VOLUME 12, ISSUE 9, SEPTEMBER 2025

Abstract: Many tea factories in Kenya still heavily rely on fuelwood for energy, leading to the release of carbon dioxide (CO2) and other pollutants, impacting the environment, and contributing to climate change. This study focuses on understanding how errors in boiler emission measurement tools, specifically flow meters, flue gas analyzers, and temperature sensors, propagate and affect the accuracy of emissions data, potentially complicating regulatory efforts and environmental assessments. In a bid to address the aforementioned issue, measurement data of the boilers under study were collected over six distinct periods from selected tea factories in Kenya, which captured a wide range of operational conditions. With the application of novel standard error propagation techniques, the uncertainties associated with each instrument in the measurements were considered. Results in this research revealed that significant variations in gas emissions readings, primarily due to errors from the instruments and general environmental fluctuations, such as temperature and humidity This paper centered on emissions of carbon dioxide, nitrogen oxides sulphur oxides and particulate matter and further explicated the effect of error propagation, showing how minor variations in sensor accuracy resulted in substantial change in under or overestimation of emissions levels. Findings in this study underscore the need for constant protocols in calibrations and the application of real-time correction methods within the equipment of boiler emissions monitoring. The analysis in this research showed that the current measurement systems might not adequately support compliance with the environmental regulations, which thereby threatens human life. Finally, concludes that the establishment of more robust methods of calibration practices in Kenya tea factories, the adoption of advanced monitoring equipment and technologies, and the adoption of real-time processes in data analysis work well to improve and mitigate errors emanating from measurement instruments. These recommendations aim to improve measurement accuracy, resulting in sustainable environmental practices in Kenya's tea industry and, by extension, contributing to climate mitigation.

Keywords: Emissions, Error propagation, Sustainability, Uncertainties, Boilers.

Abstract: This study explores whether herding behaviour exists in the Indian stock market over the period January 2022 to December 2024. Using daily sector-level returns and the Cross-Sectional Absolute Deviation (CSAD) approach, we tested whether investors tend to follow market-wide movements, and how volatility shapes this behaviour. The results show no strong evidence of herding at the aggregate level. While the squared market return term was negative as expected, it was not statistically significant. Instead, investors appeared to act more independently, suggesting that the market has matured in recent years. Interestingly, volatility was found to have a significant positive effect on return dispersion, meaning that during periods of higher uncertainty, investors responded in more diverse ways rather than moving together. The findings point to a more rational and resilient Indian equity market, where information availability and institutional participation reduce the chances of herd-driven distortions. These insights are useful for investors, fund managers, and regulators seeking to understand behavioural patterns and market efficiency in emerging economies.

Keywords: Herding behaviour, CSAD model, Investor behaviour, Market volatility, Indian stock market, Market efficiency

Abstract: This paper examines the dynamic relationship between the Bombay Stock Exchange (BSE) and key macroeconomic indicators of economic growth in India over a two-decade period from 2004 to 2024. Utilizing quarterly data for Gross Domestic Product (GDP) growth rate, Inflation Rate, and Exchange Rate (INR/USD), the study employs econometric techniques to analyze their interplay. The Augmented Dickey-Fuller (ADF) test confirms the stationarity properties of the data series. The Granger causality test reveals a unidirectional causal relationship running from the BSE Sensex to the Exchange Rate. Furthermore, multiple regression analysis identifies Inflation as the most significant positive influencer of the BSE Sensex, followed by the Exchange Rate, while GDP growth shows no statistically significant impact in the short run. The findings suggest that stock market performance is highly sensitive to monetary policy variables like inflation and forex dynamics, underscoring its role as a leading indicator in the post-liberalization Indian economy

Keywords: Stock Market Development; Economic Growth; BSE Sensex; Unit Root Test; Granger Causality; Multiple Regression; India.

Abstract: The experimental determination of proximate analysis data can be obtained easily using an ordinary muffle furnace whereas calorific value of solid fuels is a cost intensive process, as it requires spatial instrumentation and highly trained analyst to perform the experiments, compared to calorific value. Regression analysis and CIMFR formula methods have been introduced to simplify the task and also reduce the cost of analysis. An endeavor has been made in this present study to access the applicability of these correlations and regression method with a spatial emphasize on Indian coals. Correlation have been created using simple linear regression and multivariable linear Regression analysis based on proximate analysis of data sets. 15 samples were collected from different coal fields of India including the South Eastern Coalfields (SECL), Singareni Collieries Company Limited (SCCL), Central Coalfields limited (CCL), Mahanadi Coalfield Ltd. (MCL), Jindal Steel and Power Limited. The intrinsic properties were determined by carrying out proximate analysis and gross calorific value (GCV) by using bomb calorimeter. The results for intrinsic properties and the gross calorific value are given in table 1. Correlation analysis was carried out to analyze the individual effect of moisture, volatile matter, ash and fixed carbon on the gross calorific value (GCV). It is observed that moisture, ash have adverse impact and reduce the gross calorific value whereas volatile matter, fixed carbon have positive impact and increase the gross calorific value (GCV).

