International Journal of Engineering Systems Modelling and SImulation
Scope & Guideline
Fostering Interdisciplinary Collaboration for Sustainable Engineering
Introduction
Aims and Scopes
- Engineering Systems Modelling:
Focus on developing and applying models to represent complex engineering systems, facilitating understanding and optimization of performance. - Simulation Techniques:
Emphasis on the use of simulation tools and techniques to analyze and predict system behaviors under various scenarios. - Machine Learning and AI Applications:
Incorporation of machine learning and artificial intelligence to enhance system modeling, prediction, and optimization, indicating a strong trend towards data-driven methodologies. - Renewable Energy Systems:
Research on the modeling and simulation of renewable energy sources and their integration into existing systems, addressing sustainability challenges. - IoT and Smart Systems:
Exploration of Internet of Things (IoT) applications and smart systems, focusing on real-time data processing and automation. - Control Systems:
Investigation into advanced control systems, including PID and fuzzy logic controllers, for improving system efficiencies and stability. - Healthcare Applications:
Development of models and simulations aimed at improving healthcare technologies, including predictive analytics and rehabilitation systems.
Trending and Emerging
- Machine Learning Innovations:
A notable increase in the application of machine learning techniques across various domains, including healthcare, finance, and environmental monitoring, indicating a growing trend towards data-centric solutions. - Smart Infrastructure and IoT Integration:
Emerging themes in smart city applications and IoT systems are gaining prominence, focusing on real-time data usage and automation for enhanced operational efficiency. - Sustainable Energy Solutions:
Research on renewable energy systems and hybrid energy sources is trending, reflecting the global push towards sustainability and energy efficiency. - Deep Learning Advances:
The rise of deep learning methodologies in image processing and predictive analytics showcases a significant shift towards more advanced AI techniques for tackling complex problems. - Real-time Data Processing:
An increasing focus on real-time data processing and predictive analytics indicates a demand for systems capable of dynamic responses to changing environments.
Declining or Waning
- Traditional Mechanical Systems:
Research focused on conventional mechanical engineering systems appears to be declining, as the journal leans more towards advanced computational methods and smart technologies. - Basic Statistical Methods:
Basic statistical techniques are being overshadowed by more sophisticated machine learning approaches, indicating a shift towards more complex data analysis methods. - Single-Disciplinary Approaches:
There is a decreasing emphasis on research that does not integrate interdisciplinary methods, as the journal increasingly promotes studies that combine multiple engineering disciplines. - Static Modelling Techniques:
Static models are becoming less prevalent, with a noticeable shift towards dynamic models that can accommodate real-time changes and interactions in systems.
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