In order to facilitate effective communication between the V2V infrastructure, Vehicular Ad hoc Networks (VANETs) are utilized. Problems with routing, security, and node management are now plaguing VANETs that use vehicle-to-vehicle communication. New avenues for investigation into VANET routing, security, and mobility management have opened up because to intelligent transportation systems. Optimal routing for targeted traffic scenarios is one of the main issues in VANETs. Because VANET vehicles are constantly moving at high speeds, traditional protocols like AODV, OLSR, and DSDV are not suitable for this network. In a similar vein, swarm intelligence routing algorithms like Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have had some success in optimizing routing in VANET scenarios involving dense, sparse, and realistic traffic. Furthermore, most metaheuristics methods have issues with slow convergence speed, premature convergence, and becoming stuck in local optima. Hence, a new metaheuristic approach to selecting the cluster head is suggested in the study, which employs an improved wild horse optimization algorithm (IWHO). The social behaviour of wild horses served as an inspiration for the development of IWHO. The ethics of the horse informed the proposed approach. The next step is to cluster the vehicles according to the reliability of linkages criteria. Subsequently, a maintenance phase is suggested for the purpose of redistributing vehicles within the clusters and updating the cluster heads. Lastly, a MATLAB simulation is run on a real-life urban setting to assess the efficacy of the proposed strategy. A 76% decrease in change rate is indicative of improved stability, while a 37% rise in throughput and a 19% decrease in average latency are indicators of improved performance.

A numerical model of a Gas Metal Arc Welding (GMAW)-based Wire Arc Additive Manufacturing (WAAM) process was developed using the Abaqus software, with validation performed against experimental data from existing literature. The model was employed to investigate the influence of heat input and cooling time on residual stress distribution, with particular focus on longitudinal residual stress. Minimal effect was observed with increasing heat input, whereas cooling time significantly affected stress distribution. The impact of unclamping was also examined. It was determined that for heat inputs of 4000 W and 4500 W, longitudinal residual stress decreased by approximately 10% after unclamping. In contrast, for a heat input of 5000 W, longitudinal residual stress increased by 12% following unclamping. Residual stress was found to accumulate predominantly at the interface between the substrate and the deposition wall. This study provides critical insights into the thermal and mechanical behavior of WAAM processes, contributing to a deeper understanding of stress management and control in additive manufacturing of B91 steel.

All of the applications that are used in industrial processes require solutions that have a particular chemical strength of the fluids or chemicals that are being under consideration for analysis. When a full-strength solution is combined with water in the proportions that are needed, it is possible to produce the particular concentrations that are wanted. The regulation of the concentration of hydrogen peroxide which produced in an electrolysis process has been investigated over the course of this article. An examination of the impact that various controllers, such as P, PI, PID, and fuzzy logic controllers, have on the process model is presented in this work with the help of MATLAB/SIMULINK as a simulation program. Using fuzzy logic controllers showed that the rising time dropped to 0.3 seconds and the settling time to 0.4 seconds, with no overshoot or undershoot.

The development of green farmhouse technology is crucial for advancing sustainable agricultural practices in China. However, the comprehensive promotion and effective implementation of green farmhouse construction are significantly hindered by the underdevelopment and immaturity of the required technologies. This study aims to identify and analyze the key factors that impede the development of green farmhouse technology and to elucidate the interrelationships among these factors. A systematic literature review was conducted to determine the primary barriers to green farmhouse technology development. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was employed to examine the interdependencies among these factors, providing insight into their mutual influence and centrality. Subsequently, Interpretive Structural Modeling (ISM) was applied to establish a hierarchical structure, revealing the multi-level relationships among the identified barriers. Finally, the Multiplication of Cross-Impact Matrices (MICMAC) analysis was utilized to further categorize the factors based on their driving power and dependence. The findings indicate that the development of green building materials, research and development (R&D) funding, and technological expertise are the core factors impeding the advancement of green farmhouse technology. These barriers were classified into six hierarchical levels and grouped into four categories: autonomous, dependent, linked, and independent factors. Through the combined application of DEMATEL, ISM, and MICMAC, a comprehensive understanding of the hierarchical structure and the interrelationships among these barriers was achieved. The factors were further categorized into three groups: budget and funding constraints, green farmhouse technology R&D challenges, and technology promotion and selection obstacles. The insights derived from this study provide a theoretical foundation for developing strategies to overcome these impediments, thereby facilitating the broader adoption of green farmhouse technology in China.

