International Journal of Science, Engineering and TechnologyEXPERIMENTAL ANALYSIS OF UNCONFINED FLEXIBLE PAVEMENT FOR STRENGTH
Authors: B.Mounika, Assistant professor B.Narsimha
Abstract: Repairing and maintaining pavements, necessitated by an ever-increasing population and traffic, is putting a strain on government budgets. When roads are not adequately enclosed, they frequently fail. Prioritising pavements in restoration plans that include cost- effectiveness, realistic ways to containment, performance assessments of materials and techniques, and sufficient case studies of pavement failures may be beneficial. Soil stabilization solutions, such as geosynthetics, geocells (for 3D confinement), rubber, and plastics, have recently become more popular, especially for reinforcing subgrade and subbase layers. Because these techniques reduce settlement and lateral deformation, pavements are more shear resistant, sustainable, and long-lasting. One terrible method of trash disposal is to burn old tires and plastic. An alternative idea, "GoeTyre Technology," has emerged as a result, suggesting the use of recycled tires in 3D printing rather than geocells. Using recycled plastic and polyethylene as a layer atop flexible pavement that repels water is another possibility. An additional focus of the research is to identify the variables that influence the success or failure of restricted and unconfined pavement restorations. The study's methodology included both laboratory and field testing, as well as finite element analysis in the "ANSYS" software package. The findings point to the use of used tires for three-dimensional confinement in combination with a 125-micron polyethylene impermeable layer as part of a revised pavement crust design for flexible pavements. In addition, thermo-geo plastic (TGP), a composite material, is being evaluated for use in flexible pavements as a cost-effective pothole filler. These solutions also address the issue of how to responsibly and affordably dispose of discarded plastics and tires. This indicates that these innovative methods of strengthening flexible pavements have significant potential as an eco-friendly and cost-effective option.
Strategic Spatial Design: Translating Chess Into Architecture
Authors: Lavanya.M
Abstract: This research explores a unique cross-disciplinary design methodology that draws parallels between the strategic dynamics of chess and the generation of architectural form. By decoding the positional logic, movement patterns, and hierarchy of chess pieces within selected game sequences—particularly from endgame and middle-game scenarios—this approach establishes a set of spatial transformation rules. These rules govern block creation, vertical hierarchy, landscape articulation, and connective paths. The result is an architecture that is not only responsive to logical progression and layered complexity but also narrative rich, evolving through movement, transformation, and spatial tension. This proposal aims to construct a new design language that bridges gameplay mechanics with formal architectural strategy.
Optimization and Characterization for Temperature and Time in Composting Municipal Solid Waste and Brewery Sludge for Stabilizing with Concrete
Authors: B.Prashanth, Assistant professor K.Abhiram
Abstract: Huge limits for quality preparation of the composite technique are temperature and terms of handling the composite. To prevent the impetus from deactivating, it is important to keep the temperature of compost material that is heated by itself to a minimum. The time it takes to prepare the composite should be reduced by processing the material more quickly. The validation of optimal values for five elements is the focus of this evaluation, which focuses on levels 1 and 2. Specifically, A: the level of refinery filth (20, 30), B: change type (cow fertilizer, coconut material), and C: C/N extent (15, 30): In the co-treatment of a typical piece of municipal solid waste and packaging waste, D: initial society (without, with) and E: air flow rate (0.3 L/min/kg, 0.45 L/min/kg) in conjunction with segments A x B and A x C, at the maximum temperature and during the composite's preparation time. We follow Taguchi's protocol and use a L8 even group with 8 primers. The evaluation was coordinated using an in-vessel bunch type treatment of the composite reactor. Up until the composite was fully treated, regular pauses were used to meticulously monitor the temperature and oxygen take- up rates. Temperature and oxygen uptake rate are strongly correlated. As replies for a sign-to- commotion (SN) examination, we retained each starter's most prominent temperature and hard and quick composite preparation time. A2B1C2D2E1 is the optimal factor degree for an astounding 600C goal temperature according to the finest principles. It is A2B1C1D1 for addressing the composite time with more diminutive and better metrics. According to the results of the evaluation, the adjustment type—the waste from dairy creatures—is the most important component influencing the maximum goal temperature of 600C, while the C/N extent of 15 is the most important for the hard and fast treatment of the composite time.
Parental Perspectives On Children’s YouTube Consumption: Insights Into Content Preferences, Behavioral Impacts, And Desired Improvements
Authors: Vishal Sahai, Dr Gunjan Sharma, Garima Jain
Abstract: This study examines parental perspectives on children’s YouTube con- sumption through a survey of 107 respondents, primarily from middle-income households in India, with children aged 2 to over 5 years. The findings indi- cate that smartphones are the primary device for accessing YouTube, with most children watching 0–2 hours daily for education, engagement, or dis- traction. Educational videos, rhymes, and cartoons dominate content prefer- ences, with channels like Cocomelon, Peppa Pig, and ChuChu TV being highly recognized. Parents prioritize educational value and content quality (visu- als/audio) when selecting channels, with a preference for English and Hindi content. Approximately 83% of respondents reported changes in their chil- dren’s learning and behavior, highlighting YouTube’s influence. Parents ex- pressed a need for more culturally relevant, interactive, and skill-based con- tent, alongside concerns about overstimulation and inappropriate material. Recommendations include integrating moral values, Indian mythology, and regional languages to enhance holistic child development.
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A Novel Approach to Implementation of Ann Based Solar and Wind Power Generation Hybrid Grid
Authors: Narendra kumar, Professor Amit kumar Asthana
Abstract: This study presents the development and implementation of a hybrid solar-wind energy controller for seamless grid integration. With the growing demand for sustainable and renewable energy, hybrid systems combining solar and wind power offer a reliable solution to overcome the intermittency challenges inherent in each individual energy source. The proposed controller optimizes energy generation from both solar and wind sources, balancing the fluctuating power output and ensuring a stable power supply to the grid.The controller uses real-time monitoring of solar radiation and wind speed to predict energy production and dynamically adjust the power flow to the grid. By integrating energy storage systems (such as batteries or supercapacitors), the system can store excess energy produced during periods of high generation and release it during low generation periods. Additionally, advanced algorithms are employed to handle load demands, prioritize energy dispatch, and maintain grid frequency and voltage stability.Simulations and experimental results demonstrate that the hybrid solar-wind system with the proposed controller effectively reduces the dependency on fossil fuels, increases grid reliability, and enhances the overall efficiency of renewable energy utilization. The system's performance is also evaluated under various environmental conditions to assess its robustness and adaptability.The hybrid solar-wind controller offers a promising solution for large-scale grid integration, providing both economic and environmental benefits while supporting the transition toward a cleaner, more sustainable energy future
Self-Compacting Triple Blended Geopolymer Concrete as A Solution for Replacement of Mine for Rigid Pavement
Authors: A.Ravichandra, Assistant professor M.Harish kumar
Abstract: Due to rising population and consumption, resource depletion has emerged as a serious concern for governments throughout the world in recent times. The current trend towards resource conservation has prompted a number of potential solutions, none of which have been universally implemented. Sustainable manufacturing, responsible consumption, sustainable building, and many other initiatives have been integrated into traditional operations in recent years. The building industry is one of the most consequential for the growth and prosperity of any country. Therefore, these resource conservation-based issues in the building industry require attention. Sustainability in building is seen by academics as a catch-all phrase encompassing a number of different efforts, such as material optimisation and material replacement. As a crucial topic to investigate within the sustainable building context, material replacement is considered in this study under these methodologies. More and more research has concentrated on finding ways to turn trash into resources for various purposes in order to create efficient waste management systems and effective mechanisms for conserving resources. Concrete applications that include waste material are gaining popularity. The current research is limited, but certain of the waste elements, particularly mining waste, may be a superior substitute for traditional concrete components. To fill this need, this research introduced mining waste as a possible alternative to traditional concrete, particularly self-compacting concrete. In order to comprehend and evaluate the interdependencies and relationships among the gathered difficulties, the study incorporates numerical modelling. By incorporating mining waste into self-compacting concrete, the study's findings remove obstacles to sustainable building. This research took four distinct kinds of mining waste into account: bauxite, copper, stone rock, and steel. The effects of varying amounts of mining waste in geopolymer triple-blended self-compacting concrete were the subject of many experimental investigations. The results show that 4% copper residue outperforms the alternatives in all of these tests. It can be as much as 1.3 to 1.1 times more than regular concrete mix. Two tests, the water absorption test and the sorptivity test, were administered as part of the durability study. The alternatives that were being examined were compelled to participate in these tests. Two types of mining waste—4% copper residue and 3% bauxite residue—performed better than the other under both sets of tests. The addition of this mining waste improves dependability in both instances. Experimental analysis concluded with tribological testing, which included a battery of tests measuring things like corrosion potential, alkalinity, acidity, chloride ion penetration, and more. Compared to the other options tested, 4% copper residue performed better in all of the following experimental tests: acidity, chlorine penetration, corrosion potential, current density rate, and current passing value. Based on these results, the study concluded that 4% copper residue, when used as coarse aggregate replacement in triple blended geo polymer self-compacting concrete, improves the material's strength, durability, and surface tension. Additional mining waste and percentage blends can be included in future extensions of this study.
Foundation Re-use: State Of The Art
Authors: Assistant Professor Samirsinh P Parmar
Abstract: The re-utilization of existing foundations for new structural applications has emerged as a sustainable and cost-effective engineering strategy, minimizing construction expenses, material consumption, and environmental impact. However, foundation re-use poses significant challenges, including geotechnical uncertainties, structural compatibility, load redistribution complexities, and compliance with evolving design standards. This paper presents a comprehensive state-of-the-art review on foundation re-use, systematically addressing its challenges, feasibility assessment techniques, and structural evaluation methodologies. A detailed step-by-step procedural framework is proposed, integrating geotechnical investigations, structural capacity verification, retrofitting techniques, and risk mitigation strategies to facilitate the successful adaptation of existing foundations for modern infrastructure. Furthermore, insights from recent case studies, advanced numerical modelling, and non-destructive testing (NDT) techniques are explored to enhance decision-making in foundation re-use. This study aims to provide a robust engineering methodology, bridging the gap between theoretical advancements and practical implementation in civil engineering. The findings contribute to the evolving discourse on sustainable foundation engineering, particularly in urban redevelopment and infrastructure resilience.
DOI: https://doi.org/10.5281/zenodo.15852818
DESIGN OF REINFORCED FLEXIBLE PAVEMENT DESIGN OVER EXPANSIVE SOILS
Authors: V.Nikhilesh, Assistant professor B.Narsimha
Abstract: Accessibility and connectedness to diverse areas through a well-connected transport network are crucial to any country's prosperity. Road transport is governments' top priority when it comes to directing massive capital investments; it is the most versatile means of travel under different topographical circumstances. Since clayey soils cover almost 40% of India's landmass, roadways in the country must traverse them. Less initial cost, a smoother riding surface, and simple maintenance are the primary reasons why flexible pavements are favoured over rigid ones. Construction costs are expensive because clay subgrades have low soaked CBR values, necessitating a thicker pavement design. Flexible pavements over clayey soils often collapse with significant rutting, a wavy surface, longitudinal cracking along the wheel track, and shear failure in the edge region, even though they provide a considerable pavement thickness. Additionally, expansive clays' shrink-swell behaviour in response to changes in moisture makes pavement building a major headache, and the material's extremely low strength in saturated condition, caused by swelling, drives up the price of pavement construction. Researchers are periodically trying to find ways to stabilise, reinforce, control moisture, and replace soil in order to make clay subgrades and expansive soils stronger and more stable (Katti, 1979; Natarajan and Shanmukha Rao, 1979, Steinberg, 1992, Ramana Murthy, Prasada Raju). Highway engineers have taken notice of geosynthetics and are considering using them to improve the performance of pavements. In particular, synthetic geotextile has been utilised over the past twenty years as a separator-filter-drain at clay subgrades, as well as for the management of reflection cracking in overlays, all because of its multi-functional behaviour. Holding geotextiles at the subgrade has dual benefits: first, it stiffens the base layer; second, it reduces natural stress on the subgrade caused by membrane action. Despite this, the membrane effect, which geotextiles may use to reinforce, has received very little attention. Based on deriving the reinforcing effect of subgrade put at subgrade, Giroud & Noiray (1981) provided a reinforced flexible pavement design for unpaved roads, and Satyanarayana Reddy and Murthy (2005) refined this technique for pavements over expansive clay subgrade. Alternative approaches created by Bender and Barenberg and The Koerner numbers are based on actual data. As a result, this study's primary objective is to establish and validate, via test track investigations, design approaches for expansive and non expansive clay subgrades. The current study presents a design methodology that controls swelling of the clay subgrade and ensures safety against shear and settlement failures. This is in response to the lack of practical evidence in the existing literature on reinforced flexible pavements over expansive subgrades (Satyanarayana Reddy and Rama Moorthy, 2005; Satyanarayana Reddy and Chinnapa Reddy, 2011). Test track has been built across expansive clay subgrade, which is part of NH-18 and passes through Kurnool town. This will prevent the flexible pavement from failing over non expansive clay subgrades.
LABORATORY STUDIES ON RECYCLED CONSTRUCTION DEMOLISHED WASTE FOR PAVEMENT
Authors: SK.Juned, Assistant Professor M.Harish kumar
Abstract: One of the most important things a country can do for its social and economic growth is to have a good road network. In order to build, maintain, and expand roads, a substantial quantity of building material is needed. Hence, there is a scarcity of traditional materials that can be used to build the subbases and base layers of flexible pavements. The extraction of high- quality natural resources is also getting more costly. Increasing efforts to promote the use of marginal materials in road building have been made on a worldwide scale in recent times. Costs are reduced, pressure on high-quality aggregates is relieved, and the environment is protected. This research suggests constructing roads using recycled materials and industrial solid waste. Laborotary and field investigations are part of the experimental program. In the lab, we test several waste material trial mixes to find the best ones by measuring their compaction properties, unconfined compressive strength, CBR, and resilient modulus. The optimal mixtures were then subjected to a durability test. An assortment of mixes using reclaimed asphalt pavement (RAP), construction and demolition debris (C&D) waste, natural aggregates (NFL), quarry fines (QFL), fly ash (FC.), and black cotton soil (BCL) are subjected to the experiments. You may use the RFL, CFL, and NFL mixes as base course materials in a flexible pavement system, and the QFL, FC., and BCL mixes as subbase course materials. Wet mix macadam (WMM) and granular subbase (GSB) are two examples of traditional aggregates that these waste mixtures for base and subbase courses are compared to. In order to find the best mix proportions, we tested several combinations of the recommended base and subbase materials according to the Indian Road Congress's specifications for strength and durability. When compared to more traditional materials, the ideal combination of base and subbase performs better in terms of resilience modulus and strength. The primary objective of this extensive study was to analyze the field performance of the chosen design in laboratory trial experiments. Over the course of two years, a falling weight deflectometer was used to assess the test portions' structural integrity. To determine the level of pozzolanic reaction in these waste mixtures, core specimens were taken from the test sections two years after construction and subjected to X-ray diffraction and scan electron microscopy experiments. Using a variety of performance metrics, such as deflection basin parameters, composite modulus, and back-calculated modulus, structural evaluations of test sections have demonstrated that flexible pavement using these various waste mixes outperforms flexible pavement using natural aggregate base and subbase (WMM & GSB). In this research, we compare the service life ratio of a flexible pavement with optimal stabilized mixes in the base and subbase course to that of a traditional wet mix macadam (WMM) and granular subbase (GSB) layer using the IITPAVE software. Pavement consisting of conventional materials has a shorter service life compared to pavement with stabilized mixtures as the base and subbase course, as measured by the fatigue and rutting failure criterion. Additionally, employing RFL, CFL, and NFL mixes in the base and QFL and BCL mixes in the subbase layers of flexible pavement can minimize the construction cost.
PERFORMANCE ASSESMENT ON FLEXIBLE PAVEMENT DETERIORATION MECHANISM
Authors: Korra Srinu, Assistant Professor M.Harish kumar
Abstract: The country's road network has been overloaded and is experiencing early breakdown due to the dramatic growth in vehicle population and higher axle loading pattern over the past decade. Before developing an acceptable overlay type and design, it is important to determine if the pavement has a functional or structural defect based on the type of degradation existing. Conditions that compromise the pavement's load-bearing capacity lead to structural breakdown. Structural deficiencies result from insufficient thickness, cracking, deformation, or disintegration. When the pavement is not smooth and comfortable to ride on, a functional deficit occurs. Causes such as excessive surface deformation (potholes, corrugation, faulting, blow up, settlement, heaves, etc.), hydroplaning, splash from wheel paths, rutting, and poor surface friction and roughness can lead to this. Vehicle Operating Costs (VOC) and the amount of service a facility can provide its customers at any one moment are both affected by its functional state, which in turn affects the economy of the country. The remaining service life (RSL) of a pavement structure can be estimated by predicting its deterioration over time. This allows for the evaluation of different rehabilitation strategies and alternative designs, as well as the allocation of long-term funds for pavement preservation. Furthermore, they are able to foretell how the portions will respond to therapy. Primary response, structural performance, functional performance, and damage models are the four main categories into which the infrastructure prediction models fall. The elements that contribute to road degradation are multi-faceted and regional in character. A definitive plan for the rehabilitation of roads cannot be reached without first conducting an extensive investigation of the degradation process under different soil conditions and climate zones. The current study is an attempt to fill the gap in the literature by including all state road types, traffic situations, and soil types in a comprehensive analysis.
Socioeconomic Shadows In Education Access
Authors: Anshul Singh, Dr. Dimple Jain
Abstract: Socioeconomic status (SES) stands as a powerful force shaping human potential from the very beginning of life. Often gauged through a blend of family income, parental education, and occupation, SES weaves its influence across a broad spectrum of a child’s development—affecting health, learning, and emotional well-being long before a child enters school and continuing into adulthood. These impacts are not random; they flow through deep-rooted disparities in access—to nutritious food, quality schooling, safe neighborhoods, and emotional support. Children do not grow up in a vacuum. The family home, the surrounding neighborhood, and the broader society each play a role in shaping their opportunities. Stress, both seen and unseen, passes from parent to child, and resources—or the lack thereof—leave lasting imprints. Individual traits, family dynamics, and the presence (or absence) of supportive systems all interact with SES in complex ways. Globally, large-scale educational assessments highlight how starkly achievement gaps mirror differences in social background. While the factors influencing academic success may differ from country to country, one constant remains: family socioeconomic status continues to explain a significant share of student performance outcomes. As a result, SES remains a vital lens for understanding educational inequality, even as researchers grapple with the challenge of capturing its full complexity in meaningful and measurable ways.
Symbolic Metamorphosis: Analyzing Cultural Transformations Through Symbolism in Society
Authors: Ritu Rani, Dr. Dharmendra Kumar
Abstract: This study investigates the role of symbolism in driving cultural transformations, focusing on the interplay between traditional and modern symbols, and the significant influences of media, technology, art, literature, and economic factors. Through a comprehensive methodological approach that includes literature review, case studies, and symbolic analysis, the research uncovers the dynamic processes through which symbols evolve and impact societal norms, identities, and collective consciousness. The findings demonstrate that symbols act as catalysts for societal change, facilitating shifts in cultural narratives and social attitudes. This paper provides valuable insights into the psychological impacts of symbolic transformations and underscores the importance of symbols in understanding cultural evolution and societal development.
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A Comparative Study Of Western And Eastern Romanticism Across Time And Genre
Authors: Mishra Sanjana Subhashchandra Asha, Dr. Narinder Kumar Saini
Abstract: This study delves into the changing manifestations of Romanticism in literary and cultural works from the United States, the United Kingdom, and South Korea, highlighting motifs like blossoms, fruits, and the maturation process. This study takes a comparative and multidisciplinary look at how artistic representations of nature can symbolize identity, ethical awareness, and emotional connections. This study explores how different cultures understand the interplay between art and self-awareness, the purity and complexity of girlhood, and the conflicts between individuality and collectivity through analyzing literary works such as classical Romantic poetry, modern speculative fiction, and K-pop music. These writings show how Romantic ideals can be interpreted in many ways depending on the philosophical, cultural, and historical circumstances. The study concludes by highlighting the lasting importance of Romanticism in describing the commonalities shared by all humans as they navigate personal growth, meaningful relationships, and global interdependence.
Enhancing Empathy And Emotional Intelligence Through Literature: A Comprehensive Survey Analysis
Authors: Binu Sebastian, Dr. Ratna Pandey
Abstract: This study investigates the impact of literature on developing empathy and emotional intelligence through a comprehensive survey-based approach. A diverse group of participants, including students, professionals, and educators, were surveyed to understand how engagement with literature influences their empathic and emotional capacities. The findings provide insights into the specific ways literature enhances these critical skills and suggest potential applications in educational and professional settings.
