Volume 14 Issue 1

A Kinetic Study Of The Solvent Effect Of Aquo-dipolar Protic Solvent Systems On The Solvolysis Of Iso-butyl Formate

Authors: Dr. Kumari Priyanka

Abstract: The solvent effect of aquo-dipolar protic solvent was highlighted by studying the kinetics of the acid catalysed hydrolysis of Iso-butyl formate in aquo-Methanol reaction media of various compositions having 20 to 80% of methanol at five different temperatures ranging from 20 to 40°C. The sharp depletion followed by slow decrease in the rate, with gradual addition of methanol in the reaction media and also with increasing temperature of the reaction has been explained in the light of solvation and desolvation of initial and transition states to different extents. The changes in the values of iso-composition and iso- dielectric activation energies of the reaction have also been explained in the light of solvation and desolvation of initial and transition states to different extent. Increase in the numerical values of free energy of activation (ΔG*) with simultaneous enhancement in the values of enthalpy of activation (ΔH*) and entropy of activation (ΔS*) of the reaction, reveals that the methanol) acts as enthalpy dominating solvent. From the evaluated value of iso-kinetic temperature which comes to he 325.0, it is inferred that there is strong solvent-solute interaction in aquo-methanol reaction media. Effects of ionic strength and change in [H+] ion concentration on the rate of reaction have also been studied and it is concluded that the acid catalysed hydrolysis of Iso-butyl formate is ton-dipolar reaction and it follows AAC2 mechanistic pathways in aquo-MeOH reaction media.

DOI: http://doi.org/10.5281/zenodo.18241286

AI-Based Web-Application for Personalized Finance Tracker

Authors:Kirti Rastogi, Nitin, Arvind Kumar, Anurag Mall

Abstract: Managing money has become difficult for many people because of online payments, UPI apps, credit cards, and monthly subscriptions. People often forget where their money goes and faces problems in tracking their expenses and savings. Most available tools require manual work and do not give smart suggestions. To solve this problem, this project introduces an AI-Based Web Application for Personalized Finance Tracking. The system helps users record their income and expenses and automatically categorizes them using machine learning. It shows clear charts and reports so users can easily understand their spending habits. The system can also predict future expenses based on past data and remind users about important payments like EMIs or bills. A chatbot is included to answer simple questions like “How much did I spend on food last month?” This makes the system easy to use for everyone. The project is built using Python, Flask, HTML, CSS, and JavaScript, and it keeps all user data safe with secure login and encryption. Overall, this project aims to help people manage their money better, save more, and make smarter financial decisions with the help of AI.

Detecting And Preserving Digital Evidence In Decentralized Multi-Cloud And Serverless Environments

Authors: Aditya Agrawal, Abhishek, Yash Ranjan Bhargav

Abstract: The rapid adoption of multi-cloud and serverless architectures has fundamentally altered how digital evidence is generated, distributed, and lost, creating significant challenges for contemporary digital forensic investigations. Current cloud forensic practices remain largely provider-specific and assume stable infrastructure, leaving investigators without reliable mechanisms to detect, preserve, and correlate volatile forensic artifacts across decentralized cloud environments. As a result, critical evidence such as execution logs, transient identifiers, and ephemeral state information is frequently incomplete, inconsistent, or legally fragile. This paper presents a provider-agnostic forensic framework designed to support systematic detection, acquisition, and preservation of digital evidence in multi-cloud and serverless deployments. The proposed approach introduces a canonical event model, cross-provider log normalization, and a coordinated snapshotting strategy to capture transient artifacts while maintaining evidentiary integrity and provenance. Event correlation is achieved through time-aligned stitching of heterogeneous logs, enabling accurate reconstruction of distributed execution timelines. A prototype implementation was evaluated across simulated multi-cloud environments incorporating serverless workloads from multiple providers. Experimental results demonstrate improved evidence completeness and correlation accuracy compared to baseline cloud-native acquisition methods, while introducing minimal operational overhead. The findings indicate that standardized, cross-cloud forensic mechanisms are both feasible and necessary, offering practical guidance for investigators and cloud service consumers seeking legally defensible forensic readiness in decentralized cloud infrastructures.

DOI: https://doi.org/10.5281/zenodo.18534789

Futuristic Technologies In Oceanography: Advancements For Satellite-Based Earth Observation And Data Visualization

Authors: Priyanshu Raj Singh, Dr. Uttam Patil, Dr. Neeraj Agarwal

Abstract: Satellite-based Earth observation plays a critical role in oceanography, climate monitoring, disaster management, and environmental analysis. Over the years, several platforms such as MOSDAC Live, Zoom Earth, NASA Live Earth, Nullschool, and similar systems have been developed to visualize satellite-derived data and monitor Earth’s dynamic processes. While these platforms provide valuable real-time or near real- time visualization capabilities, they largely rely on traditional visualization approaches and offer limited interactivity, predictive intelligence, and user-centric analytical features. Moreover, the complexity of scientific data often restricts effective understand- ing by non-technical users and decision-makers. This paper presents a conceptual and review-based study of existing satellite-based Earth observation and data visualiza- tion platforms, with a focus on identifying their technological limitations in terms of real-time data integration, intelligent analytics, scalability, and user accessibility. Based on this anal- ysis, the paper proposes a futuristic software framework for oceanographic and Earth monitoring applications that integrates artificial intelligence and machine learning, real-time satellite data processing, smart analytical dashboards, and interactive visualization techniques. The proposed framework emphasizes automated data updates, predictive insights, cross-domain data fusion, and an AI-assisted conversational interface to enhance both scientific analysis and public understanding. The proposed approach aims to transform conventional Earth observation systems into intelligent, user-friendly, and decision- support platforms capable of supporting scientists, researchers, and non-technical stakeholders alike. This research highlights the potential of advanced visualization, AI-driven analytics, and cloud-based architectures in shaping the future of satellite-based oceanographic monitoring and Earth observation systems.

DOI: https://doi.org/10.5281/zenodo.18535772

A Study Of The Relationship Between Employee Empowerment And Job Satisfaction

Authors: Renu Bala

Abstract: This paper presents the relationship and impact of employee empowerment on job satisfaction. The objective of this study is to measure the relationship between employee empowerment and job satisfaction. Employees are the most important resource for any organization, so it is the duty of the organization to focus on the feelings, needs, health of all the employees. Employees are more likely to strive for better performance when they are given decision-making authority and responsibility. Both primary and secondary data were used for the study. Data were collected from 100 employees of manufacturing industries in Tricity (Chandigarh, Panchkula and Mohali) using questionnaire method in primary sources. Secondary sources such as relevant text books, historical events, journal articles, reviews, research papers, scholarly literature, and web-based resources were used. Descriptive method was used for this study. SPSS was used for analysis. Data was analyzed using Cronbach's alpha, correlation and regression in SPSS software. The results of this study show that employee empowerment has a positive impact on job satisfaction. This study makes it clear that a satisfied worker or employee will be truly productive.

DOI: https://doi.org/10.5281/zenodo.18537793

Mathematical Formulation Of Electromagnetic Radiation Using Vector Potential Expressions Derived From Maxwell’s Equations

Authors: Dr Abhilash S.Vasu, Priyadha Raj P G

Abstract: The electric vector potential 𝐹 plays a significant role in electromagnetic field analysis when magnetic current sources are introduced through the equivalence principle. Although magnetic currents do not exist physically, their mathematical representation greatly simplifies the analysis of radiation, scattering, and aperture problems in electromagnetics and antenna theory. This work presents a detailed derivation of the electric vector potential 𝐹 directly from Maxwell’s equations by incorporating magnetic current density into the generalized field equations. Starting from the curl relations of Maxwell’s equations, the electric field is expressed in terms of the curl of the electric vector potential, ensuring automatic satisfaction of Gauss’s law in source-free regions. By applying appropriate vector identities and imposing a suitable gauge condition, a vector wave equation governing 𝐹 is obtained. The solution of this wave equation leads to the retarded electric vector potential, which explicitly relates the magnetic current distribution to the radiated electromagnetic fields. The derived formulation provides physical insight into the radiation mechanism of equivalent magnetic current sources and establishes a dual framework to the conventional magnetic vector potential approach used for electric currents. The electric vector potential formulation is particularly advantageous for analyzing slot antennas, aperture radiation, electromagnetic scattering, and computational electromagnetics methods such as the Method of Moments and Finite Element Method. Overall, this derivation highlights the mathematical elegance and practical relevance of the electric vector potential in advanced electromagnetic radiation analysis.

IOT-Enabled Servo-Controlled Fire Detection and Suppression System

Authors: Mr.Gampala Nagendra Prasad, Koduru Vikram, Bhumireddy Mohana Sai Vamsidhar Reddy, Kuruva Anusha, Kalluri RamaDevi

Abstract: The swift increase of fire-related accidents in residential and industrial areas demands efficient and automated safety techniques. Traditional fire safety systems, which are based on passive smoke and temperature sensing, have shown limited performance in keeping up with fire safety demands. These systems have reputations for poor promptness, too many false alarms, and indiscriminate suppression mechanisms, leading to extensive collateral damage. The current paper aims to reduce these challenges with a new, cost-effective, and efficient Automated Fire Suppression System (AFSS) based on Internet of Things (IoT) and Computer Vision techniques. The proposed method makes use of ESP32-CAM, which can efficiently capture video data. Computer Vision is then applied to efficiently recognize fire using the HSV color range segmentation method. The method then makes use of a PI control mechanism to accurately locate a water nozzle to align with the center of a fire, ensuring effective fire suppression. The water flow is then initiated using a water pump. The proposed method is tested with efficient results, obtaining 94% accuracy in fire detection for different illumination conditions. The method has a response time of less than 2 seconds, ensuring efficiency in fire suppression.

DOI: https://doi.org/10.5281/zenodo.18547231

Effectiveness of A Nurse-Led Digital Wellness Program On Knowledge Regarding Coronary Artery Disease Risk and Prevention Among Bank Employees

Authors: Ravi Kumar Swami, Dr. Jogendra Sharma, Dr. Samta Soni

Abstract: Background: Coronary artery disease (CAD) remains a leading cause of morbidity and mortality worldwide, with modifiable risk factors being prevalent among working professionals, particularly bank employees who lead sedentary lifestyles. Digital health interventions offer promising avenues for health promotion and disease prevention in workplace settings. Objective: To evaluate the effectiveness of a nurse-led digital wellness program on knowledge regarding the risk of coronary artery disease and its prevention among bank employees at selected banks in Jaipur, Rajasthan. Methods: A quasi-experimental research design was employed with 100 bank employees (50 in experimental group and 50 in control group) from State Bank of India branches in Jaipur. Non-probability purposive sampling was used. The experimental group received a nurse-led digital wellness program, while the control group received no intervention. Knowledge was assessed using a structured questionnaire (reliability r=0.772) before and after the intervention. Data were analyzed using descriptive and inferential statistics. Results: In the experimental group, pre-test mean knowledge score was 17.02 (SD=4.14), which significantly improved to 34.8 (SD=4.52) post-intervention (t=2.44, p<0.05). In contrast, the control group showed no significant change (pre-test mean=15.6, SD=3.93; post-test mean=15.46, SD=4.14; t=0.008, p>0.05). In the experimental group, 74% participants achieved good knowledge levels post-intervention compared to 0% pre-intervention. Significant associations were found between post-test knowledge scores and dietary pattern, smoking habit, and alcoholic consumption (p<0.05). Conclusion: The nurse-led digital wellness program was highly effective in improving knowledge regarding coronary artery disease risk and prevention among bank employees. This intervention demonstrates the potential of technology-enabled nursing interventions for workplace health promotion.

DOI: https://doi.org/10.5281/zenodo.18582928

Multi-Agent Universes With Heterogeneous Local Physics: A Judge-Oriented Conceptual Manuscript For IJST

Authors: Suparno Samanta

Abstract: This manuscript presents an original research problem designed specifically for evaluation by the International Journal of Science and Technology (IJST). The work introduces a novel conceptual framework where multiple intelligent agents operate within a shared universe composed of regions governed by different local physical laws. Unlike existing models in physics or artificial intelligence, this framework removes the assumption of global physical consistency and instead treats physical laws as region-dependent and inferable. The intent of this manuscript is not to report experimental results, but to formally propose, justify, and contextualize a new class of research problems suitable for future theoretical and computational investigation.

DOI: https://doi.org/10.5281/zenodo.18583390

Smart Crop Protection System

Authors: Bomma Ajay

Abstract: Crop protection is the practice of protecting crop yields from different agents, including pests, weeds, plant diseases, and other organisms. India relies on a variety of crops for food production. When these crops are damaged due to animal attacks, farmers face a significant financial loss, which will affect the gross GDP of our country as well. The farmers can’t barricade entire fields for 24 hrs. Many ideas and devices existed to protect crops from animals. One of them is the electric fencing system which is mainly used nowadays. The main problem with using an electric fencing system i.e. non-secure for animals and farmers by the electric shock. So, we got an idea of a smart crop protection system, which can eliminate those problems. As per our idea, it can prevent the entry of animals into agricultural fields, and this idea can be applicable for any farm buildings & houses as well.

DOI: https://doi.org/10.5281/zenodo.18583803

Anti-Microbial and Stain Removing Effect of Herbal Dentifrfice: An In Vitro Study

Authors: Rekha Kandukuri, Dr.Shalini Kapoor Mehta, Bhavyashree N, S.S.Marry.Rejeena, Amarnath Reddy.N

Abstract: Dental plaque accumulation and extrinsic tooth staining are major contributors to oral diseases such as dental caries and periodontal disorders. Conventional dentifrices often contain synthetic antimicrobial and abrasive agents, which may cause adverse effects with long-term use. Herbal dentifrices, formulated with plant-based ingredients, are gaining attention as safer alternatives due to their antimicrobial, anti-inflammatory, and cleansing properties

A Review On: Design And Implementation Of A Citizen-Centric Crime Reporting And Safety Awareness Mobile Application

Authors: Vaishnavi Deokar, Dr. Pravin Khatkale

Abstract: Crime reporting plays a vital role in maintaining public safety and effective law enforcement. However, traditional crime reporting mechanisms are often time-consuming, location-dependent, and inaccessible during emergencies. With the rapid growth of mobile technologies, location-aware applications have emerged as a promising solution to bridge the gap between citizens and law enforcement agencies. This review paper presents a comprehensive analysis of location-aware crime reporting systems that enable users to report incidents directly through mobile or web-based applications. The focus of this review is on systems that integrate real-time crime reporting, location awareness using GPS services, crime data analysis and visualization, emergency contact access, and the use of dummy datasets for demonstration and evaluation purposes. The paper surveys existing literature related to online crime reporting platforms, cyber crime reporting systems, and intelligent crime analysis systems. A comparative discussion is provided to highlight the strengths, limitations, and research gaps in current solutions. The proposed conceptual framework emphasizes five core objectives: enabling direct crime reporting with location and description, identifying nearby police stations and crime hotspots, visualizing crime trends using charts and graphs, providing emergency contact facilities, and utilizing synthetic datasets for analysis and demonstration. The methodology section outlines the system workflow, block diagram, hardware and software requirements, and algorithmic flow for crime reporting and visualization modules. The review further discusses observed results from dummy datasets, demonstrating how crime trends and hotspot patterns can be effectively visualized to improve public awareness. Applications, advantages, limitations, and real-world applicability of such systems are also examined. This paper concludes by emphasizing the importance of location-aware crime reporting systems in smart city initiatives and highlights future research directions toward scalable, secure, and citizen-centric crime management platforms.

