Search Results (55)

The growing demand for reliable and secure vehicle-to-vehicle (V2V) communication in next-generation intelligent transportation systems has accelerated the adoption of reconfigurable intelligent surfaces (RIS) as a means of enhancing link quality, spectral efficiency, and physical layer security. In this paper, we investigate the problem of secrecy rate maximization in a cooperative dual-RIS-aided V2V communication network, where two cascaded RISs are deployed to collaboratively assist with secure data transmission between mobile vehicular nodes in the presence of eavesdroppers. To address the inherent complexity of time-varying wireless channels, we propose a novel evolutionary transformer-gated recurrent unit (Evo-Transformer-GRU) framework that jointly learns temporal channel patterns and optimizes the RIS reflection coefficients, beam-forming vectors, and cooperative communication strategies. Our model integrates the sequence modeling strength of GRUs with the global attention mechanism of transformer encoders, enabling the efficient representation of time-series channel behavior and long-range dependencies. To further enhance convergence and secrecy performance, we incorporate an improved gray wolf optimizer (IGWO) to adaptively regulate the model’s hyper-parameters and fine-tune the RIS phase shifts, resulting in a more stable and optimized learning process. Extensive simulations demonstrate the superiority of the proposed framework compared to existing baselines, such as transformer, bidirectional encoder representations from transformers (BERT), deep reinforcement learning (DRL), long short-term memory (LSTM), and GRU models. Specifically, our method achieves an up to 32.6% improvement in average secrecy rate and a 28.4% lower convergence time under varying channel conditions and eavesdropper locations. In addition to secrecy rate improvements, the proposed model achieved a root mean square error (RMSE) of 0.05, coefficient of determination ($R^2$) score of 0.96, and mean absolute percentage error (MAPE) of just 0.73%, outperforming all baseline methods in prediction accuracy and robustness. Furthermore, Evo-Transformer-GRU demonstrated rapid convergence within 100 epochs, the lowest variance across multiple runs. Full article

Timely diagnosis and prognosis based on degradation symptoms are essential steps for condition-based maintenance (CBM) to guarantee industrial safety and productivity. Most industrial machines operate under variable operating conditions. This time-varying operating condition can accelerate the machinery’s degradation process. It may have a massive influence on data and impede the process of diagnosis and prognosis of the machinery. Therefore, in this paper, to address the mentioned problems, we introduced an approach for modelling non-stationary long-term condition monitoring data. This procedure includes separating random and deterministic parts and identifying possible autodependence hidden in the random sequence, as well as potential time-dependent variance. To achieve these objectives, we employ a time-varying coefficient autoregressive (TVC-AR) model within a Bayesian framework. However, due to the limited availability of diverse run-to-failure data sets, we validate the proposed procedure using a simulated degradation model and two widely recognized benchmark data sets (FEMTO and wind turbine drive), which demonstrate the model’s effectiveness in capturing complex non-stationary degradation characteristics. Full article

Telemedicine has emerged as a vital tool for expanding healthcare access, particularly in underserved areas, yet its effectiveness is often hindered by inefficient queuing systems, fluctuating patient demand, and limited resources. This study addresses these challenges by developing a hybrid Artificial Neural Network–Particle Swarm Optimization (ANN-PSO) model aimed at improving the performance of telemedicine queuing systems. A simulation-based dataset was generated to represent patient arrivals, service rates, and queuing behaviors. An ANN was trained to predict key performance metrics, including queue intensity, system utilization, and delays. To further enhance the model’s predictive capabilities, PSO was applied to optimize critical ANN parameters, such as neuron count, swarm size, and acceleration factors. The optimized ANN-PSO model achieved high predictive accuracy, with correlation coefficients (R²) consistently exceeding 0.90 and low mean squared errors across most outputs. The findings show that optimal parameter configurations vary depending on the specific performance metric, reinforcing the value of adaptive optimization. The results confirm the ANN-PSO model’s ability to accurately predict queuing behavior and optimize system performance, providing a practical decision-support tool for telemedicine administrators to dynamically manage patient flow, reduce waiting times, and enhance resource utilization under variable demand conditions. Full article

