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Nonlinear Science and Intelligent Applications
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Nonlinear Science and Intelligent Applications (NSIA)
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ISSN (print): 3105-7837
ISSN (online): 3105-7829
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2025: Vol. 1
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Nonlinear Science and Intelligent Applications (NSIA) focuses on advancing research at the intersection of nonlinear dynamics and intelligent systems. The journal covers a broad spectrum of topics, including chaos theory, fractals, complex networks, and their integration with artificial intelligence, machine learning, and computational intelligence. Emphasizing both theoretical insights and practical applications, NSIA encourages cross-disciplinary contributions from mathematics, engineering, physics, and biomedical sciences. Published quarterly by Acadlore, NSIA releases four issues annually in March, June, September, and December.

  • Professional Service - Every article submitted undergoes an intensive yet swift peer review and editing process, adhering to the highest publication standards.

  • Prompt Publication - Thanks to our expertise in orchestrating the peer-review, editing, and production processes, all accepted articles are published rapidly.

  • Open Access - Every published article is instantly accessible to a global readership, allowing for uninhibited sharing across various platforms at any time.

Editor(s)-in-chief（1）
akif akgul
Department of Computer Engineering, Hitit University, Turkey
akifakgul@hitit.edu.tr | website
Research interests: Random Number Generators; Cryptology; Data Hiding; Blockchain; IoRT; Chaos-based Engineering Applications

Aims & Scope

Aims

Nonlinear Science and Intelligent Applications (NSIA) aims to provide an international platform for disseminating high-quality research in nonlinear dynamics, chaos theory, fractal systems, and complex networks, with a particular focus on their integration with artificial intelligence, machine learning, and computational intelligence. It aims to bridge theoretical developments with practical applications and encourages multidisciplinary approaches spanning mathematics, computer science, engineering, physics, electronics, and biomedical sciences.

NSIA promotes rigorous theoretical exploration alongside computational and experimental studies that link nonlinear dynamics with intelligent functions. It seeks to highlight research that transcends traditional disciplinary borders and contributes to a deeper understanding of complex system behaviour under nonlinearity. The journal welcomes full-length research articles, concise communications, comprehensive reviews, and well-scoped Special Issues.

NSIA is particularly attentive to reproducibility, methodological transparency, and systems-level insights. It supports open-ended contributions that document novel models, simulations, control strategies, or intelligent mechanisms rooted in nonlinear principles. All accepted papers benefit from high discoverability and editorial guidance supported by a diverse and distinguished international board.

Furthermore, NSIA highlights the following features:

  • Every publication benefits from prominent indexing, ensuring widespread recognition.

  • A distinguished editorial team upholds unparalleled quality and broad appeal.

  • Seamless online discoverability of each article maximises its global reach.

  • An author-centric and transparent publication process enhances the submission experience.

Scope

NSIA publishes original research articles, reviews, and short communications that cover both the theoretical foundations and practical implementations of nonlinear science and intelligent technologies. The journal welcomes contributions in, but not limited to, the following areas:

  • Nonlinear Dynamics and Chaos Theory: Bifurcation analysis, stability analysis, chaotic oscillators, chaos synchronisation, strange attractors.

  • Fractals and Complex Systems: Fractal geometry, multifractal analysis, complex networks, self-organising systems, emergent behaviour.

  • Chaos-Based Applications: Chaos-based cryptography, random number generation, secure communication systems, image and signal encryption.

  • Computational Intelligence and Machine Learning: Chaos-inspired algorithms, evolutionary computation, metaheuristic optimisation, hybrid artificial intelligence models.

  • Nonlinear Control and Optimisation: Adaptive control, chaos control strategies, nonlinear system optimisation, control of engineering and industrial processes.

  • Electronics and Circuits: Chaotic circuits, FPGA/ASIC implementations of nonlinear systems, nonlinear electronic devices and sensors.

