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NOTICE
> Board > Notice| [VSI] Soft Computing for Modern Engineering: Addressing Environmental Challenges Submission deadline: 25 April 2025 | ||
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Soft Computing for Modern Engineering: Addressing Environmental ChallengesSubmission deadline: 25 April 2025
In the face of pressing environmental concerns and the need for responsible resource management, achieving sustainable engineering practices has become a central focus across various industries. Traditional engineering approaches often struggle with the inherent complexities of these challenges, which frequently involve multi-criteria decision-making and the need to balance environmental, economic, and social objectives. This special issue, titled "Soft Computing for Modern Engineering: Addressing Environmental Challenges", proposes to explore the growing potential of soft computing techniques as powerful tools to bridge this gap. Soft computing, encompassing methodologies like fuzzy logic, neural networks, and evolutionary algorithms, offers a unique ability to handle uncertainty and ambiguity - characteristics that are prevalent in real-world sustainability problems. Guest editors: Dr. Masoomeh Mirrashid Prof. Abdollah Shafieezadeh Prof. Hosen Naderpour Associate Prof. Mahdi Kioumarsi Special issue information: This special issue will welcome original research articles and reviews that showcase the innovative application of soft computing methodologies in sustainable engineering. Potential topics include, but are not limited to: Energy Systems Optimization: Leveraging fuzzy logic and neural networks for optimizing energy consumption in buildings and renewable energy integration. Sustainable Materials Development: Utilizing evolutionary algorithms and fuzzy sets for designing eco-friendly materials and optimizing manufacturing processes. Life Cycle Assessment (LCA): Employing machine learning techniques to analyze the environmental impact of products and systems throughout their life cycle. Risk Management and Decision Support: Developing hybrid fuzzy-neural models for assessing environmental risks and supporting sustainable decision-making in engineering projects. Smart Infrastructure Systems: Implementing soft computing techniques for intelligent control and optimization of sustainable infrastructure systems like transportation networks and water management. Multi-Objective Optimization: Exploring how soft computing can handle multiple, often conflicting, objectives in sustainable engineering problems. This could involve optimizing cost, environmental impact, and performance simultaneously. Uncertainty Modeling and Management: Highlighting the application of soft computing for dealing with inherent uncertainties and complexities in sustainability challenges. This could involve fuzzy logic for representing imprecise data or probabilistic models for risk assessment. Data-Driven Sustainability Analysis: Showcasing the use of soft computing techniques for analyzing large datasets related to sustainability. This could include machine learning for identifying patterns in energy consumption data or using natural language processing to analyze sustainability reports. Soft Computing for Policy and Regulation Development: Exploring how soft computing can inform the development of sustainable policies and regulations. This might involve using evolutionary algorithms to design incentive programs for sustainable practices or employing fuzzy logic to assess environmental compliance. Soft Computing for Education and Awareness: Investigating the role of soft computing in promoting sustainability education and raising awareness. This could involve developing interactive learning platforms or using natural language generation to create engaging sustainability content. Social Sustainability: Encouraging submissions that explore the intersection of soft computing and social aspects of sustainability, such as promoting equitable access to resources or fostering community engagement in sustainable development projects. Life Cycle Thinking: Highlighting the use of soft computing for assessing the environmental, social, and economic impacts of products and systems throughout their entire life cycle. This could involve combining LCA methodologies with soft computing techniques. Sustainable Urban Development: Welcoming research on how soft computing can contribute to creating sustainable and resilient cities. This could encompass traffic management systems, resource optimization in buildings, or smart grid applications. Circular Economy: Exploring the use of soft computing for optimizing resource use and promoting circular economy principles in engineering design and manufacturing. This might involve designing for disassembly and recyclability or optimizing waste-to-resource conversion processes. Climate Change Mitigation and Adaptation: Showcasing how soft computing can contribute to addressing climate change challenges. This could involve developing models for climate change impact assessment, designing strategies for mitigation and adaptation, and optimizing resource allocation for climate action initiatives. Sustainable Concrete: Utilizing soft computing techniques to optimize the design, performance, and sustainability of concrete as a building material. This could include employing genetic algorithms and fuzzy logic to design low-carbon concrete mixes, using neural networks to predict the durability and lifespan of concrete structures under varying environmental conditions, or applying machine learning methods to enhance the recycling and reuse of concrete materials. Manuscript submission information: Important date:
Paper submissions for the special issue should follow the submission format and guidelines for regular papers and be submitted at Editorial Manager®.
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| 이전글 | [Ifaei박사, Sust Cities Soc./IF=10.6 JCR2%]Optimal Hybrid Renewable Microgrids via Energy Demand Control Using Media Platforms(미디어 플랫폼-에너지수요제어-최적신재생에너지마이크로그리드 시스템) | |
| 다음글 | [특허 출원, 우태용 통합, 허성구 박사] 분리막 물리-생물-화학 오염 통합모델기반 막 최적 세정 시기 예측 시스템 및 예측방법 (Membrane-informed multi-mechanistic predictive maintenance: Membrane PM)) | |