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.

Forensic odontology has become an important role for human identification, mostly when manual methods like fingerprint analysis, rugae examination, face recognition etc., are infeasible. Over the time, advance technologies have been rapidly increases in the field of forensics like digital scanning, digital imaging, advance algorithms, etc., by using these advance techniques, identification of unknown individual becomes easy. This narrative review includes the traditional and advance methods for identification purposes in conditions when the victim’s body is totally unrecognizable. The paper highlights the evolution of the methods, effectiveness of the methods, comparison and applications. The advance technologies enhance more accuracy, more effectiveness in forensic evaluations. The review also identifies the obstacles including standardization of digital dental records in hospitals, and the need for more forensic database in various regions. The future scope and more advance technologies in forensic odontology are discussed.

The emergence of social media has generated more user-produced content than ever before, producing a vast corpus of opinions, sentiments, and emotions. Sentiment analysis, an essential branch of Natural Language Processing (NLP), attempts to elicit subjective information from text data to identify the emotional tone behind user state Sentiment analysis, often called opinion mining, is a technique within Natural Language Processing (NLP) that evaluates text to determine the underlying emotional tone or subjective information it conveys. This process enables the identification and classification of sentiments—such as positive, negative, or neutral—expressed in user statements, providing valuable insights into opinions and attitudes found in written languagements. Common sentiment analysis approaches like lexical methods and ML-based algorithms have struggled to extract the complex patterns of natural language. The advent of deep neural networks has provided a solution by relying on complex models that are able to interpret contextual dependencies and variation in sentiment. This research offers an in-depth review of deep learning strategies for sentiment classification on social media. It examines major techniques, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and advanced transformer-based models such as BERT, all of which have shown significant effectiveness in analyzing the complex language found in social media posts and ensemble models which are used to improve sentiment classification performance. In addition, we discuss intrinsic challenges like sarcasm detection, domain adaptation, and data imbalance and propose methods to address these limitations. Empirical testing on benchmark data shows that deep learning-based models, especially hybrid models that combine CNN and LSTM architectures, outperform traditional models in sentiment classification tasks. The study concludes by examining how deep learning-powered sentiment analysis influences commercial applications, government operations, and academic research, while suggesting potential directions for enhancement.

भूगोल में क्षेत्रीय विविधता का अध्ययन पृथ्वी की सतह पर पाए जाने वाले प्राकृतिक एवं मानव निर्मित तत्वों में अंतर को समझने पर केंद्रित है। यह अध्ययन विभिन्न क्षेत्रों की भौतिक विशेषताओं जैसे स्थलाकृति, जलवायु, मिट्टी, वनस्पति तथा जैव विविधता के साथ-साथ सांस्कृतिक और आर्थिक पहलुओं की भिन्नताओं को स्पष्ट करता है। क्षेत्रीय विविधता का मुख्य उद्देश्य यह जानना है कि विभिन्न भौगोलिक परिस्थितियाँ मानव जीवन, आर्थिक गतिविधियों और सांस्कृतिक विकास को किस प्रकार प्रभावित करती हैं। इस अध्ययन के माध्यम से संसाधनों के समुचित उपयोग, क्षेत्रीय योजना, पर्यावरण संरक्षण तथा क्षेत्रीय असमानताओं को कम करने में सहायता मिलती है। भारत जैसे देश में, जहाँ अत्यधिक भौगोलिक और सांस्कृतिक विविधता पाई जाती है, क्षेत्रीय अध्ययन विशेष रूप से महत्वपूर्ण हो जाता है। इस प्रकार, क्षेत्रीय विविधता का विश्लेषण संतुलित विकास और सतत्‌ प्रगति के लिए एक आवश्यक आधार प्रदान करता है।

The swift rise of corporate entities in outer space—from satellite mega-constellations and suborbital tourist initiatives to asteroid-mining startups—has shown an expanding disparity between the velocity of commercial innovation and the legal framework intended to regulate it. This paper analyzes three interconnected aspects of the gap: (i) the liability framework governing private space activities under both international treaty law and domestic statutory systems; (ii) the licensing structures through which states authorize and oversee non-governmental operators; and (iii) the overarching global regulatory challenges that emerge when jurisdictions with conflicting commercial interests must collaborate in an orbital commons that transcends sovereign borders. The paper contends that the existing regulatory framework is fundamentally incompatible with the New Space era, referencing the 1967 Outer Space Treaty, the 1972 Liability Convention, recent U.S. legislation such as the Commercial Space Act of 2023, and analogous regulatory advancements in the European Union, China, and other spacefaring nations. State-centric liability rules have a hard time figuring out who is to blame when missions cross state lines. Licensing moratoriums that were meant to help a new industry grow now risk making an old one even less regulated. Finally, the lack of a binding multilateral framework for debris mitigation, spectrum coordination, and in-space servicing puts the long-term health of the orbital environment at risk. The paper ends by suggesting a series of step-by-step, risk-based changes, using examples from aviation, maritime, and environmental law, that could balance the need for new business ideas with the need to keep space safe and open for everyone.

The fast growth of social media sites has completely changed how service-sector companies plan, talk about, and carry out their marketing plans. This article examines how service businesses in Haryana, a rapidly developing state that is bridging agricultural traditions and a booming digital economy, are using social media to recruit, engage, and keep clients. The study employs secondary data from NASSCOM, FICCI, and the Internet and Mobile Association of India (IAMAI), alongside primary survey data (n=320) gathered from service-sector SMEs and consumers in key Haryana districts (Gurugram, Faridabad, Ambala, Hisar, and Panipat). It examines platform adoption trends, shifts in consumer behavior, and revenue implications from 2020 to 2024. The investigation is directed by two aims and two hypotheses. The results show that more than 76% of Haryana service SMEs currently utilize at least one social media platform for marketing.