Volume 14 Issue 4

Abstract: This paper provides a narrative review of Generative Artificial Intelligence, exploring its evolution, underlying concepts, and diverse applications across various industries. The review is conducted by searching google and google scholar using relevant keywords which in turn leads to different published articles from different websites and databases. The introduction establishes the growing significance of AI in human lives and highlights the rise of Generative A I as a powerful force in creating, innovating, and envisioning. The paper delves into different generative AI models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Recurrent Neural Networks (RNNs), Transformer-based Models, and Diffusion Models. Foundation Models, such as BERT and GPT, are introduced as adaptable models trained on broad data for diverse downstream tasks. The significance of LLMs in Natural Language Processing (NLP) and Computer Vision is emphasized, detailing their impact on text understanding, generation, translation, and information retrieval. The benefits and challenges of LLMs, ranging from natural language understanding to content moderation, are discussed, addressing concerns such as bias, ethical considerations, misinformation, and privacy. The paper concludes with an exploration of the application of Generative AI and LLMs in healthcare and business operations, showcasing their potential in personalized treatment plans, drug discovery, medical imaging, customer support automation, content creation, marketing, human resource automation, and software engineering.  

Abstract: With the growing availability of internet connectivity and the widespread use of smartphones and computers across the population, technology presents a powerful opportunity to bridge the communication gap between local household employers and skilled workers seeking employment. In response to this, the present research aims to design, develop, and evaluate a mobile-web application that serves as a platform connecting household employers with skilled workers. The main feature of the application is the use of a critiquing-based recommendation system, which assists employers in efficiently identifying workers who best match their needs and preferences. This mobile-web system is designed based on the Activity Theory Model (ATM) by Engeström (1987), which was utilized during the requirements-gathering phase. The design and development process were guided by the ADDIE model (Grafinger, 1988), ensuring a structured approach. The development process went through three iterative cycles, before producing the finalized version of the application. This version was then implemented and evaluated using the Usability Metric for User Experience (UMUX), a validated instrument by Finstad (2010), to assess effectiveness, efficiency, and user satisfaction. Results indicated strong user agreement regarding the application’s efficiency and effectiveness, with high levels of satisfaction reported. The application achieved a UMUX mean score of 94.3, indicating a high level of usability. Additionally, the critiquing feature achieved a weighted mean score of 6.8, indicating high user satisfaction with its functionality and relevance.

Abstract: This paper explores the integration of Bhagavad Gita principles with modern supply chain management (SCM) practices to propose a values-driven framework for resilient, ethical, and sustainable operations. Using a conceptual methodology supported by in-depth case studies of global corporations such as Toyota, Unilever, Patagonia, Apple, and IKEA, this study identifies philosophical insights that align with key supply chain functions. The findings demonstrate that incorporating ancient Indian wisdom can enhance decision-making, stakeholder engagement, and long-term value creation in global SCM.

Abstract:  With the emphasis of smarter, more efficient crop growing methodologies coupled with advances in sensors, Internet of Things (IoTs) and artificial illumination especially at the urban areas propel the development of indoor farming to another level as never been seen before as per compared to open and large-scale farming. However, there is a relatively big void in term of reference data availability for soilless indoor farming i.e precision farming, level of nutrients, light irradiances, yield improvement and etc. This review paper primarily aims to design and implement IoT system for real-time monitoring of multiple parameters in indoor farming. It involves using related sensors and components to monitor critical parameters in real-time. Edge computing solutions and a cloud-based storage/computing to be applied accordingly as tools to facilitate data monitoring and storage. The study includes the installation of various sensors such as nutrient quality monitoring (NPK sensors, pH, Electrical Conductivity (EC), nutrient temperature), climates monitoring (humidity, indoor room temperature, CO2 gas) and irrigation (flow, level and turbidity). All of these data then shall be transmitted accordingly to relevant processors, monitoring system and then being stored at clouds. The methodology is structured in a way that the system could be scaled-up for larger space and modular setting. Nowadays, IoT system is used widely in farming but trending towards application in indoor farming, for example, soilless lettuce indoor farming. This review paper has illustrated the bio-characteristics of the lettuce: Lactuca Sativa such as morphological or physiological parameters and biochemical parameters like basic chemical composition, chlorophyll, micro/macro minerals and so on which the parameters can be obtained by using modern IoT system. To set up soilless lettuce indoor farming, modern IoT system is utilized and the overall process from data monitoring to data harvesting can be simplified.

Abstract: A total of 496 students of Engineering and MBA were surveyed to determine their awareness about personal finance and investment. The sample consisted of 268 students of MBA programme and 228 students pursuing B.Tech. programme. These students were broadly categorized here as management students and non-management students. The following  three dimensions were primarily assessed in the study- financial behaviour, financial attitude and financial knowledge. This study shows that the level of financial awareness among college students in Bhopal is very low.

Abstract: The removal of heavy metals from industrial wastewater, particularly in paint manufacturing plants, remains a significant environmental challenge due to the toxic nature of metals such as chromium (), lead (), cadmium (), and nickel (). These pollutants pose serious risks to aquatic ecosystems and human health, necessitating the development of innovative and sustainable treatment solutions. This study explores the potential of magnesium ferrite () and its nanoform as advanced materials for heavy metal removal. Characterized by their high surface area, magnetic separability, and cost-effectiveness,  nanoparticles () demonstrate superior performance compared to their bulk counterparts in terms of adsorption capacity, kinetics, and magnetic properties. Experimental findings reveal that  offer a more efficient and eco-friendly approach to wastewater management, with enhanced reactivity and ease of recovery through magnetic separation. Furthermore, this study identifies a critical research gap in the application of surface-modified  for improved adsorption of mixed heavy metals, providing new insights into their potential for sustainable water treatment technologies. By addressing these challenges, the study underscores the promise of  as a scalable and effective solution for industrial wastewater purification.

Abstract: Diversity, Equity and Inclusion has become a ubiquitous gimmick across the world especially in the field of human resources. It has become the most critical element in the modernday HR practice. With employees spanning from Baby Boomers, Gen X, Gen Y and Gen Z and the gender variants, organizations must ensure that their policies and practices should ensure the needs and expectations of the diverse groups. Each group brings their own unique perspectives and practices and their engagement and commitment to work depends on how the organization perceives them. As organizations aim to build a stronger workforce, Diversity, Equity and Inclusion (DEI) initiatives play a crucial role in fostering a cohesive work environment. This paper explores the role of DEI initiatives in employee engagement by promoting workplace inclusivity, equitable opportunities and inter-generational collaboration. It further examines how DEI programs impact employee engagement and job satisfaction among diverse groups. The study was conducted in various manufacturing companies in Tamil Nadu and the research yielded a positive correlation between DEI and Employee Engagement.

Abstract:  Nhavare village in Shirur tehsil of Pune district, Maharashtra, India, is known to its rich faunal assemblage. Biodiversity was surveyed during the rainy season in Nhavare using a standardized walking survey technique. The photographed animals were identified with taxonomic keys. There were 24 species that were observed, out of which six species belonging to the Phylum Arthropoda while 18 species belong to the Phylum Chordata. Six resident birs species were found namely: Corvus splendens, Corvus culminates, Passer domesticus, Ardeola grayii, Milvus lineatus,  Dicrurus macrocercus. The ecological importance of Nhavare as an essential habitat for bird and other faunal diversity. Also diverse species of insects, arachnids, and reptiles to birds and       mammals were identified such as Apis dorsata, Ptyas mucosa, and Milvus lineatus signifies the ecological diversity of the area. The research also emphasizes the contribution of agriculture to local biodiversity. The record of faunal diversity in Nhavare provides a baseline for future ecological monitoring and conservation management planning. The knowledge of species composition and spatial distribution can inform biodiversity conservation and management strategies. This work highlights the importance of ongoing monitoring of biodiversity, especially in rural environments that are increasingly subject to anthropogenic pressures. It is recommended to carry out further long-term studies in different seasons to compare fluctuations in species abundance and habitat choice.

Abstract: This study examines how credit management influences the profitability of deposit money banks in Nigeria. The research is anchored on four specific objectives: (1) To analyze the effect of non-performing loans on bank profitability, ( 2) To examine the effect of loan loss provisions on the profitability of deposit money banks, (3) To assess how loans and advances influence bank profitability, and (4) To determine the effect of interest rates on bank profitability. Employing anex post facto research design, the study utilizes secondary data spanning from 2002 to 2023. The analysis employs the ordinary least squares ( OLS) model to evaluate  effects of credit management on bank profitability .Data were sourced from the Central Bank of Nigeria's statistical bulletin (2024). The study identifies that non-performing loans have a significant and negative effects and loan loss provisions have an insignificant and negative effect on profitability, whereas loans, advances, and interest rates exhibit a significant and positive effects. Based on these findings, the study recommends that non-performing loans be rigorously monitored and loan loss provisions minimized through enhanced risk management. Additionally, interest rates should be strategically reviewed for creditworthy customers to optimize profitability.

Abstract-This article presents a comprehensive review of various inverter configuration, power electronic converter topologies and controlling and optimization techniques utilized in single phase inverter fed with multi-input (wind and Solar PV Array) to power the varying load. Furthermore, this paper discusses classical as well as recent advancements in maximum power tracking of solar output including Neural Network, fuzzy logic and ANN with integration of IOT connectivity and real time enabled monitoring and communication in Smart Inverter.

Abstract: Modern companies need to transform their legacy monolithic applications into microservices as their essential approach for obtaining better scalability and flexibility and enhanced performance in cloud-native infrastructure. The text examines the process of changing monolithic frameworks into ArgoCD-managed microservices through a combination of IAM and AWS infrastructure. The study evaluates the advantages of this migration process to show how these systems combine their operations for managing deployment workflows while enhancing security measures and scaling cloud environments. The first section reviews literature that details the architectural change from monolithic systems to microservices while tracing the industry need for microservices in contemporary software development. The methodology section explains how GitOps works with ArgoCD for automatic microservice management and AWS IAM integration for security purposes. A case-based assessment provides insights into how these tools minimize the time needed for CI/CD processes and support continuous delivery functions in Kubernetes deployments. The implementation of GitOps together with ArgoCD and IAM tools leads to operational simplification through automated manual operations along with smooth microservices adoption. The connection of IAM insecurely integrates security systems to protect against deployment lifecycle vulnerabilities. This paper provides an extensive examination of the experimental work with an analysis of team adaptation to microservices combined with best practices for GitOps scaling and maintenance of microservices architectures. The article concludes with a statement about how this method demonstrates its ability to address monolithic system constraints while maximizing cloud-native application management.

Abstract: This study investigates the integration of risk management and global strategic planning in the context of a leading Indonesian telecommunications company, PT ABC Tbk. Using a qualitative case study approach, the research explores how SWOT analysis is applied alongside Enterprise Risk Management (ERM) to align organizational capabilities with environmental uncertainty. Data were collected through company reports, strategic documents, and secondary sources, and analyzed using thematic and pattern-matching techniques. The findings reveal that PT ABC implements a mature ERM framework based on ISO 31000:2018 and systematically links SWOT components to its corporate risk register. This integration enhances strategic agility, supports informed decision-making, and mitigates threats while capitalizing on opportunities. The study contributes to the literature by demonstrating a practical model of strategy-risk alignment in an emerging market setting. It also provides actionable insights for firms seeking to institutionalize risk-aware strategic planning amid technological and regulatory disruption.

Abstract: The study examined Internet usage and academic performance of students in the Faculty of Education, University of Uyo. Two research questions and hypotheses guided that study. The correlational research design was adopted for the study and the population of the study consisted of 4,292 students in the Faculty of Education, University of Uyo during the 2024/2025 academic session. The sample for the study was 385.  A structured questionnaire titled Internet Usage and Academic Performance Questionnaire (IUAPQ) was used as instrument for data collection. The instrument was subjected to a face validation and a reliability coefficient index of 0.84 was obtained using Alpha Cronbach statistics. Path analysis was used to answer the research question as well as to test the null hypotheses at .05 level of significance. The results showed that there is a significant relationship between WhatsApp usage, Facebook usage and the academic performance of students in the University of Uyo. It was recommended among others that government should show commitment towards effective integration of Information and Communication Technology into the mainstream of the teaching and learning process.

Abstract: India, a country with young people, is facing a serious problem, 83% of its unemployed are under the age of 35. The goal of this study is to make analysis in terms of different aspects of this disturbing statistic and its influence on the nation's economic and social life. Study looks at factors that contribute to high youth unemployment, such as incompatibility of education with job market demands, economic policies, and social challenges. It also looks at the long-term effects on India's economy, innovation, and social stability. The research uses a mixed method approach, using surveys, interviews, and secondary sources to gather qualitative and quantitative data. Research asserts that there are significant inadequacies in the education system, economic disparities, and social barriers that impede the employment of young people. Case studies show the effectiveness of interventions and differences in effectiveness across regions. The paper offers practical suggestions to policymakers, educational institutions, and the private sector on how to close the skills gap, reform economic policies, and foster inclusive growth, based on the analysis. Research shows the need to address youth unemployment to ensure India's economic growth and demographic dividend. This abstract encapsulates the essence of the research, highlighting the key areas of investigation, the methodology used, and the primary findings and recommendations. It provides a concise yet comprehensive overview for readers, setting the stage for the detailed exploration in the subsequent sections of the paper.

Abstract: This study examined teachers’ competencies as predictor of SS 2 students’ academic achievement in Accounting in public secondary schools in Anambra State. The study was guided by two research questions and two hypotheses. The study adopted correlational research design. The population of the study comprised 569 senior secondary students (SS2) students in Accounting (217 males and 352 females) in 267 public secondary schools in the six Education Zones of Anambra State. The sample comprised 569 Accounting students. The entire population of the Accounting students was adopted because of the manageable size. One instrument was used for data collection: Teachers’ Competencies Questionnaire (TCQ). The instrument contained 20 items spread across the clusters, A and B. The items were placed on 4-point scale of Strongly Agree (SA) – 4 points, Agree (A) - 3 points, Disagree (D) – 2 points and Strongly Disagree (SD) – 1 point. The range of scores was weighted as 4, 3, 2 and 1 respectively. On the other hand, Students’ Academic Achievement Scores (SAAS) measured the students’ academic achievement in public secondary schools in Anambra State. Academic Achievement was measured with termly scores in Accounting which were collected from Records Department, Post Primary School Service Commission (PPSSC), Awka Anambra State (2024). The coefficient value of 0.85 for teachers’ adaptability; 0.83 for teachers’ classroom management was obtained. This gives an average coefficient value of 0.84 and was considered reliable for the study. The instrument was administered to the respondents by the researcher with the help of three (3) research assistants. It was ‘on the spot hand delivery’ for the administration and retrieval of the instrument. Simple linear regression analysis was used for data analysis at 0.05 level of significance. The study found that Teachers with deep subject mastery can simplify complex concepts, provide relevant examples, and address misconceptions, leading to improved student comprehension and performance. Additionally, classroom management was found to be a strong predictor of students’ success in Accounting. A well-organized learning environment minimizes disruptions, enhances student engagement, and fosters discipline. While effective classroom management is crucial, some scholars argue that other factors, such as instructional quality and teacher-student relationships, also play essential roles in academic achievement. The study recommended that the Ministry of Education and school administrators should organize regular training programs, workshops, and professional development courses to improve teachers’ mastery of Accounting concepts.

Abstract: This chapter explores the rapid growth of digital trade and e-commerce, emphasizing the crucial role of digital technologies in global commerce and the regulatory challenges that arise from their expansion. It examines the definitions and key components of digital trade, including digital goods, services, and data flows, and highlights global trends and statistics. The chapter reviews national regulatory frameworks and their divergent approaches, focusing on the European Union, the United States, and China. It addresses legal and regulatory challenges such as data privacy, intellectual property, cybersecurity, and taxation. The role of the World Trade Organization (WTO) in shaping international rules for digital trade is analyzed, along with current discussions and dispute settlement mechanisms. The impact of digital technologies on trade facilitation and the benefits and challenges of digital platforms are discussed, supported by case studies. Emerging issues like the implications of 5G, IoT, and AI on digital trade are explored, followed by policy recommendations for harmonizing international rules and enhancing regulatory coherence. It concludes with an outlook on the future of digital trade and e-commerce, advocating for international cooperation and inclusive regulatory frameworks.

