Search Results (714)

We address the paradoxical transformation of a classical-mechanical particle motion when the space and time scales of observation pass below the uncertainty principle threshold. This is analyzed in the language of classical statistical mechanics, considering specifically many-particle systems inhomogeneous along one spatial direction. We employ the density functional formalism in its square-gradient form and find: (i) The macroscopic solution is analogous to the classical trajectory of a particle under a potential of force given by (minus) the free energy density. Whereas, (ii) fluctuations around the solution in (i) are equal to the quantum-mechanical wave functions of a particle under a potential given by the curvature of the free energy density. We illustrate this situation with three textbook examples: A particle in a box, the harmonic oscillator, and the hydrogen atom. We show that their time-independent Schrödinger equation wave functions describe, respectively, the fluctuations of a fluid interface, of critical point fluctuations, and of a confined ideal gas. At large scales, sharp probability distributions make fluctuations irrelevant; the vanishing of the first variation yields the macroscopically observable statistical-mechanical non-uniformity, equivalent to the classical particle trajectory. But at sufficiently small scales, with necessarily very few particles, distributions appear much wider, fluctuations dominate, and one obtains the Schrödinger equation (for the microscopic potential). Full article

Exchange-rate forecasting requires modelling non-stationary dynamics, heavy-tailed shocks, and complex temporal dependencies. However, forecasting performance in emerging-market currencies is fundamentally constrained by intrinsic dynamical instability, while most existing approaches are evaluated primarily through predictive accuracy rather than the predictability limits of the underlying system. This paper develops a predictability-aware framework that combines nonlinear dynamical diagnostics with a Lévy-driven neural stochastic differential equation model. Drift and diffusion are parameterized by neural networks and driven by $\alpha$-stable Lévy motion, enabling the representation of non-Gaussian fluctuations, abrupt shocks, and regime changes. To learn under discontinuous dynamics, we introduce a structurally constrained training objective based on a strong-form discretization of the underlying SDE. To characterise intrinsic predictability, we employ phase-space reconstruction and maximal Lyapunov exponent estimation. These diagnostics are interpreted as finite-sample measures of trajectory divergence and effective instability in a stochastic system, rather than evidence of low-dimensional deterministic chaos—a distinction motivated by well-documented limitations of chaos testing in financial data. Experiments on multiple West African currency pairs demonstrate competitive short-horizon forecasting performance relative to econometric and neural baselines while providing a principled framework for analysing predictability degradation under heavy-tailed stochastic dynamics. Across currencies and model classes, forecasting accuracy deteriorates beyond horizons comparable to the estimated Lyapunov time, suggesting that forecast degradation reflects intrinsic dynamical instability rather than model-specific limitations. The results support the view that reliable exchange-rate prediction is fundamentally a short-horizon problem and illustrate how stochastic dynamical modelling and predictability diagnostics can be combined to characterise forecasting limits in heavy-tailed financial systems. Full article

This exploratory study compares the Froude efficiency (η_F), intra-cyclic speed fluctuation (ICSF) and other performance determinants between two freestyle swimming techniques: double-arm backstroke and front crawl, and then demonstrates how Para swimmers with hypertonia differ from non-disabled swimmers when performing double-arm backstroke. Three-dimensional motion analysis was undertaken on three Para swimmers with hypertonia (sport classes 3–4) and eight non-disabled swimmers performing a simulated double-arm backstroke with lower limbs immobile. The non-disabled group also completed front crawl trials. Swimming speed, stroke frequency, stroke length and η_F were significantly greater, and ICSF significantly lower, during front crawl than during double-arm backstroke in non-disabled swimmers. Para swimmers’ double-arm backstroke speed was 45–52% that of the non-disabled group; their stroke length was 58–69% shorter and stroke frequency 26–53% higher. Non-disabled swimmers demonstrated higher peak elbow extension velocity during the push phase than Para swimmers (6.36 ± 1.26 rad∙s⁻¹ vs. 1.50–1.81 rad∙s⁻¹) and their η_F was approximately double the Para swimmers’ (0.33 ± 0.02 vs. 0.14–0.18). Para swimmers displayed poorer body alignment than the non-disabled group; ICSF did not differ between groups. Double-arm backstroke is slower and less efficient than front crawl. Hypertonia may reduce the efficiency of double-arm backstroke by diminishing propulsive movements and worsening body orientation. Full article

