Search Results (39,274)

Adding inspection trains to existing railway timetables is a complex task that must balance operational efficiency and service reliability, which are essential for the sustainable operation and maintenance of high-speed railway infrastructure. To address this challenge, a feasible region-based space–time network modeling approach is proposed for incorporating Comprehensive Inspection Trains (CITs) into existing railway schedules, aiming to enhance inspection efficiency while minimizing operational disruptions. Firstly, the constraints that need to be considered when scheduling for CIT are comprehensively analysed and modelled, and a mixed-integer nonlinear model with the objective of minimizing the total number of stops is constructed. In order to eliminate the difficulty of solving this model, based on the original space–time network method, more kinds of train event arcs are introduced to accurately portray the train operation process; in particular, the extra time consumed due to the acceleration and deceleration process is also reflected in the network construction process. The feasibility of various event arcs is evaluated with time windows, and the original problem finally transforms into the equivalent shortest path problem on a feasible event arc network. The processing procedure includes key stages, such as station space–time discretization, interval operation event processing, station capacity handling, and network simplification. The experimental results indicate that the approach effectively resolves all station capacity conflicts, compresses inspection durations, and optimizes the number of stops. Remarkably, the number of non-full-speed inspection sections is reduced by 43.16%, demonstrating the model’s efficiency. Additionally, the proposed approach is computationally efficient, improves timetable capacity utilization for infrastructure inspection, and supports the sustainable operation of high-speed railway systems. Full article

As calibration errors have a direct impact on epipolar consistency, rectification accuracy, and metric 3D reconstruction performance, stereo camera calibration is a fundamental requirement for high-accuracy 3D modeling and reliable digital twin data acquisition. Because current calibration workflows (based on pairwise calibration methods) lack systematic data-quality checks mechanisms, there is a clear need for more robust data selection strategies. The novelty of the approach consists in the development of a new outlier-aware stereo calibration algorithm (OutAw) that introduces a unified multi-stage approach that integrates hard geometric selection, candidate subset generation, multi-criterion ranking, bootstrap stability analysis, and triangulation assessment into a comprehensive and systematic calibration framework. Unlike conventional approaches, OutAw (through its mechanism of detecting and rejecting inconsistent pairs) redefines the calibration strategy from arbitrary to criterion-based data selection. Also, the proposed algorithm is compared with BSC (a baseline OpenCV all-pairs calibration algorithm) and InterFil (an intermediate filtered variant) using 49 stereo pairs (at 1280 × 720 resolution) captured using a planar checkerboard. OutAw algorithm achieved (using only nine image pairs) superior results (epipolar error 0.5119 px, stereo RMS 0.7666 px) to the BSC ones (epipolar error 1.3687 px, stereo RMS 1.9385 px), representing statistically significant improvements (60.5%, respectively 62.3%). OutAw geometric consistency was validated by triangulation-based metrics (square-length standard deviation 0.1140 mm and square absolute error 0.1097 mm). Contamination analysis revealed that as the outlier rate increases, the calibration process degrades progressively. Also, the results obtained highlight that geometric quality-driven image selection is critical for achieving a reliable stereo calibration for DT applications. Full article

Background: Health literacy plays an important role in medication understanding, self-management, and engagement with healthcare services among older adults. Limited health literacy may contribute to medication-related problems and reduced utilization of pharmacist-led services in geriatric populations. Methods: A cross-sectional, questionnaire-based survey was conducted among geriatric outpatients (≥60 years) attending a tertiary-care teaching hospital in Saudi Arabia. Health literacy was assessed using a four-domain functional tool—covering prescription label comprehension, understanding of healthcare instructions, confidence in completing medical forms, and comprehension of written health information—developed in alignment with established health literacy frameworks, including the Health Literacy Survey—European Union (HLS-EU) model and Baker’s conceptual framework. Participants were classified as having higher health literacy (0–2 domains impaired) or lower health literacy (3–4 domains impaired). Sociodemographic characteristics, clinical burden, medication self-management behaviors, and awareness of clinical pharmacist services were recorded. Multivariable logistic regression was used to identify factors independently associated with lower health literacy. Results: A total of 200 participants were included. Impairment in three or more domains was observed in 55.5% of participants. Lower health literacy was independently associated with older age, lower educational attainment, lower income, female sex, multimorbidity, and polypharmacy. Participants with lower health literacy reported higher rates of missed or incorrect medication dosing and unreported adverse drug reactions and lower use of medication management aids. Awareness of clinical pharmacist services and prior exposure to pharmacist counseling were significantly lower among participants with lower health literacy. Willingness to receive pharmacist counseling was higher among participants with higher health literacy and greater awareness of pharmacist roles. Conclusions: Health-literacy impairment is common among geriatric outpatients and is associated with medication self-management behaviors and engagement with pharmacist-led services. These findings highlight the relevance of functional health literacy in geriatric medication use and support further research on literacy-sensitive pharmacist-led interventions. Full article