Abstract: This study explores the long-standing and multifaceted musical rivalry between two of Zimbabwe’s most prominent artists, Jah Prayzah and Winky D, from 2010 to 2024. It investigates how this feud has evolved from early collaborations to pronounced ideological and cultural opposition, analysing its lyrical content, media portrayal, and public reception. Central to the study is the question of how this rivalry reflects deeper socio-political, generational, and class-based tensions within Zimbabwean society. The research is guided by Antonio Gramsci’s Cultural Hegemony Theory, which provides a powerful framework for understanding how dominant ideologies are reinforced or resisted through popular culture. Using a qualitative, desk-based methodology, the study relies on secondary data from newspapers, online entertainment portals, social media platforms (e.g., YouTube, Twitter, Facebook), music videos, and podcast interviews. Thematic content analysis was employed to extract patterns around state affiliation, fan division, lyrical symbolism, and political expression. Findings reveal that the rivalry is not merely artistic but represents opposing visions of Zimbabwean identity. Jah Prayzah symbolizes institutional respectability and pan-African modernity, while Winky D has become a mouthpiece for marginalized youth and urban struggles. Audience reactions and media coverage show polarized fandoms aligned with class, age, and political sentiment. The study concludes that musical rivalries, particularly in politically sensitive contexts like Zimbabwe, are rich texts of societal values and conflicts. Recommendations include protecting artistic freedom, fostering dialogue between artists and the state, and recognizing the sociopolitical power of music in shaping national consciousness. This research contributes to cultural sociology, popular music studies, and Zimbabwean media discourse by decoding how rivalry in music becomes a mirror of national tension and transformation.

Abstract: This study investigates the relationship between gold prices and stock market performance in India and other emerging markets (Brazil, China, South Africa), focusing on gold’s role as a safe-haven asset during economic crises. Employing a mixed-methods approach, we combine quantitative time-series analysis (2000–2025) with qualitative insights from investor surveys. Econometric models, including Vector Autoregression (VAR), Granger Causality tests, and predictive machine learning models, were applied to examine interdependencies between stock market indices (e.g., BSE Sensex, NSE Nifty 50, and international indices) and gold prices. The study also explores the impact of digital gold investments, technological advancements, and macroeconomic factors (interest rates, currency fluctuations, geopolitical events) on traditional gold investment behaviours. Findings confirm gold’s role as a hedge during financial crises (e.g., 2008 global financial crisis, COVID-19 pandemic), though its effectiveness varies across markets and conditions. Predictive models offer practical tools for forecasting gold price movements, benefiting investors and policymakers in volatile markets.

Abstract: This study represents the important role of dye sensitizer selection for best photovoltaic response and the photo anode thickness of semiconducting layer for determining the efficiency of dye-sensitized solar cells (DSSCs). For obtaining these objectives we employ an ex-situ methodology to prepare the composite materials. So we prepare nanocomposite by adding 20 wt.% of ZnO nanoparticles into TiO2 nanoparticles (20TZ). We fabricate dye sensitized solar cell by using the doctor blade technique. Furthermore our key findings are to extends the duration of dye loading, highlighting its significant influence on DSSC performance. The research also encompasses variations in the thickness of the semiconducting layer, providing critical insights into DSSC efficiency. Significance of present research work represent , photoanode thickness of 300-micron  20TZ-based DSSC, sensitized with 3% of N749 dye for a 2-hour loading period, achieves an impressive power conversion efficiency of 15.1%. The stability of 20TZ-based DSSCs was evaluated under continuous light illumination, revealing a minimal decrease in power conversion efficiency of 1.61% over 240 hours. Significance of present study is that we achieve power conversion efficiency 15.1% for  the assemble dye sensitized solar cell and its stability duration time.

Abstract: In the era of big data, the synergy between machine learning (ML) and data modeling has emerged as a cornerstone for predictive analytics. This article explores the integration of machine learning techniques with traditional data modeling approaches to enhance decision-making across various domains. By leveraging the strengths of both methodologies, organizations can unlock deeper insights, improve accuracy, and drive innovation. This article discusses key concepts, challenges, and applications, providing a roadmap for researchers and practitioners to harness the full potential of these technologies.

Abstract: Small islands will face a variety of very complex obstacles related to sustainable development. The Banda Islands are one of the areas that are likely to face these problems due to the high level of insularity. To avoid the problems that will be faced, various studies are needed in evaluating the status of small island sustainability on two Potential Islands as a priority for sustainable development carried out with the aim of knowing the sensitive factors that affect the development of Small Island Areas based on the dimensions of management as an improvement strategy. This research uses the Multidimensional Scaling (MDS) approach by determining five dimensions consisting of 56 Attributes. Based on the results of the study, Banda Neira Island has an average index value of 42.44 which is included in the Less Sustainable category and Banda Besar Island with an average index value of 43.91 which is included in the Less Sustainable category. From the sustainability status, it is known that sensitive factors in inhibiting sustainable development can be used as the main problems faced by the two potential islands and become a special concern in the planning process of the two potential islands.

Abstract: In this paper, I demystify the causality of Bamenda’s prominent weather elements, especially in conformity with her annual solar-zenith times, a function of sheer latitudinal location. Then I investigate the sensitivity of climate (change) impacts there on colocasia yields, and annual colocasia planting times (growing degree days), in the event to clear the fog of confusion enshrouding the minds of researchers regarding the disappearance of the plant. Research on the afore-cited growing plight has uncovered data that is skewed toward drastic weather trends, an effect of global climate change, as its main cause; it is answerable for the slothful but assured diminution of this once perennial crop. This paper, thus, as is my design, actively challenges Bamenda’s climate as pertains to this event, strongly critiquing any material that bespeaks or even hints otherwise. In order to corroborate the aforementioned hypothesis, a somewhat extensive comparative analytic study was carried out around the Mankon, Mendankwe, and Nkwen environs, but with special emphasis on the Mankon area, as it is the embodiment of Bamenda’s both peasant and beau-monde societies. Data was collected on sunshine, temperature and precipitation. Also, colocasia yields were simulated pertaining to management conditions of both (i) before what is now considered, at least nationally, as effective climate change in action, and (ii) in present (‘climate change’) times. Distortions, as were ascertained, in carbon dioxide, oxygen and nitrogen proportions in the soil, make it a recalcitrant carbon store for colocasia esculenta. Also, an until now concealed fact of the plant’s greater dependency on reliable water supply relative to good soil-nitrogen levels was revealed. A warming/ drying trend thus, a direct effect of global warming, is proving a pernicious effect on the crop. I therefore conclude that the recent disappearance of colocasia esculenta in the Bamenda area skew more toward impactful climate change ramifications than to its overexploitation.

Abstract: The termination of the Multi Fibre Arrangement (MFA) in 2005 marked a significant shift in global textile trade dynamics, introducing a quota-free regime that intensified competition. While India capitalized on expanded export opportunities, traditional sectors like Kerala's handloom industry faced substantial challenges. This study examines the post-MFA structural transformations within Kerala's handloom sector, focusing on institutional frameworks, market integration, and socio-economic impacts on weavers. The study adopts a descriptive and analytical research design based exclusively on secondary data sources. Findings reveal that despite institutional support, the sector grapples with issues such as limited market access, inadequate technological adoption, and declining youth participation. The study underscores the necessity for targeted policy interventions to revitalize this traditional industry.

Abstract: The use of artificial intelligence and machine learning techniques in emergency medicine has grown rapidly. This paper reviews and assesses studies in this field, categorizing them into three areas: prediction and detection of disease, prediction of need for admission, discharge, and mortality, and machine learning-based triage systems. Overall, the studies reviewed demonstrate the potential of artificial intelligence in improving emergency care. However, the accuracy and effectiveness of these algorithms depend on data quality. Further research is needed to validate findings and improve performance in clinical settings.

Abstract: The COVID-19 pandemic triggered a sharp global economic contraction, with global GDP declining by approximately 3% in 2020. Developing countries experienced even steeper declines, averaging 4%, with some exceeding 6.5%. Merchandise trade volumes plummeted by 13%, with China—an influential player in global trade—facing substantial economic disruptions that reverberated across its trading partners. This study analyzes the macroeconomic consequences of COVID-19, focusing on global GDP, trade patterns, and sectoral impacts. Using secondary data from the World Bank, International Monetary Fund, and national statistical agencies, a comparative analysis was conducted to assess changes in economic indicators before and after the pandemic. The study findings revealed that the post- pandemic recovery patterns varied significantly , reflecting disparities in economic resilience ,with countries possessing Strong pre-pandemic digital infrastructure, robust healthcare systems, and flexible fiscal policies recovering faster, while those reliant on labor-intensive sectors like tourism faced prolonged setbacks. Advanced economies outpaced developing nations in GDP growth and employment restoration, highlighting pre-existing vulnerabilities. Despite challenges such as inflation and supply chain disruptions, the pandemic accelerated digital transformation and infrastructure investment, offering opportunities for more resilient economic systems. To address these disparities and bolster resilience, governments should invest in digital and healthcare infrastructure, diversify supply chains, and foster regional economic cooperation. Supporting vulnerable sectors, such as SMEs and informal workers, through targeted fiscal measures and job creation programs

Abstract: Corrosion of materials such as mild steel and or zinc metal causes big losses in the economy of many countries due to the great amount of funds required in order to be minimized. The aim of the research is to investigate the corrosion of carbon steel and zinc metal in acidic medium using Lannea microcarpa leaves extract by weight loss method. It was observed that the inhibition efficiency increase with temperature for both carbon steel and zinc metal.  The adsorption of Lannea microcarpa leaves were found to obey Langmuir isotherm model for zinc metal with R2 value close to unity and that of carbon steel obeys Freundlich isotherm model.

Abstract: Artificial Intelligence (AI) is transforming the journalism industry worldwide, bringing forth new tools that can revolutionize how information is gathered, produced, and disseminated. AI’s ability to assist with data analysis, content generation, real-time transcription, audience analysis, and fact-checking has proven valuable in modern newsrooms. In the context of Nepal, a country with a vibrant yet resource-constrained media landscape, the integration of AI offers both immense opportunities and significant challenges. This study explores the various ways AI tools are impacting Nepali journalism and the ethical, professional, and infrastructural issues that come with it. Drawing from a survey of 200 journalists across Nepal and several qualitative interviews, the study outlines current AI usage, perceived benefits, and the barriers journalists face in embracing AI. A comprehensive review of global, regional, and local literature complements the analysis. The study concludes with practical recommendations for ethical and effective AI integration into Nepali journalism, underscoring the need for local innovation, journalist training, and strong regulatory frameworks.

Abstract: Drowsiness is among the primary reasons for driver caused traffic accidents. The interactive systems that have been designed to minimize road accidents by notifying the drivers are referred to as Advanced Driver Assistance Systems (ADAS). Most significant ADAS include Lane Departure Warning System, Front Collision Warning System and Driver Drowsiness Systems. In the current research, an eye state detection based ADAS system is introduced to identify driver drowsiness. To start, Viola-Jones algorithm method is utilized for identifying the face and eye regions in the current work. The eye region, detected in the present method, is classified into open or closed through utilization of a machine learning approach. Ultimately, eye conditions are inspected at time domain using percentage of eyelid closure (PERCLOS) metric and drowsiness states are calculated by Support Vector Machine (SVM). The above proposed methods are tested on 7 real individuals and drowsiness conditions are detected better accuracy, respectively.

Abstract: This study addresses the issue of students understanding the complexities of farming by developing “Harvest Horizon”, a 2D farming simulation game designed to serve as both an educational tool and an engaging interactive experience. The game employs the Cellular Automata algorithm to model realistic plant growth behavior in response to environmental conditions. The research follows an iterative Game Development Life Cycle (GDLC) methodology and is developed using the Godot Engine with GDScript for algorithm implementation, Adobe Photoshop and Aseprite for asset refinement, and Figma for user interface prototyping. The game is assessed based on the ISO 25010 software quality model. The evaluation involved two respondent groups: gamers with at least six months of gaming experience across various platforms, and technical experts with academic and professional backgrounds in computer science, information technology, or game development. Findings indicate that Harvest Horizon effectively engages players in cognitive processes such as sustained attention, cognitive flexibility, working memory, and pattern recognition through its interactive farming mechanics. The game received an overall weighted average mean interpreted as “Strongly Agree.” These results affirm the game’s capacity to deliver an immersive educational experience while adhering to essential software quality benchmarks. This study highlights the potential of game-based learning as an innovative approach to agricultural education. The successful integration of cognitive engagement and technical excellence underscores the viability of Harvest Horizon as a supplementary educational tool for those seeking to revitalize interest in farming among younger generations.

Abstract: The recent wildfires around Los Angeles devastatingly point out the relationship between nutrition security and climate change, aggravated by the Santa Ana winds that dry out the region and increase the likelihood of wildfires at speeds of 60 MPH (96.5 KPH). These fires disrupt the agricultural supply chain alongside ecosystems, limiting access to a variety of food commodities and exacerbating the already existing food insecurity challenge. The wildfire impacts in Los Angeles are not just local, but part of broader phenomena observed during bushfires in Australia and within the Amazon rainforest, where rampant deforestation poses peril to biodiversity and vital activities like carbon sequestration. Wildfires cause long-lasting damage to food production and ecosystems when combined with fires, these put tremendous stress on global food sources, including Los Angeles. The farmer's perspective indicates the heightened dependence on local food systems drives home the reality of resilient food systems that will need to be built in Australia and the Amazon. Addressing sustainability and climate change with anticipatory adaptation frameworks based on effective land management practices are required to deal with the resilient challenges. Constructing sustainable diets and resilient food systems are vital for dealing with the consequences of climate calamities. Strengthening regional partnerships is the answer to ensuring food security, agriculture with climate smart agendas while protecting soil will do the trick.

Abstract: Hospital falls represent a critical public health challenge within the United States healthcare system, affecting approximately 700,000 to 1,000,000 patients annually in acute care settings, with 30–35% resulting in injury. These incidents negatively impact patient outcomes, hospital efficiency, and healthcare costs. The complexity of fall events necessitates a technology-enabled approach to prevention and risk reduction. Advanced predictive analytics and artificial intelligence (AI) offer promising solutions to this persistent issue. This study introduces an innovative data-driven approach that integrates predictive analytics, AI-based risk assessments, and evidence-based interventions. By combining machine learning algorithms with comprehensive risk assessment protocols, healthcare institutions can develop dynamic, personalized fall prevention strategies that enhance patient safety while reducing costs. This approach demonstrates potential for significant improvements, with estimated national savings of approximately $1.82 billion annually. Participating hospitals reported outcomes such as up to 98.9% accuracy in fall risk prediction and a 66.7% reduction in fall incidents, reinforcing the role of AI in improving safety. The framework is distinguished by its integration of real-time monitoring, machine learning, and clinical workflow adaptation, allowing for responsive, patient-specific interventions that evolve during hospitalization. Furthermore, it emphasizes multidisciplinary collaboration, technological integration, and continuous performance monitoring to support a scalable and adaptive fall prevention strategy.

Abstract: Health and safety are pivotal in ensuring the protection of healthcare workers (HCWs) and patients from occupational hazards and infectious diseases. This study employed a cross-sectional design to examine the predictors of knowledge about health and safety among HCWs at a tertiary healthcare institution in Northern Nigeria. A sample of 250 HCWs was selected using stratified random sampling across various departments including clinical, nursing, laboratory, and support services. Data were collected using a structured and pretested self-administered questionnaire. The questionnaire covered demographic details and knowledge of health and safety practices. Data were analyzed using SPSS version 25. Descriptive statistics, Chi-square tests, and logistic regression analyses were conducted. The results indicated significant associations between knowledge and factors such as age, type of organization, position held, and years of service. Particularly, HCWs aged 30-39 demonstrated higher knowledge levels, while those in the 40-49 age group and those employed as contractors or support staff showed poorer knowledge. The study underscores the importance of targeted interventions and continuous professional education to enhance health and safety knowledge among HCWs in Nigerian healthcare institutions. These findings have practical implications for policy makers, hospital administrators, and training institutions aiming to strengthen the health and safety culture within the healthcare system.

