Search Results (918)

The trajectory tracking problem of underactuated unmanned surface vehicles (USVs) with unknown physical parameters arising from hydrodynamic effects is addressed using a robust control strategy based on a sliding-mode dynamic neural network identifier. To handle the unknown physical parameters, a dynamic neural network identifier with a novel structure is developed, enabling the construction of an equivalent mathematical model of the USV dynamics. To compensate for the underactuated nature of the system, a coordinate transformation is introduced. Using this transformation, together with the proposed identifier, a nonsingular sliding-mode controller is designed. Lyapunov-based analysis establishes finite-time convergence of the neural weight estimation errors to zero and convergence of the identification errors to a bounded neighborhood of zero. Furthermore, once the identification errors enter this bounded region, they asymptotically converge to zero. In addition, the closed-loop stability analysis guarantees finite-time convergence of the tracking errors. The effectiveness of the proposed identifier–controller framework is validated through simulation studies that incorporate explicit actuator saturation constraints and external disturbances to emulate realistic operating conditions. These results demonstrate the practical applicability of the proposed control strategy, as the commanded inputs remain within the physical limits of the propulsion system. Comparative results with a state-of-the-art model-based super-twisting controller show that the proposed approach achieves comparable tracking performance while eliminating the need for prior knowledge of the system’s dynamic parameters. Full article

Continuous-variable quantum communication (CVQC) operates under finite-resolution inference (finite data windows, calibration uncertainty, and estimator tolerances) and hardware control/readout limits that can be exploited by structured and adversarial disturbances. We study a feedback-inspired phase-space modulation strategy for implementation-layer resilience under DoS-like receiver-observable stress (e.g., fluctuation inflation, phase reference destabilization, or interface non-idealities), rather than proposing a protocol-level security proof. We propose a phase-first framework in which the defender selects a phase-space rotation angle $\theta$ (and, in principle, a squeezing parameter r) to minimize a receiver-observable centered second-moment degradation proxy, emphasizing containment rather than disturbance inversion. Because platforms expose different native observables, we evaluate phase-first modulation using two complementary tracks: (i) in theory/simulation, we monitor basis-dependent quadrature variance and covariance-derived summaries formed from mean-subtracted second moments so that $\Delta E_{\mathrm{cov}}$ reflects covariance inflation rather than coherent displacement; (ii) in the X8_01 hardware workflow, the readout is Fock sampling; thus, we use the shot-to-shot standard deviation ${\sigma }_N\left(\theta \right):=\sqrt{\widehat{Var}\left(N\left(\theta \right)\right)}$, where $N\left(\theta \right)$ denotes the shot-level detected count random variable at fixed $\theta$. In the reported hardware workflow, this shot-level count is formed by aggregating the returned Fock counts prior to postprocessing. We emphasize that ${\sigma }_N\left(\theta \right)$ is not claimed to estimate $Tr\left(V\right)$; it is an implementation-layer variability proxy aligned with the available readout. Our experimental validation is restricted to phase-only control instantiated as offline phase selection via one-dimensional grid search over $\theta$. Across numerical simulations and hardware phase-angle scans on Xanadu’s X8_01 photonic quantum processor, we find that static operating points can be brittle under strong DoS-like stress, whereas optimized phase selection can materially reduce a receiver-observed degradation proxy even without real-time feedback. Since $Tr\left(V\right)$ is invariant under pure rotations for phase-independent additive noise and ideal photon-number probabilities are invariant under a terminal Fock-basis phase gate, any observed $\theta$-dependence is interpreted operationally as evidence of a phase-dependent effective disturbance/measurement channel at the receiver interface. Simulation-only analyses indicate additional upside when squeezing is available, motivating future extensions incorporating higher-rate re-optimization, feedback-assisted architectures, and extended Gaussian control when available. Full article

Accurateestimation of clutter measurement density (CMD) is crucial for radar-based multi-target tracking (MTT), especially under spatially non-uniform and temporally varying environments. Existing methods, including finite mixture models, kernel density estimation, and normalizing flows, often require scene-specific tuning and exhibit limited generalization. To address these limitations, we propose HyperNCMD, a scene-adaptive CMD estimator that employs hypernetworks to dynamically generate the parameters of normalizing flows. To capture spatial variability, radar measurements are first embedded using Random Fourier Features (RFFs), and then processed by a spatio-temporal encoder that jointly models spatial structures and temporal clutter dynamics. The hypernetwork leverages the encoded embedding to adaptively produce flow parameters, enabling flexible CMD estimation across diverse environments. Lightweight data augmentation is further applied to make the estimator more robust across diverse environments, while a Feature-wise Linear Modulation (FiLM)-based fine-tuning scheme enhances test-time adaptation. Experiments on both synthetic and real radar datasets demonstrate that HyperNCMD achieves superior accuracy and robustness, achieving up to 10.5% reduction in per-point negative log-likelihood under dynamically varying conditions. These results highlight the potential of hypernetwork-driven CMD modeling for reliable radar perception in complex sensing environments. Full article