Keywords: Coal Utilization; Energy Sources; Physical Properties; Ashes; Chemical Properties; Calorific Value; Combustion Temperature; Heat Measurement; Moisture Content; Volatility;

Abstract: Companies across sectors are struggling to find suitable candidates for job vacancies because the TVET courses offered at various training institutions often do not meet the requirements of the private sector. However, there are limited studies in the Kenyan context focusing on the impact of public private partnership in curriculum development on the performance of TVET institutions. In this regard the current study has been designed to investigate the impact of public private partnership in curriculum development on performance of public Technical Vocational Education and Training Institutions in South Rift Region, Kenya. In a bid to effectively achieve this, the study adopted an Explanatory Sequential research design based on samples drawn from across the TVET institutions in South Rift Region which have public private partnership in their respective institutions. The target population was 470 trainers who included trainers of mechanical engineering departments, electrical engineering departments, building departments, management representative of TVET institutions in South rift region. Cluster, stratified random and sampling simple random technique was used. A sample size of 214 was drawn from a total population of 470 trainers. Data was collected by use of interview schedules and questionnaires from the respondents and analysed by use of simple linear regression and descriptive statistics eg frequencies, standard deviation, means using SPSS version 25. From the findings curriculum development explained 41.7 per cent variation on Performance of public Technical Vocational Education and Training Institutions From the regression coefficient β values for Curriculum development (0.433). In the qualitative analysis of interviews, the findings showed that the public TVET institutions adopted private public partnership in curriculum development to ensure performance of public TVET institutions. The study therefore concludes that PPPs in curriculum development positively and significantly affect performance of public TVET institutions. The study therefore recommends Therefore, there is need for strengthening private public partnership curriculum development to improve the performance of public TVET institutions.

Keywords: Curriculum Development, Partnership and Performance of TVET

Abstract: Effective quality assurance frameworks, such as ISO standards and national accreditation systems, promote accountability, align training outcomes with industry expectations, and foster trust in the qualifications awarded by TVET institutions. Technology does not merely complement instructional management but acts as a transformative driver of quality assurance, ensuring that TVET institutions produce graduates with competencies aligned to the rapidly changing industrial environment. Despite these aspirations, instructional management within many TVET institutions in Kenya remains traditional, with limited adoption of digital systems for monitoring teaching effectiveness, curriculum delivery, and learner assessment. Thus, the purpose of the study was to assess the effect of technology as a determinant of quality assurance implementation on instructional management in TVET institutions in North Rift Region, Kenya. The study employed mixed method research design ingrained with positivist and interpretivist philosophy. The target population comprised of 470 trainers of mechanical engineering departments, electrical engineering departments, building departments, management representatives from ISO 9001: 2015 accredited TVET institutions in the North-Rift region. Multistage sampling technique was used to select the respondents. Krejcie and Morgan (1970) table was used to calculate the sample size of 214 respondents which was proportionally allocated to the TVET institutions using Neyman Allocation formula. The primary data was collected using closed-ended questionnaires and semi-structured interview schedule. Data was analyzed using inferential statistics simple linear Regression analysis using SPSS version 25. From the findings coefficient of determination (R square) of 0.543 indicated that the model explained only 54.3 % of the variation or change in instructional management of TVET institutions. Technology (t =14.249, P<.05). The study findings indicate technology significantly affects instructional management of TVET institutions in North Rift Region, Kenya. It therefore implies that as much as TVET through technology streamlines the process of accessing scholarly support, saving trainers and trainees time and effort in locating relevant resources or seeking assistance from educators it must embrace the principles of quality assurance. This is because quality assurance in the use of technological applications in engineering lectures ensures that the tools in use are pedagogically sound.

Keywords: Technology, quality assurance, instructional management

Abstract: With increased pressure on India to achieve food security in the face of climate variability and scarce resources, the incorporation of Artificial Intelligence (AI) in conventional farm systems is a potential solution. This study investigates the use of AI-based crop monitoring systems for improving crop yields as well as reducing agricultural losses in Indian agriculture. By combining machine learning methods, IoT networks, and real-time data from field sensors, farmers are now able to recognize problems such as pests, nutrient gaps, or crop diseases much earlier than before. This allows them to respond with timely and precise interventions instead of relying on guesswork. For this study, insights were drawn from published case studies and secondary data, which show how these technologies are making agriculture more accurate, resource-efficient, and productive. Beyond operational efficiency, AI-driven crop monitoring offers long-term benefits for sustainable farming in India. For instance, precision detection of pests and diseases helps reduce unnecessary use of chemicals, which in turn protects soil health and lowers environmental damage. Another advantage is the real-time irrigation guidance that minimizes water wastage a crucial aspect for regions already facing shortages. When such tools are adapted to local farming practices and traditional knowledge, they create a useful blend of modern analytics and indigenous wisdom. This hybrid approach not only supports higher yields but also makes farming systems more resilient to climate variations and market uncertainties.

Keywords: Artificial Intelligence (AI) in Agriculture, Crop Monitoring Systems, Precision Farming, Climate Resilience, Sustainable Agriculture, Resource Efficiency.

Abstract: Despite the rising trend of artificial intelligence (AI) use in the global automotive industry, India's automotive sector has been slow to adopt AI-driven change. This paper looks at the main reasons for the lack of strong business cases for implementing AI in India's automotive ecosystem. In India's automotive industry, investing in new technology faces many hurdles. Cost-Sensitive markets, broken supply chains, weak digital systems, and a lack of skilled workers make it harder to adopt modern solutions. On top of that, traditional manufacturing practices still dominate, and there isn't much collaboration between tech startups and established manufactures. To move forward, the industry needs practical, localized solutions and stronger partnerships between the public and private sectors. There efforts can turn big ideas into real, useful applications and help bridge the gap between technology's promise and what happens on the ground. While global counterparts are using AI for predictive maintenance, autonomous driving, and smart manufacturing, Indian automotive players struggle to connect AI applications with cost-effectiveness, scalability, and practical use.

Keywords: Artificial Intelligence (AI), Automotive industry, Cost-sensitive markets, Supply chain challenges, Weak digital infrastructure, Traditional manufacturing practices.

Abstract: The aim of this study is to assess the response reduction factor (R) for raised Intze water tanks that have been seismically loaded based on their performance. When earthquakes strike, the collapse of essential lifeline buildings like elevated water tanks may have a devastating impact on communities and economies. This research utilises CSI SAP2000 software to model Intze tanks with capacity of 300 m3 and 600 m3, respectively, with staging heights of 14 m and 18 m. Various seismic zones (II-V) and medium soil types are taken into account in the nonlinear static pushover analysis, which takes into account both empty and full tank situations. Base shear, lateral displacement, and fundamental time period are some of the important performance metrics that are examined in the research. In addition, in order to evaluate the response reduction factor (R), structural performance parameters including overstrength, ductility, and redundancy are assessed. Insights for safer and more affordable seismic design of raised Intze water tanks are provided by the results, which emphasise the impact of staging height, tank capacity, and seismic zone on the structural reaction.

