Search Results (2,051)

It has been reported that quantum correction modifies the topological charges of Anti-de-Sitter Reissner–Nordström (AdS-RN) black holes in Kiselev spacetime, yielding new perspectives on topological classification. This leads us to focus on how quantum corrections and other parameters collectively influence the long-term dynamic evolution of strings. First, we analytically examine whether the strings’ motion violates the Maldacena–Shenker–Stanford (MSS) bound. Then, we employ numerical integration to study the influence of various parameters on string chaotic dynamics. Our results demonstrate that the quantum-correction parameter a, the normalization factor c, and black-hole charge Q significantly influence chaotic behavior and the violation of the MSS bound. In particular, as a increases, the system undergoes an order–chaos–order transition, whereas an increase in c or a decrease in Q drives the system from order to chaos. Full article

Reliable prediction of the Remaining Useful Life (RUL) of lithium-ion batteries (LIBs) plays a pivotal role in maintaining safe operation, enhancing system dependability, and supporting economically sustainable lifecycle planning in electric mobility and stationary energy storage applications. However, battery aging is governed by highly nonlinear, interacting, and chemistry-dependent processes, which pose significant challenges for conventional data-driven prognostic models. In this study, a unified RUL prediction framework is proposed by integrating multi-domain feature engineering, a Multi-Criteria Adaptive Selection (MCAS) strategy, and a Bidirectional Long Short-Term Memory (Bi-LSTM) network enhanced with dual multi-head attention. Degradation-relevant descriptors extracted from time, frequency, and chaotic domains are employed to capture complementary aging dynamics across battery cycling. In addition, a novel degradation-consistency indicator, termed the M-score, is introduced to characterize the regularity and stability of degradation behavior using observable electrical, thermal, and statistical signals. The MCAS mechanism systematically identifies informative and temporally stable features while suppressing redundancy, thereby improving both predictive robustness and interpretability. The resulting architecture jointly exploits adaptive feature refinement and attention-based temporal modeling to enhance the RUL estimation accuracy. The proposed framework is validated using two widely adopted benchmark datasets: the Toyota Research Institute (TRI) dataset, representing fast-charging lithium iron phosphate (LFP) cells, and the Sandia National Laboratories (SNL) dataset, which includes multiple chemistries, such as LFP, NMC, and NCA. Experimental results demonstrate substantial improvements in the RUL prediction accuracy compared with baseline Bi-LSTM and single-attention models, while systematic ablation studies confirm the individual contributions of the M-score and MCAS components. Within the evaluated datasets and operating conditions, the results suggest that the proposed framework offers a robust and interpretable data-driven solution for battery RUL estimation. However, extending its generalizability and validating its performance on unseen datasets and in real-world scenarios remain important areas for future research. Full article

Optical networks constitute the backbone of contemporary communication infrastructures, supporting massive bandwidth, low-latency services, and high levels of scalability across core, metro, and access domains. As these systems evolve toward elastic, software-defined, and multi-domain architectures, their exposure to sophisticated security threats increases significantly. This paper provides a comprehensive survey of vulnerabilities and countermeasures in modern optical networks, spanning the physical, control, and cross-layer dimensions. We analyze major architectures—including WDM, TDM, PON, EON, and IP-over-WDM—and examine how their structural properties shape their security posture. A threat taxonomy is presented covering physical-layer attacks such as fiber tapping, optical jamming, crosstalk exploitation, and signal injection; control-plane risks including spoofing, malicious signaling, and SDN manipulation; and broader cross-layer attack vectors. We review state-of-the-art defense mechanisms, including physical-layer security (PLS), spectrum randomization, chaotic optical coding, device-level authentication, survivability techniques, intelligent monitoring, and quantum-secure solutions such as QKD. By integrating insights from recent experimental and operational studies, the survey highlights emerging challenges and identifies open problems related to secure orchestration, multi-tenant environments, and quantum-era resilience. The objective is to guide researchers, engineers, and network operators toward robust and future-proof security strategies for next-generation optical infrastructures. Full article