Voltage source converters (VSCs), together with their inner current and outer power/voltage control loops, are fundamental building blocks in the modern, converter-dominated power systems, particularly within high-voltage DC (HVDC) frameworks. Selecting effective control methods for VSCs is essential to ensure the stability, power quality, and dynamic performance of HVDC grids. This paper seeks to advance the current body of research by delivering an in-depth, consistent, unified framework and systematic examination of VSC control architectures within HVDC networks. It thoroughly explores various control strategies for VSCs interfacing with HVDC grids, such as grid-following and grid-forming strategies, with particular emphasis on both stationary ($\alpha \beta$) and synchronous (dq) reference frames. Moreover, the paper provides a comprehensive analysis of the theoretical underpinnings and decoupled control strategies, like the feedforward decoupling of the d- and q-axis currents in the dq frame and the inherently decoupled structure of the αβ frame. Additionally, advanced filtering techniques, including Moving Average Filter (MAF), Cascaded Delayed Signal Cancellation (DSC), and LCL filters, are analyzed. In addition, harmonic mitigation strategies, like parallel/multiple resonant (PR) terms in the αβ frame and cascaded notch filters in the dq frame, are presented. Furthermore, precise power control approaches and synchronization methods are discussed in detail. Also, this paper presents a detailed comparison of the performance characteristics of phase-locked loop (PLL) and frequency-locked loop (FLL) in response to grid frequency variations. Moreover, this paper proposes circuit representations and VSC models in both synchronous and stationary reference frames. The simulation results corroborate the theoretical insights discussed in the paper under various operational conditions, including initial responses, grid disturbances, three-phase-to-ground temporary fault scenarios, harmonic distortions, and load imbalances, in terms of overshoot, settling time, active- and reactive-power fluctuation reduction, voltage unbalance factor, total harmonic distortion, and post-fault convergence time, all evaluated in accordance with the limits defined in EN-50160. This comprehensive comparison of the presented control strategies facilitates researchers in identifying the most appropriate controller depending on their specific application requirements. Full article

Two-dimensional (2D) human pose estimation is one of the key research directions in Computer Vision (CV), which has wide application prospects in behavior recognition, such as gesture tracking, intelligent monitoring, and identity recognition. Therefore, it has recently attracted extensive attention from academia and industry. However, although a large amount of literature has been published, existing reviews often lack a unified theoretical perspective and fail to capture the latest paradigm shifts brought by foundation models. To this end, this paper reviews the applications of deep learning in the domain of 2D body pose estimation from 2010 to 2025 through a cascading approach. First, the mainstream body pose datasets and related evaluation metrics are introduced in a comprehensive and convincing way through mathematical formulas. Subsequently, an in-depth analysis of the performance of the algorithms in single-person and multi-person scenarios, and a comprehensive comparative analysis of the strengths and weaknesses of each algorithmic model, are conducted. A comprehensive comparative analysis encompassing both traditional architectures and the latest deep learning breakthroughs are provided, specifically highlighting Vision Foundation Models (VFMs), generative Diffusion processes, and State Space Models (SSMs). Finally, the current state of research in the field of 2D human pose estimation is summarized, and three main challenges, emerging solutions, and expected development trends are pointed out. This survey is an exhaustive compilation of existing research in 2D human pose estimation, providing a blueprint for researchers in the field and laying the foundation for future research work. Full article

