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On account of the highly dynamic topology of vehicular networks, network congestion and energy utilization are greatly increased, which directly affects the performance of VANETs. So, managing traffic and reducing energy consumption in the network becomes a challenging task in such huge mobility-based VANET networks. Thus, in this paper a new traffic and cluster-based network method is introduced, namely, Traffic-Aware Clustering based Routing Protocol (TACRP). The main aim of the approach is to improve traffic management in the network as well as to reduce energy consumption in it. In the constructed network, a Traffic Management Unit (TMU) is introduced to control the entire network traffic with the help of RSUs. Vehicles with similar speed and direction are grouped into a cluster to increase the network stability and help to reduce the energy consumption of the network. The clustering model provides principles associated with vehicles leaving the clusters, joining the clusters, cluster updates and inter-cluster communication, which makes the network more stable and reliable. For instance, in the proposed work the CH selection is based on centralization, weight, distance, and energy calculation. Such network settings facilitate successfully clustering of vehicles on the road. Simulation experimental analysis showed that the proposed TACRP routing protocol achieved better results in terms of energy efficiency, throughput, packet delivery ratio, and end to end delay of the network when compared with earlier methods, such as ECHS and NRHCS.

The Smart Home Energy Management System (SHEMS) presents an innovative solution for optimizing energy consumption in residential settings by harnessing the synergy between Internet of Things (IoT) technology and Machine Learning (ML) algorithms. SHEMS offers a comprehensive suite of functionalities including monitoring, controlling, and optimizing energy usage while identifying wastage within smart homes. Its architecture comprises IoT sensors for data acquisition, an IoT gateway for preprocessing and storing data, and an Energy Management System (EMS) empowered by ML infrastructure for feature extraction and data transformation. Notably, the incorporation of the Gradient Boosting (GB) mechanism imbues SHEMS with intelligence, enabling it to analyze intricate datasets, detect patterns, and make data-driven decisions regarding energy optimization. Through ML capabilities, SHEMS adapts to dynamic usage patterns, predicts future consumption trends, and identifies opportunities for energy savings. Facilitating seamless data flow from sensors to the EMS, advanced ML techniques drive intelligent decision-making for enhanced energy efficiency. Additionally, SHEMS provides users with actionable insights and user-friendly interfaces for informed energy management, promising significant improvements in energy efficiency, cost reduction, and sustainability. The results showcase the effectiveness of the Gradient Boosting (GB) algorithm in predicting energy consumption for smart homes, with a score of 0.95, RMSE of 6.8, and MAE of 5.2. The Gradient Boosting algorithm consistently outperforms other ML algorithms, including Simple Linear Regression, Decision Tree Regression, Random Forest Regression, K-nearest neighbor Regression, and Support Vector Machine Regression.

This study focuses on the production of stir-squeeze cast hybrid composites made of aluminium with reinforcements. Base metal AA6105 is combined with SiC/TiC. TiC is changed from 3 to 9 wt% while SiC is kept constant (3wt%).Nine specimens are created using the deviation of the stir casting parameters of Stirrer speed (SP) 350-550rpm, Stir-time (TI) 15-25 min, and Reinforcement (RE) 3-19 w.t% in accordance with L9 Taguchi's design. By applying pressure of 60 MPa during squeeze casting, reinforced composites are created. Both stir casting and squeeze cast specimens are examined for their mechanical characteristics, such as tensile strength (TS) and hardness (BHN). Based on elongation, tension, and strain, TS was assessed for both processes. The outcomes showed that SQC sample L6 obtained the maximum TS of 303.19 MPa while STC specimen only managed to reach 269.88 MPa. The STC method produced a hardness value of roughly 83.56 Hv, whereas the greatest hardness value of 94.73 Hv was obtained with SQC sample L9 (SP-550 rpm, TI-25 min & RE-6%).

This paper reviews the work in the areas of machine learning’s role in bolstering renewable energy within smart grids. As the global shift towards eco-friendly energy sources such as wind and solar gains momentum, the challenge lies in managing these unpredictable energy sources efficiently. Innovative learning techniques are emerging as potential solutions to these challenges, optimising the use and benefits of renewable energies. Furthermore, the landscape of energy distribution is evolving, with a growing emphasis on automated decision-making software. Central to this evolution is machine learning, with its applications spanning a range of sectors. These include enhancing energy efficiency, seamlessly integrating green energy sources, making sense of vast data sets within smart grids, forecasting energy consumption patterns, and fortifying the security of power systems. Through a comprehensive review of these areas, this paper highlights the potential of machine learning in paving the way for a greener, more efficient energy future.

This paper offers a comprehensive review of the advancements in the realm of renewable energy, specifically focusing on solid oxide fuel cells and electrolysers for green hydrogen production. The review delves into the significance of wind energy as a pivotal renewable energy source and underscores the importance of precise forecasting for efficient energy management and distribution. The integration of machine learning-based approaches, such as Support Vector Regression and Random Forest Regression, has shown promising results in enhancing the accuracy of wind energy production forecasts. Furthermore, the paper explores the broader landscape of renewable energy generation forecasting, emphasizing the rising prominence of machine learning and deep learning techniques. As the penetration of renewable energy sources into the electricity grid intensifies, the need for accurate forecasting becomes paramount. Traditional methods, while valuable, have encountered limitations, paving the way for advanced algorithms capable of deciphering intricate data relationships. The review also touches upon the inherent challenges and prospective research avenues in the domain, including addressing uncertainties in renewable energy generation, ensuring data availability, and enhancing model interpretability. The overarching goal remains the seamless integration of renewable sources into the grid, propelling us towards a greener future.

