Applications and Applied Mathematics-An International Journal
Scope & Guideline
Bridging Theory and Application in Mathematical Research
Introduction
Aims and Scopes
- Mathematical Modeling:
The journal publishes research that develops mathematical models to describe complex systems, particularly in fields like epidemiology, fluid dynamics, and population dynamics. - Numerical Analysis and Computational Techniques:
A significant focus is on numerical methods and computational techniques for solving differential equations, optimization problems, and other mathematical challenges. - Statistical and Probabilistic Methods:
The journal features studies employing statistical methods and probabilistic models, especially in the context of risk assessment and decision-making. - Queueing Theory and Operations Research:
The analysis of queueing systems and related operations research topics is a core area, providing insights into system efficiencies and resource management. - Fractional Calculus and Differential Equations:
Research on fractional calculus and its applications in modeling dynamic systems is a recurring theme, showcasing its relevance in various scientific inquiries. - Fuzzy Logic and Decision Making:
The application of fuzzy logic in mathematical modeling and decision-making processes is explored, addressing uncertainties in data and systems. - Interdisciplinary Applications:
The journal encourages interdisciplinary research that applies mathematical principles to fields such as biology, physics, engineering, and social sciences.
Trending and Emerging
- Epidemiological Modeling:
A significant uptick in research focusing on epidemiological models, particularly in the context of infectious diseases like COVID-19, indicates a growing interest in public health applications of mathematics. - Nonlinear Dynamics and Chaos Theory:
Emerging studies on nonlinear dynamics and chaos theory suggest a trend towards exploring complex behaviors in various systems, including biological and physical models. - Fractional Differential Equations:
There is an increasing emphasis on fractional differential equations, reflecting their importance in accurately modeling real-world phenomena that exhibit memory and hereditary properties. - Machine Learning and Data-Driven Approaches:
The integration of machine learning techniques with traditional mathematical modeling showcases a trend towards data-driven methodologies, enhancing predictive capabilities. - Advanced Queueing Models:
Research on advanced queueing models, including those incorporating multiple service disciplines and complex customer behaviors, is on the rise, addressing modern operational challenges. - Multi-Criteria Decision-Making:
The application of multi-criteria decision-making frameworks, especially involving fuzzy and neutrosophic approaches, demonstrates an emerging focus on complex decision scenarios in uncertain environments.
Declining or Waning
- Traditional Analytical Methods:
There has been a noticeable decrease in papers that rely solely on traditional analytical methods without numerical or computational support, as newer techniques become preferred. - Basic Theoretical Mathematics:
Papers focusing on abstract theoretical mathematics, with little application context, are becoming less frequent, indicating a shift towards more applied research. - Static Models:
The use of static models without consideration for dynamic changes or time-dependent factors appears to be waning, as researchers increasingly prioritize dynamic and adaptable systems. - Elementary Statistics:
Research that employs basic statistical methods without advanced techniques or novel applications is less common, suggesting a trend towards more sophisticated methodologies.
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