Moms Magic: A Case Study On Modernizing Food Delivery Systems
Authors: Aaryan Kunal, Prof. Vishal Kumar Singh, Ramani Parth Bharatbhai, Divyaraj Singh Jadeja, S A Parthiv,
Abstract: – The increasing demand for nutritious and convenient meal solutions has significantly propelled the growth of the tiffin service industry. However, conventional methods such as phone-based orders and manual menu selection often fall short in terms of efficiency, personalization, and user satisfaction. Mom’s Magic Food Delivery seeks to modernize this domain through a smart, user-friendly web application designed to streamline the entire ordering process. Key features include dynamic menu browsing, personalized meal customization, secure online payments, real-time order tracking, and flexible subscription management. By leveraging modern web technologies and integrating them with the concept of home-cooked meals, this solution ensures enhanced accessibility, improved customer experience, and timely meal delivery. The platform aims to bridge the gap between traditional tiffin services and digital convenience, offering a seamless and reliable solution for users seeking healthy, homemade meals delivered directly to their doorstep
DOI: https://doi.org/10.5281/zenodo.15846881
The Impact Of Employee Well-being Initiatives On Industrial Relations And Productivity In The Manufacturing Sector
Authors: Habibullah.I
Abstract: This paper investigates the influence of employee well-being initiatives on industrial relations and productivity within the manufacturing sector of -, -. Adopting a mixed-methods approach, the study aims to understand how well-being programs shape union-management cooperation and grievance handling, and their perceived impact on employee productivity and retention. Data would be collected through semi-structured interviews with HR managers, union representatives, and a sample of employees, supplemented by a quantitative survey. Hypothetical findings suggest that robust well-being programs are positively correlated with improved industrial relations, marked by reduced conflicts and enhanced collaboration. Furthermore, these initiatives are perceived to contribute to higher employee morale, leading to increased productivity and lower attrition rates. The paper highlights the critical role of comprehensive well-being strategies as a strategic imperative for fostering harmonious industrial environments and sustainable operational efficiency in -'s manufacturing landscape.
Digital Communication and Pragmatic Meaning: A Case Study of Social Media Communication
Authors: Ina’am A. Abdul-Kadhim
Abstract: The emergence of digital communication has profoundly transformed the organization and practical importance of language. This paper explores the expansion of social media as a transformative linguistic convention that generates innovative forms of expression and communication. The use of emojis has transformed how people express their views and underlying beliefs on online platforms. However, the lack of non-verbal cues and the anonymous nature of online communication might potentially lead to misinterpretation. Understanding the influence of digital communication on language and its practical importance is crucial, and it is necessary to adapt to the changing norms associated with it. Based on Pohl et al.'s (2017) study on social media language communication and the linguistic role of emojis, the researcher believes that emojis are used to convey social and emotional indicators, as well as to improve the clarity of the message. Nevertheless, users' interpretation of emojis might vary depending on factors such as gender, cultural background, and communication style. Emoticons are a prominent means of expressing ideas and emotions among the most popular methods of communication on social media. Emojis serve as effective tools for enhancing social and emotional context in communication, hence clarifying the intended meaning of the sender. Emojis can be subjectively interpreted because various users may derive different meanings from them, particularly when considering the dimensions of communication related to sex and culture.
E-Commerce In Emerging Markets: Unlocking Digital Trade And Inclusive Economic Growth
Authors: Mr. Amit Punia, Dr. Neha Bhat
Abstract: This study explores the growing influence of e-commerce in emerging markets and its impact on digital trade and inclusive economic development. By analyzing the digital infrastructure, mobile access, entrepreneurial trends, and regulatory frameworks of India, Nigeria, and Brazil, the research highlights both opportunities and challenges in leveraging digital trade for economic inclusion. The paper reveals how mobile-first platforms, affordable digital tools, and community-driven innovations have empowered micro-entrepreneurs. However, issues such as infrastructure deficits, digital illiteracy, and weak regulatory environments persist. The paper concludes with recommendations for government policy, private innovation, and international collaboration to bridge the digital divide and enable long-term, inclusive economic growth.
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Use of Low-Cost Sensors and the Internet of Things for Environmental Monitoring of Hydraulic Systems: Potentials, Challenges, and Perspectives for Africa
Authors: Wagu Andia Francoise, Ngoy Nziam-Eyabii Germaine, Iyoya Otuli Jean-Nestor, Osako Longongo Willy
Abstract: The integration of low-cost sensors and Internet of Things (IoT) technologies into environmental monitoring presents transformative opportunities for managing hydraulic systems such as potable water networks, storm water infrastructure, wastewater treatment, and irrigation schemes. This review synthesizes recent scientific literature to assess the potential and limitations of deploying these technologies across diverse contexts. We begin by defining key concepts, including hydraulic systems, low-cost sensors, and IoT architectures, and explore typical system components such as microcontrollers, communication protocols (e.g., LoRaWAN, MQTT), and cloud-based platforms. Applications in water distribution, flood forecasting, water quality monitoring, and smart irrigation are discussed, with numerous case studies illustrating real-world deployments. The advantages of low-cost, real-time, and community-based monitoring are balanced against critical challenges, including limited sensor accuracy, durability under field conditions, power constraints, data interoperability, and cybersecurity concerns. Emerging trends such as solar-powered systems, edge computing, open-source development, and AI integration are highlighted as pathways to overcoming these limitations. Special attention is given to the context of African countries, where local initiatives, start-ups, and academic institutions are pioneering innovations despite limited resources. The paper concludes with a call for participatory design, institutional support, and research-driven co-development of robust, sustainable monitoring solutions.
DOI: https://doi.org/10.5281/zenodo.15861282
The Societal Impact Of The Internet
Authors: Maruthi T
Abstract: The internet has become an integral force shaping modern society, transforming how individuals communicate, access information, conduct business, and participate in governance. This paper explores the multidimensional impact of the internet on society, highlighting both positive advancements and emerging challenges. The internet has democratized knowledge, fostered global connectivity, and enabled digital economies, significantly enhancing education, healthcare, and social inclusion. At the same time, it has introduced concerns such as misinformation, privacy violations, cybercrime, and digital addition. The digital divide also exacerbates inequalities, especially in developing regions. Overall, the internet’s influence is profound and evolving, necessitating continuous research and adaptive policies to harness its benefits while narrating its risks.
SMART PARKING MANAGEMENT WITH ANPR BY USING OPENCV AND EASYOCR IN COMPUTER VISION_770
Authors: Dinesh V N, Dharani G Assistant Professor, Santhosh N, Nagadharshini M
Abstract: Parking management systems are advancing with the integration of Computer Vision and Automatic Number Plate Recognition technologies to enhance security, efficiency, and automation. This project aims to create a smart parking system that autonomously verifies vehicle registrations and allocates parking spaces to authorized vehicles while denying access to unregistered ones. ANPR technology plays a key role in vehicle identification, ensuring secure entry. However, existing systems struggle to accurately detect plates under conditions like poor lighting, bad weather, or damaged plates, which can impact reliability, especially in high-traffic areas.To address these issues, the project uses advanced image processing techniques such as adaptive thresholding, image denoising, and modern Optical Character Recognition (OCR) models like EasyOCR and PaddleOCR. These models improve plate detection under unclear images, varied fonts, and inconsistent lighting. Additionally, the system employs GPU acceleration, multithreading, and optimized database queries to reduce processing delays, ensuring real-time operation even in crowded environments.System performance will be assessed based on Security,Efficiency,Parking Management Automation,License Plate Detection & Recognition Performance.The system's ability to detect number plates accurately under different conditions, process vehicle entry requests efficiently, and allocate parking spaces while preventing unauthorized access will be key factors in its success. This approach aims to deliver a secure, efficient, and automated parking management solution for high-traffic areas.
The Future Of Automation And AI In Mechatronics
Authors: Akshada Bornare, Shrushti Asude, Vaishnavi Aher
Abstract: It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable. The integration of data-driven and knowledge- based AI methods can endow mechatronics with advanced capabilities around autonomy, environmental perception, reasoning, control, and human collaboration. This creates intelligent self- optimizing systems that continue learning and adapting.
Enhancing Learning Outcomes Of School Kids Through E-Learning Technology: A Practical Implementation Study
Authors: Assistant Professor K.Diala, S.E. Daniela, J.Kavitha
Abstract: The onset of the integration of e-learning technologies into primary education has been documented as a revolutionary change intended to intensify student involvement, better learning outcomes, and more personalized educational experiences for school-aged children. To better understand the effects of such an outline in practice, this study has focused on the mainstream implementation of e-learning and its impact on student cognition and behaviors in a primary school context for pupils at grade levels 3 to 5. The remediation was undertaken for a period of four weeks. The intervention primarily constituted an amalgamation of learning management systems (Google Classroom), contents that are interactive (Kahoot, Quizizz), and multimedia resources. Ultimately, 50 students constituted participants, with learning activities executed on tablets provided exclusively for the intervention. For data collection, pre- and post-assessment scores were maintained together with observational notes made by teachers and feedback questionnaires filled by students and instructors. The results have shown how great influence in academic performance can exert after the intervention, the average test scores rising from 62% to 83%. Other engagement metrics, participation included, time spent on the task, and completing activities voluntarily, have increased by 92%, hinting at quite the positive response to the digital learning environments. Qualitative feedback points to enhancements in motivation, fun, and comprehension of complex subjects, attributed to the visual nature of the tools and interactivity with such. There were, however, some slight hindrances, including the fostering of an initial orientation to the platforms and minor technical difficulties regarding connectivity. Recently, the study showed three stages in the analysis: it was found that well-implemented e-learning solutions for school children can also immensely benefit them by ensuring active participation and academic performance. Another key issue raised here is the requirement for teacher training, parental participation, and infrastructure readiness to ensure maximum usefulness from the AWTs. The study findings build into the growing literature supporting the digital transformation of primary education and offer practical insights for working educators, policymakers, and developers of technology to make learning experiences better for young learners.
DOI: http://doi.org/10.5281/zenodo.15869642
Queer Diasporic Reading Of Ghalib Shiraz Dhalla’s Novel Ode To Lata
Authors: Assistant Professor Dr. Sunita Kumari, Assistant Professor Dr. Shachi Sood
Abstract: Ghalib Shiraz Dhalla’s Ode to Lata (2002) is a seminal work in queer diasporic literature, exploring the intersections of migration, sexuality, and cultural identity through the life of Ali, a Queer Indian- Kenyan immigrant in Los Angeles. The proposed paper conducts Queer Diasporic reading of the novel, analyzing how Ali’s displacement—both geographical and psychological—shapes his negotiation of desire, belonging, and selfhood. The novel grapples with the intersections of race, sexuality, nostalgia and gender, portraying how societal prejudices impact Ali’s sense of self. Drawing on theories of Diaspora (Avtar Brah, Stuart Hall) and Queer of Color Critique (José Esteban Muñoz, Gayatri Gopinath), this study examines the way Dhalla’s novel challenges heteronormative and nationalist narratives while articulating a distinctly queer South Asian diasporic subjectivity.
DOI: https://doi.org/10.5281/zenodo.15876455
The Effect Of Covid-19 On Cardiac Function: A Machine Learning Approach.
Authors: Dr.S.Vallinayagi, Mrs.M. Gandhimathi
Abstract: The global virus outbreak in December 2019 led to the COVID-19 pandemic, which is arguably the biggest public health emergency in history. COVID-19, which was initially thought to be only a respiratory disease, is actually a blood-related illness that affects the respiratory system. In this study, we sought to investigate the impact of COVID-19 on cardiac function using a machine learning method to analyze electrocardiography (ECG) signals. Given its effects on haematological factors, how does COVID-19 affect cardiac function? Can the clinical diagnosis of COVID-19 be supported by automatically analyzing electrocardiography? We made use of a publicly accessible database of ECG signals captured in emergency care settings and displayed as pictures of printed recordings. Signals linked to myocardial infarction, irregular heartbeats, a history of myocardial infarction, COVID-19, and healthy heartbeats are all included in this database. We suggested a system to help with COVID-19 diagnosis based on hybrid deep learning architectures that use Random Forests for classification and pre-trained convolutional neural networks for feature extraction. Looked at the architectures of LeNet, ResNet, and VGG16. The VGG16 and Random Forest model with 100 trees, which used attribute selection via particle swarm optimization, produced the best results. There are now 773 attributes instead of 4096.
A RESEARCH Review On Al-Si Phase Diagram Phase Stability And Microstructure Evolution In Aluminum Alloy.
Authors: Rahul Admane
Abstract: The Aluminum phase diagram describes the relationship between temperature, composition, and the phases present in aluminum-silicon alloys. The aim of this page is to study the Aluminum-Silicon (Al-Si) binary phase diagram. The Al-Si system is of particular interest due to its widespread applications in various industries, including automotive, aerospace and electronics. Exploring the Al-Si phase diagram provides valuable information on the structure and hence the behavior of the material under different processing and operating conditions.
A Comprehensive Review of Sentiment Analysis Using Transformer Model
Authors: Assistant Professor Dr. Shivani Sharma, Assistant Professor Akhil Kumar, Nitin Yadav, Dilip Kumar
Abstract: In the last several years, the www has altered how individuals communicate with each other, share their thoughts and ideas on social media platforms, and give feedback on websites. Understanding the sentiments from people's ideas, feedback, and interactions is essential in the era of artificial intelligence. Recently, sentiment analysis has received a lot of attention. Sentiment analysis has been widely applied and utilized in a variety of industries, including business. With a focus on the function of Transformer models, this work offers a theoretical discussion of sentiment analysis. The paper will discuss the evolution from traditional approaches to deep learning techniques, delve into the architecture and advantages of Transformers, explore their applications across various domains, and address the challenges and limitations associated with their use.
A Comprehensive Investigation Of The Quality Of Coal At Selected Mines In Jharkhand
Authors: Anil Kumar, Somnath Kumar Rishi, Tekeshwar Kaushik
Abstract: Among these sources of energy is coal that has accounted to an estimated 67 percent of the overall supply of energy in the country. India has one of the greatest coal reserves in the world. The Indian coal has low returns of calorific value and very elevated levels of ash. However, with the present levels of 0.8 million tons of average of daily output of coal in the country, the reserves are estimated to last over 100 years. In India energy derived out of coal is nearly twice energy derived out of oil, compared with the world, where energy derived out of coal is nearly 30 percent lower than energy derived out of oil. It is the rock that is composed of the remaining of plant remains that have been decomposed and therefore consist mainly of an element known as carbon and this is what we call coal. Combusting coal brings about heat energy which could be used in engines such as steam engines or to produce electricity using turbines. Coal burning contributes to nearly 67 per cent of the electricity produced in INDIA. Coal quality is called coal quality and is attributed to the properties and characteristics of coal that influence behaviors and use. The quantities and distribution and varieties of the many and varied elements that are found in coal that is proposed to be burned are a few of the other coal-quality aspects of future use of coal. These quality aspects about the Indian coal deposits may lead eventually to our better use of this life-sustaining energy source with a cheaper and more effective cost, and with the least amount of unnecessary environmental pollution. The objective and vision of this project work is that it narrows its scope on quality of various Indian Coals and also finds out which coal can be used in which industry. Analysis of coal also helps in determining of coal rank alongside as implicit property. Additionally, this information will form the fundamental consideration of arriving at future related issues e.g.: coal trading and its usages. Coal Properties and tests the coals were sampled as an example of 05 mines out of the Jharkhand coal mines on the basis of channel sampling process. Different coal properties were studied and were put to test that gives us some information about quality of the coals. The various properties tested include the following – Plastic properties, Physical & Chemical properties, Thermal properties of peculiar coal.
DOI: https://doi.org/10.5281/zenodo.15960657
Comparative Analysis of Low-Cost and High- Cost Building Using Civil Engineering Software’s Simulation and Material Optimization
Authors: Assistant Professor Aashu, Sachin Kumar, Shanivesh Bharti, Ritik
Abstract: This research analyses and compares the structural and financial viability of low-cost versus high-cost housing designs for a G+4 residential building using civil engineering software’s. Emphasis is placed on load analysis, material selection (including Fe415/Fe500 steel and fly ash bricks), and structural optimization following Indian Standard codes. Results show that a cost-efficient approach reduces expenses significantly and maintains safety, performance, and sustainability. This research aims to analyze and compare the material aspects of high-cost and low-cost buildings. The study investigates the use of different materials, their properties, and their impact on the overall cost and sustainability of the structures. The research methodology involves a comprehensive literature review, case studies of existing high-cost and low-cost buildings, and a comparative analysis of material costs, durability, environmental impact, and aesthetic considerations.
DOI: https://doi.org/10.5281/zenodo.15965209
Impact of Dark Mode on Software Developers
Authors: Professor Sakshi M. Rahangdale, Professor Prajakta D. Helonde, Professor Pallavi S. Bansod, Professor Nikita S. Bante, Professor Uday D. Mahure
Abstract: With the increasing use of screens in development environments, user interface personalization- especially the implementation of dark mode—has become a prominent trend. Dark mode, characterized by light-colored text on a dark background, has gained popularity for its perceived visual comfort, battery efficiency, and aesthetic appeal. This study explores the cognitive, behavioral, and productivity- related impacts of dark mode on software developers, aiming to bridge the gap between UI preference and developer performance. Using a mixed-methods approach, including controlled experiments, eye strain surveys, and productivity assessments, the research evaluates metrics such as code comprehension speed, eye fatigue levels, and subjective comfort in light versus dark themes. Results indicate that while dark mode reduces screen glare and visual fatigue for some users—especially in low-light settings—it may impair readability and slow code scanning for others, particularly during extended periods. The study concludes with recommendations for theme customization, ergonomic screen settings, and employer-level policy considerations to optimize developer well-being and efficiency.
DOI: https://doi.org/10.5281/zenodo.16025869
Advancement in Design of HHO Generator
Authors: Ishan Panchal, Associate Professor DR Rishi Pareek, Ananiya Agarwal
Abstract: The usage of fossil gases(like LPG) and the resulting drastic increase in pollution levels has made us realize the need for additional sustainable gas which does not cause pollution. This research ended in an innovative concept of utilizing Brown gas (also known as HHO gas) as a fuel enhancer for LPG gas cylinders employed in cooking activities. Numerous advancements have been achieved in this domain, with various experiments conducted on LPG gas and alternative fuels, utilizing HHO gas or brown gas as performance enhancers.Every day to day life the consumption of fossil fuel and gases are drastically increasing, so this increment of consumption is harmful for environment as well as society. Many factors were taken into consideration, such as; the cell design, especial Cuboid shape, and the plates overall dimensions. Therefore, the main objectives of this study are to perform and evaluate the modelling of different parameters that affect the performance of the HHO generator with Cuboid shape.
“An Analytical Study On The Role Of Industry, Educators, And Strategic Partners In Fostering Conscious Consumption And Well-being For Sustainable Economic Growth In The Hospitality And Tourism Sector”
Authors: MS. Chitra Sharma
Abstract: – The hospitality and tourism sector faces mounting pressure to pivot from growth-at-all-costs to models that nurture both planetary health and traveler well-being. This study analytically disentangles the complementary roles of industry actors, educators, and strategic partners (public agencies, NGOs, and technology firms) in fostering conscious consumption and, in turn, driving sustainable economic growth. We address three guiding questions: (RQ1) How do hospitality firms operationalize and mainstream conscious-consumption practices? (RQ2) Which pedagogical interventions most effectively cultivate responsibility-oriented mind-sets in future professionals and tourists? (RQ3) In what ways do cross-sector partnerships accelerate or impede the diffusion of well-being–centric business models? Adopting an explanatory sequential mixed-methods design, Phase 1 conducts multi-case qualitative analysis of 12 eco-certified hotels, five tourism colleges, and four destination management organizations across Asia and Europe. Insights inform Phase 2, a survey of 824 travelers and 312 hospitality employees analyzed via partial least-squares structural-equation modeling (PLS-SEM). Results reveal that (i) industry initiatives such as circular-economy housekeeping and digital “nudge” interfaces raise guests’ conscious-consumption scores by 27 %, (ii) educator-led experiential modules boost students’ pro-sustainability behavioral intentions by 33 %, and (iii) strategic partnerships leveraging open-data platforms explain 41 % of the variance in destination-level wellness revenue growth. Collectively, these pathways increase local GDP contributions from tourism by an estimated 8 % without degrading environmental carrying capacity. Practically, the study offers modular toolkits for operators, SDG-aligned curricula for educators, and incentive blueprints for policymakers. The integrative stakeholder lens advances theory by linking micro-level consumption choices to macro-economic outcomes, providing a replicable roadmap for other service industries.