Black-hole accretion systems exhibit a characteristic coexistence of activities: broad-band X-ray variability, hot coronae, wide-angle winds, and both steady and discrete jets. This coexistence suggests a persistently time-dependent magnetic background in which noisy fluctuations and explosive release are both essential. In this paper, we connect them all to the storage, organization, and intermittent reconnection-mediated release of magnetic energy, and we propose a Synchronized Spin Model (SSM) in which multiple local dynamos in a rotating accretion flow are represented as interacting macro-spins. Their synchronization, partial synchronization, excursion, and reversal define a compact set of collective variables that organize both timing statistics and large-scale morphology. In this picture, multiscale magnetic reconnection converts stored magnetic energy into coronal heating, flares, intermittent outflows, and discrete jet activity, while the same synchronization dynamics produce amplitude modulation and demodulation, providing a route to 1/f-like variability, rms–flux/Taylor-like scaling, and approximately log-normal statistics of the demodulated envelope. We further argue that, although the continuous flux distribution in black-hole systems is more naturally discussed in multiplicative or log-normal terms, broader event-catalog statistics remain useful for describing suitably defined burst hierarchies, particularly by analogy with solar and stellar flare systems. The hard/soft cycle of X-ray binaries is then interpreted as motion through magnetic state space. Full article

Historic masonry towers are iconic components of the world’s architectural heritage, yet their seismic vulnerability remains to be investigated, particularly regarding the influence of vertical ground motion. This study investigates the seismic response of the Roncole bell tower, a 35 m high slender masonry structure located in Emilia-Romagna, Italy, that experienced severe damage during the 2012 Emilia earthquake sequence, presumably related to the second shock of 29 May, the epicenter of which was within approximately 5 km of the tower. In the absence of direct site recordings, a simplified seismic scenario was reconstructed using accelerograms from two nearby stations and interpolation procedures based on logarithmic attenuation relationships. Nonlinear finite element analyses were performed in Abaqus using a detailed three-dimensional model comprising approximately 263,000 tetrahedral elements and a Concrete Damage Plasticity constitutive law for masonry. Four elastic moduli of the material and multiple seismic input scenarios were considered, with and without inclusion of the vertical seismic component. Modal analysis showed that the tower response is governed by the first two dominant horizontal bending modes and one significant vertical mode involving a high percentage of participating mass. Results indicate that while horizontal excitation controls global sway behavior, the vertical component strongly amplifies axial force fluctuations and vertical displacements located close the tower base and rules the bending capacity of the tower. Nonlinear time-history analyses also revealed residual drifts close to collapse thresholds drifts under most of the scenarios considered. Simulated crack patterns closely matched the actual earthquake damage, at the base of the tower, window openings, and the façade in the tilting side. The study demonstrates that three-component seismic analyses are essential for reliable assessment of historic slender masonry towers subjected to near-source earthquakes. Full article

Predictive energy management strategies (PEMSs) have attracted increasing attention in hybrid electric vehicles (HEVs) for improving fuel economy and powertrain efficiency using anticipated driving information. For PEMS, data-driven velocity prediction is widely used to capture complex driving patterns from historical trajectories and future traffic priors, but often lacks kinematic awareness, leading to physical causality violations and long-horizon state drift. To address these issues, this paper proposes a physics-informed PEMS, where a Physics-Informed Spatio-Temporal Network (PI-STN) provides control-oriented velocity information for an MPC-based energy management controller. Specifically, to address pseudo-motion in velocity prediction under standstill conditions, a global zero-speed gating mechanism is introduced; to suppress acceleration/deceleration trends that violate vehicle kinematic causality, a causal penalty is designed; and to mitigate temporal phase misalignment between data-driven predictions and physical motion priors, a Differentiable Kalman Filter (DKF) is incorporated. At each receding horizon step, the PI-STN-predicted velocity sequence is converted into future power demand through longitudinal vehicle dynamics and used by MPC for engine–battery power allocation under SOC and engine transient constraints. Under the same tested conditions, the proposed strategy reduces engine power fluctuation by 15.1% compared with BiLSTM-Transformer, and achieves an equivalent fuel consumption of 323.74 g, outperforming Transformer-KF by 3.12%. Full article

