Search Results (233)

In this paper, we study the time-space truncated M-fractional model of shallow water waves in a weakly nonlinear dispersive media that characterizes the nano-solitons of ionic wave propagation along microtubules in living cells. A fractional wave transformation is applied, reducing the model to a third-order differential equation formulated as a conservative Hamiltonian system. The stability of the equilibrium points is analyzed, and the corresponding phase portraits are constructed, providing valuable insights into the expected types of solutions. Utilizing the dynamical systems approach, a variety of predicted exact fractional solutions are successfully derived, including solitary, periodic and unbounded singular solutions. One of the most notable features of this approach is its ability to identify the real propagation regions of the desired waves from both physical and mathematical perspectives. The impacts of the fractional order and gravitational force variations on the solution profiles are systematically analyzed and graphically illustrated. Moreover, the quasi-periodic dynamics and chaotic behavior of the model are explored. Full article

Addressing the current situation where in situ observations in the Arctic primarily target physical and a few biogeochemical parameters, leaving a gap in systematic direct observation of biological populations beneath sea ice, this study developed a polar ice-based ecological observation buoy system. Building upon conventional meteorological and oceanographic hydrographic sensors, this system innovatively integrates an underwater imaging module and key technologies such as machine learning-based automatic fish target recognition and reliable dual-channel satellite data transmission in polar environments. Its successful deployment during the 2025 15th Chinese National Arctic Research Expedition verified the system’s stability. During the initial one-month operation period (designed for a monitoring cycle of not less than one year), the data return rates for conventional and image data reached 100% and 96.8%, respectively, achieving quasi-real-time continuous observation of physical and ecological parameters at the air–sea interface in the Arctic Ocean, and it is capable of acquiring not only physical parameters but also visual observations of under-ice fauna. The system successfully acquired and transmitted images containing suspected biological targets and reference objects, providing the first in situ, image-based biological observation dataset for the central Arctic Ocean. This work establishes a new methodological capability for direct ecological monitoring, offering essential equipment support for quantifying biological presence, studying population dynamics, and informing evidence-based polar ecosystem governance. Full article

In this article, the fixed-time, quasi-consensus control problem for heterogeneous multi-agent systems (HMASs) under denial-of-service (DoS) attacks is investigated. Unlike most previous studies in this area, which focus on periodic (or single-type) DoS attacks with static event-triggered control, this paper ensures that HMASs achieve fixed-time quasi-consensus under aperiodic hybrid DoS attacks via dynamic event-triggered control. According to whether DoS attacks are known, two control protocols based on dynamic event-triggered conditions are given, which both ensure that HMASs achieve output quasi-consensus within a fixed time and exhibit less conservative triggering conditions than static event-triggered protocols. Moreover, the proof that the given dynamic event-triggered conditions can avoid Zeno-behavior is provided. Lastly, simulation examples are presented to support the obtained points. Full article

This study approaches the “Zero-Waste City (ZWC)” initiative as a quasi-natural experiment. Utilizing panel data from 273 prefecture-level cities in China from 2013 to 2023, it employs a multi-period difference-in-differences model to systematically assess the initiative’s synergistic impacts on pollution and carbon emission (CE) reductions. The findings indicate that the initiative has notably lowered both urban pollution and CEs. These results remain robust following a series of stability tests, which include dynamic effect analyses, placebo tests, and propensity score matching. Mechanism analysis suggests that the policy primarily achieves its pollution and carbon reduction goals through four pathways: green technological innovation, public participation and oversight, source control, and end-of-pipe treatment. Heterogeneity analysis further demonstrates that the policy’s effects are more pronounced in resource-based cities, regions with advanced digitalization, and areas with stringent environmental regulations. Additionally, “ZWC” initiatives notably enhance synergies between pollution reduction and carbon mitigation, especially in controlling pollutants closely associated with energy consumption, such as sulfur dioxide and particulate matter. This research provides empirical evidence and policy recommendations for promoting “ZWC” development and optimizing environmental governance systems. Full article

