VOLUME 12, ISSUE 6, JUNE 2025

Abstract: Blending natural fibers has emerged as a pivotal strategy in advancing sustainable textile production, aiming to enhance fabric performance while mitigating environmental impacts. The purpose of this study was to characterize the blended Buri and Jute fibers as Textile Material. Experimental method of research was applied for the characterization while the Developmental method was employed in the weaving of the fibers. A sensory evaluation sheet was used to determine the acceptability of the sensory attributes. The plant materials used in this study are mainly the petioles of Buri palm and the stem of Jute plant.The statistical tool used was the mean which was used to determine the level of acceptability of Textile from Blended Buri and Jute Fibers as Textile material. There were twenty-five experts who evaluated the product composed of dress shop owner, and fashion designers. The product had undergone a series of testing as to its qualities. It was tested that Blended Buri and Jute Fibers has its breaking elongation was significantly greater elasticity compared to existing blended textile fibers. As to the result of its burning test, the textile from blended buri and jute fibers was like a blended purple nutsedge (Cyperus rotundus) and lady's finger textile (albelmoschus esculentus) wherein it burns but does not melt. It shrinks from the flame. It has the odor of charred meat. The residue was black, hollow irregular bead that resembles a gritty black powder, and it was verbally interpreted as highly combustible. In the water absorption test, the result implies that the textile from a blended buri and jute fibers resulted from 44 percent of absorbency, it was like a fabric blend of 65% polyester and 35% of cotton absorbed the 12.5% amount of water. It was verbally interpreted as least absorbent. The textile from a blended buri and jute fibers in terms of washability test, result showed that there was ten (10) minutes duration of soaking on the three trials and had the same reaction to detergent wherein the textile became slightly dark and intact. The textile from blended buri and jute fibers was "Very Acceptable", considering its hand feel, rib irregularity, color shade, and plug presence. As to the evaluators comments, feedback, and suggestions, the product was unique, aesthetically pleasing and has desirable tactile qualities for consumers. Furthermore, the local government must support locally made products.

Keywords: Buri fiber, Jute fiber, blended textile, sustainable textile

Abstract: This research tested the viability of onion and garlic peels as raw materials for organic novelty paper production. The study aimed to determine the sensory characteristics in terms of texture, compactness, color shade, thickness, and finish product. The acceptability of the paper is utilized for paper bags, greeting cards, and lampshades. A developmental-experimental research approach was applied, with three treatments in varying proportions of onion and garlic peels. Treatment C (60g onion, 40g garlic) always recorded the highest grading scores, which means that a mixture and ratio of the peels determine the product's quality to a great extent. The participants were 50 individuals. Forty (40) were from Capiz State University-Main Campus: 10 instructors and 30 students. On the other hand, the remaining 10 evaluators were art and design professionals not based in Capiz State University. The participants have evaluated the products employing a Five-Point Likert Scale. Even if Treatment C was the most popular, analysis of variance (ANOVA) indicated that variations in texture between the treatments were not statistically significant (F=2.301, p=0.104). This implies that even if variations in texture exist, they are not significant enough to warrant them. Evaluators also suggested making the paper size of bags larger and investigating its application in the manufacturing of gift boxes because of its quality and resilience. Generally, the research concludes that onion and garlic peels can be utilized to produce innovative, sustainable and marketable paper products. These results affirm using agricultural waste to produce alternative paper materials, encouraging environmental sustainability and creativity in ecologically conscious product design.

Keywords: Development, Utilization, Novelty Paper, Sensory Characteristics, & Acceptability

Abstract: This research explored the possibility of oyster shell powder mixed with mica pearl powder, and beeswax. The study aimed to determine the physical properties of the Pearl Crayons in terms of visual appearance, firmness, vibrancy, texture, finished product and its fusibility, acceptability when applied to coupon bond, vellum board, and illustration board as well as the significant difference in three (3) treatments: Treatment A (5g oyster shell powder, 5g mica pearl powder, and 20g beeswax), Treatment B (10g oyster shell powder, 5g mica pearl powder, and 20g beeswax), and Treatment C (15g oyster shell powder, 5g mica pearl powder, and 20g beeswax). The instrument used in gathering the data is a sensory evaluation score sheet. The acceptability of the crayons will be evaluated among a limited sample of fifty (50) participants, likely to include thirty (30) learners, ten (10) teachers, and ten (10) artists. The data was tabulated and statistically analyzed by SPSS software using the Arithmetic Mean and the Analysis of Variance (ANOVA). The physical properties of pearl crayons showed that statistically treatment A (5g oyster shell powder, 5g mica pearl powder, and 20g beeswax) got the highest mean. The fusibility of pearl crayons, statistically treatment A (5g oyster shell powder, 5g mica pearl powder, and 20g beeswax) got the highest mean. The level of acceptability of pearl crayons statistically showed that Treatment A (5g oyster shell powder, 5g mica pearl powder, and 20g beeswax) got the highest mean. Additionally, the level of acceptability results of pearl crayons when applied to coupon bond, vellum board, and illustration board, statistically showed that Treatment A (5g oyster shell powder, 5g mica pearl powder, and 20g beeswax) got the highest mean. The significant differences found in the pearl crayons sensory qualities were visual appearance, vibrancy, texture, and finished product. But, for firmness it clearly showed no significant difference between treatments. Further research could look at different changes or treatments to improve their visual appearance, firmness, and texture. Educational institutions and art organizations should incorporate information about pearl crayons into their curricula and workshops, raising awareness about their environmental benefits and artistic potential. This fosters greater adoption of sustainable, non-toxic, and eco-friendly crayons practices among emerging and established artists alike.

Keywords: Formulation, Applicability, Acceptability, and Pearl crayons

Abstract: The Google Cloud Platform requires customers who want cloud security to fully understand the shared responsibility model, as their security obligations differ from the provider's duties. Through an in-depth analysis, the paper presents organizations with a practical approach to achieve data security along with compliance standards in Google Cloud. Google Cloud secures its underlying infrastructure, yet customers accept full accountability to protect their data applications along with configurations within the cloud system. This paper explains the security obligations between Google Cloud and its users by providing practical security implementation guidelines for robust safeguards. This document examines complete cloud security management through its key components of data protection, alongside access management and compliance guidelines, together with incident response systems. Organizations achieve risk reduction and improve security standing while meeting regulatory standards in Google Cloud through the proper use of the shared responsibility model.

Keywords: Shared Responsibility Model, Google Cloud Security, Cloud Compliance and Governance, Identity and Access Management (IAM), Data Protection in Cloud Computing, Incident Response in GCP.

Abstract: The demand for clean, renewable, and uninterrupted energy is rapidly increasing across the globe. This project presents a hybrid energy generation system designed for highway environments, utilizing both vertical axis wind turbines (VAWT) and solar panels to harvest energy from natural resources and vehicular movement. The wind energy is captured through the VAWT placed along the divider where air disturbance from passing vehicles is prominent, while solar panels capture sunlight during the day. The collected energy is stored in a battery and utilized for various applications such as automatic street lighting, electric vehicle charging, and wireless mobile charging. This dual-source system enhances power reliability and reduces dependency on non-renewable sources. The overall aim is to develop an efficient, eco-friendly energy system that supports smart highway infrastructure and aligns with sustainable development goals.

Keywords: Hybrid energy, vertical axis wind turbine (VAWT), solar power, smart street lighting, EV charging, wireless charging, renewable energy, highway infrastructure.

Abstract: The rising demand for durable and high-performance pavements has led to the exploration of polymer-modified bitumen to overcome conventional asphalt's limitations. This study investigates the effects of incorporating different polymers-including Ethylene-Vinyl Acetate (EVA), Low-Density Polyethylene (LDPE), High-Density Polyethylene (HDPE), and Ethylene-Propylene-Diene Monomer (EPDM)-on the physical and mechanical properties of bituminous mixes. The primary objective is to enhance asphalt performance under varying environmental and loading conditions, addressing issues such as rutting, thermal cracking, and premature aging. The research follows a systematic experimental approach involving standard tests such as the Marshall Stability Test, Los Angeles Abrasion Test, and Aggregate Impact Test. Bituminous mixtures were prepared with varying bitumen contents, and their behaviour was analysed in terms of stability, flow, density, voids, and binder content. Results indicate that polymer modification significantly improves the structural integrity and fatigue resistance of asphalt mixes, making them more suitable for modern road traffic demands. This study concludes that adding selected polymers at 5%, 10%, 15%, 20%, and 25% enhances the performance of asphalt pavements and contributes to sustainable road construction by enabling waste polymers. The findings support the adoption of polymer-modified bitumen in infrastructure development for improved longevity and cost-efficiency.

