International Journal of Engineering Systems Modelling and SImulation

Scope & Guideline

Connecting Researchers to the Cutting Edge of Systems Modelling

Introduction

Explore the comprehensive scope of International Journal of Engineering Systems Modelling and SImulation through our detailed guidelines, including its aims and scope. Stay updated with trending and emerging topics, and delve into declining areas to understand shifts in academic interest. Our guidelines also showcase highly cited topics, featuring influential research making a significant impact. Additionally, discover the latest published papers and those with high citation counts, offering a snapshot of current scholarly conversations. Use these guidelines to explore International Journal of Engineering Systems Modelling and SImulation in depth and align your research initiatives with current academic trends.
LanguageEnglish
ISSN1755-9758
PublisherINDERSCIENCE ENTERPRISES LTD
Support Open AccessNo
CountrySwitzerland
TypeJournal
Convergefrom 2008 to 2024
AbbreviationINT J ENG SYST MODEL / Int. J. Eng. Syst. Model. Simul.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressWORLD TRADE CENTER BLDG, 29 ROUTE DE PRE-BOIS, CASE POSTALE 856, CH-1215 GENEVA, SWITZERLAND

Aims and Scopes

The International Journal of Engineering Systems Modelling and Simulation aims to advance the field of engineering systems through innovative modelling techniques and simulation methodologies. The journal provides a platform for researchers to share their findings on various engineering domains, emphasizing the integration of advanced technologies and data-driven approaches.
  1. Engineering Systems Modelling:
    Focus on developing and applying models to represent complex engineering systems, facilitating understanding and optimization of performance.
  2. Simulation Techniques:
    Emphasis on the use of simulation tools and techniques to analyze and predict system behaviors under various scenarios.
  3. 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.
  4. Renewable Energy Systems:
    Research on the modeling and simulation of renewable energy sources and their integration into existing systems, addressing sustainability challenges.
  5. IoT and Smart Systems:
    Exploration of Internet of Things (IoT) applications and smart systems, focusing on real-time data processing and automation.
  6. Control Systems:
    Investigation into advanced control systems, including PID and fuzzy logic controllers, for improving system efficiencies and stability.
  7. Healthcare Applications:
    Development of models and simulations aimed at improving healthcare technologies, including predictive analytics and rehabilitation systems.
The International Journal of Engineering Systems Modelling and Simulation has witnessed the emergence of several themes that reflect contemporary challenges and advancements in engineering. The following sections highlight these trending areas, showcasing the journal's responsiveness to current technological and societal needs.
  1. 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.
  2. 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.
  3. Sustainable Energy Solutions:
    Research on renewable energy systems and hybrid energy sources is trending, reflecting the global push towards sustainability and energy efficiency.
  4. 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.
  5. 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

While the journal has consistently focused on several core areas, there are emerging indications that certain themes are losing traction in recent publications. The following outlines these waning scopes and their potential implications.
  1. 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.
  2. Basic Statistical Methods:
    Basic statistical techniques are being overshadowed by more sophisticated machine learning approaches, indicating a shift towards more complex data analysis methods.
  3. 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.
  4. 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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