In this article, we investigate the dynamical analysis and soliton solutions of the microtubule equation. First, the Lie symmetry method is applied to the considered model to reduce the governing partial differential equation into an ordinary differential equation. Next, the multivariate generalized exponential rational integral function method is employed to derive exact soliton solutions. Finally, the bifurcation analysis of the corresponding dynamical system is discussed to explore the qualitative behavior of the obtained solutions. When an external force influences the system, its behavior exhibits chaotic and quasi-periodic phenomena, which are detected using chaos detection tools. We detect the chaotic and quasi-periodic phenomena using 2D phase portrait, time analysis, fractal dimension, return map, chaotic attractor, power spectrum, and multistability. Phase portraits illustrating bifurcation and chaotic patterns are generated using the RK4 algorithm in Matlab version 24.2. These results offer a powerful mathematical framework for addressing various nonlinear wave phenomena. Finally, conservation laws are explored. Full article

Remote photoplethysmography (rPPG) is a non-contact technology that estimates physiological signals, such as Heart Rate (HR), by capturing subtle skin color changes caused by periodic blood volume variations using only a standard RGB camera. While cost-effective and convenient, it suffers from a fundamental limitation: performance degrades severely in dynamic environments due to susceptibility to noise, such as abrupt illumination changes or motion blur. This study presents a deep learning framework that combines two structural modifications to ensure robustness in dynamic environments, specifically modeling movement noise and illumination change noise. The proposed framework structurally cancels global disturbances, such as illumination changes or global motion, through a dual-branch pipeline that encodes the face and background in parallel after Video Color Magnification (VCM) and then performs differencing. Subsequently, it utilizes a structure that injects a Temporal Shift Module (TSM) into the Spatio-Temporal Feature Extraction (SSFE) block to preserve long- and short-term temporal correlations and smooth noise, even amidst short and irregular movements. We measured MAE, RMSE, and correlation on the standard dataset UBFC-rPPG under four noise conditions: clean, illumination change noise, Movement Noise, Both Noise and the real-world in-vehicle dataset MR-NIRP (Stationary and Driving). Experimental results showed that the proposed method achieved consistent error reduction and correlation improvement compared to the VS-Net baseline in the illumination change noise-only and combined noise environments (UBFC-rPPG) and in the high-noise driving scenario (MR-NIRP). It maintained competitive performance in motion-only noise. Conversely, a modest performance disadvantage was observed under clean conditions (UBFC) and quasi-clean stationary conditions (MR-NIRP), interpreted as a design trade-off focused on global noise cancellation and temporal smoothing. Ablation studies demonstrated that the dual-branch pipeline is the primary contributor under illumination change noise, while TSM is the key contributor under movement noise, and that the combination of both elements achieves optimal robustness in the most complex scenarios. Full article

As a key component of pure electric vehicles, the reducer plays a vital role in power transmission and overall drive system performance. This study investigates the nonlinear dynamic characteristics of helical gears with tooth root crack faults in high-speed reducers. A coupled bending–torsional–shaft dynamic model is developed, in which the time-varying mesh stiffness of cracked helical gears is calculated using an improved potential energy method. The system’s nonlinear dynamic responses under varying mesh error excitation, gear backlash, and damping ratio are numerically obtained via the variable-step Runge–Kutta method. The results reveal that under high input speed conditions, the motion of the faulted system evolves from single-period to quasi-periodic motion as bifurcation parameters change. In the stable state, fault characteristic signals are apparent, whereas under strong nonlinear vibrations and chaotic motion, they become difficult to distinguish in traditional time- and frequency-domain analyses. To address this limitation, the DBSCAN clustering algorithm is introduced, which applies machine learning to cluster the Poincaré cross-sections of the system under different motion states. This approach enables the effective classification and identification of crack-induced and fault-related noise, thereby improving the accuracy of fault detection in nonlinear dynamic gear systems. Full article

