Mahatma Gandhi Institute of Technology

This study investigates the parametric optimization of friction stir welding (FSW) for joining AA1100 alloys using the (MOGOA) multi-objective grasshopper optimization algorithm the non-dominated sorting genetic algorithm II (NSGA-II). Regression equations was employed to determine to forecast hardness and tensile strength of frictional stir welding joints, whereas tensile testing and hardness measurements were conducted to acquire the empirical evidence. The SECA–COCOSO framework, together with the empirical model, was utilized to structure experimental methodology, while empirical results and the adequacy of the predicted were evaluated through a systematic examination of difference. Five distinct instrument categories was evaluated for various amounts of input parametric rates. Optimum input parameters included a tool rotation speed of 1300 rpm, a welding speed of 60 mm/min, an axial force of 5.5 kN, and a cylindrical threaded tool pin, which demonstrated the maximum. The axial force emerged as the predominant input parameter affecting microhardness and the output tensile strength, succeeded by the tool pin shape and welding speed. NSGA-II demonstrated superior optimization relative to MOGOA. Fractography study revealed a ductile fracture in sample ‘2’, which had the highest UTS of 173.36 MPa and an improved Vickers hardness of 99.13 HV, whereas maximum hardness recorded were 93.75 HV in samples 30 throughout the empirical experiments.

The knowledge on thermodynamic irreversibility becomes indispensable in order to choose best design parameters of any thermal system. The main aim of this work is to assess thermal and hydrodynamic performance, and entropy generation analysis of a heat exchanger partially filled with aluminium metal foam. For the investigation, three different porous layer thicknesses (t) of 40, 60 and 80 mm varied from the wall side of the tube are considered. Four different pore densities of 10, 20, 30 and 45 PPI foam samples and their porosities ranging 0.90-0.95 are employed for the examination. The air flow Reynolds (Re) number is varied from 4500 to 20500. Local thermal non-equilibrium (LTNE) and Darcy-extended Forchheimer (DEF) flow models are employed in porous filled region of the heat exchanger. In the clear (non-metal foam) region of the heat exchanger, two-equations standard k-omega turbulence model is employed. Heat transfer enhancement ratio, performance evaluation criteria, 2nd law efficiency (eta 2nd) are discussed with respect to PPI and thickness of the foam sample. Additionally, total irreversibility associated with heat transfer and fluid friction increases with increasing flow Re number and found to be minimum for lower values of pore density of 30 PPI foam sample with porous layer thickness, t = 40 mm. The friction factor ratio (FFR) reduces marginally with increasing flow Re number and increases with increasing porous layer thicknesses of porous foams. Moreover, Bejan number (Be) reduces with increasing flow Re number and PPI's of metal foams. Further, for higher PPI with maximum porous layer thickness, the maximum entropy generation number (Ns) suggests that the process is more irreversible and as a result, a greater amount of energy becomes unavailable.

The incorporation of digital technologies into environmental management is critical for meeting modern challenges. Geographic Information Systems (GIS) and remote sensing provide detailed mapping of land usage and natural resources, whereas the Internet of Things (IoT) allows for real-time monitoring of air quality, water, and pollution. Big Data analytics and Artificial Intelligence (AI) improve decision-making by forecasting climate trends and maximizing conservation efforts. Blockchain enables transparency in sustainability programs like carbon credit certification and responsible supply chain tracking. Digital twins replicate environmental conditions to optimize resource consumption in smart cities and ecosystems. In the energy sector, digital platforms incorporate renewable sources such as solar, wind, and hydropower with smart networks to improve energy distribution. Precision agricultural instruments, such as drones, sensors, and self-driving tractors, improve crop yields by assessing soil health and water levels. Similarly, IoT-enabled irrigation systems save water by responding to changing weather conditions in real time. Advanced recycling technologies, aided by machine learning algorithms, improve garbage sorting, while robotic devices increase productivity in waste management facilities. These advancements extend to wildlife protection, where AI-powered cameras and drones monitor endangered species and prevent poaching. These digital developments encourage climate action, promote a circular economy, and help to achieve global sustainability goals. The United Nations Environment Program (UNEP) actively encourages projects such as the Coalition for Digital Environmental Sustainability, which aim to eliminate data gaps and accelerate progress towards the Sustainable Development Goals.

Terahertz (THz) communication is a promising enabler for next-generation wireless networks because it can support ultra-high data rates. However, severe path loss, molecular absorption, and high sensitivity to blockage significantly limit coverage and reliability. To address these challenges, this work proposes a RIS-assisted UAV positioning (RAVP) framework that integrates reconfigurable intelligent surfaces (RIS) with unmanned aerial vehicles (UAVs) and jointly optimizes RIS configuration and UAV deployment to enhance THz communications. RISs provide controllable reflections to improve propagation conditions, while UAVs enable flexible placement of RISs at advantageous locations. A reinforcement learning (RL)-based strategy that combines modified K-means clustering with gradient-based optimization coordinates user grouping, RIS phase-shift adaptation, and UAV positioning within a unified framework. Simulation results show consistent gains in link robustness, achievable data rate, and user connectivity across different network configurations compared with conventional THz systems without RISs or UAV-assisted optimization. These findings highlight the potential of coordinated RIS-UAV optimization for future 6G-enabled wireless networks, including smart-city and Internet of Things (IoT) applications.

Cases of fire in residential, industrial, and forest areas are still very dangerous to life and infrastructure because there is delayed detection and false alarm systems. A real-time smart fire detection and prevention system, which combines IoTbased multi-sensor networks with machine learning algorithms to achieve high-precision early warnings, are presented in this paper. The suggested framework uses temperature sensors, smoke sensors and gas sensors that are implemented on an edge-enabled IoT platform to continuously obtain environmental data. A trained supervised machine learning model is used to differentiate normal fluctuations and critical fire conditions, thus minimizing false alarms greatly. The system also includes a real-time visualization and emergency response coordination system powered by cloud-based assistance. Through experimental analysis, it is shown that the proposed method has high detection accuracy, low latency, and sound fire classification in a variety of operating conditions. Smart prevention module activates suppression measures and emergency signals automatically, thus providing an opportunity to suppress fire hazards within a short period of time. The findings reinforce the fact that the proposed AI-enabled IoT system can be used as a scalable, economical, and robust solution to next-generation smart fire safety applications using smart buildings and industries.