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..

Lean Management methods provide a structured and impactful approach to overcoming operational challenges in Indian desalination plants, particularly inefficiencies, high energy demands, and resource wastage. These challenges are acute in India due to rising population pressure, rapid industrialization, and climate-driven water scarcity, necessitating efficient and sustainable water treatment. This study employs a mixed-method research design, combining qualitative case studies from three desalination facilities with quantitative modeling and simulation to evaluate the applicability of Lean principles in practice. Value Stream Mapping (VSM) was a central tool, enabling detailed visualization of current-state processes and identification of bottlenecks, delays, and redundant steps. Subsequently, Lean interventions—including 5S for workplace organization, Kaizen for continuous incremental improvements, and Kanban for streamlining task scheduling and material flow—were implemented. Performance assessments before and after intervention, validated through operational data and simulations, revealed significant improvements. On average, cycle times were reduced by 15–20%, while energy consumption decreased by up to 12% per cubic meter of treated water, primarily due to minimized idle time and improved process synchronization. Resource utilization, including labor, maintenance, and chemical use, was optimized, lowering costs without compromising water purity or safety standards. Beyond operational gains, this study highlights Lean as a sustainable improvement framework aligned with India’s goals for water security, infrastructure modernization, and environmental stewardship. The findings not only address a critical research gap on Lean applications in the water sector but also provide a replicable model for desalination plants and broader utility contexts across India..

TV advertising is still the most powerful medium for influencing consumer behavior, cultural values, and societal norms. However, ethical limits of persuasion in this medium remain a subject of great contention (Murray, 1993). This research focuses on how audiences perceive and respond to ethical standards in TV commercials, specifically public opinions about honesty, fairness, and cultural sensitivity (Nelson, 2012). Using a mixed-method design, the study integrated quantitative survey findings (n = 500) with qualitative content analysis of 50 TV adverts aired in the last 12 months (Jowett & Abbott, 2013). Stratified sampling was used to provide demographic representation across gender and age groups. Statistical tests, such as Chi-square and ANOVA, uncovered significant trends: 68% of respondents saw ethical transgressions in beauty product adverts that encourage unrealizable standards, with 61% criticizing repeating themes of gender objectification (Berger, 2020). Notably, 54% of participants signaled conditional acceptance of exaggeration when worded as humor or clearly fictional, implying that audiences differentiate between false claims and creative freedom (Entman & Rojecki, 2001). Demographics also influenced ethical sentiments, with young viewers exhibiting increased tolerance for exaggeration and women recording greater sensitivity toward stereotyping (p < 0.05). The qualitative stage, carried out using thematic analysis, supported these results by identifying recurring ethical issues like deceptive health statements, reinforcement of patriarchal patterns, and occasional trivialization of culture (Sutherland, 2020). The research makes a contribution to the discipline of media ethics by placing audience voices at center stage in assessing advertising practice (Johnson, 2012).