Volume of International Discoveries and Youth Academic Works, Achievements, and Novelties - VIDYAWAN

Financial literacy, socio-demographic factors and behavioral factors were added and investigated the investment behavior of the women academician in Rajasthan, India. The primary cross-sectional study was conducted on 400 respondents from Higher Educational Institutions (HEIs) of Rajasthan by using structured questionnaire. Analysis was done by descriptive statistics and correlation analysis, cross tabulation, and Ordinary Least Squares (OLS) regression. Results indicated moderate financial literacy, with roughly 32.5% of respondents getting the financial literacy questions right, there was clearly some room for improvement in terms of financial literacy regarding understandings of inflation and compounding. The analysis further reveals that preference was given to more traditional financial instruments such as gold (52%) and fixed deposits (68.5%) with low risk. Results of the OLS regression models indicate that financial literacy (β = 0.284) is Along with income level and risk attitude, the factors of statistically significant in determining the investment behavior. Based on the micro level, this study belongs to the body of literature based on the micro level data on the not so much studied group of women academicians with regional Indian scenario and not only that but also gives the financial education its importance so as to empower women and help them in making educated investment decisions. The findings will try to document the current scenario of women academicians in terms of their financial literacy and their investment habits with an aim for highlighting the financial literacy issue among women academics. This paper has been prepared to find out financial literacy level of the women academicians and Investment behavior of women academicians and to solve the problem of low financial literacy of women academicians.

The Marathwada region of Maharashtra, India, faces chronic agricultural distress driven by severe water scarcity, recurring droughts, and rapid climate variability. Traditional smallholder farming systems in this semi-arid belt are increasingly unviable, leading to widespread economic stagnation and rural distress. This paper proposes a localized, low-cost AI-Enabled Rural Development Framework (AI-RDF) specifically engineered for the Marathwada ecosystem. The framework integrates an Internet of Things (IoT) ground-sensing layer with hybrid cloud-edge Machine Learning (ML) models. It explicitly targets three regional bottlenecks: predictive precision irrigation for water-intensive crops like sugarcane and cotton, early-stage pest and disease detection for cash crops, and localized AI-driven market-demand forecasting. Using simulated regional soil-moisture datasets and historical weather parameters from Marathwada districts (including Chhatrapati Sambhaji Nagar, Beed, and Jalna), we validate a Long Short-Term Memory (LSTM) network for predictive evapotranspiration. Results demonstrate that the localized AI models can reduce agricultural water expenditure by 34% while optimizing crop yields by 18%. Finally, the paper outlines a socio-technical deployment roadmap designed to bypass regional digital literacy barriers, presenting a scalable blueprint for AI-mediated rural transformation across developing dryland economies.

Very Large-Scale Integration (VLSI) design is a fundamental technology for developing modern integrated circuits that power consumer electronics, communication systems, automotive applications, and artificial intelligence hardware. This paper presents a comprehensive overview of the complete VLSI design flow, from Register Transfer Level (RTL) design and functional verification to logic synthesis, static timing analysis, physical design, power optimization, and semiconductor fabrication. It discusses the significance of verification, timing closure, and layout validation techniques such as Design Rule Checking (DRC) and Layout Versus Schematic (LVS) in ensuring reliable chip performance. The paper also highlights the role of Electronic Design Automation (EDA) tools and optimization strategies in designing high-performance, low-power, and manufacturable integrated circuits. This overview serves as a valuable reference for understanding the end-to-end VLSI design process and its importance in modern semiconductor engineering.

Rural electrical networks often face challenges such as voltage instability, fault recovery delays, renewable energy intermittency, and inadequate backup power systems. This paper proposes a Quantum-Inspired Artificial Intelligence (QIAI) framework integrated with Self-Healing Smart Grid technology and Vehicle-to-Grid (V2G) energy sharing. The proposed system predicts faults, automatically isolates damaged sections, and utilizes parked electric vehicles as distributed energy storage units during emergencies. Simulation-based performance evaluation demonstrates improvements in reliability, restoration time, voltage regulation, and renewable energy utilization.

