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This paper investigates the random response of hysteretic nonlinear oscillators described by the Volterra integral model, which captures material memory effects via a convolution kernel under Gaussian white noise excitation. The Gaussian basis function (GBF) approximation technique is utilized to estimate the system's steady-state probability density function (PDF). The approximately stationary probability density function is represented as a weighted sum of Gaussian basis functions in this method. The optimal weighting coefficients are determined by minimizing the residual of the stationary Fokker-Planck-Kolmogorov (FPK) equation using the least squares method with Lagrange multipliers, yielding the steady-state joint probability density function. The computed results show excellent agreement with Monte Carlo simulations (MCS), confirming the accuracy and reliability of the Gaussian basis function method and demonstrating the influences of various parameters on the hysteresis loop and the system response.

Traumatic peripheral facial paralysis is a disabling condition that may compromise facial expression, mastication, swallowing, speech, and quality of life. Although several rehabilitation approaches have been proposed, evidence regarding the long-term outcomes of oral proprioceptive stimulation remains limited. A 68-year-old man developed traumatic peripheral facial paralysis following surgery for a cerebellopontine angle tumor in 2003. After several unsuccessful rehabilitation attempts, a hyperboloid-based oral proprioceptive rehabilitation protocol was initiated in 2010. The patient performed intraoral exercises three times daily and underwent clinical follow-up for 12 years. Clinical improvement was first documented approximately 45 days after treatment initiation, with restoration of lip competence. Subsequent improvements included reduced drooling and liquid leakage, better mastication and oral bolus control, improved speech intelligibility and facial symmetry, and increased social participation. Functional gains were maintained throughout the long-term follow-up period with individualized maintenance exercises. This case describes sustained functional improvements observed during long-term follow-up after rehabilitation using a hyperboloid-based oral proprioceptive protocol. Although stimulation of the stomatognathic system may have contributed to the observed functional recovery, treatment effectiveness cannot be established from a single case. Further prospective studies using standardized outcome measures are needed to investigate the potential role of this therapeutic approach.

This research investigates the dynamic stress–strain behavior of a railway bridge beam superstructure subjected to moving train loads using advanced computational modeling. The study employs MIDAS Civil software to develop a comprehensive three-dimensional finite element model of a typical railway bridge superstructure, incorporating realistic boundary conditions, material properties, and geometric configurations. The analysis examines the temporal variation of stresses, displacements, and internal forces as multiple train axles traverse the bridge at various operational speeds. The investigation reveals critical zones experiencing maximum dynamic amplification factors, which significantly exceed those predicted by static analysis methodologies. Dynamic load allowance coefficients are calculated for different train speeds, demonstrating a nonlinear relationship between velocity and structural response amplification. The model accounts for complex phenomena including moving load effects, inertial forces, and structural damping characteristics. Results indicate that dynamic effects become particularly pronounced at specific resonance frequencies, where vertical deflections and bending moments reach peak magnitudes. The findings provide essential insights for bridge design optimization, maintenance planning, and safety assessment protocols. This comprehensive dynamic analysis framework enables engineers to accurately predict structural behavior under realistic operational conditions, ensuring adequate service life and structural integrity of railway bridge infrastructure systems while maintaining required safety margins for contemporary high-speed rail applications.

Fatigue-driving detection based on facial cues is challenging because the key fatigue-related regions, especially the eyes and mouth, are small, easily affected by illumination and occlusion, and must be detected in real time. To address insufficient small-target feature extraction and the trade-off between detection accuracy and model complexity, this paper proposes FWC-YOLOv8, an improved fatigue-driving detection framework built on YOLOv8s. First, an additional 160×160 detection head is introduced to preserve high-resolution spatial information and improve localization of small eye and mouth regions. Second, a Coordinate Attention (CA) module is embedded in the backbone to enhance position-aware feature representation. Third, the feature-fusion structure is refined to strengthen the interaction between shallow spatial details and deep semantic information. Finally, Wise-IoU, which uses a dynamic focusing mechanism, is adopted as the bounding-box regression loss to reduce the influence of low-quality samples and improve convergence. Experiments on the GX_FatigueDriving2020 dataset show that FWC-YOLOv8 achieves an mAP@0.50:0.95 of 0.640, corresponding to a 4.2 % improvement over the original YOLOv8s baseline. The model contains 17.0 M parameters and requires 35.5 GFLOPs, indicating a favorable balance between accuracy and computational cost. In addition, detected eye and mouth states are further converted into four fatigue indicators, namely PERCLOS, blink frequency, continuous eye-closure duration, and yawn frequency, and fused through a weighted multi-index assessment strategy to classify driver fatigue into alert, mild-fatigue, and severe-fatigue states. The framework combines task-specific facial-state detection with interpretable fatigue-level assessment.