Keywords: Water tank, Pushover analysis, Earthquake, Staging.

Abstract: This paper focuses on how to AI Thinking, AI literacy, and AI literature. Currently, technology of the biggest impact on everywhere is AI and its related topics including ChatGPT. AI and ChatGPT is giving an influence on many areas, job pattern, workface, and so on. This paper describes on how much importance to educate AI, AI literacy, and AI literature because the nature of working and the range of activities by AI have been changing. As AI Thinking is that relevant to users of AI systems such as, choosing inputs, working with outputs, AI developers (design and implementation of AI technologies), AI related managers (determining the platforms and organizational practices AI for use), policymakers (government and organization), and data (data for the use of AI), it is quite important for students and higher education, professionals and the public people. Because AI Thinking has a multi-purpose meaning about various aspects of AI user and it has a wide range of the management, the production, the training, and use of AI systems, it is absolutely needed to educate systematically to understand and learn with literature (this paper call as AI literature). Without introducing literature, the education of AI Thinking and AI literacy cannot implement. Through education, AI Thinking should provide guidance training as well as self-learning in professional, and structure and guidance for interdisciplinary AI teams (management and collaboration, evaluation) for industrial practice. AI Thinking should be recognized as an element of AI literacy, at least for practice-based education and professional experts. It is also important to distinguish, learn, and educate AI Thinking focused on AI practice, methodological, and context from more general-purpose AI literacy and more extended AI literature.

Keywords: AI and ChatGPT, AI Education, AI Thinking, AI literacy, AI Literature.

Abstract: The field of ballistic protection is undergoing a significant transformation characterized by the integration of cutting-edge materials, adaptive technologies, and a focus on user-centered design. This report offers an in-depth examination of the scientific and engineering principles underlying the latest advancements in armor systems, transitioning from conventional, monolithic designs to innovative, hybrid, multi-layered, and "smart" configurations. Protection is now understood to extend beyond a material's capacity to stop a projectile; contemporary armor aims to address secondary effects, such as blunt force trauma, spalling, and thermal discomfort. The integration of next-generation nanomaterials, particularly carbon nanotubes (CNTs) and graphene, is fundamental to this evolution. These materials present groundbreaking mechanical properties, including unmatched tensile strength, outstanding flexibility, and extraordinary energy dissipation, while maintaining an ultralight profile. When incorporated into composites or combined with advanced materials, such as shear-thickening fluids and self-healing polymers, CNTs and graphene enable armor to dynamically adapt to various impact situations. These advancements signal a transition from traditional defense mechanisms to dynamic, smart systems. This report highlights the crucial role of ergonomics, wearability, and thermal regulation elements that are often overlooked in conventional armor design. The advancement of ballistic protection should focus on crafting flexible, adaptive systems that integrate seamlessly with the human form and the specific demands of the mission, rather than striving for unbreakable barriers. The findings highlight a significant transformation in understanding: armor has evolved from a simple, static barrier to a dynamic, multifunctional interface designed for enhanced survivability in the intricate threat landscapes of the 21st century.

Keywords: Bulletproof standard, STF, carbon nanotubes, graphene, Protection

Abstract: Phishing has emerged as one of the most persistent and evolving threats to cybersecurity, and its impact has grown even more severe with the widespread adoption of remote work. Employees working from home or hybrid environments often lack immediate IT assistance and rely heavily on digital platforms such as email, messaging apps, and cloud services. This makes them prime targets for attackers who exploit psychological triggers such as urgency, curiosity, and authority. Recent developments, including QR-code-based quishing and large language model (LLM) generated phishing emails, have enabled adversaries to create highly convincing messages that bypass traditional spam filters and deceive users at alarming rates. Studies shows that over 30% of participants in controlled experiments fall victim to these advanced phishing strategies, highlighting the limitations of conventional awareness programs and static filtering technologies. Although advanced artificial intelligence (AI) techniques, particularly hybrid models combining BERT and CNN, have achieved near 97.5% accuracy in classifying malicious content, these systems typically function in the background and do not provide real-time, user-facing assistance. This gap leaves individuals vulnerable at the precise moment when they must decide whether to trust or reject a suspicious message. Research in behavioral science has consistently demonstrated that contextual nudges, micro-prompts, and reinforcement mechanisms are more effective in shaping long-term secure practices compared to one-off training sessions or financial incentives. This paper proposes a multistage behavioral-AI framework designed specifically for remote workers to address this gap. The framework integrates four stages: Awareness Nudges, Micro-Actions, Reinforcement, and AI-Driven Contextual Alerts. Each stage complements the others-nudges capture attention, micro-actions promote critical reflection, reinforcement cultivates long-term secure habits, and contextual alerts provide AI-powered warnings at high-risk moments. Unlike prior approaches that address technical or behavioral dimensions in isolation, this framework merges them into a single, interactive model. By aligning advanced detection capabilities with human decision-making processes, the framework aims to reduce click-through rates, encourage proactive reporting, and foster a culture of security-conscious behavior among remote employees. In doing so, it provides not just a technical solution but also a sustainable strategy that adapts to evolving phishing threats while strengthening organizational resilience.

Keywords: Phishing Defense, AI-Guided Behavioral Nudges, Real-Time Security Alerts, QR-Code Phishing (Quishing), Reinforcement Mechanisms, Contextual Micro-Prompts, Hybrid BERT-CNN Detection Model.