High-precision time synchronization among high-dynamic platforms is an important foundation for distributed detection, cooperative sensing, and networked operation of high-speed mobile platforms. In high-dynamic two-way microwave links, rapid variations in propagation geometry, Doppler-related frequency offsets, and link-quality fluctuations can break the approximate symmetry between uplink and downlink propagation. Although geometric and motion compensation can remove the dominant propagation-asymmetry term, residual asymmetric errors caused by propagation modeling errors, compensation mismatch, and link degradation may still remain and couple into clock-offset estimation, thereby reducing synchronization stability and accuracy. To address this problem, this paper proposes a modeling and joint estimation method for residual asymmetric errors in high-dynamic two-way microwave links. The post-compensation residual error is modeled as a recursively estimable dynamic state, and its rate of change is introduced to characterize the short-term evolution of the residual term. Meanwhile, a four-timestamp and frequency-offset joint observation model is constructed, in which frequency-offset information is used as an observation-level auxiliary constraint to enhance local separability among the clock offset, frequency offset, and residual link state. On this basis, a link-state-information-assisted IMM-IEKF is adopted to realize online joint estimation of clock parameters and link residual errors. Under the equivalent stochastic-error simulation setting, the proposed method effectively suppresses post-compensation residual asymmetric errors and achieves sub-nanosecond synchronization accuracy under strong-dynamic and degraded-link conditions. Full article

Aiming at the problems of diverse defect types, large-scale differences, and complex background interference in gear surface defect detection, a lightweight model, ASW-YOLO, is proposed based on YOLOv8n. By using an ADown dual downsampling module to compress feature map resolution and preserve fine-grained information. C2f_SE channel attention is introduced to enhance small-scale defect response. The CIoU is replaced with WIoU to optimize multi-scale target localization accuracy. The experiments are conducted on the gear dataset. The comparative experiments show that mAP@0.5 of ASW-YOLO reached 94.8%, an increase of 4.5% compared to YOLOv8n, with a reduction of 9.3% in parameter count and 8.5% in computational complexity. The ablation experiments confirm the effectiveness of the three modules. ASW-YOLO achieves a 4.5% increase in mAP@0.5 and a 6.1% increase in recall compared to YOLOv8n. The generalization experiments demonstrate that the mAP@0.5 fluctuation of ASW-YOLO remains below 2% under strong highlight and striped shadow. Moreover, the model maintains over 85% mAP@0.5 under motion blur. ASW-YOLO balances precision and lightweight, making it suitable for real-time quality monitoring in industry. Full article

In high-speed machining, linear toolpaths constructed from a series of short line segments are widely used but inevitably introduce tangent and curvature discontinuities at segment junctions, which may cause feedrate fluctuation and contouring error. To address this problem, this study proposes a real-time corner smoothing and feedrate interpolation method based on dual cubic Bézier transition curves and an optimal error assignment model. The main contribution lies in coupling analytical corner rounding with error allocation: the approximation error and maximum curvature of the transition curves are obtained explicitly, while the allowable tolerance is optimally distributed between approximation error and chord error so that the overall trajectory error remains within the prescribed bound. A jerk-limited look-ahead interpolator is then developed through reverse scanning and forward interpolation to satisfy geometric constraints, drive constraints, and feedrate commands. Simulation results for a three-dimensional toolpath show that the approximation error, chord error, and total trajectory error are all constrained within the preset tolerance of 0.05 mm. In the mask-machining case, the proposed method reduces the machining time to 13.9 s, corresponding to reductions of approximately 70% and 25% compared with the method without look-ahead and the method with look-ahead only, respectively. These results indicate that the proposed framework can improve motion smoothness and machining efficiency while maintaining trajectory accuracy. Full article

