Search Results (8,373)

Background/Objectives: Gait dysfunction is a major contributor to disability and reduced quality of life in Parkinson’s disease (PD). Although pharmacological treatments and exercise-based rehabilitation programs provide partial improvement, residual gait dysfunction often persists. Given these limitations, there has been growing interest in non-pharmacological and non-invasive strategies such as vibration therapy (VT). However, previous systematic reviews and meta-analyses have yielded inconsistent findings, largely summarizing the presence or absence of treatment effects without clarifying the clinical or therapeutic conditions under which VT may be most effective. Therefore, this study aimed to systematically review and synthesize evidence on the efficacy of VT for improving gait in PD and to identify clinical and therapeutic factors influencing treatment outcomes. Methods: A systematic search of PubMed, Web of Science, Embase, and the Cochrane Library was conducted with no restrictions on the search period, including studies published up to July 2025. Eligible studies included randomized and quasi-experimental clinical trials that evaluated the effects of VT on gait-related outcomes in patients with Parkinson’s disease. Data extraction followed the PRISMA 2020 guidelines, and the risk of bias was assessed using the Cochrane RoB 2 and ROBINS-I tools. Multilevel random-effects meta-analyses were conducted to estimate pooled effect sizes for gait outcomes, and meta-regression and subgroup analyses were performed based on disease stage, medication status, and vibration parameters. Results: A total of 14 studies (11 randomized and 3 non-randomized) were included. The pooled analysis showed that VT significantly improved gait performance in PD (Hedges’ g = 0.270, 95% CI: 0.115–0.424; 95% PI: −0.166–0.705). The sensitivity analysis restricted to randomized controlled trials yielded a comparable significant effect (g = 0.316, 95% CI: 0.004–0.628). Greater benefits were observed in patients with higher clinical severity, while the moderating effect of levodopa dosage was not significant. Optimal effects were identified with frequencies of 51–100 Hz, session durations ≤3 min, and 2–3 sessions per week. Improvements were evident in gait speed, cycle, and magnitude, whereas no consistent effects were observed for freezing of gait or gait variability. Conclusions: VT yields small but statistically significant improvements in fundamental gait parameters in Parkinson’s disease, particularly under optimized stimulation conditions and in individuals with greater disease severity. Although the pooled effect was modest and the certainty of evidence was rated as very low according to GRADE, these findings cautiously support the potential of vibration-based interventions as a supportive, non-pharmacological, and non-invasive adjunct within broader rehabilitation programs, rather than as a stand-alone treatment. Full article

This study aimed to evaluate the effect of whole-body vibrations (WBV) on ergonomics related to static posture during the operation of container handling machines (Portainer). A 3D numerical model of a seated man was developed using modal and harmonic analysis based on the finite element method (FEM), and implemented on the ANSYS platform to achieve this. Quantitative analyses of whole-body vibrations were carried out in actual workplaces at a port terminal in northeastern Brazil, considering the interaction between the human and the machine. A comparison was made between the real data collected at the operating sites and the values obtained from the developed model. Concerning vibration exposure, the results revealed a critical situation: in 86.2% of cases, the Acceleration of Resulting Normalized Exposure—A(8)—exceeded the alert level, and in 96.6% of cases, the Resulting Vibration Dose Value (VDV) also surpassed this threshold. Similarly, an alert level was exceeded in 97.0% of cases. According to the maximum limits established by Brazilian legislation, the acceleration from normalized exposure did not exceed the limit, while the resulting vibration dose value was surpassed in 20% of cases. The modal analysis results helped identify the critical directions of vibration response, thus supporting the assessment of human exposure effects and the structural performance of the system analyzed. The harmonic analysis showed good agreement between the model and the real acceleration data in the frequency range of 3 to 4 Hz. Full article

