This study proposes a novel framework that integrates sink relocation, joint clustering, and compressed sensing (CS) for energy-efficient data collection in Wireless Sensor Networks (WSNs). Traditional static-sink models suffer from uneven energy depletion, leading to premature network failure.

This research dives into the connection between emotional intelligence and academic performance among higher secondary students in the Jammu district, located in the Union Territory of Jammu and Kashmir. Using a descriptive survey method within a quantitative framework, the study involved 200 students selected through a simple random sampling technique.

This review article critically examines the evolution and contemporary status of bail jurisprudence in India, focusing on the provisions of the Code of Criminal Procedure (CrPC) and the New National Bail Code of India (BNSS, 2023). Bail, as a fundamental aspect of personal liberty and criminal justice, has undergone significant changes through legislative reform and judicial interpretation.

The convergence of Machine Learning (ML) and the Internet of Things (IoT) marks a pivotal shift toward the development of smart, autonomous, and data-driven systems. While IoT serves as a vast network of interconnected physical devices generating continuous, real-time data, ML algorithms process this data to uncover patterns, detect anomalies, and make predictive decisions. This synergy empowers applications ranging from predictive maintenance in industries and precision farming in agriculture to real-time traffic management and personalized smart environments. The integration of ML and IoT enhances operational efficiency, user experience, and system responsiveness across multiple domains.

The Molybdenum Blue Reaction is a classical, yet eco-conscious analytical method used for the quantitative determination of phosphate in water samples. Rooted in the principles of Green Analytical Chemistry (GAC), this study highlights the method’s minimal environmental impact due to its aqueous medium, mild reaction conditions, and low hazardous waste generation.

Wireless Sensor Networks (WSNs) are integral in various applications like environmental monitoring, healthcare, and military surveillance. However, one of the critical challenges faced by WSNs is data loss, which can significantly affect the performance and reliability of these networks. The primary causes of data loss include signal interference, packet collisions, environmental factors, and energy constraints of sensor nodes. As WSNs are often battery-powered, the depletion of energy resources can lead to intermittent connectivity or node failure, resulting in data loss. This study explores the causes of data loss in WSNs, its consequences, and the impact of dynamic network topologies and routing challenges. Furthermore, it examines mitigation strategies such as robust communication protocols, error detection and correction techniques, energy-efficient designs, redundant data storage, and adaptive routing to enhance the reliability and performance of these networks.

Impostor Syndrome (IS) is a psychological phenomenon characterized by persistent self-doubt, fear of being exposed as a fraud, and an inability to internalize success despite evident achievements. One of the key contributing factors to IS is social comparison, wherein individuals evaluate their competence and self-worth by comparing themselves to others.