National Institute of Technology Durgapur (also known as NIT Durgapur or NITDGP), formerly called as Regional Engineering College, Durgapur, is a public technical and research university in the city of Durgapur in West Bengal, India. Founded in 1960, it is one of India's oldest, most selective, and most prestigious technical universities. It is located in a campus of 187 acres (0.75 km²).NIT Durgapur is recognized as an Institute of National Importance by the Government of India under the National Institutes of Technology Act, 2007 and is one of the institutions of the National Institutes of Technology (NITs) system. The university focuses exclusively on science, technology, engineering, management, and architecture.National Institutional Ranking Framework ranked the university 9th among the NITs, 47th for engineering, and 96th overall in India in 2020. The university has academic and research collaborations with universities and research centers in India and abroad including the United States and United Kingdom and is undergoing accelerated growth through the World Bank-funded Technical Education Quality Improvement Program (TEQIP).
In this work, we show that the similarity transformation of the thermal diffusion equation in the thermal layer leads a generalization of the asymptotic theories of the dynamics of bubble growth in nucleate boiling in an infinite pool of superheated liquid. The transformation allows us to define similarity variable and to reduce the PDE for the diffusion equation to an ODE solving for the radial variation of liquid temperature and an ODE for the thickness of thermal boundary layer. Thus, the method efficiently solves the diffusion equation, eliminating the assumptions in our semi-analytical solution that ignored the thermal convection due to the moving surface of a growing bubble. We have obtained the rate of bubble growth from the energy balance at vapour-liquid interface. The work dispenses with the Rayleigh-Plesset equation for the growth rate. Interestingly, we have obtained the growth rate following Plesset-Zwick equation with the thin-thermal layer assumption. We have also studied the asymptotic features of the ODE of the thermal boundary layer. Thus, the asymptotic solution for the growth rate evolves to be our previous limiting solution at the initial stage (small-time asymptotic solution), t→0 and to be a new solution (large-time asymptotic solution) at the final stage, t→∞. These findings have been found to be well comparable with the experiments and our numerical model. Finally, we have formulated Nu as a function of Ja for t→0 and t→∞; Ja is Jakob number.
This survey explores the advancements in Visual Question Answering (VQA) technology designed for visually impaired (VI) individuals. VQA systems integrate computer vision (CV) and natural language processing (NLP). It has the potential to improve VI people's independence by translating visual information into understandable representations. Compared to other recent surveys, this survey not only focuses on the development of the VQA system but also demonstrates the challenges faced by VI people. This article provides an in-depth review of the state-of-the-art VQA systems, datasets, and methodologies, focusing on their application to assist VI users. We analyze the unique challenges faced by this demographic, such as the quality of images captured and the complexity of questions asked. The survey also highlights the specific needs of VI users and how existing VQA solutions address these needs. We discuss the role of multimodal transformers, prompt-based learning, and generative approaches in improving VQA performance. We discuss how it aligns with the Sustainable Development Goals (SDGs). Our findings emphasize the importance of developing customized VQA systems that meet the diverse requirements of VI individuals, leading the way for future research and innovation in this field. This comprehensive review aims to provide valuable insights and guidance for researchers and developers working on VQA technologies for VI people.
The rapid integration of distributed energy resources and power-electronic-interfaced loads requires sophisticated planning techniques for modern radial distribution systems (RDS). In this paper, we propose a comprehensive multi-objective optimization framework for the coordinated placement and size of electric vehicle charging stations (EVCS), distributed generations (DGs), and soft open points (SOPs) in RDS. The developed objective function concurrently minimizes loss, improves the voltage profile, enhances the power factor, reduces harmonic distortion, and maximizes the utilization of substation capacity, subject to operational and technical constraints. Teaching Learning Based Optimization (TLBO) and Harris Hawks Optimization (HHO) were used and compared to evaluate solution resilience and convergence efficiency. The outcomes showed that system coordination consistently improved network efficiency, voltage stability, feeder balance, and power quality. Coordinated multi-device integration delivered better technical performance and ensured steady operation within regulatory voltage and harmonic limits. The robustness and dependability of the suggested optimization framework were validated through statistical analysis, which showed that HHO outperformed TLBO in terms of convergence behavior and solution quality. In addition to improving technical performance, the suggested framework supports the development of sustainable power systems by encouraging the integration of renewable energy sources, grid modernization, and the adoption of electrified transportation. The study supports SDGs 7 (Affordable and Clean Energy), 9 (Industry, Innovation and Infrastructure), 11 (Sustainable Cities and Communities), and 13 (Climate Action). Overall, the suggested coordinated optimization approach offers a scalable and long-lasting solution for active distribution networks prepared for the future.
In fostering economic growth, the effects of changes in Land Use and Land Cover (LULC) plays an important role for sustainable urban development. To address this issue, an efficient hybrid model is proposed to enhance the prediction accuracy of LULC for sustainable urban development. Initially, sentinel-2 satellite images of a region of interest for the study area Durgapur in West Bengal, India, and its neighboring areas, for 2018 and 2022 are processed using the Google Earth Engine (GEE) platform. Image acquisition and preprocessing are performed to ensure the suitability of input data for classification. The preprocessed images are classified using a Convolutional Neural Network (CNN), which excels at extracting spatial features of each class from the imagery. The CNN classification method ensures high accuracy in distinguishing between LULC classes. The classified images are then processed using a Random Forest Regressor (RFR) to predict future LULC changes. The RFR effectively handles nonlinear relationships in the data to make the predictions more accurate. The proposed hybrid model predicts the changes in LULC at 5-year intervals for the years 2025, 2030, and 2035. Hence, the proposed hybrid model captures both spatial patterns and temporal trends by combining CNN and RFR for spatial feature extraction and predictive modeling, respectively. The results demonstrate a high prediction accuracy of 98.7
This work presents a novel approach to measuring centrality within m-polar fuzzy graphs (mPFG). Several properties, along with detailed descriptions of the centrality measure, influence nodes, and related indices, are thoroughly investigated. The proposed approach accounts for the various merits of each member in a network by considering individual merits (self-weight) in the centrality measurement. A limited group of people has been examined to highlight the issue and showcase the potential applications of this novel centrality evaluation technique. This comprehensive method allows for a more nuanced and detailed understanding of node influence in complex, multi-dimensional networks.