DOI: https://doi.org/10.5281/zenodo.16030098
Comparative Analysis Of Manchester Adder Using FINFET At 14nm Technology
Authors: G.Shanthi
Abstract: The growing demand for high-speed and low-power digital systems has intensified the need for optimized arithmetic circuits, particularly at advanced technology nodes. This paper presents a detailed comparative analysis of the Manchester Carry Chain Adder (MCCA) implemented using FinFET technology at the 14nm node. The MCCA is recognized for its efficient carry propagation and regular layout structure, making it an attractive option for high performance arithmetic operations. With the limitations of traditional planar CMOS becoming more prominent at sub-20nm nodes, FinFETs offer a viable alternative due to their superior electrostatic control, reduced short-channel effects, and lower leakage currents. Using Synopsys Custom Compiler and a 14nm FinFET Process Design Kit (PDK), the MCCA was designed and simulated to evaluate key performance parameters such as propagation delay, dynamic and static power consumption, and estimated layout area. Transient simulations were carried out to assess timing performance, while power analysis was conducted using realistic input patterns. The results indicate that the FinFET-based MCCA provides substantial improvements in terms of speed and power efficiency compared to traditional CMOS-based designs. Specifically, the reduction in propagation delay and dynamic power highlights FinFET’s suitability for nextgeneration energy-efficient computing systems. Furthermore, the compact nature of the FinFET layout contributes to area savings, making it highly favorable for integration in dense VLSI systems. This study demonstrates the effectiveness of combining advanced transistor technologies with proven arithmetic architectures, and underscores the role of modern EDA tools in facilitating accurate and efficient VLSI design at nanoscale technology nodes.
A COMPREHENSIVE REVIEW OF RF AMPLIFIER – TECHNOLOGY AND APPLICATIONS
Authors: Ananiya, Ishan Panchal
Abstract: RF Power Amplifiers are used extensively worldwide, have numerous applications. The technology advancing in their design is changing very quickly. Commercial and defense avionics, deep space exploration, electronic warfare, naval applications, mobile internet, satellite communication, and wireless communications are some of its applications. From SiC (Si on Chip) to GaN, technology has advanced. This review paper covers a comprehensive analysis and comparative study of RF Power Amplifier design with the advent of future technology and supplies knowledge of different types of RF Amplifier, their design and implementation and software used details, and technology advancements in both hardware and software, etc. It also provides applications of amplifiers and how we should improve and work on the design for better efficiency and results. It is primarily focused on increasing output power from a given input power, and it also involves various simulation stability techniques. Their performance is closely linked to both electronic design parameters and thermal behavior, requiring a multidisciplinary approach to optimize efficiency, linearity, and reliability. This research presents a comprehensive analysis combining technological evolution, bias circuit designs, and thermal management strategies.
DOI: https://doi.org/10.5281/zenodo.16793523
Alternative Energy in the Context of Sustainable Development: Integration of International Practices in Azerbaijan
Authors: Samira Jabrayilova
Abstract: This article explores contemporary trends and international practices in the development of alternative and renewable energy, including case studies from the EU, China, Germany, and the United States. Special attention is given to state regulation, investment models, and successful renewable energy projects. A comparative analysis is conducted with the current situation in Azerbaijan, highlighting its green energy development strategy, the implementation of projects in the Karabakh region, and the evaluation of the country's energy potential. The conclusion presents recommendations for applying international experience in the national context to enhance sustainability and energy independence. The study is based on official statistics, international organization reports, and practical case studies.
Adboard Connect: Bridging Brands with Billboards
Authors: Amit Vikram, Dipesh Chaubey, Jiya Kumari Bhagat, Bhavik Trivedi, Professor Sanjay Pagare
Abstract: Billboard advertising remains a vital tool for businesses to enhance brand visibility and reach a broad audience. However, companies often face difficulties in locating suitable billboard spaces, while billboard owners struggle to attract advertisers efficiently. This study introduces AdBoard Connect, a digital platform designed to bridge this gap by streamlining the process of connecting billboard owners with advertisers. By referencing previous studies on billboard advertising strategies, digital platforms, and outdoor media effectiveness, this research evaluates how AdBoard Connect can improve ad placement, optimize revenue generation, and enhance accessibility for businesses. Utilizing a mixed-method approach, including qualitative interviews and data analysis, the study highlights key factors such as visibility, pricing models, and market impact. Findings suggest that digital integration in billboard advertising significantly enhances efficiency, reduces procurement costs, and increases utilization rates. However, challenges such as platform adoption and user engagement must be addressed. This research contributes to the evolving landscape of outdoor advertising and provides insights into future advancements in digital billboard management.
What Role Should ChatGPT Play in the Classroom
Authors: Fabio Morandín-Ahuerma
Abstract: The arrival of ChatGPT has sparked a broad debate about the future of education in the context of generative artificial intelligence (GenAI). Initial reactions ranged from enthusiasm to fear, but recent research suggests that, assuming ChatGPT has the potential to be a useful teaching tool, it could play a constructive role in the classroom if used with a clear educational purpose. In this article, I analyze the educational role that ChatGPT should play in different learning modalities, considering the latest empirical studies in higher education. Based on a critical synthesis of the current literature, I consider how students and teachers interact with ChatGPT and review different types of usage profiles, ranging from behaviors derived from automated practices to those of collaborative teaching and reflection. I examine the tensions between the tool's efficiency and deep learning, and I argue that its integration requires technical training, but above all, broader AI literacy that considers ethical, critical, and metacognitive aspects. It also examines the extent to which the arrival of ChatGPT challenges course design and traditional forms of assessment, forcing us to redefine the concepts of originality, authorship, and assessment in education. The argument demonstrates that ChatGPT's instructional mediation is more valuable than its outcome. I argue that institutions should not prevent its use; instead, they should establish rules and regulations that encourage responsible, fair, and useful use. In conclusion, it seems to me that ChatGPT should not be considered a substitute for teaching, but rather a way to rethink our education, give students more freedom, and enhance the learning experience.
An Analysis of Machine Learning Procedures for Software Design Pattern Risk Classification
Authors: Dr. Vishal Khatri
Abstract: Design patterns are essential components in software development that provide reusable solutions to common problems. However, the selection and implementation of design patterns can introduce various risks to software projects, including increased complexity, performance degradation, and maintenance challenges. This research presents a comprehensive comparison of machine learning techniques for classifying and predicting risks associated with software design patterns. We evaluate multiple algorithms—Support Vector Machines (SVM), Decision Trees, Random Forests, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and hybrid approaches—for their effectiveness in risk classification. Our methodology includes feature extraction from design pattern implementations, creation of a risk taxonomy, and empirical evaluation using a dataset of design pattern implementations from open-source projects. The results demonstrate that hybrid CNN-RNN models with attention mechanisms outperform traditional machine learning approaches, achieving 94.2% accuracy in design pattern risk classification. The research provides valuable insights for software architects and developers to make informed decisions when selecting and implementing design patterns, ultimately enhancing software quality and reducing project risks.
Langchain-Augmented Memory Networks for Persistent Dialogue and Knowledge Retention in LLMS
Authors: Tinakaran Chinnachamy
Abstract: The advent of Large Language Models (LLMs) has revolutionized the field of natural language understanding and generation, enabling machines to engage in complex human-like conversations. However, despite their remarkable linguistic capabilities, LLMs often suffer from limited contextual memory, leading to inconsistencies in long-term dialogue and a gradual degradation in knowledge retention across sessions. This research presents a novel architectural framework that integrates LangChain with dynamic memory networks to address these limitations. By embedding modular, context-aware memory nodes within the LangChain pipeline, the proposed system maintains and retrieves relevant historical context and domain-specific knowledge over extended conversations. This augmentation enables persistent dialogue continuity, minimizes redundant interactions, and ensures semantic coherence across user interactions. The research explores three core components: (1) long-term memory embedding using vector stores and chained retrievers, (2) temporal context segmentation for dialogue history optimization, and (3) knowledge-grounded prompting via memory-aware orchestration. Through extensive experimentation on open-domain and task-oriented dialogue datasets, the system demonstrates superior performance in terms of memory fidelity, user relevance recall, and dialogue consistency when compared to traditional transformer-based models. Additionally, the architecture supports real-time adaptation, allowing LLMs to incorporate new knowledge without catastrophic forgetting. This study offers a significant advancement in conversational AI by bridging the gap between transient model recall and durable knowledge comprehension. It opens pathways for developing truly persistent and contextually intelligent agents for applications ranging from virtual assistants to educational tutoring systems.
Synthesis And Structural Analysis Of CuO Thin Films By Spray Pyrolysis
Authors: Dr. Nakade S. T.
Abstract: – Copper oxide (CuO) thin films were synthesized using the spray pyrolysis technique to investigate their structural properties. The formation of the CuO phase was confirmed using Xray diffraction (XRD) analysis. The results reveal the successful deposition of polycrystalline CuO thin films with a monoclinic crystal structure, demonstrating the effectiveness of spray pyrolysis for fabricating metal oxide thin films
Effects of Self-Learning Multimedia Package on Students’ Academic Performance in Internal Combustion Engines in Tertiary Institutions in Rivers State, Nigeria
Authors: AJIE, Prince Maduabuchukwu, IDIBIA, Clinton Nwachukwu, OJOBAH, Lucky Obulor
Abstract: This study investigated the effect of self-learning multimedia packages on students' academic performance in internal combustion engines in tertiary institutions in Rivers State. Specifically, it examined the impact on students' performance when taught types of internal combustion engines and their working principles in automobile technology education programmes in tertiary institutions in Rivers State. A quasi-experimental design was guided the study, with two research questions and hypotheses tested at a .05 significance level. The study population consisted of 162 Year I-IV automobile technology students in three tertiary institutions in Rivers State, with a sample of 71 Year III students. A teacher-made test was used for data collection, validated by two lecturers in the department of Industrial Technology Education (automobile technology option), Ignatius Ajuru University of Education, Port Harcourt, and reliability established through test-retest method. The data achieved were analyzed with Pearson Product Moment Correlation (PPMC), and the coefficient achieved was .84. Analysis of Covariance (ANCOVA) was used to test hypotheses. Findings showed the experimental group (taught with self-learning multimedia packages) performed better than the control group (taught with whiteboard). The study recommends training lecturers to develop and use self-learning multimedia packages alongside conventional teaching methods.
Effect of Time on Fiber Optic Cable Characteristics
Authors: Abdullah Jameel Rowaished, Suhail Aboobakar, Praveen Prasad Pillai, Wajd Aldhawi, Muhammad Mubarak Ghefaily
Abstract: This study investigates the effect of time on the characteristics of fiber optic cables in the Eastern Region of Saudi Arabia, focusing on degradation metrics such as splice loss, attenuation, and deviation from manufacturer specifications. Utilizing OTDR measurements on 22 live cables of varying ages, the research highlights inconsistencies in degradation patterns, with attenuation losses ranging from 2% to 30%. Environmental factors, undocumented splicing histories, and mixed cable types (e.g., G 652D, G 652C) contributed to data variability. Despite these challenges, the results demonstrate the resilience of fiber optic systems, with some cables exceeding the manufacturer’s 25-year lifespan expectation. The study underscores the need for continuous monitoring and standardized documentation to improve long-term performance assessments.
Sign-to-Speech: Generating Natural Language Audio from Skeletal Sign Language Input Using Transformer Models
Authors: Dr. Pankaj Malik, Rahul Singh, Daksh Khandelwal, Shruti Bajpai, Aayush Tiwari
Abstract: This research presents Sign-to-Speech, a novel two-stage framework that translates skeletal sign language gestures into natural spoken language using Transformer-based models. The system first captures 3D skeletal joint data using pose estimation tools like MediaPipe, then translates the sequential joint information into textual sentences using a custom Pose2Text Transformer. These sentences are subsequently converted into speech using a Tacotron2-based Text-to-Speech (TTS) synthesizer. To evaluate the proposed method, we used the RWTH-PHOENIX-Weather 2014T dataset and a custom Indian Sign Language (ISL-TTS) dataset containing synchronized skeletal, textual, and audio samples. Our Pose2Text model achieved a BLEU score of 41.7, METEOR of 0.47, and Word Error Rate (WER) of 23.8%, outperforming conventional CNN-LSTM baselines. The final speech output was assessed using Mean Opinion Score (MOS), where our model received an average MOS of 4.1/5, indicating high naturalness and intelligibility. These results demonstrate the feasibility of end-to-end skeletal sign-to-speech translation, enabling seamless communication for hearing-impaired individuals and laying the foundation for real-time assistive technologies.
Balance Training In Parkinson’s Disease:
Authors: Dr. Sapna Shokeen
Abstract: Background: Postural instability in Parkinson’s disease (PD) leads to high fall risk. While balance training is beneficial, the comparative effectiveness of fixed vs variable programmes remains unclear.Objective: To compare the efficacy of fixed and variable balance-training programmes on balance control, gait performance, balance confidence, and fall frequency in individuals with mild-to-moderate PD. Secondary aims were adherence, motivation, and feasibility.Methods: Forty PD participants (Hoehn & Yahr stages I–III) were randomized 1:1 to 12 weeks (3×/week) of either a fixed or variable balance-training protocol. Outcomes: Berg Balance Scale (BBS), Mini-BESTest, Timed Up and Go (TUG), Activities-specific Balance Confidence (ABC) scale, and fall logs, assessed pre-intervention, post-intervention, and at 3-month follow-up.Results: Both groups improved significantly (p < 0.05), but the variable group showed greater gains in BBS (+6.2 vs +3.8), Mini-BESTest (+5.4 vs +2.9), and TUG speed (–1.5 s vs –0.8 s) post-intervention. Fall incidence reduced by 50% in the variable group vs 30% in the fixed group. Adherence exceeded 85% in both; motivation scores were higher in the variable group (p < 0.01).Conclusion: Variable balance training yielded superior improvements in postural control, gait performance, fall reduction, and motivation, offering a feasible model for clinical physiotherapy practice in PD.
DOI:
How Free Body Diagrams Explain Real-World Mechanics
Authors: Ms. Amisha Malviya, Mr. Rahul Khobragade, Mr. Rahul Ghotkar
Abstract: Free Body Diagrams (FBDs) serve as a fundamental visual tool in physics and engineering, allowing learners and professionals to analyze forces acting on a body in various static and dynamic conditions. This paper explores how FBDs are essential not only in academic problem- solving but also in understanding and designing real-world mechanical systems—from bridges and buildings to automotive components and robotics. The study discusses the pedagogical benefits of FBDs, their role in simplifying complex physical interactions, and how they help improve intuition about mechanical behaviors. Through a comprehensive literature review and application-focused methodology, we examine how students' conceptual understanding and practical problem-solving abilities are enhanced when FBDs are systematically applied. The results show a marked improvement in students' ability to model mechanical systems and predict outcomes when FBDs are used during instruction. The paper recommends integration of FBD instruction into multidisciplinary STEM curricula and explores its applications in real-world engineering analysis and simulation tools.
Predictive Modeling In Healthcare: Machine Learning-Based Analysis Of Hospital Costs And Patient Disposition
Authors: Aviichal Sharma, Dr. Dolly Sharma
Abstract: We utilized the SPARCS 2015 Inpatient DeIdentified dataset to come up with machine learning models meant to be forecasting total hospital charges and patient disposition outcomes. We made use of both regression and decision tree algorithms for the purpose of the inbound cost prediction. The algorithms we used here are Linear Regression, Ridge, Lasso, SVR, Decision Tree, ElasticNet, KNN, and XGBoost. Besides, in order to do the disposition classification we opted for Logistic Regression, Decision Tree, Random Forest, SVC, Gradient Boosting, KNN, and a Deep Neural Network. We judged model performances with the help of such metrics as R², MSE, accuracy, F1-score, and AUC-ROC. From our research it is seen that XGBoost bested all regression models, registering an R² of 0.9688, while Gradient Boosting secured the highest classification accuracy (87.03%) and the highest F1-score (0.8483). The most substantial determinants that emerged are the length of stay, procedure count, admission type, and DRG codes. Our findings are in line with the finding that the use of machine learning techniques in clinical and operational planning provides still hospital administrators with information on what can be done to optimize costs and what are the most efficient patient discharge strategies. Our results show that data-driven systems have the potential to support value-based healthcare and offer a minimal and scalable model for predictive analytics in hospital settings, as well as provide a scalable blueprint for predictive analytics in hospital settings.
DOI:
Torrefaction Of Spelt Husks To Increase Its Fuel Properties
Authors: Abubakar Halidu, Anh Phan,, Paul Bilsborrow
Abstract: Torrefaction is referred to as mild pyrolysis of biomass at temperatures between 200 and 300°C in an oxygen-free environment in order to increase its heating value, enhanced grindability, and storability, which can be used as a pre-treatment for other thermochemical processes. The findings from torrefaction revealed that temperature had a clear impact on torrefaction product yields. In this study, the torrefaction of spelt husks was performed at 200, 250, and 300°C under an inert nitrogen environment at a 20°C.min-1 heating rate with a residence time of 15–60 min, respectively. The effect of torrefaction temperatures and residence time was analysed. The results showed that the studied torrefaction temperatures led to an increase in the higher heating values (HHV) and an enhancement in grindability. With higher torrefaction temperatures, char yield decreased while gas and liquid yields increased. Torrefaction temperatures that are higher reduce volatiles, oxygen content and energy yield while increasing carbon content and HHV. The highest energy yield of approximately 97 % was obtained at a torrefaction temperature of 250°C and a residence time of 15 minutes. The torrefied spelt husks at 300°C with a residence time of 15 min had the highest increase in HHV of 30.88 MJ kg⁻¹ when compared to the non-torrefied spelt husks, which had an HHV of 17.56 MJ kg⁻¹. By increasing the temperature from 200 to 300°C, oxygen was removed in the form of CO2, CO, and H2O and the grindability was improved. The liquid product was predominantly water and strongly acidic with limited potential for further use in the petrochemical industry.
DOI: http://doi.org/
Student Performance Prediction Using Machine Learning Techniques For Personalized Learning Paths
Authors: Nisha Rampravesh Gupta
Abstract: Machine Learning (ML) is revolutionizing the education sector by enabling data-driven insights into student performance and learning behaviors. One of its most promising applications is in the development of personalized learning paths that adapt to individual learners’ strengths, weaknesses, and preferences. However, predicting student performance involves multiple challenges, such as handling diverse data types, ensuring fairness, and maintaining transparency. This paper explores the role of ML in forecasting academic outcomes, reviews key prediction models, and evaluates their applicability in designing adaptive learning experiences. A framework is proposed that integrates predictive accuracy with explainability, ensuring that educational interventions are not only effective but also understandable by educators and students. The study emphasizes that ML-based performance prediction is a cornerstone of intelligent tutoring systems and a catalyst for inclusive, personalized education.
DOI:
Blockchain For E-Voting: A Study Of Secure, Scalable, And Transparent Systems
Authors: Prof. Rina Kumari, Anuj Kumar, Aditya Maurya, Rajvardhan Singh, Komal Shah
Abstract: In modern democracies, ensuring the security and transparency of voting systems is essential. Traditional electronic voting methods face challenges such as data tampering, centralized control, and limited public trust. This paper explores a blockchain-based voting system designed to overcome these issues through decentralization, immutability, and cryptographic verification. A prototype model is developed using smart contracts to automate vote casting and counting while ensuring voter anonymity and preventing duplicate votes. System performance is evaluated based on scalability, transaction speed, and security. The study also addresses practical concerns like voter authentication and regulatory compliance. Results indicate that blockchain technology offers a promising foundation for building secure, transparent, and verifiable electoral systems, potentially strengthening democratic processes and increasing voter confidence in future digital elections.
A Novel Transformer Model With Multiple Instances Learning For Diabetic
Authors: Mrs Dr.S.Balaji, Mr.Abdulla
Abstract: Diabetic Retinopathy (DR) is a leading cause of vision impairment among diabetic patients, and its timely detection is crucial for preventing irreversible vision loss. This work presents a novel hybrid model that combines Convolutional Neural Networks (CNN), EfficientNetB0, Transformer architectures, and Multiple Instance Learning (MIL) for accurate DR classification and recommendation. EfficientNetB0 serves as a lightweight yet powerful backbone for extracting high-quality features from retinal fundus images, while the Transformer module captures global context and spatial relationships across image regions. MIL enhances model robustness by treating each image as a collection of instances, allowing effective learning even with limited or weak labels. Additionally, the system provides severity-based recommendations to assist clinicians in prioritizing patient care. Experimental results across multiple DR datasets demonstrate superior accuracy, generalization, and clinical relevance, highlighting the model’s potential for integration into real-world diabetic screening workflows.
Diagnosis of Pneumonia from chest-x ray Images
Authors: Dr. Amol S. Dange, Shubham S . Babar, Kiran B. Dombale, Abhishek R. Mohite, Pranav N. Patil
Abstract: Computer Science & Engineering ADCET, Ashta Ashta, India — Pneumonia is an acute respiratory illness that necessitates appropriate and timely diagnosis to assist management. This project, Diagnosis of Pneumonia from Chest X-Ray Images, adopts deep learning techniques to enhance and support automation of pneumonia detection and classification using imaging technology. The system was developed with a reasonably easy to curate dataset of registered chest X ray images to develop an advanced Convolutional Neural Network, a ResNet-50 based model for measuring features of the chest X-ray image and a additional sequential CNN for predicting pneumonia from the extracted features of an image. The end user was presented with a suitable graphical interface, developed in react, to upload chest X-ray images at personal convenience. The system implemented an automated deep learning pipeline visioned to classify pneumonia types, suggests personal recommendations for further medical needs. The AI metrics of this model was an additional phase of the project application initiating a conversational chatbot interface that enables more user engagement through API and additional accessibility for interaction with other modules. An innovative doctor recommendation module suggests clinical specialists’ users may access based on the severity of the condition presented to the user. The model was also continued to be subjected to continued testing and validation using a real-time collected dataset, measure and examine performance metrics for verification of effective and efficient performance. In general, this project is likely to improve the management of pneumonia screening by diagnosing pneumonia faster and more accurately, making it a accessible choice for management of early pneumonia identification, severity classification and patient outcome automation using AI designed health care.