This study proposes a comprehensive framework for automated diagnostics of rotating machinery using vibration signals. The methodology integrates the stages of dataset selection and preparation, signal processing, feature extraction, dimensionality reduction and training and testing of supervised machine learning algorithms. The preparation of the dataset involves proposing a technique for addressing data imbalance, normalisation of the data amplitude, adjusting the sampling frequency, and calculating the actual rotational frequency. Then, appropriate signal filters are designed and applied, and a wide range of statistical and spectral features is extracted in the time and frequency domain. To address the challenges of high-dimensional feature spaces and allow transparency in identifying features with the highest level of importance in recognising machinery faults, a group-wise dimensionality reduction technique, Sequential Selection Algorithm (SSA), is proposed. The applicability of the proposed SSA technique is validated by comparing its performance with that of commonly used dimensionality reduction techniques, by testing its compatibility with supervised machine learning algorithms, and by analyzing its efficiency. In that sense, a set of models combining four dimensionality reduction techniques and four supervised machine learning algorithms is trained and tested, and their accuracy is compared.

The roof of a swimming pool is supported by a 33.6 m-span post-tensioned bonded prestressed concrete frame, in which the presence of a heavy upper transfer structure and superimposed filling loads significantly complicates the structural behavior. This study presents a comprehensive investigation of such structural systems based on a practical engineering project. A refined analytical framework is developed to evaluate the flexural capacity, stiffness, deflection, and crack width of prestressed concrete frame beams, with particular consideration of prestress effects and secondary internal forces induced by the transfer structure. In addition, a performance-oriented design approach incorporating ductility requirements and vertical seismic effects is proposed. A three-dimensional global analysis is adopted to capture the interaction between structural components and to improve the accuracy of dynamic response evaluation. The results indicate that the presence of the upper transfer structure significantly affects internal force redistribution, and the induced secondary internal forces must be carefully considered in design. The proposed system demonstrates favorable structural performance in terms of load-carrying capacity, ductility, and seismic behavior under heavy loading conditions. Compared with conventional design approaches, this study highlights the importance of considering the coupled effects of prestressing and transfer systems, and provides practical design recommendations, including reinforcement configurations and construction procedures. The findings offer useful guidance for the analysis and design of similar long-span prestressed concrete structures with transfer systems.

Shipboard equipment vibration is the main source of underwater radiated noise for marine vessels, and floating raft isolation (FRI) systems are widely used for vibration and noise reduction. Current land-based air tests cannot directly reflect the actual isolation performance of FRI systems in underwater service conditions. To address this gap, this paper conducts a systematic experimental study on the vibro-acoustic characteristics of a typical ship cylindrical cabin with an FRI system, in both air and underwater environments. The vibration transfer laws and underwater radiated noise characteristics of the structure are obtained, and the isolation performance of the FRI system is quantitatively evaluated. The research results can provide a direct experimental basis and reference for the design, test, and optimization of marine vibration isolation systems. Compared with previous studies that mainly considered bare or simply stiffened shells, or evaluated floating raft isolation through numerical or air-based tests, this work tests an integrated vibration source to raft with isolator to stiffened-shell to water configuration and provides source-path-receiver benchmark data for assessing the environmental robustness of FRI systems.

Heterogeneous nanocatalysts encode compositional and structural information in complex spectroscopic patterns that are difficult to quantify with single-scale analysis. In this work, a wavelet-based multiscale descriptor framework is applied to the X-ray diffraction (XRD) patterns of pure and cation-substituted hydroxyapatite nanocatalysts (Ag-, Co-, and Sr-HAP) previously reported for the selective hydration of aromatic nitriles to amides. The Discrete Wavelet Transform (DWT) and the Wavelet Scattering Transform (WST) are computed for each diffractogram and compared in terms of feature dispersion, translation invariance, and inter-sample discriminability. The DWT captures localized peak-intensity variations but retains a wide dynamic range (variance ≈ 6.2×1012) that is sensitive to amplitude and translation. The WST compresses the descriptor space by roughly ten orders of magnitude (variance ≈ 1.2×102) while preserving the sample ordering induced by cation substitution and remaining stable under small deformations. These properties yield a compact, mathematically defined, translation-invariant representation of doped-HAP diffraction fingerprints that is directly compatible with downstream chemometric and supervised-learning workflows. Because the descriptors are deterministic and fully reproducible, the framework is proposed as a candidate approach for future batch-to-batch quality assessment during doped-nanocatalyst production – a materials-processing concern relevant to manufacturing quality control – though this application remains untested against actual production-scale batch-variation data. The novelty of the present work lies in the systematic construction and quantitative comparison of DWT and WST descriptors for a family of doped nanocatalysts, providing a reproducible feature-extraction pipeline for structurally similar catalyst systems. The main limitation is the small number of samples analyzed (four catalyst compositions), which restricts the present study to descriptor characterization rather than predictive modelling; extension to larger catalyst libraries is required for full data-driven prediction of catalytic activity.