Volume 1, Issue 2

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 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.

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.

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.

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.