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State-of-the-Art Dynamical Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (20 May 2025) | Viewed by 1986

Special Issue Editor


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Guest Editor
Department of Computer Science and Automatics, University of Bielsko-Biala, Willowa 2, 43-309 Bielsko-Biala, Poland
Interests: dynamical systemss; system analysis; medical informatics; data mining; cybernetics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This forthcoming Special Issue, titled "State-of-the-Art Dynamical Systems", is poised to explore the intricate interplay between theory and practice in the realm of dynamical systems. Encompassing a broad spectrum of theoretical and practical aspects, this issue delves into systems where time is the primary driver.

Theoretical discussions within this Special Issue will span diverse domains, including spatial and temporal systems such as reaction-diffusion systems, characterized by a plethora of differential and difference equations. Furthermore, the scope extends to equations on time scales, delayed systems, and fractional derivatives. Time series analysis and digital signal processing represent pivotal areas of inquiry, reflecting the multifaceted nature of dynamical systems.

A key focus of this Special Issue lies in the qualitative analysis of dynamic systems, delving into stability, identification, observation, and control issues, particularly in the face of uncertainty. This emphasis underscores the significance of understanding the underlying dynamics for effective system management and decision making.

Practical applications showcased in this Special Issue will encompass a wide array of fields, including biomedicine, where insights into population dynamics, epidemiology, immunology, biochemical reactions, and pharmacokinetics hold profound implications. Moreover, the integration of dynamical systems theory into mechanical engineering, manufacturing, and management underscores its relevance across various industrial sectors. Additionally, the exploration of recurrent neural networks and reinforcement learning highlights the contemporary relevance of dynamical systems in advancing artificial intelligence and computational methodologies.

In summary, this Special Issue promises to offer a comprehensive exploration of the state-of-the-art in dynamical systems, bridging theoretical insights with real-world applications across diverse domains. By fostering interdisciplinary dialogue and collaboration, it aims to advance our understanding and utilization of dynamical systems in tackling contemporary challenges and fostering innovation.

Prof. Dr. Vasyl Martsenyuk
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • dynamical systems
  • difference-differential equations
  • time series
  • qualitative analysis
  • stability
  • population dynamics
  • mechanical engineering
  • control problems
  • recurrent neural networks

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Published Papers (1 paper)

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Research

14 pages, 3511 KiB  
Article
Application of Time-Series Modeling in Forecasting the Doctorate-Level Science and Technology Workforce
by Ho-Yeol Yoon and Hochull Choe
Appl. Sci. 2024, 14(19), 9135; https://doi.org/10.3390/app14199135 - 9 Oct 2024
Viewed by 1457
Abstract
The science and technology (S&T) workforce plays a crucial role in social development by promoting technological innovation and economic growth, as well as serving as a key indicator of research and development productivity and measure of innovation capability. Therefore, effective S&T workforce policies [...] Read more.
The science and technology (S&T) workforce plays a crucial role in social development by promoting technological innovation and economic growth, as well as serving as a key indicator of research and development productivity and measure of innovation capability. Therefore, effective S&T workforce policies must be established to enhance national competitiveness. This study proposes a time-series forecasting methodology to predict the scale and structural trends of South Korea’s doctorate-level S&T workforce. Based on earlier research and case data, we applied both the traditional time-series model exponential smoothing and the latest model Prophet, developed by Meta, in this study. Further, public data from South Korea were used to apply the proposed models. To ensure robust model evaluation, we considered multiple metrics. With respect to both forecasting accuracy and sensitivity to data variability, Prophet was found to be the most suitable for predicting the S&T doctorate workforce’s scale. The scenarios derived from the Prophet model can help the government formulate policies based on scientific evidence in the future. Full article
(This article belongs to the Special Issue State-of-the-Art Dynamical Systems)
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