The Internet of Things (IoT) is revolutionizing various industries by enabling devices to autonomously communicate and process data, improving efficiency and decision-making. However, a major challenge lies in the power supply of IoT devices, especially in remote areas where regular maintenance or battery replacement is impractical. Traditional battery-powered systems are limited by sustainability, cost, and environmental impact, prompting the development of energy harvesting technologies. Energy harvesting antennas, which capture ambient energy from sources like radio frequency signals, light, heat, and vibrations, present a promising solution. These antennas enable IoT devices to operate autonomously by converting ambient energy into electrical power, reducing maintenance costs and environmental waste. This paper explores the development, efficiency challenges, integration with energy storage solutions, and adaptability of energy harvesting antennas for IoT applications, highlighting their role in creating self-sustaining IoT networks..

Nephritic disease, a kidney condition with significant health implications, requires timely diagnosis and precise monitoring for improved outcomes. Traditional diagnostic approaches are often time-intensive and lack real-time precision. This study explores the application of machine learning (ML) algorithms, including Random Forests, Support Vector Machines (SVM), and Neural Networks, for early diagnosis and progression analysis of nephritic disease. Using clinical, biochemical, and imaging data, these models deliver high accuracy, with Random Forests excelling in feature interpretability, SVMs ensuring robust classification, and Neural Networks handling complex patterns..