Background: Hypoxic–ischaemic brain injury (HI) is a major contributor to neurological deficits following stroke. Understanding what happens to the smallest functional and structural unit of the central nervous system in the face of oxygen and nutrient deprivation is essential to fully comprehend the pathogenesis of diseases and disorders associated with HI, such as ischaemic stroke. Aim: The aim of this study was to develop a robust in vitro tool for initial screening of potential therapeutics and identification of diagnostic markers of brain hypoxic injury. Methods: This study details and validates a comprehensive protocol for modelling HI using differentiated SH-SY5Y neuroblastoma cells (Neuron-like Cells, NLCs). First, we optimized the differentiation process and confirmed the maturity and purity of NLCs via standard molecular markers. The NLCs exhibited functional excitotoxicity, demonstrating a graded cell death response to N-methyl-D-aspartate (NMDA), thus validating their functional application. To simulate HI, we initially optimized the oxygen-glucose deprivation (OGD) treatment using graded concentrations of CoCl₂ (0.125 mM to 2 mM) in glucose-free media. The validated NLCs were then subjected to the refined OGD protocol (1 mM CoCl₂ in glucose-free media) for 3 h, followed by various periods of reoxygenation (1 h, 3 h, 6 h, 12 h, 18 h, and 24 h). Result: Bulk RNA-sequencing revealed a distinct temporal transcriptional response to HI. Injury-associated genes, including heat shock proteins and stress markers, were significantly (p < 0.05) upregulated at 3 h of reoxygenation, peaked at 6 h, and declined thereafter, remaining above baseline at 24 h. Upstream regulator analysis identified IL-1β, TNF-α, and HIF-1α as key drivers during OGD, with additional regulators emerging during reoxygenation. TNF-α and β-oestradiol were consistently identified across time points, while TGF-β1 and NTRK1 became prominent during peak injury and later phases. Analysis of secreted factors showed increased release of inflammatory (TNF-α) and neurotrophic (β-NGF, BDNF, VEGF) mediators with reoxygenation, while maximal cell death occurred at 24 h. Conclusions: This study identifies a transient, time-dependent transcriptional cascade following hypoxic–ischaemic injury, highlighting a critical window for early neuronal response. The model provides a reproducible platform for studying neuronal injury and recovery, and identifies known (TNF-α, IL-β, and HIF-1α), context-specific (NTRK1 and TGF-β) and novel (β-oestradiol) regulators of the injury response with potential relevance for therapeutic targeting. Full article

While the Triton compiler has revolutionized GPU kernel development, its deployment on general-purpose CPUs struggles to fully utilize the underlying hardware capabilities. This is primarily due to the semantic gap between Triton’s SPMD execution model and CPU vector architectures, which leads to suboptimal utilization of vector units during complex memory accesses. In this paper, we present a comprehensive compilation and runtime co-optimization framework for Triton-CPU, specifically targeting Vector Length Agnostic architectures (VLA) like ARM SVE. At the compiler level, we propose a novel semantic reconstruction and explicit base-offset decoupling strategy, enabling native VLA gather/scatter generation and eliminating scalar loop overheads. At the runtime level, we introduce a Machine Learning-driven thread scheduling model to optimally orchestrate the synergy between Thread-Level Parallelism and Vector-Level Parallelism. Extensive evaluations on an ARM-based multi-core processor demonstrate that our framework achieves up to a 2.0× throughput improvement for compute-bound GEMM operators (peaking at 346 GFLOPS), notably outperforming the hand-optimized OpenBLAS library by up to 1.54× at small-to-medium scales. Additionally, it delivers a 1.7× speedup for element-wise workloads. Furthermore, our optimizations saturate memory bandwidth (up to 55 GB/s) for memory-bound operators with zero compilation bloat, establishing a robust, high-performance foundation for deploying deep learning models on general-purpose CPUs. Full article