Introduction: Health problems related to cardiovascular morbidity and mortality are overrepresented in patients with schizophrenia (SCH) and their rates have not declined in parallel with those of the general population. Cardiovascular diseases in patients with SCH are less likely to be diagnosed and treated, and data regarding structural and functional cardiac abnormalities—particularly those involving the left atrium (LA)—remain limited in this population. The present study is the first to provide a detailed three-dimensional speckle-tracking echocardiography (3DSTE)-derived volumetric and functional evaluation of LA properties in patients with chronic SCH compared with age-, gender- and body mass index (BMI)-matched healthy controls (HCs). Methods: A total of 36 patients with SCH were initially enrolled, from which 19 subjects (53%) were excluded due to inferior image quality. Ultimately, 17 SCH patients (mean age: 45.2 ± 7.7 years; 9 males, 53%) were compared with 40 age- and gender-matched HCs (mean age: 42.5 ± 5.7 years; 23 males, 58%). All participants underwent comprehensive two-dimensional Doppler echocardiography and 3DSTE. Results: LA volumes respecting the cardiac cycle were lower in SCH patients compared with controls. Among LA volume-derived functional properties, total and active LA stroke volumes were reduced in patients with chronic SCH, whereas passive LA emptying fraction was increased. All global and mean segmental peak LA strain parameters tended to be increased in SCH patients, with global and mean segmental LA area strain (AS) and mean segmental LA radial strain (RS) reaching statistical significance. Regarding regional peak LA strains, basal LA circumferential strain (CS) and LA-AS, as well as superior LA longitudinal strain (LS), LA-CS, and LA-AS, differed significantly between the groups. All global and mean segmental LA strain parameters measured at atrial contraction tended to be increased in chronic SCH patients, with global and mean segmental LA-AS and mean segmental LA-RS and LA-LS reaching statistical significance. Regional LA strains during atrial contraction demonstrated increased superior LA-RS, LA-CS, LA-LS and LA-AS, along with elevated mid-atrial LA-RS, LA-AS and LA-3D strain. All these abnormalities suggest reduced LA volumes in all phases of LA function, accompanied by overcompensating functional alterations. Conclusions: Chronic schizophrenia is associated with marked volumetric and functional abnormalities of the left atrium. These findings highlight the need for comprehensive cardiac functional evaluation extending beyond left ventricular-centered analysis in patients with this severe mental illness. Full article

To meet the demand for real-time and accurate diamond particle size measurement in industrial scenarios—where traditional image processing methods lack robustness in complex environments and existing deep learning models struggle to balance accuracy and efficiency—this paper proposes an integrated framework for dynamic segmentation and morphological analysis of diamond particles based on video streams. A fully automated data acquisition system consisting of a high-precision motion stage, an industrial camera, and an optical microscope is first constructed to capture dynamic particle images. Based on YOLOv8n-seg, a lightweight SPR-YOLOv8 instance segmentation model is then developed with three key improvements: a Large Separable Kernel Attention (LSKA) mechanism is introduced into the SPPF module to enhance feature discriminability; the RepBlock module is adopted in the neck network to improve feature fusion efficiency through structural re-parameterization; and a P2 small-object detection head is introduced while large-object detection branches are removed, enabling the model to focus on tiny, densely distributed particles. Finally, a contour-based geometric analysis method is proposed for particle size estimation based on segmentation results. Experimental results show that the proposed model achieves an mAP@0.9 of 0.861 while maintaining a low parameter count (0.97 M) and a high inference speed of 500 FPS. Compared with the conventional OpenCV-based method (CADPS), the proposed DPSCA framework reduces the mean absolute percentage error in particle size measurement by over 70%, while also demonstrating strong accuracy and stability in consecutive-frame tracking. Overall, this study provides a practical and efficient automated inspection solution for online quality control in superhard material manufacturing, and supplementary cross-scale experiments further demonstrate promising robustness on diamond particles beyond the primary 180–250 μm range. Full article

