Special issue of JEAM: Computational Complexity, Mathematical Sciences and Complexity Science: Artificial Intelligence and Complex Systems’ Theories and Applications - Part II (Call for Paper)
Mathematical sciences with internal sui generis vitality serve as the foundational means and language for interdisciplinary studies with an inextricable link representing a mode of thought resting upon abstraction maintaining accuracy and precision through explicit calculation, with internal abstractness and external effectiveness. In this sense, mathematical modeling signifies a process where real world situations and relations are denoted in the form of equations which ensure the understanding of the original problem and discovering new structural features related to the problem. Known as a cyclical process in which real-life problems are translated into mathematical patterns, and solutions are tested back in the particular real-life systems through the consideration of the interchanging compromise between simplicity and accuracy concerning the model. On the flip side, complexity theory along with complexity creates a bridge crossing over the quantitative and qualitative facets of real life, which enables the comprehensive contemplation of diverse systems from as cells, neurons, tissues to human beings, ecosystems to organizations, which are possible to be comprehended merely partially by traditional scientific methods. Complexity reflects the disentangling of complex, dynamic, complicated, nonlinear, adaptive, evolutionary, emergent and chaotic systems, amongst many more. Besides these, complexity provides facilitation in seeing the problems through multiple perspectives, by looking into the micro and macro issues and comprehending the way they are interdependent.
Complexity science, on the other hand, merges the two solitudes of both micro (analysis of the parts) and macro analyses (holistic analysis), ranging across human genome to evolutionary biology across the spectrum natural and human systems. When patterns in complex systems are in question, systems with multicomponents display a spontaneous form of organization into macroscopic structures with simple rules leading to unpredictable behaviors. In that regard, complexity science seeks to discover the underlying principles, theoretical aspects of emergence with an orientation to use them through applications so that medical, biological, physical and social worlds can be understood at the pedestal of emergence of chaos and order as the hallmarks of natural systems as well as designed systems. The theories of complex systems besides these points provide the ideas suggestive of the way intractable the world is considering that even the simplest of phenomena involve enormous and even incalculable complexity. Consequently, it has been observed that many applications of science have turned into multimethod cases based on evolving knowledge over time and across space.
The mathematics of data, by encompassing a multifaceted blend of mathematical techniques and models, is pivotal for tackling voluminous datasets and extracting significant insights from them. Computation of the complexity of a particular mathematical model requires the carrying out of the analyses over the run time, which is concerned with and based on the type of data (big data) identified, determined and employed along with the methods. While providing the tools required to navigate through the complexities of data, Artificial Intelligence (AI) as well as data analysis rely on foundational mathematical concepts, which can pave the way for novice perspectives, solutions to challenges and directions for the future elements to arise. To put differently, AI as characterized by mathematical models entails a deeply holistic understanding of the mechanisms of intelligence and related technologies to enable the development of the means which ensure the carrying out of the complex and time-consuming tasks efficiently and accurately. Such an interplay emerging on dynamic scales can highlight mutually enriching association between mathematics and data in the ever-evolving digitizing landscape and ecology science, computer science, informatics, medicine, biology, applied sciences, engineering, bioengineering, and so forth, towards the integration, analysis, processing of models and data-centric prediction-based domains to name some.
To these ends, the aim of our special issue is to unify and put into practice the diverse and evolving approaches to complexity theory, mathematical sciences and applied complexity science for providing a key into understanding the current and conceivable complex problems so that mathematical frameworks can serve as the plinth to understand the role of AI and future science of complexity.
In accordance with the high academic standards promoted by the JEAM, this call for papers is focused upon, but is not limited to the following themes:
1) Fractional calculus and / or Fractals and complexity
2) Quantum algorithms and complexity
3) Applications of mathematical modeling
4) Complex systems modeling and simulations
5) Probabilistic scientific computing
6) Stochastic modeling and complexity
7) Partial differential equations (PDEs) and / or learning / inference problems
8) Differential equations (PDEs, ODEs, functional differential equations, etc.) and applied complexity
9) Difference equations in real world problem-driven systems
10) Neuronal dynamics and functions
11) Big data assimilation and processing
12) Artificial Intelligence (AI) applications (i.e.: deep learning, Artificial Neural Networks, Natural Language Processing, Generative AI)
13) Multiscale deep generative neural networks
14) Theoretical aspects and applications of computational (algorithmic) complexity
15) Mathematical biology and bioengineering
16) Uncertainty quantification and / or time-delay systems
17) Bifurcation, control, oscillation and nonlinear dynamics
18) Entropy and wavelet applications
19) Quantitative methods and advanced data analyses for decision-making processes
20) Neurocomputation and neuronal circuits’ dynamics
21) Applied mathematical methodologies for modeling and analyzing data
22) Probability and statistics, signal/image processing, information theory and optimization
23) Computational complexity and analysis
Among the many other related points with mathematical, numerical and computational modeling aspects.
How to submit:
All authors must follow JEAM “Information for Authors” available at lhscientificpublishing.com/journals/JEAM-Default.aspx, and submit their papers online at editorialmanager.com/jeam/default.aspx.
Important dates:
Initial submission of articles: April 23, 2025
Closing date for initial submission: November 23, 2026
Deadline for final decision notification: December 18, 2026
(Some of the accepted papers in this special issue may be published in Journal of Applied Nonlinear Dynamics or Discontinuity, Nonlinearity, and Complexity)
All papers submitted to the special volume will be peer-reviewed in accordance with the standard procedures of Journal of Environmental Accounting and Management.
• RESEARCH articles describing original investigations relevant to Environmental Accounting and Management;
• REVIEW articles giving in-depth overviews of certain topic or reviews on research achievements.
• EDITORIALS providing important essays of the editors and invited scholars or overviews on certain topic written by guest editor(s) of special issue.
To prepare their manuscripts, authors are asked to closely follow the JEAM “Guide for Authors” at https://lhscientificpublishing.com/journals/JEAM-Default.aspx. Authors should submit their papers via the journal's online submission system. Submitted papers should not have been previously published or be currently under consideration for publication elsewhere.
Papers must be written in good English. Authors with limitations in the command of written English are recommended to have their papers edited by a ‘Native English Scientific Editor’ before the first submission to JEAM because poorly written pieces can compromise the decisions during the review process.