Keywords: Polymer Modified Bitumen; Asphalt performance Marshall Stability Test.

Abstract: The COVID-19 pandemic highlighted deep vulnerabilities even in planned urban spaces like Gomti Nagar, Lucknow. This research explores a hybrid model combining traditional urban planning with smart city strategies to create a resilient and sustainable future. Using a mixed-method approach-including field surveys, GIS mapping, infrastructure audits, and literature reviews-the study identifies key gaps in healthcare accessibility, mobility, digital governance, and community infrastructure. Comparative case studies from Bhubaneswar and Singapore provide insights into practical resilience models. The framework proposes localized 15-minute city strategies, digital twin tools, and adaptive zoning to ensure inclusive and responsive urban systems. Policy recommendations focus on decentralization, smart integration, and community participation. The findings are scalable and relevant for other emerging Indian townships. This work bridges theoretical frameworks and field realities, offering a replicable model for post-pandemic urban resilience planning. The COVID-19 pandemic exposed the fragility of even well-planned urban areas. Gomti Nagar in Lucknow, despite its planned layout and infrastructure, experienced significant case loads during the 2020-2021 waves. This study analyzes the shortcomings and proposes a hybrid model combining traditional planning principles with smart, localized resilience strategies. Global case studies, demographic data, infrastructure gaps, and GIS-based design solutions were used to frame a resilience implementation model. The methodology included field survey, mapping of healthcare and accessibility, analysis of public infrastructure adaptability, and integration of successful global models into local policy recommendations.

Keywords: Post-pandemic resilience, Gomti Nagar, 15-minute city, Smart City strategies, Urban Planning, Digital Twin, GIS Mapping

Abstract: The COVID-19 pandemic brought global disruptions across all sectors, with sports and physical activity among the hardest hit. This study explores the psychological and physical impact of the pandemic on athletes, emphasizing the mental health challenges, closure of sports facilities, and the resultant decline in physical activity levels. The abrupt suspension of sporting events, closure of training centers, and enforcement of lockdowns led to heightened psychological distress among athletes, including anxiety, depression, demotivation, and identity crises. Many athletes lost access to structured routines, coaching support, and competition, which are essential to both performance and mental stability. The closure of sports facilities disrupted regular training, causing a decline in performance readiness and contributing to emotional burnout. Simultaneously, there was a global decrease in physical activity levels among the general population and amateur athletes due to limited access to recreational spaces and fitness infrastructure. This inactivity increased the risk of lifestyle diseases and mental health disorders. The pandemic highlighted the urgent need for integrating mental health support within athletic programs and adopting digital solutions for fitness continuity. The study concludes by recommending policy reforms, hybrid training models, and enhanced psychological services to mitigate the long-term impact on athletes and the broader community. The findings underscore the importance of resilience planning and mental health prioritization in sports policy post-COVID-19.

Keywords: COVID-19, athletes, mental health, sports facility closure, physical inactivity, depression, anxiety, psychological support, pandemic impact, training disruption

Abstract: The application of Wireless Sensor Networks (WSNs) in environmental monitoring, military surveillance, and industrial automation have led to broad adoption of these networks. For the proper functioning of the network, node localization is critical, which poses a challenge in harsh NLOS and noisy environments. Cooperative localization improves the reliability of position estimation using shared data between nodes due to its reliance on inter-node communication. Incorporating localization uncertainty, probabilistic models, and Gaussian Mixture Models (GMM), offers a powerful solution. This review aims to merge the main contributions in the range-based, range-free, and hybrid localization approaches, mainly focusing on probabilistic models which provide robust precision and scaling efficiency in WSNs.

Keywords: Wireless Sensor Networks (WSNs), Node Localization, Cooperative Localization, Non-Line-of-Sight (NLOS), Probabilistic Models, Gaussian Mixture Models (GMM), Range-Based Localization, Range-Free Localization, Hybrid Localization

Abstract: This work provides one theoretical basis for cooperative localization in Wireless Sensor Networks (WSNs) with Gaussian Mixture Modelling (GMM). The work attempts to solve the problem of sensor node positions determination in unknown and hostile environment by using probabilistic clustering methods. By analysing the synthetic data and simulating studies, the proposed GMM based approach is shown to outperform traditional range-based methods in terms of the localization accuracy, with lower Root Mean Square Error and the ability to mitigate the noise, signal interference and uncertainties of the wireless channels.

Keywords: Cooperative Localization, Wireless Sensor Networks (WSNs), Gaussian Mixture Modelling (GMM), Sensor Node Positioning, Probabilistic Clustering, Localization Accuracy, Root Mean Square Error (RMSE)

Abstract: Forecasting of insurance claims is of great concern to insurance industry. In motor insurance, claim payments constitute to a significant portion of insurance's expenditure, making accurate forecasting an essential aspect. Traditional models such as Generalized Linear Models which has been widely used in predicting insurance claims often fail to capture seasonality, trends and temporal dependencies in the data leading to inaccurate forecasts. This research applied a SARIMA model to predict motor insurance claims in Kenya. The quarterly motor insurance data from 2017-2024 was obtained from IRA and analyzed through Box-Jenkins Methodology. From the time series plot, it was found that the data exhibit seasonality, with claims paid each quarter increasing continuously from the first quarter to the last in each year. SARIMA (1,1,1) (1,1,0,4) was chosen using Grid Search Optimization since it had the lowest AIC value (320.5). The suitability of this model was also confirmed through model diagnosis. A 2-year forecast graph showed a rising trend in motor insurance claims while still maintaining seasonal fluctuations that aligns well with past data. The future confident intervals widened with time indicating that there is an increase in uncertainty of the forecasts. From the analysis, the study suggests that SARIMA is a better tool for projecting seasonal motor insurance claims in Kenya. Motor insurers will minimize losses that results from inaccurate forecast by utilizing this model.

Keywords: SARIMA, Claims, Motor Insurance.

Abstract: In this research paper we have compared different models of classification using csv dataset and we tried to find out which one of them best fits for the dataset to predict the drug type and then predict the drug type based on the input features of the patient. We have performed this problem in the python programming language on Google Colab. We have used a lot of libraries and packages for the implementation of classifiers and also for plotting graph, making table, finding errors, accuracies confusion matrices etc. The dataset has a lot of classes in which the outcomes are classified and a lot of parameters which are used for the prediction of the outcomes. We have made tables for the comparison also plotted the graphs for the prediction and then we have compared the models for which among them has better efficiency for that particular dataset.

Keywords: Artificial Intelligence (AI), Machine Learning (ML), Drug Type Prediction, Support Vector Machine (SVM); Naïve Bayes, k-nearest neighbours, Random Forest, Logistic Regression.

Abstract: This experimental-development research was conducted during school year 2024-2025 aimed to formulate study formulated the tofu-taro embutido with green amaranth leaves to evaluate the sensory and acceptability qualities in terms of appearance, aroma, taste, and texture. Significant differences in sensory qualities and acceptability were also determined. The products were submitted for proximate analysis of the best treatments. The research employed a Completely Randomized Design (CRD), with four replications, and involved the evaluation of one final product by 100 tasters to gauge consumer preference. Data collection utilized scorecards with 9-Point Hedonic Scale. Statistical tools employed included the mean, Analysis of Variance, and post-hoc test. Results from sensory evaluations revealed that Treatment B (85 grams of taro flour and 15 grams of green amaranth leaves) received the highest ratings from semi-trained panelists, described as extremely appealing, pleasant, delicious and firm. Treatment B was highly preferred by consumers and was liked extremely. Statistical analysis indicated a significant difference in appearance, taste, aroma, and texture based on sensory qualities, in the acceptability of the products. There was a significant difference as to consumers' preference in favor of Treatment B (85 grams of taro flour and 15 grams of green amaranth leaves). The shelf-life analysis showed that the product remained acceptable up to two days at room temperature, after which they began to lose freshness. The microbial analysis revealed that the product complied with safety standards. Proximate analysis confirmed that the product contained nutrients good for the health of the consumers. These findings indicated that the incorporation of green amaranth leaves with taro flour in embutido formulation enhanced the sensory qualities and increased consumer acceptability.