Conductive intracardiac communication (CIC) is one of the most innovative and promising communication technologies in multi-point cardiac pacing schemes that utilize the heart as the transmission channel in recent years. Current research predominantly focuses on static channel characteristics. Although some studies have explored dynamic responses, they are largely confined to basic amplitude–frequency and amplitude–time behaviors, lacking in-depth analysis of underlying dynamic mechanisms such as path loss, shadowing, multipath, and Doppler effects. Designing CIC systems solely on the basis of static properties can result in inaccurate channel estimation, distorted channel state information (CSI), and elevated bit error rate (BER). To solve the problems of dynamic channel measurement and modeling, this paper for the first time proposes a dynamic channel modeling method for CIC based on sinusoidal response and impulse response. Firstly, we develop a physical simulation and miniaturized measurement setup to measure the dynamic cardiac channel, and analyze the amplitude–frequency characteristics and amplitude–time characteristics. The influence of factors such as instrument differences, heart rate, flow rate, and motion artifacts is also discussed. Secondly, we systematically analyze the path loss, shadowing effect, multipath effect, and Doppler effect of the CIC channel. Combined with the dynamic channel characteristics and parameters, we propose a composite fading dynamic channel model and analyze the BER performance of baseband signal transmission and On–Off Keying (OOK) modulation systems. We conclude that (1) the CIC channel exhibits capacitive characteristics. Fixed electrodes can effectively suppress motion artifacts. (2) The dynamic channel gain of CIC varies periodically with the heartbeat, and the fluctuation range of the signal is less than 1–2 dB. (3) The dynamic CIC channel presents extremely weak shadow fading, no significant multipath, and no measurable Doppler characteristics, belonging to an extremely slow-fading channel. This work provides effective dynamic channel measurement approaches and a parameter basis for the transceiver design of CIC and a reliable model for the simulation of CIC systems. Full article

A microscale vortex embedded in a cold front over the Pearl River Estuary was observed by weather radars in Hong Kong on the evening of 2 March 2026. This paper presents an observational and simulation study of this vortex. In addition to the reflectivity and Doppler velocity data, the three-dimensional wind field associated with this vortex was analyzed using two radar-based analysis methods. Updrafts were present within the vortex, and the formation of the vortex appears to be related to the horizontal wind shear within the frontal zone and vertical motion triggered by a mid-tropospheric wave. Three commercial aircraft flew across the vortex at low altitude southwest of Lantau Island. Flight data showed marked fluctuations in vertical velocity, including both upward and downward air motions, together with severe turbulence within the vortex. The vortex is therefore of both meteorological interest and operational significance for aviation safety. The event was also simulated using the Weather Research and Forecasting (WRF) model with 200 m resolution. The model reproduced the observed vertical motions and turbulence intensity reasonably well in comparison with aircraft observations. Sensitivity tests with varying sea surface temperature and local terrain over Hong Kong showed no significant impact on the formation of the vortex, confirming that the event was primarily driven by horizontal wind shear in the frontal zone and vertical motion triggered by mid-tropospheric waves. Full article

A study of agricultural yield sensitivity in Peru to climate variations is conducted from 1961 to 2024 to identify climate drivers and statistical tools for early warning and risk management. The statistical basis is year-on-year change in standardized crop yield rate (CYR) across the southeastern highlands of Peru 7–15° S, 70–77° W. Crops favoring La Nina include citrus, cotton, fruit, and sugar-cane. Based on temporal and spatial correlation and composite analysis, our findings indicate that (i) east Pacific and Caribbean sea temperatures and Atlantic upper winds provide advance warning signals of CYR fluctuations; (ii) during El Niño, the subtropical jet subsides over the Peruvian highlands, raising temperatures and lowering humidity; (iii) during La Niña, cooler temperatures conspire with rising motion and beneficial rains; and (iv) CYR fluctuations account for 26% of macro-economic variance, ~$66 B at the current value. Bringing technological information to agricultural decision making will improve resilience and help meet the twin challenges of a growing population and changeable climate. Adaptive measures are suggested to take advantage of Southern Oscillation’s influence on austral summer weather and subsequent annual crop yield. Full article