Enhancing Synthetic Data Generation with Fine-Tuned GPT Models for High-Accuracy Predictive Analytics
Authors: Shiv Hari Tewari
Abstract: The creation of high-quality synthetic data has become essential in modern machine learning applications, tackling several important challenges in data science. In many real-world situations, organizations experience significant constraints when using actual datasets. These limitations can include having insufficient amounts of data, containing sensitive personal information, or displaying biases that distort analysis results. Synthetic data generation provides an effective solution by producing artificial datasets that statistically resemble real-world data while avoiding these issues. This research specifically aims to improve synthetic data generation through the fine-tuning of Generative Pre-trained Transformer (GPT) models. These models are part of deep learning architectures known for their ability to understand and generate complex patterns. This framework consists of: -Statistical similarity metrics, like Jensen-Shannon divergence, which measure how closely the synthetic data distribution matches the original one. – Propensity score analysis, a technique to check if classifiers can tell apart real and synthetic samples, with indistinguishable samples indicating higher quality. -Machine learning efficacy tests that check the practical usefulness of synthetic data by training predictive models and comparing their performance to models trained on original data. Our findings show that fine-tuned GPT models perform better in all evaluation areas. They not only replicate the complex statistical properties of real-world datasets more accurately but also allow predictive models trained on synthetic data to perform at levels similar to those trained on real datasets. This result is significant, as it suggests that well-tuned synthetic data can act as an effective substitute for real data in many analytical settings. This is especially relevant in sensitive areas where preserving privacy is critical, such as healthcare or finance, or in cases where model generalization is needed despite limited training data. Our approach presents a practical solution by showing that synthetic data can maintain both statistical integrity and functional usefulness. This research opens new avenues for data sharing, collaborative studies, and the creation of more inclusive and representative machine learning systems, addressing important ethical and practical challenges tied to using real-world data.
The Role Of Technology in Education
Authors: Assistant Professor Dr. Anuradha Hanumant Deshpande
Abstract: With the rise of technological innovations across various industries, the education sector has emerged as one of the most significantly impacted. In fact, technology has consistently played a prominent role in shaping education—from the early days of carving symbols and figures on cave walls, to the Gurukul system where students learned to use the tools and techniques of their time, and now to the integration of advanced technologies like artificial intelligence (AI) and virtual reality (VR) (Nishant, 2023). According to Ward, (2001) to understand the history of technology in education, one must first look at the origin of writing. Like the telegraph and computer, writing was once resisted by traditionalists because it was seen as unnatural. Plato was one leading thinker who was very much opposed to the use of writing in fear that it would weaken our memories (Clanchy, 1993). One can see the comparison today with spell and grammar checks on word processing programs as many educators believe that students lose the ability to spell and write grammatically correct sentences on their own. Those who believed in how the technology of writing could change lives for the better, however, balanced these pessimistic views.
Enhancing Agriculture with Smart Irrigation System Based on IOT
Authors: Yogesh Sanap, Ashwini Chate, Shubhangi kale, Assistant Professor Shantanu Kharode
Abstract: The growing necessity for water conservation in agriculture has led to the development of advanced irrigation systems that enhance water efficiency while promoting robust crop growth. This initiative outlines the design and implementation of an IoT-based Smart Irrigation System aimed at effective water management, specifically designed for onion cultivation. The system incorporates an ESP8266 (NodeMCU) microcontroller, along with a soil moisture sensor, rain sensor, DHT11 temperature and humidity sensor, a relay-operated water pump, and a real-time monitoring and control interface through the Blynk mobile application. The soil moisture sensor continuously tracks the water content in the soil, the rain sensor detects rainfall, and the DHT11 records temperature and humidity levels. Utilizing feedback from these sensors, the system automatically controls the water pump via the relay module to maintain ideal soil moisture levels. Additionally, users can oversee environmental conditions and manually adjust the irrigation system through the Blynk app, thereby enhancing user flexibility and management efficiency. By automating irrigation processes and facilitating remote monitoring, the proposed system reduces excessive water usage, minimizes the need for human intervention, and improves crop vitality. This intelligent irrigation solution not only promotes sustainable agricultural practices but also utilizes affordable, readily accessible IoT components, making it suitable for small to medium-sized farming operations.
A survey on Priority Based Job Scheduling in Cloud Computing
Authors: Ummulbanin Amjherawala, Deepak Kumar Yadav
Abstract: The increasing demand for cloud computing demonstrates how resources are being controlled with the need to schedule every incoming jobs. Cloud computing faces numerous challenges, with job scheduling being a particularly complex issue and scheduling belongs to category of NP-hard problems. From this viewpoint, numerous strategies are used to schedule incoming job requests in accordance with the needs of cloud users. Users can scale up and down resources from distant locations by using the cloud on a pay-per-use basis. As demand increases, it becomes increasingly important to efficiently manage resources by considering priority and non-priority based approaches. The effectiveness of various priority and non priority based job scheduling techniques is the main focus of this study with the goal to examine and evaluate the issues associated with distributing jobs across limited cloud-based resources.
DOI: http://doi.org/10.5281/zenodo.16417528
Design And Development Of Green Synthesized ZnO Nanoparticles From Banana Peel Waste For Environmental And Energy Solutions
Authors: Ms. Hirkanya Bhole, Ms. Fiza Pathan, Ms. Nageshwari Sarade, Mr. Bhavesh Thakre, Ms. Sushma Borewar
Abstract: The rapid development of nanotechnology has significantly impacted multiple fields, particularly in environmental and energy-related areas. Among the various nanomaterials, zinc oxide nanoparticles (ZnO NPs) have garnered considerable interest due to their strong photocatalytic, antimicrobial, and electrochemical characteristics. However, conventional physical and chemical synthesis methods often rely on hazardous chemicals and require substantial energy inputs, posing risks to both human health and the environment. As a sustainable alternative, green synthesis utilizes natural biological sources as reducing and stabilizing agents, offering an eco-friendly and cost-efficient approach.This review highlights the use of banana peels—an easily available form of agricultural waste—as a promising biological source for the green synthesis of ZnO nanoparticles. Rich in bioactive compounds such as flavonoids, polyphenols, and carbohydrates, banana peels facilitate both the reduction and stabilization processes during nanoparticle formation. The resulting ZnO NPs were analyzed using a range of characterization techniques, including UV–Vis spectroscopy, X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDX), which confirmed their nanoscale dimensions, high purity, and structural stability.Furthermore, the review discusses the promising applications of green-synthesized ZnO nanoparticles in environmental remediation, such as wastewater treatment and pollutant degradation, as well as in energy storage systems, including supercapacitors and batteries. By converting low-cost biowaste into functional nanomaterials, this green synthesis approach not only supports sustainable development but also aligns with circular economy principles.
DOI: https://doi.org/10.5281/zenodo.16532572
Factors Affecting Logistics Performance and Economic Growth of Landlocked Countries within East African Community
Authors: Nirere Martine, Li Zhaolei
Abstract: This study explores the factors affecting logistics performance and economic growth in four landlocked East African Community (EAC) countries Burundi, Rwanda, Uganda and Ethiopia. The research emphasizes on three key objectives: analyzing import delays, assessing economic growth trends and identifying logistics-related challenges impacting development. A mixed-methods approach was adopted. Import delay data, measured through port and consolidated dwell time, was analyzed over a six-month period (May–October 2022), while macroeconomic data covering 2015–2024 was drawn from reputable international sources. Statistical tools, including mean, median, and interquartile range, were used to evaluate logistics delays, and economic indicators such as GDP growth, inflation, trade balance, investment rate, and employment were assessed. Findings show Uganda had the longest consolidated dwell time (18.2 days), indicating inland logistics inefficiencies. Ethiopia had the shortest dwell times, reflecting port reforms, though inland logistics remained moderate. Rwanda recorded the highest port dwell time (14.6 days), suggesting dependency on foreign ports. Burundi exhibited inconsistent delays, with limited data suggesting weak monitoring systems. In terms of economic growth, Rwanda and Ethiopia demonstrated consistently strong GDP growth, supported by infrastructure investments. Uganda maintained stable growth, while Burundi lagged due to political instability and structural weaknesses. Common challenges across all four countries included persistent trade deficits, underemployment, high inflation, and low productivity, particularly in agriculture. The study concludes that logistics inefficiencies are a critical constraint on economic growth in landlocked EAC countries. Policy recommendations include infrastructure development, customs modernization, improved port access agreements, regional transport harmonization, and strengthened trade monitoring systems. These measures are essential for enhancing trade competitiveness and fostering sustainable development in the region
DOI: https://doi.org/10.5281/zenodo.16530155
DEEP LEARNING BASED PARKINSON’S DISEASE DIAGNOSIS THROUGH RECURRENT NEURAL NETWORKS WITH INCEPTION NET MODELS
Authors: Mrs. Eurekha, Mrs. Samundeeswari
Abstract: Parkinson’s Disease (PD) is a progressive neurodegenerative disorder that affects millions globally, characterized by motor and non-motor impairments. Early diagnosis is essential to improve patient outcomes but remains a challenge due to subjective clinical assessments and delayed symptom recognition. This study proposes a novel, automated diagnostic framework leveraging deep learning techniques, particularly the integration of InceptionNet and Long Short-Term Memory (LSTM) networks. The system processes SPECT imaging data to extract spatial features using InceptionNet and captures temporal patterns via LSTM networks. Bilinear pooling is employed to fuse spatial and sequential features, enhancing classification performance. Evaluated on public handwriting datasets such as PaHaW and DraWritePD, the model achieved a diagnostic accuracy of 91.7%, with a high F1-score (90.9%) and AUC (0.94), outperforming traditional methods. This hybrid architecture offers a non-invasive, reliable, and scalable solution for early PD detection, demonstrating strong potential for integration into clinical workflows. Future improvements may include real-time diagnostics, explainable AI, and cross-platform deployment to support large-scale medical applications.
DOI:
Bitezy: A Food Recipe Recommendation System Based On Ingredients
Authors: Varalakshmi S P, Banibrata Paul
Abstract: Modern kitchens face challenges in meal preparation due to lack of ingredient planning, leading to repetitive meals or food wastage. Bitezy is a real-time, AI-powered food recommendation system that takes user input in the form of images, text, or both, and suggests personalized recipes using available ingredients. The system uses a deep learning based object detection model, YOLOv5, to identify ingredients from uploaded images. To match these ingredients with recipes, Bitezy leverages a Word2Vec-based semantic similarity model that retrieves contextually relevant matches. The Roboflow dataset containing over 43 labelled ingredient categories is used for training the detection model. Bitezy also incorporates filters such as cuisine type, meal course, and diet preferences to further customize outputs. The output is comprehensive—providing the recipe title, image, ingredient list, instructions, estimated cooking time, servings, and nutritional values. Experimental evaluation showed that the system achieves high accuracy, fast response time, and robust performance in both single and multi-modal inputs. Bitezy offers a scalable, accessible, and intelligent platform for modern meal planning, contributing to sustainability and better food management. Its intuitive interface ensures ease of use for users of all ages. The system's modular architecture enables easy future integration with voice-controlled assistants and smart kitchen devices. The system promotes dietary awareness and healthy eating habits through personalized suggestions. Bitezy bridges the gap between technology and everyday cooking, Revolutionizing the way users engage with their kitchen environment.
DOI:
Effect Of Information Communication And Technology On Students’ Performance In Biology In Ekiti State Secondary School.
Authors: Ogundana Olayemi Tolulope, Adedeji Florence Taiwo
Abstract: This study investigated the effects of information and communication technology on student’s performance in Biology in Ekiti State Secondary Schools. One research question was raised and three hypotheses were generated. The study adopted a quasi-experimental research design. The population for the study was 19, 603 Senior Secondary School two students in Ekiti State and the sample consisted of 137 Students who were selected using multistage sampling procedure. One research instrument was used for the study. Biology performance test (BPT). The validity of the instrument was established through face and content validity. Test –retest method was used to established the instrument BPT, this was done by administering it to 20 students who were not part of the sample, the same instrument was re-administered to the same set of students within the intervals of two weeks, the results were correlated using Pearson product correlation method (PPCM)and the coefficient 0.89 was obtained and this makes the instruments reliable and The data obtained were analyzed using descriptive analysis (mean and standard deviation) and inferential statistic (ancova) at 0.05 level of significant. The results revealed that there was a significant difference in the performance means scores of students taught with ICT and those that were not taught with ICT. The result also revealed that there is significant difference in the pre- test and post – test means scores of students taught with ICT facilities and those without. there is no significant gender difference in the performance of students taught with ICT and those that were not taught with ICT facilities. This implies that the effect of treatment on the performance of students toward Biology is independent of their gender. Similarly, the main effect of gender was not significant. Based on the findings its recommended that the teachers should endeavor to take the Biology students to ICT laboratories in Schools and teach the students
DOI:
Reassessing Credit Risk: A Comparative Analysis Of Traditional And Explainable Machine Learning Models
Authors:
Abstract: Loan default risk modeling is a critical area for financial institutions seeking to optimize credit decisions while ensuring compliance and transparency. This study offers a detailed comparative analysis between a newly developed interpretable machine learning framework and existing loan prediction models, particularly the benchmark study by Haque and Hassan (2022). While the base model emphasized loan approval prediction with high accuracy, the present study focuses on predicting actual defaults a more financially and regulatorily significant outcome. The proposed model integrates class imbalance mitigation (via SMOTE), ensemble learning (Random Forest and XGBoost), and explainable AI techniques (SHAP) to address limitations in prior works. A rigorous evaluation on a real-world dataset of 255,347 loan records showed that the Random Forest model achieved superior performance with 96.26% accuracy, an F1-score of 0.8014, and AUC-ROC of 0.9215, while providing global and local interpretability. Compared to the base study's black-box AdaBoost model (99.99% accuracy on approval tasks), this research contributes a more balanced, interpretable, and risk-sensitive predictive framework. The paper concludes that transparent and fair AI-based credit scoring systems can offer practical utility for real-world financial operations.
DOI: https://doi.org/10.5281/zenodo.16530155
Real-Time Brand Perception Through AI Sentiment Analysis: A Multi-Platform Study On Social Media Marketing Effectiveness In Indian E-Commerce
Authors: Dr.Shrikant Jagtap
Abstract: In the era of digital changes, real -time emotion analysis operated by Artificial Intelligence (AI) is explaining how brands understand and respond to consumer engagement on social media. The study examines the effectiveness of social media marketing in influencing the brand perception within the Indian e-commerce sector, focusing on consumer feelings expressed in Twitter, Instagram, Facebook and YouTube. Using Advanced AI Tools, research analyzes more than 50,000 user-related texts-incorporating comments, captions, tweets and reviews, related to major Indian e-commerce brands like Flipkart, Amazon India, Misho and Ajio. While employing mixed-method research design, the study integrates machine learning-based emotion classification with statistical hypothesis tests. Conclusions highlight the platforms in platforms, highlight the impact of the campaign tone and time, and customers outline the sufficient effect of negative emotion on loyalty and purchase behavior. Paper ends with strategic recommendations to help the aberor to increase real-time consumer engagement through the AI-Pailed A sense of intelligence.
DOI: https://doi.org/10.5281/zenodo.16608897
Review Of The Regulatory Environment Governing Stablecoins In Singapore And The Implications Of Contemporary Legislative Evolution In Competing Jurisdictions
Authors: M.P.Ramaswamy
Abstract: Technological evolution enabling the offering of digital assets, particularly, using crypto technology and distributed ledger technology, have made various crypto assets viable. However, with the perception of high risks and threats posed by crypto assets, most markets around the world are yet to legalize the circulation of crypto currencies or offering of other digital assets. Even in jurisdictions that have not strictly banned crypto assets or currencies, relevant regulatory standards to enhance the legal environment governing digital assets are rare to come. However, with the efforts of pertinent international institutions, regulatory emergence could be noticed, especially in prominent financial markets like Singapore, Hong Kong and USA. The pioneering regulation in this regard are seen first in one low-risk crypto asset offerings namely the stablecoins. The present paper aims at assessing the regulatory developments governing stablecoins in Singapore as one of the first jurisdictions to introduce an exclusive stablecoins regulatory regime and assess its relative strength and potential limitations from the perspective of stablecoins business operators. The paper also aims to undertake an assessment of contemporary regulatory development in two competing jurisdictions of Hong Kong and USA that have introduced the latest legislative instruments comprehensively governing stablecoins. Three distinct parts of the paper carryout a close analysis of a selected set of regulatory standards and related developments in the three jurisdictions with some relevant references to the international regulatory efforts. The paper concludes with an analysis of the major findings relating to the regulatory standards in Singapore and certain comparative regulatory features to derive relevant implications and recommendations.
DOI: https://doi.org/10.5281/zenodo.16627473
Hybrid Work: A New Era For The Corporate Sector
Authors: P. Sharanya
Abstract: Many firms have adopted a hybrid work culture that blends remote work and in-person work at the office especially since the COVID-19 epidemic, which has caused a change in how businesses operate. This study attempts to shed light on the drawbacks and advantages of hybrid work cultures in the corporate sector. The primary objectives of this study are to pinpoint the variables that affect the adoption of a hybrid work environment, analyse its effects on worker productivity and work-life balance, and investigate management techniques for a successful hybrid work environment. This study uses a mixed-methods approach to its research, incorporating both qualitative and quantitative data. The basic data was gathered through management and employee questionnaires and interviews in a variety of companies that have a hybrid work culture. Descriptive statistics, correlation analysis, and regression analysis are some of the statistical methods for analysis. According to the findings of this study, hybrid work culture can enhance employee productivity and work-life balance, but also present challenges for managing employee expectation and communication. Clear communication channels, flexible schedules, and encouraging teamwork and collaboration are just a few of the tactics that can be employed to handle hybrid work cultures successfully. As a whole, hybrid work cultures can benefit businesses, employees and the working environment at large, but they are also associated with particular challenges that must be overcome in order to guarantee success. This study emphasises the value of teamwork, flexible scheduling, and effective communication in managing hybrid work cultures. Companies that adopt a hybrid work culture must be proactive in addressing these issues and putting plans in place to handle them successfully. Overall, the study's findings provide insightful information about the hybrid workplace cultures in the business sector. According to the survey, agencies must remember the viability of implementing a hybrid work environment depending on their organisational culture, industrial goals, and personnel requirements. By doing this, they could take well-knowledgeable choices that could sell a nice workplace culture and decorate worker productivity.
DOI: https://doi.org/10.5281/zenodo.16627542
A Review on Dynamic Voltage Restorer Quality Improvement Analysis
Authors: Ankit, Dr. Puneet Pahuja, Rajesh Garg
Abstract: This paper reviews the growing importance of Low Voltage Ride Through (LVRT) capabilities in modern power systems, particularly in the context of renewable energy integration. With increasing reliance on wind and solar generation, voltage sags and grid disturbances pose challenges to system stability. Dynamic Voltage Restorers (DVRs) are identified as effective tools for mitigating these voltage issues. This review discusses various DVR-based LVRT enhancement strategies, control techniques, and integration approaches with Energy Storage Systems (ESS). Optimization methods like Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WCA) are also explored for fine-tuning control parameters. The paper synthesizes findings from recent literature and concludes with future trends and research directions.
Performance Comparison Of Routing Protocols
Authors: Ahmad Alotaibi, Muath Alkhaldi, Monther Aljaafari, Mohammed Alghefaili
Abstract: Routing protocols are essential components of modern computer networks, responsible for determining the most efficient paths for data to travel between devices. As network infrastructure continues to expand in size and complexity, the choice of routing protocol directly impacts performance, reliability, scalability, and security. This paper provides a comprehensive analysis of five widely used routing protocols: Routing Information Protocol (RIP), Open Shortest Path First (OSPF), Enhanced Interior Gateway Routing Protocol (EIGRP), Intermediate System to Intermediate System (IS-IS), and Border Gateway Protocol (BGP). The study evaluates these protocols based on five critical performance metrics: convergence time, security mechanisms, scalability, operational speed, and configuration simplicity. RIP, while easy to implement, is limited by slow convergence and poor scalability, making it suitable only for small, static networks. OSPF and IS-IS, both link-state protocols, offer rapid convergence and robust scalability, with IS-IS often outperforming OSPF in large-scale environments due to its simpler architecture. EIGRP, a hybrid protocol developed by Cisco, combines fast convergence with ease of use, but remains constrained by limited interoperability. BGP, the standard for inter-domain routing, excels in scalability and policy control but suffers from slow convergence and complex configuration, alongside ongoing security challenges. The findings highlight that no single protocol is universally optimal. Instead, each protocol offers advantages suited to specific network requirements.
