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Association between burnout and working motivation among rehabilitation doctors: a multicentre cross-sectional study in China
Robust resolution improvement of 3D UTE-MR angiogram of normal vasculatures using super-resolution convolutional neural network
Identifying influential spreaders based on improving communication transmission model and network structure
Opportunities for physical activity in the school environment and their association with physical activity and sedentary behavior in Brazilian adolescents
Digital twin syncing for autonomous surface vessels using reinforcement learning and nonlinear model predictive control
Abstract Current control systems for autonomous surface vessels (ASVs) often disregard model uncertainties and the need to adapt dynamically to varying model parameters. This limitation hinders their ability to ensure reliable performance under complex and frequently changing maritime conditions, highlighting the need for more adaptive and robust approaches. Therefore, this study introduces an innovative approach that integrates deep reinforcement learning (DRL) with nonlinear model predictive control (NMPC) to optimize the control performance and model parameters of ASVs. The primary objective is to ensure that the digital twin of the ASV remains continuously synchronized with its physical counterpart, thereby enhancing the accuracy, reliability, and adaptability of the digital twin in representing the vessel under complex and dynamic maritime conditions. Leveraging the capabilities of digital twins, agents can be trained in safety-critical applications within a risk-free virtual environment, minimizing the hazards associated with real-world experimentation. The DRL framework optimizes NMPC by tuning its parameters for peak performance and identifying unknown model parameters in real-time, ensuring precise and dependable vessel control. Extensive simulations confirm the effectiveness of this approach in improving the safety, efficiency, and reliability of ASVs. The proposed methods address critical challenges in ASV control by enhancing reliability and adaptability under dynamic conditions, providing a foundation for future advancements in autonomous maritime navigation and control system development.
Comparison of asymmetric and symmetric offset ablation in myopic astigmatism patients
Investigation of the hydraulic and thermal characteristics of a double concentric tubes with an inner twisted spiral tube
Abstract The characteristics of heat transfer and fluid flow of twisted spiral tubes with different pitches and depths have been investigated experimentally with respect to a conventional tube (smooth tube) as a particular reference. The effects of the twisted spiral pitch ratios, S/D hy , depth ratios, H/D hy , Reynolds number and flow arrangement on the thermal performance of a twisted spiral tube heat exchanger are investigated. Three twisted spiral tubes with different pitches, S, of 3.9, 5.2 and 8.2 mm corresponding to twisted spiral pitch ratios, S/D hy , of 0.278, 0.372 and 0.586; besides three twisted spiral tubes at different depths, H, of 0.6, 0.95, and 1.15 mm corresponding to twisted spiral height ratio, H/D hy , of 0.043, 0.068 and 0.082 are experimentally examined in this study. The Reynolds number, Re, ranges in both inner tube side and annular side are 5000–50,000 and 1400–10,400, respectively. The results revealed that the twisted spiral pitch ratios S/D hy of the 0.278achieved an enhancement of Nu by 38% compare to the smooth tube with a corresponding increase of 33.2% in the f. Also, the twisted spiral depths ratios, H/D hy , of 0.082 achieved higher Nu by 44.9%, compared to the smooth tube with a corresponding increase of 36.4% in the f. The thermal performance criteria reached 1.93 and 2.03 at S/D hy of 0.278 and H/D hy of 0.082, respectively. New correlations to expect Nu c and f c were predicted.
Mechanism of action for Troxerutin targeting the sialylation-related gene EGLN3 for the treatment of LUAD
Soy protein selectively accumulates formaldehyde
High-throughput untargeted metabolomic profiling of urinary biomarkers in acute myocarditis patients: a cross-sectional study
Author Correction: Economic renovation of a hillside drip irrigation system using drip tape inlet pressure regulators under computer-aided design
Dynamic biomarkers and Cox regression with time-dependent covariate for mortality prediction in severe fever with thrombocytopenia syndrome
Abstract Severe fever with thrombocytopenia syndrome (SFTS) is a fatal tick-borne infectious disease that lacks effective treatments. Dynamic analysis that reflects changes in the SFTS patient’s condition is needed. This study aimed to evaluate the time-dependent predictive performance of key biomarkers using a time-dependent Cox regression model. A retrospective multicenter cohort study was conducted on 440 SFTS patients hospitalized in South Korea between 2013 and 2024. Time-dependent Cox regression and time-dependent receiver operating characteristic (ROC) analyses were applied to assess the prognostic value of Blood Urea Nitrogen (BUN), Prothrombin Time (PT), and Activated Partial Thromboplastin Time (aPTT). Missing data were handled using multiple imputation. aPTT consistently demonstrated high predictive accuracy (AUC > 0.90) throughout the disease course, indicating its sustained role in coagulopathy. PT exhibited strong early-stage predictive power (AUC = 0.86 on day 2) but declined over time, reflecting its utility for early monitoring. BUN showed a progressive increase in predictive performance (AUC = 0.70 on day 2 to AUC = 0.78 on day 8), supporting its relevance in later stages of disease progression. Non-survivors exhibited significantly higher levels of BUN, PT, and aPTT compared to survivors. This study demonstrates the utility of time-dependent analysis for evaluating dynamic biomarker changes in SFTS patients. aPTT is a robust predictor throughout the disease course, while PT is valuable for early-stage assessment and BUN for later-stage management. These findings suggest the importance of integrating dynamic biomarker monitoring into clinical decision-making to improve prognosis in SFTS patients.