Secure compressive sensing (SCS) mostly benefits scenes such as IoT with finite computer resources, the fields of spaceflight and medicine, etc. Recently, research on SCS has aroused widespread interest. Nevertheless, existing work on embedding security of CS usually requires an extra cryptographic routine applied to the measurement vectors. In this paper, we proposed an SCS scheme boosted by the hyper-chaotic system, which outperforms state-of-the-art methods and endows the SCS with a high level of inherent security. Encryption and sampling processing are accomplished simultaneously in our scheme, i.e., security is achieved when sampling with a measurement matrix, which is generated by an initial-value (secret key)-driven discrete hyper-chaotic (HC) system. Moreover, the application of the HC matrix decreases both the computing and bandwidth consumption costs of secret key streams transmission compared with traditional CS-based encryption methods. Experimentally, the HC-based matrix demonstrates excellent reconstruction performance, achieving an average SSIM of 0.91 and PSNR of 29.09 dB on the Set5 dataset at a sampling ratio of 0.5, outperforming conventional matrices such as Bernoulli and Hadamard. Security analysis confirms that the system exhibits asymptotic spherical secrecy and high key sensitivity—a deviation of ${10}^{-16}$ in the initial value results in complete decryption failure. Furthermore, the scheme shows strong robustness against additive Gaussian white noise and cropping attacks, maintaining a PSNR above 15 dB even under 50% cropping. Compared to existing methods, the proposed approach reduces bandwidth consumption by transmitting only the HC initial parameters rather than the entire measurement matrix. These results demonstrate that the HC-driven SCS framework provides inherent security, high reconstruction fidelity, and practical efficiency, making it suitable for secure sensing in constrained environments. Full article

The quantum dissipative time evolution of a fluxonium under a pulsed field (kicks) is studied numerically and analytically. In the classical limit, the system dynamics is converged to a strange chaotic attractor. The quantum properties of this system are studied using the density matrix within the framework of the Lindblad equation. In the case of dissipative quantum evolution, the steady-state density matrix is converged to a quantum strange attractor that is similar to the classical one. It is shown that depending on the dissipation strength, there is a regime when the eigenstates of the density matrix are localized at a strong or moderate dissipation. At weak dissipation, the eigenstates are argued to be delocalized, which is linked to the Ehrenfest explosion of the quantum wave packet. This phenomenon is related to the Lyapunov exponent and Ehrenfest time for the quantum strange attractor. Possible experimental realizations of this quantum strange attractor with fluxonium are discussed. Full article

We systematically investigate semiclassical dynamics of the optical field produced by quantum nanoemitters (NEs) embedded in a periodic lattice of conducting nanorings (NRs), in which plasmon polaritons (PPs) are excited. The coupling between PPs and NEs through the radiated optical field leads to establishment of a significant cross-correlation between NEs, so that their internal dynamics (photocurrent affected by the laser irradiation) depends on the NR’s plasma frequency ${\omega }_p$. The transition to this regime, combined with the nonlinearity of the system, leads to a quite increase in the photocurrent in the NEs, as well as to non-smooth (chimera-like or chaotic) behavior in the critical (transition) region, where considerably small variations in ${\omega }_p$ lead to significant changes in the level of the NE pairwise cross-correlations. The chimera-like state is realized as coexistence of locally synchronized and desynchronized NE dynamical states. A fit of the dependence of the critical current on ${\omega }_p$ is found, being in agreement with results of numerical simulations. The critical effect may help to design new optical devices, using dispersive nanolattices which are made available by modern nanoelectronics. Full article

Self-organizing systems arise in complex biomechanical structures, human locomotion, and neural control hierarchies, yet quantitative methods for describing order formation and loss of stability remain limited. This study develops a mathematical framework for analyzing self-organization using entropy-based measures, indicators of chaotic dynamics, and network-theoretic structure. The approach (the LET framework) combines Lyapunov exponents with entropy families and graph metrics (algebraic connectivity, Load-Path Heterogeneity Index) to: (i) examine transitions between ordered and disordered states, (ii) assess sensitivity to perturbations, and (iii) characterize structural coherence in evolving cervical spine kinematics. Analytical models and computational validations are presented for cervical stability and post-operative Adjacent Segment Disease (ASD) using the Branney–Breen dataset. The findings indicate that entropy and chaos measures identify regime shifts and the emergence of a “stability corridor” more clearly than task-oriented indices, and provide finer resolution of dynamical variability within self-organizing processes. Network metrics complement these results by linking local segmental interactions to global structural fragility transfer. The study shows that entropy, chaos indicators, and network structure together form a consistent basis for describing self-organization in biomechanical systems, enabling quantitative comparison of dynamical regimes and improved interpretation of emergent pathological behavior. The approach utilizes a hybrid kinematic surrogate model to resolve passive and active components, bypassing direct force measurements by employing viscoelastic mechanotransduction principles. Full article