  • Signal and Image Processing: Nonlinear filtering techniques, biomedical signal analysis (EEG, ECG, EMG), pattern recognition in nonlinear domains.

  • Biomedical and Health Applications: Nonlinear modelling in physiology, intelligent diagnostic systems, chaos theory in medical imaging and bioinformatics.

  • Engineering and Industrial Systems: Nonlinear modelling and control in robotics, Internet of Things (IoT), cyber-physical systems, smart grids, and renewable energy systems.

    Emerging Technologies: Quantum chaos, nonlinear models in big data analytics, intelligent simulations and forecasting of emerging technologies

    NSIA distinguishes itself by bridging the rigour of nonlinear science with the adaptability of intelligent systems. It encourages forward-thinking research that challenges assumptions, redefines complexity, and opens new frontiers for intelligent technologies shaped by nonlinear phenomena.

Articles
Recent Articles
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Abstract

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Nonlinear dynamical systems operating under explicit constraints may exhibit qualitatively different closed-loop behaviours depending on the interaction between system coupling, feasibility boundaries, and feedback decision mechanisms. In constrained optimization-based control frameworks, such behaviour often manifests as distinct operating regimes and regime transitions that cannot be captured through local linear analysis. This study investigates constraint-induced nonlinear operating regimes arising in nonlinear model predictive control by considering quadrotor trajectory tracking as a representative constrained intelligent dynamical system. A physics-based rigid-body Newton–Euler model is embedded within a receding-horizon optimization framework with explicit actuator saturation and attitude safety constraints. Beyond conventional tracking objectives, the analysis adopts a system-level perspective to examine how nonlinear translational–rotational coupling and constraint activation jointly shape the qualitative structure of the closed-loop response. Comparative numerical simulations are conducted for both mild and aggressive reference maneuvers under varying constraint boundaries. The resulting responses reveal two dominant classes of nonlinear behaviour: constraint-inactive regimes, in which coupling-driven dynamics govern convergence characteristics, and constraint-active regimes, in which feasibility limits reallocate control authority and dominate transient response. Increased maneuver aggressiveness amplifies coupling-dominated effects, whereas tightened constraints induce regime transitions characterised by feasibility-driven dynamics. The results demonstrate that nonlinear model predictive control functions not only as an effective control strategy for constrained trajectory tracking, but also as a structured analytical tool for characterising regime-dependent behaviour in nonlinear intelligent systems. The findings provide insight into performance limitations, stability-relevant behaviour, and design trade-offs arising from the interplay between nonlinear dynamics and constraint geometry.
Open Access
Research article
Modeling and Analysis of Inductively Coupled Identical Linear Resistive–Capacitive Shunted Josephson Junction Circuits
alex stephane kemnang tsafack ,
lucienne makouo ,
oumate alhadji abba ,
noel freddy fotie foka ,
godpromesse kenne
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Available online: 10-22-2025

Abstract

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An inductively coupled system composed of two identical linear resistive-capacitive shunted Josephson junction (LRCSJJ) circuits driven by external direct current (DC) sources was modeled and analyzed in this study. The coupling between the junctions was realized through a shared inductor. The existence and nature of equilibrium states were shown to depend critically on the normalized DC bias currents applied to the junctions. It was demonstrated that the coupled LRCSJJ system admits either an unique equilibrium point or no equilibrium points at all, depending on the biasing conditions. A comprehensive linear stability analysis of the equilibrium point was carried out, revealing that its stability is jointly governed by the inductive coupling strength and the magnitude of the normalized DC currents. When a single stable equilibrium point exists, the system operates in an excitable regime. Conversely, when equilibrium points are absent, sustained oscillatory dynamics emerge. The analysis highlights the role of inductive coupling in regulating the balance between dissipation, energy storage, and nonlinear Josephson dynamics, thereby shaping the global behavior of the coupled system. These results provide fundamental insight into the controllable dynamical regimes of inductively coupled Josephson junction (JJ) circuits and may be of relevance for the design of superconducting electronic devices, including neuromorphic circuits and high-frequency oscillators, where excitability and oscillation play a functional role.