The drive system of pure electric vehicles is characterized by high transmission efficiency and a rapid torque response, with the centralized drive configuration being the most commonly adopted. To improve the dynamic performance and reliability of such systems, this study investigates the nonlinear dynamic characteristics of high-speed helical gear reducers used in electric vehicles. A coupled bending–torsional–shaft dynamic model is established, in which the time-varying mesh stiffness is calculated using an improved potential energy method. The system responses under varying mesh errors, backlash, and damping ratios are obtained through numerical integration via the variable-step Runge–Kutta method. The results demonstrate that at high input speeds, the helical gear system exhibits complex nonlinear behavior. Small backlash and minor manufacturing errors lead to stable periodic or quasi-periodic motion, whereas increasing these parameters induces dynamic instability. Moreover, enhancing the mesh damping ratio effectively suppresses chaotic responses and improves overall system stability. Full article

This article investigates the chaotic features of a novel variable-order fractional Rössler system built with Liouville–Caputo derivatives of variable order. Variable-order fractional (VOF) operators incorporated in the system render its dynamics more flexible and richer with memory and hereditary effects. We run numerical simulations to see how different fractional-order functions alter the qualitative behavior of the system. We demonstrate this via phase portraits and time-series responses. The research analyzes bifurcation development, chaotic oscillations, and stability transition and demonstrates dynamic patterns impossible to describe with integer-order models. Lyapunov exponent analysis also demonstrates system sensitivity to initial conditions and small disturbances. The outcomes confirm that the variable-order procedure provides a faithful representation of nonlinear and intricate processes of engineering and physical sciences, pointing out the dominant role of memory effects on the transitions among periodic, quasi-periodic, and chaotic regimes. Full article

This study investigates spatiotemporal b value variations and seismic interaction networks preceding the M_wg 7.1 Dingri earthquake that struck southern Tibet on 7 January 2025. Using relocated earthquake catalogs (2021–2025) and dual-method analysis combining b value mapping with Granger causality network modeling, we reveal systematic precursory patterns. Spatial analysis shows that the most significant b value reduction (Δb > 0.5) occurred north of the mainshock epicenter at seismogenic depths (5–15 km), closely aligning with subsequent aftershock concentration zones. Granger causality analysis reveals a progressive network simplification: from 73 causal links among 28 nodes during the background period (2021–2023) to 49 links among 34 nodes pre-mainshock (2023–2025) and finally to 6 localized links post-rupture. This transition from distributed system-wide interactions to localized “locked-in” dynamics reflects the stress concentration onto the primary asperity approaching critical failure. The convergence of b value anomalies and network evolution provides a comprehensive framework linking quasi-static stress states with dynamic system behavior. These findings offer valuable insights for understanding earthquake nucleation processes and improving seismic hazard assessment in the Tibetan Plateau and similar complex tectonic environments. Full article

Electric network frequency (ENF) refers to the transmission frequency of a power grid, which fluctuates around 50 Hz or 60 Hz. Videos captured in a power grid environment may exhibit flickering artifact caused by the intensity variation in the light source, thus exhibiting the flickering pattern according to the ENF fluctuation. This flicker, notable for its temporal dynamics and quasi-periodic property, acts as an effective means for video tampering forensics. However, ground-truth ENF databases are often unavailable in a real-world authentication setting, thus posing challenges in conducting ENF examination in video forensics. In addition, dynamic scenes in videos also increase the difficulty of anomaly detection in ENF signals. To address these challenges, we proposed an approach based on neural networks to detect inter-frame tampering in CMOS videos that incorporate ENF signals. To the best of our knowledge, this is the first work that deploys data-driven approach for ENF-based video forensics. Without the aid of the reference ENF dataset, we exploited the implicit ENF variation in luminance signals and transformed the video signal into a one-dimensional time series utilizing ENF priors. In addition, to alleviate the impact of moving objects that also cause the variation in luminance signal, a preprocessing stage is proposed. On this basis, we designed an anomaly detection model combining 1D-CNN and Bi-GRU to conduct experiments on static and dynamic video datasets. The experimental results demonstrate the effectiveness of our proposed method in inter-frame video tampering detection, implying its potential as a forensic tool for ENF-based video analysis. Full article