The assessment of seismic performance of existing buildings has traditionally focused on structural safety and damage limitation, often neglecting the explicit quantification of the associated economic consequences. In recent years, performance-based earthquake engineering (PBEE) frameworks have enabled a direct link between structural response and probabilistic loss estimation, allowing economic metrics to be integrated into seismic risk evaluation. Among these, the Expected Annual Loss (EAL) represents a comprehensive indicator that accounts for seismic hazard, structural vulnerability, and exposure over the building’s lifetime. This study presents a performance-based seismic loss assessment of an existing reinforced concrete building, adopting EAL as a global metric for seismic performance evaluation. The case study concerns an existing hospital building designed primarily for gravity loads and representative of a large portion of the Italian building stock. A detailed nonlinear numerical model is developed using OpenSees ver. 3.8.0, incorporating shear-critical behavior through nonlinear link elements. Structural performance is evaluated through modal analysis, pushover analysis, and nonlinear time-history analyses using a set of ground motions selected and scaled according to intensity-based criteria. Seismic losses are estimated following the FEMA P-58 methodology, implemented through the PACT software ver. 3.1.2, integrating structural response demands, component fragility functions, collapse probability, and seismic hazard curves. Probabilistic loss curves are derived, and the EAL is computed as a synthetic indicator of economic seismic performance. The results highlight the effectiveness of EAL in capturing the combined effects of seismic hazard and structural vulnerability, demonstrating its potential as a robust decision-support metric for seismic risk mitigation, retrofit prioritization, and insurance-related applications for existing buildings. Full article

Brown bear populations in Greece face multiple threats, and parasitic infections may pose an additional risk to these vulnerable animals. This study represents the first comprehensive assessment of endoparasite occurrence, prevalence, and seasonality in brown bears in Greece, in relation to geographical location and the animal’s different physiological phases. A total of 918 faecal samples were collected over a three-year period from regions with brown bear presence in Greece. For each sample, the date of collection and the coordinates of the site were recorded. Samples were examined using sedimentation, flotation, and McMaster techniques, while the Baermann method was additionally applied to a subset of 195 samples. Spatial and temporal patterns in parasite occurrence and diversity were analysed using generalised additive models (GAMs). Ten parasitic taxa were identified, with Baylisascaris transfuga being the most prevalent (39.8%), followed by Crenosoma spp. (26%), Uncinaria spp. (18.09%), and Dicrocoelium dendriticum (14.38%). Less prevalent taxa included Eucoleus aerophilus, Sarcocystis spp., Toxascaris leonina, Eimeria spp., Linguatula serrata, and Taeniidae. Μixed infections, involving two or more parasites, were detected in 22% of the samples. The prevalence of B. transfuga was higher in late autumn, with high-risk infection areas identified in both late summer and autumn. In contrast, Uncinaria spp. and D. dendriticum showed no seasonal variation, while D. dendriticum exhibited spatial clustering patterns similar to B. transfuga but without clear seasonal trends. These findings highlight the widespread occurrence and complexity of parasitic infections in Greek brown bears. Continued long-term monitoring is essential to improve understanding of transmission dynamics and the ecological processes shaping parasite distribution in this animal species. Full article

The exponential growth of machine learning (ML) applications in smart grid (SG) research over the past decade has generated a vast and fragmented body of literature that lacks systematic synthesis. This study addresses that gap by presenting a comprehensive bibliometric and science mapping analysis of the ML–smart grid (MLSG) research landscape to date, drawing on 4156 peer-reviewed publications indexed in the Elsevier Scopus database from 2009 to 2025. The principal contributions of this study are fourfold. First, it provides a rigorous quantitative mapping of MLSG publication growth from one document in 2009 to 1163 publications in 2025, representing a growth rate of 116,200%, thereby establishing a definitive baseline for tracking future scholarly development in the field. Second, it identifies the key actors driving MLSG research, including the most prolific authors (Nadeem Javaid, Alsabaan M.), leading institutions (King Saud University, Tennessee Technological University), and dominant nations (India, China, United States), which offers researchers and funding bodies actionable intelligence on collaboration opportunities and research leadership. Third, through keyword co-occurrence and cluster analysis, the study maps the three dominant thematic hotspots structuring current MLSG research—Smart Grid Security, Power Load Forecasting, and Advanced Energy Management—providing a structured intellectual framework that can guide future research prioritization. Fourth, the study delivers a critical thematic literature review of these three hotspots, synthesizing the most impactful ML methodologies and applications reported across 4156 publications, including deep learning-based intrusion detection, ensemble forecasting models, and reinforcement learning-driven energy management. Collectively, these contributions offer a robust evidence base for researchers, policymakers, and industry practitioners seeking to navigate, benchmark, and advance the field of ML-enabled smart grid systems. Full article

