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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.

Abstract— The Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) is the cornerstone of modern CMOS and VLSI technology. Accurate modelling of its characteristics is essential for circuit design and performance optimization. This paper explores MOSFET transistor modelling based on the NPTEL course “The MOS Transistor Modeling” by Prof. Navjeet Bagga. The study covers the physical structure, MOS capacitor behaviour, threshold voltage derivation, and operating regions of the MOSFET. The classical Level-1 model equations for drain current in linear and saturation regions are derived and validated through extensive LTspice simulations. Simulated output (I_D vs V_DS) and transfer (I_D vs V_GS) characteristics show excellent agreement with theoretical predictions, with errors typically below 3%. Parametric analysis of W/L ratio further confirms the linear scaling of drain current. The work highlights both the strengths and limitations of the basic Level-1 model, particularly for long-channel devices. This study successfully bridges theoretical learning from NPTEL with practical simulation skills, serving as a valuable educational resource for students in microelectronics and VLSI design.

The increasing demand for wireless communication services has resulted in spectrum scarcity despite the existence of numerous underutilized licensed frequency bands. This issue is particularly significant in rural areas where communication infrastructure is limited and broadband penetration remains low. Cognitive Radio (CR) technology has emerged as a promising solution for improving spectrum utilization through dynamic spectrum access. Spectrum sensing is the fundamental function of cognitive radio systems, enabling secondary users to identify unused spectrum without causing interference to licensed users. Traditional spectrum sensing techniques such as energy detection, matched filtering, and cyclostationary feature detection suffer from reduced accuracy under low signal-to-noise ratio conditions and dynamic wireless environments. Recent advancements in deep learning have demonstrated remarkable improvements in signal classification and spectrum occupancy detection. This paper presents a comprehensive study of deep learning-based spectrum sensing techniques for cognitive radio networks aimed at enhancing rural connectivity. Various deep learning architectures, including Convolutional Neural Networks, Long Short-Term Memory networks, Autoencoders, and hybrid CNN-LSTM models, are discussed. A deep learning-enabled spectrum sensing framework is proposed to improve detection accuracy, spectrum utilization, and communication reliability in rural environments. The benefits, challenges, performance evaluation metrics, and future research directions are also examined.

Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) has become a fundamental transmission technique for modern wireless communication systems due to its high spectral efficiency and robustness against multipath fading. Accurate channel estimation is essential for reliable data transmission in MIMO-OFDM systems. Traditional estimation methods such as Least Squares (LS) and Minimum Mean Square Error (MMSE) suffer from performance degradation under low Signal-to-Noise Ratio (SNR) conditions and rapidly varying channels. Recent advancements in deep learning have demonstrated promising capabilities in wireless signal processing tasks. This paper proposes an optimized Convolutional Neural Network (CNN)-based channel estimation framework for MIMO-OFDM systems. The proposed model learns channel characteristics directly from received pilot signals and estimates channel coefficients with improved accuracy. Simulation results demonstrate that the CNN-based estimator significantly outperforms conventional LS and MMSE estimators in terms of Mean Square Error (MSE) and Bit Error Rate (BER). The proposed approach provides enhanced robustness in dynamic wireless environments and is suitable for future 5G and 6G communication systems.

Vehicular Ad-hoc Networks (VANETs) play a critical role in enabling intelligent transportation systems by facilitating real-time communication among vehicles and roadside infrastructure. However, the highly dynamic topology, frequent link failures, and traffic congestion present significant challenges to efficient network management. Software-Defined Networking (SDN) offers a promising solution by introducing centralized control, global network visibility, and dynamic traffic management capabilities. This paper proposes an SDN-based traffic management framework for VANETs that leverages a centralized SDN controller to monitor network conditions, optimize routing decisions, and reduce communication overhead. The proposed architecture enhances packet delivery performance, minimizes end-to-end delay, and improves overall network throughput through adaptive traffic control mechanisms. Simulation-based analysis demonstrates the effectiveness of the proposed approach in managing vehicular traffic under varying network densities. The results indicate that SDN-enabled VANETs can significantly improve network efficiency, scalability, and reliability compared to conventional VANET architectures.