Abstract

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Nonlinear and inherently unstable systems continue to present significant difficulties for conventional control strategies, especially when actuator limitations and parameter uncertainty are involved. This work examines an intelligent control framework in which evolutionary optimization is combined with fuzzy logic control (FLC) to improve the stabilization and robustness of a nonlinear inverted pendulum system. A Mamdani-type fuzzy logic controller is employed to represent nonlinear feedback behavior without resorting to local linearization, while a genetic algorithm (GA) is used to tune the main scaling parameters through simulation-based optimization. The optimization procedure explicitly accounts for nonlinear system dynamics, actuator saturation, and failure-related performance penalties, allowing the evolutionary search to adjust the closed-loop response beyond heuristic parameter selection. Nonlinear time-domain simulations indicate that the optimized controller achieves faster convergence, reduced oscillatory motion, and more consistent performance than a manually tuned baseline controller. Further evaluations under different initial conditions and parameter variations demonstrate an enlarged region of attraction and stable behavior across a range of operating scenarios. These results suggest that evolutionary optimization can play an effective role in shaping fuzzy control structures for nonlinear systems by embedding robustness and performance objectives at the system level. The proposed approach offers a flexible and general framework for the intelligent stabilization of nonlinear and unstable dynamical systems and may be extended to other engineering applications characterized by strong nonlinearity and uncertainty.
Open Access
Research article
Nonlinear Electromechanical Dynamics of a DC Motor Driven by Fractional-Order Hindmarsh–Rose Neuronal Oscillations: Theory and Experiment
thepi siewe raoul ,
goune chengui géraud roussel ,
endele paul patrick ,
edima-durant hélène carole ,
atangana jacques
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Available online: 09-29-2025

Abstract

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Neuronal oscillations generated by nonlinear dynamical systems have attracted increasing attention for bio-inspired actuation and control applications. In this study, a direct electromechanical coupling between a direct current (DC) motor and neuronal signals produced by a fractional-order Hindmarsh–Rose oscillator (FOHRO) was investigated. The FOHRO was realized using an Arduino–Simulink interface and was employed as a signal-generation unit whose output voltage was used to drive a DC motor. The oscillator was treated as a nonlinear wave-shaping element capable of generating pulse-like and bursting neuronal patterns through appropriate variation of its fractional order and system parameters. The resulting electromechanical system (EMS) was modeled by consistently incorporating Newtonian rotational dynamics and electrical circuit laws. A total energy function was defined, and a scaling transformation was applied to derive an equivalent dimensionless dynamical model. Numerical simulations demonstrated that, when driven by FOHRO-generated neuronal signals, the DC motor exhibited angular velocity responses that preserve the temporal characteristics of the underlying neuronal oscillations, including spiking and bursting regimes. These findings were validated experimentally through real-time microcontroller implementation, confirming close qualitative agreement between simulation and hardware results. The proposed framework provides fundamental insights into the interaction between fractional-order neural oscillators and electromechanical actuators and suggests potential design principles for bio-inspired robotic joints and artificial articulations subjected to electrical stimulation. Such architectures may be particularly relevant for soft robotics, neuro-robotic interfaces, and adaptive actuation systems requiring rich dynamical responses derived from biologically inspired signal sources.