Enhancing random forest model prediction of gas holdup in internal draft airlift loop contactors with genetic algorithms tuning and interpretability
Bioaccumulation of polycyclic aromatic hydrocarbons from leachates of waterpipe tobacco wastes on Peronia peronii species from the Persian Gulf region
Rhodiola crenulata induces apoptosis in bone metastatic breast cancer cells via activation of caspase-9 and downregulation of MtMP activity
Predictive analysis of miners’ group unsafe behavior based on group dynamics and institutional environment
Multiobjective adaptive predictive virtual synchronous generator control strategy for grid stability and renewable integration
Abstract A novel Adaptive Predictive Virtual Synchronous Generator (AP-VSG) control strategy is proposed for enhanced grid stability and seamless renewable energy integration. The method introduces adaptive inertia and damping mechanisms combined with multi-objective predictive optimization, specifically designed for parallel-connected Self-Excited Induction Generators (SEIGs). Unlike conventional approaches requiring multiple DC conversion stages, the proposed system implements parallel operation directly in the AC domain, reducing system complexity and conversion losses. The AP-VSG control incorporates a real-time adaptation of virtual inertia (H) ranging from 1 to 4 s and damping coefficient (D) from 20 to 65 pu, responding to grid frequency deviations and Rate of Change of Frequency (RoCoF). Experimental validation with parallel-connected 2.2 kW and 5.5 kW SEIGs demonstrates a 56% reduction in maximum RoCoF (from ± 0.48 Hz/s to ± 0.21 Hz/s), 33% improvement in frequency nadir (50.85–50.87 Hz), and 41% enhancement in damping ratio. Under fault conditions, the system maintains a current limit of 1.5 pu while providing reactive support up to 0.8 pu. The multi-objective optimization framework achieves 36.7% reduction in control effort while maintaining stability margins (PM $$>45^{\circ }$$ , GM>6 dB). Statistical analysis confirms 95th percentile frequency regulation enhancement of 43.5% compared to conventional VSG control. The fault ride-through capability demonstrates voltage recovery within 100 ms with THD maintained below 3%. Experimental results verify robust performance under various grid disturbances, including voltage sags down to 0.2 pu and complete grid disconnection scenarios.
Planets larger than Neptune have elevated eccentricities
NASA’s Kepler mission identified over 4,000 extrasolar planets that transit (cross in front of) their host stars. This sample has revealed detailed features in the demographics of planet sizes and orbital spacings. However, knowledge of their orbital shapes—a key tracer of planetary formation and evolution—remains far more limited. We present measurements of eccentricities for 1,646 Kepler planets, 92% of which are smaller than Neptune. For all planet sizes, the eccentricity distribution peaks at e = 0 and falls monotonically toward zero at e = 1. As planet size increases, mean population eccentricity rises from ⟨ e ⟩ = 0.05 ± 0.01 for small planets to ⟨ e ⟩ = 0.20 ± 0.03 for planets larger than ∼3.5 Earth-radii R ⊕ . The overall planet occurrence rate and planet-metallicity correlation also change abruptly at this size. Taken together, these patterns indicate distinct formation channels for planets above and below ∼3.5 R ⊕ . We also find size-dependent associations between eccentricity, host star metallicity, and orbital period. While smaller planets generally have low eccentricities, there are hints of a noteworthy exception: eccentricities are slightly elevated in the “radius valley,” a narrow band of low occurrence rate density which separates rocky “super-Earths” (1.0 to 1.5 R ⊕ ) from gas-rich “sub-Neptunes” (2.0 to 3.0 R ⊕ ). We detect this feature at 2.1σ significance. Planets in single- and multitransiting systems exhibit the same size–eccentricity relationship, suggesting they are drawn from the same parent population.