This research successfully synthesized a novel n-n heterojunction Bi₂O₂CO₃/AgVO₃ nanocomposite photocatalyst via the in situ chemical deposition process. Characterization results strongly confirmed the formation of a tight heterojunction at the Bi₂O₂CO₃/AgVO₃ interface. The nanocomposite exhibited characteristic XRD peaks and FT-IR vibrational modes of both Bi₂O₂CO₃ and AgVO₃ simultaneously. Electron microscopy images revealed AgVO₃ nanorods tightly and uniformly loaded onto the surface of Bi₂O₂CO₃ nanosheets. Compared to the single-component Bi₂O₂CO₃, the composite photocatalyst exhibited a red shift in its optical absorption edge to the visible region (515 nm) and a decrease in bandgap energy to 2.382 eV. Photoluminescence (PL) spectra demonstrated the lowest fluorescence intensity for the nanocomposite, indicating that the recombination of photogenerated electron–hole pairs was suppressed. After 90 min of visible-light irradiation, the degradation efficiency of Bi₂O₂CO₃/AgVO₃ toward methylene blue (MB) reached up to 99.55%, with photodegradation rates 2.51 and 2.79 times higher than those of Bi₂O₂CO₃ and AgVO₃, respectively. Furthermore, the nanocomposite exhibited excellent cycling stability and reusability. MB degradation was gradually enhanced with increasing the photocatalyst dosage and decreasing initial MB concentration. Radical trapping experiments and absorption spectroscopy of the MB solution revealed that reactive species h⁺ and ·O₂⁻ could destroy and decompose the chromophore groups of MB molecules effectively. The possible mechanism for enhancing photocatalytic performance was suggested, elucidating the crucial roles of charge carrier transfer and active species generation. Full article

Accurate bearing fault diagnosis under various operational conditions presents significant challenges, mainly due to the limited availability of labeled data and the domain mismatches across different operating environments. In this study, an adaptive meta-learning framework (AdaMETA) is proposed, which combines dynamic task-aware model-independent meta-learning (DT-MAML) with efficient multi-scale attention (EMA) modules to enhance the model’s ability to generalize and improve diagnostic performance in small-sample bearing fault diagnosis across different load scenarios. Specifically, a hierarchical encoder equipped with C-EMA is introduced to effectively capture multi-scale fault features from vibration signals, greatly improving feature extraction under constrained data conditions. Furthermore, DT-MAML dynamically adjusts the inner-loop learning rate based on task complexity, promoting efficient adaptation to diverse tasks and mitigating domain bias. Comprehensive experimental evaluations on the CWRU bearing dataset, conducted under carefully designed cross-domain scenarios, demonstrate that AdaMETA achieves superior accuracy (up to 99.26%) and robustness compared to traditional meta-learning and classical diagnostic methods. Additional ablation studies and noise interference experiments further validate the substantial contribution of the EMA module and the dynamic learning rate components. Full article

Timely detection of leaks is essential for the safe and reliable operation of pressure vessels used in superconducting systems, aerospace, and medical equipment. To address the lack of efficient online leak detection methods for such vessels, this paper proposes a quasi-distributed fiber Bragg grating (FBG) sensing network combined with theoretical stress analysis to diagnose vessel conditions. We analyze the stress–strain distributions of vacuum vessels under varying pressures and examine stress concentration effects induced by small holes; these analyses guided the design and placement of quasi-distributed FBG sensors around the vacuum valve for online leakage monitoring. To improve measurement accuracy, we introduce a vibration correction algorithm that mitigates pump-induced vibration interference. Comparative tests under three leakage scenarios demonstrate that when leakage occurs during vacuum extraction, the proposed system can reliably detect the approximate leak location. The results indicate that combining an FBG sensing network with stress concentration analysis enables initial localization and assessment of leak severity, providing valuable support for the safe operation and rapid maintenance of vacuum pressure vessels. Full article

