Submissions

Multidisciplinary Physics

Description: This section focuses on integrating diverse physical principles, bridging multiple disciplines to advance our understanding of complex systems. The goal is to shed light on the behavior, control, and optimization of systems that exhibit nonlinear dynamics, emergent behavior, and intricate physics interactions, drawing on fields such as mathematics, engineering, biology, and computer science. The scope spans theoretical, experimental, and computational approaches, and welcomes interdisciplinary work connecting physics to other science and technology domains.

Potential topics: Nonlinear dynamics, Chaotic systems, Complex networks, Multiscale modeling, Fractional calculus, Nonlinear dynamics in fluids, Nonlocal models, Quantum chaos, Quantum complex systems, Emergent phenomena, Condensed matter physics, Plasma dynamics, Nonlinear optics, Turbulence, Biological physics, Population dynamics, Soft matter, Non-equilibrium processes, Control theory, System optimization, Dynamical systems in engineering.

Applied Mathematics and Interdisciplinary Applications

Description: This section aims to provide approaches to mathematical methods in different fields of science and engineering, as physics, biology, finance, computer science, and engineering applications. 

Potential topics include the synchronization of coupled systems and complex networks, multistability in discrete or continuous dynamical systems, the analysis of financial time series, cryptography, and discrete mathematics, such as graph theory and combinatorics. 

Computer and Information Systems

Description: This section is related to novel applications of chaos, optimization, machine learning techniques, and computer vision algorithms. Design, simulation, and applications of chaotic pseudo-random number generators, and novel approaches to generate real and pseudo-random numbers.

Potential topics: Classification techniques. Chaotic pseudo-random number generators. Fractional circuits and systems, Optimization of classifiers. Optimization of neural networks. Optimization of time series predictors. Optimization of circuits and systems, optimized algorithms in computer vision.

Artificial Intelligence and Cybersecurity

Description: This section aims to connect emerging concepts such as artificial intelligence (AI), neural networks, symbiotic systems, and cybersecurity to develop new techniques and open new frontiers in the field of dynamical systems and complexity.  Based on this data-driven approach, underlying patterns, chaos, and complex relationships in dynamical systems can be discovered, which might not be apparent through conventional methods. From modeling and prediction to cybersecurity, neural networks, control, and optimization, these powerful technologies can extend the boundaries of what is possible in studying complex systems. 

Potential topics: Data-set-centric dynamical systems, AI for complex systems, chaos in neural networks, cybersecurity based on multi-stability, symbiotic dynamical systems, Neuromorphic circuits for dynamical systems, and data analytics-based models for biological dynamical systems, fractal and fractional-order dynamical systems.

Electronic, Embedded, and Hardware in Dynamical Systems

Description: This section focuses on the design, implementation, and experimental validation of dynamical systems using electronic, embedded, and hardware platforms. The goal is to bridge nonlinear theory with real-world implementations, enabling the deployment of complex dynamical behaviors in physical devices and cyber-physical systems.
Potential topics: The section promotes contributions that integrate nonlinear dynamics, control, artificial intelligence, and complexity into analog, digital, and hybrid hardware platforms, including real-time and resource-constrained environments. Special emphasis is placed on experimental validation, reproducibility, and practical implementations of dynamical systems.

Machine Learning and Edge Computing for Biomedical and Healthcare Applications

Description: This section covers machine learning (ML) and deep learning (DL) methods, and their deployment on edge and embedded devices, for biomedical and healthcare applications in which physiological or clinical processes behave as nonlinear or complex dynamical systems. Contributions should address the modeling, analysis, monitoring, or control of such systems through learning algorithms, from signal and image acquisition to model design, validation, and real-time implementation near the patient, with emphasis on reproducibility, clinical relevance, and computational
efficiency.

Potential topics: ML and DL for physiological signals (EEG, ECG, EMG) and medical images (MRI, CT, PET, ultrasound); LSTM, CNNs, vision transformers, and hybrid architectures for computer-aided diagnosis; TinyML, edge AI, and embedded deployment on microcontrollers, SoCs, FPGAs, and GPUs; H-IoT, wearables, and telemedicine; chaos-based encryption of medical data; brain-computer interfaces and
assistive technologies; explainability (XAI), uncertainty quantification, and benchmarking of learning models, including small language models (SLM) and large language models (LLM) for clinical use; and ethical and governance aspects of AI in healthcare.