Keywords: Formulation, Analyses, Acceptability, Microbial and Proximate Analysis, Tofu, Taro, Embutido, Green Amaranth Leaves

Abstract: While floor planning is a fundamental skill in technical drafting, many students find manual drafting time consuming and prone to errors. This study explored how the use of the Floor Plan Creator application could improve the efficiency, accuracy, and performance of Grade 9 Technical Drafting students in producing floor plans. The main objective was to determine whether students could produce better outputs with the help and guidance provided by the digital features of the Floor Plan Creator. The study utilized a quasi-experimental research design, particularly a single-group pretest-post-test approach. Students were given the requirements and area of a specific floor plan. In the pretest, they were asked to draft the floor plan manually but had no formal instruction or prior experience in creating one. As a result, their manual outputs were generally lacking in structure and technical accuracy. Their outputs were evaluated using a scoring rubric focused on workmanship, accuracy, neatness and speed. The intervention involved introducing the Floor Plan Creator application, which guided students through the floor planning process with built-in tools, auto-dimensioning, and layout suggestions. In the post test, students used the application to recreate the same floor plan. The results showed that with the help and guidance of the Floor Plan Creator, students produced more accurate and complete outputs. Their average scores improved from "Very Satisfactory" in the pretest to "Excellent" in the post test. There was a significant difference before and after the use of Floor Plan Creator application. The mean score after the use of the intervention was higher than the mean score before the intervention. The findings demonstrate that the application not only supported the students in understanding the structure and layout but also increased their confidence and ability to meet the required drafting standards. Additionally, the low standard deviation in the post test scores indicates a consistent level of improvement across the participants, demonstrating that the Floor Plan Creator helped students achieve more uniform and higher quality outputs. Therefore, this study recommends the integration of Floor Plan Creator into the drafting curriculum, as it effectively assists students in producing high-quality outputs through a more guided and efficient process.

Keywords: Floor Plan Creator, Digital Drafting, Performance. Accuracy, Technical Drafting, Efficiency.

Abstract: The front foot stroke is a fundamental technique in cricket, employed primarily against deliveries pitched up to the batter. This study provides a comprehensive mechanical analysis of the front foot stroke, focusing on its biomechanical components, kinetic chain involvement, and principles of human motion. The stroke is dissected into five critical phases: preparation, backlift, stride, swing/impact, and follow-through. Each phase demonstrates coordinated movements involving joints, muscles, and skeletal alignment. Key mechanical principles such as Newton's Laws of Motion, force generation, balance, momentum transfer, and leverage play crucial roles in the execution of an effective front foot stroke. This analysis emphasizes the importance of correct stance, foot placement, and body alignment to generate maximum force and maintain balance. The role of the lower body in initiating momentum, coupled with the rotation of the torso and precision in arm movement, determines the timing and power of the shot. Muscle engagement, particularly of the quadriceps, gluteus maximus, deltoids, and wrist flexors, is vital in executing the stroke efficiently. Common errors such as misalignment, late swing, and unstable base are discussed, along with corrective techniques. Understanding the mechanical aspects of the front foot stroke can aid players, coaches, and sports scientists in improving technique, optimizing performance, and preventing injuries. This study highlights the significance of integrating biomechanical training in cricket coaching to refine motor skills and shot accuracy.

Keywords: Cricket biomechanics, front foot stroke, kinetic chain, Newton's laws, balance, momentum, muscle activation, sports performance, batting technique, shot execution.

Abstract: This study presents Green Deal Awareness through Augmented Reality in Primary School Education, an innovative curriculum designed to integrate environmental sustainability education with Augmented Reality (AR) technology. Developed collaboratively by five European institutions, the curriculum aims to equip pre-service teachers and educators with the skills to foster ecological and digital competencies in young learners. Structured into five modules-Green Deal Awareness, Augmented Reality, Environmental Education, AR in Education, and AR for Green Deal Awareness-the program leverages AR's immersive capabilities to enhance engagement, comprehension, and emotional connection to sustainability topics. The research employs a mixed-methods approach, combining surveys (N=250+) and interviews (N=50+) across five countries to assess educational needs and evaluate the curriculum's effectiveness. Findings highlight AR's potential in visualizing abstract environmental concepts (e.g., climate change, circular economies) and fostering active, constructivist learning. However, challenges such as technological barriers, teacher training gaps, and equitable access are identified.Pilot testing demonstrates that AR-enhanced modules significantly improve student motivation and environmental literacy, with applications like virtual field trips and interactive simulations making Green Deal principles tangible. The study underscores the importance of interdisciplinary collaboration, policy support, and localized adaptations to scale this approach. By merging cutting-edge technology with sustainability education, this curriculum offers a transformative model for preparing future generations to address global ecological challenges, aligning with the European Green Deal's vision of a climate-neutral society.

Keywords: European Green Deal, sustainability education, primary schools, environmental awareness, immersive learning, curriculum development.

Abstract: This project suggests an early diagnosis and bone cancer classification AI system with the help of deep learning methods like Convolutional Neural Networks (CNN). The system takes medical image input like X-rays, MRIs, and CT scans. Image preprocessing, tumor segmentation, feature extraction, and benign/malignant classification are steps in the methodology. The solution has achieved 92.71% accuracy, 100% precision, and 93.95% recall, which is better than conventional machine learning algorithms like SVM. The system also incorporates cloud storage and remote diagnostic, making it scalable and efficient for telemedicine.

Keywords: Bone Cancer Detection, Convolutional Neural Network (CNN), Deep Learning, Medical Image Segmentation, Tumor Classification, Image Preprocessing, Python, TensorFlow, Keras, Flask, MongoDB, Cloud-Based Diagnosis, AI in Medical Imaging.

Abstract: This study explored the impact of the Special Training for Employment Program (STEP) implemented by the Technical Education and Skills Development Authority (TESDA) on the graduates of Dumalag Vocational Technical School (DVTS) from 2021 to 2023. Employing the explanatory sequential design, this study provided a comprehensive understanding of the program's impact. The descriptive method was used for the quantitative aspect, while a phenomenological approach was applied to gather and analyze qualitative data through in-depth interview. The participants included 75 STEP graduates selected using total population sampling, all of whom met the criteria of having completed the program within the specified timeframe. The frequency, percentage, and the mean were the statistical tools used to analyze and interpret the gathered data. Findings revealed that the majority of respondents were female, aged between 26 to 35 years old, with most belonging to low-income households earning between ₱5,001 to ₱10,000 monthly. A significant number were college graduates, primarily employed in the private sector, and many came from farming or fishing backgrounds. The quantitative data indicated that the overall impact of STEP was rated "High," with productivity scoring the highest among measured variables. Qualitative analysis identified six key themes reflecting the skills developed through the program: knowledge generation, skill development, business startup, honing skills, income generation, and emotional growth. Benefits highlighted by the graduates included hands-on experience, emotional empowerment, financial independence, and enhanced job security. Despite these positive outcomes, the study also uncovered several challenges, including the lack of tools and materials during training, high startup costs, inadequate publicity of the program, and difficulties in learning technical skills. The study concludes that STEP plays a crucial role in empowering individuals through technical and vocational education, especially those from socioeconomically disadvantaged backgrounds. It recommends enhanced resource allocation, expanded support services, and strengthened partnerships to further improve the program's reach, effectiveness, and long-term impact on graduates' lives.

Keywords: STEP Program, Technical-Vocational Education, Graduate Outcomes

Abstract: Many industrial sectors generate different forms of solid waste that threaten the environment. The slag of the Basic oxygen furnace, which is considered in the steel industry as a by-product, is one of these solid wastes. The main aim of this study is Using this solid waste to be included in applications related to chemical engineering. The process of producing of the biodiesel would be the main use of this waste in this study. As it is known that the energy is essential for survival of human being, most of the sources of energy used worldwide are non-renewable sources and are produced as products from oil, coal, petroleum and natural gas, also their use impact on the environment is negative. As these resources will soon run out, the sources of renewable energy should take place as replacement. Mainly the aim of this paper is biodiesel production, which is a renewable, clean, diesel with high cetane numbers, Waste cooking oil was used as the feedstock and the basic oxygen furnace slag as the catalyst beside using the methanol in a transesterification reaction. With respect to the data that come out from the experimental work, the yield of biodiesel produced could reach 90.16% if the optimum parameters for this reaction is used which states that the methanol to oil ratio should be 20:1, while the catalyst loading should be 5%, also the temperature of the reaction should be 57oC, in addition to a 750rpm stirring rate, and the reaction time to be one hour.

Keywords: Biodiesel; Solid waste; Slag; Renewable energy.

Abstract: The fuel injector system is a crucial component in modern automotive engines, especially in four-wheelers, where efficient fuel delivery and combustion are essential for performance and emission control. This project focuses on the modelling and 3D printing of a fuel injector system used in four-wheel vehicles. Using CAD software, the injector was designed with precision to mimic real-world functionality. The design process involved analyzing the structure, function, and material considerations of fuel injector systems. Once the modelling phase was completed, the 3D model was printed using FDM (Fused Deposition Modelling) technology with PLA material. This prototype helps in understanding the design, assembly, and functioning of the fuel injection mechanism, and serves as an educational tool for automotive engineering applications. The study also emphasizes the advantages of additive manufacturing in prototyping and testing automotive components, offering a cost-effective and time-saving approach for product development.

