Search Results (763)

As the demand for air transportation surges, issues like flight conflicts and air-route congestion within multi-airport terminal areas have grown progressively more serious. Analyzing the evolution of air traffic situations in these areas can effectively enhance the air traffic’s early-warning capability, reduce flight conflicts, and alleviate air-route congestion. This paper proposes a method for analyzing the evolution of air traffic situations in multi-airport terminal areas based on flight segment–flight state interdependent network. First, a flight segment–flight state interdependent network model is established. This interdependent network model consists of an upper-layer flight state network, a lower-layer air-route network, and coupling edges. The upper-layer network is constructed with aircraft as nodes and flight conflicts between aircraft as edges. The lower-layer network takes air-routes as nodes and the connection relationships between air-routes as edges. The inter-layer coupling edges are determined by judging the relationship between aircraft and air-routes. If an aircraft is on a certain air-route, there exists a coupling edge between the aircraft node and the air-route node. On this basis, by comprehensively considering three network indicators, namely node degree, weighted clustering coefficient, and node strength, the overall air traffic situation value is obtained. Finally, experimental verification and analysis were conducted in an actual flight scenario of a multi-airport terminal area in the Guangdong–Hong Kong–Macao Greater Bay Area. The results show that the proposed method can accurately reflect the air traffic situation. The time-series analysis of the situation evolution reveals that the evolution process has chaotic characteristics. Full article

Fermentative processes are considered one of the most important technological developments in the modern transforming industry, due to this, the applied research to reach high performance standards with a crucial focus on system intensification, which is the the analysis, optimization, and control issues, are a cornerstone. The goal of this proposal is to show a novel nonlinear feedback control structure to assure a stable closed-loop operation in a continuous flow enzymatic–fermentative bioreactor with chaotic dynamic behavior. The proposed structure contains an adaptive-type gain, which, coupled with a proportional term of the named control error, can lead the feedback control trajectories of the bioreactor to the required reference point or trajectory. The Lyapunov method is used to present the stability analysis of the system within a closed loop, where an adequate choice of the controller gains assures asymptotic stability. Moreover, analyzing the dynamic equation of the control error, under some properties of boundedness of the system, shows that the control error can be diminished to close to zero. Numerical experiments are carried out, where a well-tuned standard proportional–integral (PI) controller is also implemented for comparison purposes, the satisfactory performance of the proposed control scheme is observed, including the diminishing offsets, overshoots, and settling times in comparison with the PI controller. Full article

This study proposes a lightweight double-encryption cryptosystem for 3D medical images such as Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) scans, and Computed Tomography scans (CT). The first encryption process uses chaotic pseudo-random numbers produced by a Lorenz chaotic system while the second applies Cipher Block Chaining (CBC) mode using outputs from a Pseudo-Random Number Generator (PRNG). To enhance diffusion and confusion, additional voxel shuffling and bit rotation operations are incorporated. Various sets of optimized parameters for the Lorenz system are calculated using either a genetic algorithm or a random walk. The master key of the cryptosystem is 672 bits long and consists of two components. The first component is the SHA-512 hash of the input image while the second component consists of the initial conditions of the Lorenz chaotic system and is 160 bits long. The master key is processed by a function that generates fourteen subkeys, which are then used in different stages of the algorithm. The cryptosystem exhibits excellent performance in terms of entropy, NPCR, UACI, key sensitivity, security, and speed, ensuring the confidentiality of personal medical data and resilience against advanced computational threats, and making it a good candidate for real-time 3D medical image encryption in healthcare systems. Full article

