Volume 14 | Issue 4 |

 Abstract

The high rate of urbanization has enhanced congestion, safety levels, and inefficiencies at intricate intersections of traffic, so that intelligent and autonomous traffic management systems are required. This report presents a proposed multi-modal urban junction analytics, which consists of the YOLOv8-based object-detection tool and the DeepSORT-tracker to identify fine-grained motions of heterogeneous road users. Graph Neural Networks are used to model interaction between agents and changing conflict patterns, whereas Temporal Convolutional Networks make it possible to predict short-term traffic flows. Bayesian Change Point Detection detects abnormal change and incident initiation, and self-supervised contrastive learning is more robust in little literature. Drone and CCTV feeds' multi-view fusion improves the spatial coverage and eliminates occlusion. Extracted trajectory and safety indicators are organized into a downstream signal, optimizing scalable datasets. Real-world intersection validation that can be conducted experimentally indicates that there is an enhanced accuracy when estimating the traffic flow, as well as the identification of risks and adaptive signal planning. The suggested framework provides an intelligent urban traffic management framework that will have a predictive and safety-conscious base.

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 Keywords

Urban Traffic Analytics and YOLOv8 Deep SORT Graph Neural Networks Multi-modal Fusion Traffic Safety Predictive Signal Control.

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Coral reefs are great significance in maintenance of biodiversity and the conservation of the ecosystem; however, they are facing threats due to effects of climate change, pollution and human activities. It is imperative to keep the track of the health of the coral reef. However, the existing techniques involves the manual analysis of underwater images, which takes long time to complete and not suitable for the entire ecosystem. This project aims at developing an AI-based system to automate the analyzing the coral reef using deep learning techniques. This system employs enhanced Semantic Segmentation network to locate the coral reef in the underwater images and compute the Live Coral Cover (LCC). This system also classifies the states of the reef into healthy, moderate, degrading and poor conditions. This project has been designed to include a user-friendly web-based system where the user can upload the underwater images and get the results instantly. This system is expected to be helpful in the conservation of the ecosystem

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 Keywords

Coral reef monitoring, Live coral cover (LCC), Deep learning, Semantic Segmentation, Reef health classification, Underwater image analysis, Environmental conservation.

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India has been an early adopter of sustainability reporting for listed entities. In 2012, the Business Responsibility Report (BRR) as released by the Securities and Exchange Board of India (SEBI) mandated listed entities to disclose their ESG performance. Sustainability is not just a feel-good theme. It is about the imperative of surviving in a world facing climate change, running out of resources, and social problems. This study objective is to study the evolution of sustainability reporting in India and analyse the sustainability reporting trends in India. The launch of the SDG India Index in 2018 provided the impetus for the localisation push, reaffirming States and UTs as key stakeholders in this transformative journey. The SDG India Index has been consistently improved over the years to provide a comprehensive and comparative analysis of progress on the goals. In 2025, sustainability reporting in India is transitioning from a compliance exercise to a strategic imperative, driven by stricter regulations, increased demand for transparency, and technological integration. The key trends are centred around mandatory and standardized reporting, enhanced data quality and assurance, and a focus on the entire value chain.

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 Keywords

sustainability reporting, India, Securities and Exchange Board of India, Sustainable Development, Business Responsibility and Sustainability Report.

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Clayey soils used as subgrades in highway construction often suffer from high plasticity, excessive swelling, low bearing capacity, and inadequate strength, leading to early pavement failure. To overcome these limitations in a sustainable manner, this study investigates the effectiveness of stabilizing clay with a hybrid combination of rice husk biochar, wood biochar, and lime. Biochar, a carbon-rich and porous by-product of biomass pyrolysis, has gained attention for its ability to improve soil structure and moisture behaviour, while lime remains a proven chemical stabilizer capable of inducing pozzolanic reactions and long-term strength gain. In this research, natural clay was stabilized using rice husk biochar, wood biochar, and lime, selected based on their optimum performance ranges reported in literature and confirmed through preliminary assessment. A comprehensive experimental program was conducted, including Atterberg limits, California Bearing Ratio, Moisture test, and specific gravity tests

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 Keywords

Rice Husk Biochar; Wood Biochar; Lime Stabilization; Clay Subgrade; Soil Stabilization; California Bearing Ratio (CBR); Atterberg Limits; Sustainable Construction Materials; Eco-friendly Stabilizer; Soil Improvement Techniques

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The personal laws of the citizens have played a major role in helping and supporting the secular feature of our nation. This secular aspect of the Preamble allows its citizens to practice, profess, and propagate the religion of their choice as enumerated in the fundamental rights thereby creating a constant rise in need for a homogenous law for all so that no person shall feel deprived of a right. The Uniform Civil Code (UCC) finds its place under Article 44 in Part IV of the Constitution of India, 1950 as a Directive Principle of State Policy which provides that it is the duty of the state to secure a UCC throughout the country. To bring a UCC the aim is to change the personal laws of various religious communities. The personal laws include marriage, divorce, inheritance, adoption, maintenance, and succession which form the main domain to bring about requisite change in the societal norms to enact UCC. The said code differentiates between public laws and private laws. The theme of this paper revolves around the UCC being an ancient concept that has evolved into the notion of codification of a UCC. The main objective of the lawmakers has always been to foster harmony and equality amongst all but the question lies in whether the administration will be able to do so while retaining the title of being secular; whether the need to ratify an enactment which decrees the equal personal rights to all, is recurring and if so, what measures have been taken by the respective leaderships to satiate the said need. The prime objective of this paper is to crystallize the real essence of UCC by discussing variegated strands of the UCC including but not restricted to the existence of the UCC in the radicle of the Indian realm, the compelling need and requisite for the said code to be structured and tabulated by virtue of profound judgments postulated by the learned judges of the Hon'ble Supreme Court.

