Search Results (2,458)

Road-surface distress poses a serious threat to traffic safety and imposes a growing burden on urban maintenance budgets. While modern detectors based on convolutional networks and Vision Transformers achieve strong frame-level performance, they often overlook an essential property of road environments—structural symmetry within road networks and damage patterns. We present Graph-MambaRoadDet (GMRD), a symmetry-aware and lightweight framework that integrates dynamic graph reasoning with state–space modeling for accurate, topology-informed, and real-time road damage detection. Specifically, GMRD employs an EfficientViM-T1 backbone and two DefMamba blocks, whose deformable scanning paths capture sub-pixel crack patterns while preserving geometric symmetry. A superpixel-based graph is constructed by projecting image regions onto OpenStreetMap road segments, encoding both spatial structure and symmetric topological layout. We introduce a Graph-Generating State–Space Model (GG-SSM) that synthesizes sparse sample-specific adjacency in $\mathcal{O}\left(M\right)$ time, further refined by a fusion module that combines detector self-attention with prior symmetry constraints. A consistency loss promotes smooth predictions across symmetric or adjacent segments. The full INT8 model contains only 1.8 M parameters and 1.5 GFLOPs, sustaining 45 FPS at 7 W on a Jetson Orin Nano—eight times lighter and 1.7× faster than YOLOv8-s. On RDD2022, TD-RD, and RoadBench-100K, GMRD surpasses strong baselines by up to +6.1 mAP_50:95 and, on the new RoadGraph-RDD benchmark, achieves +5.3 G-mAP and +0.05 consistency gain. Qualitative results demonstrate robustness under shadows, reflections, back-lighting, and occlusion. By explicitly modeling spatial and topological symmetry, GMRD offers a principled solution for city-scale road infrastructure monitoring under real-time and edge-computing constraints. Full article

Soil organic matter (SOM) is essential for ecosystem health and agricultural productivity. Accurate prediction of SOM content is critical for modern agricultural management and sustainable soil use. Existing digital soil mapping (DSM) models, when processing temporal data, primarily focus on modeling the changes in input data across successive time steps. However, they do not adequately model the relationships among different input variables, which hinders the capture of complex data patterns and limits the accuracy of predictions. To address this problem, this paper proposes a novel deep learning model, 2-Channel Network (2C-Net), leveraging sequential multi-temporal remote sensing images to improve SOM prediction. The network separates input data into temporal and spatial data, processing them through independent temporal and spatial channels. Temporal data includes multi-temporal Sentinel-2 spectral reflectance, while spatial data consists of environmental covariates including climate and topography. The Multi-sequence Feature Fusion Module (MFFM) is proposed to globally model spectral data across multiple bands and time steps, and the Diverse Convolutional Architecture (DCA) extracts spatial features from environmental data. Experimental results show that 2C-Net outperforms the baseline model (CNN-LSTM) and mainstream machine learning model for DSM, with R² = 0.524, RMSE = 0.884 (%), MAE = 0.581 (%), and MSE = 0.781 (%)². Furthermore, this study demonstrates the significant importance of sequential spectral data for the inversion of SOM content and concludes the following: for the SOM inversion task, the bare soil period after tilling is a more important time window than other bare soil periods. 2C-Net model effectively captures spatiotemporal features, offering high-accuracy SOM predictions and supporting future DSM and soil management. Full article

A review of the methods and applications of marker-based and markerless-based motion capture and inertial measurement units for clinical gait analysis is offered to provide readers with an important historical and legacy-guided perspective. Advantages and limitations of these methods are delineated in light of Cappozzo’s ‘considerations on clinical gait evaluation’ and Brand and Crowninshield’s ‘comment on criteria for patient evaluation tools’. Critical summaries of each manuscript that make up this Special Issue reflect consideration of the notable comments by the legacy biomechanists who had the insights to frame important issues. Full article

This paper investigates frequency regulation of an airport microgrid (AIM) through the application of an integral absolute error (IAE)-assisted control approach. The islanded AIM is initially captured using a linearized transfer function model to accurately reflect its dynamic characteristics. This model is then simplified using a first-order plus dead time (FOPDT) approximation derived via a reaction-curve-based method, which balances between model simplicity and accuracy. Two different proportional–integral–derivative (PID) controllers are designed to meet distinct objectives: one focuses on set-point tracking (SPT) to maintain the target frequency levels, while the other addresses load disturbance rejection (LDR) to reduce the effects of load fluctuations. A thorough comparison of these controllers demonstrates that the SPT-mode PID controller outperforms the LDR-mode controller by providing an improved transient response and notably lower error measures. The results underscore the effectiveness of combining IAE-based control with reaction curve modeling to tune PID controllers for islanded AIM systems, contributing to enhanced and reliable frequency regulation for microgrid operations. Full article

