Journal of Applied Mathematics and Computing

Scope & Guideline

Transforming Ideas into Computational Solutions

Introduction

Delve into the academic richness of Journal of Applied Mathematics and Computing with our guidelines, detailing its aims and scope. Our resource identifies emerging and trending topics paving the way for new academic progress. We also provide insights into declining or waning topics, helping you stay informed about changing research landscapes. Evaluate highly cited topics and recent publications within these guidelines to align your work with influential scholarly trends.
LanguageEnglish
ISSN1598-5865
PublisherSPRINGER HEIDELBERG
Support Open AccessNo
CountryGermany
TypeJournal
Convergefrom 1997 to 2024
AbbreviationJ APPL MATH COMPUT / J. Appl. Math. Comput.
Frequency6 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressTIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY

Aims and Scopes

The Journal of Applied Mathematics and Computing focuses on the development and application of mathematical theories and computational techniques to solve real-world problems across various fields.
  1. Mathematical Modelling:
    The journal emphasizes the creation and analysis of mathematical models that describe complex phenomena, particularly in fields such as epidemiology, ecology, and engineering.
  2. Numerical Analysis:
    A core focus on developing and improving numerical methods for solving differential equations, optimization problems, and integral equations, often involving advanced techniques such as finite element methods and spectral methods.
  3. Control Theory:
    Investigating control strategies for dynamic systems, particularly in relation to stochastic processes and systems influenced by delays or perturbations.
  4. Graph Theory and Coding Theory:
    Exploring properties of graphs and their applications in coding theory, including the study of various types of codes and their implications for information theory and network design.
  5. Fractional Calculus:
    Research into fractional derivatives and their applications in modeling phenomena that exhibit memory and hereditary properties, including applications in physics and engineering.
The journal has adapted to emerging trends and themes that reflect current research interests and technological advancements.
  1. Epidemiological Modeling:
    Significant attention is being given to mathematical models that address infectious diseases, particularly in light of the COVID-19 pandemic, focusing on dynamics, control strategies, and vaccination impacts.
  2. Stochastic Systems and Uncertainty Quantification:
    An increasing number of papers are focusing on stochastic models that incorporate uncertainty, reflecting a growing interest in how randomness affects system dynamics.
  3. Fractional Differential Equations:
    Research into fractional calculus has gained momentum, especially in modeling processes with memory effects, highlighting its applicability across various scientific domains.
  4. Advanced Numerical Methods:
    Emerging trends include the development of high-order numerical methods and adaptive algorithms that improve accuracy and efficiency in computational solutions.
  5. Multi-criteria Decision Making and Fuzzy Logic:
    There is a rise in research involving fuzzy logic and multi-criteria decision-making frameworks, emphasizing their relevance in complex decision environments.

Declining or Waning

While the journal has consistently published a wide range of topics, certain areas have seen a noticeable decline in focus over recent years.
  1. Traditional Optimization Techniques:
    There has been a shift away from classical optimization methods in favor of more advanced techniques that incorporate elements of machine learning and adaptive algorithms.
  2. Basic Graph Theory:
    Basic graph theory topics have become less prominent, with a noticeable trend towards more complex structures and applications, such as fuzzy graphs and quantum codes.
  3. Static Mathematical Models:
    There is a decreasing emphasis on static models that do not account for dynamics or time-varying parameters, as the trend moves towards dynamic modeling that incorporates temporal changes.

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