Abstract: Rapid urbanization is resulting in the development of slum areas throughout the city and the slum dwellers are also becoming a part of the urban population. Though the urban population was blessed with all the modern health facilities, the accessibility and utilization of health services by the slum dwellers need to be studied. The current research study was conducted to assess the utilization of antenatal care (ANC) services by pregnant women living in urban slums. A community-based cross- sectional study was carried out among a total of 306 pregnant women selected purposively who were in the third trimester of their current pregnancy and habitat of slums of Sambalpur Municipality Corporation, Odisha, India. The study was undertaken from July 2024 to December 2024. The mean age of the participants was 27.10±4.06 years with 88.1% literacy rate and 75.5% were homemakers. About fifty percent of women experienced their 1st delivery. Throughout pregnancy, all the studied participants received antenatal checkups. However, about 41 percent of women underwent ANC less than 4 times. All the deliveries were conducted at the institution.

Abstract: Artificial Intelligence (AI) has increasingly been integrated into educational technologies, with particular attention to career guidance systems. This paper presents a comprehensive synthesis of six- teen existing research studies focused on AI-based career counselling and advisory platforms. Through detailed analysis, it was found that current approaches predominantly rely on classical machine learning models such as Decision Trees, Support Vector Machines, and Naive Bayes, alongside rule-based expert systems. A small subset of studies explored the use of neural networks, such as GRUs and attention mechanisms, yet deep learning remains underutilized.[1] Moreover, significant gaps were identified: lack of personalization, limited use of psychological profiling (example MBTI or RIASEC), insufficient integration with real-time labor market data, and poor accessibility in multilingual or mobile-first con- texts-especially in the Global South. Most systems are static, institution-specific, and rely on small datasets, limiting scalability and adaptability. Educational counseling is a pedagogical and social service that involves orienting students to find the most relevant academic or professional institutions according to their educational background and preferences. Its primary goal is to help students join the right path that aligns with their skills, where they can develop themselves and realize their full potential. It caters to students at all school levels, spanning from primary to higher education. Building on these findings, this research proposes a novel AI-powered career guidance framework that leverages deep learning, user modelling, and personality traits to deliver personalized, adaptive, and culturally contextual recommendations. This system aims to bridge the gap between education and employability by incorporating psychological, academic, and future-skill analytics into a unified, intelligent decision support platform.[6]

Keywords: keywords; Artificial Intelligence, Career Guidance, Deep Learning, Machine Learning, Psychometrics, Recommender Systems, BERT, NLP, User Modelling, MBTI, RIASEC, Labor Market Analytics, Adaptive Learning, Personalization, Decision Support Systems.

Abstract: The paper analyses the complex cultural and religious importance of the Lord Jagannath Cult, emphasising its origins, the persistence of its ancient traditions, including the distinctive Nabakalebara ceremony and its inclusive aspects. The Jagannath tradition exemplifies a confluence of tribal beliefs and mainstream Hinduism, with the Jagannath Temple in Puri as a pivotal sacred site. The temple's daily and periodic rituals, conducted by the Sevayats under the oversight of the King of Puri, involve treating the deities akin to living beings, receiving care and attention comparable to that afforded to humans, which is both intriguing and indicative of religious devotion. The tale of Kanchi Vijaya is emphasised as an important folk legend, utilised by students in Odia medium schools to foster children's awareness and instil faith in God-an endeavour that traces back to the initiatives of freedom fighter Gopabandhu Das during the colonial era. Particular emphasis is placed on Nabakalebara in relation to its folk beliefs, rites, and rituals, specifically the ritualistic renewal of wooden idols that occurs periodically. This practice is derived from ancient Indian texts and is understood by scholars to represent the transmigration of the soul-the philosophical concept that, akin to the human soul's transition between bodies, deities also assume new forms. The paper contends the importance of educating children about the necessity of documenting and disseminating our rich cultural heritage, enabling them to comprehend the depth of our culture, which encompasses science, literature, folk beliefs, and coexistence.

Keywords: Nabakalebara, Brahmadaru, Lord Jagannath, Puri, Odisha.

Abstract: This research provides a thorough evaluation of the advantages and disadvantages of using intermediate storeys vs void storeys in tall structures. The choice of design features has a major influence on the functioning and sustainability of tall buildings, which have become ubiquitous in contemporary urban landscapes. In this study, we compare and contrast two different design methodologies by exploring their structural contexts and aesthetic qualities. Contrarily, void floors provide open space inside the structure, which may serve a variety of functions including increasing natural ventilation and serving as leisure places. Our research takes a holistic look of tall structures with these design features, taking into account things like structural displacement, drift, and base shear. The purpose of this article is to provide guidance to professionals as they decide between an intermediate level and a void level for a certain project. The structure is modelled in ETABS, loads are applied, and the structural behavior is analyzed.

Keywords: Tall Structures, ETABS, Seismic Analysis, Void Storey.

Abstract: Gender bias is been done in case of women works when it is not considered for Gross Domestic Product of the country most of their household works are either not paid or they are not added up in GDP. This is current study wanted to know if women takes up some economic activity by joining hands in SHGs and earn some income their household income and gets empowered economically or not.

Keywords: Self Help Group, Empowerment, Athani, Women, Status, Financial Stability

Abstract: The Indian e-commerce industry has evolved rapidly, with Amazon and Flipkart emerging as the two leading players competing for dominance. This project focuses on a comparative study of their marketing strategies to evaluate how each company engages, attracts, and retains customers. Flipkart emphasizes localized campaigns, cash-on-delivery options, regional language promotions, and mega events like the Big Billion Days, making it more relatable to Indian consumers. Amazon, on the other hand, leverages its global expertise, advanced technology, and services like Amazon Prime to build strong customer loyalty and trust. The study is based on both primary data, collected through a survey of 100 respondents, and secondary data from journals, reports, and online sources. Findings reveal that discounts, delivery speed, seasonal sales, and customer service are the most influential factors shaping consumer decisions. While Amazon is perceived as more trustworthy and globally reliable, Flipkart is favored by price-sensitive and regional customers. The research highlights that both platforms have unique competitive advantages, and their strategies collectively shape India's fast-growing digital retail ecosystem.