Background: Polytrauma remains a leading cause of mortality and disability worldwide. Although trauma-related deaths have declined in recent decades, the drivers of this trend remain incompletely understood. Traumatic brain injury (TBI) is the principal cause of death and long-term disability in polytrauma, making it a critical determinant of outcomes. This study aimed to examine long-term trends in clinical characteristics, management strategies, and outcomes of polytraumatized patients with TBI (PTBI), with a particular focus on factors influencing overall and cause-specific mortality. Methods: We conducted a retrospective observational study of a prospectively maintained trauma registry over a 25-year period (1993–2018) at the Gregorio Marañón University General Hospital (Madrid, Spain). Adult patients with PTBI were included. Epidemiological, clinical, and outcome data were analyzed globally and across four time periods. Results: Among 768 patients with PTBI, mean age was 43 years (±20), and 29% were female. Most sustained closed TBIs (96%) with concomitant severe injuries to the head, chest, and extremities (median Injury Severity Score [ISS] 27; median New Injury Severity Score [NISS] 34). Emergency surgery was required in 51%, and 84% were admitted to intensive care. Over time, the incidence of polytrauma decreased, mainly reflecting fewer traffic-related injuries following advances in prevention and legislation. Despite an increasingly older and comorbid population, ISS/NISS and early mortality declined, largely due to improvements in prehospital care and hemorrhage control. Although crude TBI-related mortality appeared unchanged (28%), this pattern likely reflects offsetting influences, including an older and more comorbid patient population, a higher relative burden of severe cases, and the limitations of mortality alone to capture gains in functional outcomes. Conclusions: Advances in trauma systems and preventive policies have substantially reduced the burden of polytrauma and improved survival. However, severe TBI remains the principal unresolved challenge, highlighting the urgent need for innovative neuroprotective strategies and greater emphasis on functional recovery. Full article

Accurate monitoring of vegetation phenology is vital for understanding climate change impacts on terrestrial ecosystems. While global vegetation greenness phenology (VGP) products are widely available, vegetation productivity phenology (VPP), which better reflects ecosystems’ carbon dynamics, remains largely inaccessible. This study introduces a novel global 500 m VPP dataset (GLASS VPP) from 2001 to 2020, derived from the GLASS gross primary productivity (GPP) product. Validation against three ground-based datasets—Fluxnet 2015, PhenoCam V2.0, and PEP725—demonstrated the dataset’s superior accuracy. Compared to the widely used MCD12Q2 VGP product, GLASS VPP reduced RMSE and bias by 35% and 63%, respectively, when validated against Fluxnet data. It also showed stronger correlations than MCD12Q2 when compared with PhenoCam (195 sites) and PEP725 (99 sites) observations, and it captured spatial and altitudinal phenology patterns more effectively. Overall, GLASS VPP exhibits a higher spatial integrity, stronger ecological interpretability, and improved consistency with ground observations, making it a valuable dataset for phenology modeling, carbon cycle research, and ecological forecasting under climate change. Full article

The increasing prevalence of Arabic text in digital media offers significant potential for sentiment analysis. However, challenges such as linguistic complexity and limited resources make Arabic sentiment analysis (ASA) particularly difficult. In addition, explainable artificial intelligence (XAI) has become crucial for improving the transparency and trustworthiness of artificial intelligence (AI) models. This paper addresses the integration of XAI techniques in ASA through a scoping review of developments. This study critically identifies trends in model usage, examines explainability methods, and explores how these techniques enhance the explainability of model decisions. This review is crucial for consolidating fragmented efforts, identifying key methodological trends, and guiding future research in this emerging area. Online databases (IEEE Xplore, ACM Digital Library, Scopus, Web of Science, ScienceDirect, and Google Scholar) were searched to identify papers published between 1 January 2016 and 31 March 2025. The last search across all databases was conducted on 1 April 2025. From these, 19 peer-reviewed journal articles and conference papers focusing on ASA with explicit use of XAI techniques were selected for inclusion. This time frame was chosen to capture the most recent decade of research, reflecting advances in deep learning and the transformer-based and explainable AI methods. The findings indicate that transformer-based models and deep learning approaches dominate in ASA, achieving high accuracy, and that local interpretable model-agnostic explanations (LIME) is the most widely used explainability tool. However, challenges such as dialectal variation, small or imbalanced datasets, and the black box nature of advanced models persist. To address these challenges future research directions should include the creation of richer Arabic sentiment datasets, the development of hybrid explainability models, and the enhancement of adversarial robustness. Full article