DOI: https://doi.org/10.5281/zenodo.18596332

Plasmonic Nanoparticles For Solar Cells: A Survey

Authors: Santosh Suryabhan Satpute, Tanay Ghosh

Abstract: Plasmonic nanoparticles are a powerful way to improve the performance of modern solar cells by changing how light works at the nanoscale. The LSPRs enabled these nanoparticles to trap light more efficiently, amplify near-field interactions, and produce energetic hot carriers that may be used for photocurrent enhancement. This review provides a comprehensive overview of the underlying physical mechanisms, the material choices, the geometries of nanoparticles, the approaches for device integration, and the modelling techniques concerning plasmonic-enhanced photovoltaic systems. More emphasis will be given to advances both in theoretical understanding and in experimental demonstrations across silicon, organic, perovskite, and dye-sensitized solar cells. Additionally, the review explains how scientists are employing simulations and computational tools to enhance nanoparticle placement, reduce parasitic losses, and forecast optical–electrical behavior. Despite the progress made, challenges remain. These include material stability, thermal effects, recombination losses, and scaling up fabrication. The survey wraps up by discussing future research directions and new opportunities in plasmonic photovoltaics, especially focusing on next-generation materials, hybrid photonic–plasmonic structures, and better hot-carrier extraction methods.

DOI: https://doi.org/10.5281/zenodo.18596382

Deep Learning For Helmet And Number Plate Detection

Authors: Ritesh Ramesh Gunjal, Dr. P. Kavitharani

Abstract: Road safety has become a critical concern due to the rapid increase in two-wheeler usage and frequent violations of helmet-wearing regulations. Manual traffic monitoring is often inefficient and error-prone, especially in high-density traffic environments. This study aims to tackle these difficulties presents an automated detecting helmets and license plates system utilizing deep learning techniques. The proposed approach employs a YOLO-based object detection model to identify riders, helmets, and the number of the vehicle plates from pictures, movies, and live surveillance feeds in real time with high accuracy. Helmet violations are detected by associating riders with helmet presence, and the corresponding number plates are isolated for further processing. The model learns from a custom annotated set of data and optimized to perform reliably under varying lighting conditions, camera angles, and traffic scenarios. The results of the experiments show that the detection is very accurate, low latency, and strong robustness, making the system work in real life time traffic surveillance and smart city applications. This study shows how well deep learning works-based computer vision systems in enhancing road safety and supporting automated traffic law enforcement.

DOI: https://doi.org/10.5281/zenodo.18596414

Sahayak: A Generative AI Framework For Personalised Education Support

Authors: Gayatri Dekhane, Rinkal Rahane, Gauri Dani, Shruti Sadgir, Sanika Dighe

Abstract: The rising demand for personalized and efficient learning solutions has accelerated the integration of Artificial Intelligence (AI) into modern education. To address this, we present Sahayak, an AI-powered teaching assistant designed to automate study material generation while enabling adaptive learning. The system begins with Optical Character Recognition (OCR) to extract text from handwritten notes, books, and images, ensuring compatibility with both traditional and digital resources. The extracted data is indexed using FAISS, providing efficient knowledge storage and semantic retrieval for large-scale educational content. Leveraging a GPT-based model, Sahayak generates quizzes, flashcards, and worksheets that are contextually aligned with the source material, thereby enhancing learning reinforcement and retention. To support personalization, K-Means clustering groups students based on their performance, enabling adaptive pathways and tailored recommendations that cater to diverse learner needs. The generated content is delivered through teacher-validated dashboards, ensuring accuracy, relevance, and pedagogical trust. This human-in-the-loop approach bridges innovation with reliability, empowering teachers while maintaining academic quality. Experimental results show that Sahayak significantly reduces teacher workload by automating repetitive tasks, while also improving student engagement, comprehension, and outcomes. The system’s design emphasizes scalability and inclusivity, making it adaptable to both resource-rich urban schools and under-resourced rural contexts. By combining automation, personalization, and teacher oversight, Sahayak demonstrates the transformative potential of AI-driven tools in reshaping education. Furthermore, it contributes to the broader vision of accessible, data-driven, and student-centered learning, aligned with global sustainable development goals.

DOI: https://doi.org/10.5281/zenodo.18596444

Review Of Vibration-Based Fault Detection Methods For CNC Spindle Bearings: Current Trends, Technological Advances, And Future Directions

Authors: Kishor Patil, Dr. Ajaykumar Thakur, Dr. Kiran Wakchaure

Abstract: This paper provides a detailed review of vibration-based fault detection methods used for CNC (Computer Numerical Control) spindle bearings, which are essential in machining operations. The review looks at traditional signal processing methods like Fast Fourier Transform (FFT) and wavelet analysis, along with new machine learning models such as Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and hybrid approaches. It also examines newer diagnostic frameworks like digital twins and predictive maintenance systems for their potential to improve fault diagnosis and prognosis. Key findings indicate that machine learning models significantly boost fault detection accuracy, but challenges remain for real-world use in industries. These challenges involve the need for real-time prediction, combining multi-modal sensor data, and making advanced methods scalable in operational settings. The paper highlights these research gaps and suggests future directions to address these issues. This includes validating techniques in real environments, merging multi-sensor data, and developing IoT-enabled fault detection systems.

DOI: http://doi.org/10.5281/zenodo.18619293

Development Of An IoT-Enabled Cyber-Physical Framework For Real-Time Defect Detection In GMAW

Authors: Mr. Vishal Kadam, Dr. A. G. Thakur

Abstract: Gas Metal Arc Welding (GMAW) remains a critical manufacturing process across aerospace, automotive and construction industries, yet traditional quality control methods rely on manual inspection and subjective assessments, leading to inefficiencies, increased rework costs and potential safety compromises. This research proposes an integrated IoT-enabled Cyber-Physical System (CPS) framework designed to enable intelligent, real-time defect detection and quality monitoring in GMAW processes by combining multi-modal sensor fusion with advanced machine learning algorithms. The framework integrates heterogeneous sensor data streams—including electrical arc signals (voltage and current), thermal imaging, acoustic emissions and torch position sensors—through a distributed edge-cloud computing architecture. Advanced deep learning models, specifically embedded system for image-based defect classification, Long Short-Term Memory (LSTM) networks for temporal pattern recognition in arc signals and ensemble methods (XGBoost optimized with Particle Swarm Optimization) for multi-sensor data fusion, are employed for real-time anomaly detection and quality classification. The proposed work involves: (1) design and development of a cost-effective IoT-based multi-sensor acquisition system with standardized data protocols; (2) implementation of a hybrid machine learning architecture capable of detecting critical defects such as porosity, lack of penetration and burn-through with enhanced accuracy and minimal latency; (3) development of a digital shadow system enabling predictive analytics for process parameter optimization and preventive maintenance; and (4) validation through experimental trials on industrial GMAW setups. Expected outcomes include achieving greater than 95% defect detection accuracy, reducing quality inspection time by 70%, enabling real-time process adaptation and providing a scalable framework adaptable to diverse welding environments and materials. This research bridges the gap between Industry 4.0 manufacturing demands and practical implementation challenges, delivering a comprehensive solution for autonomous, intelligent quality assurance in modern welding operations.

DOI: https://doi.org/10.5281/zenodo.18596485

Inclusive Iris Recognition System (ATM)

Authors: Kshitij Dushyant Jadhav, Abhijit Babasaheb Dawange, Dipak Balasaheb Bhosale, Rohit Sopan Satre, Rohit Raja Thorat

Abstract: This project presents the development of an Inclusive Iris Recognition System for Automated Teller Machines (ATMs) aimed at enhancing both security and accessibility for users. Traditional ATM authentication methods, such as PINs and cards, often pose challenges for individuals with disabilities, particularly those with visual impairments. Our system leverages advanced iris recognition technology, which offers a secure and user-friendly alternative. The project involves designing a hardware setup featuring a high-resolution iris camera integrated with a processing unit, alongside a software framework for image capture, processing, and recognition. By employing algorithms for feature extraction and machine learning techniques, we ensure accurate user identification while maintaining data security through encryption. To promote inclusivity, the system includes accessibility features such as audio prompts and adjustable camera positioning. Rigorous testing with diverse user groups demonstrates the system’s effectiveness in accurately recognizing iris patterns and its usability for individuals with varying needs. Overall, this project not only addresses the security challenges faced by ATMs but also contributes to a more inclusive banking experience, paving the way for broader adoption of biometric technologies in everyday applications.

DOI: https://doi.org/10.5281/zenodo.18596633

Development Of Quantum-Dot Luminescent Coatings For Self-Illuminating Solar Roadways: A Sustainable Approach To Night-Time Road Visibility

Authors: Maitreyee Anil Kulkarni, Sara Sandip Waghchaure, Bhavana Badrinath Deokar, Swarali Nitin Gade, Jidnyasa Kishor Salunke

Abstract: Road safety during night-time remains a critical challenge, particularly in regions with limited access to continuous street lighting infrastructure. Conventional road illumination systems rely heavily on electrical power, resulting in high energy consumption and maintenance costs. This paper presents the development of a sustainable self-illuminating roadway system based on quantum-dot (QD) luminescent coatings integrated with solar energy harvesting mechanisms. Quantum dots exhibit size-dependent photoluminescence, enabling efficient light emission under low-energy excitation. The proposed approach utilizes solar energy during daylight hours to charge embedded photovoltaic layers, which subsequently activate quantum-dot luminescent coatings during night-time conditions. The system architecture combines photovoltaic cells, energy storage modules, and photoluminescent QD coatings applied to road surfaces. Experimental analysis and simulation results demonstrate enhanced night-time visibility, reduced power dependency, and improved durability under environmental stress. The proposed solution offers an energy-efficient, low-maintenance, and environmentally sustainable alternative to conventional roadway lighting systems.

DOI: https://doi.org/10.5281/zenodo.18598073

Contactless Fever Detection Using WiFi Doppler Signal Distortion For Intelligent Health Monitoring

Authors: Nipun Pankaj Chaudhari, Samarth Rajendra Pawar, Aditya Devidas Bawche, Impreet Charanjeet Khanijo, Maitreyee Anil Kulkarni

Abstract: Fever is a primary clinical indicator of infections and inflammatory conditions, making its early detection essential for effective healthcare response. Conventional temperature measurement techniques, including contact thermometers and infrared scanners, require close proximity, manual operation, or direct line-of-sight, which restricts their usefulness in large-scale and continuous monitoring environments. This paper presents a novel, contactless fever detection framework based on WiFi Doppler signal distortion, enabling passive health monitoring without additional sensing hardware. The proposed system exploits variations in Channel State Information (CSI) and Doppler frequency shifts produced when WiFi signals interact with the human body. Changes in body temperature subtly influence RF signal amplitude, phase, and frequency characteristics due to thermal radiation and involuntary micro-movements. A structured processing pipeline comprising signal denoising, Doppler feature extraction, and statistical feature modeling is designed to capture these variations. Machine learning techniques are then applied to classify normal and elevated temperature conditions. The solution is non-invasive, cost-effective, privacy-preserving, and compatible with existing WiFi infrastructure [2],[7]. Experimental observations indicate a consistent relationship between Doppler-based RF features and body temperature variations, validating the feasibility of WiFi-assisted fever detection. This study contributes to wireless health sensing research and demonstrates the potential of RF signals for scalable and autonomous fever screening.

DOI: https://doi.org/10.5281/zenodo.18598287

Unveiling The Dynamic Crosstalk Between Agricultural Hazards And Spontaneous Pregnancy Loss

Authors: Ahana Chakraborty, Tiyasha Mishra, Priyanka Jana

Abstract: Agricultural practices expose women to a wide range of chemical, physical, and environmental hazards, which may adversely affect reproductive outcomes, particularly spontaneous abortion. This review synthesizes recent evidence (2020–2025) linking agricultural exposures—including organophosphate pesticides, herbicides, fertilizers, heavy metals, persistent organic pollutants, and occupational stressors—to miscarriage. Epidemiological studies consistently demonstrate that maternal exposure to these hazards, especially during the peri-conceptional period and first trimester, is associated with increased miscarriage risk. Mechanistic insights reveal multiple pathways by which agricultural toxins disrupt early pregnancy, including endocrine disruption, oxidative stress, placental toxicity, and immune dysregulation. Molecular genetic factors, such as polymorphisms in detoxification enzymes (e.g., PON1, GSTs), and epigenetic alterations, including DNA methylation, histone modification, and microRNA dysregulation, further modulate susceptibility and mechanistically link environmental exposures to adverse outcomes. Immunological mechanisms, including T helper cell imbalance, reduced regulatory T cells, and pro-inflammatory cytokine induction, contribute to compromised maternal–fetal tolerance. Fertilizer-related nitrate and heavy metal exposures additionally induce oxidative and epigenetic stress, highlighting the importance of cumulative and mixture effects. Overall, these findings underscore the complex interplay between environmental exposures, genetic susceptibility, and immune-epigenetic mechanisms in the etiology of miscarriage. Enhanced occupational safety, reduction of chemical exposures, and early biomarker-based risk assessment are essential to mitigate the burden of spontaneous abortion in agricultural populations. This review consolidates epidemiological, mechanistic, and molecular evidence, providing a comprehensive framework for understanding the impact of agricultural hazards on reproductive health

Live Surveillance With Actionable Intelligence: A Review

Authors: Mrs. Vibhavari Jawale, Mrs. Deepali Hajare, Arhant Sahuji, Tanay Shinde, Ananya Vaishnav, Ritesh Kadam

Abstract: The rapid advancement of computer vision and natural language processing has paved the way for new forms of intelligent video surveillance. Traditional closed-circuit television (CCTV) and motion-based monitoring systems are limited in their ability to understand contextual information, often resulting in false alarms and requiring extensive human intervention. To address this gap, recent research explores the integration of vision-language models (VLMs) and sentiment analysis for context-aware surveillance. This review focuses on emerging methodologies where image captioning models such as Salesforce BLIP are used to describe real-time video frames in natural language, followed by sentiment-driven analysis to assess the nature of the detected activity. The combination of visual understanding, language-based context generation, and sentiment inference enables systems to differentiate between benign and suspicious behavior, thereby reducing false positives and providing actionable insights. Key applications include public safety in smart cities, security in high-risk environments like airports and banks, and monitoring sensitive areas such as hospitals and military zones. The core contribution of this review is the evaluation of how VLM-based context awareness augments conventional object detection pipelines, shifting surveillance toward more explainable and human-like alerting mechanisms. Furthermore, we discuss computational challenges, accuracy limitations, and privacy concerns while highlighting the societal implications of deploying such systems, including alignment with Sustainable Development Goals (SDGs) such as fostering safe cities and reducing crime. Future directions include multimodal fusion, real-time optimization, and ethical frameworks for responsible deployment.

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Women Entrepreneurship Challenges And Prospects For The Future. A Case Of Harare

Authors: Yeukayi Dzapasi, Tawanda Laston Makombe, Witness Ukama

Abstract: Women entrepreneurship has been hailed for its importance in the socio-economic development of societies. However, the success of women entrepreneurship is still very low as compared to their male counterparts. Hence the study sought to investigate the challenges and prospects of women entrepreneurship in Zimbabwe so as to find out how best women entrepreneurship can be perpetuated in the country. The research employed a survey of registered women businesses in Harare. Data was collected from the women entrepreneurs using self-administered questionnaires. It was then analysed using mean, scores, percentages and standard deviations. Findings from the study indicated that women entrepreneurship in Zimbabwe is mainly hindered by lack of access to financial resources, shortage of raw materials, inadequate education, poor infrastructure, persistent inflation and shortage of foreign currency, gender discrimination, family commitments, and lack of government support. It however revealed that opportunities for enhanced entrepreneurship in the country include rapid advances in ICT, increased regional economic integration, increased support from developmental organisations and higher education among women in Zimbabwe. Hence the research recommended useful policies to undermine the challenges and take advantage of the opportunities in order to improve women entrepreneurship in Zimbabwe.