Abstract: This manuscript reports an extended laboratory-scale evaluation of homogeneous Fenton oxidation (Fe²⁺/H₂O₂) applied to a representative composite sample of chemical industry wastewater from an industrial estate. The work was carried out under optimized laboratory conditions (pH ≈ 3.0; Fe²⁺ = 10 mg/L; H₂O₂ = 100 mg/L) in batch mode. The Fenton process delivered substantial reductions in organic load (BOD: 240 → 28 mg/L; COD: 510 → 80 mg/L), turbidity, and certain inorganic constituents, while neutralization and filtration removed iron-rich precipitates. Photographs of untreated and treated samples and the sampling location are included, together with comparative graphs for key parameters.

Keywords: Fenton process; advanced oxidation; industrial wastewater treatment; hydroxyl radicals; BIS standards; sludge management.

Abstract: Groundwater is a critical resource in semi-arid regions of India, particularly in Karnataka where rural communities rely on borewells. The present study assesses groundwater quality in selected villages of Shahpur Taluk, Yadgir District, Karnataka. Eight source were selected, samples were selected borewells and hand pumps across Gogi-K, Gogi-P, Hoskera, and Hattigudur. Physico-chemical parameters including pH, Temperature, Total Hardness (TH), Calcium, Magnesium, Chloride, Fluoride, Nitrate, and Dissolved Oxygen were analyzed. Results indicated that while pH remained within BIS/WHO limits, hardness, calcium, magnesium, fluoride, and nitrate often exceeded permissible standards. Water Quality Index (WQI) shows that most sources as unfit for consumption to very poor, rendering them unsuitable for domestic use without treatment.

Keywords: Groundwater quality, WQI, Physico-chemical characteristics

Abstract: The construction industry's growing emphasis on sustainable practices and cost-effective materials has led to increased research into supplementary cementitious materials. This study investigates the performance characteristics of M35 grade concrete incorporating fly ash as a partial replacement for ordinary Portland cement. The research aims to evaluate the mechanical properties, durability aspects, and economic viability of fly ash modified concrete mixtures while addressing environmental concerns associated with cement production and fly ash disposal. Methodology The experimental program involved the systematic replacement of cement with Class F fly ash at varying percentages (10%, 15%, 20%, 25%, and 30% by weight). Standard concrete mix design procedures were followed to achieve the target characteristic compressive strength of 35 N/mm² at 28 days. Fresh concrete properties including workability, setting time, and bleeding characteristics were evaluated alongside hardened concrete properties such as compressive strength, split tensile strength, flexural strength, and elastic modulus. Long-term durability parameters including permeability, chloride resistance, and carbonation depth were assessed to determine the service life implications of fly ash incorporation. Key Findings: The incorporation of fly ash in M35 concrete improved strength, durability, and economy. Optimum replacement at 15-20% enhanced compressive strength beyond 28 days, with 90-day gains of 8-12% over control mixes. Pozzolanic action refined pore structure, lowering permeability and increasing chloride resistance. Workability improved with reduced bleeding, though setting time slightly increased at higher replacements. Durability tests showed 25-30% lower chloride diffusion coefficients in fly ash mixes, ensuring better performance in aggressive environments. Reduced heat of hydration minimized thermal cracking risks in mass concreting. Economic evaluation indicated 8-15% cost savings, making fly ash concrete both sustainable and cost-effective. Conclusions and Implications: The study confirms that 15-20% fly ash replacement in M35 concrete optimizes performance and sustainability. Long-term strength, durability, and reduced environmental impact make it a viable option for structural use. Improved durability and cost-effectiveness are especially beneficial for infrastructure projects. Standardizing fly ash quality and mix design guidelines is recommended for broader adoption. The research highlights the role of industrial by-products in sustainable construction. Fly ash concrete offers lower permeability, reduced heat of hydration, and economic advantages. Future studies should focus on high-volume fly ash systems and blending with other SCMs to maximize performance. This supports sustainable concrete technology advancement.

Keywords: Fly ash, M35 concrete, supplementary cementitious materials, partial cement replacement, pozzolanic reaction, strength, durability, workability, permeability, sustainable concrete, mechanical properties, carbonation, thermal cracking, cost-effectiveness, sustainability, construction, infrastructure.

Abstract: This study investigated the enhancement of peach shell lightweight concrete through glass and nylon fiber reinforcement at varying percentages (2%, 4%, 6%, and 8% by cement weight). The fiber addition resulted in a 6.6% reduction in density while significantly improving mechanical properties, with compressive strength increasing by 10.20%, split tensile strength by 60.1%, and flexural strength by 63.49%. Optimal compressive strength was achieved at 6% fiber content, while maximum split tensile and flexural strengths occurred at 8% fiber addition. Research on concrete curing methods demonstrates that steam curing at temperatures between 45°C and 80°C within 24-hour cycles produces superior concrete properties compared to normal curing, particularly for achieving high early strength in precast applications using PC42.5 cement. However, temperatures exceeding 80°C negatively impact performance. The combination of agricultural waste utilization through peach shells and synthetic fiber reinforcement, coupled with optimized curing regimes, presents a promising approach for developing lightweight concrete with enhanced mechanical characteristics suitable for diverse construction applications while promoting sustainable materials usage.

Keywords: glass fibre reinforced concrete, steam curing and normal curing.