Abstract: Groundwater exploration in hard rock terrains requires advanced geophysical techniques to identify high-yielding aquifers. This study utilizes Magneto telluric (MT) resistivity imaging to delineate subsurface fracture zones and assess groundwater potential in the Kilambakkam region. The resistivity profiles reveal a complex hydrogeological setting characterized by shallow weathered zones (50m-100m depth) with low water yield, deeper fractured aquifers (120m-250m depth) with moderate yield, and deep-seated fault zones (180m-300m depth) exhibiting high groundwater potential. Two primary borewell target zones have been identified based on low resistivity anomalies (1-6 Ωm), indicating significant water-bearing formations. The study emphasizes the importance of integrating geophysical surveys with hydrogeological data to optimize borewell placement and enhance sustainable groundwater extraction. Further validation through vertical electrical sounding (VES) and pumping tests is recommended to ensure long-term aquifer viability.

Abstract: This paper explores the interplay between Banach space theory and machine learning optimization, offering novel theoretical insights with applications in engineering and management. We establish a suite of original theorems that bridge functional analysis and data-driven models, including: (1) convergence rates for gradient descent in reflexive Banach spaces under norm-attainability conditions, (2) operator norm bounds governing neural network generalization, and (3) adversarial robustness guarantees via Lipschitz continuity in non-Euclidean settings. Methodologically, we develop Banach-space analogues of fundamental results-from SVM duality to PID control stability-while demonstrating their utility in resource allocation and time-series forecasting through Orlicz space embeddings. Our framework not only extends classical optimization theory to infinite dimensional function spaces but also provides implementable regularization strategies for deep learning. The results are substantiated by rigorous proofs leveraging weak∗ compactness, spectral radius analysis, and semigroup theory. For practitioners, we derive explicit error bounds and convergence rates applicable to high-dimensional datasets and non-smooth objectives. This work thus unifies abstract functional-analytic concepts with modern machine learning challenges, offering new tools for both theoretical analysis and algorithmic design.

Abstract: A smart helmet has been developed that can recognize hazardous conditions in the mining sector. When constructing the helmet, consideration was given to the three main types of hazards: air quality, helmet removal, and collision (miners are hit by an object). The first is the degree of concentration of dangerous gases like particulate matter, CO, SO2, and NO2. The second risky incidence was a miner removing their mining helmet. The attempted development of an infrared sensor led to the successful usage of an off-the-shelf infrared sensor to detect when the miner's helmet is on his head. According to the Head Injury Criteria (HIC), the third risky occurrence occurs when miners are struck in the head by an object with a force greater than 1000. An accelerometer was used to measure the head's acceleration, and software was used to calculate the HIC. The layout of the visualization program was completed, but it was not executed properly. The calibration tests of the accelerometer were successfully finished. Among the PCBs made were a prototype board and a breakout board. The Contiki operating system served as the foundation for the entire software implementation that controlled sensor measurement and a computation using the measured values. The undertaken design, which includes remedies to the problems identified in earlier study, is presented in this publication. 

Abstract: With the increasing integration of solar PV and wind energy in island micro-grids (MGs), the intermittent nature of non-dispatchable sources and the unpredictability of load demands are unavoidable. As a result, maintaining high reliability and system stability becomes a significant challenge. Additionally, demand-side management technologies and energy storage are commonly implemented in island MGs to mitigate the negative effects of RESs. However, these solutions also introduce uncertainties. Moreover, RESs in MGs are highly susceptible to external environmental factors such as solar radiation, temperature fluctuations, and wind speed variations, making it difficult to ensure system stability, particularly in islanded mode. To address these uncertainties, this paper considers six participant sites and proposes a Cooperative Game Theory (CGT) based on Nash Bargaining Solution (NBC) for collaboration among the participants of MG under the condition of uncertainties introduced by each participant. First energy transaction among the MG participants is modeled. Secondly, CGT using NBS is applied to model the uncertainties in dispatchable and non-dispatchable energy resources of the participants. Moreover, using NBS, a cooperative operation model among MG participants is established, which is transformed into profit maximization in cooperation, ensuring fair profit distribution and improves economic outcomes. The simulation results show that cooperation among all participants leads to an increase in their benefit values. Additionally, the findings suggest that the proposed model demonstrates strong economic performance.

Abstract: The intricate, nonlinear interactions between several process parameters make it difficult to forecast biogas and biomethane (bioCH₄) yields in anaerobic digestion systems with any degree of accuracy. These dynamics are frequently not well captured by conventional kinetic models, particularly in co-digestion systems with heterogeneous substrates. This was addressed by developing and testing a Multilayer Perceptron Artificial Neural Network (MLP-ANN) for predicting cumulative biogas and bioCH₄ volumes from the anaerobic co-digestion of soymilk dregs and cow manure based on key operational parameters: pH, Substrate Mixing Ratio (SMR), and Hydraulic Retention Time (HRT) while the temperature was constant (33 ± 1 °C). An MLP architecture with a 3-3-2 network structure was employed, comprising three input neurons, two hidden layers, and two output neurons. The model was trained and validated using standardized input variables, with hyperbolic tangent activation in the hidden layers and identity activation in the output layer. The ANN model demonstrated excellent predictive accuracy, achieving average R² values of 0.980 for biogas and 0.976 for BioCH₄ during five-fold cross-validation, with correspondingly low mean absolute error (MAE) and root mean square error (RMSE) values. Parameter estimates indicated that HRT had the most significant influence on gas production, while SMR and pH served as important supporting factors. Practical recommendations include optimizing HRT and SMR settings and maintaining stable pH levels to maximize biogas system efficiency. The study highlights the importance of ANN models for improving operational planning and optimizing biogas production processes. The developed model offers a robust tool for predicting energy recovery outcomes in anaerobic digestion systems and supports the broader adoption of machine learning in biogas process optimization.

Abstract: While the Big Bang theory remains foundational to cosmology, critical questions persist regarding the initial singularity and pre-Bang conditions. We propose a novel high-energy cosmological mechanism for the origin of the observable universe, modeled as a catastrophic icequake within the crust of a rotating tectonic iceball of cosmological scale, termed Feluc, embedded in a vast, cold medium we refer to as the Old-Water. In this framework, mechanical stress accumulation and sudden fracture in Feluc’s crystalline outer shell release a burst of energy sufficient to initiate sublimation, dissociation, and ionization of H₂O ice, giving rise to a rapidly expanding, rotating plasma cloud: the nascent B-universe. The model preserves energy conservation by linking cosmic expansion to ongoing progressive sublimation of the Last Scattering Surface (LSS), the spherical icy boundary of Feluc's cavity preserving the B-universe. The Cosmic Microwave Background (CMB) is recast as thermal radiation emitted from the LSS. Observed temperature anisotropies in the CMB are interpreted as projections of density variations within Feluc’s crust, offering a physically grounded mechanism for primordial fluctuations. By bridging glaciology, thermodynamics, and high-energy astrophysics, the model generates testable predictions that both challenge conventional cosmological theories and provide innovative solutions to persistent cosmological problems, while establishing new observational constraints for probing the universe's formation through verifiable physical mechanisms rather than abstract mathematical singularities. The unification of planetary-scale physics with high-energy astrophysical phenomena creates a robust, observationally constrained alternative to traditional creation paradigms.

Abstract: An Intrusion Detection System is the process of monitoring events within a computer network and analyzing them for unusual behavior. Moreover, IDS detects attempts at misuse, whether by authorized users or external parties who seek to abuse privileges or exploit security vulnerabilities. Computer intruders, who can be found across the internet, pose a significant threat, making it challenging to ensure that information systems are secure and maintained in a safe state throughout their lifetime and use. Intrusion Detection Systems can be software or hardware products designed to monitor system usage and identify any signs of an insecure state. This paper aims to review the methodology of intrusion detection systems and their classifications, summarizing the advantages and disadvantages of the most used approaches.

Abstract: The thematic analysis adopted in this paper is a method that examines the evolving nature of marketing management field conceptually.  Basic marketing principles has remained essential but the need for digital technological adoption coupled with artificial intelligence and human focused methods and moral standards has brought dynamics in marketing. This paper highlights several modern trends in marketing management which includes customization for customers together with digital changes and eco- friendly progressions and innovative patterns of practices. The research used theoretical frameworks for evaluation because it has revealed the exact operational hindrances and focus on current modern strategic development opportunities. In this paper is also highlighted that organizations must implement agile principles alongside data driven decisions and a compressive understanding of customer experiences for sustainable market competition. Recommendations in the analysis includes direct practitioners to adapt their marketing methods or processes to contemporary marketplace needs together with suggestions for researchers to continue with their studies into new fields of study in the marketing management perspectives.

Abstract: Predictive Maintenance (PdM) plays a pivotal role in Industry 4.0 and 5.0 by minimizing equipment downtime and optimizing performance. However, limitations such as scarce fault data, data quality issues, and model interpretability hinder its effectiveness. This study presents a machine learning-based PdM framework tailored for Vortex Oil and Gas Nigeria Ltd., leveraging synthetic sensor data and eXtreme Boost (XGBoost) regression to predict Remaining Useful Life (RUL) of industrial equipment. Using simulated data from 50 machines over 300 operational cycles, the model achieved strong performance metrics, with an RMSE of 40.73 and MAE of 32.38. A four-layer system architecture—comprising data acquisition, edge processing, cloud analytics, and user interface—enabled real-time monitoring and decision-making. The results underscore the system’s capacity to detect early failure trends and support proactive maintenance, aligning with the goals of intelligent, sustainable, and human-centric industrial operations. This research contributes a scalable, data-driven PdM solution suitable for environments with limited real-world fault data.

Abstract: This study investigates the socio-economic determinants of terrorism in Nigeria, focusing on the interplay between unemployment, literacy, and socio-economic conditions as they relate to terror incidents. Employing a mixed-methods approach, the research integrates Chi-square analysis and Elastic Net Regression (ENR) to analyze data collected from 1,537 respondents via structured questionnaires. Key findings reveal a significant correlation between high unemployment rates, particularly among youth, and increased occurrences of terrorism, aligning with Strain Theory. The study also highlights the critical role of literacy in mitigating radicalization; lower literacy rates correlate with heightened susceptibility to extremist ideologies. Additionally, socio-economic inequalities, including poverty and income disparity, are identified as significant factors exacerbating security challenges, supporting Relative Deprivation Theory. Temporal and spatial dynamics reveal that certain regions and times are more prone to violence, necessitating targeted counter-terrorism strategies. The effectiveness of government policies is critically examined, showing that inadequate policies can perpetuate grievances and violence, emphasizing the need for evidence-based interventions. The ENR model provides actionable insights into the relationships between socio-economic factors and terrorism, demonstrating that integrated approaches addressing economic, educational, and social dimensions are essential for effective counter-terrorism efforts. However, the study is limited by its reliance on self-reported data, which may introduce bias, and its focus on Nigeria, potentially limiting generalizability. Overall, this research underscores the necessity for comprehensive strategies that tackle the root causes of terrorism in Nigeria, fostering sustainable peace and development.

Abstract: The physico-chemical characteristics and phytochemical profiles of Catharanthus roseus (L.) G. Don grown in both garden soil and contaminated soil are examined in this study. The purpose of the study is to evaluate how soil pollution affects that plant’s growth characteristics, metabolic components, and secondary metabolites. While plant samples were assessed for growth metrics, and phytochemical substances like alkaloids, flavonoids, and phenolics, soil samples were examined for pH, Dissolved solids, Dissolved oxygen and Total hardness and Calcium hardness. The Qualitative test for plant extract of solvent methanol and distilled water is done by Harbone’s Qualitative method for detection of secondary metabolites i.e., Alkaloid, Tannin, Phenol, Flavonoid, Saponins and Lignin. The determination of the Total Phenolic Content, Total Tannin Content and Total Flavonoid Content was done by Quantitative analysis with the help of Folin- ciocalteu, Folin- Phenol and Aluminium Chloride methods respectively. Determination of Total Antioxidant Capacity id done by Phosphomolybdenum Assay. The test results of quantitative are all using the concentration of 1 ml of prepare sample extract. The plant extract of solvent methanol and distilled water detects many secondary metabolites like Alkaloid, tannin, phenol, flavonoid, saponins and lignin this all are detect in the phytochemical test by qualitative methods and according to quantitative analysis the pollution affects the phytochemical but the Antioxidant capacity remains same. The findings demonstrate the adaptability of Catharanthus roseus (L.) G. Don under challenging circumstances and its potential for application in phytoremediation by showing notable differences in growth and phytochemical composition between plants in contaminated areas and their consequences for environmental health and sustainable agriculture are better understood thanks to this research.

Abstract: This article interrogates the theoretical and practical perspective of corroboration of a Global Single  Currency (GSC) within a single frame of digitally assimilated global economy. It conceives the basic proposition of value equalization (stable value), exchange unification and unified form of currency as instruments for reconciling global financial disparities, intensifying trade efficiency and enhancing a unified economic hub cross-border. Indeed, it has concurred through a resemblances analysis of the US Dollar (USD), Indian Rupee (INR) and EUR (Euro), and a review of literature, the article also traverse the evolving role of digital money and its  core possibilities as the fundamental  framework of worldwide single financial spectrum based on inclusivity and interoperability.

Abstract: Finishing this thesis has been a significant journey of philosophical and personal revelation rather than just an academic undertaking:Samuel F. Migallos, PhD, RN, mentor, whose support in challenging conventional wisdom and delving into the complex web of ideas has been a beacon of hope;My heartfelt gratitude flows to my family, whose unwavering support, boundless understanding, and eternal love remain steadfast, a constant bonfire through every storm life may bring;Dr. Sarah Bernadette L. Baleṅa, as chairperson, Dr. Cherry Mae M. Manual, Dr. Anna Liza A. Saus, and Dr. Lea F. Elivera, my esteemed scientific panels I extend my heartfelt thanks;  Dr. Sarah Bernadette L. Baleṅa, chairperson, Dr. Cherry Mae M. Manual, and Dr. Anna Liza A. Saus, my validators. Your scholarly suggestions have woven a tapestry of wisdom throughout the various stages of my research, enriching its essence and elevating its impact with your discerning guidance; Sr. Zoselie Escolano, O.P., Prioress General, and Sr. Aida T. Frencillo, O.P., Local Superior of the Holy Rosary Convent, along with the beloved Dominican Sisters of the Trinity, I extend my heartfelt gratitude. Your generous provision of resources and unwavering support have been the nurturing light guiding my journey toward this degree. However, words may fall short, but my deep appreciation resonates in the silence of my soul; The twelve novice nurse participants, I extend my heartfelt gratitude to my cherished friends who lent their support through referrals, my Renal Dialysis Unit family, San Pedro Hospital, San Pedro College, and all those who quietly worked behind the scenes. Your contributions have woven a tapestry of kindness and support, enriching my journey in ways words alone cannot capture;Above all, I offer my deepest gratitude to the Blessed Holy Trinity—Father, Son, and Holy Spirit—alongside all the angels and saints in Heaven and the revered Blessed Virgin Mary. Your divine grace and eternal guidance have been my steadfast source of strength, for which my heart sings its profound thanks.

Abstract: Mathematics is considered a difficult subject by many students and parents. However, at the higher secondary level, the study of mathematics is of prime importance because it opens doors to many professional studies. Hence, at this level, teachers handling the subject face pressure from multiple directions for better results — from higher authorities as well as from students’ parents. Amidst such challenges and issues, along with personal matters, there is a strong possibility that mathematics teachers experience stress that affects their daily routines. Therefore, the stress experienced by mathematics teachers is a major subject of research. The current study is an inquiry into the demographic differences among mathematics teachers, conducted by surveying a random sample of 200 mathematics teachers from higher secondary schools in the Kollam district of Kerala state, India. A standardized occupational stress scale developed by Meenakshi Sharma and Satvinderpal Kaur (2014) was used to assess the stress levels of teachers. Data analysis revealed a significant difference in occupational stress with respect to the locality of the institution and the type of school management. However, the difference was not substantial with respect to gender or teaching experience. The findings emphasise the need of appropriate interventions to mitigate the stress among mathematics teachers.