While existing curricula often focus on theoretical aspects of sustainability, they frequently fail to equip students with practical design skills required by the green industry. To address this disconnect, this study seeks to answer: How can a structured pedagogical framework effectively enhance students’ ability to translate abstract sustainability principles into concrete technical solutions? This study introduces a comprehensive CDIO-based framework reform for Embedded Intelligent Systems education, weaving sustainability throughout every phase. We put forward a “Sustainable CDIO Capability Model” that charts a progressive pathway—starting from basic resource awareness and advancing through to sophisticated sustainable system innovation. Our four-dimensional teaching strategy brings this model to life: first, project-based learning driven by real sustainability challenges; second, a hybrid ecosystem blending online resources, hands-on practice, and immersion in green industry contexts; third, hierarchical team-based pedagogy backed by personalized support mechanisms; and fourth, a multi-dimensional assessment system that weights energy efficiency, resource stewardship, and social value creation alongside conventional metrics. We implemented this approach with Intelligent Science and Technology majors at Wuhan Institute of Technology. The results show the model effectively bridges the persistent gap between dry technical content and the practical demands of green industry. Students made substantial gains not merely in core engineering capabilities—system architecture, hardware-software co-development—but crucially in sustainable design awareness and their capacity to untangle complex sustainability challenges. This work offers a readily transferable framework for embedding Education for Sustainable Development (ESD) into engineering curricula worldwide. It provides practitioners with a concrete, tested model for cultivating the next generation of engineers who naturally think and act with sustainability in mind. Full article

Background: Patients with diabetes mellitus and nonobstructive coronary artery disease (NOCAD) may remain at increased cardiovascular risk despite the absence of flow-limiting stenosis. Quantitative coronary CT angiography (CCTA) enables comprehensive assessment of anatomical, functional, and inflammatory imaging biomarkers beyond luminal stenosis. This study aimed to evaluate the prognostic value of an automated multiparametric CCTA-derived imaging framework for risk stratification in patients with NOCAD, with exploratory assessment in those with diabetes mellitus. Methods: This retrospective single-center study included 485 patients with NOCAD who underwent CCTA between January 2020 and December 2021. Automated CCTA analysis was performed to quantify plaque burden, high-risk plaque features, CT-derived fractional flow reserve (CT-FFR), and perivascular fat attenuation index. The primary endpoint was major adverse cardiovascular events (MACE) during follow-up. Prognostic associations were assessed using Kaplan–Meier analysis, Cox regression, and hierarchical models. Results: During a median follow-up of approximately three years, MACE occurred in 56 patients. Patients with diabetes had a higher event rate than those without diabetes. Increased plaque burden, high-risk plaque features, elevated perivascular fat attenuation index, and reduced CT-FFR were associated with adverse outcomes. The fully integrated model combining anatomical, functional, and inflammatory CCTA-derived biomarkers improved risk stratification compared with plaque-based assessment alone. Conclusions: Automated multiparametric CCTA phenotyping may provide complementary prognostic information for risk stratification in patients with NOCAD. The diabetes-specific findings should be considered exploratory and require validation in larger prospective cohorts. Full article

