Search Results (3,349)

Background/Objectives: Postoperative monitoring of microvascular free flaps is critical for early detection of vascular complications and flap survival. Nurses play a central role in this process; however, qualitative evidence on their experiences and challenges remains limited. This study explored nurses’ experiences in free tissue flap care to identify clinical practices, challenges, and improvement needs. Methods: A phenomenological qualitative design was used. Data were collected through semi-structured interviews with nine nurses experienced in free tissue flap care, recruited via purposive and snowball sampling. Interviews were conducted online and lasted 30–45 min. Data were analyzed using content analysis with MAXQDA 2025. Inter-researcher reliability was 97%. Results: The findings were categorized into four main themes and seventeen subthemes: (1) clinical monitoring and evaluation in the care process, (2) challenges and difficulties, (3) emotional and professional reflections, and (4) suggestions for improving care. Nurses reported that flap care requires intensive monitoring, rapid decision-making, and close collaboration with physicians, especially within the first 24–48 h. Monitoring was largely based on observation and experience due to the lack of standardized protocols. Major challenges included high workload, frequent assessments, and donor site management. Emotional burden, stress, and responsibility were also prominent. Conclusions: Free flap care is a complex and demanding process for nurses. The lack of standardized monitoring tools and protocols is a key gap. Developing structured tools, improving training, and strengthening multidisciplinary collaboration may enhance patient safety and care quality. Full article

This study addresses the stratified and turbulent tropospheric delays that impede interferometric synthetic aperture radar (InSAR) deformation monitoring in alpine canyon regions. We introduce a tropospheric delay model that incorporates time-varying Gaussian coefficients and coupled zenith wet delay (ZWD) by combining diverse multi-source data. This model was incorporated into StaMPS for InSAR processing. Evaluation results demonstrated that (1) the model accurately captured seasonal and diurnal tropospheric variations, achieving a root mean squared error (RMSE) of 2.01 cm relative to the GNSS reference data; (2) the model corrected stratified and turbulent delays and reduced interferometric phase standard deviation (STD) by 9.28% compared to the Generic Atmospheric Correction Online Service (GACOS); and (3) the deformation accuracy improved by 19.07% over GACOS. Discussion results indicate that accounting for time-varying Gaussian coefficients is essential and that coupling ZWD to rectify turbulent delays outperformed the filtering method. The observed negative interferogram corrections result from the random intensity of turbulent delays. These findings confirm the effectiveness of the proposed model for high-precision InSAR deformation monitoring in complex alpine terrains. The proposed model aims to enhance studies of tropospheric delay variations in alpine canyon regions and to mitigate such delays in InSAR-based geological hazard monitoring. Full article

Despite the large amount of successful existing methods and frameworks for planning sets of multiple unmanned aerial systems (UASs), there is still a lack of coordination frameworks that are capable of coping with real-world operational conditions. This paper presents U-Plan, an integrated management framework for the coordination of multi-UAS missions. U-Plan is designed to plan, schedule, monitor, and replan a heterogeneous set of UASs to complete point of interest (PoI) visiting missions while ensuring that all the generated trajectories are safe, feasible, and compliant with the required PoIs’ arrival times, UAS kinematics and energy constraints, and the existing 3D no-fly zones (NFZs). U-Plan is designed as a practical tool for strongly dynamic missions and is built upon three core components: (1) an NFZ-aware route computation method that explicitly accounts for NFZs prior to vehicle routing problem (VRP) optimization, resulting in shorter NFZ-safe routes; (2) a trajectory smoothing module that ensures the generation of kinematically feasible trajectories for fixed-wing UASs; and (3) a mission supervision module for real-time monitoring and replanning in case of changes in the UAS, mission, wind speed, or airspace restrictions. To validate the proposed architecture, we conducted rigorous experiments utilizing the VECTOR-SIL autopilot and Visionair Ground Control Station to realistically replicate the behavior of certified fixed-wing autopilots under various weather conditions using the exact same hardware and flight control software that runs onboard the physical drones. The validation shows U-Plan’s capacity to efficiently satisfy complex mission requirements with strong scalability. Due to its high computational efficiency, U-Plan enables online mission replanning, allowing UAS fleets to seamlessly adapt to changes that are typical of real-world operational scenarios. Full article