Accurate short-term traction load forecasting is crucial for optimizing railway operations. However, the strong fluctuations in high-speed railway loads and the general neglect of the physical relationships between adjacent substations in existing studies pose significant challenges to reliable short-term forecasting. To address these issues, this paper proposes a Lag-Adaptive Gradient Aware Network (LAGA-Net). Unlike isolated forecasting methods, LAGA-Net explicitly combines the physical information of train motion with deep learning methods to achieve collaborative load forecasting between adjacent traction substations (TSs). Specifically, it first calculates the cross-correlation coefficients of the load curves of adjacent TSs to quantify the train lag process and achieve load time-series alignment, effectively utilizing the historical load of upstream substations as prior information for load forecasting at this station. Based on this, a dual-stream gradient sensing encoder is proposed to capture the load amplitude and high-frequency pulses of the two TSs, improving the prediction accuracy of the model in highly volatile scenarios. Finally, an adaptive cross-attention mechanism based on Gaussian masks is designed to achieve spatiotemporal coupling and collaborative forecasting of the loads of two adjacent TSs using the aligned load representation information. Extensive experiments on real adjacent traction substation datasets demonstrate that LAGA-Net significantly outperforms existing state-of-the-art benchmark methods in terms of multi-step prediction and peak prediction accuracy, and exhibits strong robustness to operational uncertainties. Full article

Mastication and swallowing are complex physiological processes involving the coordinated activity of multiple tissues in the oral cavity, facial region, and laryngeal system. Some detection methods suffer from limitations such as insufficient information acquisition and inadequate temporal feature analysis. To address these issues, this study proposes a conceptual method for analyzing the state of masticatory and swallowing movements. It integrates maxillofacial electromyographic (EMG) signals with laryngeal movement signals. The goal is to preliminarily explore state analysis of masticatory and swallowing movements over time. A designed gain-adjustable conditioning circuit processes and acquires these signals: maxillofacial EMG signals from EMG electrodes and laryngeal movement signals from flexible PVDF piezoelectric sensors. These two signal streams complement each other’s missing information, enabling comprehensive detection of the state of masticatory and swallowing movements. To address time-point labeling in mastication and swallowing, a sliding-window-based dispersion calculation method was employed to extract characteristic signal nodes, which were then accurately associated with their corresponding physiological motion states. We combined temporal features such as the zero point, onset of fluctuations, characteristic peaks, and baseline recovery from electromyographic (EMG) signals and laryngeal movement signals. This allowed us to establish a correspondence between key time points in the mastication and swallowing processes. The coefficient of determination (R²) for the pressure–voltage linear fit of the PVDF flexible piezoelectric sensor was 0.99446. The pressure resolution was approximately 0.08 kPa. Response times were no more than 15 ms for the EMG channel and no more than 10 ms for the PVDF pressure channel. These results indicate that this method is feasible for extracting oral movement time parameters in healthy subjects. Full article

With the development of shipping to harsh marine environment, it is very important to understand the transient behavior of a marine diesel engine in high sea conditions. Wave-induced hull motion will lead to severe load fluctuations and air-fuel ratio imbalance. In this study, an integrated simulation platform coupled with environmental loads, hull dynamics, propeller characteristics and a high-fidelity thermodynamic engine model was constructed to explore the response characteristics of the propulsion system. The model integrates a zero-dimensional multi-zone combustion method, turbocharger dynamic characteristics and an incremental PID governor, and has been verified based on the bench test data of TBD234V12 diesel engine and the 20 m Wigley standard ship. The simulation results under the sea conditions from level 7 to 9 show that the transient load has a nonlinear amplification effect. Specifically, from sea state 7 to sea state 9, the engine load fluctuation range expands by 2.0 times, while the main peak amplitude of speed fluctuation increases by 3.7 times. Furthermore, the peak exhaust pressure rises by 1.8 times, and the exhaust temperature fluctuation amplitude broadens by 35%. Frequency domain analysis further identified the low-frequency energy concentration phenomenon in the exhaust pressure spectrum and the precursor characteristics of compressor surge. The research results quantify the deterioration law of thermodynamic stability and mechanical stress under wave disturbance, and provide an important reference for the formulation of an engine robust control strategy and fatigue life assessment under high sea conditions. Full article