The objective of this study is to examine the impact of Chief Executive Officer (CEO) attributes on corporate performance in Jordan, a representative frontier market. The analysis focuses on four key CEO attributes, comprising two socio-demographic variables—age and educational—and two corporate governance-related ones—tenure and origin. Return on assets (ROA) and return on equity (ROE) are used as proxies for firm performance. Using a sample of 416 firm-year observations from companies listed on the Amman Stock Exchange (ASE) during 2015–2023, the study employs the system GMM methodology to estimate dynamic panel data models, addressing potential endogeneity and capturing the dynamic nature of firm performance. The results show that CEO age has a positive but insignificant effect, whereas CEO education and tenure significantly enhance firm performance. Conversely, CEO origin has a statistically negative impact on firm performance, reflecting the value of insider CEOs. The significant effects of CEO education, tenure, and origin—observed within the models that also incorporated firm- and country-level controls—reflect their incremental contribution to firm performance in frontier markets. Robustness checks, including controls for the COVID-19 pandemic and industry effects, confirm these findings. The study contributes to the literature by demonstrating the applicability of established theories—namely Upper Echelons, Stewardship, Resource Dependence, and Human Capital Theories—while identifying the CEO traits that drive success in frontier markets. It also offers practical guidance for shareholders, board directors, and policymakers in designing effective leadership and governance strategies. Full article

With the widespread adoption of space–air–ground integrated networks (SAGINs) in next-generation wireless communications, intelligent reflecting surfaces (IRSs) have emerged as a key technology for enhancing system performance through passive link reinforcement. This paper addresses the prevalent issue of channel state information (CSI) uncertainty in practical systems by constructing an IRS-assisted multi-hop SAGIN communication model. To capture the performance degradation caused by channel estimation errors, a norm-bounded uncertainty model is introduced. A simulated annealing (SA)-based phase optimization algorithm is proposed to enhance system robustness and improve worst-case communication quality. Simulation results demonstrate that the proposed method significantly outperforms traditional multiple access strategies (SDMA and NOMA) under various user densities and perturbation levels, highlighting its stability and scalability in complex environments. Full article

This cross-sectional study analyzed the lower limb (LL) behavior in terms of gait asymmetry and joints’ kinematic parameters, comparing people with multiple sclerosis (pwMS) and unaffected individuals. Methods: Data from 15 patients, EDSS ≤ 4.5, and 15 healthy control volunteers were gathered. The VICON Motion Capture System (14 infrared cameras), NEXUS software, Plug-in–Gait skeleton model and reflective markers were used to collect data for each subject during five gait cycles on a plane surface. Biomechanical analysis included evaluation of LL joints’ range of motion (ROM) bilaterally, as well as movement symmetry. Results: Comparative biomechanical analysis revealed a hierarchy of vulnerability between the groups: the ankle is the most affected joint in pwMS (p = 0.008–0.014), the knee is moderately affected (p = 0.015 in swing phase), and the hip is the least affected (p > 0.05 in all phases). The swing phase showed the most significant left–right asymmetry impairment, as reflected by root mean square error (RMSE) values: swing-phase RMSE = 9.306 ± 4.635 (higher and more variable) versus stance-phase RMSE = 6.363 ± 2.306 (lower and more consistent). Conclusions: MS does not affect the joints structurally; rather, it eliminates the ability to differentiate the fine-tuning control between them. The absence of significant left–right joint asymmetry differences during complete gait cycle indicates dysfunction in the global motor control. Full article

To quantitatively analyze the macroscopic diffusion process of chloride ions in existing concrete members, the Peridynamic Differential Operator (PDDO) was introduced to formulate a discrete format for Fick’s second law, and a simulation model was established and validated. Subsequently, the influence of specific or randomly distributed defects in the concrete is reflected by adjusting the coefficients in the model’s global matrix. Moreover, the complex geometry of concrete members is captured by employing a point set-based spatial discretization approach. The model also accommodates for the complex corrosion conditions encountered in practice by imposing different boundary conditions. These features allowed for the simulation and validation of chloride ion diffusion experiments on concrete under natural environmental conditions. The study further analyzed how factors such as defects, diffusion coefficients, boundary conditions, and the geometric shape of members influence the macroscopic diffusion process. The findings indicate that the numerical model based on the PDDO can effectively quantify the macroscopic diffusion of chloride ions in existing concrete members. It provides fundamental data for the durability maintenance of concrete infrastructures and potentially reduces their carbon footprint by preventing unnecessary rehabilitation or reconstruction. Full article

