Search Results (12,909)

This study investigates the influence of particle morphology on two-dimensional (2D) single rock particle breakage using the combined finite-discrete element method (FDEM) coupled with fractal dimension analysis. Three key shape descriptors (elongation index EI, roundness index Rd, and roughness index Rg) were systematically varied to generate realistic particle geometries using the Fourier transform and inverse Monte Carlo. Numerical uniaxial compression tests revealed distinct morphological influences: EI showed negligible impact on crushing strength or fragmentation, and Rd significantly increased crushing strength and fragmentation due to improved energy absorption and stress distribution. While Rg reduced strength through stress concentration at asperities, suppressing fragmentation and elastic energy storage. Fractal dimension analysis demonstrated an inverse linear correlation with crushing strength, confirming its predictive value for mechanical performance. The validated FDEM framework provides critical insights for optimizing granular materials in engineering applications requiring morphology-controlled fracture behavior. Full article

Advances in OLED display technology have increased the demand for high-performance luminescent materials, yet efficient red emitters with narrow emission spectra remain rare. Here, two new polycyclic compounds (O-QA and S-QA) are designed by incorporating oxygen/sulfur into a carbonyl/nitrogen skeleton. Photophysical and theoretical studies reveal their hybridized local and charge-transfer state characteristics. In toluene, O-QA and S-QA show photoluminescence peaks at 586/579 nm with narrow emission spectra, while doped films exhibit strong red emissions peaking at 598/600 nm with high PL quantum yields of 67%/60%. The OLEDs using these emitters achieve red electroluminescence (EL) peaks at 598/602 nm, and attain maximum external quantum efficiencies of 7.36%/14.54%. This work may provide guidance for the development of narrow-spectrum red emitters based on carbonyl/nitrogen frameworks. Full article

Traffic flow prediction is a critical component of intelligent transportation systems, playing a vital role in alleviating congestion, improving road resource utilization, and supporting traffic management decisions. Although deep learning methods have made remarkable progress in this field in recent years, current studies still face challenges in modeling complex spatio-temporal dependencies, adapting to anomalous events, and generalizing to large-scale real-world scenarios. To address these issues, this paper proposes a novel traffic flow prediction model. The proposed approach simultaneously leverages temporal and frequency domain information and introduces adaptive graph convolutional layers to replace traditional graph convolutions, enabling dynamic capture of traffic network structural features. Furthermore, we design a frequency–temporal multi-head attention mechanism for effective multi-scale spatio-temporal feature extraction and develop a cross-multi-scale graph fusion strategy to enhance predictive performance. Extensive experiments on real-world datasets, PeMS and Beijing, demonstrate that our method significantly outperforms state-of-the-art (SOTA) baselines. For example, on the PeMS20 dataset, our model achieves a 53.6% lower MAE, a 12.3% lower NRMSE, and a 3.2% lower MAPE than the best existing method (STFGNN). Moreover, the proposed model achieves competitive computational efficiency and inference speed, making it well-suited for practical deployment. Full article

The objective of this study was to provide a comprehensive review of the types, properties, and potential applications of nanomaterials in phototherapy for caries prevention. This scoping review follows the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Review (PRISMA-ScR). Two researchers independently searched English-language publications in Pubmed, Embase, and Web of Science on 25 February 2025. Publications that reported nanomaterials in phototherapy for caries prevention are included. They screened 229 publications and included 38 publications. These 38 publications were categorised into three groups: nanomaterials in photodynamic therapy (25/38, 66%), nanomaterials in photothermal therapy (9/38, 24%), and nanomaterials in combined photothermal and photodynamic therapy (4/38, 10%). Nanomaterials in photodynamic therapy generate reactive oxygen species under light, causing oxidative damage that kills microbes. In photothermal therapy, nanomaterials convert light energy into heat, inducing protein denaturation and membrane rupture, which eliminate microbes. These nanomaterials were incorporated into dental materials like adhesives and topical anti-caries agents. Among the 38 publications, 29 were laboratory studies, 8 were animal studies, and 1 was a human trial. Studies showed that some nanomaterials inhibit cariogenic microbes under light. However, most of the studies were laboratory or animal studies. More human trials are essential to translate their use into clinical care. This review underscores the potential of nanomaterials in phototherapy—leveraging photodynamic and photothermal mechanisms to eliminate caries-causing microbes—as a promising, minimally invasive strategy for caries prevention. Full article

