Volume 6.
Issue 03.

This study presents an evaluation of crash boxes focusing on analyzing and validating their energy absorption capacity. Crash boxes serve as crucial components in vehicle safety systems, designed to absorb kinetic energy during collisions and mitigate the impact forces on occupants and structures. The research involves a comprehensive analysis of crash box designs, material properties, and structural configurations using numerical simulations and experimental validation techniques. Various parameters, including geometry, material selection, and impact conditions, are investigated to assess their influence on the energy absorption capabilities of crash boxes. Experimental tests are conducted to validate the numerical models and evaluate the real-world performance of crash boxes under controlled impact scenarios. The findings provide valuable insights into optimizing crash box designs for enhanced energy absorption and improved vehicle safety.

Artificial Intelligence (AI) has emerged as a transformative technology for improving resource allocation, operational efficiency, and cost optimization across diverse industrial sectors. This study evaluates the impact of AI-powered resource allocation systems on project efficiency and cost reduction through a comprehensive literature-based comparative analysis. This study looks at the use of AI in energy systems, oil and gas, infrastructure and smart grid systems and focuses on machine learning, deep learning, anomaly detection and predictive analytics. It also introduces a cross-domain framework through which the impact of AI optimization models on the management of resources, the forecasting of demand, anomaly detection, and decision making can be evaluated. The results show that, as compared to traditional rule based systems, AI systems have a significant positive impact on prediction, resource consumption, operational costs, and the efficiency of the system overall. In addition to this, the integration of AI methods (statistical methods + deep learning) can be effectively utilized within complex and uncertain environments, and the increasing use of AI across systems within the industrial field demonstrates that resource optimization systems can be made to be both scalable and flexible. However, model generalization, insufficient data, and a lack of structure are some of the significant challenges that limit the adoption of these systems. AI systems for resource allocation show great potential for more positive project outputs and for the establishment of operational efficiency that is sustainable in both the short and long terms.

This study explores the optimization of passivation techniques for monocrystalline solar cells through the assessment of carbon content in silicon-carbon alloys. Passivation layers play a critical role in enhancing the efficiency and performance of solar cells by reducing recombination losses at the semiconductor surface. Silicon-carbon alloys offer a promising avenue for passivation due to their tunable properties and compatibility with existing manufacturing processes. By systematically varying the carbon content in silicon-carbon alloys, this research investigates its impact on passivation quality, surface recombination velocity, and photovoltaic device performance. Characterization techniques such as spectroscopic ellipsometry, surface photovoltage measurements, and photoluminescence imaging are employed to assess passivation layer thickness, interface quality, and carrier lifetime. The findings provide valuable insights into the role of carbon content in optimizing passivation effectiveness and offer pathways for enhancing the efficiency and stability of monocrystalline solar cells.

This study investigates the potential of utilizing electric school buses equipped with vehicle-to-grid (V2G) technology as a portable energy solution to enhance community resilience during and after power outages caused by natural disasters and shortages. The first objective of this research is to advance our understanding of disaster resilience, particularly during the critical response period—from the onset of the disruption event to the stabilization of recovery efforts. The second objective is to translate fundamental insights gained from this understanding into practical applications. To achieve these objectives, the study encompasses various analyses. Firstly, economic losses resulting from power outages are quantified, providing insights into the broader impacts of energy disruptions. Additionally, the performance of power grid systems is evaluated to assess their resilience and effectiveness in responding to and recovering from adverse events. Furthermore, the study includes calculations for mobilization (response) time, crucial for assessing the timeliness and efficiency of emergency response efforts. Finally, the viability of employing electric bus V2G technology as an emergency power solution is thoroughly analyzed. This encompasses assessing factors such as response time, load recovery or supply, system performance pre- and post-recovery, and other resilience metrics. By integrating these multifaceted analyses, this research aims to offer comprehensive insights into the potential of electric school buses as a resilient energy solution for communities facing power disruptions.