To accurately diagnose the potential faults such as jamming and incomplete opening and closing of high-voltage disconnectors during long-term operation, this paper proposes a fault diagnosis method based on the fusion of time-frequency domain energy features of the body-side vibration signal. This method extracts short-term energy in the time domain and the marginal spectral energy of the sub-signals processed by variational mode decomposition (VMD) as features in the frequency domain, and constructs a feature set that can effectively represent different states through feature fusion. This enables the distinction between six states, namely normal closing, normal opening, closing jam, opening jam, closing not in place, and opening not in place. On this basis, the particle swarm optimization (PSO) algorithm is adopted to optimize the hyperparameters of the support vector machine (SVM), and the fault diagnosis model is obtained. The fault simulation experiment was conducted on the ZF12B type disconnector, and the experimental results show that the recognition accuracy of the proposed method reaches 98.33%, which is superior to the compared method, verifying the effectiveness and superiority of the proposed method. Full article

Background: Individuals with generalized joint hypermobility (GJH) often exhibit altered sensorimotor control, which may contribute to balance and proprioception deficits. This study investigated the effects of sensory training applied to the plantar surface on sensorimotor organization components, including light touch, vibration, two-point discrimination, proprioception, muscle strength, and balance, in individuals with GJH. Methods: This randomized controlled trial included 65 asymptomatic individuals aged 18–25 years with a Beighton score of 5 or higher. The participants were randomly assigned to either the treatment (n = 32) or control (n = 33) group. The treatment group was given a 2-week home program that included plantar sensory training and an informative brochure on healthy foot care; the control group was given only the brochure. Light touch, two-point discrimination, vibration sense, proprioception, muscle strength, and balance parameters were assessed before and after the intervention. Results: Compared to the control group, the treatment group demonstrated significant improvements in light touch (p < 0.01), two-point discrimination (p < 0.01), vibration sense (p < 0.01), and proprioceptive accuracy (p < 0.01). Balance performance improved markedly in the posterolateral direction (+8.3 cm, p < 0.01), while anterior and posteromedial directions showed moderate but nonsignificant gains. Muscle strength showed no statistically significant changes across groups (p > 0.05). The control group exhibited no meaningful pre-post changes. Conclusions: Sensory training directed at the plantar surface results in positive changes in various components of sensorimotor organization in individuals with GJH. Full article

This paper introduces an effective approach for rotatory fault diagnosis, specifically focusing on centrifugal pumps, by combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and feature-level integration. Centrifugal pumps are critical in various industries, and their condition monitoring is essential for reliability. The proposed methodology addresses the limitations of traditional single-sensor fault diagnosis by fusing information from acoustic and vibration signals. CEEMDAN was employed to decompose raw signals into intrinsic mode functions (IMFs), mitigating noise and non-stationary characteristics. Weighted kurtosis was used to select significant IMFs, and a comprehensive set of time, frequency, and time–frequency domain features was extracted. Feature-level fusion integrated these features, and a support vector machine (SVM) classifier, optimized using the crayfish optimization algorithm (COA), identified different health conditions. The methodology was validated on a centrifugal pump with various impeller defects, achieving a classification accuracy of 95.0%. The results demonstrate the efficacy of the proposed approach in accurately diagnosing the state of centrifugal pumps. Full article

Existing multivariate signal decomposition methods insufficiently account for the mechanical characteristics of gear systems, limiting their capability in fault feature extraction. To address this limitation, we propose a novel method, Multivariate Blaschke-based Mode Decomposition (MBMD). In MBMD, multivariate vibration signals are modeled as multi-dimensional responses of the gear system. Using Stochastic Adaptive Fourier Decomposition (SAFD), these signals are represented as a unified combination of Blaschke products, enabling adaptive multi-channel information fusion. To achieve modal alignment, we introduce the concept of Blaschke multi-spectra, reformulating the decomposition problem as a spectrum segmentation task, which is solved via a joint spectral segmentation algorithm. Furthermore, a voting-based filter bank, designed according to gear fault mechanisms, is employed to suppress noise and enhance fault feature extraction. Experimental validation on gear fault signals demonstrates the effectiveness of MBMD, showing that it can efficiently integrate multivariate information and achieve more accurate fault diagnosis than existing methods, providing a new perspective for mechanical fault diagnosis. Full article