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Effectiveness of Phonetics Training for Indian Students: Pedagogical, Technological, and Intelligibility Perspectives

Authors: Martina Dealon Henriques, Pyarelal Singh

Abstract: The success of phonetics and pronunciation training has since been rekindled in English language teaching, especially where more than one language is used as a common phenomenon as observed in India, where English is a second language and an important means of academic and professional mobility. However, though a large number of learners in India speak English, there are various difficulties connected to the segmental accuracy, stress, rhythm, and intelligibility because of the influence of the first language, lack of a pronunciation emphasis in the classroom, and examination-oriented teaching. This paper reviews the performance of phonetics training in Indian students through combining both empirical and theoretical studies with technology. A narrative review methodology was adopted that analyzed forty peer-reviewed studies and authoritative sources and found the instructional approaches, learning outcomes, and contextual constraints. The result of the findings is that explicit phonetics instruction (particularly mixed with communicative practice and computer-aided pronunciation training) has a significant positive effect on intelligibility, learner confidence, and speech comprehensibility. Nevertheless, there are still constant gaps in the training of teachers, the integration of the curriculum and equal access to pronunciation technology. The paper concludes that phonetics training is not only pedagogically competent but also socially requisite in Indian context, as long as it is oriented towards intelligibility with the help of technology and placed in the communicative language teaching structures.

Intelligent Sign Language Interpretation System Using Multi- Modal Deep Learning Architectures

Authors: Samruddhi Vijay Wakalkar, Sanskruti Vijay Wakalkar, Siddhi Nanasaheb Hon, Shravani Kishor Mahale, Gauri Sanjay Lad

Abstract: This project presents a real-time American Sign Language (ASL) recognition system using a standard webcam. Communication between deaf or hard-of-hearing individuals and the hearing community is often limited by the high cost and limited availability of professional interpreters. To address this, the proposed system employs an ensemble deep-learning approach that combines a Convolutional Neural Network (CNN) for hand shape recognition, a Graph Neural Network (GNN) to capture finger and joint relationships, and a Vision Transformer to focus on key visual regions while minimizing background noise. By fusing these complementary models, the system achieves enhanced recognition accuracy. The framework was trained and evaluated on a dataset of approximately 87,000 labeled images covering the complete ASL alphabet along with additional gestures such as space and delete. Experimental results demonstrate an accuracy exceeding 95%, outperforming existing methods. The system supports real-time interaction with an average inference time of about 85 milliseconds per gesture. It is deployed through a browser-based interface and requires no specialized hardware beyond a standard webcam. This solution provides an accessible, low-cost alternative to traditional interpretation services and promotes inclusive communication across educational, healthcare, and public environments.

DOI: https://doi.org/10.5281/zenodo.18619156

Gesture-Based Touchless Control System Using Computer Vision

Authors: Shweta Barhate, Samiksha Patil, Vaibhavi Ghodke, Aniket Chavan, Harshwardhan Dahatonde

Abstract: Gesture-based interaction is quickly becoming a viable replacement for touchscreens, particularly where hygiene or accessibility is a concern. This study details a touchless control system that uses computer vision to read hand gestures in real-time. By merging image processing with a deep-learning tracking model, our approach can identify specific poses and motion-based gestures. The process is broken down into capture, preprocessing, feature extraction, classification, and execution. We designed the system to handle various lighting environments and backgrounds to ensure it works reliably in everyday scenarios. Performance tests show high accuracy and low latency, allowing for smooth interaction without the need for wearables or physical touch. This makes the system useful for applications ranging from healthcare and public kiosks to smart homes. The findings demonstrate that computer vision is an effective tool for building safer, user-friendly touchless interfaces.

DOI: http://doi.org/10.5281/zenodo.18619257

AI-Powered Farming And Marketplace Solution

Authors: Aparajita Biswal, Chetan Patil, Sanket Mapari, Chaitanya Patil, Pallapu Pravalika

Abstract: The AI-Powered Farming and Marketplace Solution is an innovative web-based application designed to connect farmers directly with customers, eliminating the need for middlemen and ensuring fair pricing for agricultural produce. The platform provides a structured marketplace where farmers can list their crops, while customers can easily browse and purchase products. With dedicated modules for farmers, customers, and government authorities, the system enhances efficiency and transparency in the agricultural sector. Farmers can manage their profiles, track sales, and access valuable market insights, while customers can directly engage with producers for fresh and high-quality goods. To further support farmers, the portal integrates multiple advanced features such as weather forecasting using the OpenWeatherMap API and a machine learning-powered crop prediction system to help farmers make informed cultivation decisions. A built-in news feed ensures users stay updated with the latest agricultural trends, government schemes, and market rates. Additionally, the platform offers multilingual support, including languages like Marathi, making it accessible to a diverse range of users. The system is secured with two-factor authentication, ensuring data privacy and safe transactions for all users. Technically, the Agriculture Portal is developed using HTML5, CSS, Bootstrap, JavaScript, and jQuery for the frontend, while the backend is powered by PHP and Python, with MySQL handling data storage. Secure and seamless transactions are facilitated through the Stripe payment gateway, while APIs such as SendGrid for email services and News API for real-time updates further enhance functionality. By integrating modern technology with agriculture, this platform serves as a one-stop solution for improving the efficiency, profitability, and sustainability of farming practices.

Rcc Structures In Different Seismic Zones II & IV In India Using Staad Pro. And Rcdc

Authors: Aditya Kumar, Dr. Shubha Agrawal

Abstract: The thesis is based on a comparative study of configuration of RC Frame buildings irregular plan. With the increase in demand of high-rise buildings, the concern for safety also increases. High Rise Buildings are against lateral forces like wind load and seismic loads. To ensure safety against these factors, many tools are applied in the structure. In this thesis, we have Analysis and Time History Analysis. Both of these analyses are done under elastic limit, For Response Spectrum Method. Seismic safety of reinforced concrete buildings is a critical concern in regions of high seismic activity, particularly in areas classified under Seismic Zone II & Zone IV, where structures are subjected to severe ground motion and significant lateral forces. The present dissertation focuses on the seismic response analysis of storey reinforced concrete building located in Zone II & Zone IV, with special emphasis on studying the influence of different grades of reinforcing steel on structural behavior under earthquake loading. The primary objective of the study is to assess how variations in steel grade affect key seismic response parameters, including storey displacement, inter-storey drift, base shear, and dynamic characteristics of the building. The research adopts an analytical methodology using dynamic seismic analysis techniques in accordance with the provisions of IS 1893 (Part 1): 2016. A three-dimensional analytical model of the residential storey building is developed using standard structural analysis software.

Performance of Wealth Management Firms in India and the United States Post COVID-19: A Comparative Framework

Authors: Badal Dewani, Yuvika Nagaych

Abstract: This research examines the performance of wealth management companies in India and the US in the Post-COVID-19 Era, highlighting market fluctuations, changes in user sentiment, expansion, and liquidity trends. Drawing on a detailed daily trading record from January 2021 to December 2025, the study comprises a total of 12 wealth management sector giants across both economies. Employing summary statistics, cross evaluation and a SWOC assessment, the research reveals that Indian firms —despite operating with a smaller asset base of approximately 0.5 trillion dollars—are projected to triple their AUM by 2028, backed by quick tech adoption, favourable demographics and supporting government policies are growing fast, but they struggle with low profits, complexed regulation and increased market ups and downs. On the other hand, US firms operate within a mature ecosystem with over $ 50 trillion in AUM and exhibit greater stability and earnings, driven by advanced robo-advisory systems, robust institutional struc- tures, and solid investor trust. However, they must contend with tough competition and adapt to changing regulatory requirements. By combining firm-level and country-level insights, this study builds on current knowledge of global wealth management and emphasizes the need to align expansion with long-term sus- tainability. The findings provide valuable guidance to policymakers, investors, and professionals in the field who seek to strengthen and enhance openness in the evolving financial landscape.

DOI: https://doi.org/10.5281/zenodo.18630754

AI-based companion system for medicine and currency recognition for visually impaired users

Authors: Akash Sn, Adithiya S, Muni Praharsha M

Abstract: Visual impairment restricts independent recognition of everyday objects such as medicines and currency, leading to medication errors and financial dependency. This paper presents an AI-based companion system designed to assist visually impaired users in real-time medicine and currency recognition. The system utilizes computer vision and Convolutional Neural Networks (CNNs) to classify medicines based on packaging features and identify currency denominations using distinctive visual patterns. Optical Character Recognition (OCR) is integrated to extract drug names and dosage information from printed text. The system provides audio feedback through text-to-speech and supports voice commands for hands-free interaction. Designed for deployment on low-cost smartphone or embedded platforms, the proposed solution ensures affordability and portability. Experimental results demonstrate high recognition accuracy under varying environmental conditions. The system enhances user independence, reduces reliance on caregivers, and improves safety in medication management and financial transactions, highlighting the potential of AI-driven assistive technologies.

Ganga River Pollution In Kanpur City And Its Effects On Aquatic Life, Terrestrial Animals, And Human Health

Authors: Dr Amit Kumar Awasthi

Abstract: The Ganga River, a spiritual and physical lifeline for millions, faces an existential crisis from pollution, with Kanpur city representing its most critical and chronic hotspot. This industrial metropolis, famed for its leather tanneries and textile mills, contributes a massive, toxic load of chemical and biological contaminants to the river. This comprehensive review paper synthesizes decades of research to assess the magnitude and sources of pollution in the Kanpur stretch of the Ganga, and to evaluate its multidimensional effects on aquatic life, terrestrial animals dependent on the riverine ecosystem, and human health. The analysis reveals alarmingly consistent patterns: dissolved oxygen (DO) levels often plummet below 3 mg/L, biochemical oxygen demand (BDO) and chemical oxygen demand (COD) frequently exceed permissible limits by factors of 10-20, and heavy metals (Chromium, Lead, Cadmium, Mercury) and toxic organics are pervasive. The consequences for aquatic biota are devastating, including severe loss of biodiversity, dominance of pollution-tolerant species, bioaccumulation of toxins in fish, and large-scale fish kills. Terrestrial animals, especially livestock and wildlife consuming contaminated water, suffer from morbidity, reproductive failures, and heavy metal poisoning. For the human population, direct exposure through bathing, ritualistic practices, and indirect exposure via contaminated food and water leads to a high burden of waterborne diseases, dermatological conditions, and heightened risks of cancers, neurological disorders, and hepatic/kidney damage from chronic heavy metal intake. The review concludes that despite regulatory frameworks and intervention programs like the Namami Gange, pollution in Kanpur remains a complex, entrenched challenge due to inadequate infrastructure, enforcement gaps, and socio-economic dependencies on polluting industries. Urgent, systemic interventions prioritizing zero-liquid discharge, advanced sewage treatment, and a "One Health" approach integrating ecological and public health monitoring are recommended to reclaim the health of the river and the communities it sustains.

DOI: http://doi.org/10.5281/zenodo.18629633

Autonomous Rover Control System With Adaptive AI

Authors: Mirza Abrar Baig

Abstract: Autonomous rover operation in unstructured and uncertain environments requires control systems that are both adaptive and provably stable. Classical model-based approaches often exhibit limited robustness under terrain variability, sensor noise, and actuator uncertainty, while purely learning-based methods lack formal safety guarantees. This paper proposes a hierarchical autonomous rover control framework that integrates adaptive artificial intelligence with model predictive control and Bayesian risk awareness. The proposed architecture combines multi-sensor perception, reinforcement learning–based decision making, and adaptive model predictive control to enable real-time learning while ensuring bounded closed-loop behavior. A Lyapunov-based stability analysis establishes uniform ultimate boundedness of the system under bounded disturbances. Extensive experimental validation is conducted using both simulation and real-world rover platforms, including terrain disturbance injection, Monte Carlo trials, energy-aware evaluation, simulation-to-reality comparison, and ablation studies. Statistical reliability is demonstrated through 95% confidence intervals and significance testing, confirming that the proposed approach achieves faster convergence, lower steady-state error, and reduced energy consumption compared to baseline and single-method controllers. The results demonstrate that the proposed hybrid adaptive AI framework provides a robust, energy-efficient, and practically deployable solution for safety-critical autonomous rover applications.

DOI: http://doi.org/10.5281/zenodo.18631355

International Journal of Recent Development in Engineering and Technology Paper Template Format

Authors: Sonali Raut, Anuja Raut, Payal Rathod, Aishwarya Walke, Ashwini Meshram

Abstract: Road accidents are one of the major causes of death and severe injuries worldwide, often due to delayed medical assistance. To address this issue, the Accident Identification and Alerting System has been developed to automatically detect accidents and send immediate alerts to emergency services and nearby contacts. The system aims to reduce the time between the occurrence of an accident and the arrival of help, thereby increasing the chances of saving lives. This system is built using various sensors such as an accelerometer, vibration sensor, and gyroscope, which continuously monitor the motion and orientation of the vehicle. When a sudden impact or unusual movement is detected, it is interpreted as an accident by the microcontroller (such as Arduino, NodeMCU, or Raspberry Pi). The GPS module then captures the exact geographical location of the vehicle, while the GSM or IoT module sends this information as an alert message to predefined emergency contacts, rescue teams, or nearby hospitals. The alert message includes vital details such as the accident location (latitude and longitude) and time of occurrence, enabling quick and accurate response. Additionally, the system can be enhanced with IoT connectivity, allowing data to be sent to a cloud platform for real-time monitoring and analysis. This feature enables authorities to track accident-prone areas and improve road safety measures. The system can also be integrated with a buzzer or alarm to alert nearby people immediately after a crash. Overall, this project provides an efficient, low-cost, and reliable solution for automatic accident detection and alerting. By using real-time communication and location tracking, the proposed system minimizes human intervention, ensures faster emergency response, and significantly reduces fatalities caused by delayed medical assistance.

Platelet-Rich Plasma In Androgenetic Alopecia:An Evidence-Based Clinical And Biological Review

Authors: Prabhu Chandra Mishra

Abstract: Background: Androgenetic alopecia (AGA) is a progressive, androgen-mediated disorder characterized by follicular miniaturization. Platelet-rich plasma (PRP), an autologous biologic therapy enriched with growth factors, has emerged as a regenerative treatment modality in dermatology. Objective: To critically evaluate current scientific evidence regarding the efficacy, mechanisms, safety, and clinical application of PRP in androgenetic alopecia. Methods: A structured literature review was conducted using electronic databases including PubMed, Scopus, and Google Scholar. Randomized controlled trials (RCTs), prospective studies, and systematic reviews published between 2013 and 2025 were analyzed. Results: Most controlled studies demonstrate statistically significant improvement in hair density, hair shaft thickness, and global photographic scores following PRP therapy. However, variability in preparation protocols, platelet concentration, leukocyte content, activation methods, and outcome assessment limits inter-study comparability. PRP demonstrates a favorable safety profile with minimal adverse events. Conclusion: PRP represents a promising regenerative therapy for AGA, particularly in early to moderate disease. Standardization of preparation techniques and long-term, large-scale randomized trials are essential to establish definitive treatment guidelines.

The Need for the Exploration of Wind Energy as a Source of Power Supply in Rivers

Authors: Odu Elendu Victor

Abstract: Rivers State, Nigeria, faces significant energy challenges characterized by inadequate electricity supply, over-dependence on fossil fuels, and limited access to reliable power infrastructure. This study examines the potential for wind energy exploration as an alternative power source in Rivers State. Through a comprehensive analysis of wind energy potential, current energy challenges, and policy frameworks, this research demonstrates the viability of wind energy development in the region. The study reveals that Rivers State has moderate wind energy potential with average wind speeds ranging from 2.1 to 4.5 m/s and power densities of approximately 81.07 W/m² at 10-meter heights. Key findings indicate that strategic wind energy development could contribute significantly to the state's energy security while supporting climate change mitigation efforts. The research concludes with recommendations for policy development, infrastructure investment, and stakeholder engagement to facilitate wind energy deployment in Rivers State.