Abstract: Cardiovascular disease (CVD) continues to be a leading cause of death worldwide. Early detection is critical for timely intervention and reducing mortality. Although vast medical data is generated daily, effective utilization of this data for accurate prediction remains a challenge. This study applies data mining techniques to multiple publicly available datasets, including the Cleveland Heart Disease dataset, Framingham dataset, and UCI Heart Disease dataset, to develop predictive models for heart disease detection. Using the Knowledge Discovery in Databases (KDD) methodology, three classifiers-J48 Decision Tree, Naïve Bayes, and Artificial Neural Network (ANN)-were trained and evaluated. Results indicate high classification accuracy across all datasets, with J48 achieving the highest average performance (accuracy ~94.8%). The study demonstrates that data mining can efficiently predict heart disease and offers decision support tools for clinicians to enhance diagnostic consistency.

Keywords: KDD, Data Mining, Heart Disease, Decision Tree, Neural Network, Naïve Bayes, Cleveland Heart Disease Dataset, Framingham Dataset.

Abstract: Phishing is one of the most widespread and damaging cyber threats, exploiting human psychology and technological weaknesses to steal sensitive information. This project focuses on detecting and preventing phishing attacks by classifying various tactics used by attackers, such as fake websites, fraudulent login portals, and social engineering techniques. A machine learning-based detection engine is developed, supported by feature extraction from URLs, email headers, and web content. Different models including Random Forest, SVM, and Decision Trees are evaluated using accuracy, precision, recall, and F1-score metrics. The proposed system integrates a real-time browser extension and dashboard for monitoring phishing attempts, while also emphasizing user training and awareness. Experimental results demonstrate the effectiveness of combining technical detection methods with behaveoral education, thereby enhancing user protection. This research highlights the importance of adaptive, multi-layered approaches for combating phishing and contributes to building more resilient cybersecurity frameworks.

Abstract: This study investigates the critical, yet often underexplored, challenges faced by Tribal Community Entrepreneurs in engaging directly in Export Entrepreneurship. Despite a clear inclination, a significant impediment is the Lack of Knowledge and perceived Confidence among these entrepreneurs regarding direct export processes. The primary objective of this research is to develop insights that can Guide and Initiate Tribal Entrepreneurs towards independent export ventures. Employing a questionnaire-based methodology, data was collected to understand the perceptions and experiences of tribal entrepreneurs. Key findings reveal a strong desire among tribal individuals to participate in international trade independently, yet this ambition is significantly hampered by fear and a profound lack of knowledge concerning the intricate details of the exporting process. Specifically, substantial knowledge gaps were identified regarding payment methods and financial commitments associated with export activities. The study concludes by emphasizing the urgent need for structured awareness programs and targeted initiatives to disseminate comprehensive knowledge about export procedures, particularly financial intricacies, thereby empowering tribal communities to actively and confidently participate in the global market.

Keywords: Tribal Entrepreneurship, export Entrepreneurship, Tribal Communities, Export Challenges, Knowledge Gaps, Financial Literacy, Community Development.

Abstract: Artificial intelligence (AI) has emerged as a game changer in modern healthcare, providing novel answers to long-standing issues in medical diagnosis, treatment, and patient care. AI systems can analyze massive volumes of clinical data, detect complicated patterns, and aid in evidence-based decision-making by employing advanced machine learning algorithms, natural language processing, and computer vision. AI applications include early disease detection, medical imaging analysis, customized medicine, drug development, hospital workflow optimization, and virtual health assistants. These developments not only increase the precision and efficiency of healthcare delivery, but they also lower costs and improve patient outcomes. Despite its enormous promise, the use of AI in healthcare raises concerns about data privacy, ethical considerations, algorithmic bias, and the need for regulatory frameworks. This article discusses the role of AI in healthcare, including its primary applications, benefits, limitations, and future potential for developing intelligent, patient-centric health systems. Artificial intelligence (AI) has emerged as one of the most significant technical developments affecting the healthcare industry, transforming how medical services are supplied and administered. Unlike traditional systems, which rely primarily on manual interpretation of clinical data, AI provides healthcare practitioners with sophisticated capabilities for processing large-scale information in real time, uncovering hidden relationships and generating actionable insights. AI systems can detect subtle anomalies in medical images, predict disease risks based on genetic and lifestyle factors, and assist in complex diagnostic decisions that were previously limited to specialized expertise.

Abstract: The rapid growth of e-commerce, mobile wallets, and digital banking services has changed the way people and businesses conduct financial transactions. While these advancements have increased convenience and accessibility, they have also created significant security risks, such as identity theft, phishing assaults, account takeovers, and large-scale financial crime. Traditional rule-based security systems, while helpful in some cases, are becoming ineffective in fighting fraudsters' developing strategies. In this context, Artificial Intelligence (AI) has emerged as a powerful tool for improving online payment security by enabling intelligent, adaptive, and real-time fraud detection. AI-powered security models can scan massive volumes of transactional data, user activity, and contextual information to uncover tiny irregularities that traditional detection approaches often miss. Neural networks, random forests, and support vector machines are common machine learning methods used to create predictive models that can accurately distinguish between legal and fraudulent transactions. Unsupervised learning techniques are also used to find hidden patterns and detect previously unknown dangers, while Natural Language Processing (NLP) can help identify phishing emails, phony websites, and suspicious conversations. The combination of AI and real-time monitoring enables preemptive responses by stopping questionable transactions before they are conducted, reducing financial loss and increasing customer trust. Furthermore, AI systems are constantly learning and adapting to new attack vectors, making them resilient to developing attacks. Future improvements, like explainable AI, federated learning for privacy-preserving fraud detection, and blockchain integration, are projected to improve the security and transparency of online payment systems. This paper underlines the expanding relevance of Artificial Intelligence in protecting online payments, assesses the efficacy of current AI-based solutions, and underscores the importance of continual innovation in developing strong, scalable, and trustworthy financial systems.