Keywords: Fuel injector system,3D Printing, CAD Modelling, PLA Material

Abstract: Food innovation plays an important role in enhancing nutritional value, promoting health conscious eating, and creating sustainable alternatives to conventional food products. It focused on evaluating the sensory qualities such as appearance, aroma, taste, and texture as well as the overall acceptability, and nutritional content of the developed products. Using an experimental developmental research design, the study followed a Completely Randomized Design (CRD) with three treatments and replications. Sensory data were gathered from 110 evaluators using the 9-Point Hedonic Scale, with results analyzed through arithmetic mean and ANOVA. Findings revealed that all three papaya meaty bite variants papaya balls, papaya loaf, and papaya nuggets received favorable sensory evaluations. Papaya loaf scored highest in taste and texture, while papaya nuggets led in appearance and aroma. Although slightly lower in some aspects, papaya balls still met the acceptable range across all sensory qualities. In consumer acceptability, papaya nuggets emerged as the most preferred product, followed by papaya loaf and papaya balls. Statistical analysis revealed significant differences in appearance and aroma, while taste and texture showed no significant differences, indicating consistent quality in those attributes. In terms of overall acceptability, appearance was the only quality with a statistically significant difference, emphasizing the impact of visual appeal on consumer preference. Papaya nuggets were selected for microbial and proximate analysis, which confirmed their safety, nutritional value, and extended shelf life. These findings support the product's potential for commercialization and its value in sustainable food product development.

Keywords: Sensory Qualities, Microbial and Proximate Analysis, Papaya Meaty Bites

Abstract: Steam turbines, which convert thermal energy from steam into mechanical energy, work on the principle of thermodynamic expansion. High-pressure steam passes through multiple blade stages, causing the rotor to rotate and generate power. This project focuses on the modeling and thermal analysis of a steam turbine to assess its performance and efficiency. A detailed 3D model of the turbine is developed to simulate the thermodynamic expansion of high-pressure steam through multiple stages of blades, which causes the rotor to spin and generate mechanical power. The thermal analysis investigates heat distribution, temperature variations, and thermal stresses within the system under various operating conditions. By examining these factors, the study aims to optimize turbine design, enhance efficiency, and improve durability. The findings contribute to a deeper understanding of the thermal behavior of steam turbines, offering valuable insights for improving their performance and reliability in power generation, industrial applications, and marine propulsion.

Keywords: Thermal Analysis, Steam Turbine, 3D Modeling, ANSYS Simulation

Abstract: Crowd funding has become a popular method for non-governmental organizations (NGOs) to raise funds for various causes. However, traditional crowdfunding platforms suffer from issues such as high fees, lack of transparency, and susceptibility to fraud. This paper presents a blockchain-driven framework designed to secure NGO crowdfunding transactions, leveraging the Ethereum blockchain with the Arbitrum Sepolia Layer 2 testnet for enhanced scalability and reduced costs. The framework includes a decentralized application (DApp) with user and admin modules, enabling users to create and view campaigns, donate to approved campaigns, and track fund utilization transparently. Smart contracts automate the approval and donation processes, ensuring that only verified campaigns receive funds and that all transactions are recorded immutably on the blockchain. By eliminating middlemen and providing a trustless environment, this framework significantly reduces the risk of fraud and increases donor confidence. The implementation demonstrates the potential of blockchain technology to revolutionize the crowdfunding sector, particularly for NGOs, by offering a secure, transparent, and efficient platform for fundraising..

Keywords: Blockchain, Crowdfunding, Smart Contracts, NGO, Transparency, Ethereum

Abstract: The manual harvesting of leafy vegetables is a labor-intensive, time-consuming, and often ergonomically challenging process, facing pressures from rising labor costs and availability issues. This abstract presents the concept and design framework for an autonomous robot specifically developed for the selective cutting and collection of leafy vegetables (e.g., lettuce, spinach, kale) in field or controlled environments. The proposed system integrates computer vision for plant identification, maturity assessment, and precise localization of the optimal cutting point. A robotic manipulator equipped with a specialized cutting end-effector performs the harvesting action with minimal damage to the plant and surrounding crops. Subsequently, an integrated collection mechanism efficiently gathers the harvested produce. The system aims to improve harvesting efficiency, reduce reliance on manual labor, minimize crop loss, ensure consistent quality through selective harvesting, and operate autonomously within the cultivation area. This technology represents a significant advancement in agricultural automation, contributing to the development of precision agriculture and sustainable farming practices by optimizing resource utilization and operational throughput in leafy green production.

Keywords: Agricultural Robotics, Collection System, Smart Farming, Selective Harvesting, Leafy Vegetables.

Abstract: This paper introduces a cutting-edge AI-powered web application designed to revolutionize interview preparation. Built using Next.js, integrated with large language model (LLM) APIs, and enhanced by modern UI frameworks, the platform delivers an interactive and personalized experience. Key features include AI-generated mock interviews, structured roadmaps for technical learning, book-based study content, and dynamic performance feedback. Unlike traditional LLM interactions such as ChatGPT, this application provides goal-oriented, context-aware guidance with an intuitive UI/UX. It bridges learning gaps through a structured approach and leverages analytics to offer actionable insights, enabling users to track progress and improve systematically throughout their preparation journey.

Keywords: AI Interview Preparation, Next.js, Full-stack Development, Gemini API, LLM, TailwindCSS, Roadmaps, Automated Feedback, EdTech, React.js.

Abstract: The study made an in-depth analysis of the relationship between academic motivation and self-concept of higher secondary school students to determine the levels of academic motivation and self-concept and explored the impact of self-concept on academic motivation of the higher secondary school students located in Chanchal Block-I. 500 (Rural 250 + Urban 250) male students five government-aided higher secondary schools were randomly selected as sample for this study who participated in the process of data collection. Academic Motivation Scale (AMS) was used to measure extrinsic and intrinsic academic motivation and the Self-Concept Questionnaire (SCQ) developed by R. K. Saraswat (1984) was used as a tool for data collection to determine the level of self-concept of the students. The study revealed that the majority of the students had medium level of academic motivation and self-concept and there was no significant difference in the students' academic motivation and self-concept scores between rural and urban higher secondary male students. It also showed that the correlation between academic motivation and self-concept of the students was significant.

Keywords: Academic motivation, Self-concept, Higher Secondary Male Students

Abstract: Heart Cardiovascular disease continues to be one of the leading causes of death globally, especially in under-served and rural communities where access to sophisticated diagnostic tools is limited. This study explores the use of machine learning (ML) and explainable artificial intelligence (XAI) to provide accessible, reliable, and interpretable early detection of heart disease. Leveraging a synthetically generated dataset modelled on common patient profiles based on features like age, cholesterol, chest pain type, ECG readings, and maximum heart rate we developed and evaluated four ML models: Logistic Regression, Random Forest, Gradient Boosting, and XG Boost. Our work proposes a two-tier diagnostic framework: a lightweight, mobile-friendly model for community-level screening, and a more advanced model for clinical environments. We employed SHAP (SHapley Additive exPlanations) to ensure model interpretability and transparency, critical for clinical adoption. The results are promising, with the mobile-tier model achieving 81% accuracy and the clinical-tier model reaching 89%. These findings underscore the potential of interpretable AI to democratize cardiac care, particularly in areas lacking medical infrastructure. Future directions include integrating wearable devices and telemedicine to support real-time monitoring and broader health equity.

Abstract: A study on domestic waste water characterization has been performed followed by the design of sewage treatment plant. The present study involves the analysis of pH value, total solids, total suspended solids, hardness, acidity, alkalinity, chloride, chlorine, BOD, DO and heavy metals such as Iron, Copper, Zinc, Magnesium, Nickel, Chromium, Lead, Calcium, Aluminum, Silicon, Potassium. A sewage treatment plant is quite necessary to receive the domestic and commercial waste and removes the materials which pose harm for general public. Its objective is to produce an environmentally-safe fluid waste stream (or treated effluent) and a solid waste (or treated sludge) suitable for disposal or reuse (usually as farm fertilizer). The samplings of the domestic waste from hostels have been done in different times of the day to have an average data of the measured parameters. The average values of pH, Turbidity, Acidity, Chloride, Residual Chlorine, Hardness, Total Solid, BOD, DO, Alkalinity, Total Iron Content, Zinc Content, Potassium, Copper, Magnesium, Nickel, Chromium, Lead, Calcium, Aluminum and Silicon are found out. A sewage treatment plant has been designed with the treatment units, a bar screen of dimension 1.7m, an aeration tank of dimension 4.5 x 4.5 x 3.7 m3,a collection pit of diameter 4m and depth 5 m.