DOI: https://doi.org/10.5281/zenodo.18655166

Electromagnetic Compatibility Performance Of A Carbon Fiber-Based Thermal Pad For Industrial And Wearable Textile Applications

Authors: Elif Altürk, Nazlı Tatar, Hasan Tezcan

Abstract: Carbon fiber-based heating elements are increasingly utilized in industrial and wearable textile applications due to their flexibility, lightweight structure, and uniform heat distribution. In such applications, electromagnetic compatibility (EMC) and user safety are critical performance criteria, particularly for products operating in close contact with the human body. In this study, a carbon fiber-based heating pad integrated into a textile-compatible structure was developed and experimentally evaluated in terms of radiated electromagnetic emissions and electrostatic discharge (ESD) immunity. Radiated emission measurements were conducted in accordance with EN IEC 55014-1 over the frequency range of 30 MHz to 1000 MHz. ESD immunity was assessed using contact and air discharge methods under positive and negative polarity conditions. The results demonstrate that the developed heating pad meets the relevant EMC requirements, with emission levels remaining below regulatory limits and no permanent performance degradation observed during ESD testing. These findings confirm the suitability of carbon fiber-based heating pads for industrial and wearable textile systems.

DOI: https://doi.org/10.5281/zenodo.18667233

Design and Implementation of an Iot-Enabled Smart Doorbell with Real-Time Face Recognition and Full- Duplex Audio Communication

Authors: Mr. A. Muni swamy, Saraswathi, Srikanya, Snehitha, Sai kumar reddy, Dastagiri

Abstract: This project proposes a Smart Doorbell System using an way communication, Face recognition. ESP32-CAM that integrates face detection, face recognition,real-time alerts, and remote interaction for enhanced home security.The camera continuously monitors the entrance and captures images whenever a visitor appears. These images are processed by a recognition module that compares detected faces with a user manageddatabase. If the visitor is a known person, the system either suppressesnotifications or sends a simple message to the user containing the identifiedperson’s name. If the visitor is unknown, an instant alert with their capturedimage is sent to the user’s mobile device. The system also supports live videostreaming and two-way audio communication, enabling users to interactwith visitors remotely. Secure communication protocols ensure safe datatransfer and privacy. This project demonstrates an efficient combinationof embedded vision, IoT communication, and smart home automation,offering a reliable and user-friendly door security solution.

DOI: https://doi.org/10.5281/zenodo.18668435

Environmental and Human Health Implications of Thermal Power Generation in Baghdad Metropolitan, Iraq

Authors: Saeib A. Alhadi Faroun, Monadil Kadim Hazaa, Zahraa Qasim Jarad, Riyam Riyadh Kareem

Abstract: Baghdad, the capital of Iraq, faces a chronic and severe electricity deficit, compelling a heavy reliance on a network of thermal power plants. These facilities, predominantly fueled by natural gas, heavy fuel oil (HFO), and diesel, are critical for meeting the city's energy demands but are also significant sources of environmental degradation and public health risks. This research paper synthesizes existing scientific literature, governmental reports, and data from international organizations to provide a comprehensive analysis of the multifaceted environmental implications of thermal power generation within the Baghdad metropolitan area. The primary impacts examined include severe air pollution from emissions of sulfur oxides (SOx), nitrogen oxides (NOx), particulate matter (PM2.5 and PM10), and heavy metals, which exacerbate respiratory and cardiovascular diseases among the population. The paper further explores the consequences of water resource utilization, focusing on thermal pollution and chemical contamination of the Tigris River, the city's primary water source. Issues related to solid waste management, specifically the disposal of coal ash (where applicable) and fly ash and their potential for soil and groundwater contamination are also discussed. Furthermore, the present research quantifies the contribution of these plants to national greenhouse gas emissions, linking local operations to global climate change challenges and Iraq's particular vulnerability to its effects. The socio-economic burdens, including escalating healthcare costs and reduced quality of life, are analyzed in the context of the city's already stressed infrastructure. Finally, the paper evaluates a range of mitigation strategies and policy recommendations, from technological retrofits like flue-gas desulfurization and electrostatic precipitators to a strategic transition towards renewable energy sources, particularly solar power, and enhanced energy efficiency measures. The present study concludes that the environmental and health costs of Baghdad's current reliance on conventional thermal power are unsustainable and underscore the urgent need for a comprehensive, integrated energy and environmental policy to secure a healthier and more sustainable future for the city.

DOI: https://doi.org/10.5281/zenodo.18668845

Electric Bus Systems in India: Technology, Performance and Maintenance

Authors: Aditya aniruddh Mishra

Abstract: The rapid electrification of public transportation in India represents a critical transition toward sustainable mobility, reduced greenhouse gas emissions, and improved urban air quality. Electric buses (E-buses) are emerging as a key component of this transformation; however, their long-term technical reliability, battery degradation behaviour, and lifecycle performance under Indian operating conditions remain insufficiently understood. This research presents a comprehensive multi-scale analysis of electric bus systems, focusing on battery degradation modelling, mechanical reliability assessment, seasonal performance variation, and lifecycle optimization within the Indian context. The study investigates lithium-ion battery chemistries commonly used in Indian electric buses, including Lithium Iron Phosphate (LFP) and Nickel Manganese Cobalt (NMC), with emphasis on depth of discharge (DoD), C-rate effects, equivalent full cycle (EFC) counting, and thermal stress impacts. A predictive degradation framework is developed to estimate state of health (SoH) under variable duty cycles and climatic conditions. Results indicate that temperature variations above 35°C and high C-rate charging significantly accelerate capacity fade and internal resistance growth, directly influencing operational range and battery lifespan. Mechanical reliability analysis is conducted through Failure Mode and Effects Analysis (FMEA) and stress evaluation of suspension, axle, braking, and chassis systems. The increased vehicle mass due to battery integration is shown to elevate suspension and structural fatigue risks compared to conventional diesel buses. Seasonal performance evaluation highlights summer-induced thermal derating, monsoon-related electrical ingress risks, and winter-associated capacity reduction. A techno-economic assessment incorporating total cost of ownership (TCO) and lifecycle cost modelling demonstrates that despite higher initial acquisition costs, optimized charging strategies, predictive maintenance, and trained driver intervention significantly improve economic viability. The research further integrates preventive maintenance frameworks, telematics-based diagnostics, and driver training impacts on energy efficiency. The findings contribute to the development of a predictive reliability and lifecycle optimization framework tailored to Indian operating environments. This study provides actionable insights for policymakers, fleet operators, and manufacturers to enhance electric bus deployment strategies, improve battery longevity, and ensure sustainable public transportation systems.

DOI: https://doi.org/10.5281/zenodo.18677506

Sustainable Flood Protection Through L and U Shaped Barriers Design in Urban Areas

Authors: Dr. B.Raghunath Reddy, A.Jamaluddin, C.Chandra, k.Kavya

Abstract: Urban flooding has become a major environmental and infrastructural issue due to rapid urbanization, unplanned development, and changing rainfall patterns caused by climate change. The reduction of natural infiltration areas and the increase in impermeable surfaces have led to excessive surface runoff, resulting in frequent and severe urban floods. To mitigate these impacts, there is a growing need for innovative and sustainable flood protection systems that combine engineering efficiency with environmental responsibility. This project proposes a Land U-shaped barrier design as a sustainable flood protection solution for urban areas. The U- shaped barrier is designed to function as both a retention and diversion structure, effectively managing storm water during heavy rainfall events. Its geometry ensures enhanced hydraulic performance by guiding excess water flow while minimizing erosion and structural stress. The design promotes sustainability by utilizing eco-friendly and locally available materials, such as reinforced soil, recycled aggregates, and permeable layers that encourage groundwater recharge. Hydrological and structural analyses were conducted to assess the system’s performance under various flood conditions. Results show that the Land U-shaped barrier can significantly reduce flood depth, protect nearby infrastructure, and maintain structural stability even during high-intensity rainfall. Additionally, the system supports urban aesthetics by serving as a landscaped green corridor or pedestrian pathway during dry conditions, aligning with the principles of sustainable urban drainage systems (SUDS). Overall, the proposed Land U-shaped barrier offers a cost-effective, resilient, and environmentally compatible approach to flood protection in urban environments. By integrating modern engineering with sustainable design principles, this project contributes to the development of climate-resilient infrastructure and improved urban water management systems.

DOI: https://doi.org/10.5281/zenodo.18678199

Exploring Micro-Enterprises and Their Transformative Impact on Tribal Communities

Authors: Narendra Behera, Dr. Manoj Kumar Sharma

Abstract: Micro-enterprises, characterized by their modest scale and low startup requirements, present a unique opportunity to uplift tribal communities by generating income and fostering employment. This study explores the diverse micro-enterprises prevalent in tribal areas, such as handicrafts, food processing, and agriculture, which leverage local skills and resources. Despite their potential, tribal communities face challenges in adopting micro-enterprises, including lack of awareness, risk aversion, and cultural resistance. This research investigates the major causes of reluctance among tribal populations and proposes strategies to overcome barriers, emphasizing the role of awareness-building, risk mitigation, and comprehensive support systems. By addressing these challenges, microenterprises can flourish, empowering tribal communities and contributing to sustainable development.

Synthesis, Structural Characterization, And Antioxidant Assessment Of Fused Triazole Derivatives And Triazoles

Authors: Aijad Khan, Dr. Parimeeta Chanchani

Abstract: Fused heterocyclic triazole derivatives and triazoles such as thiadiazoles, Schiff bases, thiadiazine, thiadiazepine, and among others, were prepared and characterized using MS (mass), IR (infrared), and 1H NMR (proton NMR). Using DMSO as a solvent, the triazole derivatives were tested for antioxidant activity using DPPH scavenging activity.

Emotional Intelligence and Quality of Work Life Among Employees of HCL-BPO

Authors: N. Deepanraj

Abstract: The study adopts a descriptive and correlational research design to analyze the association between EI and QWL among selected employees. Primary data were collected through structured questionnaires measuring Emotional Intelligence and Quality of Work Life dimensions, while secondary data were obtained from journals, books, and previous research studies. Statistical tools such as percentage analysis, mean scores, and correlation analysis were used to interpret the data. The findings indicate a positive and significant relationship between Emotional Intelligence and Quality of Work Life, suggesting that employees with higher emotional competence demonstrate better stress management, improved interpersonal relationships, and greater job satisfaction. However, the study also highlights that organizational policies, workload, and management practices play a crucial role in shaping overall work-life quality. The research concludes that while Emotional Intelligence enhances employees’ ability to cope with workplace challenges, sustainable improvement in Quality of Work Life requires both individual development initiatives and structural organizational support.

Study of Bioremediation Kinetic of Oil-Contaminated Water Utilizing Maize Bran as Nutrient Support

Authors: Dr. Malachy. O.Ugwuoke, Engr. Agu Anthony, Dr.Okoye Japheth .O

Abstract: This study investigated the improvement of bioremediation kinetics of crude oil-contaminated water utilizing maize bran as nutrient support in combination with Aspergillus niger fungus. The proximate analysis revealed that maize bran contains essential macro- and micronutrients such as carbon, nitrogen, phosphorus, potassium, calcium, sodium, iron, cellulose, lignin, and proteins, which provide a rich nutrient matrix for microbial activity. Fourier Transform Infrared (FTIR) spectroscopy confirmed the presence of functional groups that support microbial growth and hydrocarbon degradation. Bioremediation experiments were conducted by introducing Aspergillus niger and maize bran into crude oil-polluted water and monitoring parameters such as pH, microbial population, and total petroleum hydrocarbons (TPH) over 35 days. Results showed a significant reduction in TPH, demonstrating enhanced microbial degradation compared to the control setup without maize bran. Kinetic modeling revealed that the biodegradation process fitted well with both first-order and second-order models, with correlation coefficients (R²) greater than 0.8, indicating reliable predictive performance. The rate constants further validated maize bran’s efficiency in accelerating crude oil degradation. The findings suggest that organic biocarriers like maize bran offer a sustainable, cost-effective, and environmentally friendly alternative to inorganic fertilizers in hydrocarbon bioremediation by supplying nutrients that stimulate microbial growth.

DOI: https://doi.org/10.5281/zenodo.18695144

Effects of Instructional Charts and Pictures on Students’ Academic Achievement in Pythagoras Rules in Abuja

Authors: Ale Florence Olajumoke, Sylvester Orobosa Okwuoza

Abstract: Instructional materials such as charts and pictures are important in the teaching and learning of mathematics. This is because they help students to visualize the concepts that are being taught. In other words, they help to remove abstract nature of some concepts and make lessons learnt to be permanent. A quasi- experimental research design was adopted. Therefore, this study investigated the effect of instructional charts and pictures on students’ academic achievement in Pythagoras rules in Abuja. Two research questions were answered and two null hypotheses were tested at 0.05 level of significance. A sample of 105 junior secondary two (JSS2) participated in the study. The instrument for data collection was Mathematics Achievement Test which contained 20 objective test items validated by specialists in mathematics and measurement and evaluation. The reliability index of the instrument established using Kuder Richardson 20 was 0.76. The results of the study showed that charts and pictures have significant effects on the academic achievement of students in mathematics in Abuja. It was therefore recommended among others that training, seminars and workshops should constantly be organized for mathematics teachers on the appropriate use of instructional materials for effective teaching and learning.

DOI: https://doi.org/10.5281/zenodo.18709647

Green Synthesis of Silicon Dioxide from Eleusine Indica and Its Characterisation

Authors: Sabira Bi M. A. Shaikh, Anurag Naik

Abstract: Eleusine indica L.commonly known as the Indian goosegrass, wire grass or crowfoot grass belonging to Poaceae or Gramineae family was scrutinized in this study. The leaf blades were studied to observe the appearance of Phytolith structures which are microscopic silica bodies produced by many grasses which aids in their physical support. The extraction of silica was accomplished by acid treatment of these leaves in two cycles for 16 hours at 80°C each cycle. The oxidation process of synthesized silica was carried out using Hydrogen peroxide for 16 hours at 80°C to produce silicon dioxide (SiO2). The characterization studies were processed using X-ray diffraction, scanning electron microscope and energy dispersive X-ray (SEM – EDX), Raman spectrometer, Fourier Transform Infra-red spectroscopy (FT-IR). The obtained results were interpreted and compared with the standard. The comparative observations were reported.

DOI: https://doi.org/10.5281/zenodo.18721155

Development Of A TL-Moment-Based Estimation Method For K3D-II

Authors: Muhammad Nura, Zahrahtul Amani Zakaria

Abstract: Strong parameter estimation for flexible multi-parameter distributions is essential for hydrological analysis of extreme values in a heavy-tailed, contaminated environment. Even though the Three-parameter Kappa Type-II (K3D-II) distribution is flexible for tail modeling, traditional L-moments and maximum likelihood estimation (MLE) are not very stable and should be used with more robust methods for reliable regional frequency analysis. A trimmed L-moment (TL-moment) estimation framework was developed for K3D-II. Closed-form expressions for the first four TL-moments were derived from the quantile function. Parameters were estimated sequentially: the shape parameter via bounded root-finding using TL-skewness, followed by direct estimation of location and scale. A Monte Carlo simulation was used to evaluate performance [10,000 replications] across light-, moderate-, and heavy-tailed regimes, sample sizes, and contamination levels of 0, 5, 10, and 20% based on Bias, RMSE, and relative efficiency. The TL-moment estimator was more stable and less sensitive to extremes as compared to L-moments or MLE. TL-moments preserved the same bias =0.15, RMSE=0.20, and 90 percent efficiency, and MLE worsened (bias=0.60, RMSE=0.65, and efficiency=50 percent). Best results were obtained with the shape parameter under heavy-tailed conditions. Moderate symmetric trimming TL (1,1) provided the most satisfactory balance of robustness, efficiency, and extreme-quantile reliability. The TL-moment framework improves robustness, identifiability, and numerical stability in K3D-II estimation. It builds on classical L-moment theory by adding resistance to contamination via trimming, supporting reliable at-site and regional hydrological frequency analysis.

The Impact of Customer Relationship on Resource Mobilization; a Case Study on Selected Commercial Banks in Ethiopia.