Self-oscillating systems convert steady external stimuli into sustained motion, enabling diverse applications in robotics, energy absorption, optics, and logic. Inspired by the adhesion–detachment behavior of climbing plants, we propose a novel light-powered self-adhesion oscillator comprising an elastic strip–substrate structure and a weight suspended by a photo-responsive liquid crystal elastomer fiber. By integrating a nonlinear beam deformation model with Dugdale’s cohesive model, we develop a nonlinear dynamic framework to describe the self-adhesion behavior of the elastic strip. Quasi-static analysis reveals two distinct operating modes: a static mode and a self-adhesion mode. Under constant light exposure, the liquid crystal elastomer fiber undergoes light-induced contraction, increasing peeling force and triggering a sudden transition from adhesion-on to adhesion-off. Upon entering the adhesion-off state, the fiber recovers its contraction, leading to a sharp return to the adhesion-on state. This cycle sustains a four-stage oscillation: gradual peeling, abrupt adhesion-off, gradual adhering, and abrupt adhesion-on. Furthermore, we identify the critical conditions for initiating self-adhesion and demonstrate effective control over the oscillation period. The system exhibits key advantages including amplitude controllable oscillation, widely tunable frequency, well-defined motion trajectories, and structural simplicity. These characteristics suggest promising potential for applications in self-healing adhesion systems, rescue devices, military engineering, and beyond. 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

Fiber-optic sensing systems based on a forward transmission interferometric structure can achieve high sensitivity and a wide frequency response over long distances. However, there are still shortcomings in its ability to position multi-point vibrations and detect low-frequency vibrations, which limits its usefulness. To address these challenges, we study the viability of merging long-range forward-transmission distributed vibration sensing (FTDVS) with high spatial resolution optical frequency-domain reflectometry (OFDR), forming the first reported hybrid distributed sensing method between these two methods. The probe light source is shared between the two sub-systems, which utilizes stable linear optical frequency sweeping facilitated by high-order sideband injection locking. As a result, this is a new approach for the FTDVS method, which conventionally uses fixed-frequency continuous light. The method of nearest neighbor signal replacement (NSR) is proposed to address the issue of discontinuity in phase demodulation under periodic external modulation. The experimental results demonstrate that the hybrid system can determine the position of vibration signals between 0 and 900 Hz within a sensing distance of 21 km. When the sensing distance is extended to 71 km, the FTDVS module can still function adequately for high-frequency vibration signals. This hybrid architecture offers a fresh approach to simultaneously achieving long-distance sensing and wide frequency response, making it suitable for the combined measurement of dynamic (e.g., gas leakage, pipeline excavation warning) and quasi-static (e.g., pipeline displacement) events in long-distance applications. Full article

Background/Objectives: Dance, particularly hip-hop, offers a dynamic means of fostering physical activity (PA) and encouraging movement in health-related initiatives among children and youth in educational environments. Hip-hop offers benefits across motor, physical, social, and mental domains. Given the importance of PA in early development, and the preschool period as a sensitive phase for acquiring motor skills, this study aimed to examine the effects of the “Grow+” hip-hop program on motor competence (MC), perceived motor coordination (PMCoor), and PA levels in preschoolers. Methods: A quasi-experimental within-subjects design was used, including 37 children aged 3 to 4 (M = 4.29 ± 0.58). The intervention included two 4-week hip-hop periods, separated by a 4-week break. Four assessments were conducted using the MCA battery (MC), PA’s pictorial scales, and questionnaires completed by caregivers and educators (PMCoor). Data were analyzed using repeated measures ANOVA and Spearman correlations. Results: MC and PA levels showed a nonsignificant but positive trend across the study. Significant improvements in MC were observed during intervention periods, while no significant changes occurred during the break. Educators’ perceptions of PMCoor remained unchanged, despite improvements in MC. Conclusions: The findings suggest that the “Grow+” hip-hop program contributed meaningfully to improvements in MC and PA levels among children in early childhood. These findings accentuate the potential efficacy of structured rhythmic movement interventions in promoting motor development throughout early childhood, thereby supporting their integration into early childhood education curricula. Full article