Abstract: The rapid growth of urban population, the lack of spaces in the cities and the high cost of land have already forced the developers to focus on the high-rise buildings. As the height of the building increases, the lateral load resistant system becomes more important than gravity load resistant system. That's why it is important to define lateral load resistant system in high rise buildings. So, the lateral load resistant systems such as shear wall and diagrid are introduced since they are better in terms of cost, aesthetic and performance. However, the diagrid structural system has become more popular these days due to its efficiency and aesthetic look provided by the unique geometric configurations of the system. In this study, a comparative analysis has been done on the buildings with different lateral load resisting systems. Five different building model of G+9 story building has been modeled with shear wall and diagrid structure to compare their performance. The design is analysed for seismic zone V and medium soil condition as per IS 1893:2016 using ETABS software. The building is kept the same except for the lateral load resisting system. From the patterns of the results, it was concluded that building models with the combination of shear wall and diagrid module has better performance in term of the maximum story displacement, story stiffness, story drift, base shear, and time period.

Keywords: Shear Wall, ETABS, Seismic Analysis, Composite Buildings.

Abstract: Reinforced concrete is the material of choice for the majority of building constructions. This choice is based on a number of factors, including the accessibility of the necessary components, the expertise of the builders, and the practicality of the design rules. Due to its dangerous formwork and high dead load, R.C.C. is no longer cost-effective. On the other hand, composite construction is an innovative idea in the building sector. It may be economically prohibitive to wait the building of each level while concrete columns are cast due to the usage of new composite techniques that enable the assembly of multi-story structural frames to continue at speed. Composite beam-columns have been widely employed for building in Japan due to their excellent earthquake resistant qualities, which have been known for a long time. In order to promote the adoption of this effective mixed-use building method, it was vital to establish seismic design requirements for commonly used structural systems in India. This project presents a comparison of several construction characteristics. This research examines the use of bracings and infill walls to mitigate the effects of seismic loading. With the use of RCC and composite columns, ETABS was used to study a G+15 residential multi-story building's response spectrum, equivalent static technique, and push-over analysis. A total of twenty-four models are tested in a range of seismic zones and soil types. Values for variables such as base shear, time period, frequency, storey bending, and storey torsion are among the outcomes that are evaluated for these twenty-four models.

Keywords: Response spectrum analysis, ETABS, RCC column, Composite column, storey drift, storey shear

Abstract: Recruitment is one of the most critical processes in human resource management, yet manual resume screening is time-consuming, costly, and prone to bias. Automated resume screening powered by machine learning offers a scalable solution to filter resumes efficiently and fairly. This study explores the development of an intelligent resume screening system using natural language processing (NLP) and supervised learning algorithms to classify resumes into relevant job categories. Data preprocessing techniques, such as tokenization, stopword removal, and TF-IDF vectorization, were employed to extract meaningful features from textual resumes. Models including Logistic Regression, Random Forest, and Support Vector Machines (SVM) were trained and evaluated on labelled datasets. The results demonstrated that machine learning-based screening achieved an accuracy of over 85% in categorizing resumes for IT roles (Software Developer, Data Analyst, Data Engineer). This research highlights the potential of machine learning to reduce HR workload, improve candidate-job matching, and minimize human bias, while also addressing ethical concerns such as fairness and transparency in AI-driven recruitment.

Abstract: This study suggests a thorough deep learning framework for identifying yoga poses and providing in-the-moment instructions. Using a standard RGB webcam, a custom dataset comprising six commonly performed asanas-Bhujangasana, Padmasana, Shavasana, Tadasana, Trikonasana, and Vrikshasana-was captured indoors Using a hybrid CNN-LSTM architecture and Open Pose-based pose estimation, the method models temporalcontinuity and spatial key point configurations in brief sequences. Configurations andtemporal continuity in short sequences. Three tests Three settings are temporal voting on45-frame windows (~15 s), frame-wise classification, and real-time webcam inference withan invisible participant. In real-time evaluations, the system attains 9892% accuracy,9938% accuracy at the windowed vote level, and 9904% accuracy at the frame level. (i) asuccinct yet effective spatiotemporal model for yoga recognition; (ii) a reproduciblepipeline made entirely of RGB inputs; (iii) the elimination of temporal pooling and thresholding strategies; and (iv) a publicly available data set complete with evaluation protocolsThe proposed framework offers a workable way to incorporate posture-awareness features into in-home coaching programs, rehabilitation settings, and consumer fitness applications.

Keywords: Human activity recognition, Yoga, Open Pose, CNN-LSTM, Spatiotemporal modeling, Real-time systems.

Abstract: Neurodevelopmental disorders (NDDs) usually develop during early childhood and affect a person's ability to think, feel, and interact with others. Common conditions associated with NDDs include ASD, ADHD, as well as other issues like intellectual disabilities, learning challenges, and cerebral palsy. This study introduces a machine learning method to classify different types of NDDs into multiple categories using both traditional and deep learning models. A dataset containing more than 6,000 entries was created, including information about gender, age, and clinical symptoms. The data was cleaned and prepared using label encoding and feature scaling methods. Two models were built and tested: a Support Vector Classifier (SVC) with a linear kernel and a DNN that uses a fully connected structure based on CNNs. The SVC model achieved an accuracy of 84%, while the deep learning model performed better, with an accuracy of 88%. This was verified using ROC curves and AUC analysis. These findings demonstrate that deep learning is effective in identifying complex patterns in data and may help enhance diagnostic processes for individuals with neurodevelopmental disorders.

Keywords: svc, linear svc, machine learning, mental health, neurodevelopmental, CNN.

