Annals of Mathematical Sciences and Applications

Scope & Guideline

Exploring the Frontiers of Mathematical Innovation

Introduction

Delve into the academic richness of Annals of Mathematical Sciences and Applications 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
ISSN2380-288x
PublisherINT PRESS BOSTON, INC
Support Open AccessNo
Country-
Type-
Converge-
AbbreviationANN MATH SCI APPL / Ann. Math. Sci. Appl.
Frequency2 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressPO BOX 43502, SOMERVILLE, MA 02143

Aims and Scopes

The Annals of Mathematical Sciences and Applications focuses on advancing the field of mathematical sciences through innovative research and applications. The journal aims to publish high-quality articles that contribute to various mathematical theories and their practical implementations.
  1. Applied Mathematics:
    The journal emphasizes applied mathematical techniques across diverse fields, including engineering, physics, and biology, showcasing the relevance of mathematical theories in solving real-world problems.
  2. Numerical Analysis:
    A significant focus lies in numerical methods and algorithms, addressing computational challenges and developing efficient techniques for solving complex mathematical problems.
  3. Optimization and Control Theory:
    Research in optimization and control theory is prevalent, with studies exploring algorithms and frameworks for enhancing decision-making processes and system controls.
  4. Mathematical Modeling:
    The journal publishes works on mathematical modeling of phenomena in various disciplines, particularly those that involve dynamic systems and population dynamics.
  5. Theoretical Developments:
    Theoretical advancements in mathematical sciences, including new algorithms, mathematical proofs, and conceptual frameworks, are regularly featured, contributing to the foundational knowledge in the field.
Recent publications have highlighted several trending and emerging themes within the journal, reflecting the evolving landscape of mathematical sciences and their applications. These themes indicate areas of increased interest and innovation among researchers.
  1. Machine Learning and Deep Learning Applications:
    There is a notable increase in research integrating machine learning and deep learning with mathematical modeling, particularly in areas such as image processing, facial recognition, and optimization, showcasing the growing intersection of mathematics and AI.
  2. Fractional Calculus and Its Applications:
    The rise of fractional calculus in mathematical modeling, particularly for complex systems, indicates an emerging trend as researchers explore its implications in various fields, including physics and engineering.
  3. Uncertainty Quantification:
    Research focusing on uncertainty analysis and quantification in mathematical models, especially in biological and ecological contexts, is gaining traction, highlighting the importance of robust modeling in uncertain environments.
  4. Geometric and Topological Methods:
    Emerging studies on geometric and topological methods, particularly in applications related to medical imaging and data analysis, reflect an increasing interest in these areas within mathematical sciences.
  5. Computational Fluid Dynamics (CFD):
    There is a growing emphasis on computational techniques for fluid dynamics, particularly in relation to viscoelastic fluids and complex systems, indicating a trend towards practical applications of mathematical theories in engineering.

Declining or Waning

While the journal maintains a broad range of topics, certain themes have shown a decline in frequency or prominence in recent publications. This trend may indicate a shift in research priorities or emerging areas of focus.
  1. Classical Differential Equations:
    Research centered on classical differential equations has become less frequent, suggesting a possible shift towards more complex or generalized forms of differential equations in contemporary studies.
  2. Static Mathematical Models:
    There appears to be a waning interest in purely static mathematical models, as recent publications increasingly favor dynamic models that incorporate time-dependent variables and interactions.
  3. Basic Statistical Methods:
    Basic statistical methods are appearing less frequently in favor of more advanced analytical techniques, such as machine learning and deep learning applications, reflecting a trend towards more sophisticated data analysis.

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