Abstract: The runaway growth of web technology has resulted in an unprecedented volume of data being produced and published on the web each day. Social networking sites such as Twitter and Facebook have turned into indispensable zones for individuals to share thoughts, experiences, and opinions around the world. Sentiment analysis which involves the extraction and analysis of opinion from text, is central to gauging public feeling, monitoring trends, business strategy, and customer satisfaction with regards to unstructured and heterogeneous nature of Twitter data, most research has been conducted on how to use sentiment analysis methods to classify opinion as positive, negative, or neutral. In this paper, sentiment analysis of social media data is investigated based on a Twitter dataset, utilizing machine learning methods such as Long Short-Term Memory (LSTM) networks for precise sentiment classification.

Abstract: Global warming is mainly the results of human activities on the environment. It is the leading cause of change in weather patterns. The activities are linked to different ways of generating and using energy. Researches have revealed that 80% of households in Sub-Sahara Africa use traditional wood fuel. This implies that chunks of trees are usually cut yearly to provide the fuel. It leads to greenhouse gas emissions and deforestation of up to 39 hectares per year. Unfortunately, the households have limited access to ICS.  The high costs of stoves, lack of government support and unwillingness to substitute traditional cooking practices with clean cooking methods limit the uptake of the improved stoves. This means that there is more use of biomass fuel, which highly increases GHG emission. For instance, each household is estimated to use approximately 4 bags of charcoal annually. The yearly charcoal demand for the entire continent is approximately 4 billion bags. To meet this demand, about 715 million trees have to be cut annually. ICS present a promising solution for conserving trees. As per the terminal evaluation on the usage of the stoves, only 25% of the trees that are currently burned to produce charcoal will be required to meet the annual charcoal demand. The 537 million trees that shall be preserved will sequent 0.013425 GtCO2 in a single year. Working towards Paris Agreement strategies on GHG emissions, ICS usage in Africa will therefore enable CDR up to 5.034% within a 25-year period. To achieve this, strategies aimed at increasing adoption rate should be implemented. If households outside Africa that are still using traditional stoves adopt ICS for cooking, we should expect even better and more promising changes. The GHG emissions will have to drop leading to a decrease in global warming.

Abstract: As we are aware of the fact that, most of Indians have agriculture as their occupation. Farmers usually have the mind-set of planting the same crop, using more fertilizers and following the public choice. By looking at the past few years, there have been significant developments in how machine learning can be used in various industries and research. So, we have planned to create a system where machine learning can be used in agriculture for the betterment of farmers. India is an Agricultural Country and its economy largely based upon crop productivity. So we can say that agriculture can be pillar of all business in our country. Selecting of always crop is very important in the agriculture planning. Many researchers studied guess of yield rate of crop, guess of weather, soil categorizing and crop classification for agriculture planning using machine learning techniques. Many changes are required in the agriculture department to improve changes in our Indian economy. We can improve agriculture by using machine learning system which are applied simply on farming sector. Along with all advances in the machines and technologies used in farming, functional information about different matters also plays a significant role in it. The concept of this paper is to implement the crop selection method so that this method helps in solving many agriculture problems. This enhances our Indian wealth by maximizing the yield rate of crop production. In our project crop is predicted by algorithm namely Recurrent Neural Network (RNN) as proposed and Random Forest (RF) as existing and its accuracy is calculated and compared with other algorithms.

Abstract: Horticulture has emerged as a transformative force in the agricultural landscape of Mulshi Taluka, Pune District, significantly impacting the economic well-being of local farmers. This study aims to analyze the role of horticulture in enhancing farmers' income, employment opportunities, and market accessibility while assessing its overall contribution to rural development. Using statistical models and government reports, we evaluate the economic benefits derived from horticultural activities compared to traditional farming. Our findings suggest that farmers engaged in horticulture experience higher income growth, improved employment generation, and greater resilience to climatic fluctuations. Additionally, initiatives such as farmer cooperatives and fruit export clusters have contributed to the increased profitability and sustainability of horticulture-based agriculture in the region. The study also highlights the need for continued policy support, infrastructure development, and innovative agricultural techniques to maximize the sector’s potential. By leveraging statistical equations to quantify the economic benefits, this research provides a data-driven perspective on the effectiveness of horticulture as a catalyst for agricultural transformation.

Abstract: The current food shortages due to increasing population, especially in developing countries calls for intensification of efforts in the development and adoption of technologies to enhance food production. Soil type and quality is key for crops to grow produce maximally, hence the soil should be properly managed. This is where technology involvement becomes necessary. Smart agriculture using Internet of Things (IoT) system can help farmers to analyse soil properties, monitor and control crops intake of vital nutrients. This research developed a more cost effective Online IoT-based technology for soil testing, analysing soils for better nutrient advisory to cucurbitaceous farmers. The research adopted an experimental method and used the object oriented analysis and design methodology. An IoT based system with Arduino Uno, ESP8266 Node Microcontroller and NPK sensors was developed and used to collect and analyse soil data. The system when deployed will assist farmers to test soil type and nutrient qualities in real/offline time and receive experts advice on cucurbitaceous crops in order to increase crop quality and yields.

Abstract: The diagnostic procedure of Chest X-ray (CXR) relies on subjective manual report generation which takes an excessive amount of time. The combination of CNNs for feature extraction together with NLP for text generation through deep learning techniques demonstrates effective potential in solving this problem. The automated report generation allows the radiological report process to become more efficient and maintain higher consistent standards. Integration of NLP and CNNs in the system enables image analysis through CXR images which results in the production of thorough and reliable radiological reports. The automated system provides both fast reporting capabilities with enhanced detection precision and improved treatment services. Deep learning used for CXR image-based report generation represents a transformative opportunity for radiology which produces more effective diagnostics while benefiting both medical professionals and their patient subjects.

Abstract: In the development of smart factories, advanced vibration analysis is essential since it allows for real-time machinery and equipment monitoring and diagnostics. Smart sensor integration signifies continuous collection, processing, and analysis of vibration data to identify early indications of mechanical failures and maintain maximum performance in intricate industrial systems. With a focus on how artificial intelligence (AI) is revolutionizing conventional diagnostic approaches, this chapter examines the most recent developments in vibration analysis techniques. Machine learning and deep learning algorithms are used in AI-driven vibration diagnostics to identify complex defect patterns that traditional techniques could overlook. On top of that, that combine vibration data from several sources improves diagnostic precision and resilience, making it possible to identify problems in big, networked systems. By integrating sensor data with advanced signal processing techniques like wavelet transformation and Fast Fourier Transform (FFT), a complete image of system health is produced, allowing for predictive maintenance and minimizing downtime. This chapter shows how as we go toward Industry 5.0, AI, sensor technology, and vibration data fusion work together to improve smart factory operations, increase overall system reliability, and facilitate the long-term growth of manufacturing sectors.

Abstract: Social media platforms like Twitter, Instagram, and Facebook have exploded with user-generated content, offering a goldmine of data for understanding emotions online. But detecting nuanced feelings—like joy, anger, or surprise—in short, informal posts is far harder than basic sentiment analysis (which just labels things as "positive" or "negative"). This paper introduces a smarter way to detect emotions in microblogs by blending cutting-edge NLP and machine learning. Our key innovation? A hybrid model that combines the deep contextual understanding of transformer-based models (like BERT) with emotion- specific classifiers. Unlike older methods, our system doesn’t just skim the surface—it picks up subtle emotional cues, even in messy, slang-filled posts. We also tackle real-world challenges: emojis, sarcasm, and ever-changing internet slang. By fine-tuning our model on a diverse dataset (covering emotions from disgust to fear), we outperform traditional tools, especially for tricky cases like mixed emotions in a single tweet. The results? More accurate emotion tracking for applications like mental health monitoring, brand sentiment analysis, and real-time social media trends.

Abstract – Material management plays a vital role in construction, significantly impacting project costs, efficiency, and overall success. In both commercial and residential projects, materials contribute over 60% of total expenditures, emphasizing the need for an efficient management system. Traditional manual tracking methods often result in inefficiencies, delays, and cost overruns. However, technological advancements, particularly System Application and Product (SAP) software, have revolutionized material management by optimizing procurement, inventory control, and consumption tracking. Implementing SAP enhances productivity, minimizes waste, and ensures timely project completion. An efficient material management system ensures the right quality and quantity of materials are procured, delivered, and handled effectively at a reasonable cost. This study compares traditional methods with SAP-based solutions, highlighting their advantages. A survey conducted across construction firms in Chennai provides insights into current material management practices. The research focuses on SAP’s impact on cost control, inventory management, procurement efficiency, and overall operational effectiveness in the construction industry.

Abstract: Wireless networks are a fundamental part of modern communication, offering convenience and flexibility. However, they also present unique security challenges due to their broadcast nature and vulnerability to unauthorized access. This project focuses on the process of wireless network penetration testing — a method used to evaluate the security posture of Wi-Fi networks through ethical hacking techniques. The objective of this project is to identify potential vulnerabilities in wireless networks and demonstrate how attackers might exploit these flaws. Techniques such as passive scanning, packet sniffing, deauthentication attacks, handshake capturing, and password cracking are employed using tools like Wireshark, Aircrack-ng, and Kali Linux. The project also explores advanced threats including rogue access points and Evil Twin attacks.

Abstract: Detection of skin cancer remains a crucial medical issue because it determines the effectiveness of melanoma treatment. The current detection systems experience performance limitations because of limited labeled data which results in overfitted models that produce narrow potential outcomes while demonstrating poor generalization for unknown skin lesion classes. This research proposes solving classification challenges through the implementation of Conditional Generative Adversarial Networks which produces synthetic images that replicate the natural variability seen in real-world skin lesion scans. Synthetic images from CGANs enhance CNN training sets while improving their capabilities to identify various types of skin cancer. The proposed system exists as a platform which trains CGANs on real skin lesion datasets to produce matching synthetic imagery that amalgamates with original datasets before creating an extended CNN training set. The evaluation of proposed CNN models uses real skin lesion images with their performance evaluated through accuracy and sensitivity while measuring specificity and F1 score metrics. Model performance improves when training augmentation techniques are used instead of original image sets resulting in enhanced robustness and precision together with generalized results. Additional incorporation of synthesized data leads to substantial advancement in detecting skin conditions which dermatologists identify rarely. The research has established CGANs as promising tools for generating synthetic medical images which address data deficit challenges while showing foundationally that data augmentation strengthens deep learning model capabilities.

Abstract: The white goods markets, which includes appliances like refrigerators, washing machines, and air conditioners, microwaves have traditionally been dominated by global brands. However, indigenous brands are gaining more attention in emerging markets and understanding what drives consumers to choose them is essential. This paper presents a conceptual framework to explore the key factors that influence consumer buying behaviour towards indigenous brands in the white goods sector. The framework combines insights from consumer behaviour highlighting factors such as product quality, price, aſter-sales service, cultural relevance, and national pride. It also looks at how trust, perceived service quality, and brand loyalty play a role in the decision-making process. By offering a comprehensive understanding of these factors, the study aims to provide valuable guidance for marketers and policymakers in promoting indigenous brands. The study shed light on the challenges, opportunities and future outlook of white goods in indigenous brands. This framework also suggests future research areas to validate these factors and improve indigenous brand strategies.

Abstract: The increasing adoption of online payments and e-commerce platforms has amplified the threat of credit card fraud. As fraudsters continuously develop advanced techniques to bypass traditional security systems, it becomes essential to deploy smart, adaptive solutions. This study focuses on leveraging machine learning—specifically Random Forest and Classification and Regression Trees (CART)—to build a high-performance fraud detection system. Using a publicly available dataset from Kaggle, the model analyzes transaction records to uncover patterns indicative of fraudulent behavior. Emphasis is placed on accuracy, scalability, and the potential for real-time deployment. The implemented model achieved an impressive accuracy of 99.78%, with strong precision and recall scores. The paper discusses the methodologies applied, evaluates the outcomes, and recommends directions for future development.

Abstract—Timely detection and classification of crop diseases are essential for maintaining agricultural productivity as well as the quality of food. Many traditional disease identification methods are labor-intensive, time-consuming, and human-error- prone. Recently developed techniques based on computer vision and deep learning add efficient, automated alternatives for disease detection. This work proposes a deep learning-based system utilizing a Residual Network (ResNet50) for automatically diagnosing and classifying rice diseases, a specialized Convolu- tional Neural Network (CNN) form. Rice, ranking third after wheat and maize in global cereal production, is critical for food security. The proposed ResNet50-based method outperforms all aspects of existing processes in accuracy, recognizing and categorizing multiple rice disease types. Experimental results reveal that the system leads to early diagnosis and intervention, eventually leading to better crop management, higher yield, and enhanced grain production quality. In addition, the proven robust performance of the model signifies its real-world applicability for agricultural purposes. Thus, integrating deep learning techniques such as ResNet50 into agricultural disease management practices will contribute to more sustainable farming practices and lower crop loss outcomes. This research underscores the transforma- tional potential of AI in precision agriculture and global food security sustainability.

Abstract: Chatbot can be termed as software capable of chatting with humans using artificial intelligence. Such software is employed to accomplish jobs like replying to users instantaneously, giving them information, assisting them to buy goods and improving customer services. In this paper, the overall working principle, and the fundamental concepts of artificial intelligence based chatbots and associated concepts along with their application in different industries like telecommunication, banking, health, customer call centers and e-commerce are discussed. Moreover, the donation service implemented for telecommunication service provider area demonstrates using the proposed architecture.

Abstract: This initiative focuses on developing innovative genetic modification techniques to create thornless plants, enhancing agricultural efficiency and sustainability.  Using advanced biotechnology, including CRISPR-Cas9, it ensures precise gene edits while preserving beneficial traits like yield and disease resistance.  The goal is to improve safety and productivity in farming and gardening by eliminating thorns.  A key aspect is establishing a platform to oversee genetic modifications, monitor edits, and assess plant growth.  This system streamlines the process for researchers, promoting ecological sustainability by reducing mechanical pruning and increasing crop yields, ultimately improving agricultural efficiency and safety.

Abstract: Driver alert system is a project to detect the drowsiness of the driver with help of CNN—Convolutional Neural Network. In recent years, deep learning methods, particularly convolutional neural networks (CNNs), have shown promising results in detecting driver drowsiness. In this detection system, we are going to build a non-contact technique for judging different levels of driver alertness and facilitate early detection of a decline in alertness during driving. In such a case when drowsiness or yawning is detected, a warning alarm is issued to alert the driver. In our project, we will employ a convolutional neural network which detects the states of the eyes and mouth from the ROI images, and also we will be using OpenCV for gathering the images from the webcam and feeding them into the deep learning model which will classify whether the person’s eyes are ‘Opened’ or ‘Closed’. The model is trained on a large dataset of facial expressions representing different drowsiness levels to improve its accuracy and performance. By continuously monitoring the driver’s facial landmarks in real time, the system can ensure proactive intervention. The use of non-invasive techniques also ensures that the driver remains comfortable and undistracted. The alert mechanism can include both sound and visual cues to effectively bring the driver’s attention back to the road. Furthermore, the system is designed to operate efficiently even under varying lighting conditions and with different facial features. The CNN model is optimized for low-latency predictions to make sure alerts are timely. In conclusion, the proposed driver alert system using CNN shows great potential in improving road safety by detecting and alerting drivers. In the future, this system can be integrated with other vehicle safety mechanisms like automatic braking or steering control. It can also be enhanced with infrared sensors to work accurately during nighttime or in low-light environments. Additional features like tracking head movement or blink duration can further strengthen the model’s reliability. The project can be extended to support multi-driver environments, such as public transport systems. We can also deploy the system on edge devices like Raspberry Pi for real-time, on-board processing. With continuous advancements in AI and hardware, the scope for such smart safety systems is vast. This system not only ensures driver safety but also safeguards passengers and pedestrians by minimizing the chances of road accidents caused due to fatigue. The implementation of such technologies reflects the growing importance of AI in real-world safety-critical applications. Moreover, the system can be further enhanced by incorporating machine learning algorithms that adapt to individual driver patterns, offering personalized alerts based on their driving behavior.