This paper presents a unified dynamic-modeling and error-analysis framework for an FTR (force-to-rebalanced)-operated MEMS ring gyroscope. Starting from an equivalent mass-point representation of the ring resonator, a dynamic model including stiffness and damping errors is first established. Principle-related inertial-acceleration errors and structural errors are then analyzed within the same framework. The results show that, under practical rate-measurement conditions, inertial-acceleration errors have negligible effects on both the drive and sense modes. In contrast, structural errors, including modal-frequency perturbation, damping-decay-time mismatch, mass-distribution mismatch, and electrode angular misalignment, impair drive-mode amplitude control and frequency tracking, introduce in-phase bias components into the sense-mode output, and produce quadrature signals through frequency coupling. The analysis further indicates that electrostatic mode matching should be implemented in two steps: quadrature-stiffness correction followed by modal-frequency tuning. The proposed model provides a concise and physically transparent basis for resonator design, parameter identification, and control compensation in high-performance MEMS ring gyroscopes. Full article

The occurrence of short-wave corrugation with wavelengths of 32–44 mm on curved sections of urban express railway lines is particularly pronounced, yet the underlying initiation mechanisms have remained insufficiently understood. Furthermore, conventional mitigation strategies—including the installation of rail dampers and passive grinding—entail substantial maintenance expenditures, thereby hindering their large-scale application. To elucidate the initiation mechanisms of rail corrugation and to formulate effective control measures, the characteristic corrugation parameters under various track structure configurations across an entire alignment were first measured and systematically analyzed. Dynamic interaction models between vehicles and three distinct track typologies were subsequently developed, together with a comprehensive analytical framework for corrugation evolution. The wheel–rail dynamic response characteristics and corrugation growth rates corresponding to each track type were examined, and the wheel–rail coupled vibration modes that exacerbate corrugation propagation in urban express lines were identified. The instantaneous wear behavior of the rail under differing creep regimes was also investigated, leading to the proposal of a novel self-mitigating approach for rail corrugation. The results demonstrate that the excitation frequency of rail corrugation is predominantly confined to the 600–700 Hz range, exhibiting a fixed-frequency characteristic that remains invariant with respect to curve radius, track structure type, and operational speed. An interesting finding is that, although the intrinsic vibration properties of different track structures diverge significantly, the third-order bending resonance of the rail segment situated between bogie wheels is largely unaffected by track-borne vibrations and manifests as a localized wheel–rail resonance within the vehicle–track coupled system. This particular resonance markedly accelerates corrugation development and is identified as the critical governing factor for corrugation initiation in urban express lines, regardless of the underlying track configuration. Furthermore, rail instantaneous wear displays a substantial phase shift under varying creep conditions, with the wear profiles under creep saturation (full sliding) and low creep (rolling–sliding) exhibiting a distinct anti-phase relationship. This insight underpins a novel self-wear suppression strategy: by intentionally mixing rolling–sliding and full-sliding operational regimes, destructive interference between the out-of-phase wear contributions is achieved, resulting in a considerably attenuated corrugation growth rate compared with exclusive rolling–sliding operation. This methodology thus offers a promising and fundamentally new alternative for the long-term management of rail corrugation through intrinsic wheel–rail interaction. Full article

Dual-motor electric drive axles (e-axles) can realize basic torque vectoring through motor-torque allocation. However, without an inter-wheel power-transfer path, they still face structural limitations under motor torque–speed envelopes and severe left–right adhesion asymmetry. To address this issue, this paper proposes a reconfigurable dual-motor e-axle based on fixed-carrier compound planetary gear trains and two cross-axle clutches. By switching between controlled-slip and lock-coupled states, the proposed topology creates a switchable inter-wheel power-transfer path. As a result, it enhances yaw-rate regulation capability under high-adhesion conditions and improves escape capability under severe adhesion asymmetry. A unified kinematic–static analytical framework is established to derive closed-form capability boundaries and compact structural indices for parameter matching. Vehicle-level co-simulation on a representative rear-wheel-drive platform is then carried out for validation. Under severe split-$\mu$ conditions, the peak high-adhesion wheel torque increases from 241.72 to 695.57 N·m, and the escape time decreases from 0.43 to 0.19 s. In a representative high-adhesion step-steer case, the mean yaw-rate tracking error is reduced from 6.75 to 0.20 deg/s, while the mean differential wheel torque reaches 1.83 times that of the baseline mode. The other high-adhesion cases show the same trend. These results verify the vehicle-dynamics significance and engineering feasibility of the proposed architecture. Full article