Keywords: Characterization, sewage, treatment plant.

Abstract: These days, cloud computing has emerged as the greatest way for customers and different IT enterprises to solve space-related problems. The user might consider the data's privacy and genuine integrity. The existing cryptographic approaches can be used to improve cloud computing data security. This study suggested a hybrid cryptography method based on hash functions and visual cryptography techniques for cloud data storage security. The user computes the hash value or hash digest of the file before uploading it to the cloud for storage. On the cloud side, the data is stored and encrypted. The integrity of the data has been preserved if both hash values are the same. The MATLAB 8.3 program is used to carry out the simulation.

Keywords: Hash, VCS, Hybrid, Data, Cryptography, Security, Cloud, IOT, Server etc.

Abstract: The integration of machine learning and Internet of Things (IoT) sensors has significantly advanced crop monitoring and disease detection methods. This study introduces an innovative approach that combines IoT sensors with live image capture to monitor crop health and identify plant diseases in real time. The proposed system utilizes high-resolution cameras to obtain live images of crops, while IoT sensors collect critical environmental and soil data. These images are then analyzed using enhanced machine learning algorithms trained on large datasets to accurately detect and classify plant diseases. By identifying early signs of disease, the system enables timely intervention, minimizing crop losses. Compared to traditional methods, the fusion of sensor data with image analysis greatly improves the precision of disease detection. The relevance of this research lies in its potential to transform agriculture by equipping farmers with real-time, actionable insights that support better crop management, increased yield, and sustainable farming practices.

Keywords: IoT sensors, crop monitoring, machine learning, disease detection, real-time monitoring, agricultural technology, live image capture, precision agriculture, smart farming, environmental data.

Abstract: In many applications, weather forecasting (WF) research is an essential endeavor. These applications always require accurate WF. The purpose of this study is to validate different machine learning (ML) classifiers for wet weather prediction. Several machine learning techniques, including support vector machines (SVM), decision trees (DT), and artificial neural networks (ANN), are validated and tested using the Koggle weather dataset. Extended surveys of ML and NN-based WF techniques are also included in this study. The different ML-based classifiers are validated by comparing their prediction accuracy. The five data classes are classified using four features. Overall, it is discovered that the verified accuracy of ANN and Random Forest is 84.35% each.

Keywords: Weather Forecasting, Rain, Classifier, Machine Learning. SVM, ANN, DT and Random Forest etc.

Abstract: Polycystic Ovary Syndrome (PCOS) is a condition that affects women during their reproductive years. This project aims to reduce the risk of serious health complications by enabling early detection of PCOS through advanced machine learning techniques. Using a dataset from Kaggle that includes both clinical and physical attributes of women, the project focuses on predicting PCOS effectively. Additional features integrated into the system include a menstrual cycle tracker, customized diet and yoga plans, PCOS detection via ultrasound imagery, and access to virtual doctor consultations. To support this, three distinct machine learning models have been developed: PCOS Model 1, which achieved 97% accuracy using the XGBoost algorithm; PCOS Model 2, with 92% accuracy using Random Forest; and the Image PCOS Model, which attained 96% accuracy using a Convolutional Neural Network (CNN). These models significantly enhance early diagnosis efforts and contribute to creating a holistic, user-friendly platform for managing women's health.

Keywords: Consultation, detection, hormonal, PCOS, XGBoost, ovary, menstrual etc.

Abstract: This paper presents a comprehensive analysis of these movements from mechanical perspectives, integrating principles of biomechanics Understanding the mechanical dynamics of movement enhances performance and reduces injury risk in both athletic and clinical populations. This analysis contributes to better training protocols, rehabilitation strategies, and ergonomic designs. The paper concludes by emphasizing the need for interdisciplinary research to advance the field of human movement science and calls for the integration of emerging technologies like motion capture and electromyography in biomechanical studies. This work serves as a resource for students, coaches, physiotherapists, and researchers aiming to optimize movement efficiency and health outcomes.

Keywords: biomechanics, human motion, mechanical efficiency, sports science, rehabilitation.

Abstract: Classification of grain quality is essential to the agricultural and food processing industries; without this proper identification of the grain types and impurities, it may lead to potentially reduced standards and increased safety risks to consumers. In this project, such comparative analysis has been performed with respect to the two most popular deep learning models-MobileNetV2 and ResNet50-which are applied to grain images for classification. Both models were trained and tested using a custom dataset developed-by the use of various classes of grains-for accuracy, prediction confidence, and class-wise performance. Furthermore, the system will incorporate a user-friendly web interface developed using Streamlit, enabling uploading of grain images and classification results together with visualizations of model confidence. Results indicate the trade-off between lightweight efficiency in MobileNetV2 and rich deeper representation in ResNet50. This work would show the employability of deep learning models into accessible web applications for applied grain inspection tasks while imparting knowledge about the model selection for embedded or real-time scenarios.

Keywords: Grain Classification, Deep Learning, MobileNetV2, ResNet50, Streamlit, Image Recognition, Web-Based Deployment.

Abstract: Software project management has evolved significantly over the last few years. With increasing software system sizes and complexity, conventional project management falls short. Artificial Intelligence (AI), including machine learning and natural language processing tools, is increasingly used to enhance software project management through automation and predictive decision support. AI contributes to improved decision-making, optimized resource utilization through data-driven insights, and proactive risk management. This paper puts forward the application of AI in software project management today. It outlines the primary tools and techniques employed, indicates the advantages that they provide, and identifies problems that project teams continue to experience. The research employs both theoretical studies and actual-world cases to gain a greater depth of knowledge about the subject. The findings highlight that AI is immensely beneficial in Agile project teams. AI may be employed in automating daily work, anticipating threats, and assisting in decision-making in real time. Nevertheless, issues like resistance to change, untrained resources, privacy of data, and having transparent AI systems persist. As much as AI produces improved results in most aspects of a project, it is researched insufficiently concerning long-term effects. The paper fills some of the gap by offering a plain explanation of how AI is applied today in software project management. It also proposes future research must emphasize responsible use of AI, human-AI collaboration, and monitoring long-term performance. Organizations should be ready to evolve and adopt robust ethical principles in order to realize the principle potential of AI.

Keywords: Artificial Intelligence, Software Project Management, AI-Driven Decision Making, Agile Development, AI Ethics, Case Studies, Predictive Analytics.

Abstract: This study aimed to characterize Calamondin (Citrofortunella microcarpa) in paper making using green binders. This also aimed to produce sustainable and eco-friendly products utilizing agro-waste materials. It also specifically sought to evaluate the physical characteristics, acceptability and significant difference in terms of appearance, texture, foldability, fiber dispersion, and opacity of paper from Calamansi pulp using green binders. Developmental research design was used, formulating three treatments. The participants of the study were composed 25 professionals (handmade paper maker, interior designer, architectural and drafting instructors, and local artists) and 25 students (BIndTech drafting students). Score cards with Five-point Likert Scale was used to obtain the data. The mean and Analysis of Variance (ANOVA) were used to analyze the data into alpha level set at 0.05. Results of the data collected revealed that the physical characteristics of the calamondin (Citrofortunella microcarpa) in paper making using green binders, in terms of appearance, Treatment B (500g calamondin fruit pulp with 500g taro runner pulp) is the best. In terms of texture, foldability, fiber dispersion, and opacity, Treatment A (500g calamondin fruit pulp with 387g malabar spinach stem pulp) got the highest mean scores. The product also revealed that there is no significant difference in terms of appearance and fiber dispersion, however, in terms of texture, foldability and opacity has significant difference. Treatment A (500g calamondin fruit pulp with 387g malabar spinach stem pulp) turned out to be "Very Acceptable" in terms of appearance, texture, foldability, fiber dispersion, and opacity. Better heavy pressing equipment and more other product conversions and developments were suggested and recommended by the experts to further enhance the quality and to maximize the functionality of the calamondin handmade paper with green binders.

Keywords: Handmade paper, Green Binders, Eco-friendly, Sustainable, Agro-waste, Product Conversion.

