Daily Archives: June 12, 2026

Authors: A. Deepthi, S.Dakhita Sri, B. Vamsi, P.Prabhakar Reddy

Abstract: The continuous demand for lightweight, fuel-efficient, and environmentally sustainable automobiles has encouraged researchers and manufacturers to replace conventional metallic components with advanced composite materials. Automotive suspension springs, which are essential components responsible for supporting vehicle loads, absorbing road shocks, and maintaining ride comfort, have attracted significant attention for weight reduction. Conventional steel springs offer excellent strength and durability; however, their high density contributes substantially to the unsprung mass of vehicles, negatively influencing fuel economy, acceleration, and dynamic response. Hybrid automotive composite springs, fabricated using combinations of carbon, glass, aramid, and natural fibers reinforced with polymer matrices, provide an effective solution due to their superior specific strength, high fatigue resistance, excellent corrosion resistance, and improved vibration damping characteristics. The development of finite element analysis (FEA) techniques has further facilitated accurate prediction of the structural behavior of hybrid composite springs under static, dynamic, impact, and cyclic loading conditions. This review presents a detailed discussion on the evolution of hybrid composite springs, material selection, design methodologies, finite element modeling techniques, failure mechanisms, manufacturing methods, optimization approaches, and future research directions. The study highlights the potential of hybrid composite spring systems to replace conventional steel springs in next-generation automotive suspension systems.

DOI: http://doi.org/10.5281/zenodo.20672857

Published by: vikaspatanker

Authors: S,Shiva Kumar, P.V.R.Ravindra Reddy

Abstract: The drive shaft is a critical mechanical component responsible for transmitting torque from the transmission system to the wheels or other rotating components in automobiles, aerospace systems, and industrial machinery. Conventional steel drive shafts possess high strength but contribute significantly to the overall system weight, resulting in increased fuel consumption and reduced efficiency. In recent decades, fiber-reinforced polymer (FRP) composite materials such as carbon fiber reinforced polymers (CFRP), glass fiber reinforced polymers (GFRP), and hybrid composites have emerged as promising alternatives due to their superior specific strength, high stiffness-to-weight ratio, corrosion resistance, and improved damping characteristics. The advancement of finite element analysis (FEA) tools has enabled researchers to accurately predict the structural behavior of composite drive shafts under torsional, bending, buckling, vibration, and fatigue loading conditions. This review presents a comprehensive study of the design methodologies, material selection criteria, finite element modeling approaches, failure theories, optimization techniques, and recent developments in composite drive shaft technology. A critical comparison of different composite materials and FEA approaches is discussed, highlighting their advantages, limitations, and future research opportunities.

DOI: http://doi.org/10.5281/zenodo.20672813

Published by: vikaspatanker

Authors: Parul Tyagi, Dr. Brij Mohan Singh

Abstract: Kidney disease is a growing global health challenge requiring early, accurate, and automated diagnostic solutions. This paper introduces KidneyNet, a deep learning framework designed for automated kidney disease detection and classification from Computed Tomography (CT) scan images. KidneyNet leverages the power of transfer learning through ResNet50, enhanced with custom classification layers and advanced data augmentation strategies, to classify kidney CT images into four categories: cyst, normal, stone, and tumor. The proposed system is compared against two baseline architectures — Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) — using a publicly available dataset of 12,446 kidney CT images. Experimental results demonstrate that KidneyNet (ResNet50) achieves superior performance with an accuracy of 92%, precision of 91.44%, recall of 92%, and an F1-score of 91.72%, outperforming both ANN (86% accuracy) and CNN (89% accuracy). These findings confirm the effectiveness of deep residual transfer learning as a reliable computer-aided diagnostic tool for kidney disease classification.