Authors: Megbaru Misikir Tashu

Abstract: This study entitled “customer relationship on resource mobilization; a case study on selected Commercial banks in Ethiopia”. To achieve the aim of the study, five explanatory variables: trust, commitment, communication, competency and conflict handling were regressed against resource mobilization. In this study both primary and secondary data collection methods were used. The primary sources of data for the study were collected through questionnaire survey from customers and customer service managers. Moreover, in order to support the questionnaire survey, additional information was obtained through unstructured interview with selected branch managers. Finally, the gathered information were analyzed by descriptive, correlation and ordered logit regression. The major findings of the study indicated that trust, commitment, communication, and competency customer relationship marketing practices were contributed positively on the effort of Commercial banks in Ethiopia in resource mobilization While, conflict handling practices in Commercial banks in Ethiopia was identified as a one constraints for resource mobilization. The study also recommended the bank to design and providing continuous training sessions that emphasizing customer service handling (customer relationship marketing) for employees to develop skill, attitude and abilities gap and to fill customer handling gaps identified under this study.

DOI: https://doi.org/10.5281/zenodo.18722998

An Assessment of Vegetation Cover and Tourism Potential in Kakoijana Reserved Forest, Bongaigaon District, Assam

Authors: Musrufa Ahmed, Rimlee Bora

Abstract: Reserved forests play a vital role in biodiversity conservation, ecosystem regulation, and the provision of ecosystem services, while also offering opportunities for sustainable, nature-based tourism. This study assesses the spatial pattern of vegetation cover and evaluates the tourism potential of Kakoijana Reserved Forest, located in Bongaigaon district of Assam, using geospatial techniques. Vegetation cover dynamics were analyzed through the Normalized Difference Vegetation Index (NDVI) derived from satellite data for the years 2013 and 2023. NDVI values were classified into no vegetation, low vegetation, and high vegetation categories to examine spatio-temporal changes in forest condition. The results reveal a positive shift in vegetation health over the decade, marked by a reduction in non-vegetated and low-vegetation areas and a significant increase in high-vegetation cover. This improvement indicates enhanced forest regeneration, increased biomass, and improved ecological health, likely supported by conservation efforts and reduced anthropogenic pressure. In addition, the study evaluates the current tourism status and future potential of Kakoijana Reserved Forest. The forest is internationally recognized as a key habitat for the endangered Golden Langur (Trachypithecus geei), along with several other rare and threatened species, making it an emerging eco-tourism destination in western Assam. Existing tourism activities are well-managed and non-exploitative, with active community participation and basic infrastructure support. The findings suggest that Kakoijana Reserved Forest holds substantial potential for sustainable and community-based eco-tourism, which can simultaneously support biodiversity conservation and local livelihood development. An integrated approach to vegetation assessment and tourism planning is essential to ensure long-term ecological sustainability and responsible tourism growth.

DOI: https://doi.org/10.5281/zenodo.18738077

MLCC Capacitors: Failure Mechanisms, Reliability Perspectives, and Emerging Challenges

Authors: Avadhut Kumbhar, Akshay Khot

Abstract: Multilayer Ceramic Capacitors (MLCCs) are among the most widely deployed passive components in contemporary electronic systems, supporting applications ranging from consumer electronics to safety- critical automotive platforms. Continuous scaling, cost reduction, and material transitions. Most notably the shift from Precious Metal Electrode (PME) to Base Metal Electrode (BME) technology. This have enabled higher capacitance density and miniaturization while simultaneously introducing new reliability challenges. Historically, capacitor failures have accounted for a significant portion of electronic field failures, and recent trends indicate increasing early-life and wear-out failures even in low-voltage MLCCs. This paper presents an overview of MLCC construction, standardization, and applications, followed by a detailed analysis of dominant failure mechanisms observed in automotive environments. Mitigation strategies and design considerations aligned with automotive reliability standards are also discussed.

DOI: https://doi.org/10.5281/zenodo.18738528

A Comprehensive Literature Review On Block Chain Enabled Deep Learning Frameworks For Smart Learning Environments

Authors: Mrs. Vidhya Rani M, Dr. Vijayalakshmi S

Abstract: The integration of Blockchain Technology (BCT) and Deep Learning (DL) has emerged as a transformative approach to addressing the challenges of data security, transparency, and personalization in smart learning environments (SLEs). Blockchain provides decentralized, tamper-proof storage of learner records and ensures data authenticity, while deep learning offers powerful predictive analytics for personalized and adaptive education. This paper presents a literature review on blockchain-enabled deep learning frameworks in education, examining recent advances, architectural models, and practical implementations. The review highlights how existing studies have applied blockchain for credential verification, secure content sharing, and distributed trust management, while deep learning techniques have been employed for student performance prediction, intelligent tutoring, and adaptive feedback. Despite these advancements, significant research gaps remain, particularly in the areas of real-time deployment, scalability, latency, interoperability across institutions, and privacy-preserving learning analytics. By systematically analyzing current trends, this review identifies open challenges and future directions for developing efficient, secure, and scalable blockchain.DL frameworks tailored to smart learning environments.

DOI: https://doi.org/10.5281/zenodo.18738880

INTELLIGENT MULTI-SENSOR FUSION FRAMEWORK FOR REAL-TIME INDUSTRIAL FAULT PREDICTION AND ENERGY OPTIMIZATION

Authors: Mr. H. Raghunatha Rao, A. Tejunisa, B. Naimneesha, M. Rajeswari, J. Keerthi

Abstract: Industrial machinery failures and energy inefficiencies represent critical challenges in modern manufacturing environments, with unplanned downtime costing manufactures between $50,000 to $250,000 per hour depending on production scale and industry sector. Traditional reactive maintenance approaches and scheduled preventive maintenance programs fail to detect early-stage equipment degradation patterns, resulting in catastrophic failures, extended production interruptions, and substantial financial losses. This paper presents an intelligent multi-sensor fusion framework for real-time industrial fault prediction and energy optimization utilizing internet of Things (IOT) technology, advanced signal processing techniques, and machine learning algorithms. The proposed system integrates an ESP32 microcontroller as the edge computing device with multiple sensor modalities including MPU6050 three-axis MEMS accelerometer for vibration analysis, ACS712 Hall-effect current sensor for electrical parameter monitoring, resistive voltage divider for power consumption tracking, and DS18B20 digit temperature sensor for thermal condition assessment. The ESP32 continuously samples sensor data at 500 Hz frequency and transmits multi-parameter information via-WIFI WebSocket protocol to a python-based computational server. The server performs Fast Fourier Transform (FFT) analysis on time-domain vibration signals to extract frequency-domain spectra in the 30-500 Hz industrial machinery range, revealing characteristic fault signatures associated with bearing defects, rotor imbalance, shaft misalignment, and mechanical looseness. Multi sensor data fusion combines vibration frequence patterns with electrical parameter anomalies and thermal deviations to improve fault classification accuracy by 40-60% compared to single-parameters monitoring systems. A professional web-based dashboard provides oscilloscope-level visualization with real-time time-domain waveforms, frequency spectrum charts, electrical parameter displays, and thermal monitoring capabilities.

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Machine Learning for Predicting Cement Zonal Isolation Quality Using Mixed Telemetry and Limited Cement Evaluation Data

Authors: Jeremiah Ifeanyi Okoroma, Ikechi Igwe

Abstract: Sustained casing pressure (SCP), prevention of cross-flow between intervals of formation, and reliable cement zonal isolation depend on attaining integrity of the well over time and eliminating cross-flow between intervals. The standard cement evaluation devices (CET) like ultrasonic, acoustic logs have high fidelity but tend to be few, costly to purchase or become inaccessible in high angle, deepwater or slimhole completions. The paper builds a machine-learning (ML) model to forecast the quality of cement zonal isolation with mixed data; rig-site telemetry, mud logging parameters, pumping schedules, and a few cement evaluation logs. The gradient boosting and sequence learning networks were combined in a hybrid and trained on multi-well data sets with full and partial cement logs. The findings indicate that the ML workflow predicts the quality of isolation with an accuracy of 92% and a mean absolute error of 0.07 and an AUC of 0.89, and on the other hand, the isolated empirical correlations and physics-based rules perform worse. The model has been able to generalize on wells having no cement logs and has allowed the proactive definition of risky intervals and the use of optimal cementing plans. The results indicate that combining various telemetry signals with scarce evaluation data can be highly useful in predicting cement quality and lessening the utilization of expensive wireline cement logs.

DOI: https://doi.org/10.5281/zenodo.18740327

Infusing Artificial Intelligence For Transformative Impact In Academic Libraries: A Technical Perspective

Authors: Dr. Gaurav Kumar Jaiswal

Abstract: Academic libraries constitute the epistemic infrastructure of higher education institutions, serving as knowledge repositories and research facilitation environments. The rapid evolution of Artificial Intelligence (AI), particularly in machine learning (ML), natural language processing (NLP), and knowledge graph engineering, is catalyzing a paradigm shift in academic library ecosystems. AI-driven systems enable semantic information retrieval, automated metadata generation, predictive collection development, and intelligent decision support. Through API-based interoperability and large-scale metadata aggregation, AI platforms deliver personalized recommendation systems, automate repetitive cataloguing tasks, optimize search precision, and enhance bibliometric analytics. This study critically evaluates advanced AI tools and frameworks integrated within academic libraries to improve discoverability, research productivity, and scholarly communication. The findings suggest that AI architectures significantly strengthen metadata harmonization, citation intelligence, and dynamic knowledge visualization while raising essential ethical and infrastructural considerations.

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PCOS and Women’s Health: Examining the Influence of Modern Living Conditions in Coimbatore City

Authors: Dr.P.Manochithra

Abstract: Polycystic Ovary Syndrome (PCOS) is one of the most common endocrine disorders affecting women of reproductive age worldwide. Characterized by irregular menstrual cycles, hormonal imbalances, and the presence of ovarian cysts, PCOS has become a significant health concern, particularly in urban areas where modern lifestyle habits are rapidly evolving. This study focuses on understanding the prevalence and impact of PCOS among women in Coimbatore City, Tamil Nadu, and examines how modern living conditions—such as dietary habits, stress levels, sedentary behavior, and environmental factors—contribute to the onset and progression of the condition. Data was collected through surveys and interviews with healthcare professionals and affected individuals. The findings suggest a strong correlation between lifestyle factors and the increasing incidence of PCOS. This study highlights the need for greater awareness, early diagnosis, and lifestyle interventions to manage and prevent PCOS-related complications.

DOI: https://doi.org/10.5281/zenodo.18741354

Security Architecture for Cloud Computing

Authors: Gayatri Mudaliar, Khusbhu Modi

Abstract: Cloud computing has become a foundational technology in modern information systems by providing on-demand access to computing resources such as storage, processing power, and networking over the Internet. Organizations increasingly adopt cloud platforms to enhance scalability, flexibility, and cost efficiency. However, the migration of sensitive data and critical applications to remote infrastructures introduces significant security challenges. These challenges include data privacy risks, multi-tenancy vulnerabilities, regulatory compliance concerns, and lack of direct infrastructure control. This paper presents a comprehensive analysis of cloud security architecture, including service models, deployment models, architectural layers, and major security threats. It further discusses protection mechanisms such as encryption, identity and access management (IAM), accountability frameworks, secure key management, and compliance monitoring. The study aims to provide a structured understanding of designing secure cloud architectures while balancing performance and operational efficiency.

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Synergistic Influence of Tool Pin Geometry and Axial Force on Microstructure and Mechanical Performance of A356 Aluminium Alloy Friction Stir Welds

Authors: Mr. Jaspreet Singh, Dr. Rakesh Kumar, Ms. Saloni Spall, Mr. Jagjit Singh

Abstract: This study presents an investigation on the effect of tool pin profile and axial force on the mechanical and microstructural properties of friction stir welded A356 aluminium alloy joints while keeping rotational speed and welding speed constant. Experiments were conducted using three different tool pin profiles (cylindrical, tapered, and threaded) and three axial force levels (4, 6, and 8 kN). The welded joints were evaluated in terms of tensile strength, microhardness and impact strength. Results indicate that threaded pin profile combined with moderate axial force (6 kN) produced defect-free joints with superior mechanical performance. The study demonstrates that tool geometry and axial force play a critical role in improving joint quality in friction stir welding of A356 aluminium alloy.

DOI: https://doi.org/10.5281/zenodo.18753146

Blockchain-Based Decentralized Food Supply Chain

Authors: Valarmathi.P, Abimanyu.S, Ashwin Kanna.V, Harish.R

Abstract: The global food supply chain faces significant challenges including lack of transparency, food fraud, contamination risks, traceability issues, and inefficient logistics management. Traditional centralized systems are vulnerable to data manipulation, single points of failure, and limited stakeholder accountability, compromising food safety and consumer trust. This paper proposes a blockchain-based decentralized food supply chain system that leverages distributed ledger technology to ensure transparency, traceability, and security throughout the entire food production and distribution process. The proposed framework integrates smart contracts to automate transactions, verify authenticity, and enforce compliance with food safety standards. Each stakeholder—including farmers, processors, distributors, retailers, and consumers—participates as a node in the blockchain network, recording immutable transactions at every stage from farm to fork. The system incorporates IoT sensors for real-time monitoring of environmental conditions such as temperature, humidity, and storage duration, ensuring product quality and reducing spoilage. Implementation of cryptographic hashing and consensus mechanisms guarantees data integrity and prevents unauthorized modifications. Performance evaluation demonstrates enhanced traceability, reduced fraud incidents, improved recall efficiency, and increased consumer confidence. The proposed decentralized architecture provides a robust, transparent, and tamper-proof solution for modernizing food supply chain management and ensuring global food security.

DOI: https://doi.org/10.5281/zenodo.18753593

Heavy Metal Contamination Assessment in Agricultural Soils of Pb-Zn Mining Areas: A Case Study of Ebonyi State, Nigeria

Authors: Osayande, A.D

Abstract: This study examined the level of heavy metal contamination in the agricultural soils of the Pb-Zn mineralization zone of Ameri, Ameka, Enyigba, and Ishiagu in Ebonyi State, Nigeria. Thirty-three soil samples were collected for analysis for the concentrations of Pb, Zn, Cu,Cr and Cd, using the Atomic Absorption Spectrophotometry (AAS) method. Physicochemical parameters such as pH, EC, and TOC were also determined. The study found that the soil pH ranges from 2.0 to 6.93, averaging 4.17. The results of the heavy metals analyzed ranked as follows: Pb > Zn > Cu > Cd > Cr. The study found that the mean concentrations of lead(Pb) in the excavated soils and quarry soils were 65.76 ppm and 57.03 ppm, respectively, which is higher than the control values. The Contamination Factor (CF) results showed moderate to high contamination levels for Pb and Cd. The study also found that the Enrichment Factor (EF) values indicated the level of anthropogenic activities on the environment. The Pollution Load Index (PLI) confirmed that there is a level of deterioration of the soil environment. The research concludes without doubting the level of heavy metal contamination which is very high, and remediation measures should be put in place to save the environment.

Biometric Blood Group Identification Using Fingerprint Images With EfficientNet And Confidence-Aware Deep Learning

Authors: Mrs. A. Adaikkammai, Kumaran A, Monish MD

Abstract: Blood group identification is a fundamental requirement in medical diagnostics, transfusion procedures, and emergency healthcare. Conventional blood grouping techniques rely on invasive serological testing, which requires laboratory infrastructure, trained personnel, and time. This paper presents a non-invasive fingerprint-based blood group identification system using deep learning. The proposed approach employs an EfficientNet-based convolutional neural network for automatic feature extraction and classification from fingerprint images. To enhance system reliability, a confidence-aware decision mechanism is incorporated to avoid uncertain predictions. Additionally, explainable artificial intelligence techniques are used to visualize fingerprint regions influencing the prediction. Experimental evaluation using fingerprint image datasets demonstrates that the proposed method serves as an efficient, transparent, and deployable decision-support system for academic and prototype-level healthcare applications.