Abstract:  This project proposes a chatbot system that integrates Retrieval-Augmented Generation (RAG) and a Knowledge Graph (KG) to address queries on Indian law. The system dynamically adapts to the complexity of user queries to optimize performance and accuracy. For straightforward questions, the RAG module retrieves relevant legal documents and generates concise answers. For complex, multi-faceted queries, the system employs a multihop reasoning approach using the knowledge graph to derive accurate and context- aware responses.To enhance accessibility and inclusivity, the chatbot supports multiple regional languages, enabling users from diverse linguistic backgrounds to interact with the system in their preferred language. This hybrid design ensures efficient computation by selectively engaging the KG only when necessary, reducing resource usage while maintaining high accuracy.The chatbot provides users with seamless access to legal insights, offering quick answers for simple queries and in- depth reasoning for complex cases. By combining the strengths of RAG and KG, this system aims to revolutionize legal assistance by improving accessibility, precision, and usability for both legal professionals and the general public, empowering users to navigate Indian law with confidence and efficiency.

Abstract: This paper presents the development and implementation of a data-driven Crop Recommendation System designed to assist farmers in making informed decisions regarding optimal crop selection. The system is deployed as a responsive web application featuring a user-friendly interface, allowing easy access and interaction for end-users. The application comprises three main modules: the Home Page, which introduces the system and outlines its significance; the User Guide Page, which provides step-by-step instructions for effective usage; and the Crop Recommendation Page, where users input specific environmental and soil parameters to receive crop suggestions tailored to their conditions.The core of the recommendation engine is a Random Forest Classifier, selected for its high accuracy and robustness in handling agricultural datasets. The model was trained on a comprehensive dataset that includes critical features such as nitrogen, phosphorus, potassium levels, temperature, humidity, pH, and rainfall. By analyzing these inputs, the system delivers personalized crop recommendations that align with the user's agro-climatic conditions, thereby enhancing decision-making for better agricultural outcomes.The model’s performance was evaluated using standard classification metrics, confirming its effectiveness in providing reliable crop suggestions. This intelligent system addresses a crucial need in modern agriculture for precision-based recommendations that can lead to increased crop productivity and resource optimization. The solution promotes sustainable farming practices by leveraging machine learning techniques and facilitating easy access through a web-based platform.

Abstract: The secure voting system using blockchain technology is a transformative solution aimed at enhancing the transparency, integrity and efficiency of electoral processes. By leveraging the decentralized and tamper-proof nature of blockchain, the system ensures secure voter authentication, immutability of votes and end-to-end transparency in the voting process. Each vote is recored as an encrypted transaction on the blockchain, data integrity and preventing unauthorized alterations. The system incorporates features such as multi-factor authentication, vote tracking, and privacy-preserving mechanisms protect vote anonymity. This innovative approach address common challenges such as voter fraud, manipulation, and lack of trust offering a reliable and scalable framework for secure and transparent elections.

Abstract: This project is aimed at women artisans in “Imaginative Artisans” for transformational purposes to empower them with a special site where they can display their unique crafts and talents. By using MongoDB, Express.js, React.js, Node.js (MERN stack), the project seeks to bridge the gap between talented artisans and potential buyers to promote economic independence, recognition and community support. In essence, it solves the problem of many brilliant female artisans who have no platform specifically designed for them where they can display their skills and earn sustainable income. The project will build an intuitive and visually appealing platform that meets the different needs of both buyers and craftsmen through thorough research, user- centered design as well as agile development methods. Some key features include: authentication of users; ability to manage profiles; display of products; processing orders; having community forums and personalized suggestions. This makes the platform scalable, flexible and interactive thereby ensuring a smooth user experience since it is powered by MERN stack. The project also focuses on data privacy and security by putting in place adequate measures that would protect the user information and ensure compliance with data protection laws. To keep up with the changing needs and desires of its customers, the project seeks to develop further and improve itself over time, through continuous iteration, user feedback, performance monitoring.

Abstract: This paper presents the development of an expense tracking web application designed to assist individuals in managing personal finances. Using Python, the Django framework, and MongoDB, this system provides features such as expense categorization, data visualization, and authentication. With its user-friendly interface and secure database integration, this application aims to enhance financial management practices. The paper explores the design, implementation, and future potential of the system.

Abstract: The increasing environmental concerns related to synthetic plastics have spurred interest in biodegradable alternatives derived from renewable sources. This study focuses on the preparation and comparative analysis of biodegradable plastics synthesized from corn and potato starches. The bioplastics were developed through plasticization with glycerol and crosslinking with acetic acid. Their mechanical, thermal, and degradation properties were tested and analyzed. Results indicated that corn starch-based plastic exhibited superior solubility (11.6%) and biodegradability (complete degradation within 15 days), while potato starch-based plastic showed higher thermal resistance. these findings indicate the potential of this material as a viable alternative to low-density polyethylene (LDPE) for various packaging applications.

Abstract: Covid 19 vaccine had played an important role in controlling the spread of COVID virus. But post effects of the COVID  vaccine has become major public health issues World wide. The immediate and later effects of Covid 19 vaccine has shown many symptoms which shows closest resemblance to Dushi Visha according to Ayurvedic science.A Google survey was conducted which proves the same. Dushi Visha, a concept in Ayurveda, refers to the accumulation of toxins in the body, leading to various health issues. This study aimed to investigate the efficacy of Ayurvedic interventions in managing Dushi Visha. A comprehensive treatment approach, including Panchakarma, herbal remedies, and dietary modifications,can be  administered to pateint. Which may help to get significant reductions in Dushi Visha symptoms and improvements in overall well-being. This study highlights the potential of Ayurvedic medicine in addressing Dushi Visha and promoting holistic health. Aim: -To prove Covid 19 vaccine as Dushivisha                                                                       Objectives: To investigate the immediate and post effects of COVID 19 vaccine on the body. To evaluate the relationship between the COVID 19 vaccine and DushiVisha.

Abstract: This study evaluated the impact of on-street parking on traffic speed under mixed traffic conditions in Nekemte City, Ethiopia. Data on the traffic volume, speed, and parking parameters were collected at selected mid-block locations during peak hours. The results showed that the speed of vehicles, particularly heavy vehicles and trucks, decreased significantly with increased traffic volume compared with other categories. The vehicular speed noticeably diminished as parking levels increased. A multiple linear regression model was developed using the SPSS software to analyze the percentage reduction in speed. The model indicated that for a given road width at a location with on-street parking, as parking turnover, duration, accumulation, or traffic volume increased, the percentage reduction in speed also increased. Conversely, as the road width increased, the percentage reduction in speed decreased, and vice versa, whereas the other parameters remained constant. The study concluded that the volume-speed relationship capacity of each road in the parking area and away from it could be determined, and models for percentage speed reduction could be developed using road width, parking parameters, and traffic composition. This study provides recommendations for maintaining a maximum speed reduction of 40% at selected locations for street parking based on the road width, traffic volume, parking turnover, duration, and accumulation.

Abstract: This study produces a web-based Guidance Record Management System (GRMS) designed to simplify record-keeping in guidance offices. It allows students to submit and manage their records, which counselors can access for sessions. The system also includes exploratory data analysis (EDA) features for data-driven decision-making. The admin dashboard presents visualizations and insights using EDA with Python and a Decision Tree algorithm, offering valuable information on student behavior and academic performance. The system is built using the PHP framework Laravel, CSS and Tailwindcss for styling, JavaScript, Python's Pandas library, along with Matplotlib and Seaborn for data visualization, MySQL as the database, and Apache as the server. It is evaluated according to the ISO 25010 standard, which provides a framework for assessing software quality, ensuring that the system meets key requirements for functionality, reliability, usability, and performance. The study successfully identified the respondents' genders as male and female. Evaluation results reveal that both male and female users, as well as technical respondents, strongly agree on the acceptability and usability of the Guidance Record Management System. Male users provided an overall average mean of 3.4, while male technical respondents rated it slightly higher at 3.5. Similarly, female users and technical respondents both rated the system with an average mean of 3.5. These results indicate a consistent positive reception across both groups, affirming the system’s effectiveness and ease of use for its intended audience.

Abstract: Vertical farming presents a hopeful approach for tackling issues related to reliable food access, environmental responsibility, and the growth of cities. This innovative agricultural method involves growing crops in vertically stacked layers within controlled environments, optimizing space and resource use while enhancing yields. Recent advancements in automation have significantly improved the efficiency and sustainability of vertical farming. By minimizing water and pesticide use, and shortening supply chains through proximity to urban centres, vertical farming reduces transportation costs and carbon emissions. Economic benefits include year-round production and premium pricing for pesticide-free produce. However, challenges remain, including high initial investments and energy demands. From an environmental perspective, vertical farming uses less land and water than conventional agriculture. This review highlights vertical farming’s potential to improve global food security and support sustainable urban development. It also emphasizes the need for further research and collaboration to overcome barriers, with continued innovation and supportive policies essential for its widespread success.

Abstract: As Technology keeps advancing rapidly, the risk of data leakage is on the increase. centralized servers are often attacked in order to control the flow of data and divert the actual network direction and breach data privacy. Data can be manipulated on a higher scale once the centralized systems have been attacked and the existing IDS fails to recognize a breach in the network server.  In order to have a more robust privacy of data and accurate functioning of the IDS through ML and DL approaches to curb cyber-attacks and network attacks, the use of a decentralized ML model would be effective. This approach aims at introducing a decentralized network where data will be shared in protective nodes and each node will have an existing IDS that will not be exhausted with a huge workload.  Federated Learning, which is a sub domain of DML, would offer solutions by enhancing local models on edge devices without the need and reliance of a centralized server [16]. This approach will reduce the risk of data exposure by ensuring that data stays on the source device and can only be accessed and controlled from that source only.  The outcome of this research would vividly outline the effectiveness of decentralization of data and the efficiency of IDS on specialized points of the data network to protect the exchange and control of data without a maximum risk of data leak. 

Abstract: The detection of abnormalities in medical images plays a pivotal role in early diagnosis and treatment. This paper presents a hybrid approach that combines K-Nearest Neighbors (KNN) and deep learning techniques to improve medical image classification and anomaly detection. The method applies KNN for classifying images such as MRI, CT, and X-ray scans, focusing on abnormality detection in brain images. By integrating KNN classifiers with feature extraction methods, the approach addresses challenges such as class imbalance and small datasets, resulting in improved detection accuracy. The effectiveness of the proposed method is demonstrated on a medical image dataset, showing significant improvements in both classification and anomaly detection tasks.

Abstract: The heavy reliance of electronic communication tools to conduct businesses across the world has facilitated business transactions and other related aspects of business. An evaluation on the effectiveness of resolving e-commerce disputes by alternative dispute resolution methods (ADR) is dwelled upon in this article. There are many problems associated by relying on the traditional adjudication processes in courts to resolve e-commerce transaction disputes amongst which are; high cost, waste of time, corruption and jurisdictional problems. Disputes arising from e-commerce transaction are most often complicated to be resolved by courts based on this.  This article raises questions on the effectiveness of using ADR mechanisms to resolve such disputes and shows its advantages. It also raises questions on the effectiveness of using the normal courts processes. The article is timely because of the heavy reliance nowadays on e-commerce and the frequency of disputes arising from such. The methodology used is qualitative with the use of the doctrinal research methods. The findings of this research is beneficial to the business world, business persons, those charged with conducting ADR and students of business and commercial law.  Its findings reveal that processes like arbitration, mediation and online dispute resolution are more effective and accepted by contending parties in resolving their disputes as opposed to litigation. It is therefore recommended that e-disputants should use the opportunities and tools provided under ADR and Online dispute resolution (ODR) to resolve their disputes. That the ODR framework and guidelines should be properly established as a dispute resolution mechanism with worldwide recognition. It is also recommended that reciprocity amongst states should be encouraged in solving e-commerce transactions disputes. ADR methods being more effective in resolving e-commerce disputes, the education community is encouraged to incorporate e-commerce dispute settlement courses in their syllabuses with continuous training on the topic. That countries should encourage reciprocity to easily recognize and enforce ADR and ODR decisions.

Abstract: Artificial Intelligence (AI) is reshaping education, sparking debates on whether it will replace teachers or enhance their role. While AI-powered tools personalize learning, provide real-time feedback, and automate administrative tasks, they lack the human touch essential for motivation, emotional intelligence, and deep learning. Bandura’s theory highlights the importance of social interaction in learning—something AI can facilitate but not replicate. Similarly, Bloom’s Taxonomy shows how AI can handle lower-order thinking tasks (like recall and comprehension), freeing teachers to focus on higher-order skills such as analysis, evaluation, and creativity. By integrating AI thoughtfully, education can become more student-centered, efficient, and engaging. Instead of replacing teachers, AI can empower them to foster critical thinking, problem-solving, and holistic development, creating a balanced, technology-enhanced learning environment.This study explores Teacher-AI Collaboration using Bandura’s Social Learning Theory and Bloom’s Taxonomy, demonstrating that AI can support, rather than replace, educators.

Abstract: Organizational culture plays a pivotal role in shaping both employee satisfaction and overall organizational performance. A positive and well-aligned culture fosters a work environment where employees feel valued, motivated, and committed to their roles. This study investigates the relationship between organizational culture and job satisfaction by analyzing data collected from 101 employees across various departments. The structured survey focused on critical cultural elements such as leadership styles, ethical behavior, communication transparency, fairness in treatment, opportunities for professional growth, employee recognition, and compensation practices. The analysis revealed a strong correlation between a supportive, ethical, and inclusive organizational culture and higher levels of job satisfaction. Employees who perceived their organization as fair, communicative, and growth-oriented reported greater engagement and loyalty. These findings emphasize the importance of cultivating a positive workplace culture to boost employee morale and reduce turnover. The study concludes with practical recommendations aimed at enhancing employee engagement, satisfaction, and long-term organizational success through cultural development initiatives.

Abstract: In the context of rapid development of digital technologies, organizations increasingly turn to the concept of digital transformation to improve their competitiveness and efficient operations. In this regard, the development and application of suitable tools for choosing a digital transformation strategy is one of the important applied tasks of each organization. This fact indicates the need to select advanced analytical tools. In the article, the author conducts a comprehensive study aimed at creating an innovative model for assessing digital transformation strategies. The model combines innovative methods of multi-criteria analysis, in particular multi-criteria decision making, fuzzy logic and other mathematical approaches that allow for a comprehensive analysis and development of the most optimal strategies for the development of an organization. Based on the results of the study, the optimal strategy for the object under study was determined for the purpose of successful digital transformation. Further, the main conclusions and promising areas for further research are formulated.

Abstract: Cloud computing offers a flexible and efficient method for data sharing, benefiting both individuals and society. However, users may hesitate to store shared data on external servers due to concerns about the sensitive nature of the information. To address this, implementing cryptographic access control is essential. Identity-based encryption (IBE) serves as a valuable cryptographic approach to establishing secure data-sharing systems. Nonetheless, access control needs to be dynamic. When a user's authorization expires, there must be a mechanism to revoke their access, ensuring they cannot retrieve either previously or newly shared data. To achieve this, we introduce the concept of Revocable-Storage Identity-Based Encryption (RS-IBE), which ensures both forward and backward security by incorporating user revocation and cipher text updates. We further present a concrete RS-IBE construction and validate its security within a defined model. Performance comparisons highlight the scheme's advantages in functionality and efficiency, making it a viable and cost-effective solution for secure data sharing. Additionally, implementation results showcase its practical applicability. The RS-IBE scheme is proven to be adaptively secure under the decisional ℓ-DBHE assumption in the standard model. Comparative analysis confirms that our approach is efficient and functional, making it suitable for real-world applications.

Abstract: Good performance in Chemistry is a prerequisite for students aspiring to pursue Science, Technology, Engineering, and Mathematics (STEM) courses in higher education. However, persistent poor performance in Chemistry among secondary schools in Trans Nzoia East Sub-County has raised concerns among education stakeholders. This study examined the influence of classroom management strategies on students’ performance in Chemistry. The study specifically sought to determine the influence of classroom management strategies, students’ psychological environment, and teachers’ characteristics on Chemistry performance. Anchored on McGregor’s (1960) Instructional Management Theory, a descriptive survey research design was adopted. The target population comprised 8495 respondents, with a sample size of 385 selected through simple random and purposive sampling. Data collection involved questionnaires, interviews, and document analysis. Descriptive statistics were applied for quantitative data analysis using SPSS Version 25.0, while qualitative data were analyzed thematically. The findings revealed that classroom management strategies significantly influenced Chemistry performance (72.7%), while the classroom psychological environment (68.9%) and teacher characteristics (81.4%) also demonstrated substantial effects. The study recommends prioritizing effective classroom management practices to enhance Chemistry outcomes in secondary schools. These insights will assist teachers and educational policymakers in aligning classroom management with Kenya National Examination Council (KNEC) standards, contributing to improved academic results.