Predictive maintenance of railroads is essential to prevent costly disruptions. A critical aspect of this maintenance is ensuring the integrity of the pantograph-catenary system, where copper alloy wires experience continuous friction and wear. The degradation rate and condition of these wires are vital factors in planning maintenance activities. Current wear rate prediction methods are largely theoretical and often inaccurate, overlooking essential contextual details. Additionally, wire condition data frequently show inaccuracies and inconsistencies in spatial and temporal resolution, complicating the feasibility of using data-driven approaches. This research investigates a data-driven framework to accurately predict wear rates, emphasizing data processing and optimized data use. A dataset spanning nine years of Dutch railway infrastructure measurements is used, employing various machine learning techniques to determine the most effective approach. Findings indicate that, in 95% of cases, average wire thickness can be predicted with a precision of ±0.12 mm over a four-year period. This study advances the field by proposing a framework that addresses measurement errors, a common challenge in sensor-based assessments, making data-driven maintenance a more reliable option. Full article

This study presents the configuration design and attitude control of a moving-mass coaxial dual-rotor UAV (MMCDRUAV) for indoor applications. Compared with existing configurations, the proposed configuration avoids additional actuation mass and improves the control authority. Based on these improvements, a promising micro UAV platform with a high payload ability for agile indoor flight could be developed. Ground validation tests demonstrated its maneuverability, as provided by a moving-mass control (MMC) module requiring only the repositioning of existing components (e.g., battery packs) as movable masses. For trajectory tracking, an adaptive backstepping active disturbance rejection controller (ADRC) is proposed. The architecture integrates extended-state observers (ESOs) for disturbance estimation, parameter-adaptation laws for uncertainty compensation, and auxiliary systems to address control saturation. Lyapunov stability analysis proved the existence of uniformly ultimately bounded (UUB) closed-loop tracking errors. The results of the ground verification experiment confirmed enhanced tracking performance under real-world disturbances. Full article

In view of the limitations of traditional rigid joint robots, cable-driven parallel robots (CDPRs) have shown significant advantages such as wide working space and high payload-to-weight ratio by replacing rigid connectors with flexible cables. Therefore, CDPRs have received widespread attention in the academic community in recent years and been applied to many fields. This review systematically reviews and categorizes the research progress in related fields in the past decade, focusing on mechanical structure design, mainstream mathematical models, and typical planning and control algorithms. In terms of mechanical structure, the advantages and disadvantages of three types of mainstream configurations and their application scenarios are summarized in detail. As for mathematical models, the dynamic modeling methods and various disturbance compensation models are mainly sorted out, and their action mechanisms and inherent limitations are explained. In terms of planning and control, four main research directions are discussed in detail, and their core ideas, evolution context, and development prospects are deeply analyzed. Although significant results have been achieved in the field of CDPR research, it is still necessary to continue to explore the direction of configuration diversification and intelligent autonomy in the future. Full article

This survey offers a comprehensive overview of advancements in Artificial Intelligence in Graphic Design (AIGD), with a focus on the integration of AI techniques to enhance design interpretation and creative processes. The field is categorized into two primary directions: perception tasks, which involve understanding and analyzing design elements, and generation tasks, which focus on creating new design elements and layouts. The methodology emphasizes the exploration of various subtasks including the perception and generation of visual elements, aesthetic and semantic understanding, and layout analysis and generation. The survey also highlights the role of large language models and multimodal approaches in bridging the gap between localized visual features and global design intent. Despite significant progress, challenges persist in understanding human intent, ensuring interpretability, and maintaining control over multilayered compositions. This survey aims to serve as a guide for researchers, detailing the current state of AIGD and outlining potential future directions. Full article