Robotic belt grinding is an effective and widely adopted finishing method for superalloys, offering notable advantages such as high material removal capability, low heat input, and reduced workpiece damage. In addition, robots can readily integrate multiple sensors—such as infrared radiation cameras, force sensors, and high-speed cameras—which facilitate real-time monitoring of the grinding process and thereby enhance grinding quality control. With the establishment and continuous advancement of large-scale artificial intelligence (AI) data models, new breakthroughs have emerged in the optimization of robotic grinding processes. Owing to its dexterous workspace and advantages in high flexibility and cost-effectiveness, robotic belt grinding has become a critical process for the precision forming of complex curved components such as aero-engine blades and blisks. However, factors such as the limited absolute accuracy of industrial robots, time-varying grinding contact states, and significant transient boundary effects make it difficult for the current constant-parameter open-loop machining mode to simultaneously meet the demands for high material removal efficiency and high surface integrity on complex profiles. This paper systematically reviews the technologies for precision control and process optimization of robotic belt grinding aimed at pointwise precise material removal. First, the structural composition of the robotic belt grinding system and the material removal mechanism are analyzed. Then, centered on the compensation concept, a hierarchical progressive technical framework is outlined, covering geometric calibration compensation, force/position hybrid online compensation, transient entry boundary compensation, and system-level comprehensive compensation of multi-source errors, with a comparison of the applicable scenarios and the effects on shape and property control at each level. Furthermore, under the support of effective compensation, the collaborative optimization methods of material removal modeling, multi-objective optimization of process parameters, force-constrained trajectory planning, and intelligent adaptive processes are elaborated. Finally, current technical bottlenecks are summarized, and future trends in next-generation adaptive grinding technology driven by digital twins and embodied intelligence are envisioned. This review aims to provide a systematic theoretical reference for the high-precision and intelligent upgrading of robotic precision grinding systems. Full article

The present integrative umbrella review aims to provide a comprehensive overview of the evidence and practices related to mental health and career transitions in elite sport toward the implementation of service provision through digital interventions. Following PRIO guidelines, an extensive search across five databases (2015–2025) identified 52 eligible manuscripts (e.g., conceptual, review, and position studies). Data extraction focused on mental health, dual-career pathways, career transition challenges and needs, and identity-related issues among high-performance athletes. The findings revealed a strong consensus that athlete well-being is shaped by the dynamic interaction of mental health symptoms, sport-specific stressors, identity processes, and structural conditions across the athletic lifespan. Mental health vulnerabilities (e.g., anxiety, depression, disordered eating, and distress) were consistently reported, particularly during injury, deselection, and retirement. Dual-career engagement, diversified identities, and proactive career planning emerged as key protective factors, while stigma, limited literacy, and uneven access to psychological services remained persistent barriers. Five main thematic areas (Matrix 1) operationalized in ten higher-order intervention domains (e.g., Matrix 2, screening, monitoring, literacy, and others) and 14 potential online implementation strategies (Matrix 3) were identified. However, the evidence highlights fragmented implementation and a lack of scalable, cross-national tools to support athletes during and beyond their competitive careers. Therefore, a harmonized, evidence-based, multidimensional framework for the development and implementation of digital support resources has been proposed. This integrative review underscores the need for integrated, culturally sensitive, and digitally enabled support systems to promote sustainable transitions and long-term athlete well-being. Full article