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The long-term resilience of classical cryptographic systems has been challenged by recent advances in quantum computing, particularly through algorithms capable of undermining number-theoretic security assumptions. In this context, a simulation-driven comparative evaluation of Rivest-Shamir-Adleman (RSA) and the BB84 Quantum Key Distribution (QKD) protocol was conducted to elucidate their respective computational, physical, and practical security characteristics. RSA was assessed using OpenSSL implementations across key sizes ranging from 1024 to 4096 bits, with performance quantified through processing time and CPU utilization under controlled experimental conditions. A 31-fold increase in RSA key generation time was observed when scaling from 1024-bit to 4096-bit keys, although overall performance remained compatible with conventional hardware and existing communication infrastructures. In contrast, BB84 was examined using the Qiskit and NetSquid simulation frameworks to analyze photon transmission distance, channel noise, and Quantum Bit Error Rate (QBER) dynamics. The results demonstrate that BB84’s security arises from quantum mechanical principles, with QBER increasing linearly as eavesdropping probability was varied. The comparative analysis reveals that RSA continues to provide practical advantages in software compatibility and computational efficiency. Conversely, BB84 offers a quantum-resistant framework suitable for long-term secure communication. These findings suggest that sustainable cryptographic security is most effectively achieved through hybrid architectures that integrate classical and quantum paradigms, enabling near-term operational feasibility while ensuring future-proof protection against quantum adversaries.

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Potato production is critically influenced by foliar diseases such as Early Blight and Late Blight, which continue to threaten global food security. Although visual inspection remains widely used, such assessments are subjective, time-consuming, and difficult to scale, creating a pressing need for automated and reliable diagnostic frameworks. In this study, the classification performance and computational efficiency of four state-of-the-art Convolutional Neural Network (CNN) architectures—the Residual Network with 50 layers (ResNet-50), Densely Connected Network with 169 layers (DenseNet-169), EfficientNetV2-B3, and InceptionV3—were systematically benchmarked for the identification of healthy potato leaves and those affected by Early Blight or Late Blight using the publicly available PlantVillage dataset. Accuracy, precision, recall, and F1 score were employed to characterize predictive performance, while parameter count and giga floating-point operations per second (GFLOPS) were used to assess computational efficiency. High-level classification capability was consistently achieved across all models, with overall accuracies ranging from 98% to 99%. DenseNet-169 achieved the highest classification accuracy at 99% with fewer than 13 million parameters, and EfficientNetV2-B3 attained 98% accuracy while exhibiting tsshe lowest GFLOPS requirement. The results indicate that architectures designed for parameter efficiency and feature reuse, such as DenseNet-169 and EfficientNetV2-B3, provide accuracy that is comparable to or surpasses that of less efficient baseline models while offering significant advantages in resource efficiency. These findings reinforce the strong potential of lightweight and high-performance CNN architectures to support scalable, real-time agricultural disease diagnostic systems, particularly in regions where computational resources and technical expertise may be limited.

Abstract

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Accurate identification of node intrusion behavior in computer networks remains challenging due to the highly dynamic and complex nature of modern network environments, where benign activities are frequently misclassified as malicious events. To address the degradation in detection reliability resulting from such misjudgments, an intrusion identification framework based on an enhanced Recursive Residual Network (RRN) was developed. A statistical classification paradigm was incorporated to process the heterogeneous characteristics of network node intrusion data, enabling a more robust separation of normal and anomalous activity patterns. Features associated with abnormal nodes were extracted, and the range of intrusion-related behavioral deviations was optimized iteratively through an error-minimization function, allowing the model to adapt effectively to fluctuations in network states. A recursive structure derived from Recurrent Neural Network (RNN) principles was subsequently embedded within the residual regression architecture, through which node credit values were continuously iterated and updated to refine the distinction between legitimate behavior and genuine intrusion attempts, enhancing the stability of intrusion identification. In the final stage, a potential loss metric was computed to quantify the expected impact of detected anomalous behaviors on network assets, thereby enabling abnormal behaviors to be classified rigorously as intrusion events when their estimated loss exceeds a critical threshold. Experimental results demonstrate that the proposed method achieves high sensitivity, maintains a stable attack-type identification rate throughout the evaluation period, and reduces the trust value of compromised nodes below 0.15 within 75 seconds, indicating strong effectiveness in distinguishing authentic intrusion behaviors from normal variations. The overall findings suggest that the enhanced RRN offers a resilient and adaptive mechanism for intrusion behavior identification under conditions of complex network dynamics.
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