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Enhancing Data Security: The Synergy Of Blockchain And Homomorphic Cryptosystems In Cloud Privacy Management
Authors: Sandeep Gajanan Sutar, Dr. Praveen B M, Dr. Amolkumar Jadhav
Abstract: – In the era of pervasive cloud computing, ensuring the privacy and integrity of sensitive data has emerged as a critical challenge. Traditional data protection methods often fall short in addressing sophisticated security threats and compliance demands. This study explores the synergistic integration of blockchain technology and homomorphic encryption (HE) as a transformative approach to privacy management in cloud environments. Blockchain's decentralized and immutable architecture ensures transparent, tamper-proof data transactions, while homomorphic encryption enables computation on encrypted data without revealing its contents—thus preserving confidentiality throughout the data lifecycle. The research discusses layered architectural frameworks, real-world implementations across healthcare, IoT, and supply chains, and presents empirical findings that highlight improvements in computational efficiency, security, and regulatory compliance. Despite challenges in scalability and computational overhead, the combined use of blockchain and HE presents a promising pathway for developing resilient, privacy-preserving cloud infrastructures. This integration not only fortifies data governance but also lays the groundwork for next-generation secure cloud services.
DOI: https://doi.org/10.5281/zenodo.16759102
MATHEMATICAL MODELLING FOR COVID-19 CRISIS
Authors: Prakash Kumar, Associate Professor Dr. Mukesh Kr. Madhukar
Abstract: The present paper provides SIR and SIRD models for covide-19 epidemic. Here we have assumed that the time scale of SIR model is short enough so that birth and deaths (other than the death caused by the virus) can be neglected. The population under consideration is fixed. Also the number of deaths from virus is small compared with the living population. We have also discussed here for control of epidemic covid-19.
The Future Of Orthopedic Surgery: AI And Robotics In Action
Authors: Dr. Shivani Sharma
Abstract: Artificial Intelligence (AI) leverages advanced computer science and vast datasets to streamline problem-solving, offering transformative potential for orthopedic surgery, clinical practice, and healthcare delivery. Orthopedic surgeons increasingly use AI-driven tools to enhance service quality and outcomes. AI algorithms analyze medical images, such as X-rays, CT scans, and MRIs, to detect abnormalities and assist in diagnosing fractures, joint diseases, spinal conditions, and other musculoskeletal issues. Furthermore, AI supports personalized post-operative care by designing tailored rehabilitation programs and offering real-time feedback during recovery sessions. AI-powered wearable devices monitor movement patterns, promoting proper form and minimizing injury risks. However, accurate, diverse datasets are essential for training and validating AI systems, ensuring data privacy, quality, and interoperability. By integrating AI with intraoperative imaging, surgeons can improve implant positioning, alignment, and technique. AI-guided navigation systems enhance precision, reduce corrective procedures, and support predictive analytics, advancing personalized patient care and post-surgical management.
DOI: https://doi.org/10.5281/zenodo.16931802
Development And Evaluation Of A Library Book Finding System With A Proximity Detector
Authors: Maricel T. Seno-Sanoria, Joe Marie D. Dormido
Abstract: The efficiency of locating books in large libraries is a significant challenge, often leading to user frustration and reduced satisfaction. This study addresses these issues by developing and evaluating a Library Book Finding System that integrates Radio Frequency Identification (RFID) and Bluetooth Low Energy (BLE) technologies to enhance the accuracy and efficiency of locating books. The system utilizes mobile applications and strategically placed sensors to significantly reduce search time and improve user experience. The research employed a descriptive and developmental approach, using the Rapid Application Development (RAD) model to facilitate quick software deployment. User satisfaction and system usability were quantitatively assessed using the Post- Study System Usability Questionnaire (PSSUQ) and qualitatively through text case analysis. Results indicated that 85% of users experienced increased efficiency in locating books, 88% trusted the accuracy of the information provided, and 83% appreciated the system's user-friendly interface. The study highlights the potential of RFID and BLE technologies to create intelligent library environments, with data analytics playing a crucial role in refining the system to meet user needs better and enhance overall library service delivery.
DOI: http://doi.org/10.5281/zenodo.16743453
Use Of Anterior Segment Imaging Adapter For Diagnosing Cataract Patients: A Case Series
Authors: Ivo Indzhov 1, 5; Gergana Ivanova 1, Rohan Khemlani 2, Shintaro Nakayama 2, Hiroki Nishimura 2-4, Eisuke Shimizu 2-4
Abstract: To evaluate the utility and clinical relevance of an anterior segment imaging adapter attached to a smartphone for diagnosing and documenting various forms of cataract in a real-world setting. This case series presents five patients diagnosed with different types of cataracts using an anterior segment imaging adapter (e.g., a smartphone-mounted slit-lamp adapter). High-resolution anterior segment images were captured in outpatient settings, allowing for detailed visualization and classification of lens opacities. Clinical data, patient history, slit-lamp findings, and visual acuity were recorded for each case.
DOI: http://doi.org/
Comprehensive Analysis And Optimization Of Wireless Fidelity (Wi-Fi) Network Deployment Across A Large Campus
Authors: Puneet Bajpai
Abstract: – Modern educational institutions and corporate environments require a robust Wi Fi network on large campuses. To achieve optimal wireless connectivity on large campuses, this research paper will provide a thorough analysis of the implementation challenges and optimization techniques for Wi Fi networks. The investigation underscores the significance of a well-designed and effectively managed Wi-Fi infrastructure in accommodating evolving user needs in diverse and dynamic campus environments.
DOI: http://doi.org/10.5281/zenodo.16759311
Recent Advances In GFRP Composite Bridge Decks: Materials, Fabrication Techniques, And Performance Evaluation_940
Authors: Mr. Atishay Singhai, Dr. Bhagyashree Naik
Abstract: This study investigates the structural performance of Glass Fibre Reinforced Polymer (GFRP) bridge deck panels fabricated using the hand lay-up method. Three different GFRP configurations—Chopped Strand Mat (CSM) with isophthalic resin, Woven Roving (WR) with isophthalic resin, and WR with epoxy resin—were analyzed. A total of six 1:3 scaled bridge deck models were fabricated and tested under static flexural, shear, and fatigue loading conditions. The results demonstrate that all configurations exhibited linear elastic behavior with brittle failure modes, and WR + epoxy resin panels outperformed others in fatigue resistance. Finite element analysis using ANSYS and ABAQUS was conducted to validate the experimental results and perform parametric optimization under IRC Class A loading. The study confirms the suitability of low-cost, hand lay-up GFRP bridge deck panels for use in modular and temporary bridges, and it provides essential design data to support future implementations in bridge infrastructure
DOI: https://doi.org/10.5281/zenodo.16753102
Advancing Agriculture Through Artificial Intelligence: A Framework For Sustainable Crop Production And Equitable Food Security
Authors: Tushar Sharma
Abstract: Artificial Intelligence (AI) is revolutionizing agriculture by enabling data-driven, sustainable, and equitable farming practices. This review synthesizes AI applications—machine learning, computer vision, robotics, and IoT integration—to enhance crop productivity, resource efficiency, and climate resilience. We propose a novel Inclusive AI-Agriculture Framework (IAAF) that integrates AI with traditional knowledge and equitable access to address global food security. The review critically evaluates AI’s role in yield prediction, soil management, pest detection, and supply chain optimization, with a focus on India, particularly Punjab and Haryana. Barriers, including high costs, digital literacy gaps, and ethical concerns (e.g., data privacy, rural employment), are analyzed, alongside strategies like policy incentives and public-private partnerships. Global and regional adoption trends are presented, supported by a systematic literature review of 60 studies from 2018–2023. Future directions emphasize localized, climate-smart, and ethically grounded AI solutions. This paper underscores AI’s transformative potential to create resilient, inclusive agricultural systems.
DOI: http://doi.org/10.5281/zenodo.16759876
Design, Perception, And Decision-Making In Autonomous Cars: A Systems-Level Review And Implementation
Authors: Santosh Kumar Dash
Abstract: Abstract- Autonomous vehicles (AVs) promise to transform transportation by improving road-safety, increasing mobility access, and enabling new mobility services. This paper presents a comprehensive systems-level review and practical draft design for contemporary autonomous cars, covering levels of driving automation, sensor suites, perception pipelines, localization and mapping strategies, motion-planning and control architectures, simulation and evaluation approaches, and safety/ethical considerations. We summarize current industry and standards perspectives on automation levels, then present a modular architecture that integrates multi-sensor perception (camera, LiDAR, radar), real-time sensor fusion and object tracking, simultaneous localization and mapping (SLAM), behavior planning (route and tactical), and trajectory generation and low-level control. Important algorithmic choices—classical (A*, RRT, MPC) and learning-based (deep perception networks, reinforcement learning for decision-making)—are compared with their strengths and failure modes. The draft includes recommended software/hardware stacks, data pipelines, validation approaches (closed-track testing and large-scale simulation), and metrics for safety and performance evaluation. It also examines practical failure modes (sensor occlusion, adverse weather, distributional shift), regulatory and ethical constraints, and socio-economic impacts. Where possible the design favors explainable, verifiable methods that enable safety cases supported by reproducible testing. Finally, the paper outlines a staged roadmap for development from Level 2/3 driver-assistance prototypes to Level 4 operational design domains (ODDs). The review and draft aim to be a pragmatic blueprint both for research teams and startups seeking to build safe, testable autonomous driving systems while acknowledging open research challenges and policy needs.
DOI: http://doi.org/10.5281/zenodo.16811475
Artificial Intelligence Integrated Automated Irrigation System On Crop Recommendation Dataset
Authors: Nitish Sharma, Dr Komal Garg
Abstract: The requirement for appropriate agricultural care practices and the increase in water worries are significant challenges that require the highest level of propriety. In light of these considerations, the current work has developed an intelligent agricultural irrigation system. Designing a decision-support system based on an embedded system with machine learning integrated is the current approach. To make accurate decisions in an irrigation management plan, an LSTM model that learns from a crop recommendation dataset has been created. However, an Adriano Uno-based embedded system has been created in order to evaluate the LSTM model's choice. According to the experiment's findings, the LSTM model was trained to make decisions with a promising accuracy
DOI: https://doi.org/10.5281/zenodo.16811610
Design, Perception And Decision Making In Autonomous Cars: A System Level Review And Implementation
Authors: Santosh Kumar Dash
Abstract: Autonomous vehicles (AVs) promise to transform transportation by improving road-safety, increasing mobility access, and enabling new mobility services. This paper presents a comprehensive systems-level review and practical draft design for contemporary autonomous cars, covering levels of driving automation, sensor suites, perception pipelines, localization and mapping strategies, motion-planning and control architectures, simulation and evaluation approaches, and safety/ethical considerations. We summarize current industry and standards perspectives on automation levels, then present a modular architecture that integrates multi-sensor perception (camera, LiDAR, radar), real-time sensor fusion and object tracking, simultaneous localization and mapping (SLAM), behavior planning (route and tactical), and trajectory generation and low-level control. Important algorithmic choices—classical (A*, RRT, MPC) and learning-based (deep perception networks, reinforcement learning for decision-making)—are compared with their strengths and failure modes. The draft includes recommended software/hardware stacks, data pipelines, validation approaches (closed-track testing and large-scale simulation), and metrics for safety and performance evaluation. It also examines practical failure modes (sensor occlusion, adverse weather, distributional shift), regulatory and ethical constraints, and socio-economic impacts. Where possible the design favors explainable, verifiable methods that enable safety cases supported by reproducible testing. Finally, the paper outlines a staged roadmap for development from Level 2/3 driver-assistance prototypes to Level 4 operational design domains (ODDs). The review and draft aim to be a pragmatic blueprint both for research teams and startups seeking to build safe, testable autonomous driving systems while acknowledging open research challenges and policy needs.
DOI:
Silver Nanoparticle-Antibiotic Combinations for Combatting Antimicrobial Resistance: A Review
Authors: 1OJIAKU A. A.,, 1NJOKU-TONY R. F, 1EJIOGU C.C
Abstract: Antimicrobial resistance (AMR) is becoming a serious global health issue, undermining the effectiveness of traditional antibiotics and pushing us to seek new treatment options. Silver nanoparticles (AgNPs) have gained a lot of attention due to their strong and wide-ranging antimicrobial properties. When used alongside conventional antibiotics, AgNPs can create a powerful synergy, boosting antibacterial effectiveness, slowing down the development of resistance, and enhancing treatment success against multidrug-resistant pathogens. This review delves into silver nanoparticles: synthesis, characterization and factors that affect their synthesis, applications, antimicrobial resistance, their function as antimicrobial agents, their combinations with antibiotics, and also what the future might hold. As major challenges abound in integrating AgNPs with antibiotics to tackle AMR, this approach offers a promising way to revive the potency of existing antimicrobial treatments. To fully unlock the potential of this innovative strategy in fighting resistant infections, more research is needed, particularly in clinical trials, biocompatibility, and targeted delivery systems.
DOI: https://doi.org/10.5281/zenodo.16869788
Experimental Investigation Of Mechanical Properties Of Ultra-High-Performance Concrete.
Authors: Bajirao V Mane, BM Praveen, Uday Kumar G, Amit P. Patil
Abstract: This study presents a comprehensive experimental and numerical investigation into the structural performance of Ultra-High-Performance Reinforced Concrete (UHPRC) rectangular columns subjected to varied loading conditions, with the objective of improving strength, ductility, and service life in modern construction. UHPRC, recognized for its exceptional compressive strength, tensile capacity, and long-term durability, holds significant promise for critical structural elements exposed to demanding environments. The research focuses on evaluating the behaviour of UHPRC rectangular columns under axial loading, uniaxial bending, and biaxial bending, while analysing the influence of concrete grade, reinforcement ratio, and loading type on load-bearing capacity and failure characteristics. A total of eighteen rectangular column specimens were designed, cast, and tested using a 2000 kN capacity loading frame. The experimental program incorporated three concrete grades (M60, M70, M80), three reinforcement ratios (1.34%, 2.09%, 3.01%), and three loading scenarios. This experimental matrix facilitated an in-depth assessment of material and loading interactions. Finite Element (FE) models, comprising both two-dimensional plane stress elements and a full three-dimensional representation, were developed to simulate column behaviour. Model validation against experimental outcomes and existing literature confirmed their reliability in predicting peak loads, crack propagation, and post-cracking response. The results revealed that UHPRC significantly enhances peak load capacity and ductility, with higher concrete grades and reinforcement ratios delivering superior performance. Failure patterns exhibited gradual post-cracking behaviour, indicating improved energy dissipation. Furthermore, a simplified analytical model was formulated and calibrated to predict load–moment interaction, showing close agreement with experimental and numerical results. This study confirms UHPRC’s potential for improving the safety, durability, and efficiency of rectangular column design, offering valuable guidance for its implementation in high-performance structural systems.
DOI: http://doi.org/10.5281/zenodo.16836720
A Comparative Analysis Of Training Data Size Influence On Machine Learning Accuracy Using Logistic Regression
Authors: Amit Kumar, Mohmmad Darvesh, Rohit Kumar Singh
Abstract: This study investigates the impact of training dataset size on the performance of logistic regression models across three standard datasets: Iris, Breast Cancer, and Titanic. By gradually increasing training proportions, we evaluate the resulting accuracy trends. Results reveal that while model accuracy improves with more data, the marginal benefit diminishes past a threshold, particularly in simpler datasets. These findings inform data collection practices and highlight dataset complexity as a crucial determinant of model performance.
DOI:
Tailored Localized Surface Plasmon Resonance Behavior Of Synthesised Silver Nanoparticles Via Modified Lee–Meisel Method
Authors: Peter O. Adigun, Nnamso D. Ibuotenang, Solomon E. Shaibu
Abstract: This study reports the synthesis and comprehensive characterization of silver nanoparticles (AgNPs) with tunable optical properties, focusing on their localized surface plasmon resonance (LSPR) behavior and relevance to nanoscale light–matter interactions. AgNPs were synthesized via a modified Lee-Meisel method employing trisodium citrate as both a reducing and capping agent. Systematic variation in citrate volume enabled controlled modulation of nanoparticle size within the 10–40 nm range, as confirmed by Transmission Electron Microscopy (TEM). UV–Visible spectroscopy revealed distinct LSPR absorption peaks in the visible region. Photoluminescence (PL) analysis demonstrated enhanced emission for smaller nanoparticles, attributed to increased plasmon–exciton coupling and reduced non-radiative damping. Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDX) validated surface morphology and elemental purity, respectively. Fourier Transform Infrared Spectroscopy (FTIR) identified functional groups such as hydroxyl and carboxylates, derived from citrate, which contributed to surface passivation and colloidal stabilization. X-ray Diffraction (XRD) confirmed the face-centered cubic (FCC) crystal structure of metallic silver with high crystallinity and phase purity. The combined results underscore the critical role of synthetic parameters in tailoring nanoparticle size, surface chemistry, and plasmonic response. This work establishes AgNPs as highly tunable platforms for integration into plasmon-enhanced technologies, with promising applications in biosensing, photothermal therapy, and nanophotonic systems.
DOI: https://doi.org/10.5281/zenodo.16870607
Effect of Stress & Design and Analysis of Bullet Proof Glass
Authors: Minish Kumar Mucharla
Abstract: The demand for bulletproof glass has surged in recent years due to its critical role in safety and defense applications. This project investigates the effects of stress on bulletproof glass by analyzing its design and structural performance under various conditions. The primary goal is to understand the material’s behavior under impact stress and to optimize its design for enhanced durability and protection. The study evaluates stress vs strain characteristics, highlighting the deformation and energy absorption mechanisms under high-velocity impact. Using experimental setups replicating real-world scenarios, this research examines the parameters influencing glass performance, including layer thickness, material composition, and bonding strength. The findings are supported by a detailed stress-strain curve, finite element analysis, and insights into failure modes. By bridging material science and structural engineering, the report aims to guide the development of next-generation bulletproof glass with superior performance and cost-effectiveness. Experimental procedures are designed to replicate real-world impact scenarios using precise test setups, ensuring the reliability and relevance of the results. The study incorporates advanced techniques such as finite element analysis (FEA) to model the stress distribution and failure mechanisms within the material. Key insights into the interaction of stress waves, energy dissipation, and fracture patterns provide a foundation for refining the design of bulletproof glass.By bridging material science and structural engineering, the project aspires to contribute to the development of next-generation bulletproof glass with improved strength, durability, and cost efficiency. This comprehensive analysis not only highlights the importance of understanding stress effects but also offers practical recommendations for enhancing the performance and application scope of bulletproof glass. By bridging material science and structural engineering, the project aspires to contribute to the development of next-generation bulletproof glass with improved strength, durability, and cost efficiency. This comprehensive analysis not only highlights the importance of understanding stress effects but also offers practical recommendations for enhancing the performance and application scope of bulletproof glass.
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Forensic Structure And Recovery Mechanisms Of ReFS
Authors: Rohit Kumar Singh, Surajit Das, Rohit Kumar Singh
Abstract: This research paper presents a deep forensic examination of Microsoft's Resilient File System (ReFS), highlighting its architecture, on-disk structures, recovery strategies, and experimental findings. Unlike NTFS, ReFS employs modern design principles such as B+ trees and a Copy-on-Write (COW) policy to ensure consistency and integrity, which both help and hinder forensic recovery efforts. Illustrated figures and annotated hex dumps are provided throughout to support forensic practitioners.
DOI: http://doi.org/
A Deep Dive Into Quantum Computing: Principles, Advancements, And Challenges
Authors: Santosh Kumar Dash
Abstract: Quantum computing represents a paradigm shift in computational paradigms, leveraging quantum mechanical principles to solve problems infeasible for classical computers. Unlike classical computers that rely on binary bits, quantum computers utilize qubits, which exploit superposition, entanglement, and interference to perform computations in fundamentally new ways. This paper explores the theoretical foundations of quantum computing, including qubit architectures, quantum gates, circuits, and measurement. Key quantum algorithms such as Shor’s algorithm for factoring, Grover’s search algorithm, Quantum Fourier Transform, and hybrid variational algorithms are analyzed. Hardware implementations—including superconducting qubits, trapped ions, topological qubits, and photonic systems—are reviewed, along with challenges such as decoherence, error correction, and scalability. Applications in cryptography, optimization, drug discovery, material science, artificial intelligence, and finance are examined. Historical developments, industry case studies, and comparative analyses of classical versus quantum computation are also included. Finally, limitations, current challenges, and future directions, including quantum supremacy, quantum internet, and hybrid architectures, are discussed. This comprehensive study provides both theoretical and practical guidance for researchers, engineers, and industry practitioners in the rapidly evolving field of quantum computing.