The application of ceramic particle-reinforced metal matrix composites (CPRMMCs) in the nuclear power sector is primarily dependent on their mechanical and thermal properties. A comprehensive understanding of the structure–property (SP) linkages between microstructures and macroscopic properties is critical for optimizing material properties. However, traditional studies on SP linkages generally rely on experimental methods, theoretical analysis, and numerical simulations, which are often associated with high time and economic costs. To address this challenge, this study proposes a novel method based on Materials Informatics (MI), combining the finite element method (FEM), graph Fourier transform, principal component analysis (PCA), and machine learning models to establish the SP linkages between the microstructure and thermodynamic properties of CPRMMCs. Specifically, FEM is used to model the microstructures of CPRMMCs with varying particle volume fractions and sizes, and their elastic modulus, thermal conductivity, and coefficient of thermal expansion are computed. Next, the statistical features of the microstructure are captured using graph Fourier transform based on two-point spatial correlations, and PCA is applied to reduce dimensionality and extract key features. Finally, a polynomial kernel support vector regression (Poly-SVR) model optimized by Bayesian methods is employed to establish the nonlinear relationship between the microstructure and thermodynamic properties. The results show that this method can effectively predict FEM results using only 5–6 microstructure features, with the R² values exceeding 0.91 for the prediction of thermodynamic properties. This study provides a promising approach for accelerating the innovation and design optimization of CPRMMCs. Full article

We study the large-time behavior of solutions to Darcy–Boussinesq equations with a non-vanishing scalar acceleration coefficient, which model buoyancy-driven flows in porous media with spatially varying gravity. First, we show that the system admits steady-state solutions of the form $(\mathbf{u},\rho ,p)=(\mathbf{0},{\rho }_s,p_s)$, where ${\rho }_s$ is characterised by the hydrostatic balance $\nabla p_s=-{\rho }_s\nabla \mathrm{\Psi }$. Second, we prove that the steady-state solution satisfying $\nabla {\rho }_s=\delta (x,y)\nabla \mathrm{\Psi }$ is linearly stable provided that $\delta (x,y)<{\delta }_0<0$, while the system exhibits Rayleigh–Taylor instability if $\mathrm{\Psi }=gy$, ${\rho }_s={\delta }_{0}g$ and ${\delta }_0>0$. Finally, despite the inherent Rayleigh–Taylor instability that may trigger exponential growth in time, we prove that for any sufficiently regular initial data, the solutions of the system asymptotically converge towards the vicinity of a steady-state solution, where the velocity field is zero, and the new state is determined by hydrostatic balance. This work advances porous media modeling for geophysical and engineering applications, emphasizing the critical interplay of gravity, inertia, and boundary conditions. Full article

This study proposes an improved bidirectional dynamic jump point search (JPS) algorithm to address key challenges in mobile robot path planning, including excessive node expansions, poor path smoothness, safety concerns, and extended search times. The core novelty of this algorithm lies in the introduction of adaptive weight coefficients in the heuristic function and dynamic constraint circles to optimize node expansions. Specifically, the adaptive heuristic function dynamically adjusts the weight coefficients based on the current position relative to the target point, significantly accelerating path searches while ensuring accuracy. Additionally, a dynamically constrained circle is introduced, which defines an adaptive search region, prioritizing node expansions within its boundary and effectively reducing unnecessary searches. Moreover, the jump point selection rules have been optimized to eliminate hazardous nodes and further improve path safety and practicality. Simulation tests conducted on grid maps with varying complexities clearly demonstrate that the proposed algorithm considerably reduces search times by up to 66.27% compared with conventional A*, traditional JPS, and bidirectional JPS methods. Finally, physical mobile robot experiments further validate the effectiveness and real-world applicability of the proposed algorithm. Full article

The rail surface condition is a critical factor influencing wheel–rail adhesion performance. To address the engineering challenges associated with existing rail surface condition identification models, such as high-parameter complexity, significant computational delay, and the difficulty of onboard deployment, a lightweight rail surface condition identification method integrating knowledge distillation and transfer learning is proposed. A rail surface image dataset is constructed, covering typical working conditions, including dry, wet, and oily surfaces. A “teacher-student” collaborative optimization framework is developed, in which GoogLeNet, fine tuned via transfer learning, serves as the teacher network to guide the MobileNet student network, which is also fine tuned through transfer learning, thereby achieving model compression. Additionally, an FP16/FP32 mixed-precision computing strategy is employed to accelerate the training process. The experimental results demonstrate that the optimized student model has a compact size of only 4.21 MB, achieves an accuracy of 97.38% on the test set, and attains an inference time of 0.0371 s. Integrating this model into the estimation system of the maximum adhesion coefficient for heavy-haul locomotives enhances estimation confidence, reduces estimation errors under varying operating conditions, and provides real-time and reliable environmental perception for optimizing adhesion control strategies. This approach holds significant engineering value in improving adhesion utilization under complex wheel–rail contact conditions. Full article