Cracking in asphalt layers of rigid–flexible composite pavements under coupled ambient temperature fields and traffic loading represents a critical failure mode. Traditional models based on uniform temperature assumptions inadequately capture the crack propagation mechanisms. This study developed a thermo-mechanical coupling model that incorporates realistic temperature-modulus gradients to analyze the compressive-shear behavior and simulate crack propagation using the extended finite element method (XFEM) coupled with a modified Paris’ law. Key findings reveal that the asphalt layer exhibits a predominant compressive-shear stress state; increasing the base modulus from 10,000 MPa to 30,000 MPa reduces the maximum shear stress by 22.8% at the tire centerline and 8.6% at the edge; thermal stress predominantly drives crack initiation, whereas vehicle loading governs the propagation path; field validation via cored samples confirms inclined top-down cracking under thermo-mechanical coupling; and the fracture energy release rate (G_f) reaches a minimum of 155 J·m⁻² at 14:00, corresponding to a maximum fatigue life of 32,625 cycles, and peaks at 350 J·m⁻² at 01:00, resulting in a reduced life of 29,933 cycles—reflecting a 9.0% temperature-induced fatigue life variation. The proposed model, which integrates non-uniform temperature gradients, offers enhanced accuracy in capturing complex boundary conditions and stress states, providing a more reliable tool for durability design and assessment of composite pavements. Full article

Transparent objects are prevalent in various everyday scenarios. However, their reflective and refractive optical properties present significant challenges for conventional optical sensors. This difficulty makes the task of generating dense depth maps from sparse depth maps and high-resolution RGB images a critical area of research. In this paper, we introduce SRNet-Trans, a novel two-stage depth completion framework specifically designed for transparent objects. The approach is structured into two stages, each primarily focused on leveraging semantic and depth information, respectively. In the first stage, RGB images and sparse depth maps are used to predict a relatively dense depth map. The second stage then takes the predicted depth from the first stage, along with the sparse depth map, to generate a final dense depth map. The depth information produced by the two stages is complementary, allowing for effective fusion of both outputs. To enhance the depth estimation process, we integrate a self-attention mechanism in the first stage to better capture semantic features and introduce geometric convolutional layers in the second stage to improve depth encoding accuracy. Additionally, we incorporate a global consistency-based fine depth recovery technique to further refine the final depth map. Extensive experiments on the large-scale real-world TransCG dataset demonstrate that SRNet-Trans outperforms current state-of-the-art methods in terms of depth estimation accuracy. Full article

Ground deformation monitoring in mining areas is essential for hazard prevention and environmental protection. Although interferometric synthetic aperture radar (InSAR) provides detailed phase information for accurate deformation measurement, its performance is often compromised in regions experiencing rapid subsidence and strong noise, where phase aliasing and coherence loss lead to significant inaccuracies. To overcome these limitations, this paper proposes JOTGLNet, a guided learning network with joint offset tracking, for multiscale deformation monitoring. This method integrates pixel offset tracking (OT), which robustly captures large-gradient displacements, with interferometric phase data that offers high sensitivity in coherent regions. A dual-path deep learning architecture was designed where the interferometric phase serves as the primary branch and OT features act as complementary information, enhancing the network’s ability to handle varying deformation rates and coherence conditions. Additionally, a novel shape perception loss combining morphological similarity measurement and error learning was introduced to improve geometric fidelity and reduce unbalanced errors across deformation regions. The model was trained on 4000 simulated samples reflecting diverse real-world scenarios and validated on 1100 test samples with a maximum deformation up to 12.6 m, achieving an average prediction error of less than 0.15 m—outperforming state-of-the-art methods whose errors exceeded 0.19 m. Additionally, experiments on five real monitoring datasets further confirmed the superiority and consistency of the proposed approach. Full article

Gallium nitride (GaN) power devices have attracted extensive attention due to their superior performance in high-frequency and high-power applications. However, the reliability and lifetime prediction of these devices under various operating conditions remain critical challenges. In this study, a hybrid approach combining finite element simulation and deep learning is proposed to predict the lifetime of GaN power devices. COMSOL Multiphysics (V6.3) is employed to simulate the thermal and mechanical stress behavior of GaN devices under different power and frequency conditions, while capturing key degradation indicators such as temperature cycles and stress concentrations. The variation in temperature over time can reflect the degradation of the device and also reveal the fatigue damage caused by the long-term accumulation of thermal stress on the chip. LSTM performs exceptionally well in extracting features from time series data, effectively capturing the long-term and short-term dependencies within the time series. By using simulation data to establish a connection between the chip temperature and its service life, the temperature data and the lifespan data are combined into a dataset, and the LSTM neural network is used to explore the impact of temperature changes over time on the lifespan. The method mentioned in this paper can make preliminary predictions of the results when sufficient experimental data cannot be obtained in a short period of time. The prediction results have a certain degree of reliability. Full article