Abstract: Artificial Intelligence (AI) is considered as one of evolutionary technologies, which enhances the efficient management of different business and industrial contexts, while having its own risks and limitations as well.  Even though the developed countries are significantly utilizing AI technologies, practical usage of AI applications within developing countries including Sri Lanka is considerably lower. With the context of Sri Lanka holds a significantly lower rank in most of AI based global rankings indices and contains considerably lower amount of AI based research, this study is aiming to identify the existing and prospective AI applications within Sri Lankan businesses. Annual reports of 279 public listed companies which belongs to 20 industry groups have been studied to identify the reported contents relevant to existing and prospective AI usage, among which only 26 companies contained the relevant content. Through the content analysis, current and prospective AI usage within different industry groups in Sri Lanka have been identified and elaborated within this report.

Abstract: Farmers cultivating solanum tuberosum  often experience significant financial setbacks due to plant diseases that adversely affect crop health and yield. Early blight and late blight are particularly destructive diseases that, if not detected and managed early, can reduce crop productivity. Here we will use machine learning approach, Convolutional Neural Networks (CNNs), that detects and classifies these diseases, on its own, through image analysis of Solanum Tuberosum leaves. The developed system offers a rapid, accurate, and user-friendly diagnostic tool that enables farmers to take prompt action to manage and mitigate disease spread effectively. The primary goal is to reduce crop losses and enhance disease management, ultimately contributing to improved agricultural efficiency. Early disease detection through this technology allows for more effective disease control, leading to healthier crops and improved yields. The solution not only enhances crop monitoring but also provides an affordable way to protect farmers' livelihoods, fostering greater sustainability and productivity in Solanum Tuberosum farming.

Abstract: This study offers an in-depth review of the performance and strategic importance of merchant banking services in India, contextualized within the country's evolving financial ecosystem. The historical evolution of merchant banking is traced from its origins in medieval Europe to its formal emergence in India during the late 1960s. Initially focused on issue management, merchant banking in India has expanded to encompass a broad spectrum of financial services such as underwriting, portfolio management, corporate advisory, and mergers and acquisitions. Adopting a descriptive and analytical methodology, the research synthesizes insights from over 25 peer-reviewed articles, regulatory documents, and financial reports published between 2009 and 2024. The findings highlight a significant performance disparity between public and private sector merchant banks. Private sector institutions have consistently demonstrated superior agility, innovation, and profitability. Factors such as evolving regulatory frameworks, rapid digital transformation, and the emergence of small and medium enterprises (SMEs) and startups have amplified the role of merchant banks in fostering capital market growth and supporting economic development. Despite notable advancements, the sector continues to face challenges, particularly in areas of regulatory compliance, investor protection, and comprehensive risk management. The study underscores the need for a more robust regulatory environment, capacity-building initiatives, and technology-driven innovation to improve the efficiency and transparency of merchant banking operations. The paper concludes with strategic recommendations aimed at strengthening the contribution of merchant banks to inclusive and sustainable financial growth in India. These include promoting public-private collaboration, enhancing digital infrastructure, and aligning services with the needs of emerging sectors. By addressing these critical areas, merchant banking can better support India’s broader economic objectives in an increasingly competitive and digitized global financial landscape.

Abstract:  This review paper provides a comprehensive examination of the changing behavior of Indian consumers in the context of online fashion apparel shopping. With the rapid rise of internet penetration, smartphone usage, and digital payment infrastructure, India has witnessed a significant transformation in retail consumption patterns, particularly in the fashion segment. This review delves into the key factors influencing consumers’ online apparel purchasing decisions, including individual characteristics such as brand consciousness, price sensitivity, fashion awareness, and impulsiveness. It also explores technological enablers like ease of website navigation, accessibility, information search capabilities, and perceived usefulness—elements that enhance consumer engagement and trust. Additionally, the review investigates transactional and socio-psychological considerations such as perceived risk, convenience, social influence, and lifestyle alignment. Drawing upon a wide array of empirical studies and theoretical frameworks—including the Consumer Styles Inventory and the Technology Acceptance Model—this paper synthesizes insights to present a holistic view of online shopping behavior in the Indian fashion market. The findings underscore both the opportunities and challenges associated with e-commerce, such as consumer satisfaction, delivery logistics, product authenticity, and privacy concerns. Ultimately, this review highlights the critical need for businesses to understand consumer motivations and behavior to capitalize on the growth of India’s online fashion retail sector.

Abstract: Older adult cancer survivors face a range of physical, psychological, and social challenges that can significantly impact their quality of life. This comprehensive review aims to identify key challenges and opportunities for enhancing quality of life in older adult cancer survivors. A systematic search of the literature identified studies published in the last 10 years that examined quality of life outcomes in this demographic. The review highlights the importance of comprehensive geriatric assessments, personalized care planning, rehabilitation and exercise programs, and psychosocial support in improving overall well-being. Furthermore, the review emphasizes the need for innovative strategies to integrate these interventions into standard oncology practice and to advocate for policy changes that support implementation efforts.

Abstract: The Solvent effect of aqueous aprotic solvent was expressed by studying the kinetics and mechanism of alkali catalyzed hydrolysis of ethylnicotinate in aqueous 1,4-dioxane system having various compositions (20% to 80% v/v) and different temperature range 20°C, 30°C and 40°C affected by the addition of ZnO-nanoparticles. ZnO-nanoparticles increase surface area, catalytic property and polarity of the alkali catalyzed hydrolysis reaction, hence, rate and the specific rate constant value changes. From the kinetic study we found that decrease of rate and specific rate constant (k) of the reaction with gradual addition of organic co-solvent. The change in iso-composition activation energy (EC), iso-dielectric activation energy (ED), solvation and desolvation of initial state and transition state, the number of associated water molecules with activated complex were also calculate and explained. The thermodynamic parameters enthalpy of activation (∆H*), entropy of activation (∆S*) and free-energy of activation (∆G*) were also calculated. The addition of ZnO-nanoparticles in reaction mixture enhances conductivity specific rate constant EC, ED and other above given parameters.

Abstract: By virtue of studying existing literature, this study attempted to comprehend the scope of green marketing and the concept underpinning it. It tried to capture the customers’ demand and perception towards green products and marketing strategy. Studied in depth were the processes on how environmental deterioration is a major issue and the challenges and possibilities that green marketing strives to achieve. Also this study attempted to analyze the change in behavior of consumers towards accepting eco-friendly products for sustainable living. Eco-friendly goods also contribute to our climate. Study also tried to analyse that future scope of the products and its usability for the industry along with the consumers.

Abstract: Drawing upon Sikh philosophy and socio-cultural transformation as a lens, coupled with the violence of Partition in the Punjab region, this paper addresses the different aspects of women's experiences in pre-Partition Punjab. It seeks to redirect the narrative from a Sikh victimization approach to the masculinity of Punjabi—and Sikh—women's agency and their active participation on different timelines in history. In autobiographies, primary sources and secondary literature, the research looks for the origins of woman empowerment and Sikhism. It discusses the fundamentals of equality from Guru Nanak through Mata Sundri Ji and subsequent leaders and also looks at the contribution of Mai Bhago. The research explores women's agency in religious, political, and cultural life with Langar and Phulkari, and social reform movements and subsequently the freedom struggle. Even at the time of Partition, Sikh women emerged as survivors of trauma and mobilisers since they protected horrible violence. The paper attempted to portray the intersection of Sikh women's religion and community to illuminate how these women created new Sikh identities while strengthening traditions in the context of modernity. These women's histories, in their efforts to reclaim them, made a notable contribution to Sikh women's history and emphasize the changes in Punjab's socio-political context that occurred in the pre-Partition and post-Partition period. Their narrative, excluded from the mainstream histories, along with those of other Sikh women makes one reconstruct them as rooted and empowered social historical persons whose spirit and cultural identity set fire to society.

Abstract: In today’s competitive job market, efficient and accurate resume screening is crucial for recruiters and hiring managers. Traditional manual resume review processes are time-consuming and prone to human error, which can lead to overlooking qualified candidates. This project aims to develop an automated system for resume analysis using Python, Natural Language Processing (NLP), and Applicant Tracking System (ATS) algorithms. The proposed solution leverages NLP techniques to extract key information from resumes, such as personal details, educational background, work experience, and skills. Additionally, ATS algorithms are employed to score and rank resumes based on their relevance to specific job descriptions, facilitating a more streamlined and objective hiring process. The system is designed to enhance the efficiency of resume screening by reducing the time and effort required for initial resume screening while improving the accuracy of selection. This report details the development, implementation, and evaluation of the proposed resume analysis system, highlighting its potential benefits and limitations.

Abstract: The significance of predicting crime therefore arises from the possibility of making reliable assumptions that serve the intended organizations in preventing crime. Traditional crime models suffer from three main limitations: low density of data, difficulties for quantifying significant information from parameters that probed space-time, and low adaptability of the model to areas or crimes not envisaged in its dataset. To tackle these problems, the paper offers a brand new multi-module crime prediction system based on the modern machine learning methodologies, which operates on multivariate time-space data. The model consists of three sub-models: The system includes the proposed Attention-based Long Short Term Memory (ATTN- LSTM), a temporal spatial, bidirectional LSTM, as well as a combination of spatial-temporal worksheets is a Fusion Learning Framework (FLF) combined with the Dynamic Learning Fusion Tool (DLF). The DLF module refines the model by adding its refinement of the outputs of the various sub-models hence in- creasing on accuracy. Also, the transfer learning method cuts the training time because it uses features from similar datasets. The implemented model is checked on extensive crime datasets from San Francisco and Chicago jurisdictions; the MAE, MSE, R2 and SMAPE which were used in the evaluation of performance show R2 of about 0.92—0.97 which depicts the model performance is accurate. This approach allows for predicting the hourly crime rates for various type of crime and representing these results graphically in a form of pie charts, which can be useful for policemen. This work will be continued in the future to reduce training time and improve the applicability of the model to cases where there is little or no data on certain types of crime. Although MDPIS is quite efficient for MEP training, problems like longer training time and data scarcity are still present, and later versions of this forecasting tool can theoretically solve these problems thereby providing an environment of real-time prediction.

Abstract: Driver alert system is a project to detect the drowsiness of the driver with help of CNN—Convolutional Neural Network. In recent years, deep learning methods, particularly convolutional neural networks (CNNs), have shown promising results in detecting driver drowsiness. In this detection system, we are going to build a non-contact technique for judging different levels of driver alertness and facilitate early detection of a decline in alertness during driving. In such a case when drowsiness or yawning is detected, a warning alarm is issued to alert the driver. In our project, we will employ a convolutional neural network which detects the states of the eyes and mouth from the ROI images, and also we will be using OpenCV for gathering the images from the webcam and feeding them into the deep learning model which will classify whether the person’s eyes are ‘Opened’ or ‘Closed’. The model is trained on a large dataset of facial expressions representing different drowsiness levels to improve its accuracy and performance. By continuously monitoring the driver’s facial landmarks in real time, the system can ensure proactive intervention. The use of non-invasive techniques also ensures that the driver remains comfortable and undistracted. The alert mechanism can include both sound and visual cues to effectively bring the driver’s attention back to the road. Furthermore, the system is designed to operate efficiently even under varying lighting conditions and with different facial features. The CNN model is optimized for low-latency predictions to make sure alerts are timely. In conclusion, the proposed driver alert system using CNN shows great potential in improving road safety by detecting and alerting drivers. In the future, this system can be integrated with other vehicle safety mechanisms like automatic braking or steering control. It can also be enhanced with infrared sensors to work accurately during nighttime or in low-light environments. Additional features like tracking head movement or blink duration can further strengthen the model’s reliability. The project can be extended to support multi-driver environments, such as public transport systems. We can also deploy the system on edge devices like Raspberry Pi for real-time, on-board processing. With continuous advancements in AI and hardware, the scope for such smart safety systems is vast. This system not only ensures driver safety but also safeguards passengers and pedestrians by minimizing the chances of road accidents caused due to fatigue. The implementation of such technologies reflects the growing importance of AI in real-world safety-critical applications. Moreover, the system can be further enhanced by incorporating machine learning algorithms that adapt to individual driver patterns, offering personalized alerts based on their driving behavior.

Abstract: The combination of Healthcare Internet of Things (HIoT) and Generative Artificial Intelligence (GenAI) has offered an eminent opportunity to develop highly personalized, real-time healthcare offerings. However, the need for data security, cloud dependency, and sensitivity in handling medical information have come in the way of mass adoption. This paper proposes a secure Federated GenAI framework for HIoT networks to allow distributed training and generation of AI models directly on edge devices like wearables and mobile health sensors. By combining federated learning protocols with privacy-preserving mechanisms and generative AI, the system minimizes the need for transmitting raw data to centralized cloud servers. This architecture asserts data sovereignty, minimizes the risk of data breaches, and preserves the performance of models operating across distributed nodes. The experimental evaluation showed the framework achieves competitive accuracy levels for health monitoring tasks along with strong privacy guarantees and communication efficiency. Therefore, our results signal that secure Federated GenAI can be a credible base for developing scalable and ethical AI-driven healthcare systems, particularly in settings that are resource-constrained or laden with regulations.

Abstract: Scientific studies have demonstrated that Nanoparticles have become a promising contributor towards Alzheimer’s disease (AD) research prospects offering significant advantages in imaging diagnostics as well as therapy. These particles offer better prospects in terms of solubility, permeability and bioavailability due to their nanoscale size which enables effective drug delivery to particular brain areas while reducing off-target side-effects. Ursolic acid (UA), a bioactive compound with promising therapeutic properties, faces challenges in clinical applications due to its poor solubility and low bioavailability. To address these limitations, UA was encapsulated using three different polymer-based nanoparticle formulations: Gum Acacia (UGNPs), Eudragit (UE-NPs), and Gum Ghatti (UAGG-NPs). This study compares the physicochemical properties, drug loading efficiency, release profiles, and antioxidant activities of these UA-loaded nanoparticles. All formulations demonstrated effective encapsulation and sustained drug release, with notable variations in stability, antioxidant activity, and enzyme inhibition. The findings suggest that polymer-based nanoparticles significantly enhance UA’s therapeutic potential, with each formulation exhibiting distinct advantages. Further analysis unravels the comparative benefits of these delivery systems, highlighting their implications for oxidative stress-related and neurodegenerative disorders. UA-Nps demonstrated strong acetylcholinesterase (AChE) inhibition, suggesting a potential role in neurodegenerative disease therapy. This comparative analysis highlights the advantages of Eudragit-based nanoparticles for enhanced UA delivery while also recognizing the therapeutic potential of Gum Acacia and Gum Ghatti formulations. The safety, effectiveness and long-term consequences of nanotechnology-based UA formulation for AD treatment and management require further clinical research. The initial step in the same direction has been taken throughout this investigation and analysis.

Abstract: This research investigates the intricate landscape of inheritance rights for nonbinary individuals within the context of Indian legal frameworks. As the recognition of diverse gender identities expands, the study critically examines existing laws governing inheritance, evaluating the extent to which they acknowledge and protect the rights of nonbinary individuals. Through an in-depth analysis of Indian inheritance laws and a comparative exploration of international legal precedents, the research illuminates the specific challenges faced by nonbinary individuals in the inheritance process. The study employs case studies to provide a nuanced understanding of real-life scenarios, highlighting discriminatory practices, social biases, and cultural impediments that nonbinary individuals encounter when asserting their inheritance claims. By adopting an intersectional lens, the research delves into the complex interplay of gender identity with other factors, such as socioeconomic status and geographical location, influencing inheritance outcomes. Proposing targeted legal reforms, the research seeks to address the identified gaps and advocate for inclusive practices that align with the evolving understanding of gender diversity. The findings of this study contribute to the broader discourse on gender inclusivity within Indian legal systems and provide actionable insights for policymakers, legal practitioners, and advocacy groups. The ultimate aim is to foster a legal environment that recognizes and protects the inheritance rights of nonbinary individuals in India.