This study investigates the influence of curing temperature (explored at 10 °C, 20 °C, and 30 °C) on the volume changes of alkali-activated slag (AAS) pastes with the aim of expanding existing knowledge on alkali-activated materials (AAMs). The focus was on autogenous and thermal strains, internal relative humidity (IRH), heat flow and cumulative heat, setting times, and workability. The results indicate that increasing the curing temperature to 30 °C reduces autogenous shrinkage, likely due to changes in the elastic modulus and viscoelastic properties, while promoting swelling, especially for higher molarities. The coefficient of thermal expansion (CTE), related to thermal strains, is higher when the curing temperature is increased, but its development is delayed. The IRH is influenced more by the activating solution’s molarity than by curing temperature, although temperature does affect the initial IRH. The study also revealed that higher curing temperatures accelerate chemical reactions and reduce setting times. The initial workability was significantly affected by the solution-to-binder ratio, while higher temperatures decreased workability, especially at higher molarities. These findings contribute to the understanding of how curing temperature influences the durability of AAS pastes, offering insights into optimized construction practices under varying environmental conditions. Full article

The objective identification of depression using physiological data has emerged as a significant research focus within the field of psychiatry. The advancement of wearable physiological measurement devices has opened new avenues for the identification of individuals with depression in everyday-life contexts. Compared to other objective measurement methods, wearables offer the potential for continuous, unobtrusive monitoring, which can capture subtle physiological changes indicative of depressive states. The present study leverages multimodal wristband devices to collect data from fifty-eight participants clinically diagnosed with depression during their normal daytime activities over six hours. Data collected include pulse wave, skin conductance, and triaxial acceleration. For comparison, we also utilized data from fifty-eight matched healthy controls from a publicly available dataset, collected using the same devices over equivalent durations. Our aim was to identify depressive individuals through the analysis of multimodal physiological measurements derived from wearable devices in daily life scenarios. We extracted static features such as the mean, variance, skewness, and kurtosis of physiological indicators like heart rate, skin conductance, and acceleration, as well as autoregressive coefficients of these signals reflecting the temporal dynamics. Utilizing a Random Forest algorithm, we distinguished depressive and non-depressive individuals with varying classification accuracies on data aggregated over 6 h, 2 h, 30 min, and 5 min segments, as 90.0%, 84.7%, 80.1%, and 76.0%, respectively. Our results demonstrate the feasibility of using daily wearable-derived physiological data for depression recognition. The achieved classification accuracies suggest that this approach could be integrated into clinical settings for the early detection and monitoring of depressive symptoms. Future work will explore the potential of these methods for personalized interventions and real-time monitoring, offering a promising avenue for enhancing mental health care through the integration of wearable technology. Full article

The available historical documents for the city of Alexandria indicate that it was damaged to varying degrees by several (historical and instrumentally recorded) earthquakes and by highly destructive tsunamis reported at some places along the Mediterranean coast. In this work, we applied the neo-deterministic seismic hazard analysis (NDSHA) approach to the Alexandria metropolitan area, estimating ground motion intensity parameters, e.g., peak ground displacement (PGD), peak ground velocity (PGV), peak ground acceleration (PGA), and spectral response, at selected rock sites. The results of this NDSHA zonation at a subregional/urban scale, which can be directly used as seismic input for engineering analysis, indicate a relatively high seismic hazard in the Alexandria region (e.g., 0.15 g), and they can provide an essential knowledge base for detailed and comprehensive seismic microzonation studies at an urban scale. Additionally, we established detailed tsunami hazard inundation maps for Alexandria Governorate based on empirical relations and considering various Manning’s Roughness Coefficients. Across all the considered scenarios, the average estimated time of arrival (ETA) of tsunami waves for Alexandria was 75–80 min. According to this study, the most affected sites in Alexandria are those belonging to the districts of Al Gomrok and Al Montazah. The west of the city, called Al Sahel Al Shamally, is less affected than the east, as it is protected by a carbonate ridge parallel to the coastline. Finally, we emphasize the direct applicability of our study to urban planning and risk management in Alexandria. Our study can contribute to identifying vulnerable areas, prioritizing mitigation measures, informing land-use planning and building codes, and enhancing multi-hazard risk analysis and early warning systems. Full article