DOI: https://doi.org/10.5281/zenodo.20671785

Published by: vikaspatanker

Authors: Amit Kumar, Raj Kumar

Abstract: Agriculture has long been the backbone of rural economies and continues to serve as the primary source of livelihood for a large segment of the population. However, declining farm profitability, unpredictable climatic conditions, and increasing production costs have created significant challenges for farmers. In response to these challenges, agro-tourism has emerged as an innovative approach that enables farmers to diversify their income sources while promoting rural culture and agricultural heritage. The present study investigates the impact of agro-tourism on the economic empowerment of farmers. The study is based on primary data collected from 150 farmers engaged in agro-tourism activities through a structured questionnaire. A quantitative research approach was employed to analyze the relationship between agro-tourism participation and various dimensions of economic empowerment, including income enhancement, employment generation, entrepreneurial development, and financial independence. The findings indicate that agro-tourism has contributed significantly to improving farmers' economic conditions by generating additional income opportunities, creating local employment, and encouraging entrepreneurial initiatives. Furthermore, agro-tourism has enhanced the financial security and self-reliance of farming households. The study concludes that agro-tourism can serve as an effective strategy for promoting sustainable rural development and strengthening the economic position of farmers. Therefore, greater support in terms of infrastructure development, training programs, marketing assistance, and policy initiatives is essential to unlock the full potential of agro-tourism in rural areas.

Published by: vikaspatanker

Authors: Gulshan Kumar, Meentu Grover

Abstract: The rapid growth of Unified Payments Interface (UPI) has transformed the way people conduct financial transactions in India. With the widespread availability of smartphones, internet connectivity, and government initiatives promoting a cashless economy, UPI has become one of the most preferred digital payment methods among consumers. This study examines the impact of UPI adoption on consumer spending patterns in India and explores how digital payment convenience influences purchasing behavior. The research investigates key aspects such as transaction frequency, spending habits, impulse buying tendencies, budgeting practices, and consumer preferences for digital payments over traditional cash transactions. Primary data were collected through a structured questionnaire administered to UPI users from diverse demographic backgrounds. The findings indicate that UPI adoption has significantly increased the ease and speed of transactions, encouraging more frequent purchases and reducing dependence on cash. Consumers reported greater convenience in managing daily expenses, while businesses benefited from faster and more transparent payment processes. The study further reveals that although UPI promotes financial accessibility and convenience, it may also contribute to increased discretionary spending due to the ease of making instant payments. The findings highlight the growing role of digital payment systems in shaping consumer financial behavior and supporting India's digital economy. The study offers valuable insights for policymakers, financial institutions, and digital payment service providers seeking to enhance consumer engagement and promote responsible digital financial practices.

Published by: vikaspatanker

Authors: Dr. Sahil Nazir, Natasha

Abstract: The banking sector plays a vital role in the economic development of India by mobilizing savings and channelizing funds into productive investments. The present study aims to compare the financial performance of public and private sector banks operating in India. The study evaluates the performance of five public sector banks and five private sector banks using financial indicators such as profitability, asset quality, liquidity, customer satisfaction, and operational efficiency. Primary data were collected from 200 respondents comprising customers of selected banks through a structured questionnaire. Secondary financial indicators were used to support the comparative analysis. Statistical tools such as percentage analysis, mean score analysis, and independent sample t-test were employed for data interpretation. The findings reveal that private sector banks outperform public sector banks in terms of customer satisfaction, service quality, digital banking facilities, and profitability. Public sector banks, however, enjoy higher customer trust, wider geographical coverage, and greater government support. The study concludes that while private banks exhibit superior financial efficiency, public sector banks continue to maintain a significant presence due to their reliability and extensive branch network. The research provides valuable insights for policymakers, banking professionals, and investors.

Published by: vikaspatanker

Authors: Dr. Raja Sekhar Koduru, Saranya P K

Abstract: As a result, the competition for consumers' attention has been increased because of the rapid development of digital marketplaces. At the same time, the current approaches to consumer analytics are based on the use of self-reported measures and do not reflect subconscious processes. Neuromarketing or neuroscience marketing can be described as the application of neuroscientific methods to understanding consumer behavior. In other words, neuromarketing can be used to investigate the mechanisms of making purchasing decisions. This paper introduces the neuromarketing analytics framework based on EEG, ET, and GSR technologies to predict purchase intent in digital marketplaces. Based on data collected from 120 participants who were shown e-commerce product listings, spectral EEG features (theta, alpha, beta, and gamma bands), ET measures (fixation duration, saccade amplitude, and pupil dilation), and GSR phasic responses have been extracted. The proposed deep learning model combines TCN and multi-head attention architecture and achieves 89.2% accuracy in predicting purchase intent. The performance of the proposed model significantly outperforms unimodal baseline models (EEG-based: 76.4%; ET-based: 78.1%; GSR-based: 71.2%). The most significant predictors of purchase intent are found to be gamma band power (30-45 Hz) during product exposure and pupil dilation change.