Volumetric video streaming holds transformative potential for telemedicine, enabling immersive remote consultations, surgical training, and real-time collaborative diagnostics. However, transmitting sensitive patient data (e.g., 3D medical scans, surgeon head/gaze movements) raises critical privacy risks, including exposure of biometric identifiers and protected health information (PHI). To address the above concerns, we propose SecureTeleMed, a dual-track encryption scheme tailored for volumetric video based telemedicine. SecureTeleMed combines viewport obfuscation and region of interest (ROI)-aware frame encryption to protect both patient data and clinician interactions while complying with healthcare privacy regulations (e.g., HIPAA, GDPR). Evaluations show SecureTeleMed reduces privacy leakage by 89% compared to baseline encryption methods, with sub-50 ms latency suitable for real-time telemedicine applications. Full article

(1) Background: Integrating assessment with training helps to enhance precision prosthetic rehabilitation of trans-radial amputees. This study aimed to validate a self-developed closed-loop rehabilitation platform combining accurate measurement in comprehensive assessment and immediate interaction in virtual reality (VR) training in refining patient-centered myoelectric prosthesis rehabilitation. (2) Methods: The platform consisted of two modules, a multimodal assessment module and an sEMG-driven VR game training module. The former included clinical scales (OPUS, DASH), task performance metrics (modified Box and Block Test), kinematics analysis (inertial sensors), and surface electromyography (sEMG) recording, verified on six trans-radial amputees and four healthy subjects. The latter aimed for muscle coordination training driven by four-channel sEMG, tested on three amputees. Post 1-week training, task performance and sEMG metrics (wrist flexion/extension activation) were re-evaluated. (3) Results: The sEMG in the residual limb of the amputees upgraded by 4.8%, either the subjects’ number of gold coins or game scores after 1-week training. Subjects uniformly agreed or strongly agreed with all the items on the user questionnaire. In reassessment after training, the average completion time (CT) of all three amputees in both tasks decreased. CTs of the A1 and A3 in the placing tasks were reduced by 49.52% and 50.61%, respectively, and the CTs for the submitting task were reduced by 19.67% and 55.44%, respectively. Average CT of all three amputees in the ADL task after training was 9.97 s, significantly lower than the pre-training time of 15.17 s. (4) Conclusions: The closed-loop platform promotes patients’ prosthesis motor-control tasks through accurate measurement and immediate interaction according to the sensorimotor recalibration principle, demonstrating a potential tool for precision rehabilitation. Full article

The swift growth of the low-altitude economic ecosystem and Unmanned Aerial Vehicle (UAV) swarm applications across diverse sectors presents significant challenges for engineering managers in terms of effective performance evaluation and operational optimization. Traditional evaluation methods often struggle with the inherent complexities, dynamic nature, and multi-faceted performance criteria of UAV swarms. This study introduces a novel Bayesian Network (BN)-based multicriteria decision-making framework that systematically integrates expert intuition with real-time data. By employing variance decomposition, the framework establishes theoretically grounded, bidirectional mapping between expert-assigned weights and the network’s probabilistic parameters, creating a unified model of subjective expertise and objective data. Comprehensive validation demonstrates the framework’s efficacy in identifying critical performance drivers, including environmental awareness, communication ability, and a collaborative decision. Ultimately, our work provides engineering managers with a transparent and adaptive tool, offering actionable insights to inform resource allocation, guide technology adoption, and enhance the overall operational effectiveness of complex UAV swarm systems. Full article