The phenomenon of extensive erosion of silty submarine slopes in the Yellow River delta has been well documented in numerous studies. Due to poor drainage and high compressibility, silty sediments are particularly prone to pore pressure buildup and accumulated seepage under wave and current action, which can influence sediment erodibility (e.g., the critical bed shear stress and the erosion rate under various bed shear stresses). To date, there remains a lack of parametric formulation to quantitatively characterize the erodibility of silty sediments with the coupled effects of the hydraulic gradient of upward seepage and the slope gradient. In this study, a series of laboratory experiments were conducted to explore the erodibility of silt sediments from the Yellow River delta under varying hydraulic gradients of upward seepage and slope gradients. The results reveal that both upward seepage and increased slope gradients can enhance the erodibility of silty sediments. Specifically, as the seepage gradient increases from 0.1 to 0.8, the critical Shields parameter required for initiating silty particle motion decreases linearly, with a reduction rate of 0.01 per 0.1 increase in the seepage gradient, independently of changes in slope gradient. Additionally, the erosion coefficient of silty sediments grows exponentially with rising seepage gradients, with its average growth rate accelerating with increasing slope inclination. For flat sediment beds, the erosion coefficient influenced by upward seepage can be up to five times that in the absence of seepage. An empirical formula for calculating the critical Shields parameter and an erosion model incorporating upward seepage gradient and slope effects were developed through multiple regression analysis, providing an experimental basis for numerical simulations of scour in silty submarine slopes under combined waves and currents. Full article

This study develops an evaluation system to assess the integration levels of the seven urban agglomerations in the Yellow River Basin. Based on the weighted comprehensive indicator-based evaluation and Dagum’s Gini decompositions, it evaluates the integration of these urban agglomerations as well as their regional disparities from 2010 to 2022. The results show the following: (1) During the study period, the overall integration level of these urban agglomerations exhibited a general upward trend, although significant gaps still exist, with a spatial pattern of “lower reaches > middle reaches > upper reaches”. Moreover, after 2019, the integration accelerated markedly, indicating that the Yellow River Strategy has positively influenced the integration of these urban agglomerations. (2) Significant differences exist between the urban agglomerations in different dimensions of integration, although the gap has shown a fluctuating but narrowing trend. In addition, the degree of integration across different dimensions has been increasing annually for all urban agglomerations, except for the Shandong Peninsula Urban Agglomeration. The focus of integration varies among these urban agglomerations due to their differing stages of development. (3) In terms of regional disparities, the overall Gini coefficient displayed a “reverse U-shaped” decline, suggesting that while the gap in integration between the urban agglomerations has been narrowing over time, imbalances persist. Inter-group differences are the primary source contributing to the overall disparities in the integration levels of the urban agglomerations in the Yellow River Basin. Full article

Superfine cement is widely used in building reinforcement and repair, special concrete manufacturing, and environmental protection engineering due to its high toughness, high durability, good bonding strength, and environmental friendliness. However, there are some problems in superfine cement slurry, such as high bleeding rate, prolonged setting time, and consolidated body volume retraction. In this article, on the premise of using the excellent injectability of superfine cement slurry, the fluidity, setting time, reinforcement strength, and volume expansion rate of novel expansive superfine cement slurries with varying proportions were analyzed by adding expansion agent UEA, naphthalene-based water reducer FDN-C, and triisopropanolamine accelerating agent TIPA. The results show that under most mix ratios, the bleeding rate and fluidity of the novel superfine cement slurry initially increase and decrease with rising water-reducing agent dosage. The initial setting time generally decreases with accelerating agent dosage, reaching a minimum value of 506 min, representing a 33.68% reduction compared to the benchmark group (traditional superfine cement). Under normal conditions, the compressive strength of the net slurry consolidation body is positively correlated with expansion agent dosage, achieving maximum strengths of 8.11 MPa at three days and 6.93 MPa at 28 days; these values are respectively higher by 6.7 MPa and 2.6 MPa compared to those in the benchmark group. On the seventh day, the volume expansion rate of the traditional superfine cement solidified sand body ranges from −0.19% to −0.1%, while that for the corresponding body formed from the novel superfine cement is between 0.41% and 1.33%, representing a difference of 0.6–1.43%. After the on-site treatment of water and sand-gushing strata, the core monitor rate of the inspection hole exceeds 70%. The permeability coefficient of the stratum decreases to a range between 1.47 × 10⁻⁶ and 8.14 × 10⁻⁶ cm/s, resulting in nearly a thousandfold increase in stratum impermeability compared to its original state. Hence, the findings of this research hold practical importance for the future application of such materials in the development of stratum reinforcement or building repair. Full article

