The current research investigated how the locals are feeling about the rising tourist traffic in Varanasi, particularly in terms of perceived economic, lifestyle-related and cultural effects of tourism development. The descriptive and analytical research design was chosen and primary data were obtained through a structured questionnaire among 200 local residents according to the five-point Likert scale. The proposed hypotheses were tested by a correlation analysis by Pearson. The results showed that the interest of the local residents was mostly positive towards the growth of tourism because they understood that it led to economic growth, creation of jobs, development of infrastructure and promotion of culture. Nevertheless, issues connected with increased cost of living, congestion, disruption of life style and cultural commercialization were also noticeable. The test of hypotheses proved that the perceived economic, lifestyle-related and cultural effects of tourism were statistically significantly associated with the attitudes of residents, with the strongest effect of the economic one. The paper came to a conclusion that the growth of tourism in Varanasi was mostly supported by the populations but on the basis of equal sharing of the benefits, proper management of the negative effects, and maintenance of the cultural authenticity. The results highlight the significance of the sustainable and community-based tourism planning in the heritage and pilgrimage sites.

Soil health management is fundamental to sustainable cotton production, supporting enhanced crop productivity, environmental stewardship, and resilience to climate variability. Cotton, a globally significant cash crop, depends heavily on soil quality due to its high nutrient demands and long growing season. Unsustainable cotton practices—such as intensive tillage, monocropping, and excessive chemical inputs—have degraded soil structure, reduced organic matter, and disrupted nutrient cycles. This has led to lower yields, increased costs, and heightened vulnerability to pests, diseases, and climate stress. Soil health management adopts holistic strategies that maintain or improve soil biological activity, chemical fertility, and physical structure. Key practices include cover cropping, organic amendments (e.g., compost, green manure), crop rotation, reduced tillage, integrated nutrient management, and precision irrigation. These practices enhance soil organic carbon, improve water infiltration and retention, and support beneficial soil biota essential for nutrient cycling. Empirical evidence shows that cotton systems with improved soil health exhibit higher yields, reduced input requirements, and greater economic returns. Additionally, healthy soils contribute to environmental sustainability by reducing greenhouse gas emissions, minimizing nutrient leaching, and enhancing biodiversity. Soil health monitoring—via soil testing, biological indicators, and remote sensing—allows farmers to make informed management decisions tailored to field conditions. While barriers such as knowledge gaps, initial investment costs, and institutional support constraints exist, targeted extension services and policy incentives can accelerate adoption. This article reviews the principles and practices of soil health management and demonstrates its indispensable role in achieving long-term sustainability in cotton production.

The increasing sophistication of global supply chains and the reliance on external suppliers has rendered the assessment of vendor performance an important managerial role. The traditional vendor assessment systems are usually manual, subjective and time consuming and this makes it difficult to implement them in dynamic business settings. The current research paper is a proposal of an AI-based vendor performance rating system that allows objective and consistent and data-driven assessment of a vendor based on key performance indicators like the overall performance of the vendor, timeliness in performance, adherence to quality standards, and reliability of its services. The research methodology was a quantitative one, where the historical data of organizational vendors and the scoring methods based on machine learning were used to produce composite performance scores. A systematic grouping of the vendors was done to Excellent, Good, Average and Poor and the data analyzed in relation to the descriptive statistical procedures. The results indicate that the majority of the vendors demonstrate good delivery and quality-conformance performance and the small percentage reveals the existence of the performance gap that requires correction activity. The opportunities of AI-based assessment systems to reduce subjectivity, enhance transparency and make informed decisions are identified in the paper, which would ultimately lead to the optimization of vendor management and the supply chain.

In recent years, owing to the rapid evolution of machine learning, especially deep learning, and its outstanding performance in the domain of image processing an increasing number of scholars are turning to convolutional neural networks (CNNs) to address image denoise challenges. To tackle image denoise issue, this paper introduces the implementation of a deep Generative Adversarial Network for image de-hazing. Unlike traditional de-hazing methods that rely on per-pixel loss, our approach leverages a perceptual loss function. This function captures high-level image features by utilizing pre-trained models from ImageNet, effectively mitigating the shortcomings associated with per-pixel loss functions, particularly their sensitivity to minor variations in single pixels, even when the images are perceptually similar.