Vestnik Sankt-Peterburgskogo Universiteta Seriya 10 Prikladnaya Matematika Informatika Protsessy Upravleniya

Scope & Guideline

Cultivating Interdisciplinary Research for Complex Problem Solving

Introduction

Welcome to the Vestnik Sankt-Peterburgskogo Universiteta Seriya 10 Prikladnaya Matematika Informatika Protsessy Upravleniya information hub, where our guidelines provide a wealth of knowledge about the journal’s focus and academic contributions. This page includes an extensive look at the aims and scope of Vestnik Sankt-Peterburgskogo Universiteta Seriya 10 Prikladnaya Matematika Informatika Protsessy Upravleniya, highlighting trending and emerging areas of study. We also examine declining topics to offer insight into academic interest shifts. Our curated list of highly cited topics and recent publications is part of our effort to guide scholars, using these guidelines to stay ahead in their research endeavors.
LanguageMulti-Language
ISSN1811-9905
PublisherST PETERSBURG UNIV PRESS
Support Open AccessNo
CountryRussian Federation
TypeJournal
Convergefrom 2017 to 2024
AbbreviationVESTN ST PET U-P MAT / Vestn. St.-Peterbg. Univ. Ser. 10 Prikl. Mat. Inform. Protsessy
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address6TH LINE VO, 11, ST PETERSBURG 199004, RUSSIA

Aims and Scopes

The journal 'Vestnik Sankt-Peterburgskogo Universiteta Seriya 10 Prikladnaya Matematika Informatika Protsessy Upravleniya' focuses on the intersection of applied mathematics, computer science, and control processes. It aims to publish cutting-edge research that contributes to theoretical advancements and practical applications in these fields.
  1. Applied Mathematics:
    Research that develops mathematical theories and models to solve real-world problems, particularly in engineering, physics, and economics.
  2. Control Theory:
    Studies focused on designing and analyzing control systems, including optimal control, stability analysis, and feedback mechanisms.
  3. Data Science and Machine Learning:
    Exploration of algorithms and statistical models that enable computers to perform tasks without explicit instructions, with applications in various domains such as healthcare and environmental science.
  4. Computational Methods:
    Development and application of numerical algorithms for solving complex mathematical problems, particularly in simulations and modeling.
  5. Game Theory:
    Analysis of competitive situations where the outcome depends on the actions of multiple agents, with applications in economics, politics, and biology.
Recent publications reflect a dynamic evolution in the journal's research focus, highlighting emerging themes that resonate with current scientific and technological advancements.
  1. Neural Networks and AI Applications:
    There has been a notable increase in studies applying deep learning and neural networks to various fields, such as healthcare and agriculture, indicating a growing interest in artificial intelligence.
  2. Renewable Energy and Sustainability:
    Research focusing on control systems for renewable energy sources and sustainable practices is on the rise, aligning with global trends towards sustainability and energy efficiency.
  3. Cyber-Physical Systems and Security:
    Emerging topics related to the security and efficiency of cyber-physical systems indicate a growing concern for the integrity and safety of interconnected systems in various applications.
  4. Complex Systems and Network Analysis:
    An increasing number of papers address complex interactions within networks, whether in communication, economic systems, or control processes, reflecting a broader interest in interdisciplinary approaches.
  5. Stochastic Processes and Optimization:
    There is a growing trend towards exploring stochastic models and optimization techniques, showcasing the need for robust decision-making frameworks under uncertainty.

Declining or Waning

While the journal has consistently focused on several core themes, certain areas have shown a decline in publication frequency, suggesting a potential shift in research interest or relevance.
  1. Traditional Statistical Methods:
    There appears to be a decreasing emphasis on classical statistical analysis techniques, perhaps due to the rise of more advanced computational and machine learning approaches.
  2. Physical Systems Modeling:
    Research related to classical physical systems modeling (e.g., mechanics) is less frequently addressed, indicating a shift towards more abstract or computationally intensive models.
  3. Biological Systems Control:
    Topics focused specifically on biological or ecological control systems, such as epidemic modeling, have become less prominent, possibly due to saturation in this niche or a shift towards broader environmental applications.

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