Abstract: As per the rapid growth of IoT devices and smart city services, there are new demands regarding data management systems. These autonomous systems need better infrastructure to handle real-time data. As per current technology standards, 5G wireless provides very fast speed and can connect one million devices in one square kilometer area. Regarding smart cities, this technology offers major changes due to its quick response time and high data capacity. This study checks how 5G affects city data systems as per a mixed approach using literature review, performance tests, network simulations, and three case studies. The research covers three international cities regarding 5G implementation - Seoul, Singapore, and Barcelona. Basically, 5G reduces delay by over 90% to 1-5 ms and increases speed by 100 times to 10+ Gbps, which is the same as enabling real-time data processing and continuous high-volume sensor data transfer. The mmWave technology in 5G networks surely provides high speeds, but it also causes high path loss. Moreover, this path loss happens because mmWave signals weaken quickly when they travel through the air. Basically, radio frequency propagation models help determine signal strength, coverage area, and outage probability by providing the same essential information about signal path loss and fading through theoretical or practical methods. A new method actually combines edge computing with federated learning to spread out data work in smart cities. This approach definitely helps process information locally instead of sending everything to one central place. Technology surely helps cities use data for better governance, but strong cybersecurity systems are needed to protect information. Moreover, city planners must design these systems keeping citizens at the center to ensure everyone gets equal access and prevent data theft. As per this analysis, technology can change how cities manage data, but there are problems regarding scalability, security, and system integration that need more research.

Keywords: 5G, Edge computing, IoT, MIMO (Multiple-Input Multiple-Output), Millimeter wave (mmWave) transmission, Ultra-Reliable Low Latency Communication (URLLC), Enhanced Mobile Broadband (eMBB), Massive Machine Type Communication (mMTC).

Abstract: Gender and Racial discrimination are key issues of the Indian situation. The crux of the article is to see to even though laws to mandate equal and fair treatment. Yet we find evidence of discrimination at work. It can be due to both "sticky floor" and glass ceiling. There is evidence of discrimination across gender and racial classes. In order to see that discriminated groups achieve justice , social policy decisions are needed. Economic empowerment and equal opportunities are two policy decisions required in this scenario.

Keywords: Discriminate, Decomposition, Wage gap, Gender

Abstract: This study looks at the growing issue of cyberbullying and the possibilities of AI-based technologies as a preventative approach. The study addresses the problem of traditional detection methods failing to keep up with the severity of bullying and the quick changes in online communication. How effectively can AI-based solutions detect and prevent cyberbullying on various social media sites is the primary study question. The research will employ a mixed-methods approach, combining a quantitative study of a simulated dataset with a comprehensive literature evaluation of existing AI content moderation solutions. The study will also include a qualitative component, like a case study, to evaluate the effectiveness and user experience of a specific AI-based application. Significant findings should demonstrate that while AI can significantly improve detection speed and accuracy, managing the nuances and context of online communication requires a hybrid approach that combines AI and human monitoring. The significance of this research lies in its ability to direct the development of more useful and effective technologies, which will eventually lead to safer online environments and less psychological harm from cyberbullying.

Abstract: Traditional blood group detection methods based on serological testing are invasive, use a lot of resources, and take too much time. Recent research shows that deep learning and biometrics can offer a non-invasive option by analysing fingerprints and blood smear images. MobileNetV2 and other CNN architectures have been used before, but we still need better and more accurate methods. This paper presents a MobileNetV4-based framework for predicting blood groups. The system uses fingerprint datasets with improved preprocessing methods, including normalization, augmentation, and noise reduction. MobileNetV4 is fine-tuned with transfer learning to classify blood groups into eight categories: A+, A−, B+, B−, AB+, AB−, O+, O−. The results show better accuracy, a smaller model size, and faster inference times compared to MobileNetV2, ResNet50, and DenseNet121. This makes it suitable for real-time mobile and edge deployment in healthcare. This research helps develop non-invasive, fast, and scalable diagnostic methods for detecting blood groups.

Keywords: Blood Group Prediction, MobileNetV4, Deep Learning in Healthcare, Non-Invasive Diagnostics, Fingerprint Recognition,CNN ,image Classification,Neural Network Optimization,Lightweight CNN Models Medical Image Analysis, Model Accuracy & Precision, Digital Health,Predictive Analytics,Personalized Medicine Point-of-Care Testing,Biomedical Signal Processing,Clinical Decision Support,Healthcare Informatics,Patient Monitoring,Preventive Diagnostics,Smart Healthcare Systems.

Abstract: Genetically modified (GM) crops are one of the most important intersections of science, technology, and law in contemporary agriculture. Their development has transformed food production by increasing yields, pest resistance, and climate resilience. The rapid expansion of biotechnology, notably the introduction of GM crops, has altered agricultural methods, food production, and global trade. These advancements, however, raise significant issues about the intersection of science, technology, and law. This study investigates the relationship between scientific innovation and legal regulation of GM crops, focusing on India and offering comparative viewpoints from other jurisdictions. It demonstrates how technical advancements frequently surpass legal frameworks, posing issues in biosafety, intellectual property rights and environmental sustainability. The study emphasizes the necessity for unified policies that strike a balance between innovation and safety by examining national and international legal approaches. Ultimately, it contends that effective regulation of genetically modified crops necessitates not only scientific rigour and technological innovation, but also strong legislative systems that address ethical, environmental, and socioeconomic problems.

Keywords: Genetically Modified (GM) Crops, Biotechnology Regulation, Science and Technology, Intellectual Property Rights.

Abstract: With the rapid growth of e-commerce and online financial services, digital payments have become an integral part of daily transactions. However, this convenience also increases the risk of fraudulent activities such as identity theft, phishing, fake transactions, and unauthorized access. Fraud detection in digital payments is therefore a critical challenge to ensure secure and trustworthy financial systems. This research/project focuses on developing intelligent fraud detection mechanisms using advanced techniques like machine learning, deep learning, and data mining. By analyzing transaction patterns, user behavior, and anomaly detection, the proposed system can identify suspicious activities in real-time. Various supervised and unsupervised learning algorithms are applied to classify transactions as genuine or fraudulent. Additionally, feature engineering and model optimization techniques are employed to improve accuracy and reduce false positives. The outcome of this study aims to provide a reliable fraud detection framework that enhances the security of digital payments, reduces financial losses, and builds customer trust in online transactions. The system can be integrated into banking applications, e-wallets, and other financial platforms for real-world implementation.