Neuromorphic circuits emulate the brain’s massively parallel, energy-efficient, and robust information processing by reproducing the behavior of neurons and synapses in dense networks. Memristive technologies have emerged as key enablers of such systems, offering compact and low-power implementations. In particular, locally active memristors (LAMs), with their ability to amplify small perturbations within a locally active domain to generate action potential-like responses, provide powerful building blocks for neuromorphic circuits and offer new perspectives on the mechanisms underlying neuronal firing dynamics. This paper introduces a novel second-order locally active memristor (LAM) governed by two coupled state variables, enabling richer nonlinear dynamics compared to conventional first-order devices. Even when the capacitances controlling the states are equal, the device retains two independent memory states, which broaden the design space for hysteresis tuning and allow flexible modulation of the current–voltage response. The second-order LAM is then integrated into a FitzHugh–Nagumo neuron circuit. The proposed circuit exhibits oscillatory firing behavior under specific parameter regimes and is further investigated under both DC and AC external stimulation. A comprehensive analysis of its equilibrium points is provided, followed by bifurcation diagrams and Lyapunov exponent spectra for key system parameters, revealing distinct regions of periodic, chaotic, and quasi-periodic dynamics. Representative time-domain patterns corresponding to these regimes are also presented, highlighting the circuit’s ability to reproduce a rich variety of neuronal firing behaviors. Finally, two unknown system parameters are estimated using the Aquila Optimization algorithm, with a cost function based on the system’s return map. Simulation results confirm the algorithm’s efficiency in parameter estimation. Full article

Fractional-order chaotic systems have received increasing attention over the past few years due to their ability to effectively model memory and complexity in nonlinear dynamics. Nonetheless, most of the research conducted so far has been on constant-order formulations, which still have some limitations in terms of adaptability and reality. Thus, to evade these limitations, we present a recently designed four-dimensional hyperchaotic Chen system with variable-order fractional (VOF) derivatives in the Liouville–Caputo sense. In comparison with constant-order systems, the new system possesses excellent performance in numerous aspects. Firstly, with the use of variable-order derivatives, the system becomes more adaptive and flexible, allowing the chaotic dynamics of the system to evolve with changing fractional orders. Secondly, large-scale numerical simulations are conducted, where phase portrait orbits and time series for differences in VOF directly illustrate the effect of the order function on the system’s behavior. Thirdly, qualitative analysis is performed with the help of phase portraits, time series, and Lyapunov exponents to confirm the system’s hyperchaotic behavior and sensitivity to initial and control parameters. Finally, the model developed demonstrates a wide range of dynamic behaviors, which confirms the sufficient efficiency of VOF calculus for modeling complicated nonlinear processes. Numerous analyses indicate that this research not only shows meaningful findings but also provides thoughtful methodologies that might result in subsequent research on fractional-order chaotic systems. Full article

To capture bus passenger flow fluctuations and address the problems of slow convergence and high error in machine learning parameter optimization, this paper develops an improved Whale Optimization Algorithm (IWOA) integrated with a Bidirectional Gated Recurrent Unit (BiGRU). First, a Logistic–Tent chaotic mapping is introduced to generate a diverse and high-quality initial population. Second, a hybrid mechanism combining elite opposition-based learning and Cauchy mutation enhances population diversity and reduces premature convergence. Third, a cosine-based adaptive convergence factor and inertia weight strategy improve the balance between global exploration and local exploitation. Based on the correlation analysis between bus passenger flow and weather condition data in Harbin, and combined with the fluctuation characteristics of bus passenger flow, the data were divided into windows with a 7-day weekly cycle and processed by fuzzy information granulation to obtain three groups of fuzzy granulated window data, namely LOW, R, and UP, representing the fluctuation trend and spatial characteristics of bus passenger flow. The IWOA was employed to optimize and solve parameters such as the hidden layer weights and bias vectors of the BiGRU, thereby constructing a bus passenger flow fluctuation trend and spatial prediction model based on FIG-IWOA-BiGRU. Simulation experiments with 21 benchmark functions and real bus data verified its effectiveness. Results show that IWOA significantly improves optimization accuracy and convergence speed. For bus passenger flow forecasting, the average MAE, RMSE, and MAPE of LOW, R, and UP data are 2915, 3075, and 8.1%, representing improvements over existing classical models. The findings provide reliable decision support for bus scheduling and passenger travel planning. Full article