Abstract: The increasing demand for sustainable construction materials has led to significant research into alternative binders that can partially replace Ordinary Portland Cement (OPC) without compromising structural performance. This study investigates the partial replacement of cement with Ground Granulated Blast Furnace Slag (GGBS) and Alccofine in mortar, focusing on mechanical and durability properties. GGBS, a by-product of the steel industry, and Alccofine, an ultra-fine supplementary cementitious material, are known for their pozzolanic and latent hydraulic properties. Various mix proportions were prepared by replacing OPC with GGBS and Alccofine. Tests conducted on fresh and hardened cement mortar included workability, compressive strength, split tensile strength, and durability assessments such as water absorption. The results demonstrated that the combined use of GGBS and Alccofine enhanced mortar performance, with improved strength development and durability due to refined pore structure and reduced permeability. This study concludes that GGBS and Alccofine are viable supplementary cementitious materials, contributing to the development of high-performance and eco-friendly cement mortar.

Keywords: Alccofine, GGBS, Supplementary Cementitious Materials, Mortar, Durability, Compressive Strength, Sustainability

Abstract: The construction industry seeks high-performance mortar for strong and stable structures. However, industrial by-products like Sugarcane Bagasse Ash (SCBA) and Ground Granulated Blast Furnace Slag (GGBS) pose environmental and health concerns due to disposal issues. Interestingly, incorporating SCBA and GGBS into mortar can enhance its properties, such as water permeability and strength. This study aims to investigate the mechanical and durable properties of mortar using SCBA and GGBS as partial cement replacements, promoting sustainable construction practices. Five mortar mixes were prepared with varying SCBA content (5%, 10%, 15%, and 20%) and a constant GGBS content (30%). The samples were cast into 50x50x50 mm cube and 160x40x40 mm beam, cured for 7, 28, and 56 days, and then evaluated for their hardened properties and quality.

Keywords: Mortar, Mechanical and Durable properties, Industrial waste.

Abstract: The Online SalonFinder Website is a web-based platform created to make the process of booking salon appointments easier and more convenient. In the traditional salon environment, customers often deal with challenges like long waiting times, uncertain service availability, and limited access to information about services. This system overcomes those problems by offering a digital solution where users can effortlessly explore different salons, check service details, and schedule their appointments online.

Keywords: Salon Booking, HTML, CSS, JavaScript, MySQL, PHP, Online Portal

Abstract: New trends in e-commerce play a significant part in the growth of technology through the internet, and the availability of modern devices and their sophisticated functions have triggered an increase in use for many. Choosing the proper product can be challenging due to the vast array of products showcased on websites, leaving customers feeling tired. These circumstances increase the rivalry between global commercial sites, which builds the need to work proficiently to increase financial profits. Simplifying the user experience is the goal of innovative technology. Visual search is the next innovation that will decouple users from the reliance on keyboards and open a new world of opportunities. Discover how the advancements in visual search technology are influencing future internet search strategies. Human and animal brains can easily detect objects, but computers struggle with this task. The latest technology is being developed, and we are witnessing these revolutionary innovations making our lives easier. As visual content continues to dominate digital platforms, traditional search methods struggle to deliver accurate and intuitive results. This paper explores the transformative potential of vector databases in enabling efficient and intelligent visual search solutions. By converting images into high-dimensional vectors, these databases allow for similarity-based retrieval far beyond keyword matching. Leveraging machine learning and deep neural networks, visual features are encoded and compared using vector embeddings. This new paradigm not only enhances search relevance and speed but also supports scalable and real-time applications across eCommerce, healthcare, and media. The study highlights architecture, use cases, and performance benchmarks of vector-based systems.

Keywords: Visual Search, Vector Databases, eCommerce Innovation, Machine Learning, Deep Neural Networks

Abstract: Accurate estimation of claims is fundamental in the insurance industry for maintaining financial stability and ensuring effective risk management. Traditionally, deterministic approaches, including the chain ladder and Bornhuetter-Ferguson models, have been used to estimate reserves, considering their ease of implementation. However, these models fail to capture the inherent uncertainty associated with unpredictable future variations since they provide point estimates, and hence may result in improper reserve allocation (over-reserving and under-reserving). In contrast, the proposed stochastic model, specifically the bootstrapping technique, introduces a probabilistic framework to quantify reserve variability and provide a distribution of possible outcomes. The goal of this study is to evaluate the effectiveness of the stochastic model as compared to deterministic approaches in quantifying uncertainty, a subject that is largely underexplored in the Kenyan market. In particular, modeling is done for the Incurred but not Reported (IBNR) reserve using real data obtained from a local and already established general insurance company in Kenya (CIC Insurance). Both the deterministic and stochastic approaches are applied on the data, and the model performance is assessed based on accuracy in reserve prediction, mean square errors, confidence intervals, and volatility. The findings demonstrate the advantages of integrating stochastic models in the claim reserving process since they provide a detailed view of uncertainty. The insights support actuarial decision-making and enhance assessments of capital adequacy, hence protecting insurance companies against solvency risks. The study highlights the necessity of integrating stochastic approaches into reserving procedures to enhance robustness of actuarial valuation practices.

Keywords: Uncertainty, Claim Reserve, stochastic, Deterministic.

Abstract: Employee attrition poses a critical challenge to organizations, leading to increased costs, reduced productivity, and disruptions in workforce stability. This project aims to address this challenge by leveraging data analytics and machine learning to analyse employee behaviour and predict attrition trends. By employing a robust dataset and sophisticated algorithms, the study identifies key factors such as job satisfaction, work-life balance, compensation, and career advancement opportunities that contribute to employee turnover. The project utilizes advanced machine learning techniques, including classification algorithms, to predict the likelihood of employee attrition with high accuracy. The analysis reveals actionable insights into attrition patterns, helping organizations proactively mitigate turnover risks. The machine learning model developed in this study integrates data preprocessing, feature selection, and hyperparameter optimization to enhance predictive performance, ensuring practical utility in real-world scenarios. This research highlights the significance of data-oriented decision-making in human resource management. By understanding the drivers of attrition, organizations can implement targeted interventions to enhance employee satisfaction and retention. The results of this study demonstrate the potential of machine learning oriented solutions to support strategic workforce planning, thereby fostering a more engaged and sustainable workforce.

Keywords: Employee attrition, Machine learning, Predictive analytics, Workforce management, Employee retention strategies.

Abstract: Personalization has become essential for success in the ever-changing world of ecommerce. One of the cutting-edge trends is the use of persona-based commerce catalogs, which customize product recommendations and displays based on segmented customer personas derived from behavioral and demographic data. The evolution of eCommerce demands highly personalized shopping experiences. This paper presents a persona-based commerce catalog system powered by Artificial Intelligence (AI) and implemented using the Spring Framework. By analyzing user behavior, preferences, and demographic data, the system dynamically generates product catalogs tailored to individual personas. The integration of AI-driven recommendation engines with Spring's modular architecture ensures scalability, performance, and maintainability. This approach enhances user engagement, boosts conversion rates, and provides a seamless shopping experience. The study also explores the system's architecture, implementation challenges, and potential impact on customer satisfaction and business outcomes in modern eCommerce platforms. The challenge of product overload on ecommerce platforms can lead to decision fatigue for users. Persona-based catalogs address this by aligning product recommendations with specific user needs and characteristics. This white paper discusses the concept of implementation using AI with the Spring framework, and its direct impact on company profitability.

Keywords: Personalization, Persona-Based Commerce, Artificial Intelligence (AI), Spring Framework, Recommendation Engine, User Engagemen, eCommerce Optimization

Abstract: India's eCommerce market is experiencing exponential growth, driven by increasing internet penetration, smartphone usage, and a vibrant festival calendar that shapes consumer behavior. Geotagging, the process of embedding geographic metadata into digital transactions, enables eCommerce platforms to deliver hyper-localized experiences tailored to state-specific festivals and evolving customer preferences. This white paper explores how geo-tagging can enhance personalized marketing, inventory management, and delivery optimization during festivals like Diwali, Pongal, Onam, and Durga Puja, ultimately driving sales. Supported by illustrative diagrams, it examines benefits, challenges, and strategic recommendations for Indian eCommerce businesses.

Keywords: eCommerce, Geo-tagging, Personalized Marketing, Festival Sales, Customer Preference

Abstract: The internal eCommerce platform is a cornerstone of the company's employee reward system, allowing employees to redeem earned points for products. Despite its potential, the platform remains underutilized as many employees continue to shop on external websites due to familiarity or convenience. This results in missed opportunities to drive engagement and maximize reward value. PointSaver is a browser extension that intelligently intercepts product searches on external eCommerce platforms and displays matching items available on the company's internal reward platform. It seamlessly redirects users to the internal site, encouraging point-based purchases. This initiative not only boosts platform usage and revenue but also enables the deployment of timely surveys to capture valuable product feedback and item suggestions, fostering continuous improvement. This white paper outlines the capabilities of PointSaver, its technical implementation, business impact, and strategic value in enhancing user engagement and data-driven decision-making.

