy and offers an intuitive interface for farmers, ensuring ease of use and effective adoption.

Abstract: The 21st century has brought unprecedented environmental problems to the fore, with Nigeria facing so many struggles in enhancing social development, economic growth and environmental sustainability. Therefore the purpose of this study is to determine the complexities of environmental governance in Nigeria, revealing the tensions between federal and state authorities, the role of civil society, and the impact of international agreements on national policy. In this research, secondary method of data collection was utilized by conducting a critical analysis of existing literature and policy frameworks. This study reveals key problems such as, inadequate institutional capacity, corruption, and conflicting interests, as well as opportunities for innovation, community engagement, and sustainable development. The findings shows the need for an inclusive and more effective governance framework that integrates environmental concerns into the strategies of national development, promotes stakeholder participation, and utilizing technology and international cooperation to address the pressing environmental challenges facing Nigeria. This research contributes to the broader discourse on environmental governance in developing countries, as it offers insights for policymakers, practitioners, and researchers seeking to ascertain the complexities of sustainable development in the 21st century.

Abstract: This paper reviews the impact of Corporate Social Responsibility (CSR) projects on infrastructural and social development, drawing from diverse case studies in sectors like healthcare and education. It analyzes how CSR initiatives contribute to societal advancement through resource mobilization, innovation, and addressing community needs, with examples from organizations such as Coca Cola, Walmart, and the Widus Foundation in Angeles City. The review highlights CSR’s positive effects on hospital and educational infrastructure, including technological upgrades and improved accessibility. However, it also identifies challenges such as governance issues, lack of transparency, regulatory barriers, and the perception of CSR as opportunistic. The paper calls for stronger policy frameworks, better governance, and transparent practices to enhance CSR’s sustainability and impact. Recommendations include community engagement, alignment with ethical standards, integration into business strategies, and collaboration with reliable partners to ensure more effective and sustainable CSR initiatives.

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Abstract: Land use means the use of land. Land use is any sort of permanent or cyclic human interference to satisfy his demand. Land use is the application of human controls systematically to the key elements of any ecosystem tries to manipulate it. Land use is intimately linked with advanced technology. It is impossible to grow crops on dry poor humus desert soil but after large scale reclamation it is possible. There is close relationship between man and land. The relationship can be expressed as – i) man is treated as social being and the land as inexhaustible resource, ii) the land and the person who use it, iii) man’s influence on land use as a means of production. Land use mainly deals with the problems of the regions. It is mainly confined to the optimum use of limited land among the alternative major type of land use. Infrastructural and institutional factors play an important role in determining land use. The land use is mainly the use in which the land in a certain area is put in a specified time. Land use creates continuous field of tension between human desire and existing available resources.

Abstract: The growing demands of urban waste management require intelligent, sustainable, and automated solutions. This study presents the design and development of a Smart Waste Bin System that leverages microcontroller technology and Internet of Things (IoT) (SWAMIS) connectivity to address the inefficiencies of conventional waste disposal methods. The system integrates an ESP32 microcontroller with ultrasonic sensors to monitor waste levels in real-time and incorporates an infrared sensor for automated, contactless lid operation, enhancing hygiene and user convenience. Once the waste exceeds a predefined threshold, the system transmits notifications via wireless communication modules to a centralized monitoring platform, enabling prompt and efficient waste collection and management.  A mobile application, based on Bluetooth technology, facilitates remote user interaction with the bin. The prototype demonstrates improved waste collection efficiency, reduced overflow incidents, and lower operational costs, particularly in high-traffic urban areas. Through performance testing, the system has proven reliable, scalable, and adaptable for smart city applications. The findings highlight the potential of microcontroller-based smart bins as a viable solution for addressing modern waste management challenges, thereby contributing to environmental sustainability and public health.