Abstract: Mobile phones have become the basic necessity of life for people living both in the urban and rural communities in developed, developing and underdeveloped countries of the world. Presence of electricity is very important in making the best use of this basic device because it helps to keep it constantly available for use. As a result of lack of electricity in many developing areas despite electricity being basic, many of the populace residing in these places usage of mobile phones has been challenging because they find it difficult to maintain constant use of their mobile phones which requires the presence of electricity for charging them. The use of alternative source of energy to maintain charge for the mobile phone is a means to surmount this challenge. Various alternative source of energy for charging the mobile phones exist among which are wind energy, thermal energy, hydro power, nuclear energy and solar energy. Of all the sources of energy, solar energy plays a vital role at powering isolated applications and has the ability to be made available for small use. The objective of this work is to provide alternative source of energy for the charging of mobile phones which is an essential communication device for people living in the urban or rural communities. This work has provided an isolated multiple mobile phones charger that makes use of solar energy as its energy source. It makes use of 12V which it accessed from the solar panel which  is reduced to 5V at its output for charging the mobile phones. It was discovered that it took the developed mobile multiple cell phones charger 60 minutes (1 hour) to charge an Infinix Android phone to its full capacity with the use of a battery bank and 120 minutes (2 hours) when use with only a Polyvinyl cell (Solar panel). It was also observed that the time taken to full charge is the same when used with the conventional Alternating Current (AC) source. This shows that the developed mobile phones charger is able to perform at the same rate the conventional source of energy performs because it took it the same time to charge the mobile phones, thus making it a good means and alternative to mobile phones charging. Therefore, rural dwellers and people living in areas in which there are no constant supply of electricity through the public utility companies or no supply at all can still make use and enjoy constant supply of power to there mobile phones through the use of this isolated mobile phone charger.

Abstract: Leadership is an idea that transcends recorded history. Various historical, social, and cultural factors have shaped how leadership is understood and practiced across different cultures. Leadership is seen through a fresh lens by the Indian Knowledge Systems (IKS) in India, a nation rich in history and culture. This article delves into the complex web of Indian Knowledge Systems (IKS) and how they relate to different types of leadership. Examining the interdependent nature of ancient Indian knowledge and modern leadership models, this study compares and contrasts several leadership paradigms. We want to provide a thorough comprehension of how IKS may guide and improve leadership practices in many settings by referencing historical writings, contemporary understandings, and academic literature.

Abstract: The document presents a comprehensive study on the modeling and simulation of battery management systems (BMS) using Dymola, focusing on electric vehicle (EV) applications. It explores the behavior of two battery models with differing complexities: a simplified model with linear state-of-charge (SOC) dynamics and an advanced model incorporating real-world effects such as transient responses, self-discharge, and RC (resistor-capacitor) network dynamics. The detailed model simulates non-linear SOC-open-circuit voltage (OCV) relationships, energy losses, and thermal effects, offering insights into battery performance under varying operating conditions. The research compares charge-discharge cycles with a focus on transient voltage behavior, internal resistance energy losses, and thermal considerations. Significant measurements like SOC development, energy efficiency, and thermal stability are compared, citing the influence of design factors such as pulse currents, self-discharge rates, and RC values. The results highlight the necessity of advanced model approaches for the optimization of battery systems in electric vehicles to achieve working efficiency, safety, and durability. This project showcases Dymola's ability to simulate multidomain systems properly, connecting theoretical modeling and real-world applications in contemporary energy storage devices

Abstract: Due to the ever-increasing housing deficit, there is an increase in the level of unwholesome practices leading to Building Project Collapse (BPC) in Nigeria, especially Lagos state. This study examined the role Stage Certification has played in the correction of the unwholesome practices to reduce BPC Lagos State, Nigeria. The study adopted a descriptive research design. The targeted population is infinite in the study area Lagos State. The sample size is determined using the formula adopted by Sheldon for infinite population. And a sample size of 384 was uased. Both primary and secondary data were used. The secondary data was extracted from literature of past researchers and stakeholders to establish the yearly frequency of BPC in Lagos state while the primary data source was collected using structured questionnaires. The collected data was analyzed using descriptive and inferential statistics using SPSS (Statistical Package for the social sciences). The findings revealed among others that, that the level of awareness of the stage certificate among the various stakeholders is insignificant, based on the frequency of occurrence of BPC for the Pre and Post introduction of the stage certification policy. It was therefore recommended among others that, the Lagos state government should enhance stakeholder engagement to foster a culture of compliance, discussions on regulatory policies with developers, homeowners, and other stakeholders, and increase public awareness campaigns. 

Abstract: Currently, there is a trend of using non-caloric high-intense sweeteners, as substitutes for sugars in food, carbonated and non-carbonated beverages to acquire the required sweet taste. Steviol glycosides have been recognized as a non-caloric natural sweetener bearing European food additive number of E960. Stevioside is the major component in steviol glycosides followed by rebaudioside A. The aim of the study was to evaluate the stevioside and rebaudioside A in different food and beverages matrixes found in the market of Sri Lanka using validated reverse phase HPLC method. validation of the method performed using USP reference standard. The Limit of detection for stevioside and rebaudioside A were 2.6 mg/kg and 3.9 mg/kg respectively. Linearity range for stevioside and rebaudiosideA was 2 to 500 mg/kg and 5 to 500 mg/kg with a correlation of coefficient of 0. 9999.Accuracy as the percentage of recovery for both analytes were between 80.7% - 118.7% for different spiked levels. In conclusion, the developed analytical procedure was sensitive, simple, accurate to determine stevioside and rebaudioside A in food, beverage and their commercial preparations and it was found that most of products were within the acceptable range.

Abstract: The integration of Artificial Intelligence (AI) into User Experience (UX) design has introduced significant transformations by enhancing efficiency, creativity, and decision-making. This study examines the critical factors influencing the adoption of AI-powered design tools among UX professionals in Sri Lanka. Employing a quantitative research approach, data were collected via a structured survey and analyzed using Structural Equation Modeling (SEM) through SmartPLS 4. Results reveal that positive attitudes toward technology, effort expectancy, performance expectancy, and facilitating conditions significantly predict adoption intention, while perceived risks and social influence exhibit limited influence. This research advances theoretical understanding by validating the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) within the UX context. Practical implications are drawn for encouraging AI adoption by creating supportive environments and enhancing perceptions of ease and usefulness. Limitations and directions for future cross-cultural and longitudinal research are discussed.

Abstract: Traditional project management processes are susceptible to manual intervention, leading to inefficiencies, delays, and transparency loopholes. This research proposes an AI and ML-enabled intelligent project management system that automates processes, optimizes resource utilization, and ensures real-time monitoring and client interaction. The system uses supervised ML models and heuristic algorithms for intelligent task allocation, actionable insights, and transparency between the stakeholders. With the integration of a visually interactive dashboard, Gantt charts, and NLP-enabled feedback loops, the system improves collaboration and operational effectiveness.

Abstract: The insurance industry has experienced significant growth, providing policyholders with risk management and financial security through various insurance policies. These policies, including life, health, motor, freight, fire, and home insurance, offer protection against unforeseen events. The pandemic has transformed the industry, pushing insurers to adapt their strategies. Despite facing challenges, insurers played a crucial role during the pandemic and benefited from increased awareness about insurance importance. As a result, insurance adoption surged, with a 30-40% increase in health and life insurance policy purchases. This growth has brought new challenges, prompting insurers to innovate and meet evolving customer expectations. This study aims to analyze the growth of the insurance industry in Ernakulam city, focusing on health, life, and motor insurance, and understanding policyholders' experiences and motivations for opting for insurance products during the pandemic.

Abstract: The agricultural sector is experiencing a substantial transformation with the introduction of drone technology. Unmanned aerial vehicles (UAVs), commonly referred to as drones, are revolutionizing agricultural practices by enabling efficient data acquisition, crop surveillance, and resource allocation. This review paper aims to provide a comprehensive analysis of agricultural drone technology, examining their mechanisms, classifications, applications, the role of autonomous drones, and the algorithms governing their operations. The results highlight the potential of drones to enhance agricultural productivity, optimize resource utilization, and promote sustainable farming practices.

Abstract: Differs factors have been identified as the causes of the high rate of Building Project Collapse with little reference to difference types of conflict among building professionals. While some studies have examined possible conflict among professional colleague in building projects, their study was only focused at developing a model to prevent conflicts in general. This study assessed the level of contribution of what is term “Egoistic Conflict” on building project collapse in Lagos state.  This study adopted descriptive research design. The population of the study is infinite as it comprises of all stakeholders-home owners, engineers, surveyors, staff of relevant government agencies etc., in the informal Building Projects in Lagos state. Thus, for convenience an online contact of respondents through social medium was used. Using the formula adopted by Sheldon for infinite population, a sample size of 384 was drawn from the population. A random sampling technique was used based on time interval of responses. Data was collected through a self-developed questionnaire. The collected data was analysed using inferential statistics using SPSS (Statistical Package for the social sciences). The findings revealed that, Ego Conflicts explains 45.5% of Building Project Collapse in Lagos State, Nigeria. It was therefore recommended among others that periodic seminars and workshop should be organised for home owners and developers on what is a structural job and the scope of individual discipline in the industry.

Abstract: The advent of Machine Learning (ML) has brought transformative changes to the landscape of online and distance education. This paper explores the pivotal role of ML in advancing sustainable online and e-learning within higher education by enhancing adaptability, personalization, and learner engagement. This review outlines the emergence and forms of ML that comprised supervised, unsupervised, semi-supervised, reinforcement, and deep machine learning. It went further to highlight the pedagogical implications and practical applications of these various machine learning in virtual learning environments. It also examines the evolving role of Learning Management Systems (LMS) and User Experience (UX) in shaping learners’ engagement and retention. In addition,  it presents information on  the potency of  machine learning as relates to the human components by predicting student behavior, emotions and performance which has been  the greatest  concern of scholars in its integration in educational processes thereby  bridging emotional, behavioral, and cognitive gaps in virtual instruction, Thus, this paper advocates for the integration of ML as a sustainable solution to personalize learning, prediction of  student  behavior and performance while fostering  inclusive, data-driven pedagogical practices in higher education.

Abstract: A simple and reproducible spectrophotometric method has been developed for the determination of Azilsartanmedoxomilin bulk and pharmaceutical dosage forms. The method is based on the extraction of the drug into organic layer of the dye TPooo in presence of 0.1 N hydrochloric acid and the absorbances were measured at 485 nm. The method was optimized using eight parameters. The linearity range of  Azilsartanmedoxomil  with TPooo was found to be 0.5 – 3.0 ml; 400 µg/ml. The developed method was found to be precise and accurate from the statistical validation of the analytical data. The proposed method has been successfully applied for analysis of dosage formulations.

Abstract: Public sector banks have made significant contributions to economic and social progress, but they face challenges that impact their efficiency and effectiveness. Inefficiencies, governance issues, and political interference hinder their performance. To stay relevant, PSBs must adapt to changing market conditions, technological advancements, and evolving regulations in a rapidly evolving financial landscape. This study explores the evolving role, challenges, and future prospects of public sector banks in the modern era, examining their history, institutional dynamics, and policy requirements to inform their strategic direction and future trajectory.

Abstract: The Crowd funding Platform with Payment Gateway Integration is a dynamic solution designed to connect project creators with potential backers, enabling seamless fundraising for a variety of initiatives. The platform allows users to create, manage, and promote campaigns while providing backers with a secure and intuitive interface for contributions. Integrated payment gateway support ensures hassle-free transactions with multiple payment options, including credit cards, digital wallets, and bank transfers. Advanced features such as campaign analytics, reward management, and social sharing tools enhance campaign visibility and effectiveness. By fostering transparency, security, and accessibility, the platform empowers individuals and organizations to bring their ideas to life through collective support.

Abstract: This study investigates the challenges affecting the implementation of fire safety measures in commercial buildings in Awka, Anambra State, Nigeria. Rapid urbanization and commercial growth have increased fire risks due to inadequate safety infrastructure, weak regulatory enforcement, and limited public awareness. Using a descriptive survey design, data were collected from 213 industry professionals-including architects, structural engineers, and builders-using a structured questionnaire. Data were analyzed using frequency analysis for this study. Key barriers identified include inadequate regulatory enforcement, financial constraints, lack of technical expertise, and limited public engagement. Insufficient fire safety training and lack of routine drills further contribute to low preparedness among building occupants. The study recommends stricter regulatory oversight, increased financial support, enhanced technical training for professionals, and mandatory safety drills. These strategies are essential to improve compliance, mitigate fire risks, and safeguard lives and property in Awka's commercial spaces. The research provides valuable insights for policymakers, industry professionals, and stakeholders in fire safety management.

Abstract: This study presents a numerical method for solving Volterra integral equations of the second kind using shifted Chebyshev polynomials. Volterra integral equations arise in various scientific and engineering applications, including population dynamics, physics, and control systems. Due to their complexity, obtaining analytical solutions is often challenging, making numerical techniques crucial. We employ shifted Chebyshev polynomials as basis functions to approximate the solution, transforming the integral equation into a system of algebraic equations. The shifted Chebyshev polynomials offer excellent approximation properties, improving convergence rates and accuracy. The proposed method is analyzed for stability and efficiency, and numerical experiments demonstrate its effectiveness in solving different classes of Volterra integral equations. The results highlight the advantages of the approach compared to traditional numerical methods.

Abstract: AgroVision is a mobile-centric artificial intelligence- driven platform, particularly designed to enhance the efficiency and sustainability of contemporary agriculture operations, specif- ically focusing on small-scale farmers in resource-constrained areas. AgroVision offers personalized crop prescriptions using soil pH, moisture, and nutrient levels, as well as for weed and crop detection through the YOLOv8 algorithm. In contrast to hardware-locked proprietary agricultural innovations, AgroVi- sion can execute seamlessly on mobile devices via a Flutter app, allowing farmers to take pictures of their fields and input soil data directly. From this analysis, the insights provided by AgroVision are tailored to the user so that decisions can be made regarding maximized crop yield, deepening ecological impact, and ecological footprint minimization. While the development team faced challenges with low computational power and a lack of varied training data, they were still able to robustly optimize the models and apply data augmentation techniques to guarantee consistent system performance across different operational scenarios. Focused on bridging the accessibility gap for precision farming technologies and fostering data-driven practices in agriculture, AgroVision addresses gaps related to sustained and inclusive agricultural advancement.

Abstract: The rapid advancement of artificial intelligence has led to the emergence of highly realistic synthetic media, commonly known as deepfakes. While this technology offers creative potential, it also presents significant cybersecurity threats, including misinformation, identity theft, political manipulation, and fraud. This paper explores the application of AI-driven techniques for the detection of deepfakes as a critical component of modern cybersecurity. We review the state-of-the-art approaches in deepfake detection, focusing on deep learning models such as Convolutional Neural Networks (CNNs), Transformer architectures, and hybrid models that leverage visual and audio inconsistencies. The study also evaluates existing datasets and performance benchmarks, highlighting current limitations and challenges in real-world deployment. Our findings underscore the need for robust, explainable, and generalizable AI systems to combat the evolving threat of deepfakes and ensure digital media integrity. Furthermore, we emphasize the importance of evaluating dataset biases, adversarial threats to detection models, and the scalability of detection systems in real-world settings such as social media platforms. Future directions include the integration of AI with cryptographic verification, multimodal detection strategies, blockchain-based authentication, and real-time analysis tools for proactive defense against synthetic media attacks.

Abstract: This study explores the therapeutic potential of Nigella sativa (Black Seed) and its bioactive compounds in treating various skin conditions, particularly fungal infections. Fungal skin diseases, caused by pathogens like Malassezia and Candida, are increasingly prevalent and often challenging to manage. The paper delves into the pharmacological properties of Nigella sativa, focusing on its antimicrobial, antifungal, and anti-inflammatory effects. The study also reviews its role in managing acne vulgaris, fungal acne, and other dermatological disorders. Additionally, the paper examines the skin microbiome's impact on these conditions and the synergistic effects of Nigella sativa’s components with conventional treatments. The findings highlight the promising therapeutic applications of Nigella sativa, particularly its bioactive compound thymoquinone, in addressing skin infections and improving skin health. This research contributes to the growing body of knowledge on alternative treatments for skin diseases, suggesting Nigella sativa as a valuable adjunct in dermatological care.

