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Research on Semantic Segmentation of Fish-Eye Images for Autonomous Driving
Full text linkAbstract: Fisheye cameras, valued for their wide field of view, play a crucial role in perceiving the surrounding environment of vehicles. However, there is a lack of specific research addressing the processing of significant distortion features in segmenting fish-eye images. Additionally, fish-eye images for autonomous driving face the challenge of few datasets, potentially causing over fitting and hindering the model\u27s generalization ability.
Based on the semantic segmentation task, a method for transforming normal images into fish-eye images is proposed, which expands the fish-eye image dataset. By employing the Transformer network and the Across Feature Map Attention, the segmentation performance is further improved, achieving a 55.6% mIOU on Woodscape. Additionally, leveraging the concept of knowledge distillation, the network ensures a strong generalization based on dual-domain learning without compromising performance on Woodscape (54% mIOU)
Case Study of Optimized Cascaded Phase Change Thermal Energy Storage Unit
Full text linkPhase change materials are paid increasing attention by the scholars in the past decades. For maintaining a relatively constant renewable energy output as a competent alternative for fossil fuel replacement, phase change storage unit are widely studied. However, for better pursuit of thermal energy performance and energy efficiency, only single staged phase change storage unit is not enough for increasing thermal requirements. Therefore, a triple staged phase change thermal energy storage unit have been proposed in this study. Meanwhile, correspondent comparison with single staged counterpart have been conducted along with related optimization. It has been concluded that the proposed thermal storage unit achieved 334.95 J/s & 41.54% / 186.37 J/s & 53.22 % in thermal energy exchange rate & exergy efficiency during endothermic/exothermic respectively. 3/21.4 L/min are the optimal circulating heat transfer fluid flowrates for endothermic/exothermic (better discharging rate)/ exothermic (better energy efficiency). Moreover, 100/9/15 are the best temperatures of circulating water for endothermic/ exothermic (better discharging rate)/ exothermic (better energy efficiency)
Regional Strategies for PV-Based Sustainable Energy in GCC and Europe
Full text linkThis study analyzes the feasibility of the production of solar power as a sustainable solution to the climate and energy crisis, focusing on two areas in particular: the Gulf Cooperation Council countries and Europe. Focused attention goes to emerging economies in analyzing how solar power can aid in reducing carbon dioxide emissions and attaining sustainable development. Comparative analysis is performed for the GCC region and Europe on the basis of unique solar system design ability and the performance of photovoltaic (PV) systems, taking into account realistic environmental and working parameters. In this comparative design research, mathematical equations were applied to determine the number of required solar panels to achieve determined energy efficiency in both regions. A novel mathematical modeling approach was designed and presented as new equations to achieve a correct estimate of the quantity of PV panels required from the energy requirement and a parameter called the Power Generation Factor (PGF). The practical design approach employed in the present work synchronizes the technical aspects of electrical system design with broader sustainable development goals, particularly for developing nations. By creating new mathematical equations for estimating how many PV panels would be necessary based on desired energy yields and incorporating the PGF, the study underscores the photovoltaic efficiency and panel requirement disparity between the GCC and European regions. These disparities owe to differences in solar energy generation and electricity generation factors between regions. The suggested approach is scalable and can be applied to other areas with comparable energy requirements, making it a tough and adaptable planning instrument for sustainable energy infrastructure deployment
Thermal Fluid and Chemical Analysis of Ionized Air Test Injection for Cold Start Emission Mitigation
Full text linkThe cold start period of an internal combustion engine (ICE) is a dominant source of hydrocarbon (HC) and carbon monoxide (CO) emissions. This paper analyzes an innovative ejector system for distributing ionized air directly into the exhaust system to accelerate pollutant oxidation. Analytical results demonstrate that the system achieves an ejection ratio ω = 0.187 at an exhaust temperature of 450 K, while a 180 W heater provides the necessary thermal compensation to maintain the mixture temperature above the 380 K activation threshold. Experimental validation confirmed that ionized air initiates low-temperature oxidation (afterburning), evidenced by a 5.0% vol. increase in CO2 concentration. This synergistic fluid-thermal and chemical strategy reduces the catalyst’s critical inactivity period from 120 s to 85 s, offering highly efficient method for meeting stringent emission regulations
Artificial Intelligence and Economic Security in EU Macro-Level Smart Energy Systems: A Sustainability-Driven Governance Framework
Full text linkThis study develops a governance-centered perspective on EU macro-level smart energy systems by linking artificial intelligence to economic security under sustainability pressures, volatility, and geopolitical stress. The core problem addressed is the absence of a comparative analytical framework that connects AI functions to economic security while preserving institutional diversity and trade-offs across EU member states. The objective is to conceptualize economic security as an interdependent governance configuration conditioned by AI-enabled capabilities. The research applies a conceptual–analytical governance modelling approach. Economic security is decomposed into affordability, supply resilience, market stability, innovation capacity, social vulnerability, and fiscal exposure. Core AI functions are mapped by governance role and institutional locus, combined with ideal-type archetype construction and risk–control calibration linking AI-induced risks to oversight, accountability, and resilience mechanisms. The findings show that AI shapes economic security primarily through governability rather than efficiency gains alone. Distinct governance configurations emerge, reflecting systematic trade-offs between resilience-building, market efficiency, social protection, and fiscal discipline. AI-induced risks, such as opacity, automation bias, and cyber vulnerability, function as direct economic security channels requiring explicit governance controls. The study is framework-building and does not provide empirical estimates. Archetypes may overlap in practice, and macro-level analysis masks subnational and sectoral heterogeneity, requiring future operationalization, empirical clustering, and multi-level validation. The framework offers practical value for comparative benchmarking and policy design by aligning AI deployment with accountability, interoperability, cybersecurity, and fairness requirements. Its originality lies in repositioning AI as a governance-conditioning variable and integrating sustainability, security, and systemic risk into a unified comparative architecture
Exploring Corporate Social Responsibility Enhancement with AI Strategies into Business Development: Literature Review
Full text linkThis a qualitative research approach investigates to explore how corporate social responsibility enhancement with artificial intelligence strategies into business development and discover the strategies that improve the CSR initiatives by assisting AI tools. The literature on AI-driven CSR that has emerged between 2018-2025 is reviewed in this study with a focus on how it affects customer satisfaction, business development, and digital leadership. AI technologies also provide businesses with cutting-edge capabilities for transparent reporting, real-time environmental monitoring, and predictive analytics to foresee environmental and social issues through rely on AI processes and adopting strategies that enhance and develop the businesses.