Abstract: Chronic Kidney Disease (CKD) is a progressive condition that often remains undetected until its later stages, leading to severe health complications and increased mortality. Therefore, this study presents the implementation of a machine learning-based system for early CKD prediction using the logistic regression algorithm. The study addresses the problem of delayed CKD diagnosis due to vague early symptoms and limited screening efficiency in traditional clinical workflows. The development of the system utilizes a clinical dataset from the UCI Machine Learning Repository made up of 400 patient records with 24 features, including demographic, clinical and laboratory parameters. Data preprocessing techniques were employed including label encoding, imputation of missing values and feature scaling for improving the quality of the data. Then, feature selection was conducted using the SelectKBest method with mutual information to identify the most relevant predictors. The logistic regression model was trained and evaluated using an 80:20 data split and the result of the implementation presents that the model achieved an accuracy of 97.5%, precision of 96.7%, recall of 98.2%, F1-score of 97.4% and a ROC-AUC score of 0.99. This work underscores the effectiveness of logistic regression in medical diagnostics and highlights the value of machine learning in facilitating early detection and timely treatment of CKD.

Abstract: Energy exists in numerous forms, which include heat, light, and radiation. We aim to optimise solar radiation at low, moderate, and high temperatures. Concentrating collectors, which are also, referred to as focussing collectors, capture direct radiation over a wide area and then focus it onto a small absorber area. Compared to flat-plate collectors, these collectors are more effective in producing high temperatures. The parabolic dish is what we use most often. A focussing receiver deliberately absorbs this concentrated radiation. Parabolic dish solar concentrators have recorded high operating temperatures and high conversion efficiency. The substantial opportunity for employing parabolic dish concentrators in various sectors boosts research and development. Concentrating collectors have been employed as heating technologies in numerous types and constructions in the past few decades. For estimating the intensity of a beam falling on a surface, the beam flux quantity arriving from the sun's axis must be transformed to values that are comparable and related to the surface's normal path. We aim to establish this pattern. The geometry of solar irradiation is one of the necessary factors. Different types of receivers have been studied by changing the depth and width of the parabolic dish collectors, which affects their focal point, how well they concentrate sunlight, and the temperatures they produce. We apply several factors to a parabolic dish to calculate its different rim angles. The focal length and diameter are the parameters. The parabola's diameter and focal length vary. In addition to the amount of solar energy received, the rim angle also affects the process of creating the parabolic dish. As a consequence, the flux dispersal and geometric rearrangement are altered for each scenario. The larger value of the focussing point in the parabolic dish leads to an increase in rim angles. Designing a parabolic collector featuring variable rim angles is the aim of the current research. The investigation examines a range of dish diameters and establishes the focus point to dish diameter ratio for every scenario during the evaluation. We employ extra precautions when choosing the right size for the reflecting materials. To achieve the least amount of deformation and strain while minimising the total mass of the dish, the dimensions of the segments and rings that hold the reflecting surface are increased.

Abstract: This paper presents the comprehensive development of a desktop-based virtual assistant application that leverages advanced speech recognition and natural language processing (NLP) technologies. The system is developed using Python programming language and is seamlessly integrated with Google Gemini to enhance performance and understanding of user intent. The assistant provides a user-friendly conversational interface for executing a variety of system-level tasks, such as launching desktop applications, recording the screen, retrieving general information, and automating repetitive commands. The primary objective of this project is to significantly enhance user productivity and improve overall system accessibility, especially through intuitive voice-based interaction. In today's world, many visually impaired, physically disabled, and elderly individuals suffer from social isolation and a diminished sense of independence in their everyday lives. Voice assistant systems provide a highly promising solution to this challenge by enabling hands-free, natural, and intuitive interaction with digital technology. This approach allows users to carry out essential tasks, access necessary information, and communicate with others without the need for visual cues or manual input. The proposed project introduces a Python-based voice assistant system specifically designed with the needs of blind, elderly, and physically challenged individuals in mind. The system is tailored to improve their quality of life by promoting continuous engagement, enhancing digital accessibility, and encouraging greater independence. By integrating both speech recognition and text-to-speech capabilities, the assistant can understand verbal commands, respond with synthesized speech, and perform vital functions such as setting alarms or reminders, reading incoming messages, providing weather or news updates, and accessing online content. This work demonstrates the transformative potential of AI-powered voice technologies in fostering inclusivity, supporting vulnerable populations, and empowering individuals with special needs through smart, accessible digital interaction.