Background: Three-dimensional gait analysis, supported by advanced sensor systems, is a crucial component in the rehabilitation assessment of post-stroke hemiplegic patients. However, the sensor data generated from such analyses are often complex and challenging to interpret in clinical practice, requiring significant time and complicated procedures. The Gait Deviation Index (GDI) serves as a simplified metric for quantifying the severity of pathological gait. Although isokinetic dynamometry, utilizing sophisticated sensors, is widely employed in muscle function assessment and rehabilitation, its application in gait analysis remains underexplored. Objective: This study aims to investigate the use of sensor-acquired isokinetic muscle strength data, combined with machine learning techniques, to predict the GDI in hemiplegic patients. This study utilizes data captured from sensors embedded in the Biodex dynamometry system and the Vicon 3D motion capture system, highlighting the integration of sensor technology in clinical gait analysis. Methods: This study was a cross-sectional, observational study that included a cohort of 150 post-stroke hemiplegic patients. The sensor data included measurements such as peak torque, peak torque/body weight, maximum work of repeated actions, coefficient of variation, average power, total work, acceleration time, deceleration time, range of motion, and average peak torque for both flexor and extensor muscles on the affected side at three angular velocities (60°/s, 90°/s, and 120°/s) using the Biodex System 4 Pro. The GDI was calculated using data from a Vicon 3D motion capture system. This study employed four machine learning models—Lasso Regression, Random Forest (RF), Support Vector regression (SVR), and BP Neural Network—to model and validate the sensor data. Model performance was evaluated using mean squared error (MSE), the coefficient of determination (R²), and mean absolute error (MAE). SHapley Additive exPlanations (SHAP) analysis was used to enhance model interpretability. Results: The RF model outperformed others in predicting GDI, with an MSE of 16.18, an R² of 0.89, and an MAE of 2.99. In contrast, the Lasso Regression model yielded an MSE of 22.29, an R² of 0.85, and an MAE of 3.71. The SVR model had an MSE of 31.58, an R² of 0.82, and an MAE of 7.68, while the BP Neural Network model exhibited the poorest performance with an MSE of 50.38, an R² of 0.79, and an MAE of 9.59. SHAP analysis identified the maximum work of repeated actions of the extensor muscles at 60°/s and 120°/s as the most critical sensor-derived features for predicting GDI, underscoring the importance of muscle strength metrics at varying speeds in rehabilitation assessments. Conclusions: This study highlights the potential of integrating advanced sensor technology with machine learning techniques in the analysis of complex clinical data. The developed GDI prediction model, based on sensor-acquired isokinetic dynamometry data, offers a novel, streamlined, and effective tool for assessing rehabilitation progress in post-stroke hemiplegic patients, with promising implications for broader clinical application. Full article

The operation of an aero-engine involves various non-stationary processes of acceleration and deceleration, with rotational speed varying in response to changing working conditions to meet different power requirements. To investigate the nonlinear dynamic behaviour of cracked blades under variable rotational speed conditions, this study constructed a rotating blade model with edge-penetrating cracks and proposes a component modal synthesis method that accounts for time-varying rotational speed. The nonlinear response behaviours of cracked blades were examined under three distinct operating conditions: spinless, steady speed, and non-constant speed. The findings indicated a competitive relationship between the effects of rotational speed fluctuations and unbalanced excitation on crack nonlinearity. Variations in rotational speed dominated when rotational speed perturbation was minimal; conversely, aerodynamic forces dominated when the effects of rotational speed were pronounced. An increase in rotational speed perturbation enhanced the super-harmonic nonlinearity induced by cracks, elevated the nonlinear damage index (NDI), and accentuated the crack breathing effect. As the perturbation coefficient increased, the super-harmonic nonlinearity of the crack intensified, resulting in a more complex vibration form and phase diagram. Full article