Hotelling’s T-squared statistic provides an interpretable framework for multivariate fault detection; however, its rolling implementation is highly sensitive to transient casewise outliers in the reference window. Such abnormal observations may inflate the sample covariance matrix, enlarge the monitoring boundary, and consequently mask subsequent moderate fault signals. This study proposes a robust rolling Hotelling fault detection method, denoted as RRH-FD, to reduce this masking effect. The proposed method estimates the rolling reference center and scatter matrix using reweighted minimum covariance determinant (RMCD) estimators, while each newly arriving observation is evaluated directly as a potential fault signal. The monitoring threshold is obtained using a robust Hotelling approximation rather than the classical Hotelling distribution. A simulation study was conducted under both clean and contaminated rolling reference scenarios. Under clean reference windows, the proposed robust procedures remained competitive with the classical rolling Hotelling detector, showing only a modest efficiency loss. Under contaminated reference windows, RRH-FD substantially improved detection performance. The adaptive RRH-FD method reduced the average detection delay by approximately 37.6% relative to the classical rolling detector, while the fixed MCD fraction 0.85 version achieved an approximate reduction of 42.4%. The proposed methods also improved early detection rates within the first 25 and 50 post-fault monitoring points. Boundary inflation was quantified using the log-determinant ratio between the classical sample covariance matrix and the RMCD scatter estimate. This analysis further confirmed that the advantage of RRH-FD becomes more pronounced as the classical covariance boundary is more strongly inflated by transient outliers. An R package, RRHFD, was developed to facilitate implementation and reproducibility. Full article

Accurate assessment of winding condition for power transformers is critical for ensuring the stable operation of modern power systems. Vibration signal has been regarded as an effective and promising evaluator for winding diagnosis. While on-line vibration monitoring offers the continuous, non-invasive and in-service assessment for winding condition, establishing precise correlations between the variable vibration patterns and specific winding condition remains challenging. To this end, an off-line vibration response analysis (VRA) technique was presented in the paper. Specifically, vibration frequency response (VFR) curves, indicating the winding response, were first obtained when the transformer was excited by the developed vibration response testing system, consisting of constant current variable-frequency power supply, intermediate transformer, accelerometers, data acquisition, control and analysis system. The VFR curves were then quantitatively and comprehensively described through four kinds of correlation indices. Finally, hierarchical integration strategy was proposed to aggregate those indices into quantitative criterion for condition assessment. The proposed method was validated on a real transformer under both normal and fault conditions, demonstrating superior performance. Notably, a 10% decrease in the evaluation criterion indicates an incipient winding looseness, while a reduction of 25% or more suggests severe looseness, prompting timely maintenance recommendations. Full article

Real-time perception of coal–biomass blending during conveyor-belt transport remains challenging because of local aggregation, particle overlap, and illumination variation. In this study, a laboratory-scale conveyor-belt image dataset covering different coal mass fractions, illumination conditions, and particle sizes was constructed. Whole-image classification, cropped-ROI classification, direct regression, horizontal bounding box (HBB)-based detection, oriented bounding box (OBB)-based detection, and RT-DETR-L detection baselines were compared using YOLO-series and auxiliary models. Coal mass fraction was estimated using a frequency-weighted statistical strategy that converts frame-level predictions into continuous estimates. YOLOv8-cls achieved an average RMSE of 13.98 percentage points (pp), indicating the influence of background interference in whole-image classification. Among HBB models, YOLOv8m achieved the lowest mean RMSE of 6.10 pp but required higher computational cost. Compared with YOLOv8n, YOLOv8n-OBB reduced the average RMSE from 9.02 to 6.90 pp by providing a more compact material-region representation and reducing background redundancy. These results show that OBB representation improves the stability of lightweight models. The proposed method provides a feasible vision-based soft-sensing approach for online trend monitoring of coal–biomass blending under lightweight deployment. Full article

Current deep learning methods for tool wear monitoring suffer from poor interpretability and struggle to reveal the intrinsic relationship between signals and wear states. To address this issue, this paper presents an interpretable tool wear monitoring method based on Sparse Identification of Nonlinear Dynamics (SINDy). Multi-domain features are extracted from cutting force and acoustic emission signals to construct a time series. The SINDy algorithm is used to identify ordinary differential equations that describe the evolution of tool wear. An iterative “predict-validate-correct” mechanism is applied to optimize model parameters. Experimental results show that the mean absolute percentage error (MAPE) between the predicted and actual values is below 6%. Moreover, the optimal model demonstrates an average MAPE as low as 0.067% in cross-condition tests. This study provides an effective solution for online tool wear monitoring that achieves high precision, strong generalization, and physical interpretability. Full article

