Green Human Resource Management (Green HRM) integrates environmental sustainability into all HR processes—from recruitment and training to performance evaluation and compensation—to foster a workforce that understands, values, and implements eco-friendly practices. By embedding environmental management into HR activities, Green HRM promotes sustainable operations, minimizes negative environmental impact, and cultivates a green organizational culture..

This research paper examines the evolving role of artificial intelligence (AI) in modern project management and its implications for 2025. Through a comprehensive analysis of current literature, industry reports, and case studies across multiple sectors, we identify key trends and technologies shaping the future of project management. The study reveals that AI adoption in project management is accelerating, with technologies such as machine learning, predictive analytics, and natural language processing transforming traditional processes. Our findings indicate that by 2025, AI will significantly impact project outcomes through enhanced forecasting accuracy, automated task management, intelligent resource allocation, and proactive risk mitigation. The research also highlights implementation challenges including technical integration issues, organizational resistance, skills gaps, and ethical considerations. Case studies across construction, software development, healthcare, manufacturing, and financial services demonstrate quantifiable benefits including 15-40% improvements in efficiency, 20-30% cost reductions, and 25-50% enhanced risk identification. This paper contributes to both theoretical understanding and practical application by providing a framework for AI implementation in project management and a roadmap for organizations navigating this technological transition. The implications suggest a fundamental shift in the project manager’s role from tactical oversight to strategic orchestration, with AI handling routine tasks while humans focus on complex decision-making, stakeholder management, and innovation..

This study presents a comprehensive comparative analysis of the State Bank of India (SBI) and ICICI Bank, two leading financial institutions in India. The evaluation spans critical dimensions such as branch network, employee strength, total assets, profitability, asset quality, and operational efficiency. SBI emerges as the largest bank in India in terms of branch network, workforce, total assets, and overall business volume, highlighting its vast outreach and dominance. .

Wireless Sensor Networks (WSNs) consist of sensor nodes that collect and transmit environmental data. The primary challenge in WSNs is managing energy consumption, particularly as sensor nodes rely on limited battery power. Energy consumption is mainly driven by communication, sensing, and computation tasks, with communication being the most energy-intensive. Effective energy management strategies, such as energy-efficient routing protocols, duty cycling, data aggregation, and energy harvesting, aim to prolong the network's lifespan and improve performance. Hierarchical routing protocols like LEACH and PEGASIS, alongside data aggregation, reduce transmission load and minimize energy consumption. Energy harvesting, utilizing renewable sources like solar and thermal energy, offers a promising solution for long-term deployments. However, challenges such as variability in energy sources and the need for specialized hardware persist. Accurate energy consumption models are essential for optimizing energy use, and issues related to network scalability and security trade-offs need to be addressed for enhanced network efficiency..

The diverse uses of magnetic nanoparticles in biomedicine, environmental cleanup, energy storage, and data recording systems have highlighted their relevance in the fast evolution of nanotechnology. Because of their biocompatibility, adjustable surface chemistry, and distinctive superparamagnetic characteristics, magnetite (Fe₃O₄) nanoparticles have garnered a lot of interest. The benefits of plasma-based synthesis over traditional chemical and physical approaches are shown in this work, which investigates magnetite nanoparticles that have been fabricated and characterized with the use of plasma. Instead of using harmful reducing agents, plasma-assisted procedures allow for more precise control over particle size, shape, crystallinity, and purity; this makes them a more environmentally benign option. To comprehend the connection between synthesis parameters and the magnetic characteristics that emerge, structural, morphological, and magnetic characterizations take center stage. .

The rapid growth of cybercrime has challenged traditional forensic methods, necessitating advanced approaches for analyzing complex digital evidence. This study examined the application of machine learning (ML) models across eight cybercrime datasets, including intrusion logs, financial fraud, phishing, malware, insider threats, darknet activity, blockchain records, and IoT botnet traffic. Supervised models such as Random Forest, ANN, and SVM showed high accuracy in structured datasets, while deep learning architectures (CNN, LSTM, Autoencoder–LSTM) excelled in unstructured or sequential data. Graph-based models, particularly Graph Neural Networks, provided valuable insights for blockchain forensics. Comparative analysis revealed that ensemble approaches leveraging multiple ML paradigms enhance operational readiness and judicial admissibility..