This paper presents a detailed investigation of the deterministic and stochastic dynamics of a noise-driven forced nonlinear oscillator in a periodically driven framework. An overlap-mapping approach is used to compare multiple traveling-wave solutions and verify the structural consistency among distinct solution families. The qualitative behavior of the system is further characterized through geometric and stability-based analysis, supported by two- and three-dimensional phase portraits, time-series responses, and reconstructed three-dimensional attractors to examine periodic and chaotic regimes under varying parameters and initial conditions. The sensitivity to parameter perturbations is quantified and the distribution of final states is analyzed to identify chaotic regions in the phase space. The high-dimensional chaotic nature of the dynamics is rigorously confirmed through Lyapunov exponent estimation, Poincaré sections, and return-map analysis, collectively demonstrating strong sensitivity to initial conditions and systematic transitions induced by parameter variations. These results provide a comprehensive dynamical description of the nonlinear oscillator and contribute to a deeper understanding of noise-influenced nonlinear driven systems. Full article

This study presents the mathematical formulation of a symmetry-compact three-step algorithm (TSA) for the numerical computation of the spatio-temporal generalized FitzHugh–Nagumo equation (FHNE), a class of one-dimensional time-dependent initial-boundary value partial differential equations. The proposed symmetry-compact TSA is constructed using the Lagrange polynomial as the basis function, yielding a structurally balanced and computationally compact formulation with an inherent symmetry that facilitates automatic step-size adaptation over the integration interval. The symmetry-compact nature of the formulation enhances numerical stability while maintaining a reduced computational footprint, thereby improving both accuracy and efficiency when compared with existing numerical schemes. Prior to the application of the TSA, the FHNE is discretized in space, resulting in a system of ordinary differential equations suitable for time integration. Rigorous analyses of the stability and convergence properties of the symmetry-compact TSA are carried out to establish the reliability and robustness of the method. The performance of the proposed algorithm is quantitatively assessed using absolute error, maximum error, root mean square error, and central processing unit time for selected spatio-temporal test cases of the FHNE. The numerical results and corresponding solution profiles clearly demonstrate that the symmetry-compact TSA delivers superior accuracy, enhanced computational efficiency, and improved stability characteristics relative to existing methods, particularly in the presence of stiffness and chaotic dynamics. Full article

Chaotic systems, with their characteristics of high sensitivity to initial conditions, pseudo-randomness, and ergodicity, provide high-quality pseudo-random sequences. Graph theory, through mechanisms such as vertex mapping, path traversal, and graph partitioning, can enhance data confusion and diffusion capabilities. This research designs an image encryption method that combines graph theory and chaotic systems. Firstly, a four-dimensional discrete chaotic system is constructed based on the Hénon map, and its chaotic characteristics and high complexity over a wide range of parameters and initial values are verified using Lyapunov exponents and permutation entropy. Secondly, an encryption framework based on a dynamic adjacency matrix from graph theory is proposed: image pixels are mapped to a dynamic graph structure, and sparse adjacency matrices are generated using chaotic sequences to achieve pixel scrambling based on graph traversal; then, chaotic sequences are used for feedback diffusion with pixel values to enhance the confusion effect. Multiple sets of experiments verify its effectiveness and robustness in terms of key sensitivity, statistical analysis, resistance to differential attacks, and resistance to cropping attacks. Full article

The proliferation of Internet of Things (IoT) devices and services creates not only significant benefits but also new security threats. Classical information encryption techniques are not suitable for resource-constrained edge modules, thereby generating the demand for lightweight and efficient data protection algorithms. This work presents a novel dynamical parameter estimation scheme for chaotic oscillators, applied to physical-layer authentication (PLA). The proposed approach relies on the receiver’s capability to estimate a selected parameter of the transmitter’s oscillator determined by circuit configuration from the received chaotic signal using a locally synchronized oscillator, thereby enabling secure authentication based on a hardware-encoded identifier. The scheme is intended to complement a chaos-based wireless sensor network (WSN) architecture, where sensor nodes (SNs) implement analog chaotic oscillators, and the gateway operates discrete-time models. The Vilnius chaotic oscillator was chosen to validate the proposed PLA scheme. A rigorous bifurcation analysis of analytical, SPICE and discrete oscillator models was first conducted to identify parameter regions that preserve chaotic dynamics, establishing correspondence between models to guarantee the feasibility of parameter estimation across implementations. The digital realization of the parameter estimator demonstrated accurate and stable operation, with a small and nearly constant estimation relative error not exceeding 1.01%. Key performance metrics were analyzed, including estimation time, precision, and noise robustness. A tradeoff between estimation speed and accuracy was identified, particularly under noisy channel conditions. Finally, the influence of the receiver’s native oscillator parameter on distinguishable transmitter parameter ranges was demonstrated, highlighting the configurability and security potential of the proposed system against unauthorized transmissions. Full article