DOI: http://doi.org/10.5281/zenodo.20666451

Published by: vikaspatanker

Authors: Shilpa Hiwale, Dr B V V Siva Prasa

Abstract: The rapid growth in both the volume and complexity of enterprise data has significantly accelerated the adoption of Artificial Intelligence (AI), particularly within the life sciences industry. This paper explores how AI-driven data ecosystems can enable commercial excellence by integrating predictive, prescriptive, and cognitive analytics within a unified framework. The study combines quantitative analysis of customer, sales, and operational datasets with insights from academic research and real-world industry practices. The findings suggest that organizations adopting integrated AI ecosystems are better positioned to enhance forecasting accuracy, improve customer engagement, and enable faster, more informed decision-making. The data used were business-related datasets sourced from Kaggle and data were gathered using a quantitative and analytical research approach. Using Python-based machine learning frameworks, about 50,000 records of customer, sales, demand, churn and operational data were analyzed. Different analytical models such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and Artificial Neural Networks were used to discover the customer behavior, sales forecasting, customer segmentation, and prediction of risk. The results show that AI-powered analytics have a significant impact on improving the accuracy of predictions, customer retention, business intelligence, and operational efficiency. The most significant factors influencing customer churn were the customers' satisfaction and the customer segmentation and demand forecasting for marketing targeting and resource optimization. The study also shows that AI-powered analytical systems can aid in intelligent decision-making by converting vast amounts of business information into commercial intelligence that is useful for business decisions. The proposed data ecosystem framework will leverage AI to provide predictive, prescriptive and cognitive analytics that will enhance the performance and competitiveness of organizations. The study adds to the body of literature on AI-powered business transformation and offers valuable insights for organizations aiming to adopt data-driven approaches for sustainable commercial success.

DOI: https://doi.org/10.5281/zenodo.20666345

Published by: vikaspatanker

Authors: Dr Yaganti Krishnapriya, Talari Manohar

Abstract: The increasing demand for energy worldwide and the introduction of renewable energy sources make it necessary to move from conventional electrical distribution systems to smart electrical distribution systems. This study proposes an Internet of Things (IoT)-based framework for continuous real-time monitoring of electrical distribution systems. It employs IoT sensors, edge computing nodes, and a cloud-based analytical system for the continuous monitoring of electrical distribution systems. Moreover, the system uses a novel adaptive load scheduling (ALS) algorithm that is based on a hybrid LSTM-XGBoost model to accurately forecast future consumer loads. With the ALS algorithm, the system can predict consumer loads with an accuracy of 96.2% (RMSE = 0.034). Finally, the Model Predictive Control (MPC)-based load control system lowers peak demand and energy expenses by 27.4% and 19.8%, respectively, in a testbed with 200 residential customers.

DOI: http://doi.org/10.5281/zenodo.20666042

Published by: vikaspatanker

Authors: Gurpreet Singh, Dr. Sahil Nazir

Abstract: Employee motivation is one of the most important determinants of workplace productivity and organizational success. Motivated employees demonstrate greater commitment, efficiency, creativity, and job satisfaction, which ultimately contribute to improved organizational performance. In today's competitive business environment, organizations continuously strive to develop motivational strategies that encourage employees to perform at their best. The present study examines the impact of employee motivation on workplace productivity using primary data collected from 200 employees working in different organizations. The study investigates various motivational factors such as salary and incentives, recognition and rewards, career advancement opportunities, work environment, leadership support, and training and development programs. Data were collected through a structured questionnaire and analyzed using percentage analysis, mean score analysis, correlation analysis, and chi-square testing. The findings reveal that employee motivation significantly influences workplace productivity. Salary and incentives emerged as the most important motivational factor, followed by career growth opportunities and recognition programs. The study also found a strong positive relationship between employee motivation and productivity. Employees who reported higher motivation levels demonstrated better performance, increased efficiency, and greater organizational commitment. The study concludes that organizations should implement comprehensive motivational strategies to enhance employee productivity and achieve long-term success.

Published by: vikaspatanker