Nailfold capillaroscopy has earned its place as a cornerstone of clinical assessment in systemic sclerosis (SSc). Its ability to detect early microvascular changes, distinguish primary from secondary Raynaud’s phenomenon, and contribute to disease classification has fundamentally reshaped the clinical approach to early diagnosis and disease stratification. The recognition of distinct capillaroscopic patterns offers a structured framework for tracking disease evolution and identifying patients who warrant closer surveillance or proactive therapeutic intervention. The inclusion of capillaroscopic abnormalities in the ACR/EULAR 2013 classification criteria validates its diagnostic importance and facilitates identification of patients with early or limited cutaneous disease. Beyond diagnosis, emerging evidence supports prognostic applications, particularly for predicting digital ulcers, though the predictive value for other organ complications requires further validation. As a non-invasive, safe, and reproducible technique, capillaroscopy is particularly well-suited to long-term disease monitoring. Quantitative scoring systems allow for rigorous, objective tracking of microangiopathic progression and hold considerable promise as outcome measures in clinical trials targeting vasculopathy. Ongoing technological advances, particularly in automated image analysis and integration with functional assessment tools, promise to enhance the clinical utility of capillaroscopy while reducing operator dependency. Standardization efforts and validation of capillaroscopic parameters as clinical trial endpoints will be crucial for realizing the full potential of this technique. Full article

This paper investigates the reliable stabilization and $L_2-L_{\infty }$ performance control problem for discrete-time descriptor systems described by Takagi–Sugeno (T-S) fuzzy models under stochastic data dropouts and actuator faults. In view of the practical situation that system states are usually unmeasurable, a novel observer-based proportional–derivative (PD) control strategy is proposed. Different from traditional state feedback, the PD structure effectively alleviates the inherent structural constraints of descriptor systems and relaxes the conditions for system regularity and causality. By constructing a parameter-dependent Lyapunov functional and using the Schur complement lemma, sufficient conditions are derived in the form of linear matrix inequalities (LMIs) to guarantee the stochastic stability of the closed-loop system and the prescribed $L_2-L_{\infty }$ performance. The effectiveness and superiority of the proposed methodology are verified through extensive numerical simulations on two practical case studies, namely, a bio-economic system and a DC motor system. In the case of actuator faults and data dropouts the observer achieves accurate state tracking, and the peak value of the system output is strictly constrained. The research results confirm that the method has strong robustness against data dropouts and actuator faults. Full article

Objective: Myocardial infarction (MI) triggers inflammation and fibrosis that drive the progressive impairment of cardiac function. Yet most pharmacological studies still depend on single-time-point histological or imaging endpoints and lack longitudinal, non-invasive assessments of treatment response. Electrocardiography (ECG) detects conduction and repolarization abnormalities tightly associated with myocardial injury and structural remodeling. However, ECG monitoring in mice is limited by rigid or invasive hardware, which restricts its use for longitudinal assessment of cardiac structure and function. Approach: Here, we propose an ECG-based non-invasive post-MI cardiac remodeling assessment approach and develop a flexible electrocardiographic monitoring microsystem (FECMS). Using the anti-remodeling drug (colchicine) therapy in an MI mouse model (Sham n = 4, MI n = 7 survivors, Col n = 7 survivors) for validation, we longitudinally track drug-induced changes in ECG parameters and systematically evaluate their concordance with functional, structural, and molecular indicators of cardiac injury and remodeling. Results: Colchicine treatment induced progressive shortening of the QRS and QT intervals and gradual stabilization of the PR interval. These interval changes were accompanied by increased EF and FS, decreased LVESV, reduced myocardial fibrosis and inflammatory infiltration, and lower plasma troponin I levels at the endpoint. Correlation analyses revealed strong relationships between drug-induced changes in ECG parameters and functional recovery and inhibited structural remodeling. Significance: The FECMS provides a new, non-invasive tool for longitudinal cardiovascular drug evaluation. This approach has the potential to complement or reduce reliance on terminal histological endpoints and to facilitate the optimization of dosing strategies in preclinical cardiovascular pharmacology. Full article

In single-carrier underwater acoustic (UWA) communication systems, sparse multipath channels and long delay spreads pose significant challenges to adaptive equalization, often leading to limited steady-state accuracy and degraded detection performance. To address this issue, this paper proposes a data reuse-based ${\ell }_1$-regularized proportionate recursive least-squares algorithm (DR-${\ell }_1$-PRLS) for sparse adaptive equalization. The proposed method incorporates a data reuse (DR) mechanism into the ${\ell }_1$-PRLS framework, enabling multiple equivalent uses of each received–reference sample pair without increasing pilot overhead. Meanwhile, by combining the proportionate update strategy with the ${\ell }_1$ sparsity constraint, the structural information of sparse channels can be more fully exploited, thereby improving parameter estimation accuracy. Numerical simulations are conducted to evaluate the proposed method in terms of convergence behavior, tracking capability, computational complexity, and bit error rate (BER), and comparisons are made with LMS, RLS, PRLS, ${\ell }_1$-PRLS, and DR-PRLS algorithms. Simulation results show that, under sparse underwater acoustic channel conditions, DR-${\ell }_1$-PRLS achieves lower steady-state error and better BER performance while maintaining good tracking capability, thereby demonstrating its effectiveness and robustness for sparse adaptive equalization in single-carrier underwater acoustic communications. Full article