In dynamic and information-rich contexts, systems must be capable of making instantaneous, context-aware decisions. Such scenarios require optimization methods that are both fast and flexible. This paper introduces an innovative hardware-based intelligent optimization framework, deployed on FPGAs, designed to support autonomous decisions in real-time systems. In contrast to conventional methods based on a single chaotic map, our scheme brings together six separate chaotic generators in simultaneous operation, orchestrated by an adaptive voting system based on past results. The system, in conjunction with the Secretary Bird Optimization Algorithm (SBOA), constantly adjusts its optimization approach according to the changing profile of the objective function. This delivers first-rate, timely solutions with improved convergence, resistance to local minima, and a high degree of adaptability to a variety of decision-making contexts. Simulations carried out on reference standards and engineering problems have demonstrated the scalability, responsiveness, and efficiency of the proposed model. These characteristics make it particularly suitable for use in embedded intelligence applications in sectors such as intelligent production, robotics, and IoT-based infrastructures. The suggested solution was tested using post-synthesis simulations on Vivado 2022.2 and experimented on three concrete engineering challenges: welded beam design, pressure equipment design, and tension/compression spring refinement. In each situation, the adaptive selection process dynamically determined the most suitable chaotic map, such as the logistics map for the Welded Beam Design Problem (WBDP) and the Tent map for the Pressure Vessel Design Problem (PVDP). This led to ideal results that exceed both conventional static methods and recent references in the literature. The post-synthesis results on the Nexys 4 DDR (Artix-7 XC7A100T, Digilent Inc., Pullman, WA, USA) show that the initial Q16.16 implementation exceeded the device resources (128% LUTs and 100% DSPs), whereas the optimized Q4.8 representation achieved feasible deployment with 80% LUT utilization, 72% DSP usage, and 3% FF occupancy. This adjustment reduced resource consumption by more than 25% while maintaining sufficient computational accuracy. Full article

By applying the Non-Perturbative Approach (NPA), the corresponding linear differential equation is obtained. Aimed at organizational investigation, the resulting linear equation is used. Strong agreement between numerical calculations and the precise frequency is demonstrated, and the reliability of the results acquired is established by the correlation with the numerical solution. Additionally, this study explores a new control process to affect the stability and behavior of dynamic motorcycle systems that vibrate nonlinearly. A multiple time-scale method (MTSM) is applied to examine the analytical solution of the nonlinear differential equations describing the aforementioned system. Every instance of resonance was taken out of the second-order approximations. The simultaneous primary and 1:1 internal resonance case ($\mathsf{\Omega }\approx {\omega }_{eq},$${\omega }_2\approx {\omega }_{eq}$) is recorded as the worst resonance case caused while working on the model. We investigated stability with frequency–response equations and bifurcation. Numerical solutions for the system are covered. The effects of the majority of the system parameters were examined. In order to mitigate harmful vibrations, the controller under investigation uses (PD) proportional derivatives with (PPF) positive position feedback as a new control technique. This creates a new active control technique called PDPPF. A comparison between the PD, PPF, and PDPPF controllers demonstrates the effectiveness of the PDPPF controller in reducing amplitude and suppressing vibrations. Unwanted consequences like chaotic dynamics, limit cycles, or loss of stability can result from bifurcation, which is the abrupt qualitative change in a system’s behavior as a parameter. The outcomes showed how effective the suggested controller is at reducing vibrations. According to the findings, bifurcation analysis and a control are crucial for designing vibrating dynamic motorcycle systems for a range of engineering applications. The MATLAB software is utilized to match the analytical and numerical solutions at time–history and frequency–response curves (FRCs) to confirm their comparability. Additionally, case studies and numerical simulations are presented to show how well these strategies work to control bifurcations and guarantee the desired system behaviors. An analytical and numerical solution comparison was prepared. Full article