Keywords: Internal eCommerce Platform, Employee Rewards, PointSaver Extension, User Engagement, Data-Driven Decision-Making

Abstract: The rapid generation of organic waste has led to significant environmental challenges, including improper waste disposal and greenhouse gas emissions. Composting organic waste using vermicompost provides a sustainable solution to manage biodegradable materials effectively. This study focuses on composting the organic fraction of municipal solid waste using vermicompost. The project involves the collection of organic waste such as mirchi waste(2kg), fruit waste (3kg) and vegetable waste (3 kg) from local markets. Three experiments were conducted to analyze the physical and chemical properties of the organic waste like pH, nitrogen content, phosphorus, potassium, organic carbon, and micronutrients like zinc, iron, manganese, and copper. Quantity of vermicompost applied was varied - 250 g, 500 g and 750 g and applied systematically, ensuring proper mixing to promote microbial activity. The properties were quantified both before and after composting. The lab results indicate significant increase in proportion of Nitrogen (59%), Phosphorous (43.5%) and Potassium (29%) whereas the organic carbon content and the C:N ratio exhibited a declining pattern as the quantity of vermicompost was increased. The study concludes that using vermicompost accelerates the composting process, improves the quality of compost and offers a viable method for managing organic waste sustainably. The findings suggest that compost produced through this method can be used as a high-quality.

Abstract: Cardiovascular disease is one of the primary global health issues since it leads to the death of millions of people each year worldwide. To advance the treatment outcomes and alleviate the resulting health care pressure, an early diagnosis plays a vital role. We review in this paper whether the VGG-16 model, specifically one of CNN architectures, may be used to detect CVD automatically with the help of the analysis of medical images. VGG-16 exploits a deep, sequential arrangement by employing small 3x3 convolutional filters to obtain a fully connected configuration capturing fine spatial detail in echocardiogram images, MRI images, and CT scan images for identifying patterns in cardiovascular illnesses that traditional methods cannot match. This investigation further points to the dataset pre-processing technique that has the capacity of enhancing generalization, with regard to model explanations for its prediction and crucially significant for its adoption in a clinical setting. These results ultimately prove that the VGG-16 model is a potentially sound early CVD detector tool and a promising addition to diagnostic practices, especially in contexts with limited access to healthcare professional expertise. The current review contributes to the growing body of literature on the role of deep learning in medical imaging and advocates for the incorporation of AI technologies into routine clinical workflows for enhanced patient care.

Keywords: Cardiovascular Disease, ECG, VGG-16, Convolutional Neural Network, AI-driven diagnostics

Abstract: The present study investigates the physico-chemical and bacteriological characteristics of water at the origin point of the Moran River in Alka, Balrampur, Chhattisgarh, India. Surface water quality is a critical factor in determining its suitability for various purposes including drinking, irrigation, and aquatic life sustainability. A comprehensive evaluation was conducted based on 13 chemical and 4 physical parameters. The results were compared against the BIS (Bureau of Indian Standards) acceptable and permissible limits to determine potability and safety. The water sample from the origin point revealed a pH of 6.0, slightly below the acceptable limit, indicating mild acidity. Most parameters including TDS, chloride, sulphate, calcium, and bacteriological components were well within the acceptable range, confirming relatively good water quality. However, slight deviations in turbidity and residual chlorine were noted. These results suggest that the water is generally suitable for consumption and ecological balance with minor treatment. The findings provide essential baseline data to support local environmental policies and water management strategies.

Keywords: Moran River, Water Quality Assessment, Alka Village, Balrampur, Physicochemical Parameters, BIS Standards, Drinking Water, Surface Water.

Abstract: Greater complexity in current computer networks, introduced in response to cloud computing, Internet of Things (IoT), and 5G technologies, has made complex approaches towards managing, optimizing, and securing network systems prominent. The traditional network management techniques, rooted primarily in strict rules and human intervention, are unable to cope with the amount of data and dynamics of current networks. Therefore, the use of Artificial Intelligence (AI) in networking is becoming a game-changer. Artificial Intelligence (AI) through such technologies as machine learning (ML), deep learning (DL), reinforcement learning (RL), and natural language processing (NLP) can indeed assist in enhancing the design, management, and security of the network system. There are a number of ways by which AI can optimize the networks. Traffic patterns for smart traffic control as well as resource allocation can be forecast using machine learning algorithms. The anomaly detection capabilities of machine learning-based systems also provide real-time security attack detection, hence mitigating the impact of attack vectors such as Distributed Denial of Service (DDoS) or malware attacks. Lastly but not the least, AI is capable of offering self-healing networks that automatically detect faults and heal themselves as required without human intervention, a business of unimaginable value in enormous systems. Reinforcement learning is very beneficial for dynamic routing and load balancing through constant adjustment of network parameters to changing conditions. Other applications of AI in the networks include optimization of Quality of Service (QoS), where applications with high priority such as video streaming or gaming are assigned the bandwidth, they require to function efficiently. In addition, with edge computing and 5G networks, the work of AI is ensuring that network resources are optimally distributed to edge devices for maximum scalability and performance. However, there are some limitations in the use of AI for networking. It needs to be extensively tested with data privacy issues, interpretability of the model, and computational complexity of the AI model. The requirement for high real-time performance puts constraints in processing the network, which can be itself a limiting factor for the use of AI in some applications. Even with these challenges, the potential of AI to revolutionize networking cannot be overstated, and work on network systems with AI at its foundation will probably yield more intelligent, more autonomous, and more secure networking choices.

Keywords: Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Natural Language Processing (NLP), Network Management, Traffic Optimization, Anomaly Detection, Self-healing Networks, Network Security, Distributed Denial of Service (DDoS), Quality of Service (QoS), Edge Computing, 5G Networks, Autonomous Networks, Network Optimization, Real-time Performance.

Abstract: In this paper, we examined the risk character of the NSEASI index across 10 years (January 1, 2013 - August 31, 2023), consisting of around 2,590 valid trading days following intensive cleaning and outliers adjustment of the data. A daily log return was calculated and shown as a high-risk, low-reward market, with average log returns of 0.0018 and an 11.73% daily volatility. It had extremely high kurtosis (328.199) and almost zero skewness (0.009), implying that the distribution of returns was very skewed to extremes and was not skewed. The characteristic function-based Value at Risk (VaR) model was applied in a stochastic volatility system to rectify the flaw of traditional risk models in the face of this heavy-tailed behaviour. Realistic stochastic dynamics of volatility of returns were obtained using parameter estimation using the method of moments. Comparative analysis using Delta, Delta-Gamma, and Monte-Carlo simulation techniques revealed that the fat-tailed behaviour of the return distribution was better captured when using the CF-based and Monte-Carlo-based approaches. The estimates of VaR at the 5% and 1% confidence levels based on CF (2.80 and 5.10) were significantly higher than those of the Delta and the Delta-Gamma method, which underestimated tail risk. It provides formal backtesting via the Kupiec and Christoffersen tests. It performs a sensitivity analysis and discusses policy implications in the context of financial regulation and corresponding portfolio risk management. We would conclude that CF-based VaR is a more practical and theoretically-grounded alternative to more common methods, in non-Gaussian settings that characterize emerging markets; nevertheless, our findings demonstrate the shortcomings of standard Gaussian-based models in turbulent emerging markets like Kenya. This article recommends the use of advanced stochastic methods in the field of financial risk management and regulation. Future research opportunities include introducing the dynamics of jump-diffusion processes, modeling interdependencies at the constituent level, and improving the dynamic portfolio risk estimation.

Keywords: Value at Risk (VaR), Stochastic Volatility, Characteristic Function, Emerging Markets, NSEASI Index.

Abstract: The Present research work describes the synthesis, spectral and antibacterial studies on the complexes of few complexes of Ni (II) with amide group containing ligands. The characterizations of the compounds have been carried out on the basis of elemental analysis, infrared, electronic spectra and magnetic susceptibility studies. Antibacterial activities of these ligands and complexes have also been reported on S.aureus and E.coli bacteria. The diffuse reflectance spectrums of the complexes show bands in the region 9165 cm-1 to 27027 cm-1 assignable to 3A2g (F) 3T2g (F),3A2g (F) 3T1g(F) 3A2g (F) 3T1g (P)transitions. These are also typical of octahedral environment around the manganese. The magnetic moment (2.87 BM) of the complex indicates high octahedral environment. The microwave method of synthesis of complexes have been found easier, convenient and eco-friendly.