Track quality is key to ensuring the safety and comfort of passengers and freight in railway systems. However, continuous monitoring is rarely implemented due to its high cost and technical complexity. This paper introduces a methodological framework based on machine learning and optimization algorithms for developing onboard track quality monitoring systems using inertial measurements. The workflow addresses crucial, often overlooked aspects such as sensor location, integrating them with downstream processes. The methodology was validated through its application to longitudinal level quality estimation. Synthetic acceleration signals were generated using multibody simulations under parameter configurations defined through a Design of Experiments framework. A multi-objective optimization approach was applied to determine the optimal combination of sensors, balancing estimation accuracy and efficiency. Among the evaluated models, XGBoost achieved a root mean square error of 0.175 mm on the test set, requiring only two acceleration signals and vehicle speed. The use of features derived from wavelet spectra instead of traditional statistical descriptors reduced the estimation error by approximately 20%. These results demonstrate the feasibility of constructing low-cost, data-driven monitoring systems for track quality assessment and highlight the benefits of a structured methodology integrating data generation, sensor analysis, and learning algorithms. Full article

Background: Although menstrual cycle-based training has attracted increasing attention in endurance sports, research has predominantly focused on ergometric parameters. However, the subjective perspectives and lived realities of athletes remain relatively underexamined. Accordingly, this study aimed to explore performance perceptions and self-regulatory experiences of female endurance athletes within real-life training and competitive contexts. Methods: Qualitative semi-structured interviews were conducted with twelve female endurance athletes (ages 18–42) across triathlon, running, swimming, cycling, and skiing. Data were analysed inductively using descriptive thematic analysis in MaxQDA. Results: Six themes emerged related to menstrual cycle experiences: body awareness and cycle-related perceptions; the influence of expectations and self-efficacy on perceived performance; heterogeneous approaches to cycle-based training; training and recovery adjustments; the ambivalent role of digital tracking tools; and communication openness and barriers. Overall, cycle-based training was applied inconsistently and served more as a framework for interpreting physical symptoms than as a means of optimising performance. Conclusions: In this sample of endurance athletes, cycle-related effects on performance and symptom perceptions were primarily shaped by biopsychosocial factors rather than physiological considerations alone. The menstrual cycle supported self-regulation, but rigid interpretations may risk reinforcing negative expectancies. These insights extend existing work by foregrounding athlete-centred, flexible approaches over deterministic training models. Full article

The deployment of large language models as autonomous retrieval agents over unstructured knowledge bases gives rise to a persistent structural conflict between probabilistic neural generation and deterministic physical execution. While agentic paradigms facilitate complex multi-hop retrieval, their unconstrained generative nature frequently violates strict syntactic requirements. This systemic vulnerability directly triggers execution hallucinations, such as fabricated API parameters or malformed schemas. Consequently, these syntax-driven failures force systems into redundant trial-and-error recovery loops, resulting in severe computational inflation that degrades both token efficiency and inference latency. To resolve this reliability–efficiency dilemma, this paper proposes RAG-CoT-MCP, a neuro-symbolic architecture that orthogonally decouples probabilistic cognitive planning from deterministic tool execution. By integrating the Model Context Protocol (MCP) as a strict system-level validation boundary, the framework ensures that latent reasoning trajectories manifest exclusively as syntactically valid operations. Exhaustive empirical evaluations across four disparate datasets—incorporating a multi-dimensional LLM-as-a-Judge framework, rigorous ablation studies, and granular cost tracking—validate the proposed approach. The findings demonstrate that RAG-CoT-MCP compresses network-level execution error rates from 45.2% (in unconstrained baselines) to a mere 6.0%, yielding substantial enhancements in semantic comprehensiveness and logical coherence compared to existing baselines. Counterintuitively, by proactively intercepting malformed actions and redirecting computational resources from reactive error handling to valid causal deduction, the framework drastically reduces redundant token consumption and achieves the lowest overall inference latency. Ultimately, this study establishes that deterministic execution constraints do not hinder agentic flexibility; rather, they serve as a fundamental prerequisite for deploying robust, high-speed, and cost-effective knowledge retrieval systems. Full article