Zoonotic gastrointestinal parasites such as Toxocara canis and Ancylostoma caninum are a public health concern, particularly in tropical and urban environments. This study evaluated Mexican veterinarians’ perceptions, knowledge, and deworming practices regarding these parasites and their zoonotic risks. A nationwide online survey obtained 717 fully completed responses from veterinarians across all Mexican states, exceeding the required sample size. Inclusion criteria required participants to be active small-animal practitioners with no missing data on core deworming questions; veterinarians working exclusively in pharmacies, feed stores, boarding facilities, dog daycares, or grooming services were excluded. Overall ESCCAP guideline compliance was 34.2%. Compliance was highest in northern states (41.8%) and lowest in southern states (23.5%). Deworming practices in lactating dogs showed uniformly low adherence, and no state reached moderate compliance for puppies aged 1–3 weeks. Compliance with the recommended puppy deworming frequency was notably higher. Compliance with the recommended adult deworming frequency was very low (9.8%), while coprological monitoring was recommended by 43.4% of respondents. Professional formation was the strongest predictor of overall guideline adherence across nearly all criteria. The 16–20-year experience group showed the highest overall compliance. Sex was not a significant predictor of overall ESCCAP compliance; the only significant sex difference was observed for coprological monitoring, where female veterinarians showed higher compliance rates. These findings suggest that academic training, years of experience, and geographic region are independently associated with guideline adherence, underscoring the value of standardized national protocols and continuing education to strengthen zoonotic risk awareness among veterinarians in Mexico. Full article

Background/Objectives: Despite hematoxylin and eosin (H&E) staining remaining the cornerstone of histopathological diagnosis, substantial intra- and inter-laboratory variability persists. This issue is increasingly relevant in Digital Pathology, where staining inconsistency may affect whole-slide image interpretation and the performance of image analysis algorithms. In the present work, we evaluated the diagnostic adequacy and non-inferiority of a novel tabs-based H&E histochemical staining method compared with conventional liquid reagents. Methods: Fifty formalin-fixed paraffin-embedded tissue samples from routine practice were sectioned in duplicate and stained either conventionally or using H&E Stain Tabs. After slide review, 14 representative tissue samples were selected, scanned at 40× magnification, and used to generate 24 matched image pairs at different magnifications. A blind online survey was completed by 13 expert pathologists using high-quality monitors. Participants assessed overall staining preference and rated stromal, epithelial, cytoplasmic, and nuclear staining quality. Non-inferiority was tested using a predefined margin of −0.10, and paired rating differences were analyzed using the Wilcoxon signed-rank test. Results: Across 312 paired evaluations, the tabs-based method was preferred in 120 cases (38.5%), conventional staining in 118 cases (37.8%), and no preference was expressed in 74 cases (23.7%). The tabs-based method met the criterion for non-inferiority compared with standard staining (z = 2.7). Rating-scale analysis showed significantly better stromal evaluation with the tablet-based method (z = 2.638; p = 0.008), whereas no significant differences were observed for epithelial, cytoplasmic, or nuclear staining. All evaluated images were considered diagnostically adequate. Conclusions: The tabs-based H&E stain was non-inferior to the conventional method and showed particularly favorable performance in the assessment of stromal components. These findings support its potential role in improving staining reproducibility and standardization, particularly in Digital Pathology workflows where pre-analytical and analytical consistency is critical. Full article

Temperature monitoring is critical for lithium-ion battery (LIB) safety and performance, yet instrumenting every cell in a commercial pack remains impractical due to cost and wiring constraints. Existing sensorless methods rely on either physics-based thermal models requiring extensive parameterization or nonlinear recurrent estimators that cannot integrate sensor feedback when measurements become available. Motivated by these limitations, this paper proposes a Deep Koopman observer that enforces linear latent dynamics, enabling direct compatibility with Kalman filtering. The observer estimates surface temperature from four standard BMS signals and two exponential moving averages of squared current that capture thermal memory at distinct time scales, operating in two modes: fully sensorless for uninstrumented cells, or sensor-fused via a one-state EKF when a thermistor is available. Evaluated under strict cell-to-cell split across twelve drive cycles and five ambient temperatures, the open-loop observer achieves 17% lower error than the strongest reproduced CNN-LSTM baseline without online resistance identification or thermal-model simulation, and the EKF path delivers a further 35% reduction over the open-loop estimate. The evaluation is limited to a single cell chemistry and manufacturing batch; cross-chemistry and aging validation remain for future work. Full article