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Real-Time Marine Life Detection Using Yolo Based Object Detection Models

Authors: Anguraju K, Dharaneesh K. S, Jeeva D, Mohamed Sharukkhan M

Abstract: Real-time detection of marine organisms is essential for effective ocean surveillance, ecological research, and marine conservation. This study proposes a YOLO-based object detection approach for the fast and accurate identification of marine life in underwater environments. By utilizing the single-stage architecture of YOLO, the system achieves high detection speed without compromising accuracy. The model is trained to recognize various marine species, including fish, turtles, and other underwater organisms. To overcome underwater imaging challenges such as poor illumination, color attenuation, and background noise, appropriate preprocessing techniques are applied to enhance input data quality. The trained model processes live video streams and performs real-time inference with low computational latency. Experimental evaluation shows that the proposed method delivers reliable detection performance and real-time efficiency, making it suitable for deployment on embedded systems, underwater robots, and autonomous marine vehicles. The proposed framework contributes to continuous marine ecosystem monitoring and supports data-driven conservation strategies.

DOI: https://doi.org/10.5281/zenodo.18755527

PhiURL – Graph based phishing url detection using Loopy Belief Propogation

Authors: Rutuja Hole, Riya Dandawate, Rohit Patil

Abstract: Phishing attacks continue to pose a significant threat to online security by exploiting user trust through deceptive URLs and malicious web resources. Traditional phishing detection approaches largely rely on isolated feature-based classification techniques, which fail to capture the relational dependencies that naturally exist among web entities such as domains, URLs, and structural attributes. This paper presents a theory-driven framework that models phishing detection as a probabilistic inference problem over a graph structure. URLs and their associated characteristics are represented as interconnected nodes within a graphical model, enabling relational reasoning across the network. To infer the likelihood of phishing behaviour, Loopy Belief Propagation (LBP) is employed as an approximate probabilistic inference mechanism capable of handling cyclic graph structures. The proposed framework emphasizes formal graph construction, probabilistic modelling, and message-passing inference without reliance on implementation-specific heuristics. By reasoning collectively over relational dependencies, the model provides a robust theoretical foundation for phishing detection under uncertainty. This work contributes a formalized approach that bridges graph- based learning and probabilistic inference for cybersecurity applications.

DOI: https://doi.org/10.5281/zenodo.18755619

An Integrated Deep Learning Approach for PCOS Diagnosis using Ultrasound images and Clinical parameters

Authors: Thenmozhi, Ramya R, Suganya S, Susithra R

Abstract: Polycystic Ovary Syndrome (PCOS) is a common hormonal disorder among women of reproductive age, characterized by complex clinical symptoms and varying ultrasound findings, which often make diagnosis challenging. To address these limitations, this study proposes an integrated deep learning–based diagnostic framework that combines ovarian ultrasound images with key clinical parameters. Convolutional Neural Networks (CNNs) are utilized to automatically learn representative features from ultrasound images, enabling the identification of ovarian morphological patterns such as follicular distribution and ovarian size. Simultaneously, relevant clinical data including age, body mass index, hormonal levels, menstrual history, and metabolic indicators are analyzed using a neural network model. The learned features from both modalities are fused to improve diagnostic performance. The proposed approach minimizes reliance on subjective clinical assessment and manual feature extraction. Experimental evaluation demonstrates that the integrated model achieves superior accuracy, sensitivity, and specificity compared to single-modal diagnostic methods. This framework provides an effective and non-invasive decision-support tool for early and reliable PCOS diagnosis.

DOI: https://doi.org/10.5281/zenodo.18755808

Stegablock: Dual-Layer Steganography and Watermark System

Authors: Mrs.M.Lavanya, Yogeswaran R, Pragedeeswaran S, Palanivel M

Abstract: This project introduces an advanced dual-layer security framework that integrates steganography and digital watermarking to strengthen data confidentiality and ownership verification. The proposed system follows a two-stage security model: in the first stage, sensitive information is invisibly embedded within digital media such as images, audio, or video using steganographic techniques, while the second stage applies resilient watermarking methods to ensure copyright protection and verify content authenticity. Least Significant Bit (LSB) manipulation is utilized for efficient data concealment, and Discrete Wavelet Transform (DWT) or Discrete Cosine Transform (DCT) techniques are employed for robust watermark embedding. This combined approach delivers high embedding capacity along with strong resistance to common signal-processing and malicious attacks. The layered architecture guarantees continued protection even if one security layer is breached. Potential applications include secure data transmission, intellectual property protection, medical imaging security, and digital forensic analysis. Experimental results confirm improved imperceptibility, robustness, and overall security compared to conventional single-layer techniques, making the framework well suited for high-security and mission-critical applications.

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AutoChain Nexus: Blockchain-based Smart Contract System For Secure Vehicle Registration And Ownership Transfer”

Authors: Vishakha Patil, Mihir Patil, Pallavi Patil, Ms. Tejashree Pangare

Abstract: AutoChain the word itself gives the idea about our project, Auto stand for Automobile and Chain stands for Blockchain. the ownership transfer and vehicle registration was managed traditionally that is with the help of the paper work. These conventional methods often suffer from the inefficient behaviour such as time complexity, lack of transparency, susceptibility which may lead to fraud, tampering of data and untrusting parties of human errors.Smart contract is a platform which does not include an intermediatory between two parties or individuals. As we know that purchasing or transferring vehicle is a pretty risky job which cannot be taken for granted. Traditionally all the vehicle registration work or transfer work was done on paper work.And keeping securely that hard copies might lead to false copies or misplaced. So to avoid all these stuffs we invented our system.

Catalyzing Young Innovators: Organizational Practices And Participation Patterns In INSPIRE–MANAK

Authors: Sajad Hussain Mir, Javaid Ahmad Dar, Sehreen Shakeel, Bilquees Fatima

Abstract: The INSPIRE–MANAK Scheme aims to promote innovation and scientific temper among school students; however, its effectiveness depends on awareness, institutional support, and stakeholder engagement. The present study examined the implementation of the INSPIRE–MANAK Scheme in District Pulwama by collecting primary data from students, teachers, and Heads of Institutions using structured questionnaires. The findings reveal a clear disparity between institutional awareness and student participation. While 85.71% of HOIs and 79.54% of teachers reported awareness of the scheme, only 42.40% of students were aware of INSPIRE–MANAK. Formal awareness initiatives reached merely 35.54% of students, and only 13.28% had attended any orientation or workshop, with just 12.89% expressing satisfaction. Student participation remained low, with only 16.10% having registered for the scheme, primarily due to lack of awareness (44.92%), time constraints (18.75%), and difficulty in understanding the process (11.70%). Although teachers demonstrated willingness to support innovation, with over 86% agreeing or strongly agreeing to motivating students, 75% reported mentoring as challenging due to limited resources and training. The study concludes that despite high awareness at administrative and teacher levels, inadequate training, weak institutional mechanisms, and insufficient outreach significantly constrain student engagement. Strengthening structured awareness programmes, teacher capacity building, and institutional support systems is essential to enhance the scheme’s impact at the grassroots level.

Reducing Resource Energy Wastage for Smart Homes

Authors: P Madhavan, Bhavanasi Sai Mourya, V Udaykiran, Yarragunta Prakash

Abstract: Electricity is an essential requirement in modern life, and the increasing dependence on electrical systems demands efficient monitoring and maintenance. In many cases, irregularities in electrical machines and power usage remain undetected, which may affect system performance and safety. This project proposes an IoT-based smart monitoring system that continuously observes electrical parameters and machine operating conditions in real time. Whenever any irregularity is detected, the system automatically identifies it and sends alerts to the user through an online platform. The proposed system also supports timely corrective actions by providing necessary information to rectify the identified irregularities before they lead to major failures. By enabling continuous monitoring, early detection, and preventive maintenance, this project improves system reliability, enhances operational safety, and supports efficient utilization of electrical energy. The implementation of this IoT-based solution contributes to smarter electrical system management and reduced manual intervention.

Real Time AI Voice Agent Interview Platform

Authors: Ms. Dipti Patil, Siddhesh Patil, Pushkar Rane, Swaraj Salavi

Abstract: The growing complexity of the current recruitment processes, the preparation of interviews has become under a lot of pressure, and, although the traditional methods of the preparation are united and incomplete in certain aspects, they do not fully serve the needs of each candidate. The concept introduced in this paper involves an AI-based career preparation platform, which combines automated resume parsing, simulated interviews, and a full- fledged career readiness assessment in one platform. The platform reads any resume that has been uploaded by the user and identifies the skills, education, projects, and work experience and matches the profile to the job candidates desired. To solve a vital problem in which technically skilled individuals fail to pass HR interviews, the system rolls out a behavioral, situational and personality-based interview coaching AI-powered HR Interview Coach which trains users to answer specific questions and offers tutorials and feedback to improve their interviewers. Furthermore, Technical Interview Practice Engine is added to enhance technical readiness in the main areas including data structures, databases, operating systems, object- oriented programming, and programming languages on the basis of theory application, coding problems, question and answer, and debugging. The platform also has aptitude, soft skill training, creative learning modules, company-specific training tools, and placement tracking systems. Intuitive feedback using analytics and the support of personalized learning paths decrease the time spent in preparation and enhances confidence and interview performance. The system proves to be effective to students, job seekers, and educational programs through experimental evaluation where there is better role preparedness and better interview results.

Vaultify : Secure, Compressed & Categorize File Management Platform

Authors: Ms. Dipti Patil, Aditya Lal, Harsh Patil, Karan Pendhari

Abstract: In today’s digital environment, organizations and individuals generate and manage vast amounts of information in multiple formats. This data is not efficiently handled by various websites, and users have to use multiple applications to sort, compress, and securely store this data. This project started from the simple problem that people lose or misplace files all the time, so we built a system that not only stores documents but actually keeps them organized without you doing much. When you upload something, it runs through an OCR scan, grabs the important text, and either drops it in the right folder or makes a new one if needed. Security is a priority proper logins, end-to-end encryption with public/private keys, so even if someone gets their hands on the file, it’s just gibberish to them. We also didn’t want the storage to get bloated, so files that can be compressed are compressed, images are cleaned up but still look fine, and a hashing system spots duplicates so you’re not storing the same thing over and over. All these bits together make something that’s secure, efficient, and actually pleasant to use instead of feeling like a chore. With time, the system can be scaled to support thousands of documents without losing speed and thus proves to be as effective for a small team as it is for a giant organization. It's built to learn, so if storage requirements expand or new security threats emerge, features can be added without disrupting the current workflow. In short, it's a built-to-last tool, not a temporary fix.

Capture And Learning Intelligence Platform (CLIP)

Authors: Siddhant Thorve, Ashish Thakur, Harsh Thakur, Ms. Pooja Patil

Abstract: In traditional academic settings, students who miss live lectures often struggle to catch up due to a lack of structured resources and peer interaction. This project presents A-CLIP (Capture and Learning Intelligence Platform), a hybrid intelligent system designed to bridge this gap by transforming static video recordings into a comprehensive, interactive learning environment. Unlike standard video players, A-CLIP creates a "study-like" atmosphere where absent students can engage with material more deeply than in a physical classroom. The platform utilizes a microservices-inspired architecture, combining a Node.js orchestrator for real-time state management with a specialized Python service for high-performance AI tasks. For every uploaded lecture, the system automatically generates detailed, structured study notes using Llama 3.2 and curates supplementary external resources from the web, ensuring students have access to a wealth of context beyond the video itself. To reinforce mastery, an AI-driven adaptive quiz engine generates assessments with difficulty tiers (Beginner and Pro), locking advanced modules until foundational concepts are understood. Furthermore, the platform simulates the social aspect of learning through a "Study Room", where students can watch Siddhant Thorve. 2026, ISSN (Online): 2348-4098 ISSN (Print): 2395-4752 International Journal of Science, Engineering and Technology synchronously, video chat via WebRTC, and collaborate on shared whiteboards. This holistic approach ensures that missing a lecture no longer results in a learning deficit, but rather offers an opportunity for personalized, resource-rich study.

Vernafy: AI-Powered NLP for Multi-Modal Language Translation and Interaction

Authors: Shubham Gupta, Shreesh Vichare, Anuj Wavekar, Ms. Pooja Patil

Abstract: Language diversity presents a significant challenge in effective communication, particularly in multi- lingual regions such as India. While existing translation systems provide partial solutions, most operate in isolation—handling text, speech, or visual data independently resulting in fragmented interactions and loss of contextual meaning. Vernafy is proposed as an AI-powered multimodal Natural Language Processing (NLP) platform designed to enable seamless, context aware translation and interaction across multiple modalities, including text, speech, and images. The system integrates advanced NLP techniques with speech recognition, text-to-speech synthesis, optical character recognition, and summarization to deliver accurate and natural communication in real time. By unifying these capabilities into a single, user-friendly interface, Vernafy enhances accessibility for educators, businesses, con- tent creators, and users with linguistic or sensory limitations. Special emphasis is placed on preserving tone, intent, and cultural nuances to ensure meaningful interactions rather than mechanical translations. Additionally, Vernafy supports linguistic inclusivity by enabling regional and lesser-known languages to coexist with globally dominant languages, thereby contributing to cultural preservation and digital equality. The proposed system demonstrates how multimodal AI can be effectively leveraged to overcome language barriers, improve cross-lingual communication, and create an inclusive digital ecosystem.

Hardware Architecture and Component Selection for Automotive Driver Monitoring Systems

Authors: Akshay Amrutkar

Abstract: Driver Monitoring Systems (DMS) are safety-critical embedded vision platforms used to monitor driver attentiveness, drowsiness, and distraction. Automotive DMS hardware must support multi-gigabit video transmission, low latency processing, deterministic behaviour, EMI robustness, and compliance with automotive functional safety standards. This paper presents a comprehensive embedded hardware architecture for a DMS consisting of a camera module with FPD-Link or GMSL serializer, an ECU processing unit with deserializer and serializer, and a remote TFT display module. Detailed coverage includes MIPI CSI-2 and DSI protocols, FPD-Link and GMSL selection, connector and cable considerations, Insertion loss budgeting, Power over Coax (PoC) inductor and filter calculations.

DOI: https://doi.org/10.5281/zenodo.18796655

SMART COMB: An AI-Powered Smart Hair Comb For Scalp Health And Hair Therapy

Authors: Ms. Pooja Patil, Shreya Patil, Khushi Nakhwa, Bhumisha Koli

Abstract: The Smart Hair Comb is a technology-based grooming device created to examine scalp conditions and assist users in choosing suitable treatment methods. It operates by gathering live readings related to scalp temperature and surrounding moisture levels. These measurements help in understanding the present scalp condition and in recommending appropriate corrective care. The device includes a small digital screen that presents readings and suggestions in a clear format, allowing users to easily interpret the results. Its lightweight structure supports routine use and encourages individuals to maintain consistent scalp care habits. The design focuses on simplicity so that it can be used comfortably without technical expertise. This project reflects the practical implementation of sensor-driven systems in personal wellness equipment, promoting better awareness of scalp health over time.

THE IMPACT OF RECOGNITION PRACTICES ON EMPLOYEE COMMITMENT IN MODERN ORGANIZATIONS

Authors: Dr.R.Varalakshmi, Dr.M.Neelabai, Dr.G.Muruganandham

Abstract: Employee recognition programs are widely acknowledged as a powerful tool for improving employee motivation, engagement, and organizational commitment. This study examines the relationship between recognition practices and employee commitment using correlation, regression, and ANOVA analysis. Data were collected from 120 employees across service-sector organizations using a structured questionnaire. The results show a strong positive relationship between employee recognition and organizational commitment. Regression analysis indicates that recognition practices significantly predict employee commitment. The study concludes that structured recognition programs enhance employee loyalty, reduce turnover, and strengthen organizational culture.