Keywords: Microwave, amide, Nickel (II), Antibacterial

Abstract: Neurodegenerative disorders like Alzheimer's, Parkinson's, and brain tumors are increasingly becoming challenging for healthcare systems worldwide owing to their multifaceted manifestations and delayed detection. Early diagnosis is important but is usually compromised by the dependence on specialist interpretation, sophisticated diagnostic equipment, and lengthy procedures. This article presents Brainalyze, an intelligent, web-based neuroimaging platform that streamlines and automates brain MRI analysis by integrating tractography, FA computation, and machine learning-based disease prediction into an easy-to-use interface. The platform operates on multiple imaging formats (PNG, JPEG, NIfTI), processes them with open-source tools such as DIPY and NiBabel, and classifies disease states based on a trained Convolutional Neural Network (CNN) for image data and a Random Forest classifier for structured metadata. FA values are computed to evaluate hemispheric integrity and 3D white matter streamlines are visualized using tractography. Results are displayed in the form of interactive charts and visualizations in an interactive web-based dashboard developed using Streamlit. To further improve usability and interaction, Brainalyze features NeuroBot-a chatbot AI that helps users interpret the analysis findings, provide explanations, and advise on using the system. This platform solves major accessibility, technical complexity, and interpretability of neuroimage analysis limitations. The platform is intended for clinicians, educators, and researchers who need stable, efficient, and explainable insights into brain health. With its scalable, open-source, and modular architecture, Brainalyze provides an extensive solution that caters to diagnostic as well as academic usage. The ability of the system to be deployed in the cloud and its future possibility for integration in PACS makes it a viable option for actual clinical settings and interprofessional education.

Keywords: Neuroimaging, Tractography, Machine Learning, Brain MRI, Fractional Anisotropy, AI Chatbot, Streamlit

Abstract: Srirampura has a lake that has fresh water and is located approximately 8km away from the city center. The largest and oldest lake in Mysore is named after Mysore Maharaja Krishnaraja Wodeyar Ш, who established it in 1828. One of the best places to visit for relaxation and rejuvenation in Mysore is the lush surroundings that cover an area over 260 acres. This lake is a picturesque setting for guests to unwind amidst lush vegetation and a calming body of water. It also takes pride in having over 250 different species of birds. To ensure their availability for future generations, conservation of natural resources involves protecting and sustainably managing resources like water, soil, forests, and biodiversity. Lake development and protection are crucial for the long-term health and sustainability of these precious ecosystems.

Keywords: Conservation, Lakes, Ecosystem, Development.

Abstract: Mathematics anxiety is a major psychological barrier that affects students' performance in Mathematics and has negative educational outcomes. Academic motivation refers to the mental processes that push goal-focused behavior in school settings. The present study examines the level of mathematics anxiety and academic motivation among adolescents and evaluates the association of mathematics anxiety and various subscales of academic motivation with an emphasis on gender. For this study descriptive survey research method was employed. 300 students selected for the study through purposive sampling technique as sample. Mathematics Anxiety Scale and Motivated Strategies for Learning Questionnaire were used to collect the required data. Collected data were analyzed through appropriate statistics. Mann-Whitney u tests confirm significant gender contrast in test anxiety scores, with no significant differences in other subscales, such as self-efficacy, cognitive strategy use, intrinsic value and self-regulation. Furthermore, spearman rank correlation shows significant negative correlations between mathematics anxiety and most of the academic motivation subscales, suggesting that as mathematics anxiety increases, academic motivation subscales decrease (r = -0.42, p

Keywords: Mathematics Anxiety, Academic Motivation, Gender difference and Adolescents

Abstract: Swiss ADME web tool is empolyed for study of Absorption, Distribution, Metabolism and Excretion (ADME) properties of 5-Fluorouracil (5FU). A chemotherapy drug 5-Fluorouracil (5FU) decelerate the growth of cancer cells for treatment of different types of cancer like breast cancer ,colon or rectal cancer, pancreatic cancer and stomach cancer

Keywords: 5-Fluorouracil, SwissADME, drug discovery, Lipinski's rule of five

Abstract: A series of novel 3-Nitrobenzaldehyde thiosemicarbazone complexes with three transition metal i.e. Mn(II), Ni(II) and Cu(II) having the general composition [Mn(3NBT)2Cl2], [Ni(3N BT)2Cl2] and [Cu(3NBT)2Cl2]{where 3NBT= 3-Nitrobenzaldehyde thiosemicarbazone, have been synthesized by the reaction of thiosemicarbazide with 43-Nitrobenzaldehyde by conventional heating as well as microwave irradiations method followed by complexation with transition metals. The synthesized compounds have been characterised by elemental analysis, melting point determination, FTIR, UV-visible spectral analysis. The synthesized ligands and their new metal complexes have been screened in vitro for antibacterial activity against Escherichia coli, Staphylococcus aureus and Bascillus subtilis bacteria.

Keywords: Thiosemicarbazones, Microwave irradiation, Transition metal complexes.

Abstract: Skin diseases affect millions globally, posing screening challenges due to complex lesion characteristics and limited access to medical expertise. Traditional screening methods are time consuming, often requiring extensive laboratory testing. Deep learning and machine learning techniques have gained significant traction in recent years, serving as powerful tools in tackling complex problems, particularly in areas requiring substantial prior knowledge, such as biomedicine. With the challenge of inadequate medical resources, these methods have found impactful applications in disease screening, emerging as a pivotal research focus on dermatology. This project aims to develop an automated skin disease screening system using machine learning and deep learning techniques. The system is designed to accurately identify skin diseases, enhance early detection, address existing challenges in screening and ensure accessibility and affordability for all. This provides a concise review of the classification of skin diseases, leveraging Convolutional Neural Networks (CNN) and K-Nearest Neighbors (KNN) to analyse skin lesion characteristics and evaluate imaging technologies. By exploring the strengths of CNNs due to its high performance in image classification and feature extraction. KNN providing evidence by identifying similar images, making it an explainable AI model. This study presents an Evidence based screening system a virtual dermatology platform leveraging cutting-edge artificial intelligence and deep learning techniques for efficient skin disease classification. Using pre-trained models like GoogleNet, EfficientNet, ResNet, DenseNet, MobileNet and achieving a classification accuracy of 97% through EfficientNet. significantly reducing screening time and cost. The proposed system optimizes preprocessing, transfer learning, model training and cross-validation, significantly improving accuracy. The results highlight AI's potential to revolutionize dermatological screening, reducing costs and improving early detection.

Keywords: Convolutional Neural Network; K-Nearest Neighbors; Evidence based screening; EfficientNet;

Abstract: The purpose of the study is to find working nature of the employees. The study was conducted with due reference to Hybrid Working Model. It is the combination of both Work from Office (WFO) and Work from Home (WFH). Team Building, Mental Health and Wellbeing, Productivity, Time Management in Hybrid Work Model was focused on this study. Data has been validated with analytical tools and founded that there is an increasing trend in working in hybrid work model. In this study a sample (N=120) employees filled out a Survey containing Questionnaire on the Hybrid Work Model. The study adapts the research methodologies comprising of Percentage analysis, Regression analysis and Correlation analysis. The results indicate that Employees prefer Hybrid Work Model for their Comfort. This also explained the flexibility of the employees in demographic nature. In prioritizing the long run of working employees, there is the necessity to embrace the hybrid workplace model.

Keywords: Work from Home (WFH), Work from Office (WFO), Hybrid Work Model.

Abstract: This study investigates the historical performance, risk-return profile, and correlation of green bonds relative to traditional fixed income securities. Using monthly data from January 2021 to December 2024, the research evaluates the price trends of IRFC green bonds and PFC traditional bonds. Findings reveal that although traditional bonds achieved higher peak prices, green bonds exhibited more consistent and stable performance over time. The price correction phase for green bonds was notably smoother, reflecting reduced volatility and better resilience. Key risk-return metrics-including the Sharpe ratio, standard deviation, and beta-further reinforce the attractiveness of green bonds. With a Sharpe ratio of 64.76 and a standard deviation of 60.47%, green bonds deliver strong risk-adjusted returns and moderate volatility, making them suitable for conservative and ESG-focused investors. Additionally, the beta value of 0.0182 indicates minimal market sensitivity, highlighting their potential role in portfolio diversification. Moreover, the correlation analysis shows a strong positive relationship (r = 0.9847) between the returns of green and traditional bonds, suggesting their performance is influenced by common economic and financial drivers. The study concludes by affirming that green bonds not only offer a favourable risk-return balance but also align closely with the broader fixed income market trends. These findings contribute to a better understanding of green financial instruments and support their inclusion in sustainable investment strategies.

Keywords: Green Bonds, Traditional Fixed Income Securities, Bond Performance, ESG Investment.