As the world is technologically advancing, the integration of FSO communication in non-terrestrial platforms is transforming the landscape of global connectivity. By enabling high-data-rate inter-satellite links, secure UAV–ground channels, and efficient HAPS backhaul, FSO technology is paving the way for sustainable 6G non-terrestrial networks. However, the stringent requirement for precise line-of-sight (LoS) alignment between the optical transmitter and receivers poses a hindrance in practical deployment. As non-terrestrial missions require continuous movement across the mission area, the platform is subject to vibrations, dynamic motion, and environmental disturbances. This makes maintaining the LoS between the transceivers difficult. While fine-pointing mechanisms such as fast steering mirrors and adaptive optics are effective for microradian angular corrections, they rely heavily on an initial coarse alignment to maintain the LoS. Coarse pointing modules or gimbals serve as the primary mechanical interface for steering and stabilizing the optical beam over wide angular ranges. This survey presents a comprehensive analysis of coarse pointing and gimbal modules that are being used in FSO communication systems for non-terrestrial platforms. The paper classifies gimbal architectures based on actuation type, degrees of freedom, and stabilization strategies. Key design trade-offs are examined, including angular precision, mechanical inertia, bandwidth, and power consumption, which directly impact system responsiveness and tracking accuracy. This paper also highlights emerging trends such as AI-driven pointing prediction and lightweight gimbal design for SWap-constrained platforms. The final part of the paper discusses open challenges and research directions in developing scalable and resilient coarse pointing systems for aerial FSO networks. Full article

Mid-infrared molecular sensing faces challenges in simultaneously achieving high-resolution qualitative identification and quantitative analysis of multiple biomolecules. To address this, we present a tunable mid-infrared sensing platform, integrating the simulation of a single-layer graphene square-aperture array sensor with a machine learning algorithm called principal component analysis for advanced spectral processing. The graphene square-aperture structure excites dynamically tunable localized surface plasmon resonances by modulating the graphene’s Fermi level, enabling precise alignment with the vibrational fingerprints of target molecules. This plasmon–molecule coupling amplifies absorption signals and serves as discernible “molecular barcodes” for precise identification without change in the structural parameters. We demonstrate the platform’s capability to detect and differentiate carbazole-based biphenyl molecules and protein molecules, even in complex mixtures, by systematically tuning the Fermi level to match their unique vibrational bands. More importantly, for mixtures with unknown total amounts and different concentration ratios, the principal component analysis algorithm effectively processes complex transmission spectra and presents the relevant information in a simpler form. This integration of tunable graphene plasmons with machine learning algorithms establishes a label-free, multiplexed mid-infrared sensing strategy with broad applicability in biomedical diagnostics, environmental monitoring, and chemical analysis. Full article

The reliability of hydraulic valves is critical for the safety and efficiency of industrial systems. While vibration and pressure sensors are widely deployed for condition monitoring, leveraging the heterogeneous data from these multi-sensor systems for accurate remaining useful life (RUL) prediction remains challenging due to noise, outliers, and inconsistent sampling rates. This study proposes a sensor data-driven framework that integrates multi-step signal preprocessing, time–frequency feature fusion, and a machine learning model to address these challenges. Specifically, raw data from vibration and pressure sensors are first harmonized through a multi-step preprocessing pipeline including Hampel filtering for impulse noise, Robust Scaler for outlier mitigation, Butterworth low-pass filtering for effective frequency band retention, and resampling to a unified rate. Subsequently, vibro-acoustic features are extracted from the preprocessed sensor signals, including Fast Fourier Transform (FFT)-based frequency domain features and Wavelet Packet Decomposition (WPD)-based time–frequency features, to comprehensively characterize the valve’s degradation. A health indicator (HI) is constructed by fusing the most sensitive features. Finally, a Kernel Principal Component Analysis (KPCA)-optimized Random Forest model is developed for HI prediction, which strongly correlates with RUL. Validated on the UCI hydraulic condition monitoring dataset through 20-run Monte-Carlo cross-validation, our method achieves a root mean square error (RMSE) of 0.0319 ± 0.0090, a mean absolute error (MAE) of 0.0109 ± 0.0014, and a coefficient of determination (R²) of 0.9828 ± 0.0097, demonstrating consistent performance across different data partitions. These results confirm the framework’s effectiveness in translating multi-sensor data into actionable insights for predictive maintenance, offering a viable solution for industrial health management systems. Full article