Electric vehicles (EVs) are becoming increasingly popular because of their high efficiency, reduced emissions, and lower maintenance requirements. However, brake disc overheating remains a significant challenge, especially during emergency braking, downhill driving, and regenerative braking transitions. Excessive heat causes brake fade, increased wear, thermal cracking, and reduced braking performance, affecting vehicle safety and component life. Conventional ventilated brake discs rely only on natural air cooling, which is often insufficient under severe operating conditions. This paper proposes a novel adaptive self-cooling brake disc that combines passive phase-change material (PCM) microcapsules with bio-inspired airflow channels. The PCM microcapsules are embedded within selected regions of the brake disc to absorb excess thermal energy during high-temperature operation through latent heat storage. Simultaneously, airflow channels inspired by the branching structure of leaf veins improve air circulation and convective heat transfer without requiring additional power consumption. Unlike active cooling systems, the proposed design is completely passive, lightweight, and maintenance-free. A mathematical heat transfer model is developed to evaluate transient temperature distribution inside the brake disc. The proposed concept is compared with conventional ventilated brake discs using theoretical thermal analysis. Expected results indicate that the adaptive cooling system can reduce peak brake disc temperature by approximately 18–25%, improve cooling rate by nearly 30%, and increase brake component life by reducing thermal stress. The proposed system offers a promising solution for next-generation electric vehicles by improving braking reliability while maintaining energy efficiency.

The rapid growth of electricity demand, renewable energy integration, and digital communication technologies has transformed conventional power systems into Smart Grids. Smart Grid technology enables real-time monitoring, fault detection, bidirectional communication, and efficient energy management. This paper reviews the architecture, applications, advantages, challenges, and performance analysis of Smart Grid systems. A comparative study using simulated data demonstrates improvements in transmission efficiency, fault recovery time, and renewable energy utilization.

Financial literacy, socio-demographic factors and behavioral factors were added and investigated the investment behavior of the women academician in Rajasthan, India. The primary cross-sectional study was conducted on 400 respondents from Higher Educational Institutions (HEIs) of Rajasthan by using structured questionnaire. Analysis was done by descriptive statistics and correlation analysis, cross tabulation, and Ordinary Least Squares (OLS) regression. Results indicated moderate financial literacy, with roughly 32.5% of respondents getting the financial literacy questions right, there was clearly some room for improvement in terms of financial literacy regarding understandings of inflation and compounding. The analysis further reveals that preference was given to more traditional financial instruments such as gold (52%) and fixed deposits (68.5%) with low risk. Results of the OLS regression models indicate that financial literacy (β = 0.284) is Along with income level and risk attitude, the factors of statistically significant in determining the investment behavior. Based on the micro level, this study belongs to the body of literature based on the micro level data on the not so much studied group of women academicians with regional Indian scenario and not only that but also gives the financial education its importance so as to empower women and help them in making educated investment decisions. The findings will try to document the current scenario of women academicians in terms of their financial literacy and their investment habits with an aim for highlighting the financial literacy issue among women academics. This paper has been prepared to find out financial literacy level of the women academicians and Investment behavior of women academicians and to solve the problem of low financial literacy of women academicians.