So that, through this research which recommended to integrate AI into CSR initiatives can boost a company’s reputation, data driven decision making, and positioning in the market. Also, conducting more future research to examine how AI strategies help to enhance CSR initiatives in various industries and sectors which to develop the businesses
AI-Driven Engineering for Urban Problem Solving: Recent Advances and Case Studies
Full text linkThe integration of Artificial Intelligence (AI) techniques within engineering processes has significantly reshaped the design and operation of urban environments. This paper investigates recent applications of AI-driven methods in engineering, with particular attention to urban-scale problems, including mobility optimization, infrastructure monitoring, and adaptive systems for resource management. We survey multiple case studies, illustrating how AI systems have been employed not merely as analytical tools but as active agents in autonomous decision-making pipelines. The review highlights emerging patterns, challenges, and opportunities in the deployment of AI within city-scale engineering ecosystems
Robust Global Sensitivity Analysis for Robust Design under Parameter Uncertainty
Full text linkAbstract: Based on the theory and method of robust design, the robust global sensitivity analysis of products or systems under parameter uncertainty is discussed. A basic idea of the author is to define the robust sensitivity that is the importance measure of the design variables for product functional response function distribution. The Taylor series of moments of the functional response function is carried out, and the approximate analytical formulas of robust global sensitivity are obtained by using the importance measure model based on variance. Finally, a numerical example is given to illustrate the operation principle of this method, and an engineering example is given to verify the correctness of this method
Autonomous Robotics Math Curriculum Development Using C Coding Language to Increase Student Attitudes and Learner Outcomes
Full text linkAbstract: Educational robotics is increasingly becoming incorporated into K12 instructional curriculum. The addition of autonomous robotics into mathematics lessons increases student engagement and attitudes towards robotics and STEM. This mixed methods study provides educators with an autonomous robotics curriculum, developed in C coding language, to increase learner attitude outcomes towards robotics and STEM. According to research from Vollstedt et al. (2007) as society progresses, students need to increase their knowledge of science, mathematics, engineering, and technology (STEM) to compete with the rest of the world and to efficiently utilize the new technologies that are introduced. This study was conducted at a STEM school in a small suburb of Boise, Idaho. Thirty-two fifth grade students participated in the study incorporating qualitative observations and quantitative surveys. The study concluded that coding using C coding language is one way of increasing attitudes towards robotics and STEM. Future curriculum development and research using autonomous robotics is needed to provide educators with tools to increase learner attitude outcomes towards robotics and STEM
Design Principle and Development Trends of Silicon-Based Anode Binders for Lithium-ion Batteries: A Mini Review
Full text linkAbstract: Silicon (Si), recognized as a promising alternative material for the anodes of lithium-ion batteries, boasts a high theoretical specific capacity and abundant natural availability. During the preparation of silicon-based anodes, binders play a pivotal role in ensuring the cohesion of silicon particles, conductive agents, and current collectors. The structure and performance of these binders are critical for the mechanical stability, electrical conductivity, and stress dissipation capacity of the anodes. This review initially outlines the structural characteristics of various binders, including linear, branched, and three-dimensional cross-linked types. It then delves into the relationship between the structure and properties of these binders in the context of their application in high-performance lithium-ion batteries, focusing on their mechanical properties, electrical conductivity, and self-healing capabilities. Particular attention is given to the design strategies for binders that facilitate stress dissipation, with an emphasis on integrating multifunctional polymer binders renowned for their superior conductive and self-healing features. Such binders contribute to the formation of a robust three-dimensional network structure via multiple bonding mechanisms, including chemical, non-covalent, and coordination interactions. This configuration significantly enhances the adhesion between silicon particles, thereby facilitating the efficient dissipation of stress, which is a key aspect for ensuring the long-term cycling stability of lithium-ion batteries. Lastly, the paper explores future development directions for silicon anode binders, advocating for a thorough investigation into the synergy of diverse structural and functional combinations, with the aim of advancing the performance and practical application of silicon-based lithium-ion batteries