“Labour welfare means such services, facilities and amenities which may be established in, or in the vicinity or undertaking to enable persons employed there in to perform their work in healthy and congenial surroundings and to provide them with amenities conductive to good health and good morals” - International Labour Organisation. Indian Constitution provides labour welfare through promotion of welfare of the labour for human condition of work and securing to all workers leisure, social and cultural opportunities. Labour welfare is measure to promote the efficiency of labour and this activities designed for the promotion of the economic, social and cultural well being of the employees. The various welfare measures provided by the employer will have immediate impact on the health, physical and mental efficiency, alertness, morale and overall efficiency of the workers and thereby contributing to the higher productivity. In addition to this the workmen needs protection from certain disaster which sovereign their efficiency. Social security measure provided by employer will act as a protection to the workers. Social security objectives at providing collective measures to protect the employee of a community against social risk as their individual resources are hardly adequate to after protection against hardship. Both assistance and social insurance from integral parts of the system of social security. Labour welfare provides the extra quantity to industrial relationship which ever a adequate wage alone cannot provide. The term labour welfare includes anything concluded for intellectual, physical, moral and economic betterment of worker by government or by other agencies over and above what laid down by law in various contingencies like illness, unemployment, disability and death which have direct impact on the well being of the worker and the dependent.

Abstract: The study investigated the impact of digital education on the academic performance of secondary students in Idemili North L.G.A, Anambra State. It aimed to discover the effectiveness and ineffectiveness of digital education in schools. The researcher employed a qualitative research approach to evaluate if there was a relationship between digital education and the academic performance of students. The study employed a case study design, and data collection was done through the completion of written interviewsby filling in the open-ended questions, while face-to-face oral interviewsto further receive viable information on the subject under investigationThe sampling approach used in this study is stratified sampling. Purposefully, 30 respondents were sampled and categorized into 15 separately for the two schools under study. The principal, teachers, and students made up this number on the ratio of 1:4:10, respectively, for each school. The three strata that were sampled were the students, principals, and teachers of the selected schools who comprehend the impact of digital education on students’ performance. Those schools were chosen firstly because of their proximity and accessibility to the researcher. In addition, literatures were reviewed. The study revealed that digital education has benefits for students; these include easy access to information, accessibility of online resources, collaboration enhancement, and creation of personalized learning. The major negative effect of digital education is distraction; it causes students to engage in cyber fraud, immodest lifestyle, and watch pornography. Also, it was found that lack of digital gadgets for effective learning, unavailability of the internet and lack of basic knowledge on digital education, lack of good supervision, and lack of digital facilities are other factors that affect digital education in schools. Therefore, it was recommended that parents, teachers, government, and secondary school stakeholders join in a collaborative effort to make sure digital education is accessible to students in secondary schools. Also, parents should start teaching their children moral discipline right from home; another commendation was that teachers should be held responsible for guiding the students on digital education in secondary school; therefore, they should upgrade themselves to modern-day teachers. Thus, concerning this issue of the negative impact of digital education that is ravaging lives because people cannot defend themselves online, it was recommended that all hands should be on deck to avoid the wrong use of the internet that affects people negatively in colleges and societies.

Abstract: The integration of artificial intelligence (AI) in recruitment has transformed traditional hiring practices, offering efficiency, scalability, and data-driven decision-making. However, the implications of AI on Diversity, Equity, and Inclusion (DEI) remain complex and multifaceted. This paper investigates how AI-driven recruitment tools affect DEI outcomes, examining both the potential benefits and inherent risks. Through an analysis of current literature and emerging trends, this research identifies the key challenges and opportunities for aligning AI recruitment technologies with DEI goals. Recommendations for ethical implementation and the role of human oversight are also explored.

Abstract: This study investigates the impact of internal capabilities, particularly Results-Based Management (RBM) and Deep Knowledge Implementation (DKI), on the sustainability of Small and Medium Enterprises (SMEs) in Mozambique, between 2005-2023. Using Structural Equation Modeling (SEM) and Principal Component Analysis (PCA), we analyze data from Mozambican SMEs to explore how these management practices influence organizational resilience and long-term survival. Our findings reveal that while RBM significantly contributes to SME sustainability, DKI presents mixed effects, suggesting the need for a more integrated approach to internal knowledge application. Additionally, statistical models reveal that firms with high absorptive capacity benefit more from DKI, emphasizing the moderating role of organizational learning. This study contributes to the literature on SME management by providing empirical insights into the role of internal capabilities in emerging markets.

Abstract: This study examines the impact of deep knowledge integration and results-based management on the sustainability of small and medium-sized enterprises (SMEs) in Mozambique. Based on a quantitative analysis of SMEs operating between 2005 and 2023, we test two hypotheses: (i) firm mortality increases with age, and (ii) adopting results-oriented management practices enhances SME survival rates. The methodology employs regression analysis and structural equation modeling (SEM) to assess the relationships between organizational knowledge, operational efficiency, and business success, supported by reliability and validity statistical testing. Findings indicate that deep knowledge practices (e.g., continuous improvement, tacit knowledge sharing) combined with results-driven management (e.g., structured managerial processes, performance goal setting) contribute significantly to performance and longevity. However, the relationship between firm age and mortality is nonlinear - while younger firms demonstrate adaptive flexibility, older firms benefit from structured processes - suggesting that survival is contingent on managerial and knowledge-based factors rather than age alone. This article provides empirical evidence on the internal determinants of SME resilience in challenging economic environments, contributing to sustainable management literature by demonstrating that deep knowledge integration and performance-driven strategies improve organizational sustainability. Policy recommendations include enhancing SME knowledge management frameworks and implementing government-led capacity-building initiatives to optimize long-term impact.

Abstract: The world's largest rivers often display meandering features in their middle and lower courses, leading to the formation of oxbow lakes. These meandering rivers frequently change their paths along the floodplain and creating frequent meandering scars and paleochannels. The Bhagirathi River in West Bengal is a prominent example marked by numerous meander loops and oxbow lakes. Over the past decades , it has experienced frequent cut-offs. Consequently, a few villages have suddenly become disconnected from the mainland due to a cut-off, causing physical isolation of certain villages from the mainland. This study explores the socio-economic impacts experienced by the communities residing within these isolated oxbow lakes regions. A household survey was conducted among 102 respondents from Chupi Char (located in the Purbasthali 1 block of Purba Bardhaman district), focusing on demographic profiles and occupation changes before and after the cut-off. Both quantitative and qualitative data were collected through interviews and structured questionnaires. Key findings reveal that the age group between 30-60 years old experienced the most significant impacts, while younger individuals reported minimal effects due to the meander cut-off. Additionally, there has been a shift in occupation, with the majority of people transitioning from primary activities to secondary activities due to land submergence following the cut-off. Land erosion, waterlogging, and crop damage have adversely affected the region. This unfortunate situation has changed significantly due to social development and support in recent times, leading many to flee from hazardous conditions. 

Abstract: This study addresses an important and critical problem in the area of information technology. The software development life cycle, also known as development models, is the subject of software management practices that analyze software development. The main purpose of this research is to craft a development model that meet the requirements of various system and eliminate the defect of earlier model. This study suggests a hybrid model that incorporates elements from the five most popular development models: waterfall, iteration, spiral, V-shaped, and extreme programming. Through some modifications, the model suggested in this study maintains some of the characteristics and advantages of previous models. As a result, it avoids and resolves many of the software issues that plagued previous models. Therefore, the model we have just suggested is an integrated model that applies to most software applications and systems.

Abstract: Standardization is required to guarantee the quality and purity of herbal drugs. Despite being an essential part of many Ayurvedic formulations, Apamarga's (Achyranthes aspera Linn.) kshara (alkaline ingredient) has not yet been used in its standard manufacturing method (SMP). Establishing standard manufacturing method (SMP) for Apamarga Kshara is the goal of this work. In the process, the ash sediments that are left over after washing are frequently thrown down. To prize fresh Kshara, we tried continuously washing the sediments in the exploration by adding water. Apamarga was gathered from the vindicated and neighbourhood. Standard procedures were followed to prepare Kshara, and a primary physicochemical profile was created. Indeed in consecutive wetlands, it has been noted that the ash produces Kshara. This study aims to estimate the histopathological goods of Apamarga Kshar(an alkaline medication deduced from Achyranthes aspera) on albino rats. Apamarga Kshar is extensively used in Ayurvedic drug, particularly in Kshar Karma remedy for its Chedana(slice), Lekhana(scraping), and Shodhana(sanctification) parcels. Despite its remedial significance, limited data is available on its cellular- position goods. This experimental study was conducted to assess any possible towel changes or pathological differences after administration of Apamarga Kshar.

Abstract: Gender is a common term whereas gender discrimination is meant only for women, because females are the only victims of gender discrimination. Recognizing women’s right and believing their ability are essential for women’s empowerment and development. Gender Stereotype is a long-term problem in our society and females are discriminated various ways in India, although legally women have equal rights. So, there is a great need to focus the society on gender issues so that there would be no discrimination based on gender. Women empowerment through gender stereotypes is one of the key factors to unlock the potential of women. This paper elaborates strategies that should be adopted in our society to promote gender equality. As we know “Power of women as the greatest potential for the growth of the economy”. To achieve the empowerment of all women and girls will establish policies and institutions to build a society where men and women support each other. Many activities need to focus where we lack gender responsive indicators and sex disaggregated baseline data and having limited monitoring information. There should be study on some constraints to assess possible differences in participation, benefits and impact between men and women. Gender development or sustainable inclusive development is one the prominent phenomenon which has emerged after the publication of Human Development Report. 

Abstract: Nigeria, a major oil-producing country in Africa, has long been plagued by the persistent problem of oil pipeline vandalism. Sabotage and illegal tapping of oil pipelines have resulted in significant economic losses, environmental damage, and disruptions to the country's energy supply. This study explores the potential utilization of horizontal directional drilling (HDD) as a viable solution to help eliminate oil pipeline vandalism in Nigeria. The objective of this literature review is to showcase the effectiveness of HDD technology in preventing or reducing incidents of pipeline vandalism as compared to conventional methods of pipeline installation. By examining case studies and scholarly literature, this paper will analyze the advantages, challenges, and economic feasibility of implementing HDD techniques to mitigate oil and gas pipeline vandalism in Nigeria. Firstly, the study will explore the advantages of HDD technology, including its ability to install oil pipelines underground and mitigate accessibility to potential vandals, enabling the creation of subsurface pathways, reducing the visibility and potential vulnerability of infrastructure. In addition, this literature review will shed light on the challenges associated with implementing HDD in Nigeria. The economic feasibility of implementing HDD technology in Nigeria’s oil and gas sector will be assessed. A cost-benefit analysis will be conducted, comparing HDD with traditional methods of oil pipeline installation. The findings of this study will contribute to the existing body of knowledge regarding methods to combating oil and gas pipeline vandalism in Nigeria, thereby providing useful insights to policymakers, infrastructure developers and relevant stakeholders, encouraging them to consider HDD as a potent solution to mitigate oil and gas pipeline vandalism.

Abstract: This research study investigates consumer behavior in the context of online apparel shopping by focusing on the role of gender, purchase frequency, and preference patterns. A sample of 100 respondents from the twin cities of Hyderabad and Secunderabad in Telangana state was surveyed by using convenience sampling using Google forms and social media platforms to understand the significance of various factors such as door delivery, product range, convenience, discounts, and payment options in shaping purchase decisions of apparels though online mode. The primary data thus collected was cross-tabulation and chi-square tests were conducted to examine associations between gender and purchase preferences as well as between frequency and mode of shopping. The results revealed that while gender does not significantly influence purchase preference or frequency, a strong association was found between purchase preference and shopping frequency. The descriptive statistics show that door delivery and product range are the most important factors for consumers when buying apparels online. The study finally concludes with practical suggestions for e-retailers and identifies avenues for future research in this area.

Abstract: Organizational decisions and actions are a balance between two interacting forces; an entrepreneurial aspiration and market dynamics. Marketing is the strategic interface and a function for market engagement. Theorization in marketing has been addressing specific organizational issues, but falls short in conceptualizing a unified marketing theory. This research examines objectives of marketing research, relationships and questions if they are grounded in a foundational understanding of ‘what the market is?’, the very environment to which organizations are inherently tied. We derive basic characteristics of market reflexively from the current theorization around market and marketing, where marketing has been a seller-oriented function and market is a mix of discrete parts and activities. Beyond these concepts, we identify and redefine market as a three-dimensional domain. Accordingly, we conceptualize market-based perspective of organization and its marketing function. The three-dimensional market enables an environment through ‘Marketing Objectives of Interaction’ consisting of guiding principles for interaction between the market and the organization. Organization and the objectives of interaction, together create an ‘Organization-Market’ composite making the organization a participant, along with many other participants who constitute the market environment.  The composite presents the dynamic nature of organization’s marketing and its complexity in dealing with the multitude of market variables. Together these concepts evolve a new approach for theoretical advancements in understanding market, various facets of marketing and relationships in a dynamic organizational context. This framework offers a novel approach for marketing research, facilitating integration with organizational studies on design, strategy and management.

Abstract: The integration of Wi-Fi technology into solar inverters has significantly enhanced the operation and management of both on-grid and off-grid solar systems. These smart Wi-Fi solar inverters enable real-time monitoring, remote control, and automated optimization of solar energy systems, making them more efficient, cost-effective, and user-friendly. On-grid systems allow users to connect to the public electricity grid, enabling energy export and contributing to grid stability, while off-grid systems provide energy independence, especially in remote or rural areas, through battery storage. The Wi-Fi connectivity in smart inverters facilitates remote monitoring, performance tracking, and fault detection via smart phones or web interfaces, offering users the ability to manage energy production and consumption at their convenience. Furthermore, Wi-Fi-enabled inverters enhance predictive maintenance and offer real-time alerts, minimizing downtime and reducing maintenance costs. However, the widespread adoption of these systems faces challenges, including connectivity issues, cyber security risks, and higher initial costs. This paper examines the technical components, benefits, and limitations of smart Wi-Fi solar inverters, providing a comprehensive analysis of their role in improving the efficiency and sustainability of on-grid and off-grid solar energy systems. Through case studies and market trends, it concludes that Wi-Fi-enabled smart inverters are a key innovation in the transition to cleaner, more connected, and efficient solar energy solutions.

Abstract: Crowd funding is an emerging concept that bridges the gap between entrepreneurs and investors by facilitating online fundraising for various projects through dedicated platforms. In recent years, the crowd funding industry has seen remarkable growth, raising billions to support ventures ranging from individual art ventures to innovative startups. This model has immense potential to improve the financial ecosystem for businesses, benefiting both small entrepreneurs and large enterprises. This article examines the essence of crowd funding, its benefits, associated risks, and the challenges it poses to startups in India. Crowd funding serves as a transformative tool, enabling entrepreneurs to translate their innovative ideas into viable business ventures. With a growing economy and a growing educated middle class, India has the potential to redefine the fundraising landscape for startups. This global phenomenon is rapidly impacting both developing and developed countries. By connecting investors with startups and small businesses through online platforms, crowd funding removes traditional barriers to entry. A well-structured approach to crowd funding can provide a vital alternative funding source for emerging startups and SMEs and empower young entrepreneurs to achieve their business ambitions.

Abstract: -This project presents a comprehensive Online FMCG (Fast-Moving Consumer Goods) Management System designed to streamline and modernize the management of retail operations for FMCG products. The system offers an intuitive and responsive interface for managing inventory, tracking order delivery, processing orders, and handling customer and supplier information. It includes modules for real-time stock updates, automated billing, online payment integration, and dynamic reporting dashboards. With features such as low-stock alerts, and product categorization, the system aims to enhance operational efficiency and reduce manual effort. Additionally, it supports multi-user roles with secure access control, allowing admins, staff, and suppliers to interact with the system according to their privileges. By leveraging modern web technologies and cloud-based architecture, the application ensures high availability, scalability, and data integrity. This solution caters to the needs of retailers, wholesalers, and distributors, making the management of FMCG products more organized, accessible, and efficient.