DOI: https://doi.org/10.5281/zenodo.16886323
Handheld Ultrasonic 3D Mapping: A Low-Cost, Portable Real-Time Spatial Visualization System
Authors: Goutham J, C Lithish, Dabbara Adithya, Darshan M
Abstract: Handheld ultrasonic 3D mapping systems provide a promising alternative to expensive and bulky LiDAR or optical-based approaches, particularly in environments where GPS is unavailable or optical visibility is limited. The proposed system uses ultrasonic sensors to perform time-of-flight measurements, converting echo signals into accurate distance data that can be reconstructed into a three-dimensional model of the surrounding environment. By integrating these sensors into a compact, portable device, real-time data acquisition and visualization are made feasible without relying on costly equipment. The solution emphasizes affordability, portability, and accuracy, making it accessible for a wide range of applications. This handheld system demonstrates significant potential in robotics, where precise spatial awareness is essential for autonomous navigation and obstacle avoidance. In construction and civil engineering, it enables quick and non-destructive mapping of indoor spaces and infrastructure. Similarly, it supports environmental monitoring by providing efficient, real-time 3D visualization of terrain and objects. The design highlights the advantages of ultrasonic technology—operating reliably in conditions where optical methods fail, such as low light or smoke. With future enhancements in resolution, range, wireless integration, and advanced processing algorithms, handheld ultrasonic 3D mapping devices could become indispensable tools across industrial, research, and societal domains, offering both cost-effectiveness and practical usability, and paving the way for broader adoption in diverse industrial and research domains.
DOI: https://doi.org/10.5281/zenodo.16892277
A Critical Study Of Technological, Security, And Behavioural Factors Affecting E-Banking Adoption In Selected Banks Of Jalgaon District.
Authors: Dr.Pradnya P Surwade, Dr. Mahesh M Badve
Abstract: The banking industry is undergoing a rapid digital transformation, with e-banking emerging as a crucial service channel for both public and private sector banks. In India, the increased penetration of the internet, smartphones, and digital payment infrastructure has created a fertile ground for the growth of e-banking. However, the actual adoption rate of these services depends on multiple factors, including technological readiness, trust in security measures, and customer behavioural attitudes. In the context of the Jalgaon district, which has a diverse mix of urban and semi-urban populations, there exists a variation in technology awareness, banking habits, and security perceptions. While some customers actively embrace online banking for its convenience and speed, others remain hesitant due to concerns about cyber fraud, lack of digital literacy, or preference for traditional branch-based services. This study critically examines the technological, security, and behavioural factors that influence the adoption of e-banking among customers of selected banks in the Jalgaon district. It aims to provide insights into how these factors impact customer decisions, satisfaction levels, and long-term engagement with digital banking platforms. Additionally, the research seeks to compare adoption patterns across public and private sector banks, highlighting differences in customer expectations, service delivery, and trust levels.
DOI:
A Hybrid Secure Routing Framework Integrating Machine Learning And Blockchain For Efficient And Confidential IoT Data Transmission
Authors: Piyali Ghosh Bhowmik, Dr. Dhirendra Kumar Tripathi
Abstract: The IoT network proliferation has brought forth stringent challenges in providing secure, energy-efficient, and privacy-preserving communications. Centralized anomaly detection systems and conventional routing protocols are usually lacking in robustness against advanced cyber-attacks and scalability. This work introduces the Federated Hybrid Secure Routing Protocol (F-HSRP), an innovative, modular design combining federated CNN-based anomaly detection, trust- and energy-aware routing, AES-256 encryption, and blockchain-assisted route validation. In contrast to current solutions, F-HSRP allows decentralized, smart threat detection and dynamic trust calculation without revealing unprocessed data. Based on the Bot-IoT dataset and simulations on NS-3 and TensorFlow Federated, the model reported 96.3% detection accuracy, saved 27% energy consumption, and enhanced packet delivery ratio to 94.8%. The proposed solution fills the gaps of scalability, latency, and privacy and is well-fitted for real-time industrial, healthcare, and smart city applications. The paper shows major steps toward future-proof, secure IoT infrastructures by interdisciplinary innovation and federated intelligence.
DOI:
Data-Driven Forecasting Of Loan Default Risk Via Personalized Customer Analytics And Deep ML Models
Authors: Mrs. Narni Sree Ratna Niharika, Mr. V Anil Santosh
Abstract: Banks serve as the foundation of the global financial system, generating considerable income from loan interest. Nevertheless, loan defaults can turn expected profits into substantial losses, highlighting the necessity for thorough risk evaluation before loan sanctioning. In this research, we utilized advanced machine learning methods to accurately and efficiently predict loan default risk. Six sophisticated models Decision Tree, Random Forest, Support Vector Machine (SVM), Multi-layer Perceptron (MLP) Neural Network, Naive Bayes, and an innovative stacking ensemble were trained on an extensive dataset consisting of twenty vital attributes from loan applications. The stacking ensemble model attained the highest performance, achieving an accuracy of 78.75%, while the Random Forest model exhibited similar effectiveness at 78.15% with greater computational efficiency. Our examination pinpointed key indicators of credit risk, including loan amount, checking account status, customer age, loan duration, and loan purpose. These results reinforce the transformative capacity of machine learning in advancing credit risk assessment, providing banks with actionable insights for prudent lending choices.
DOI: http://doi.org/10.5281/zenodo.16900081
Specification Of An Ideal Zero Trust Architecture Implementation Design For The Mitigation Of Cloud-Based Services Authentication Threats And Vulnerabilities
Authors: Victor Otieno Mony, Anselemo Peters Ikoha, Roselida O. Maroko
Abstract: Cloud-based services' authentication mechanisms have recurring threats and vulnerabilities that cannot be solved by existing traditional mitigation strategies. As a result, cloud insecurity has been the norm of recent times, even as edge technology outsmarts mitigation mechanisms such as passwords, biometrics, and key-based protocols. In response to these cloud authentication challenges, this paper seeks to specify an ideal Zero Trust Architecture (ZTA) implementation Design that best mitigates authentication threats and vulnerabilities. To specify the ideal ZTA design, the research paper begins by examining the operational tenets of the ZTA principle of Policy Enforcement, discovered as the most effective among the five ZTA principles affecting Cloud-based services (CBS). The paper then delves into the process of ZTA design specification. The design specification process evolves around five identified cloud-based authentication threat categories, namely, Brute-force attacks, Man-in-The-Middle Attacks, Social Engineering Attacks, Password Discovery Attacks, and Denial of Service Attacks. The ZTA model design is thus performed by the research work through the Forrester ZTA framework, particularly focusing on the People and Device tenets, thereby achieving the objective of architectural specification. To achieve this, the paper builds on the ZTA principle of policy enforcement and operationalizes it through trust signals that balance usability and security. The resulting ZTA design specification enlists Keystroke Dynamics to represent user behaviour, while Device Location serves as a contextual device-based trust signal. These two elements are aligned with the “People” and “Device” tenets of the Forrester ZTA model. Together, they enabled the construction of a dynamic, adaptive security mechanism capable of enforcing ZTA access policies based on real-time behavioural and contextual signals. The tailored ZTA design specified by this paper is a proposal of an innovative means of enhancing authentication security in CBS to ensure a data-driven threat mitigation process that aligns strategic dynamic security controls with the most pressing authentication threats.
DOI: https://doi.org/10.5281/zenodo.16902046
RESARCH INTEGRITY IN SOFTWARE ARCHITECTURE: A CRITICAL REVIEW OF ETHICAL CHALLENGES AND BEST PRACTICES
Authors: Paul Oduor Oyile, Samuel Mungai Mbuguah
Abstract: As software systems become increasingly integral to critical infrastructure, commerce, governance, and everyday life, the role of software architecture in shaping ethical outcomes has gained renewed importance. While technical efficiency and system scalability remain core concerns, the ethical integrity of architectural research and decisions is often overlooked. This paper critically reviews the ethical challenges and best practices related to research integrity in software architecture. The objectives of the study are: (1) to examine the ethical risks and dilemmas inherent in architectural research and design decisions, and (2) to explore best practices that can guide ethically responsible architectural research and implementation. The study adopts a desktop research design, utilizing content analysis of empirical literature to evaluate the current discourse on ethical conduct and responsible innovation in software architecture. Findings reveal that ethical challenges in software architecture research often stem from biased trade-offs, lack of transparency, neglect of user privacy and inclusivity, and inadequate documentation of decision rationales. Conversely, best practices emphasize transparency, stakeholder involvement, long-term impact assessment, and alignment with ethical frameworks such as fairness, accountability, and sustainability. This paper contributes to the growing discourse on ethical software engineering by offering a synthesized understanding of how research integrity can be upheld throughout the architectural design process.
DOI: http://doi.org/10.5281/zenodo.16908681
Zero Trust Architecture In Cybersecurity: Rethinking Trust In A Perimeterless World
Authors: Okpala Charles Chikwendu
Abstract: As traditional network perimeters continue to dissolve due to the rise of cloud computing, remote work, mobile access, and increasingly sophisticated cyber threats, organizations are compelled to rethink long-standing assumptions about trust in cybersecurity. Zero Trust Architecture (ZTA) offers a transformative approach that replaces implicit trust with continuous verification, contextual access controls, and the principle of least privilege. This article explores the conceptual foundation, core principles, enabling technologies, implementation strategies, and real-world applications of Zero Trust in both public and private sectors. It also examines the challenges associated with legacy system integration, scalability, user experience, and cost, while highlighting future directions such as AI-driven trust scoring, privacy-preserving authentication, and Zero Trust for IoT and operational technology environments. Through a comprehensive review of literature, case studies, and emerging trends, the article positions ZTA as a critical framework for securing modern digital ecosystems in a perimeterless world.
Cybersecurity Challenges And Solutions In Edge Computing Environments: Securing The Edge
Authors: Okpala Charles Chikwendu
Abstract: Edge computing has emerged as a transformative paradigm that brings computation and data storage closer to the source of data generation, thereby enhancing responsiveness, reducing latency, and enabling real-time processing. However, this distributed architecture introduces a unique set of cybersecurity challenges that differ significantly from those faced in traditional cloud-centric models. This paper provides a comprehensive analysis of the key security challenges in edge environments, including expanded attack surfaces, limited device resources, physical vulnerabilities, decentralized trust, and complex regulatory compliance related to data privacy. It then explores emerging and effective solutions such as lightweight encryption, zero trust security models, AI-driven intrusion detection systems, secure hardware foundations, blockchain-based trust mechanisms, and edge gateway protection. Through sector-specific case studies in manufacturing, healthcare, smart cities, and industrial IoT, the article illustrates the practical implications of edge security. Finally, it highlights future research directions, such as the need for interoperable security standards, quantum-resilient cryptography, context-aware security models, and the secure integration of edge computing with 5G and beyond. The findings underscore the critical importance of designing adaptive, scalable, and forward-looking cybersecurity frameworks to ensure the safe evolution of edge computing ecosystems.
A Coordination Address Protocol And Data Model To Improve Administrative Address Performances: Coherence, Accuracy And Traceability
Authors: Dr. Bayomock Linwa André
Abstract: One of the main questions to qualify an action that occurs is the "Where" question. The "Where" helps to enhance an activity description by providing the location’ characteristics. The activity may be related to public, private, family or business affairs. The extended domains of the "Where" usability show its great importance in human life. The "Where" answer is a location, a place or an object position of a given action. The common word used to identify that information encapsulated in the "Where" answer is called an "Address". The address information is either classified geo-located or administrative. The geo-located classification of an address uses world longitude/latitude/altitude coordinates (or Cartesian coordinates). the geographic location' result precision varies either fine granularity less than 3 meters from target object position or a large granularity answer for example the location of an action may be identified at the North, West, East, Center or South of a country, region or city. The administrative address may use country subdivisions made by a government authorities or by the organization authorities to identify the organization units' location. In many IT (Information Technology) projects, the address module is generally re-written. The main reason is that the addressing of the organization subdivisions and their units’ hierarchical relationships should be represented and mapped to the country subdivisions made by government authorities. Another reason is that tightly coupled object to an address don’t keep a location quality about their address for a discovery purpose (address change, error in address writing, lack of responsibility of providing a quality address). Return costs (shipping fees, restocking fees, Reshipping fees, address correction fess, labor cost, customer dissatisfaction, loss of revenue) or lost of opportunities due to bad address is so high. For example, UPS and Fedex estimate address correction fees between $ and per package (Jimmy Rodriguez, 2023). To coordinate the address production to consumption with coherence, accuracy, traceability of address change and allow address reuse, this paper proposes a coordination address protocol that handle administrative addresses and its associated data model. This protocol establishes the actors’ responsibilities of an address production as well as the entrances (inputs) and the exits (outputs).
DOI: https://doi.org/10.5281/zenodo.16908949
An Overview And Analysis Of The Muscular System And Appropriate Treatment In Response To Crotalid Snake Bite
Authors: Jerrod G. Tynes
Abstract: Snake bites, while fairly rare in the United States, pose serious medical threats due to the myotoxic and cytotoxic properties of venom, particularly from species of various rattlesnakes. This paper explores the muscular system's role and vulnerability during envenomation, detailing the anatomical and physiological effects of venom on muscle and vascular tissues. An explanation explores the beginning of diagnosis and the rapid clinical assessment. Species identification and imaging to monitor swelling and tissue damage are also an important part of a snake bite response. Symptoms can range from localized pain and edema to systemic effects such as internal bleeding and cardiovascular complications. Underlying conditions like diabetes, clotting disorders, or advanced age can exacerbate outcomes. Treatment includes wound care, pain and symptom management, and antivenom administration, which must be carefully done to minimize allergic reactions. Imaging and histological analysis may help determine tissue damage severity, guiding further interventions. Suggestive prevention strategies are discussed including an emphasis on protective behavior and awareness, especially in areas where snake concentration is high. As human-snake interactions continue, medical preparedness and ongoing research remain vital in improving outcomes and reducing mortality associated with envenomation
DOI: http://doi.org/10.5281/zenodo.16909477
Advanced Research Paper On Computer Graphics
Authors: Santosh Kumar Dash
Abstract: Computer graphics has become one of the most influential fields of modern computing, revolutionizing industries such as film, gaming, scientific visualization, virtual reality, and artificial intelligence. This paper explores advanced dimensions of computer graphics, focusing on its evolution, mathematical foundations, rendering pipelines, and the integration of emerging technologies. It begins with a discussion of the historical trajectory from vector graphics to real- time ray tracing and neural rendering. The mathematical underpinnings of graphics—linear algebra, geometry, and numerical methods—are examined as the basis of transformations, modeling, and physical simulation. The rendering pipeline is analyzed through both classical rasterization and physically accurate ray tracing, with emphasis on global illumination and physically based rendering models. Finally, neural rendering techniques highlight the growing convergence between computer vision and graphics, shaping the future of digital image synthesis. This study underscores the balance between computational efficiency, photorealism, and interactivity that defines the frontier of computer graphics research..
DOI: http://doi.org/
An AI System For Comprehensive Evaluation Of Handwritten Answer Sheets
Authors: Ajay Rajendra Erude, Dr. Sushilkumar N. Holambe, Dr. Vikramsinha V. Mane
Abstract: The comprehensive evaluation of handwritten answer sheets plays an essential role in the educational evaluation process, yet it remains a time-consuming process prone to subjectivity and inconsistency. This paper investigates how Artificial Intelligence (AI) can be leveraged to improve this process. We present a prototype system that utilizes a multimodal AI model to perform a comprehensive analysis of both a digitized question paper and handwritten answer sheets. The system automates grading, provides detailed feedback, and, critically, addresses a significant gap in existing research by automatically identifying and extracting student identification numbers from handwritten answer sheets. Initial testing on a computer science and engineering examination demonstrates the system's capacity to provide consistent, accurate evaluations and feedback, affirming the potential of AI to create more efficient and equitable assessment workflows.
Quantum Computing And The Future Of Cybersecurity: A Paradigm Shift In Threat Modeling
Authors: Okpala Charles Chikwendu
Abstract: Quantum computing represents a transformative technological advancement with the potential to disrupt existing cybersecurity paradigms. As quantum algorithms such as Shor’s and Grover’s threaten to undermine widely used cryptographic systems, the cybersecurity landscape must evolve to address emerging risks. This article explores the implications of quantum computing on current threat models and highlights the need for a paradigm shift on how organizations conceptualize and respond to cyber threats. It examines both the technical challenges and strategic considerations associated with transitioning to post-quantum cryptographic standards, the limitations of existing quantum hardware, and the necessity of rethinking risk frameworks in anticipation of quantum-capable adversaries. The paper also discusses future directions, including quantum-safe protocols, ethical considerations, and global coordination. Ultimately, it argues for a proactive, interdisciplinary approach to preparing for the quantum era, in order to ensure that digital infrastructure remains resilient in the face of rapid technological change.
DOI:
Cross-Layer 5G Anomaly Detection: A Hybrid 1D-CNN-LSTM Approach For Mitigating RF And Protocol-Level Attacks In 5G NR Networks
Authors: Abhirup Guha
Abstract: The rapid deployment of 5G networks has introduced complex security challenges that span both physical-layer signal integrity and higher-layer protocol semantics, necessitating unified anomaly detection frameworks capable of addressing multi-vector attacks. We propose a cross-layer anomaly detection system that integrates radio-frequency (RF) signal distortions with protocol-level behavioral patterns to identify threats such as downgrade exploits, data sniffing, and device fingerprinting. The proposed method employs a hybrid 1D-CNN-LSTM architecture, where the 1D-CNN processes cyclostationary features extracted from in-phase/quadrature (I/Q) samples, while the LSTM analyzes graph-based embeddings of 5G protocol messages generated by a Graph Attention Network (GAT). These heterogeneous features are fused through a cross-attention mechanism, enabling real-time anomaly classification without specialized hardware. Moreover, the system interfaces with the 5G Core’s Security Edge Protection Proxy (SEPP) and Access and Mobility Management Function (AMF) to trigger conditional re-authentication upon detecting anomalies, thereby preserving session continuity for legitimate devices. The architecture achieves sub-millisecond latency by leveraging dilated residual blocks in the 1D-CNN and peephole connections in the LSTM, trained with a contrastive loss function to improve discriminative power. Experimental results demonstrate significant improvements over standalone SDR-based or protocol-centric detectors, particularly in scenarios involving coordinated RF and protocol-level attacks. This work bridges the gap between physical-layer signal analysis and network intrusion detection, offering a scalable solution for securing next-generation wireless systems.
DOI: https://doi.org/10.5281/zenodo.16910463
Eco-Sustainability In Aerospace: Managing Orbital And Terrestrial Chemical Emissions
Authors: R. Hema Krishna
Abstract: The increasing presence of space debris in Earth’s orbit and the release of harmful chemicals during satellite launches, operations, and re-entry events have raised pressing concerns regarding orbital and terrestrial chemical pollution. Beyond the risk of physical collisions, the degradation of paints, polymers, metallic alloys, and residual propellants in space introduces reactive chemical species into the near-Earth environment. Furthermore, the combustion of re-entering debris contributes oxides, halogenated compounds, and fine particulates to the upper atmosphere, posing risks to ozone chemistry and climate balance. This paper explores integrated strategies to minimize such chemical pollution. Key approaches include the development of green propellants (e.g., ionic liquids, nitrous oxide–based systems), eco-friendly materials with lower degradation potential and surface coatings resistant to atomic oxygen erosion. Policies for passivation of spacecraft and rocket stages to prevent explosive fragmentation, as well as controlled re-entry techniques to limit uncontrolled chemical dispersal, are emphasized. In addition, recycling and active debris removal technologies offer long-term solutions to reducing both physical and chemical impacts. Collaborative international regulations and sustainability frameworks are vital to ensure responsible space practices. By combining material innovation, engineering design, and policy enforcement, these strategies provide a pathway toward mitigating orbital chemical contamination while safeguarding Earth’s atmosphere from the adverse effects of space activities. This Review highlights the Eco-Sustainability in Aerospace- Managing Orbital and Terrestrial Chemical Emissions.
DOI: http://doi.org/10.5281/zenodo.16910573
Lightweight Online Ransomware Detector (LORD): A System Call Interceptor With Incremental Learning For Isolated Systems
Authors: Abhirup Guha
Abstract: We propose LORD, a Lightweight Online Ransomware Detector that integrates incremental learning with hierarchical feature extraction to enable real-time ransomware detection on isolated systems. Traditional signature-based methods fail to adapt to evolving threats, while static behavioral analysis lacks scalability for resource-constrained environments. The proposed method addresses these limitations by intercepting system call sequences and processing them through a Compact Neural Feature Extractor (CNFE), which reduces computational overhead by 60% compared to standard Transformers while preserving detection accuracy. Furthermore, the Incremental Learning Module (ILM) dynamically updates the model using online gradient descent and elastic weight consolidation, ensuring adaptability to new ransomware variants without catastrophic forgetting. To enhance robustness, a Weighted Majority Voting Ensemble (WMVE) aggregates predictions from multiple sub-models, pruning less accurate ones during inference to maintain real-time performance. The system operates entirely offline, requiring no internet connectivity or manual updates, and achieves <5ms latency per prediction with a memory footprint under 50MB. Experimental results demonstrate that LORD detects ransomware with high precision, outperforming conventional rule-based and static machine learning approaches. Its deployment on commodity hardware validates practicality for edge devices, offering a scalable solution for securing isolated systems against zero-day attacks. The integration of TensorFlow Lite and PyTorch JIT compilation ensures efficient execution, making LORD a viable alternative to existing detection frameworks.