Human perception of urban streetscapes plays a crucial role in shaping human-centered urban planning and policymaking. Traditional studies on safety perception often rely on labor-intensive field surveys with limited spatial coverage, hindering large-scale assessments. To address this gap, this study constructs a street safety perception dataset for Wuhan, classifying street scenes into three perception levels. A convolutional neural network model based on transfer learning is developed, achieving a classification accuracy of 78.3%. By integrating image-based prediction with spatial clustering and correlation analysis, this study demonstrates that safety perception displays a distinctly clustered and uneven spatial distribution, primarily concentrated along major arterial roads and rail transit corridors by high safety levels. Correlation analysis indicates that higher safety perception is moderately associated with greater road grade, increased road width, and lower functional level while showing a weak negative correlation with housing prices. By presenting a framework that integrates transfer learning and geospatial analysis to connect urban street imagery with human perception, this study advances the assessment of spatialized safety perception and offers practical insights for urban planners and policymakers striving to create safer, more inclusive, and sustainable urban environments. Full article

This study presents a comprehensive study integrating machine learning, life cycle assessment (LCA) and heuristic optimization to achieve a low-carbon medical waste (MW)-to fuel process. A detailed process simulation coupled with cradle to gate LCA is employed to generate a dataset covering diverse process operation conditions, embodied carbon of supplying H₂ and the associated carbon emission factor of MW treatment (CEF). Four machine learning techniques, including support vector machine, artificial neural network, Gaussian process regression, and XGBoost, are trained, each achieving test R² close to 0.90 and RMSE of ~0.26. These models are integrated with heuristic algorithms to optimize operating parameters under various green hydrogen mixes (20–80%). Our results show that machine learning models outperform the detailed process model (DPM), achieving a minimum CEF of ~1.3 to ~1.1 kg CO₂-eq/kg MW with higher computational stabilities. Importantly, the optimization times dropped from hours (DPM) to seconds (machine learning models) and the combination of Gaussian process regression and particle swarm optimization is highlighted, with an optimization time under one second. The optimized process holds promise in carbon reduction compared to traditional MW disposal methods. These findings show machine learning can achieve high predictive accuracy while dramatically enhancing optimization speed and stability, providing a scalable framework for extensive scenario analysis during waste-to-energy process design and further real-time optimization application. Full article

This paper focuses on enhancing master cylinder pressure control in pressure-sensorless Electro-Hydraulic Brake (EHB) systems. A novel control strategy is developed, integrating a Piecewise Sliding Mode Controller (Piecewise-SMC) with an Extended Sliding Mode Observer (ESMO) based on a newly derived pressure–position–velocity model that accounts for rack position and velocity effects. To handle external disturbances and parameter uncertainties, the ESMO provides accurate pressure estimation. The nonlinear EHB model is approximated piecewise linearly to facilitate controller design. The proposed Piecewise-SMC regulates motor torque to achieve precise pressure tracking. Experimental validation under step-change braking conditions demonstrates that the Piecewise-SMC reduces response time by 31.8%, overshoot by 35.8%, and tracking root mean square error by 9.6% compared to traditional SMC, confirming its effectiveness and robustness for pressure-sensorless EHB applications. Full article

This study focuses on the Guangdong-Hong Kong-Macao Greater Bay Area and employs a multi-model coupling method of InVEST-Bathtub-GeoSOS-FLUS to predict and analyze the impacts of sea level rise and rapid urbanization on ecological source areas by the year 2100. The InVEST model is used to delineate areas with higher habitat quality scores as ecological source areas. The Bathtub inundation model predicts the impact ranges under three different sea level rise scenarios by 2100. The FLUS model simulates the land-use pattern of the Greater Bay Area in 2100. Finally, the raster calculator is used to conduct overlay analysis and accurately calculate the impact on ecological source areas under the combined effects of sea level rise and urban expansion. The results show that by 2100, the proportion of cultivated land in the Greater Bay Area is expected to decrease from 24.95% to 10.55%, while the proportion of urban land will increase from 7.69% to 26.84%. Under the dual impacts of the three sea level rise scenarios and urbanization, the affected areas of ecological source areas will reach 109.88 km², 125.05 km², and 255.10 km², respectively. This study provides an important basis and decision-making support for the sustainable planning and scientific management of ecological source areas in the Greater Bay Area. Full article