Academic attention has long been accorded to Yan Zhitui and his Family Instructions for the Yan Clan; however, the Confucian philosophical dimensions of his thought remain underexplored. This article will analyze his concepts of Virtue and Happiness alongside his thoughts on the Mandate of Heaven to explore how he found his place in the chaotic landscape of the Northern and Southern Dynasties. Reacting to the contemporary trend of pursuing high-ranking posts and generous emoluments while disregarding morality, Yan Zhitui first defined the connotations of Virtue and Happiness. He then established a causal relationship between them through the correspondence between Name and Reality, an act which reestablished the central position of Virtue. To address the conflict between Virtue and Happiness, his response was to trace its root cause and divide the Mandate of Heaven into two dimensions: Virtue and Time. Transcendental assurance for the core status of Virtue and the unity of Virtue and Happiness is provided by the former dimension, while the latter’s uncontrollability, in comparison, offers an explanation for contradictory realities. Based on this understanding, Yan Zhitui’s guidance urged individuals to adhere to the Way and uphold virtue while observing the macro situation and micro signs, and to wait for the right moment for the unity of Virtue and Happiness to be realized. The flourishing of Buddhism during the Northern and Southern Dynasties was also a significant influence; therefore, Yan Zhitui’s thoughts on the Mandate of Heaven absorbed Buddhist karma theory. This process formed a model that employed Confucianism as its foundation and Buddhism as its supplement. Full article

This study introduces the Double-Diamond Reactor (DDR), a novel planar passive microreactor designed to overcome the following conventional limitations: inefficient mass transfer, high flow resistance, and clogging. The DDR integrates splitting–turning–impinging (STI) hydrodynamic principles via CFD-guided optimization, generating chaotic advection to enhance mixing. Experimental evaluations using Villermaux–Dushman tests showed a segregation index ($X_s$) as low as 0.027 at 100 mL·min⁻¹, indicating near-perfect mixing. In BaSO₄ nanoparticle synthesis, the DDR achieved a 46% smaller average particle size (95 nm) and narrower distribution (${\sigma }_g=1.27$) compared to reference designs (AFR-1), while maintaining low pressure drops (<20 kPa at 60 mL·min⁻¹). The DDR’s superior performance stems from its hierarchical flow division and concave-induced vortices, which eliminate stagnant zones. This work demonstrates the DDR’s potential for high-throughput nanomaterial synthesis with precise control over particle characteristics, offering a scalable and energy-efficient solution for advanced chemical processes. Full article

Classifying study objects into groups is facilitated by fuzzy classifiers based on a set of rules and membership functions. Typically, the characteristics of the study objects are used to establish the criteria for classification. This work arises from the need to design fuzzy classifiers in contexts where real data is scarce or highly random, proposing a design based on statistics and chaotic maps that simplifies the design process. This study introduces the development of a fuzzy classifier, assuming that three features of the population to be classified are random variables. A Mamdani fuzzy inference system and three pseudorandom number generators based on one-dimensional chaotic maps are utilized to achieve this. The logistic, Bernoulli, and tent chaotic maps are implemented to emulate the random features of the target population, and their statistical distribution functions serve as input to the fuzzy inference system. Four experimental tests were conducted to demonstrate the functionality of the proposed classifier. The results show that it is possible to achieve a symmetric and robust classification through simple adjustments to membership functions, without the need for supervised training, which represents a significant methodological contribution, especially because this indicates that designers with minimal experience can build effective classifiers in just a few steps. Real applications of the proposed design may focus on the classification of biomedical signals (sEMG), network traffic, and personalized medical assistance systems, where data exhibits high variability and randomness. Full article

Kolmogorov–Sinai (KS) entropy is an indicator of the chaotic behavior of entire systems from an information-theoretic viewpoint. Here, we used KS entropy values derived from the heart sounds of four fetus–mother pairs to identify the changes in fetal and maternal informational patterns during the four phases of pregnancy (early, mid, late, and postnatal). Time-series data of the heart sounds were reconstructed in a five-dimensional phase space to obtain the Lyapunov spectrum, and KS entropy was calculated. Statistical analyses were then conducted separately for the fetus and mother for the four phases of pregnancy. The fetal KS entropy significantly increased from early pregnancy to the postnatal period (0.054 ± 0.007 vs. 0.097 ± 0.007; p < 0.001), whereas the maternal KS entropy decreased in late pregnancy and then significantly increased after birth (0.098 ± 0.002 vs. 0.133 ± 0.003; p < 0.001). The increase in KS entropy with the course of fetal gestation reflects an increase in information generation and adaptive capacity during the developmental process. Thus, changes in maternal KS entropy play a dual role, temporarily enhancing physiological stability to support fetal development and helping to rebuild the mother’s own adaptive capacity in the postpartum period. Full article