Security Risk Assessment Of Blockchain-Enabled Cloud Storage Architectures Threat Modelling And Mitigation Strategies

Authors: Dr. Pankaj Malik, Manish Parihar, Mayankraj Ahirwar, Amay Gupta, Sanskar Jain

Abstract: Blockchain-enabled cloud storage architectures have emerged as a promising approach to enhance data integrity, transparency, and decentralized trust in distributed environments. However, integrating blockchain with cloud storage infrastructures introduces new security challenges, including consensus manipulation, smart contract vulnerabilities, key-management weaknesses, and cross-layer attack surfaces. This paper presents a comprehensive security risk assessment framework for blockchain-enabled cloud storage architectures using structured threat modelling and systematic risk evaluation techniques. A layered architectural model comprising the cloud layer, blockchain layer, and integration interface is analyzed using the STRIDE threat modelling approach and attack tree methodology. Identified threats are evaluated using a semi-quantitative risk matrix considering likelihood, impact, and exploitability factors. The results indicate that while blockchain integration significantly reduces risks related to data tampering, repudiation, and centralized trust failure (risk reduction observed in integrity-related threats by approximately 35–45% compared to traditional cloud models), it introduces elevated risks in consensus-level attacks, smart contract exploitation, and key management compromise. High-severity risks were primarily associated with poorly audited smart contracts and insufficient access control at the integration layer. The proposed mitigation strategies—including permissioned consensus mechanisms, formal smart contract verification, multi-factor decentralized identity management, and layered cryptographic controls—demonstrate substantial risk reduction when applied within the architectural model. The study concludes that blockchain integration improves transparency and integrity assurance but shifts the overall security posture rather than eliminating risk. A balanced architectural design with structured threat assessment and layered mitigation controls is essential for secure deployment. The findings provide actionable guidance for cloud architects, security analysts, and researchers designing next-generation blockchain-integrated cloud storage systems.

Real Time Facial Emotion Recognition Using Deep Learning

Authors: Ms.N.Sowmiya, Ms.A.Anitharani, Dr.T.C.Kalaiselvi

Abstract: Real-time facial emotion recognition using deep learning has become an important research area in computer vision and affective computing. The objective of this study is to design and implement a robust system capable of detecting and classifying human emotions from live video streams with high accuracy and low latency. The proposed framework utilizes Convolutional Neural Networks (CNNs) to automatically extract discriminative facial features from input images and classify them into basic emotional categories such as happiness, sadness, anger, fear, surprise, disgust, and neutrality. The system consists of three main stages: face detection, pre-processing, and emotion classification. Face regions are detected using a deep learning-based detector, followed by normalization and resizing before being fed into the CNN model. To enhance performance, transfer learning techniques with pre-trained models are applied and fine-tuned on benchmark facial expression datasets. Experimental results demonstrate that the model achieves reliable accuracy under varying lighting conditions, head poses, and facial orientations while maintaining real-time processing speed. The proposed system has potential applications in healthcare, education, human-computer interaction, surveillance, and assistive technologies, providing an intelligent solution for emotion-aware systems. Nonverbal notes conveyed by facial expressions are extremely important for interpersonal relationships. A frequent part of human mechanical interfaces is the automatic facial expression detection. It can also be used in clinical practice and behavioural science. People actually recognize facial expressions, but machine-based detection of solid expression remains a challenge. Expressions can be seen as distortions in the face and changes in facial pigmentation or their spatial relationships or changes in facial pigmentation in terms of automatic detection.

EduWay-Personalized Learning Platform

Authors: Nitin Dhawas, Rohan Thakre, Dipali Khairnar

Abstract: In today’s dynamic educational and professional landscape, there is a growing demand for intelligent platforms that offer personalized learning and career development. This study presents EduWay—an AI-driven Integrated Learning and Career Pathway System—designed to generate individualized learning paths and suggest career directions based on user-specific data gathered through an initial interactive survey. By analyzing users’ academic backgrounds, career interests, and professional goals, EduWay crafts actionable roadmaps to help learners progress effectively. The system integrates modern technologies such as React for a responsive frontend, Spring Boot for a scalable backend, and AI/ML algorithms to power smart learning path generation and career mapping. It enhances learner motivation through gamified features including badges, leaderboards, and progress tracking. Furthermore, EduWay encourages collaborative learning with community features like team challenges and discussion groups. This paper outlines the platform’s conceptual structure, technological implementation, and potential future advancements such as adaptive agent-based learning and labor market analysis. By aligning academic progress with industry needs, EduWay positions itself as a comprehensive tool for individualized education and skill development.

Efficient And Accurate Cloud-Assisted Medical Pre-Diagnosis With Privacy Preservation

Authors: V.Narasimha Swamy, M.Bhuvaneswari, M.Pavan Kumar Reddy, S.Sai Sindhuri

Abstract: Cloud-based healthcare services help doctors deliver quick assessments and early diagnoses, even when patients are far from hospitals. However, sending medical data and diagnostic models to the cloud raises serious privacy concerns because sensitive patient information may not always be safe. The NAIAD framework addresses this by using encrypted kNN and secure Mahalanobis Distance calculations, allowing the cloud to process medical queries without ever seeing the actual data. It also speeds up the search process using a hierarchical encrypted index tree. While NAIAD provides good privacy and accuracy, it still has gaps in data optimization, fine-grained user control, and transparency of the results.The proposed system enhances NAIAD by focusing on smarter data preparation, stronger protection, and better verification. It filters and encrypts only meaningful medical features before outsourcing them, reducing computation and improving performance. Enhanced access control ensures that patients, doctors, and administrators can securely interact with the system while keeping sensitive records fully protected. The system also adds a result-verification mechanism so patients can confirm that the cloud processed their data honestly without modification or tampering. Additionally, the framework introduces better data organization, reduced redundancy, and improved communication efficiency—giving quicker responses during diagnosis. Security layers are strengthened to withstand modern cyber-attacks, ensuring long term trust in the system

Design and Implementation of Smart Multi Socket Power System for Efficient Monitoring, Control and Management of Domestic Appliances

Authors: K. Karthik, M. Harish, S. Samadhushek, D. Vinothkumar

Abstract: The rapid growth of electrical and electronic appliances in modern households has increased the demand for intelligent, safe and efficient power distribution systems. Conventional switchboards and extension sockets provide limited control, lack monitoring features and may lead to energy wastage and safety risks. The design and implementation of a Smart Multi-Socket Power System using the ESP32 microcontroller for voice control and remote switching of domestic appliances. The system integrates multiple 6A and 16A sockets within a single unit, where each socket can be independently controlled through mobile based ON/OFF commands and voice assistance. The ESP32 acts as the central control unit, enabling wireless communication and real-time system operation through Wi-Fi. Voltage and current sensors are used to measure electrical parameters of connected appliances. The ESP32 processes these values to estimate power consumption, allowing users to monitor energy usage effectively. To enhance safety, a Miniature Circuit Breaker (MCB) is incorporated to protect against overload and short circuit conditions.

DOI: https://doi.org/10.5281/zenodo.18833524

Short Circuit Fault Detection and Automated Reporting System for Power Utility Using Stm32 and Lora in Distribution Line

Authors: V. Balakrishnan, S. Boobalan, R. Karthikraja, Mr. P. Sridhar

Abstract: Short circuit faults in electrical distribution lines result in excessive current flow, equipment damage, safety hazards and prolonged power outages if not detected and reported promptly. This paper presents a short circuit fault detection and automated reporting system for power utilities using an STM32 based control system and LoRa communication technology. Line current is continuously monitored using an ACS712 current sensor and the controller analyzes the sensed data to identify abnormal current conditions indicative of a short circuit fault. Upon fault detection, a solid state relay is activated to isolate the affected section of the distribution line, thereby preventing further damage. Simultaneously, fault information is transmitted wirelessly through a LoRa (SX1278) module to the utility monitoring unit. The received data is processed and the fault status is displayed to enable rapid maintenance action. The proposed system reduces fault detection time, minimizes manual inspection and enhances the reliability and safety of modern power distribution networks.

DOI: https://doi.org/10.5281/zenodo.18833746

Animal Deterrent System for Domestic Areas

Authors: Dr. S.Ragul, S.Joel, S.Manikandan, S.Selvakumar

Abstract: Animal intrusion in domestic areas is an increasing concern due to urbanization and reduced natural habitats. This project presents an Animal Deterrent System for Domestic Areas employing dual frequency ultrasonic sound to enhance repellent effectiveness. The system generates ultrasonic waves that alternate between two frequency ranges to prevent animal adaptation. In addition to ultrasonic deterrence a high intensity LED flashing module is incorporated to provide a visual stimulus that further discourages animal entry, particularly during low light and night time conditions. The system controls frequency switching and LED flashing operations to ensure reliable performance with low power consumption. The proposed system is non-lethal, eco-friendly and suitable for residential environments. Experimental observations demonstrate improved deterrence compared to single frequency systems, making the proposed approach a practical solution for domestic animal intrusion control.

DOI: https://doi.org/10.5281/zenodo.18834354

Design and Fabrication of Electrical Platform Trolley

Authors: A.Bharath, I.Gobikrishanan, S.Sakthivel, Dr.P.Arul

Abstract: Efficient material handling in educational institutions is essential to reduce manual effort and improve workplace safety. Conventional trolleys require continuous human force, leading to fatigue and reduced productivity. This paper presents the design and fabrication of an electrical platform trolley powered by a 24V, 250W BLDC geared motor. The system incorporates a rechargeable battery, motor controller, and handle-mounted forward–reverse switching mechanism for smooth operation. A reinforced steel platform and rubber wheels provide structural strength and stability for load transportation within campus environments.

DOI: https://doi.org/10.5281/zenodo.18835003

Analysis Of A Symmetric 15-Level Multilevel Inverter With Polarity Generation Unit

Authors: Kailash Kumar Mahto

Abstract: This paper presents a symmetric 15-level multilevel inverter (MLI) topology designed to achieve enhanced voltage level generation with a reduced number of power electronic components. The proposed configuration is systematically divided into a level synthesis unit and a polarity reversal unit (H-bridge), enabling efficient voltage step formation and full AC output generation. The topology employs twelve power switches, including bidirectional switches in the level generation stage, which facilitate flexible current conduction and proper voltage insertion. Operating in symmetric mode with equal DC voltage sources, the inverter generates fifteen distinct voltage levels, including zero, by selectively inserting or bypassing the DC sources through appropriate switching combination. A Carrier-Based Sinusoidal Pulse Width Modulation (CB- PWM) technique is utilized to regulate the switching operation and enhance output waveform quality. The performance of the proposed topology is validated through simulation analysis under RL load conditions. The results demonstrate that the inverter produces a high-quality staircase output waveform with reduced total harmonic distortion (THD), improved voltage utilization, and efficient device operation.

Enhancing_Computational_Efficiency_In_Modal_Transient_Analysis_Through_Nastran_Restart_Method

Authors: Mr. Ranjit Mane, Mr. Kishor Raut

Abstract: Vehicle operation occurs in a highly dynamic environment, necessitating the use of dynamic simulation methods for accurate durability analysis. In the automotive sector, predicting structural life and damage during the design phase is essential to meet warranty requirements and ensure long-term reliability. To replicate real-world loading conditions, the industry employs torture track recipes—specialized test tracks designed to simulate extreme road conditions. Data collected from these tests, in the form of time-domain road excitations, is used for digital validation of vehicle structures. Given the limitations of current computational resources, simulating vehicle durability tests and performing structural fatigue life assessments require efficient methodologies and advanced CAE tools. Modal transient analysis in MSC Nastran offers a practical solution for evaluating time-dependent dynamic responses of vehicle structures, particularly the Body-in-White (BIW). This paper presents an effective and resource-conscious approach to vehicle dynamic durability analysis using the restart method in modal transient analysis. Although this technique has been validated across multiple programs, this paper presents two case studies to demonstrate its feasibility and accuracy in stress analysis and fatigue damage prediction. Also the automation script is designed to initiate the cold start run, followed by the automatic execution of multiple restart runs. The results confirm that the restart method significantly reduces runtime while maintaining analytical fidelity, making it a viable option for modern automotive durability simulations.

DOI: https://doi.org/10.5281/zenodo.18846461

Multi Link Suspension Hotspot Identification And Co-relation

Authors: Abhijit Swami, Gopal Gautam

Abstract: The suspension system in an automobile plays a critical role in ensuring ride comfort, vehicle handling, and overall safety. It serves as the interface between the vehicle body and the wheels, absorbing road irregularities and maintaining tire contact with the road surface. Automotive suspension system durability refers to the ability of the suspension components to withstand operational stresses over time without failure or excessive degradation. In modern automotive suspension systems, the multi-link suspension offers superior ride and handling characteristics due to its geometric flexibility and precise wheel control. This paper presents a comprehensive methodology for simulating the behaviour of the longitudinal link, camber link, toe link, and upper link in a multi-link suspension architecture using finite element analysis (FEA). The study emphasises robust modelling strategies, realistic boundary condition definition, and accurate load-path representation under representative driving scenarios, including acceleration, braking, and road-induced excitations. Component-level FEA is employed to evaluate stress distribution and fatigue characteristics, which are subsequently correlated and validated through bench-level experimental testing. Further, the paper outlines a correlation methodology between simulated results and physical test data. The results demonstrate a high degree of correlation between the simulated and test data, affirming the robustness of the proposed methodology. The findings enable early-stage design optimization, reduce prototyping iterations, and enhance confidence in virtual validation processes for suspension systems.

Spinning String In Quantum Mechanics

Authors: Spiros Koutandos

Abstract: In this short paper we examine how a flux tube called the spinning string leads to quantum effects.

IDS Based Model for Industrial Control Systems Using Artificial Neural Network

Authors: Anthony Vivian Onyinyechi, Umejuru Daniel, Vinani Nuka Precious

Abstract: In the present scenario of rapidly changing technology, the industrial control system (ICS) is the backbone of critical services like power generation, production units, and transport services. However, with the increasing interconnectivity of the components of the ICS, they are also increasingly exposed to various cyber-attacks, which may have varying effects from operational bugs to possible threats to public safety. The hybrid nature of the ICS network, along with the need for continuous real-time monitoring, creates a challenge in identifying possible threats to the ICS network. A cyber-attack on an industrial control system can lead to system unavailability, loss of production, economic loss, and even potential threats to public safety in extreme cases. This is a point of concern for a wide range of stakeholders who use ICS for their day-to-day business activities. Traditional security solutions are no match to the advanced nature of cyber-attacks, thus requiring the development of innovative solutions that can offer effective protection against cyber-attacks and unauthorized access. The proposed research work aims to make industrial control systems cyber-attack proof using the capabilities of artificial intelligence (AI) and deep learning (DL) models. The focus is on designing a Deep Learning-Based Intrusion Detection System (IDS) that can detect and mitigate port scanning and Distributed Denial of Service (DDoS) attacks in real-time on ICS networks, as the current IDS systems may offer some level of security but are not capable of dealing with the ever-changing nature of cyber-attacks. The proposed research work uses the Rapid Application Development (RAD) method, where the data from the ICS is collected and preprocessed to enable effective feature extraction and development of the model. The diagnostic parameters used in the proposed research work include the confusion matrix, accuracy, precision, recall, and F1-score. The proposed model was validated using a sample of the HAI 21.04 dataset and achieved an average accuracy of 98.58%, thus proving the effectiveness of the proposed model in detecting normal and abnormal patterns in the ICS data.

Smart Parking System With Dynamic Slot Detection

Authors: Dr. D Siva, N.Shalini, M.Amrutha, A.Imam Basha, K.Suresh

Abstract: The Smart Parking System with Dynamic slot Detection is an intelligent solution designed to address the growing challenges of urban parking management. The rapid increase in urban vehicle usage has created significant parking challenges, including congestion, long search times, and inefficient utilization [1][3]. Traditional Parking Systems rely on manual monitoring or fixed sensors, which are costly and difficult to maintain to address these issues, this project proposes a Smart Parking System with Dynamic slot Detection using an ESP32-Cam. The ESP32-Cam captures real-time images of the parking area and process them using an machine learning model to detect slot occupancy [4]. The system dynamically updates the entry gate and exit gate occupied or free and send the information to a web dashboard. The solution eliminates the need for individual IR/ultrasonic sensors, reduces installation cost, and provides high accuracy. The proposed system enhances user convenience by guiding drivers to nearest available slot and improves overall parking management efficiency in smart cities.