This study examines the difficulties in implementing sustainable building practices in the construction industry, with a particular emphasis on the lack of details and cost views as the main barriers. The Use of the material in concrete, combining environmental advantages as well as limitations, and utilizing recovered solid waste in geopolymer composites for sustainable building are all explored in this research. With regard to sustainable materials and technologies in the construction sector, the research studies provide a thorough overview that points the way for future investigation and implementation.

The integration of renewable energy and the optimization of energy use are crucial components of sustainable energy transitions aimed at mitigating climate change. Modern technology, notably the Internet of Things (IoT), offers a myriad of applications within the energy sector, spanning energy supply, transmission and distribution, and demand. IoT holds the potential to enhance energy efficiency, boost the share of renewable energy sources, and reduce the environmental impact of energy consumption. This review article synthesizes existing literature on IoT applications in Hydelenergy systems, with a particular focus on smart grids. Furthermore, it delves into the enabling technologies of IoT, including cloud computing and data analysis platforms. Challenges in deploying IoT in the energy sector, such as privacy and security concerns, are also addressed, with proposed solutions like blockchain technology. This review serves as a comprehensive resource for energy policymakers, economists, and managers, providing insights into IoT’s role in optimizing policymakers, economists, and managers, providing insights into IoT’s role in optimizing energy systems.

Water is the basis for human survival and socioeconomic development, but rapid population growth, rise in industries, and agriculture pose a threat to the quality and availability of freshwater resources. The greater part of international water treatment techniques seeks to ensure that the drinking water is safe through coagulation, flocculation, sedimentation, filtration, disinfection, and pH correction processes. While their reliability has been amassed over the years, these techniques have huge limitations in terms of scalability, efficiency, and ability to adjust to new, emerging contaminant risks. Some of the state-of-the-art technologies in terms of water treatment, discussed in this paper, include nanotechnology, membrane filtration, advanced oxidation processes, and biological treatments. Nanotechnology utilizes Nano adsorbents and catalytic materials to remove contaminants efficiently with antibacterial effects. On the other hand, advanced membrane filtration technology improves water flux and antifouling properties while raising contaminant rejection. AOPs use hydroxyl radicals to degrade organic and inorganic pollutants effectively. Biological treatments utilize microorganisms for biodegradation and hence are sustainable and effective. This paper therefore, discusses the outcomes of such innovative methods in terms of efficiency, application, and future potential to underline recent developments and future prospects of water treatment technologies. Application of these innovative approaches into water treatment frameworks will enhance water purification by surmounting the drawbacks of the conventional techniques that ensure reliable supplies of clean water globally. The paper has focused on in-depth analysis of the processes being developed in improving water quality and access with respect to global health and socio-economic development.

The study explores the pivotal role of Geographic Information Systems (GIS) in shaping marketing decisions, with a special emphasis on consumer behaviour patterns. Distinct studies are scrutinized, spanning diverse applications of GIS in marketing, from spatial consumer behaviour in small towns to the visualization of consumer sales promotions. The analysis encompasses the integration of GIS with methodologies such as spatial point pattern analysis, kernel density estimation, and RFID systems, offering insights into optimal retail site locations, consumer preferences in shopping centers, and the spatial distribution of data. The findings underscore GIS's capacity to enhance decision-making processes, offering a valuable resource for marketers seeking to leverage spatial intelligence for strategic advantage.

In the current study, the investigation of heat transfer and fluid flow Characteristics of Pure water when pass through a double tube heat exchanger (DTHX). This investigation has been conducted across various Reynolds Number to gain insights into their performance also conducted a computational fluid dynamics (CFD) simulation using the ANSYS-FLUENT 22 R1 software. The study employed mathematical models and thermophysical properties of nanofluids and water, which were sourced from existing literature. The analysis focused on comparing pure water, 1% Al2O3/H2O nanofluids. The investigation considered various operating variable as Reynolds Number and temperature across the inner, and outer tubes. Specifically, the Reynolds Number of a range of 2500 to 5500 at 80°C, and 2500 at 15°C for the respective tubes. Key findings are that friction factor for pure water, 1% alumina nf, 2% alumina nf, and 3% alumina nf is increased by 4.61%,11.42%,15.06% and 16.21% as compared to Gnielinski correlation in existing literature at a Reynolds Number of 2500 and this increase in friction factor is 5.66%, 13.79%, 18.03% and 19.61% respectively at Reynolds number of 5500. Nusselt number (Nu) for pure water, 1% alumina nf, 2% alumina nf, and 3% alumina nf is increased by 24.92%, 50.04%, 59.90% and 64.31% as compared to Gnielinski correlation in existing literature at a Reynolds Number of 2500 and this increase is 10.84%, 28.68%, 35.31% and 41.55% respectively at Reynolds number of 5500. The heat transfer coefficients (hi) for pure water, 1% alumina nf, 2% alumina nf, and 3% alumina nf is increased by 3.17%, 7.29%, 8.49% and 8.94% as compared to Gnielinski correlation in existing literature at a Reynolds Number of 2500 and this increase is 8.04%, 18.49%, 21.54% and 22.64% respectively at Reynolds number of 5500.