This study, based on multi-source data fusion and risk index models, has developed a comprehensive methodological system for evaluating the risk of forest fires caused by human factors. The system starts with four dimensions, i.e., exposure, hazard factors, vulnerability, and prevention and control capabilities, and constructs an evaluation framework with 19 secondary indicators. It also establishes single-category risk index models for four types of dominant fire sources: agricultural activities, religious ceremonies, tourism, and power distribution lines. Through weighted synthesis and exponential smoothing algorithms, it achieves daily dynamic risk forecasting. The research took the typical forest areas in the Mentougou, Changping, and Yanqing districts of Beijing as the application demonstration areas, collecting meteorological data, geographic information data, risk census ledgers, online hiking trajectories, and 2530 social survey questionnaires to complete the local parameter calibration and validation of the model. The retrospective analysis of 22 typical human-caused fire cases from 2018 to 2025 shows that the risk percentile of the ignition points in all cases was above 87.8%, indicating that the model has a good risk identification capability. Based on the evaluation results, differentiated control measures for different types of fire sources were proposed. The research results have been integrated into Beijing’s forest fire risk monitoring and early warning system, providing a scientific tool for the refined management of human-caused fire sources. Full article

Background: Digital health technologies have expanded access to home fetal heart rate (FHR) monitoring devices, enabling fetal surveillance outside clinical settings. However, evidence on women’s awareness, acceptance, and experiences with these devices remains limited. Objective: To assess awareness, adoption, user experience, perceived reassurance, and attitudes toward home FHR monitoring among pregnant and postpartum women. Methods: A cross-sectional online survey was conducted using a structured questionnaire distributed via Google Forms. Eligible participants were women aged ≥18 years who were currently pregnant or had been pregnant within the previous two years. The survey evaluated awareness and use of home FHR monitoring devices, usage patterns, sources of recommendation and instruction, emotional responses, perceived reassurance, mobile application integration, and overall attitudes. Descriptive statistics and exploratory subgroup analyses were performed. Results: A total of 225 women completed the survey; 166 (73.8%) reported using a home FHR monitoring device during pregnancy. Most users reported positive emotional experiences, with calmness as the most common response. Home monitoring was generally perceived as reassuring, and many participants felt calmer on days of device use. Gynecologists were the primary source of device recommendations and usage instructions. Participants highlighted the importance of professional guidance, clear instructions, and mobile application support. Primiparous women had significantly higher adoption rates than multiparous women (p < 0.001). Conclusions: Home FHR monitoring was widely accepted and commonly perceived as reassuring. These devices may support patient-centered prenatal care when accompanied by appropriate professional guidance. Further prospective studies are needed to assess their clinical utility, safety, and integration into prenatal care pathways. Full article

Current landslide displacement prediction models often suffer from insufficient integration between physical mechanisms and data-driven approaches, weak model generalizability, and limited operational applicability. To address these issues, this study develops a physics-informed online learning framework for landslide displacement prediction. The core of this framework is a Physics-informed Long Short-Term Memory network (Phys-LSTM). By embedding discretized forms of the stress balance, creep constitutive, and kinematic equations as hard constraints into the LSTM’s gating mechanisms and loss function, the model ensures physically consistent predictions and enhanced interpretability throughout the learning process. Leveraging real-time data streams from the Sichuan Provincial Geological Hazard Monitoring and Warning Platform, we developed an online processing pipeline for real-time multi-source data ingestion, automated quality control, spatiotemporal alignment, and physics-informed feature engineering. A progressive three-stage learning algorithm was designed to support model cold-start, incremental training, and rolling prediction. Validation across 45 model-development landslide sites and one independent application case demonstrated the framework’s significant superiority over traditional models in displacement prediction accuracy (RMSE ≤ 1.78 mm, R² ≥ 0.96), cross-site generalization stability, and its capability to capture accelerated deformation phases. This research indicates that deeply integrating geomechanical prior knowledge into an online learning framework can effectively improve the reliability, interpretability, and operational applicability of landslide displacement prediction models, thereby providing methodological support for subsequent landslide early warning applications. Full article