With the rapid development of transportation infrastructure, bridges increasingly face prominent issues of dynamic response and fatigue damage induced by vehicle–bridge interaction (VBI). To effectively suppress the coupled vibrations and enhance both vehicle ride comfort and bridge service life, this paper proposes an active inerter-spring-damper (ISD) suspension system based on Particle Swarm Optimization (PSO) algorithm and Linear Quadratic Regulator (LQR) control. By establishing a VBI model considering general boundary conditions and employing the modal superposition method to solve the system response, an LQR controller is designed for multi-objective optimization targeting the vehicle body acceleration, suspension dynamic travel, and tire dynamic load. To further improve control performance, the PSO algorithm is utilized to globally optimize the LQR weighting matrices. Numerical simulation results demonstrate that, compared to passive suspension and unoptimized LQR active suspension, the PSO-LQR control strategy significantly reduces vertical body acceleration and tire dynamic load, while also improving the convergence and stability of the suspension dynamic travel. This research provides a new insight into the control method for VBI systems, possessing both theoretical and practical engineering application value. Full article

In this study, a nonlinear dynamic model is developed to examine the stability and vibration behavior of a self-balancing electric Segway operating over irregular stochastic terrains. The Segway is treated as a three-degrees-of-freedom cart–inverted pendulum system, incorporating elastic and damping effects at the wheel–ground interface. Road irregularities are generated in accordance with international standard using high-order filtered noise, allowing for representation of surface classes from smooth to highly degraded. The governing equations, formulated via Lagrange’s method, are transformed into a Lorenz-like state-space form for nonlinear analysis. Numerical simulations employ the fourth-order Runge–Kutta scheme to compute translational and angular responses under varying speeds and terrain conditions. Frequency-domain analysis using Fast Fourier Transform (FFT) identifies resonant excitation bands linked to road spectral content, while Kernel Density Estimation (KDE) maps the probability distribution of displacement states to distinguish stable from variable regimes. The Lyapunov stability assessment and bifurcation analysis reveal critical velocity thresholds and parameter regions marking transitions from stable operation to chaotic motion. The study quantifies the influence of the gravity–damping ratio, mass–damping coupling, control torque ratio, and vertical excitation on dynamic stability. The results provide a methodology for designing stability control systems that ensure safe and comfortable Segway operation across diverse terrains. Full article

The failure to accurately identify abnormal vibrations in protected metro areas is a serious threat to the operational safety of metro tunnels and trains, and there is currently no suitable method for effectively improving the accuracy of abnormal vibration identification. To address this issue, an accurate method for identifying abnormal vibrations in a metro reserve based on spatially correlated dense measurement points is proposed. First, by arranging distributed optical fibers along the longitudinal length of a tunnel, dynamic strain vibration signals are extracted via phase-sensitive optical time-domain reflectometry analysis, and analysis of variance (ANOVA) and Pearson correlation analysis are used to jointly downscale the dynamic strain features. On this basis, a spatial correlation between the calculated values of the features of the target measurement points to be updated and its adjacent measurement points is constructed, and the spatial correlation credibility of the dynamic strain features between the dense measurement points and the target measurement points to be updated is calculated via quadratic function weighting and kernel density estimation methods. The weights are calculated, and the eigenvalues of the target measurement points are updated on the basis of the correlation credibility weights between the adjacent measurement points. Finally, a support vector machine (SVM) and back propagation (BP) identification model for the eigenvalues of the target measurement points are constructed to identify the dynamic strain eigenvalues of the abnormal vibrations in the underground tunnel. Numerical simulations and an experiment in an actual tunnel verify the effectiveness of the proposed method. Full article