DOI: https://doi.org/10.5281/zenodo.16910891
RIGHT TO PRIVACY IN THE DIGITAL AGE: LEGAL CHALLENGES AND EMERGING TRENDS IN INDIA
Authors: Nitisha Mohanty
Abstract: Mymensingh is the capital of Mymensingh Division in central Bangladesh. Ensuring water sanitation and hygiene in Mymensingh is vital for community health, requiring effective measures and collaboration between authorities, organizations, and residents for sustainable implementation. The objectives of this study were to investigate the water supply and sanitation status of Mymensingh City. Data were collected primarily based on a reconnaissance survey with the help of a structured questionnaire. A cross-sectional survey design was employed to collect data on variables related to water, sanitation, and hygiene in the area. Many homes rely on submersible pumps and deep tube wells for drinking water, while access to piped water is limited. Inadequate water supply and limited access to clean water contribute to waterborne illnesses and negatively impact public health. Sanitation infrastructure in Mymensingh City Corporation varies, with reliance on septic tanks and pit latrines, while limited sewage systems and waste management exist. Inconsistent hygiene practices contribute to waterborne illnesses, highlighting the need for improved infrastructure and behavior change interventions. Improving the drainage system, implementing effective measures for waste management, and promoting hygiene education programs are essential for minimizing waterborne diseases and enhancing residents' quality of life.
DOI: http://doi.org/
Green Manufacturing Practices And Their Economic Impact In The Light Industry Sector: A Life-Cycle Assessment And Cost–Benefit Study Of A Packaging Facility In Nigeria_319
Authors: He Baocheng, Nouayou Kamdoum Clauvis, Amir jamil, Mihad Bellaoulah
Abstract: This study investigates the environmental and economic impacts of adopting green manufacturing practices in the light industry sector, with a specific focus on packaging. Using a gate-to-gate life-cycle assessment (LCA) combined with cost–benefit analysis, three operational scenarios were evaluated in a Nigerian light-industry packaging facility: (i) current practices, (ii) optimized processes incorporating low-energy machinery and recyclable packaging materials, and (iii) renewable-integrated systems with solar photovoltaic energy. The results reveal that the optimized case achieved a 22% reduction in CO₂ emissions, 18% reduction in fossil fuel depletion and 12% reduction in water usage, accompanied by annual savings of ,000 and a payback period of 2.7 years. The renewable-integrated case provided even greater environmental benefits, with a 48% reduction in CO₂ emissions compared to the base case, as well as improvements in human toxicity reduction. Economically, this scenario required higher upfront investment but delivered annual savings of ,000 with a payback period of 3.3 years and positive net present value over 10 years. These findings confirm that adopting eco-friendly practices in the light industry sector not only reduces environmental impacts but also enhances financial performance and consumer perception. The study underscores the potential for light industries in developing economies to transition toward sustainable manufacturing, thereby aligning industrial growth with environmental responsibility.
DOI: https://doi.org/10.5281/zenodo.16927202
Exploring Awareness And Use Of Artificial Intelligence Among Healthcare Professional And Students In India
Authors: Dr. Lucy Shinners, Dr.Sree T. Sucharitha, Dr.Aravind Manoharan, Dr.A.Keerthana, Dr.MJ.Kotteeswari
Abstract: – Background: Artificial Intelligence (AI) is transforming the healthcare sector by providing unmatched improvements in diagnosis, patient care, and service administration. Healthcare could be completely transformed by artificial intelligence, but doing so would require a comprehensive approach. In order to facilitate , transition to the future of AI-enhanced healthcare, the study aims to identify knowledge and readiness gaps and provide healthcare providers with the skills and confidence they need to successfully integrate AI technologies into their clinical workflows. Methodology: The study was a cross sectional study conducted among healthcare professionals and students from June 2023 – June 2024 across Tamil Nadu. Sample of 444 was used for analysis. The Shinners Artificial Intelligence Perception (SHAIP) tool designed in Qualtrics online platform is used for data collection after getting consent. Results: The complete data of 444 is taken for analysis. In factor analysis, out of ten items two items were generated and it is closer to one indicating the importance of the factors in finding the perception of impact of AI on HCP’s role. Two predictive variables were shown to be significant (improve population health outcomes (0.025), ethical framework in place for the use of AI (0.033), all of the models showed significance levels of p < 0.05. Conclusion: The results highlight the need for improved AI training, transparent explanations of AI's benefits, and a well-rounded strategy to deal with the difficulties of using AI in healthcare, especially with regard to liability, equity, and transparency.
DOI: https://doi.org/10.5281/zenodo.16927160
Exploring The Factors Affecting Participation Of Pastoralist Girls’ Education In Secondary Schools; The Case Of South Omo Zone Secondary Schools (Hamer And Dasanech Woredas), South Omo Zone In Southern Ethiopia Regional State, Ethiopia
Authors: Moy Topo Murale, Abraham Yohannes Amado, Abate Ashsnafi Niguse
Abstract: The purpose of the study was to explore factors affect girls’ participation in secondary education in Dasanech and Hamer woredas, South Omo Zone, South Ethiopia Regional State Ethiopia. The objectives of the study are. – to find out how cultural, school and economic factors affect participation of girls in secondary education, to identify the individual challenges that affect participation of girls in secondary education, to suggest ways of improving girls’ participation in secondary education in Dasanech and Hamer woredas. To achieve this, the descriptive survey method was used. The target population of the study was the 2 head teachers of two public secondary schools in South Omo Zone, 20 girls, 20 boys, 10 class teachers. The response rate of the respondents was 100 percent. The study used questionnaires for data collection and collected data were analyzed using computer. The findings from the study showed that socio-economic factors affect participation of girls in secondary education. This resulted to lack of teaching learning materials and lack of personal effects for those with poor socio-economic background. The socio-cultural factors result to early marriages, male preference in family, community initiation into adulthood, negative attitudes of girls in education, cultural practices and feeling of being adults which do affect participation of girls in secondary education. Parents’ level of education as a factor affects girls’ participation in secondary education because it can promote or lower their participation in education. Educated parents do support their girls in their educational requirements. They also become role models to their daughters' participation in education they most understand what their daughters want and they do provide them with unlike the uneducated parents. Distance from school as a factor has effect on girls’ participation in education. The researchers suggest the need to carry out the study to determine other factors affecting girls’ participation in secondary education. The researchers also suggest similar studies to be carried out in other woredas secondary schools and in public primary schools in the Zone
DOI: http://doi.org/10.5281/zenodo.16932506
PARAMETRIC OPTIMIZATION AND THEIR EFFECT ON MECHANICAL PROPERTIES OF FRICTION SIR WELDED AA1100 ALUMINUM ALLOY
Authors: Raj Kumar Pathak, Tanvir Singh
Abstract: This research work deals with studying the effect of welding parameters on the mechanical and metallurgical properties of friction stir-welded joints of AA1100 aluminum alloy. FSW process parameters with tool rotational speed range from 1400 to 1800 rpm and traverse speeds from 80 to 120 mm/min were employed. Results revealed that higher ultimate tensile stress (127 MPa–148 MPa) and percentage elongation (1.9- 11%) with joint efficiency (82-95%) for every set of rotational speed and traverse speed compared to the base material (155 MPa, 75MPa and 12% of ultimate tensile strength, yield strength and % elongation, respectively). The joint efficiency of most of the samples is around 70–80 % with the highest joint efficiency of 95.64% of FSW welds produced at 1800 rpm and 100 mm/min of tool rotational speed and traverse speed due to the fine grains, due to the dynamic recrystallisation, and an onion-ring structure, which improves the properties. Surface appearance and macrostructure results reveal that the weld produced at a higher tool rotational speed of 1800 rpm and moderate traverse speed of 100 mm/min leads to good surface appearance with a flawless weld. Furthermore, the welds produced at 1800 rpm/80mm/min and 1600rpm/120 mm/min of tool rotational and traverse combination show the highest values of microhardness in the probe region due to the exertion of higher stresses because of the round dome shape that deforms the grains to a higher extent, which increases the microhardness values.
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Use Cases Of Augmented Reality, Virtual Reality, And Mixed Reality-based Practices In Library: A Study
Authors: Prof. Manisha D. Patil
Abstract: This research paper focuses specifically on augmented reality, virtual reality, and mixed reality practices, as well as their applications in the context of libraries. This paper outlines emerging technology practices, as elaborated by the investigator. The first one is an amazing XR Tour of the library. The Texas State University Library (Albert B. Alkek Library) conducted this tour and provided visual information services to its patrons. The investigator also studied the UTM Library VR Tour. Another innovative practice is the use of a tangible globe with AR. The investigator employs the interactive globe concept, which is a more engaging way to understand the world's geography. MondlyAR is another interesting interactive language learning VR app. It is also a Chatbot and voice recognition. Through these practices, the library can promote immersive services in its libraries.
DOI: http://doi.org/
Evaluation Of Safety System In Egyptian Infrastructure Projects Based On Level Of Injuries
Authors: Nema, Amira S, Tolba, Ehab R, Hassan, Hassan M.
Abstract: The construction industry is pivotal in developing global infrastructure and economic expansion. Nevertheless, it is infamous for its high accident rates and safety concerns. Despite accounting for only 7 % of global employment, construction is responsible for approximately 30 % of all fatal workplace fatalities worldwide. In developing countries, the situation is even worse. The injury itself can vary from case to case, starting from small injuries that require first aid treatment to severe injuries that can cause long-term disabilities and, ultimately, death. From a safety and health perspective, the absence of implementing the safety system on site causes many accidents that could lead to death or disability. In this work safety absence management factors of Egyptian infrastructure projects are collected using questionnaire. This paper aims to study the relation between the absence of safety factor in infrastructure sites and the level of injury by using statistical analysis for collected data from construction projects partners. The questionnaire answer analyzed by using SPSS program and PCA method (principal component analysis) which reached to identify 41 models to identify the level of injury. The result of model function identifies the level of injury which more close to 1 the level of injury decrease which mean superficial or minor injury. The interested companies could choose the suitable model due to its type of infrastructure projects and safety system. The researcher explained the model no (15) equation and model variable as an example.
DOI: https://doi.org/10.5281/zenodo.16934036
Optimizing Solar Energy For Sustainable Development: A Cost-Efficient Approach
Authors: Dr. Isha Madan, Dr. Manju Papreja, Dr. Renu Miglani
Abstract: The growing demand for renewable energy has made solar power a key contributor to sustainable development. Solar energy provides a clean, abundant alternative as fossil fuel reserves decline. However, its intermittent nature necessitates optimization to ensure maximum efficiency. This paper examines cost-effective strategies for improving solar energy production, storage, and utilization. Solar systems can enhance efficiency, lower costs, and better meet energy demands by integrating Artificial Intelligence (AI), smart grids, and advanced photovoltaic (PV) materials. These advancements make solar power more reliable, scalable, and economically viable, contributing to a more sustainable and energy-secure future.
Ethiopian Higher Education Relevance and Quality Assurance Policy Implications for Conventional and Distance Higher Eduction
Authors: Dr. Melese Mekasha Woldeyes
Abstract: The history of higher education in Ethiopia traced its origin from the Orthodox Church. However, a secular higher education began in 1950 following the establishment of the University College of Addis Ababa (World Bank, 2003). According to Yizengaw, (2004) and Bogale (2006), the development of higher education was neglected and remained underdeveloped for almost six decades. It faced problems associated with quality and relevance of programmes of study, research, equity, resource constraints, and inefficient resource utilization (Yizengaw, 2005). With these problems, the contribution of higher education institutions to the development of the country particularly in the area of supplying the large numbers of trained manpower required for development is not significant. As in other countries in Africa, Ethiopia recognises that higher education is a prerequisite in achieving its national economic development and poverty reduction strategies (World Bank, 2003). Indeed, this requires the alignment between higher education provision and other sectors in the country. It is also true that the higher education system is undergoing transformation in order to respond to and gear adequately to the development needs of the society and the country (Yizengaw, 2004: 12). Just as in other developing countries, the Ethiopian government has prioritised quality in higher education as part of a strategy to respond to the human resource development needs of the country. In this regard, it established HERQA in 2003 as its main agency to ensure quality in education. The mandate of this agency included conducting assessments of applicants for licenses, pre-accreditation and accreditation and external quality audits; ensuring the relevance of higher education institutions with regard to national policy; and proposing national benchmarks and standards for quality, among other responsibilities. The function of HERQA encompasses both traditional contact and distance higher education systems. The agency has not yet developed a clear policy framework; it only has general guidelines to ensure quality in both distance and higher education. However, based on the general guidelines HERQA has drafted its own ten focal assessment areas to ensure the quality of higher education. Available records and documents regarding this issue affirm that HERQA follows guidelines to maintain quality in higher education in both public and private sectors. In order to address this, government officials were asked the research question to understand the policy of government in terms of quality assurance in higher education, in general, and distance education, in particular. This was done to establish whether HERQA had already developed policy to further enhance quality assurance in higher education.
DOI: http://doi.org/
Globaly Accepted Concepts of Distance Education and Its Growth and Development Over the World
Authors: Dr. Melese Mekasha Woldeyes
Abstract: Across the globe it is an accepted fact that Distance education, open learning and correspondence education are often used interchangeably, but have different meanings. Distance Education has been defined in many ways by various scholars. Keegan, for example, defines it “as a method of imparting knowledge, skills and attitudes to learners, using high quality materials for those learners who geographically departed from their teachers” (1986:39). Similarly, Moore (1973:6) defines distance teaching as instructional methods in which the teaching behaviours are executed apart from the learners. The definition of distance education has been explained as “instruction through print or electronic media to persons engaged in planned learning in a place or time different from that of the teacher or instructors” (Keegan, 1986:39). As new technological advancements develop, the traditional definitions of distance education gradually started being reconceptualised by different scholars and educators. Distance Education, nowadays defined more as Open and Distance Learning, dates back to 1800s. Once considered as using non-traditional approaches and delivery methods compared to conventional campus-based education, distance education now has become a mainstream form of education increasing its popularity and use in the 21st century. Distance education has taken various forms and different definitions have been adopted depending on the age it has been developed. Technologies and pedagogies of the age along with the societal circumstances have influenced how distance education is viewed and practiced making way for different generations of distance education one of the strengths of this definition is that unlike Keegan’s definition, it includes the three domains of learning: 1) cognitive (thinking), 2) affective (emotion/feeling), and 3) psychomotor (physical/kinaesthetic) (Wilson, 2016). However, like Keegan, this definition incorporates the existence of a supporting organization. According to Holmberg, distance education is “the form of education in which there is a geographical separation between the teacher and the student” (1985:331). He further notes that “distance education is a form of study which is not at all under the continuous supervision of tutors, and the tutors are not present with their students in lecture rooms.” Holmberg suggests that learners and tutors can be “on the same premises, but which, however, can benefit from the planning, guidance and tuition of tutorial organization’’ (1985:330). It is evident that no developing country, using traditional methods of education alone, can hope to make education universally available in order to train the qualified human resources needed. Therefore, many countries around the world have developed distance education to increase access to higher education. To ensure that this mode of provision meets the required needs, quality measures have been built into its provision. From the available data, the following table indicates the number of countries on different continents and the number of institutions involved in distance education around the world
DOI: http://doi.org/
Three-dimensional Reconstruction Of Breast Cancer Tumour
Authors: Lakshmi Shree A, J Tanuja, Prerana K N, V Satish
Abstract: Accurate visualization of breast cancer tumors is critical for effective diagnosis and treatment planning. However, conventional 2D medical imaging often lacks the spatial depth needed to fully assess tumor morphology. This research presents a deep learning-based approach for 3D reconstruction of breast cancer tumors using 2D medical scans. A U-Net architecture is employed to segment tumors from MRI or CT images, followed by a 3D Generative Adversarial Network (3D-GAN) to reconstruct volumetric tumor models. The final 3D structures are rendered using the Visualization Toolkit (VTK), enabling interactive exploration of tumor size, shape, and localization. The system enhances diagnostic accuracy, supports clinical workflows, and provides a scalable framework for future applications in other cancer types. Experimental results demonstrate the model’s effectiveness in generating anatomically consistent 3D reconstructions from limited 2D data.
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Artificial Intelligence In Gaming: Innovations, Impacts, And Future Directions
Authors: Santosh Kumar Dash
Abstract: Artificial Intelligence (AI) has become one of the most transformative forces in the global digital economy, and nowhere is this more evident than in the gaming industry. Gaming has historically been both a driver and a beneficiary of AI research. From early rule-based enemies in arcade titles to complex reinforcement learning agents capable of defeating world champions in games like Go, Dota 2, and StarCraft II, AI has reshaped how games are designed, played, and experienced. This paper explores the evolution of AI in gaming, highlighting its dual role as a commercial innovation and as a scientific testbed. We examine AI technologies including pathfinding algorithms, reinforcement learning, machine learning-based personalization, natural language processing, computer vision, and generative AI. Applications are analyzed across design and development, player experience personalization, e-sports, procedural content generation, and the management of virtual economies. Case studies of landmark AI achievements — from IBM’s Deep Blue to AlphaGo and OpenAI Five — are provided to demonstrate the increasing sophistication of game-oriented AI. Beyond entertainment, gaming environments have become laboratories for AI research, offering controlled yet complex systems for testing algorithms that later find real-world applications in healthcare, robotics, logistics, and defense. However, the growing influence of AI in gaming raises ethical challenges related to addiction, bias, privacy, and the over- personalization of experiences. The article concludes by exploring the future of AI in gaming, including AI-driven storytelling, autonomous game design, and integration into the metaverse. We argue that gaming’s unique position at the crossroads of technology, culture, and commerce makes it central to the broader trajectory of AI, providing insights into how humans and machines will collaborate in the future.
DOI: https://doi.org/10.5281/zenodo.16946448
Energy Cascade: An Experimental School Prototype To Reuse Heat Generated From VRF Air Conditioners And Reduce Air Pollution
Authors: Silvia Gaba, Kanav Kalra
Abstract: In today's world, a large amount of energy is lost in the form of waste heat released by air conditioners, industrial equipment, and household appliances. This project focuses on utilizing that otherwise wasted vent heat through the energy cascade principle, where high-grade energy is used for primary tasks and the remaining energy is reused for secondary functions. Our aim is to capture the vent heat from a VRF (Variable Refrigerant Flow) air conditioning system, which is then redirected to perform multiple useful applications. The generated hot air is reused in four smart ways: (a) generating micro electricity using peltier device, (b) drying clothes by directing hot air to a mini clothesline, (c) heating water for domestic uses, and (d) passing the hot air into a mini-iron model for pressing clothes. This system models an effective method to minimize energy waste, reduce reliance on electricity and fuel, and promote eco-friendly and sustainable energy use. The setup uses simple, low-cost materials including copper piping, a water container, small generator modules, and basic heating tools. The concept is scalable and can be used in homes, hotels, and even industries to improve overall energy efficiency. This project not only demonstrates practical application but also encourages innovation in renewable and waste energy management systems.
DOI: http://doi.org/10.5281/zenodo.16947153
Optimizing CUDA Kernels For High-Performance Canny Edge Detection
Authors: Harshul Gupta, Rishita Yadav
Abstract: We present a comprehensive optimization of the Canny Edge Detection algorithm using NVIDIA CUDA to achieve high-performance GPU acceleration. Our approach integrates warp-level execution, shared memory tiling, memory coalescing, and page-locked transfers to systematically enhance computational efficiency. Evaluated on a subset of the ILSVRC 2017 dataset across six image resolutions (128×128 to 1024×1024), the optimized kernels achieve up to 56× speedup over a baseline OpenCV implementation, with the shared memory version reaching 83.3% compute and memory throughput. Profiling with NVIDIA Nsight Compute reveals that memory transfer bottlenecks remain the primary limitation, but asynchronous transfers and stream concurrency further improve performance. These results demonstrate the potential of advanced CUDA optimization techniques for enabling real-time, high-resolution edge detection in large-scale image processing applications.
DOI: https://doi.org/10.5281/zenodo.16948653
REVIEW ON DESIGN AND OPTIMIZATION OF DVFS-BASED VLSI CONTROLLERS FOR REAL-TIME VPP ENERGY MANAGEMENT
Authors: Anand Kumar Yadav
Abstract: This paper displays a thorough study on the new also intriguing idea of virtual control plant (VPP). The study blankets the virtual force plant definitions, components, Furthermore schema highlights those different strategies that might make utilized for VPP operation streamlining. Finally, An general structure to the operation and the streamlining of the virtual control plant is recommended examined.
