The National Institutes of Technology (NITs) are the central government-owned-public technical institutes under the ownership of Ministry of Education, Government of India. They are governed by the National Institutes of Technology, Science Education and Research Act, 2007, which declared them as institutions of national importance and lays down their powers, duties, and framework for governance. The act lists thirty-one NITs. Each NIT is autonomous, linked to the others through a common council known as the Council of NITSER, which oversees their administration and all NITs are funded by the Government of India.These institutes are among the top-ranked engineering colleges in India and have one of the lowest acceptance rates for engineering institutes, of around 1 to 2 percent. In 2020, National Institutional Ranking Framework ranked twenty four NITs in the top 200 in engineering category. The language of instruction is English at all these institutes. As of 2021, the total number of seats for undergraduate programs is 23,997 and for postgraduate programs 13,664 in all the 31 NITs put together.
Large Language Models (LLMs) and Evolutionary Computation (EC) are increasingly being combined to support automated optimization, algorithm design, and adaptive decision-making. This survey reviews the bidirectional interaction between these two paradigms and examines how their complementary strengths can be leveraged in hybrid intelligent systems. First, we analyze how EC can enhance LLM-based systems through prompt optimization, hyperparameter tuning, and architecture search. Second, we review how LLMs can improve EC by supporting metaheuristic design, surrogate reasoning, adaptive operator control, and heuristic generation. We further discuss emerging co-adaptive frameworks in which LLMs and EC interact through iterative feedback loops. Beyond summarizing recent developments, the survey provides a structured perspective on interaction mechanisms, application patterns, and methodological challenges, including computational cost, reproducibility, interpretability, benchmarking, and generalization. The paper concludes by outlining open research questions and future directions for developing more robust, transparent, and scalable LLM-EC systems.
Abstract A heat recovery and management system for municipal solid waste decomposition in an open dumpsite is crucial for preventing urban heat island effects and reducing greenhouse gas emissions. The temperature rise has reached the ignition point in open dumpsites, increasing serious environmental issues. The present study systematically evaluated heat output from the Ariyamangalam dumpsite in Tiruchirappalli, using landfill degradation and transport equations combined with thermodynamic principles, and estimating smoldering potential for different waste fractions. The results indicate that the existing physical composition produces 1,348 MJ per tonne of waste. A combined approach of remote sensing and machine learning was adopted to investigate surface thermal characteristics and predict future surface temperatures. Thermal infrared sensor data and high-resolution satellite images were used to derive the spatial distribution of temperature. The thermal footprint mapping was conducted in ArcGIS (version 10.4.1), and variations in surface temperatures were correlated with biomining activities, which increased temperatures by 2°C–5°C across seasons. At the same time, the random forest algorithm identified surface temperature trends using Landsat imagery from 2004 to 2024. The random forest executed well with an out-of-bag error of 9.5% and an R 2 of 0.81. The outcomes highlight the significant thermal load caused by waste decomposition and biomining. Without efficient recovery, heat accumulation at the dumpsite is a growing concern, intensifying fire risks and local climate stress.
Marine bacteria are present almost everywhere in the ocean environment and are essential to many biogeochemical processes. The perspectives of ecologists and evolutionary biologists on the significance of microbes in ecosystem function are shifting as a result of exploring the marine microbiomes. This is especially true in ocean habitats, where microbes comprise the bulk of the biomass and are responsible for the majority of the planet's key biogeochemical cycles, including those that influence the global climate. Emerging research suggests that many ecosystem services provided by coastal marine environments depend on intricate interactions between groups of microbes and the environment or their hosts. The structure, variety, and functional capability of marine microbial populations have been revealed on a global scale thanks to recent developments in molecular ecology techniques. Over-recent-decades, industrialization and urbanization have led to widespread contamination of oceans. These contaminants accumulate in seawater and sediments, particularly in coastal areas, posing risks to marine ecosystems and human health. Marine microorganisms possess diverse catalytic abilities and extreme environmental tolerance, making them suitable for bioremediation of toxins. Effective-degradation of pollutants often depends on syntrophic-interactions within microbial communities, highlighting the importance of understanding their collaboration and communication for marine resource management. Here, we assess the current level of knowledge about marine microbiome research and highlight key issues within this developing field of study. The review aims to enhance understanding of marine microbiome's roles and potential uses in biogeochemical analysis, biotechnology, and environmental remediation, which could support sustainable and circular business models for future generations.
Hydrogen is one of the major pillars of the low-carbon economy, but its wide application is limited by difficulties in storage. Metal hydrides (MHs) represent excellent candidates for storage since they are characterized by high volumetric density, reversibility, and safety. This article provides a thorough and integrative review of novel generation MH storage technologies based on intermetallic, complex, magnesium, and chemical hydrides with special focus on thermodynamics and kinetics of these processes. Major restrictions, including high temperatures of desorption, slow kinetics, and cycling instability, are discussed in conjunction with current advanced approaches to address the issue of MH properties improvement, such as catalyst addition, nanoscale modifications, composite materials, and HEA engineering. Special attention is paid to innovative HEA materials, which can improve hydrogen mobility and binding energies due to composition engineering and lattice distortion. In addition, a rapid growth in the use of artificial intelligence algorithms for the fast development of new materials with tailored features and accurate hydrogen storage property prediction is described. Relevance to practice is supported by examples involving hydrogen fuel cells for transport and space applications. Although MH-based storage technologies still have certain drawbacks, such as heat management, material stability, and environmental aspects, they have great promise as reliable and scalable platforms for hydrogen storage.
Abiotic stressors such as drought, salinity, extreme temperatures, and heavy metal toxicity pose significant threats to global agricultural productivity. This review examines the diverse ways in which abiotic stress factors impact plant growth and survival. Also, it explores the emerging role of nanoparticles (NPs) in enhancing plant resilience and sustainability under such adverse conditions. Nanoparticles—owing to their unique physicochemical properties, high surface-to-volume ratio, and nanoscale dimensions—offer innovative solutions for improving nutrient uptake, water-use efficiency, and stress tolerance in plants. It highlights how various NPs (e.g., TiO₂, ZnO, Fe₃O₄) modulate stress-responsive pathways, including the activation of antioxidant enzymes and hormonal regulation, to mitigate oxidative damage and enhance photosynthetic efficiency. While the benefits of nanoparticle applications are promising, this article also addresses potential risks, including environmental accumulation, microbial toxicity, and genotoxicity. The paper concludes by emphasising the need for regulated, sustainable use of nanotechnology in agriculture and calls for further interdisciplinary research to optimise nanoparticle formulations and application strategies for climate-resilient farming.