DOI: https://doi.org/10.5281/zenodo.18898146

Automated Website Generator Using Backend-Driven Code Synthesis And Real-Time Deployment

Authors: Akanksha Patil, Mr. Parth Bhoir, Mr. Manish Bhoir, Mr. Smit Deshmukh

Abstract: Website development traditionally requires expertise in frontend design, backend logic, and deployment workflows, making it time-consuming and costly for non-technical users and small organizations. Although existing website builders provide template-based solutions, they lack backend automation, scalability, and dynamic code generation capabilities. This paper presents an Automated Website Generator, a backend-driven system that creates fully functional, responsive websites using minimal user input. The proposed solution integrates Next.js for frontend rendering, Flask-based APIs for backend automation, and database-driven template management to dynamically generate, compile, and deploy websites in real time. The system eliminates manual coding by automating layout selection, code synthesis, and deployment, thereby reducing development effort while maintaining flexibility and scalability. Experimental evaluation demonstrates improved development efficiency, faster deployment cycles, and enhanced usability compared to traditional website creation approaches.

Ransomware Detection Using Behavioral Analysis And Machine Learning: A Comprehensive Review

Authors: Promise Enyindah, Daniel Okon

Abstract: Among notable threat within rising digital risks is ransomware, known for deep consequences – yearly monetary damages climb into multiple billions, alongside disruptions felt by critical operations such as healthcare systems, financial institutions, and government bodies. Since older methods based on fixed signatures prove ineffective against evolving code structures, modern approaches shift attention toward real-time conduct of harmful software after activation. Behavior seen during runtime provides clearer indicators compared to unchanging markers. With adaptive malware variants and widely available hacking resources increasing complexity, different defensive paths emerge as vital. A potential direction focuses on teaching models to identify faint signals of malicious behavior. Research indicates some sophisticated architectures spot ransomware accurately in over 99 out of every 100 trials. Among them are networks modeled after biological vision, those designed to follow temporal patterns, together with hybrid approaches merging several strategies. Despite lingering obstacles, advancement in automatic detection moves forward at a consistent pace. From observed behaviors – alterations in saved files, active system operations, internet-based data movements – to encryption patterns, distinctions emerge between malicious software and standard applications. These indicators form the basis for analysis within openly available datasets, while challenges such as false signals or evasion tactics adopted by adversaries are considered alongside them. Practical constraints influencing real-world deployment weave into each point made across the review. Future directions surface through interpretable artificial intelligence systems, adaptive defense frameworks, collaboration enriched by live threat intelligence feeds. Insight gained from this exploration aids progress toward more effective defenses targeting harmful file-locking behavior.

DOI: https://doi.org/10.5281/zenodo.18918647

Performance-Based Analysis of a G+6 Reinforced Concrete Shear Wall Structure Under Earthquake Loads

Authors: Dr.V. Srinivas, R Ashwitha, K Naresh Kumar, M Sharath, MD Fowzan

Abstract: The principle objective of this project is to analyse and design a multi-storeyed residential building [G + 6 (3 dimensional frame)] using STAAD Pro. The design involves load calculations manually and analyzing the whole structure by STAAD Pro. The design methods used in STAAD-Pro analysis are Limit State Design conforming to Indian Standard Code of Practice. STAAD.Pro features a state-of-the-art user interface, visualization tools, powerful analysis and design engines with advanced finite element and dynamic analysis capabilities. From model generation, analysis and design to visualization and result verification, STAAD.Pro is the professional’s choice.We considered a 3-D RCC frame with plan dimensions of 38.39 meter in x-direction and 11.78 meter in z-direction. The y-axis consisted of G + 6 floors. The ground floor height was 2.45 meter and rest of the 6 floors had a height of 3 meter. The structure was subjected to self weight, dead load, live load and seismic loads under the load case details of STAAD.Pro. Seismic load calculations were done following IS 1893-2000. The materials were specified and cross-sections of the beam and column members were assigned. The supports at the base of the structure were also specified as fixed. The codes of practise to be followed were also specified for design purpose with other important details. Then STAAD.Pro was used to analyse the structure and design the members. In the post-processing mode, after completion of the design, we can work on the structure and study the bending moment and shear force values with the generated diagrams. We may also check the deflection of various members under the given loading combinations. The design of the building is dependent upon the minimum requirements as prescribed in the Indian Standard Codes. The minimum requirements pertaining to the structural safety of buildings are being covered by way of laying down minimum design loads which have to be assumed for dead loads, imposed loads, and other external loads, the structure would be required to bear. Strict conformity to loading standards recommended in this code, it is hoped, will ensure the structural safety of the buildings which are being designed. Structure and structural elements were normally designed by Limit State Method.

DOI: https://doi.org/10.5281/zenodo.18923426

Performance-Based Structural Analysis and Design of a G+4 Residential Reinforced Concrete Building Under Gravity and Lateral Loads

Authors: Mrs.P Sri Vidya, D Narendar, G Akshaya, M Govind Raju, J Siddaratha

Abstract: Rapid urbanization and population growth have increased the demand for multi-storeyed residential buildings, especially in urban and semi-urban regions where land availability is limited. The present project deals with the analysis and design of a G+4 residential building using both manual calculations and STAAD.Pro V8i software. The building is a reinforced cement concrete (RCC) framed structure consisting of four residential floors above ground level, designed as per the provisions of IS 456:2000, IS 875 (Part 1 & 2), and other relevant Indian Standard Codes. The planning and drafting of the building were carried out using AutoCAD, ensuring compliance with National Building Code (NBC-2016) requirements related to setbacks, ventilation, room dimensions, and safety provisions. The structural elements such as slabs, beams, columns, and isolated footings were designed using the Limit State Method. Loads considered in the analysis include dead loads, live loads, and floor loads as per codal specifications. The modeling of the structure was performed in STAAD.Pro by defining geometry, assigning material properties (M30 concrete and Fe500 steel), applying support conditions, and generating load combinations. The analysis results such as bending moments, shear forces, axial loads, and deflections were obtained through the post-processing mode. These results were compared with manual design calculations to evaluate accuracy and efficiency. The comparison shows that the analysis results from STAAD.Pro and manual calculations are nearly similar, with slight variations in reinforcement detailing. The use of software significantly reduces computation time and minimizes human errors while ensuring safe and economical design. The project concludes that STAAD.Pro provides reliable and efficient structural analysis and design results for multi-storeyed residential buildings.

DOI: https://doi.org/10.5281/zenodo.18923480

Digital Fortification: A Deep Dive Into Cryptomator

Authors: Jithendra, Lahari, Uday Krishna, Shrinath

Abstract: With cloud computing and distant collaboration, security of personal information against unauthorized access has never been more important. Cryptomator, an open-source-based client-side encryption computer program employed to encrypt cloud storage files like Google Drive, Dropbox, and OneDrive, is the focus of this paper. In comparison to other server-side encrypted cloud security software, Cryptomator offers end-to- end and transparent encryption in an attempt to give users full control over the confidentiality of their data without requiring any kind of cryptographic knowledge. The study is deep into Cryptomator’s internal workings and technical architecture, its virtual encrypted drives, key handling, and file encryption with the AES-256 algorithm. Its zero-knowledge design, platform neutrality, and ease of use are displayed. The study has to be readable to people and groups. Critically examined, the paper summarizes Cryptomator’s native features, discusses its efficiency and vulnerabilities, and looks back at its application in today’s security measures. Apart from that, the paper also discusses vulnerabilities like metadata disclosure and enterprise integration limitations and suggests future directions for development. In summary, this study attests to the relevance of software like Cryptomator in the current digital assistance due to surveillance, loss of data, and third-party access to information being areas of concern.

Computational Fluid Dynamics Analysis Of Meandering Open Channel Flow Influenced By Circular Vegetation Patches

Authors: Ali Hamza, Naveed Anjum, Zaheer Ahmed

Abstract: This study explores how circular vegetation patches effect flow characteristics in a meandering open channel functional under steady conditions. Two channel setup were considered for assessment one with a smooth bed and no vegetation and another incorporating rigid circular vegetation patches located along consecutive bends. The comparison tells that the introduction of vegetation considerably alters the flow pattern. A clear reduction in extreme velocity around 55% was observed along with the creation of wake regions behind the areas which contributed to energy loss and flow reducing. In calculation the presence of vegetation controlled to a significant weakening in turbulence related parameters. Turbulence intensity reduced by nearly 40% while turbulent kinetic energy was reduced by nearly 50%. These reductions advise that vegetation plays an effective role in damping large scale turbulent waves even though local mixing still happens near the vegetation foundations. Overall the vegetated arrangement established greater movement stability higher hydraulic resistance and a relocation of turbulent energy throughout the channel. The findings emphasize the standing of vegetation in promoting eco-hydraulic balance reducing the chances of erosion and supporting environmentally sustainable river.

DOI: https://doi.org/10.5281/zenodo.18933294

Real-Time Cyber Threat Monitoring And Analysis

Authors: Professor Snehal chitale, Prashant shingote, Aditya Surve, Nikhil Suravkar

Abstract: The exponential growth of digital infrastructure and the increasing sophistication of cyber adversaries have made threat detection and situational awareness critical challenges for modern organizations, as traditional monitoring methods often rely on manual analysis and fail to keep pace with the velocity of online information. To overcome these limitations, this project presents an integrated platform for Real-Time Cyber Incident Monitoring and Analysis. The system autonomously aggregates unstructured data from diverse public sources, including social media platforms and news feeds, to extract critical indicators of compromise. It utilizes a machine learning engine to filter irrelevant noise, classify incidents by severity, and compare this data against historical patterns to identify genuine security events. The platform also includes a dynamic visualization dashboard that allows analysts to monitor live threat feeds, track regional incident trends, and receive instant alerts to accelerate response times. To enhance decision-making and operational efficiency, the system incorporates automated severity scoring and detailed event logging. In addition, region-specific filtering—specifically for the Indian cyber space—is provided to help organizations align their defense strategies with local threat landscapes. The proposed solution aims to reduce the time between incident occurrence and detection, improve analyst productivity, and support proactive cybersecurity measures. Functional evaluation and system testing indicate that the tool effectively streamlines the intelligence lifecycle and provides accurate, real-time situational awareness.

Blockchain-Enabled Healthcare Data Transmission

Authors: Surya R, Dr.R.Nagarajan

Abstract: The Healthcare Sector Faces Major Challenges In Maintaining Secure, Private, And Real-Time Access To Patient Medical Records. Traditional Centralized Systems Are Vulnerable To Data Breaches, Unauthorized Access, And Single Points Of Failure, Which Can Critically Impact Patient Safety. To Overcome These Issues, This Paper Proposes Healthchain, A Blockchain- Enabled Healthcare Data Transmission And Real-Time Patient Monitoring System. Healthchain Employs A Python-Based Blockchain With SHA-256 Proof-Of-Work, RSA-2048 Cryptography, And AES-256 Encryption To Securely Store And Protect Patient Records On An Immutable Distributed Ledger. The System Continuously Monitors Patient Vitals Such As Heart Rate (BPM) And Blood Oxygen (Spo₂) And Updates Them In Real Time Using Websocket Communication. Critical Health Data Is Automatically Recorded On The Blockchain, While Smart Contracts Manage Emergency Access And Patient Consent. The System Is Built Using Flask, Flask-Socketio, Mongodb, And Pydantic, With A Responsive Frontend Developed Using Jinja2 Templates And Chart.Js For Real-Time Visualization. It Also Supports Role-Based Access Control For Admins, Doctors, And Patients, Along With Automated PDF Health Reports. Healthchain Demonstrates How Blockchain, Real-Time Data Monitoring, And Secure Cryptographic Techniques Can Be Integrated To Create A Tamper-Proof, Transparent, And Patient-Centric Healthcare Data Management System.

DOI:

Energy Efficiency Optimization In Hyperscale Data Centers

Authors: Samuel N Nimaful, Joel Holison, Gloria O. Darkoh, Augustine Hanyabui, Faith Esther Holison, Laureta Tatenda Nyamsutswa

Abstract: Hyperscale data centers—very large, industrialized facilities that deliver cloud-scale compute, storage, and networking—have become a central node in the global energy system because they concentrate electrical load, water demand, and waste heat while enabling rapidly growing digital services and AI workloads. Over the past decade, efficiency gains in hyperscale infrastructure and IT operations have helped decouple growth in compute demand from the growth in electricity use, but the acceleration of GPU/accelerator-based AI is now reintroducing strong upward pressure on both energy and cooling capacity requirements (Masanet et al., 2020; International Energy Agency [IEA], 2025; U.S. Department of Energy [DOE], 2024).[1]

DOI:

Challenges And Opportunities In Big Data Analytics

Authors: Miss. Shweta Guru Chikmal

Abstract: Big Data Analytics has become a crucial technology in the modern digital era, enabling organizations to extract meaningful insights from vast amounts of data. With the rapid growth of data generated through social media, IoT devices, and online transactions, traditional data processing methods are no longer sufficient. This paper explores the major challenges faced in Big Data Analytics, including data privacy, security, storage, and processing complexities. At the same time, it highlights the opportunities that Big Data presents in various sectors such as healthcare, banking, education, and business. The study is based on secondary data collected from journals and research articles. The findings indicate that while challenges exist, the opportunities provided by Big Data Analytics significantly outweigh them, making it an essential component of modern decision-making systems.

DOI: https://doi.org/10.5281/zenodo.19841184

Design And Estimation Of Led Billboard Using Tekla Software

Authors: M. Sakthivel, P. Aarthi, K. Bharani, BG. Madhumitha

Abstract: This project focuses on the design and estimation of an LED billboard using Tekla Structures software. LED billboards are widely used for digital advertising due to their high visibility, energy efficiency, and long service life. The study involves the structural design of the billboard using steel components such as columns, beams, bracings, and base connections to ensure strength, stability, and safety under various loading conditions. Tekla software is used for 3D modeling, detailing, quantity estimation, and preparation of fabrication drawings. The project also includes estimation of construction materials such as steel, concrete, bolts, and foundation requirements to determine the overall project cost. By using BIM technology, manual errors are reduced and project accuracy and efficiency are improved. The proposed design ensures durability, economical construction, and effective structural performance for modern advertising applications. Different loads acting on the structure are considered to ensure safety and durability. Tekla software helps to reduce manual errors and improves accuracy in design and estimation. The main objective of this project is to create a safe, economical, and efficient LED billboard structure using modern BIM technology.

DOI: https://doi.org/10.5281/zenodo.20383788

Design And Estimation Of Metro Station Using Tekla

Authors: S. Kavipriya, A. Jabeen, R. Keerthana

Abstract: The project titled as Design and Estimation of Metro Station Using Tekla Structures-“2025 focuses on applying advanced Building Information Modeling (BIM) techniques for the efficient planning, structural design, and cost estimation of a modern metro station. The main objective of this study is to create a precise and fully integrated 3D model that enhances accuracy, constructability, and coordination throughout the project lifecycle. Using Tekla Structures, the complete structural framework of the metro station-including columns, beams, slabs, staircases, platforms, and roof trusses-is modeled with high dimensional accuracy. The software enables the automatic generation of general arrangement drawings, fabrication drawings, and material take-off reports, significantly reducing manual drafting errors and improving project efficiency. The estimation process is directly linked to the 3D model, allowing real time updates in quantity and cost calculations whenever design modifications occur. This approach enhances decision-making, reduces material waste, and ensures better cost control. The integration of BIM principles further supports clash detection, visualization, and data management, ensuring seamless coordination between design, fabrication, and construction teams. Overall, this project demonstrates that Tekla Structures is an effective digital tool for achieving higher precision and productivity in metro station projects. It provides a comprehensive solution for modeling, detailing, and estimation, thereby promoting sustainable and technology-driven infrastructure development.

DOI: https://doi.org/10.5281/zenodo.20383849