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In the gas and industry, erosion that is brought on by particles in pipe bends is a severe issue that can lead to failure or equipment malfunction. The computational fluid dynamics (CFD) approach is primarily utilized in the presented study in order to investigate the erosion distributions as well as particle trajectories in pipe bends under various influencing conditions. Throughout upstream petroleum production activities, crude oil as well as eroded sand from formation zones is frequently transported together via pipes up to flow stations and between flow stations and pipe. The rotator fin is propelled by flow momentum in the stream-lines which are particle-laden flow pipe walls, particularly at the elbows, causing erosive damages, which could result in costly and disastrous system failure. Thus, calculating the erosion rate while the system is operating is essential to predict failures and preventing them. Of all fittings used in the piping systems, the elbows are the most prone to experience erosions brought on by oil-carried rotator fins that veer off course and strike the walls as they pass through the bent portions of elbows. The numerical simulation-based erosion prediction model was used in order to calculate relative erosion severity so as to lessen erosive damage caused through the solid rotator fin. Physical features such as particle tracking, flow turbulence, and erosion simulation were merged in this work to create the potentials needed to fully represent the present issue. The computational simulation related to crude oil flow offers comprehensive insights, but it also allows for the avoidance of significant expenses and laborious attempts associated with conventional experiments. The new analysis provides invaluable physical information that may be utilized to assess oil recovery and employ the model as an alternate particle-laden flow management tool. Additionally, it might pinpoint limiting processes and elements; develop a computer-aided tool to optimize and design future pipe systems for increasing their lifetime by enhancing their erosion resistance, which would undoubtedly save a significant amount of cost and time.

Amid growing concerns over global climate change and the need for sustainable infrastructure development, remote communities such as Rigolet in Newfoundland and Labrador (NL), which primarily rely on diesel generators, face unique challenges and opportunities. This study proposes a transition to a hybrid energy system (HES) that integrates wind and solar energy with battery storage and diesel generator backups. The feasibility and implications of this transformation in Rigolet were assessed using HOMER Pro software, contrasting it with the current diesel-centric model. The feasibility, environmental impact, and economic implications of implementing a HES in Rigolet were thoroughly examined. The methodology employed includes a detailed simulation and optimization of the HES configuration suitable for 125 households with a population of 327. The findings reveal that integrating wind and solar electricity with the existing diesel infrastructure, coupled with battery storage, reduced diesel consumption by 352 tons per year and Carbon Dioxide (CO₂) emissions by 929 tons per year. Additionally, other pollutants such as Carbon Monoxide (CO), Particulate Matter (PM), Sulfur Dioxide (SO₂), and Nitrogen Oxide (NO) were significantly reduced. The proposed system demonstrates a reasonable Net Present Cost (NPC) of \$5.17 million with a Levelized Cost of Energy (LCoE) of \$0.22/kWh. This shift towards a HES not only illustrates significant environmental advantages and an increase in the percentage of renewable energy but also provides economic benefits through cost reductions over the long term compared to the existing diesel-dependent configuration. The proposed system provides a reliable and sustainable energy solution for Rigolet, presenting a replicable and innovative model for other similar remote locations aiming for a greener future.

Container vessel accidents risk maritime safety and the environment, and understanding their causes and consequences is vital to developing effective preventive measures. This study analyzes the distribution of latent factors and active events related to container vessel accidents by applying the Human Factors Analysis and Classification System (HFACS) derived NASAFACS framework. The study employs a varied dataset comprising different types of container vessel accidents that occurred worldwide from 2010 to 2021. Findings suggest that latent factors, i.e., 'Preconditions,' are the predominant causative agents behind container vessel accidents, followed by 'Acts,' which involve active events leading to them. Damage to vessels is usually the most common outcome, and container loss and environmental pollution are sizeable. Collision incidents frequently involve both latent factors and active errors, while fire incidents typically are solely driven by latent ones; other accident types, including heavy weather damage, grounding, and allision incidents, show evidence of both latent and active factors; heavy weather damage incidents tend to exhibit higher incidences of environmental pollution than other accident types. This research offers unique insight into container vessel accidents, underlining the need for enhanced securing practices, accurate cargo declaration, and stricter cargo stowage compliance to improve safety and reduce pollution.