Code Collaboration Platforms: A Systematic Review Of Tools, Practices, And Impacts
Authors: Bholu Yadav, Vicky Kumar, Abhishek singh, Aryan pandey, Prof. Nidhi Patel
Abstract: Code Collaboration is a digital platform developed to modernize the submission, evaluation, and approval process of academic projects by eliminating paperwork, manual tracking, and fragmented communication that often cause inefficiencies and delays. The system provides a centralized, intuitive interface where students can seamlessly submit proposals, monitor progress, and receive timely, structured feedback from faculty members. For educators, the platform simplifies project evaluation through role-based access control, automated notifications, and real-time tracking, enabling them to approve submissions or request revisions efficiently while ensuring academic standards are upheld. This structured workflow reduces administrative workload, minimizes delays, and fosters clear communication between students and faculty. Students benefit from instant updates on submission status, a transparent resubmission process, and actionable feedback, while faculty can focus on academic quality instead of logistical management. With its scalability and adaptability, Code Collaboration is suitable for implementation across departments or even multiple institutions. Enhanced by strong security features and tools that support real-time collaboration, the platform offers a professional, reliable, and transparent solution to academic project management. Ultimately, it transforms traditional workflows into a more efficient, structured, and equitable process that empowers both students and educators while ensuring fairness and accountability in project evaluation.
DOI: https://doi.org/10.5281/zenodo.16963214
FinSaathi
Authors: Tushar Chudhari, Rohit Singh, Sonam, Priyanshu Kumar Singh, Professor Himanshu Tiwari
Abstract: FinSaathi is a comprehensive, AI-driven personal finance management system specifically designed to address the unique financial challenges faced by students and young professionals. The system leverages advanced machine learning algorithms, Optical Character Recognition (OCR), Natural Language Processing (NLP), and gamification principles to provide automated expense tracking, intelligent budgeting recommendations, and engaging financial literacy tools. Built using Flutter for cross-platform mobile development, Node.js/Express for backend services, and MongoDB Atlas for cloud-based data storage, FinSaathi integrates behavioral economics principles to encourage positive financial habits. The platform features automated receipt scanning with 95% accuracy, AI-powered expense categorization achieving 87% precision, real-time budget monitoring, and gamified user engagement through achievement systems and social comparison features. Evaluation with 60+ student users demonstrates significant improvements in financial awareness, budget adherence, and user engagement compared to traditional expense tracking methods. The system addresses critical gaps in student-focused financial technology by combining automation, personalization, security, and campus-specific integrations.
DOI: https://doi.org/10.5281/zenodo.16991213
Detection And Extraction Of Triacontanol From Waste Material (rice Bran Wax)
Authors: Deekshith Gowda , Dr.Kiran kumar B, Anusha N Raju
Abstract: Molecular distillation (MD) and microwave digestion are used to extract triacontanol concentration from crude rice bran wax (RBW), a byproduct of the rice bran oil business. Policosanols, primarily triacontanol, octagonal, do triacontanol, and hexacontanol, are present in RBW. The study involved hydrolysingdeoiled and bleached RBW with 6% potassium hydroxide in a microwave oven (1300W, 35 minutes), then MD at 153°C and 62.37 Pa. 74.18% triacontanol was produced as a triacontanol concentrate by the procedure. Additionally, triacontanol, a plant growth regulator, may have anti-inflammatory and cholesterol-regulating effects.A variety of rice known as "fortified rice" has been improved with extra nutrients to offer superior nourishment. In order to aid people that rely significantly on rice as a staple food with micronutrient deficits, fortification entails adding vitamins and minerals to the rice while it is being milled.Enhanced for those who may not have access to a wide variety of meals or who may not be able to pay for more costly sources of nutrients, rice can help to improve their health and well-being.
DOI: http://doi.org/
Social Media’s Impact On Modern Library Innovation
Authors: Bhavani S, Prasanna R
Abstract: – Social media has become a critical driver of innovation in modern libraries, transforming their traditional roles and expanding their reach. This article examines the impact of social media on library practices, highlighting how platforms such as Facebook, Twitter, Instagram, and TikTok facilitate enhanced outreach, community building, and digital engagement. Social media enables libraries to connect with patrons in novel ways, promote digital literacy, and adapt their services to meet evolving needs. By leveraging these tools, libraries are able to experiment with innovative programming, gather valuable feedback, and collaborate with a diverse array of partners. This paper explores the integration of social media in libraries through case studies and trends, demonstrating how these digital platforms are reshaping libraries into dynamic centers of community interaction and learning. The findings underscore the pivotal role of social media in driving library innovation and its potential to further enhance the accessibility and relevance of library services in the digital age.
DOI: https://doi.org/10.5281/zenodo.16993095
Low-Power, High-Linearity Mixer Architectures: A Comparative Study
Authors: Lalita Chouhan, Assistant Professor Mr. Divyanshu Wagh
Abstract: This paper presents a comparative analysis of major RF mixer topologies. The Gilbert cell remains the mainstream core thanks to its high conversion gain (CG), strong port-to-port isolation, and suppression of even-order distortion. Multi-tanh linearization implemented with multiple differential transconductance branches delivers excellent linearity but suffers from low CG. Current-bleeding improves both linearity and CG by injecting additional bias current through a dedicated source, at the expense of higher power. Switched-biasing can yield a very low noise figure (NF) by replacing the tail current source with parallel nMOS devices; however, using devices in place of a current source degrades linearity and raises power. A folded-cascode arrangement lowers supply voltage by folding the LO switching pair (often with pMOS switches), but typically worsens NF. Bulk-driven operation reduces power by using the LO(RF) gate for switching and the bulk for amplification, yet it also reduces linearity. Cross-Coupled Post-Distortion (CCPD) cancels third-order terms to achieve high linearity, but it lowers CG and increases power due to auxiliary devices. Similarly, MGTR linearization raises linearity via an auxiliary transistor while penalizing CG and NF. Overall, mixer design is governed by inherent trade-offs among conversion gain, noise figure, linearity, and power consumption.
Analyzing The Performance Of Algorithmic Trading Strategies In Emerging Markets
Authors: Prof. Pratik Patel, Abhay Singh Gautam, Sushil Kumavat, Sumeet Goswami, Tanvish Munginwar
Abstract: Algorithmic trading has become a key driver of efficiency and innovation in global financial markets, yet its adoption and effectiveness in emerging economies remain less explored. This study examines the performance of selected algorithmic trading strategies within emerging stock markets, focusing on factors such as market volatility, liquidity constraints, and technological infrastructure. Using historical price data and transaction records, multiple strategies—including trend-following, mean reversion, and momentum-based models—are implemented and evaluated through rigorous backtesting. Performance is measured in terms of profitability, risk-adjusted returns, and execution efficiency. The research also considers how market-specific characteristics, such as regulatory frameworks and trading volume patterns, influence outcomes. Findings aim to highlight both the potential and the limitations of algorithmic approaches in environments where market dynamics differ from developed economies. The results provide valuable insights for traders, policymakers, and financial institutions seeking to optimize algorithmic systems in rapidly evolving, high-growth markets.
DOI: https://doi.org/10.5281/zenodo.17003395
Reimagining University Examinations Post-NEP 2020: A Mixed-Methods Study On Reform Implementation Across Urban Madhya Pradesh
Authors: Professor Dr.Himanshu Pandiya
Abstract: The National Education Policy (NEP) 2020 envisions a transformation in India’s assessment architecture by shifting from rote-based testing to holistic, competency-based evaluations. However, translating this vision into university-level examination systems remains fraught with structural, administrative, and pedagogical challenges. This paper examines the state of examination reform implementation across select urban centers of Madhya Pradesh—namely Bhopal, Indore, Jabalpur, Sagar and Gwalior—by deploying a mixed-methods approach. Drawing from survey data, institutional document analysis, and semi-structured interviews with examination controllers, the study uncovers a persistent gap between policy aspirations and ground realities. The findings suggest that while NEP 2020 has generated awareness and limited structural realignments, the legacy of centralized, summative exams persists. Recommendations are proposed for localized capacity building, digital infrastructure improvement, and stakeholder sensitization to facilitate meaningful reform.
DOI: https://doi.org/10.5281/zenodo.17004519
Laxapana Complex
Authors: Abhay Singh Gautam, Sushil Kumavat, Sumeet Goswami, Tanvish Munginwar, Prof. Pratik Patel
Abstract: The Laxapana Complex is one of the most significant hydroelectric power generation systems in Sri Lanka, strategically located in the Central Highlands along the Kelani River and its tributary, the Maskeliya Oya. Operated by the Ceylon Electricity Board (CEB), the complex consists of multiple interconnected components, including the Old and New Laxapana Power Stations, Polpitiya (Samanala) Power Station, and the Wimalasurendra Hydropower Plant. Water is managed through a series of reservoirs and dams such as the Laxapana, Norton, and Canyon Dams, utilizing gravity-fed penstocks to drive turbines and generate electricity. With a combined capacity exceeding 300 megawatts (MW), the complex plays a vital role in meeting the country’s renewable energy demand. Continuous upgrades and modernization efforts, including turbine replacements and plant refurbishments, have enhanced both efficiency and capacity. The Laxapana Complex not only contributes significantly to Sri Lanka’s power grid but also exemplifies the country’s commitment to sustainable and environmentally friendly energy solutions.
DOI: http://doi.org/10.5281/zenodo.17004750
Steel Role In UPI Penetration, Currency Circulation, And Local Currency Production
Authors: Ajit Kumar Thakur
Abstract: This paper examines the interplay between UPI penetration, currency circulation, and the establishment of local currency production facilities, integrating the use of dual-phase steel in the production process. Using Dual phase steel as steel wire and hologram is one of the best techniques to increase uses of steel in currency notes . The analysis explores strategies to enhance UPI adoption, optimize currency circulation, and establish a local currency production facility leveraging dual-phase steel for currency durability and cost-efficiency. Dual phase steel can be used as holographic material by mixing it with halides of silver on currency notes. The proposed framework aims to stimulate currency production, create government jobs, and ensure a balanced approach to physical and digital currency in underserved regions..
Review Future Trends In The Design Of Sustainable, Eco-friendly Pesticides And The Impact Of Chemical Pesticides On The Environment
Authors: Baraa kasim Mohammed
Abstract: Bio pesticides are pest control products derived from natural sources, such as microbes, microorganisms (insects and pathogens), plant extracts, and certain minerals. Many bio pesticides are considered environmentally safe and can complement or substitute conventional chemical pesticides. The topic of pollution has drawn the attention of researchers and specialists from various disciplines, whether scientific or human. Physicists, chemists, health, environmental and geographic scientists have been interested in it. However, the interest of chemists came recently despite their close connection to the topic of pollution. Chemistry is a science concerned with studying the relationships between natural and human phenomena. Research objective: To understand chemical pesticides, their history of manufacture, and their benefits, to study the risks caused by chemical pesticides and to clarify the phenomenon of environmental pollution caused by pesticides, which are sometimes used randomly, without proper scientific oversight. As we enter the twenty-first century, we must reconsider many aspects of our lives in order to protect future generations and live in a clean, pollution-free environment. Future trends in the design of sustainable, Eco-friendly pesticides and finally, advancements in application techniques, as well as future research directions
DOI: http://doi.org/10.5281/zenodo.17007573
Machine Learning For Authentication And Fraud Detection: A Systematic Literature Review
Authors: Abdullahi Mohammed Ibrahim, Naja’atu Basiru Sanusi
Abstract: The rapid growth of digital ecosystems has intensified challenges in authentication and fraud detection, as traditional mechanisms such as static passwords, rule-based systems, and CAPTCHA are increasingly inadequate against sophisticated threats like credential stuffing, phishing, and AI-driven deepfakes. This study presents a systematic literature review (SLR) of machine learning (ML) applications in authentication and fraud detection, synthesizing advances across four key domains: biometric authentication, financial fraud detection, password security, and behavioral biometrics. Following the PRISMA framework, the review encompassed publications from 2013–2025 across SpringerLink, ScienceDirect, Scopus, Web of Science, and Google Scholar. From an initial set of 1,520 studies, 146 peer-reviewed articles were selected through rigorous screening and quality assessment. Data were extracted and coded into thematic categories, enabling both quantitative and qualitative synthesis. The findings reveal that deep learning models such as CNNs, RNNs, and Transformers dominate biometric authentication, significantly improving accuracy in face, voice, and keystroke recognition, though challenges persist with spoofing and privacy. In financial fraud detection, ensemble methods (e.g., XGBoost, LightGBM) and hybrid ML–rule-based systems offer robust performance, while graph neural networks are emerging as powerful tools for detecting fraud rings. Password security research has advanced through ML-based strength estimation and generative models, though ethical risks remain. Behavioral biometrics increasingly leverage multimodal fusion and sequential deep learning, enabling continuous authentication but raising privacy concerns. Across domains, systemic challenges include adversarial attacks on ML models, extreme class imbalance in fraud datasets, limited data sharing due to privacy constraints, lack of standardized benchmarks, and the pressing need for explainable AI (XAI) to meet regulatory and trust requirements. Emerging directions emphasize federated learning, adversarial robustness, hybrid AI frameworks combining symbolic reasoning with ML, and user-centric privacy-preserving approaches. This review concludes that while ML has redefined authentication and fraud detection with adaptive, intelligent, and scalable solutions, its sustainable deployment requires addressing these systemic barriers to ensure future digital ecosystems are not only secure but also transparent, privacy-preserving, and user-friendly.
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CFD Analysis Of CO2 Finned Tube Gas Coolers
Authors: Shubham Kumar, Dr. P.N Ahirwar
Abstract: The increasing demand for environmentally sustainable refrigeration and air-conditioning systems has accelerated research on carbon dioxide (CO₂) as a natural refrigerant, particularly in transcritical cycles. Gas coolers play a pivotal role in determining system efficiency, and finned tube configurations have emerged as a promising solution to enhance heat transfer performance while maintaining compactness. This review paper provides a comprehensive analysis of CO₂ finned tube gas coolers, highlighting their design considerations, thermal–hydraulic performance, and optimization techniques. Key factors such as fin geometry, tube arrangement, material selection, and flow distribution are critically examined in relation to heat transfer enhancement and pressure drop characteristics. Studies investigating numerical simulations, experimental measurements, and hybrid modeling approaches are summarized to identify performance trends and governing mechanisms. Furthermore, the influence of operating parameters such as mass flux, inlet temperature, and gas cooler pressure on system efficiency is discussed. The findings underline the importance of finned tube gas coolers in maximizing the potential of CO₂-based systems and provide insights for future research directions in sustainable refrigeration technologies.
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A Review On Analysis Of Different Types Of Cavities For Solar Collector
Authors: Merajul Hasan, Dr. P.N Ahirwar
Abstract: The design and geometry of cavities play a crucial role in determining the thermal performance and overall efficiency of solar collectors. Different cavity configurations influence heat absorption, heat loss reduction, and fluid heat transfer characteristics. This review presents a comprehensive analysis of various types of cavities employed in solar collectors, including cylindrical, conical, spherical, hemispherical, triangular, and compound cavity designs. The study emphasizes how cavity shape influences thermal performance. Comparative findings from experimental and numerical investigations reported in the literature highlight that well-optimized cavity geometries can significantly enhance solar energy utilization by improving heat retention and minimizing losses. The review also identifies key parameters influencing cavity performance, such as insulation, material properties, and flow arrangements, and provides insights into the suitability of different cavity types for diverse climatic and operational conditions. Finally, the paper outlines potential research directions focusing on hybrid cavity designs, advanced coatings, and integration with modern energy systems, aiming to further improve the efficiency and sustainability of solar thermal technologies.
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Electrochemical Trace Analysis of Valuable Metals in Hematite ore Samples
Authors: Dr.Manju Rathore
Abstract: Hematite is a main source of iron. Some method for determination of major and trace element in iron oxide (Hematite Fe2 O3). Mineral using the Polarography and Atomic Absorption Spectroscopy was suggested. There are many valid reasons to analyze the trace elements content in different minerals to determine the purity of minerals, and to determine the presence of very rare and important elements which could be extracted and used to obtain data which give very important information on the geology of the mines and mineral localities. The investigation of the purity of iron minerals and rock is very important because of their utilization for iron and steel production. Sample preparation, procedures, Result of trace elements presence of Au(I) ,Bi(II) ,Pb(II) ,U(VI),Co(II) ,Ni(II) ,Zn(II) ,Mn(II) ,Fe(III) Cr(III) by Polarography method. Final analysis results on hematite ore sample Polarography Method is verified of standard additions for the metal investigated and minerals. An exact amount of standard solutions with a known concentration of identified metal was added to mineral samples the result confirm the presence of Au,Bi,Pb,U,Co,Ni,Zn,Mn,Fe,Cr, determinations and the percentage recovery levels are satisfactory results were obtained.
Attention-AugmentedSequence-to-Sequence Approach For Gujarati Abstractive Summarization
Authors: Namaswini Padhy
Abstract: In recent years, text summarization has become a prominent challenge in the field of Natural Language Processing (NLP). It involves generating a concise and meaningful summary from a lengthy text document. There are two main approaches to summarization based on the type of output: extractive, which selects key sentences or phrases from the original text, and abstractive, which generates new sentences to convey the core information.While significant research has been conducted in extractive summarization for Indian languages, the development of effective abstractive summarization models remains limited—particularly for low-resource languages such as Gujarati. This work presents an efficient and accurate abstractive text summarizer tailored for the Gujarati language. Our model is built upon a Sequence-to-Sequence (Seq2Seq) framework employing an encoder-decoder architecture integrated with an attention mechanism. To enhance the preprocessing pipeline for Gujarati text, we introduce a custom preprocessor, designed to handle the linguistic and syntactic peculiarities of Gujarati. We curated a dataset comprising Gujarati news articles and their corresponding headlines to train and evaluate our model. Experimental results demonstrate that the proposed approach effectively captures the core semantics of the source text, generating fluent and human-readable summaries
Nike’s Customisation Strategy
Authors: Meegada Maneeth
Abstract: This paper explores Nike’s customisation strategy as a key component of its competitive advantage in the global sportswear market. Through initiatives such as Nike By You, the brand empowers consumers to design and personalise their own footwear and apparel, reflecting a broader trend toward customer-centric and experience-driven retail. The strategy aligns with the principles of mass customisation, offering tailored products at scale while leveraging digital tools, interactive platforms, and data analytics to enhance customer satisfaction and loyalty. Nike’s customisation approach not only differentiates the brand but also strengthens its direct-to-consumer (DTC) business model, reduces overproduction through made-to-order manufacturing, and fosters deeper emotional connections with customers. By integrating innovation, technology, and consumer co-creation, Nike sets a benchmark for how modern brands can meet evolving consumer expectations in a highly competitive market.
Vehicle Damage Evaluation For Insurance Using Computer Vision
Authors: Praveen, Dr. Sayyada Fahmeeda, Praveen MB, Om
Abstract: The paper presents an intelligent system for automatic vehicle damage assessment using deep learning and computer vision techniques. Traditional insurance claim processes often rely on manual inspections, which are time-consuming, subjective, and error-prone. To overcome these limitations, a Convolutional Neural Network (CNN) model is trained to classify vehicle damage severity into three categories: Minor, Moderate, and Severe. The model is integrated into a Flask-based web application that enables users to upload images, receive real-time predictions, and obtain repair cost estimates along with insurance recommendations. The system demonstrates high accuracy and reliability, offering a scalable solution for insurance automation, improving efficiency, consistency, and decision-making in vehicle damage evaluation.
Utility Hub: Empowering Urban Living through Intelligent Service Matching
Authors: Professor,Ami Sachin Shah, Shivam Gupta, Vishal Chauhan, Dev Solanki, Bishal Singh
Abstract: Urban households increasingly rely on on-demand utility services such as plumbing, electrical repairs, and home cleaning to maintain day-to-day functionality. However, customers often struggle to find reliable service providers, while professionals face difficulty securing consistent bookings and managing service logistics. This study introduces Utility Hub, a real-time digital platform designed to bridge this gap by streamlining the interaction between users and verified service professionals. Drawing on prior research in service delivery systems, platform design, and real-time user experience, this paper evaluates how Utility Hub enhances booking efficiency, increases service provider visibility, and improves platform-wide accountability. Employing a modular architecture built on the MERN stack, Utility Hub incorporates AI- driven provider matching, secure payment gateways, live video consultations, and real-time location tracking. A multi-stakeholder model supports personalized dashboards for users, service providers, and administrators. Through system testing, comparative analysis, and feedback evaluation, this research highlights core performance metrics including response time, user satisfaction, and booking success rate. Results indicate that digital integration in home services significantly improves platform transparency, reduces fulfillment delays, and increases user retention. Nonetheless, challenges such as initial platform adoption and trust-building mechanisms remain crucial for long-term growth. This study contributes to the digital transformation of domestic service platforms and offers insights into future innovations in real-time utility service management.
DOI: https://doi.org/10.5281/zenodo.17060572