Evaluating grouting effectiveness in deep shafts remains difficult because water-control performance is jointly governed by hydraulic response, seepage-path sealing, grout-body quality, and surrounding rock stability under complex hydrogeological conditions. In this study, an integrated evaluation and seepage analysis framework was developed for the Lianhuashan Phosphate Mine shaft project in Zhongxiang City, Hubei Province, China. Multi-source engineering data from hydrogeological observations, geophysical detection, construction records, and laboratory tests were used to evaluate six representative working faces, and a two-dimensional Darcy flow model was established to interpret the seepage-control mechanism. The evaluation results show differences among the treated sections: the auxiliary shaft at the −29.8 m outlet achieved the highest comprehensive score of 74.79, whereas the main shaft at +13 m showed the weakest performance, with a score of 50.16. Overall, three sections were rated as good, two as moderate, and one as poor. The dominant controls on grouting effectiveness are total shaft inflow, surrounding rock integrity/stability, seepage point number, and sealing-related indices. Numerical simulations further show that grouting reduced total shaft inflow from 6.6080 to 2.0198 m³/h, corresponding to a reduction of 69.43%, and shifted the main hydraulic-gradient concentration from the shaft wall to the outer boundary of the grouted ring. Reducing grouting ring permeability from 5.10 × 10⁻¹³ to 1.00 × 10⁻¹⁴ m² further lowered shaft inflow to 0.2929 m³/h and increased water-control efficiency to 95.57%, whereas increasing ring thickness from 8 to 16 m reduced shaft inflow from 2.7063 to 1.7260 m³/h. In addition, moving the water-rich zone away from the shaft reduced total inflow from 2.5503 m³/h at X_f = 10 m to 2.0079 m³/h at X_f = 26 m. These results indicate that effective shaft grouting depends on the coordinated control of inflow suppression, conductive-path sealing, and structural stabilization. The proposed framework provides a practical basis for grouting evaluation and water hazard control in deep shafts under complex hydrogeological conditions. Full article

Automated mapping of vertical traffic signs and horizontal road markings is essential for road safety and Intelligent Transportation Systems (ITS). Traditional methods are labor-intensive, while existing automated solutions often lack a unified approach or are proprietary, limiting research accessibility and reproducibility. This paper presents a comprehensive framework for identifying these assets using a low-cost, vehicle-mounted action camera. A distance-aware frame extraction strategy is introduced to minimize data redundancy and ensure high spatial diversity. Specific strategies address the class imbalance inherent in real-world driving, ensuring robust detection for infrequent sign categories. Deep learning models handle the distinct geometries of vertical and horizontal assets, employing segmentation-based annotation for irregular road markings. Experimental results show high performance, with leading YOLO-based architectures achieving an F1-score of 0.92 for vertical signage and 0.96 for horizontal markings. By transforming raw visual data into structured georeferenced information, this framework facilitates the generation of High-Definition (HD) maps and digital inventories, supporting road authorities in proactive maintenance planning and regional road safety assessments. Full article

Urban–rural integration (URI) represents a pivotal pathway to realizing sustainable development within urban–rural spatial systems. It is of paramount importance in addressing the challenge of reconciling ecological conservation with high-quality development in the Yellow River Basin. Leveraging panel data from 78 cities in the Yellow River Basin spanning the years 2006–2023, this research constructs an evaluation index system that encompasses five dimensions: population, economy, society, ecology, and space. Through the comprehensive application of kernel density estimation, exploratory spatiotemporal data analysis, and panel quantile regression models, a systematic analysis of the spatiotemporal evolution patterns and transition mechanisms of URI is conducted. The results disclose that URI in the Yellow River Basin demonstrates a trend of “overall enhancement with regional disparities”. From 2006 to 2023, the URI of the basin witnessed an average annual growth rate of 2.86%. Spatially, it presented distinct features: high-level agglomeration in the lower reaches, accelerating-growth path dependency accompanied by internal divergence in the middle reaches, and balanced yet low-level development in the upper reaches. The local spatial evolution of URI follows a pattern characterized as “predominant stability and limited transitions”. In detail, high-level regions sustain their advantages, low-level regions encounter obstacles in achieving breakthroughs, and the spillover effects between adjacent regions remain relatively restricted. The driving mechanisms exhibit significant “phase-spatial” dual heterogeneity, with four distinct patterns identified. In light of these findings, policy recommendations are put forward, including the establishment of a multi-scale, coordinated spatial governance system. Full article