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Uniform Civil Code, Uttarakhand, Directive Principles of State Policy, Fundamental Rights, Article 44, Article 25

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improper crop selection, inefficient fertilizer usage, and delayed detection of plant diseases, which can reduce crop yield and quality. This project presents a software-based system for crop and fertilizer recommendation along with plant disease detection using machine learning techniques. The system analyzes soil parameters including nitrogen, phosphorus, potassium, pH, and moisture to recommend the most suitable crop and appropriate fertilizers based on nutrient requirements. In addition, the system detects plant diseases at an early stage using image processing and deep learning models. A convolutional neural network (CNN) is used to analyze plant leaf images and accurately identify diseases, along with suggesting suitable treatment measures. The proposed system provides a user-friendly interface and supports data-driven decision-making, helping farmers improve productivity, reduce losses, and adopt sustainable agricultural practices.

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 Keywords

Precision Agriculture, Machine Learning, Crop Recommendation, Fertilizer Recommendation, Plant Disease Detection, Image Processing, Convolutional Neural Network (CNN), Soil Nutrient Analysis.

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The Indian steel sector, as part of the country's infrastructural dreams and economic sovereignty, is undergoing a paradigm shift driven by Industry 4.0 principles. This change goes beyond a technological upgrade and will lead to a paradigm shift in human capital strategies. This paper examines a symbiotic and yet discordant association between digital transformation (DT) and human resource management (HRM) practice in this vital industry. The study uses a one- longitudinal case study methodology, which will utilize an in-depth approach to study, focusing on the example of Jindal Steel and Power Limited (JSPL), a well-known leader in technological adoption. Four research objectives (to map the DT initiatives at JSPL; to analyze the effect on strategic HRM functions; to define the mediating role of organizational culture and leadership in this interaction; and to synthesize a contextual framework of human-technology integration in capital-intensive sectors in the emerging economies) guide the study. Triangulation of data was based on 42 semi-structured interviews, non-participant observation, and internal document and performance metrics analysis between 2019-2024. It has been found that DT is a disruptor and enabler of HRM. Although it has resulted in the establishment of data-centric roles, AI-driven recruitment, and VR-based training, it has also contributed to the creation of a so-called digital skills dichotomy, employee anxiety, and the creation of conflicts between algorithmic management and tacit and experiential knowledge. The results of the study confirm that the success of DT does not depend on the technological sophistication but the active, human-oriented approach of HRM based on transparent change management, ongoing dialogue, and investment in the skills of the soft adaptation alongside the hard technical adaptation. The study has contributed to the emerging body of literature on HRM 4.0 in emerging economies and provides evidence-based suggestions to practitioners and policymakers, who are aiming at future-proofing the conventional manufacturing industries.

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 Keywords

Digital Transformation; Strategic Human Resource Management; Industry 4.0; Indian Manufacturing; Technological Change.

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Artificial Intelligence (AI) is reshaping training and development in organizations by enabling personalized, adaptive, and data-driven learning. AI-powered tools analyze employee behavior, identify skill gaps, and recommend tailored learning paths, enhancing engagement and performance. Applications include intelligent tutoring systems, Chabot's, predictive analytics, and content curation. Real-world examples from IBM, Amazon, Bank of America, DHL, Microsoft, and healthcare organizations demonstrate AI's effectiveness in improving skill development, training efficiency, and employee retention. Challenges such as implementation costs, data privacy, and algorithmic bias must be managed carefully. Future trends point to AI integration with virtual reality, adaptive learning platforms, and continuous reskilling initiatives. Overall, AI offers organizations a strategic advantage in developing a competent and agile workforce.

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 Keywords

Artificial Intelligence, Generative AI Revenue, HR Analytics, Enhanced Productivity, Operational Cost Reduction

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This paper presents an AI-enabled edge-based system for real-time grid fault detection and analysis. Electrical parameters such as voltage, current, frequency, and phase angle are continuously monitored using sensors. The data is processed locally on an edge device such as Raspberry Pi or ESP32 integrated with a lightweight AI model. The trained machine learning model identifies abnormal patterns and classifies fault types including single line-to-ground, line-to-line, and three-phase faults. Immediate alerts are generated and transmitted to the control center through IoT communication. By performing analytics at the edge, the system minimizes latency, reduces bandwidth usage, and ensures rapid fault isolation. This approach enhances grid reliability and supports smart grid automation. The increasing complexity of modern power systems demands fast and reliable fault detection mechanisms to ensure system stability and safety.

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 Keywords

KEYWORDS : Artificial Intelligence, Edge Computing, Fault Detection, Smart Grid, IoT, Power System Protection

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 Abstract

Recent advancements in generative modeling have significantly enhanced the capability of text-to-image synthesis systems. Despite these improvements, achieving precise spatial control over generated content remains a persistent challenge. This paper introduces an adaptive region-aware conditioning framework designed to improve controllability in diffusion-based generative models. The proposed approach dynamically integrates spatially localized conditioning signals derived from textual prompts, enabling fine-grained manipulation of specific regions within generated images. By incorporating region-level attention mechanisms and adaptive weighting strategies, the model effectively aligns semantic descriptions with corresponding spatial locations. Experimental evaluations demonstrate that the proposed method outperforms conventional conditioning approaches in terms of spatial accuracy, visual coherence, and semantic consistency. The findings suggest that adaptive region-aware conditioning provides a promising direction for controllable and interpretable text-to-image synthesis.

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 Keywords

Diffusion Models, Text-to-Image Synthesis, Region-Aware Conditioning, Generative AI, Spatial Control, Deep Learning.

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