Chaotic systems, governed by deterministic nonlinear equations yet exhibiting highly complex and unpredictable behaviors, have emerged as valuable tools at the intersection of mathematics, engineering, and information security. This paper presents a comparative study of the Lorenz and Rössler systems, focusing on their dynamic complexity and statistical independence—two critical properties for applications in chaos-based cryptography. By integrating techniques from nonlinear dynamics (e.g., Lyapunov exponents, KS entropy, Kaplan–Yorke dimension) and statistical testing (e.g., chi-square and Gaussian transformation-based independence tests), we provide a quantitative framework to evaluate the pseudo-randomness potential of chaotic trajectories. Our results show that the Lorenz system offers faster convergence to chaos and superior statistical independence over time, making it more suitable for rapid encryption schemes. In contrast, the Rössler system provides complementary insights due to its simpler attractor and longer memory. These findings contribute to a multidisciplinary methodology for selecting and optimizing chaotic systems in secure communication and signal processing contexts. Full article

Artificial protozoa optimizer (APO), as a newly proposed meta-heuristic algorithm, is inspired by the foraging, dormancy, and reproduction behaviors of protozoa in nature. Compared with traditional optimization algorithms, APO demonstrates strong competitive advantages; nevertheless, it is not without inherent limitations, such as slow convergence and a proclivity towards local optimization. In order to enhance the efficacy of the algorithm, this paper puts forth a multi-strategy fusion artificial protozoa optimizer, referred to as MSAPO. In the initialization stage, MSAPO employs the piecewise chaotic opposition-based learning strategy, which results in a uniform population distribution, circumvents initialization bias, and enhances the global exploration capability of the algorithm. Subsequently, cyclone foraging strategy is implemented during the heterotrophic foraging phase. enabling the algorithm to identify the optimal search direction with greater precision, guided by the globally optimal individuals. This reduces random wandering, significantly accelerating the optimization search and enhancing the ability to jump out of the local optimal solutions. Furthermore, the incorporation of hybrid mutation strategy in the reproduction stage enables the algorithm to adaptively transform the mutation patterns during the iteration process, facilitating a strategic balance between rapid escape from local optima in the initial stages and precise convergence in the subsequent stages. Ultimately, crisscross strategy is incorporated at the conclusion of the algorithm’s iteration. This not only enhances the algorithm’s global search capacity but also augments its capability to circumvent local optima through the integrated application of horizontal and vertical crossover techniques. This paper presents a comparative analysis of MSAPO with other prominent optimization algorithms on the three-dimensional CEC2017 and the highest-dimensional CEC2022 test sets, and the results of numerical experiments show that MSAPO outperforms the compared algorithms, and ranks first in the performance evaluation in a comprehensive way. In addition, in eight real-world engineering design problem experiments, MSAPO almost always achieves the theoretical optimal value, which fully confirms its high efficiency and applicability, thus verifying the great potential of MSAPO in solving complex optimization problems. Full article

With the rapid development of big data and artificial intelligence, the problem of image privacy leakage has become increasingly prominent, especially for images containing sensitive information such as faces, which poses a higher security risk. In order to improve the security and efficiency of image privacy protection, this paper proposes an image encryption scheme that integrates face detection and multi-level encryption technology. Specifically, a multi-task convolutional neural network (MTCNN) is used to accurately extract the face area to ensure accurate positioning and high processing efficiency. For the extracted face area, a hierarchical encryption framework is constructed using chaotic systems, lightweight block permutations, RNA cryptographic systems, and bit diffusion, which increases data complexity and unpredictability. In addition, a key update mechanism based on dynamic feedback is introduced to enable the key to change in real time during the encryption process, effectively resisting known plaintext and chosen plaintext attacks. Experimental results show that the scheme performs well in terms of encryption security, robustness, computational efficiency, and image reconstruction quality. This study provides a practical and effective solution for the secure storage and transmission of sensitive face images, and provides valuable support for image privacy protection in intelligent systems. Full article