The Marathwada region of Maharashtra, India, faces chronic agricultural distress driven by severe water scarcity, recurring droughts, and rapid climate variability. Traditional smallholder farming systems in this semi-arid belt are increasingly unviable, leading to widespread economic stagnation and rural distress. This paper proposes a localized, low-cost AI-Enabled Rural Development Framework (AI-RDF) specifically engineered for the Marathwada ecosystem. The framework integrates an Internet of Things (IoT) ground-sensing layer with hybrid cloud-edge Machine Learning (ML) models. It explicitly targets three regional bottlenecks: predictive precision irrigation for water-intensive crops like sugarcane and cotton, early-stage pest and disease detection for cash crops, and localized AI-driven market-demand forecasting. Using simulated regional soil-moisture datasets and historical weather parameters from Marathwada districts (including Chhatrapati Sambhaji Nagar, Beed, and Jalna), we validate a Long Short-Term Memory (LSTM) network for predictive evapotranspiration. Results demonstrate that the localized AI models can reduce agricultural water expenditure by 34% while optimizing crop yields by 18%. Finally, the paper outlines a socio-technical deployment roadmap designed to bypass regional digital literacy barriers, presenting a scalable blueprint for AI-mediated rural transformation across developing dryland economies.

Very Large-Scale Integration (VLSI) design is a fundamental technology for developing modern integrated circuits that power consumer electronics, communication systems, automotive applications, and artificial intelligence hardware. This paper presents a comprehensive overview of the complete VLSI design flow, from Register Transfer Level (RTL) design and functional verification to logic synthesis, static timing analysis, physical design, power optimization, and semiconductor fabrication. It discusses the significance of verification, timing closure, and layout validation techniques such as Design Rule Checking (DRC) and Layout Versus Schematic (LVS) in ensuring reliable chip performance. The paper also highlights the role of Electronic Design Automation (EDA) tools and optimization strategies in designing high-performance, low-power, and manufacturable integrated circuits. This overview serves as a valuable reference for understanding the end-to-end VLSI design process and its importance in modern semiconductor engineering.

Rural electrical networks often face challenges such as voltage instability, fault recovery delays, renewable energy intermittency, and inadequate backup power systems. This paper proposes a Quantum-Inspired Artificial Intelligence (QIAI) framework integrated with Self-Healing Smart Grid technology and Vehicle-to-Grid (V2G) energy sharing. The proposed system predicts faults, automatically isolates damaged sections, and utilizes parked electric vehicles as distributed energy storage units during emergencies. Simulation-based performance evaluation demonstrates improvements in reliability, restoration time, voltage regulation, and renewable energy utilization.

Electric vehicles (EVs) are becoming increasingly popular because of their high efficiency, reduced emissions, and lower maintenance requirements. However, brake disc overheating remains a significant challenge, especially during emergency braking, downhill driving, and regenerative braking transitions. Excessive heat causes brake fade, increased wear, thermal cracking, and reduced braking performance, affecting vehicle safety and component life. Conventional ventilated brake discs rely only on natural air cooling, which is often insufficient under severe operating conditions. This paper proposes a novel adaptive self-cooling brake disc that combines passive phase-change material (PCM) microcapsules with bio-inspired airflow channels. The PCM microcapsules are embedded within selected regions of the brake disc to absorb excess thermal energy during high-temperature operation through latent heat storage. Simultaneously, airflow channels inspired by the branching structure of leaf veins improve air circulation and convective heat transfer without requiring additional power consumption. Unlike active cooling systems, the proposed design is completely passive, lightweight, and maintenance-free. A mathematical heat transfer model is developed to evaluate transient temperature distribution inside the brake disc. The proposed concept is compared with conventional ventilated brake discs using theoretical thermal analysis. Expected results indicate that the adaptive cooling system can reduce peak brake disc temperature by approximately 18–25%, improve cooling rate by nearly 30%, and increase brake component life by reducing thermal stress. The proposed system offers a promising solution for next-generation electric vehicles by improving braking reliability while maintaining energy efficiency.

The rapid growth of electricity demand, renewable energy integration, and digital communication technologies has transformed conventional power systems into Smart Grids. Smart Grid technology enables real-time monitoring, fault detection, bidirectional communication, and efficient energy management. This paper reviews the architecture, applications, advantages, challenges, and performance analysis of Smart Grid systems. A comparative study using simulated data demonstrates improvements in transmission efficiency, fault recovery time, and renewable energy utilization.