We study the planar repulsive two-center Coulomb system in the presence of a uniform magnetic field perpendicular to the plane, taking the inter-center separation a and the magnetic field strength B as independent control parameters. The free-field system $B=0$ is Liouville integrable and the motion is unbounded. The magnetic confinement introduces nonlinear coupling that breaks integrability and gives rise to chaotic bounded dynamics. Using Poincaré sections and maximal Lyapunov exponents, we characterize the transition from regular motion at $aB=0$ to mixed regular–chaotic dynamics for $aB\ne 0$. To probe the recoverability of the dynamics, we apply sparse regression techniques to numerical trajectories and assess their ability to capture the equations of motion across mixed dynamical regimes. Our results clarify how magnetic confinement competes with two-center repulsive interactions in governing the emergence of chaos and delineate fundamental limitations of data-driven model discovery in nonintegrable Hamiltonian systems. We further identify an organizing mechanism whereby the repulsive two-center system exhibits locally one-center-like dynamics in the absence of any static confining barrier. Full article

High-accuracy short-term electric load forecasting is essential for ensuring the security of power systems and enhancing energy efficiency. Power load sequences are characterized by strong randomness, non-stationarity, and nonlinearity over time. To improve the precision and efficiency of short-term load forecasting in microgrids, a hybrid predictive model combining Complementary Ensemble Empirical Mode Decomposition (CEEMD) and a multi-strategy enhanced Whale Optimization Algorithm (WOA) with Long Short-Term Memory (LSTM) neural networks has been proposed. Initially, this study employs CEEMD to decompose the short-term electric load time series. Subsequently, a multi-strategy enhanced WOA with chaotic initialization and reverse learning is introduced to enhance the search capability of model parameters and avoid entrapment in local optima. Finally, considering the distinct characteristics of each component, the multi-strategy improved WOA is utilized to optimize the LSTM model, establishing individual predictive models for each component, and the predictions are then aggregated. The proposed method’s forecasting accuracy has been validated through multiple case studies using the UC San Diego microgrid data, demonstrating its reliability and providing a solid foundation for microgrid system planning and stable operation. Full article

The heavy use of digital images across network systems has become a major concern regarding data confidentiality and unauthorized access. Conventional image encryption techniques hardly achieve high security levels efficiently, especially in real-time and resource-constrained environments. These challenges motivate the development of more robust and efficient encryption mechanisms. In this paper, a dual-chaotic image encryption framework is developed where two complementary chaotic systems are combined to effectively realize confusion and diffusion. The proposed method uses a chaotic permutation mechanism to find the pixel positions and enhanced chaotic diffusion to change the pixel values for eliminating the statistical correlations. An extended family of piecewise affine chaotic maps is designed to enhance the dynamic range and complexity of the diffusion process for strengthening the resistance capability against cryptographic attacks. Intensive experimental validations confirm that the proposed scheme well obscures the visual information and strongly reduces the pixel correlations in the encrypted images. High entropy values, uniform histogram distributions, high resistance to differential attacks, and improved robustness are further evidenced by statistical and security analyses compared to some conventional image encryption techniques. The results also show extremely low computational overheads, hence allowing for efficient implementation. The proposed encryption framework provides more security for digital image transmission and storage, and the performances are still practical. Given its robustness, efficiency, and scalability, it is equally adequate for real-time multi-media applications and secure communication systems, hence promising to offer a reliable solution for modern image protection requirements. Full article

Millimeter-wave technology helps achieve antenna miniaturization and high gain, but it is limited by factors such as short wavelength, high transmission loss, and high signal-to-noise ratio, which put higher requirements on the accuracy and computing speed of signal processing methods. The weak signal detection method based on the Duffing oscillator is suitable for detecting and estimating the parameters of such signals, but its intermittent chaotic state brings difficulties in phase determination and limited frequency detection accuracy. This article proposes a Heterodyne Duffing equation, which analyzes system properties through bifurcation diagrams, timing diagrams, and phase diagrams. Based on this, signal detection and frequency estimation models are designed, and frequency detection accuracy and calculation time are discussed. The analysis and simulation results show that the phase state discrimination speed and accuracy of the Heterodyne Duffing oscillator (HDO) are superior to the traditional Duffing equation-based intermittent chaotic state method. It has adjustable frequency resolution, overcomes the inherent 0.03ω frequency detection error limitation of the traditional Duffing oscillator, and has a significant advantage in phase state discrimination speed. The frequency estimation method based on the proposed HDO can better meet the frequency resolution and real-time requirements of millimeter-wave sensor signals. Full article