河北交通职业技术学院

In this study, through a combined approach of numerical simulation and experimental verification, the aerodynamic performance of a two - stage centrifugal compressor for hydrogen fuel cell vehicles with different leading - edge inclinations was analyzed. The results indicate that: The impeller with a leading - edge inclination can enhance the operating range of the two - stage compressor. A positive leading - edge inclination can improve the stall margin of the compressor, enabling the compressor to achieve a better boosting effect under low - flow conditions. A negative leading - edge inclination can not only delay the choking flow of the compressor but also optimize the pressure distribution on both sides of the blade, refine the internal flow within the impeller, and reduce the occurrence of leakage.

With the rapid growth of global maritime trade, the efficient and safe management of maritime traffic has become increasingly critical. This study proposes a comprehensive framework for ship trajectory prediction and maritime traffic congestion identification based on Automatic Identification System (AIS) data. We integrate spatiotemporal analysis with deep learning techniques, specifically combining Graph Convolutional Networks (GCN) and Gated Recurrent Units (GRU) to form a Temporal Graph Convolutional Network (T-GCN) model. This model effectively captures both spatial dependencies among ships and temporal dynamics in traffic flow. Furthermore, we introduce a congestion measurement indicator based on the Speed Performance Index (SPI) to quantify and identify congestion levels in maritime routes. The proposed method not only enhances the accuracy of ship trajectory prediction but also enables proactive congestion warnings, contributing to improved maritime safety and operational efficiency. Experimental results demonstrate the effectiveness of our approach in real-world scenarios.

The authors developed a data-driven decision support framework for intercity rail transit network planning and used it to empirically investigate the impact mechanisms of intercity rail transit networks on economic spatial spillovers within the Yangtze River Delta core metropolitan area. The findings, which are based on panel data from 2013 to 2022 and integrate the spatial Durbin model with bootstrap mediation tests, reveal that network connectivity exerts a significant positive influence on economic spillovers. This process is facilitated through dual mediation pathways: (a) IND (contributing 41.5%) and (b) MIG. Furthermore, significant node heterogeneity was identified, with the spillover intensity of primary hubs being 2.9 times that of ordinary nodes. The results provide robust evidence for formulating differentiated rail network planning and industrial synergy policies and offer precise decision-making support for regional integration.

To explore the diagnostic value of infrared thermography and receiver operating characteristic curve (ROC) analysis in point sensitization of primary dysmenorrhea (PD) patients with cold-induced blood stasis pattern. Forty-seven cold-induced blood stasis pattern PD patients (PD group) and 47 healthy subjects (normal group) were included. Infrared thermography was used to detect the point temperature of abdominal conception vessel and meridians of kidney, stomach, and spleen, as well as lumbosacral governor vessel and bladder meridian of the subjects during menstruation. The Youden index was calculated by ROC, and the optimal diagnostic boundary value of point sensitization was calculated according to the Youden index. The area under curve (AUC) was used to evaluate the diagnostic value of temperature in point sensitization, and the sensitization rate of point was calculated with the optimal diagnostic boundary. Temperatures of Guanyuan (CV4), Qugu (CV2), left Huangshu (KI16), left Zhongzhu (KI15), right Siman (KI14), right Qixue (KI13), right Dahe (KI12), bilateral Tianshu (ST25), left Daju (ST27), bilateral Shuidao (ST28), right Guilai (ST29), right Daheng (SP15), and left Fujie (SP14) on the abdomen in the PD group were significantly lower than those in the normal group (P<0.05). The AUC value was 0.62–0.66, indicating that the temperature of these points has certain diagnostic value in distinguishing point sensitization. Guanyuan (CV4) showed the highest sensitization rate (80.95

This should be informative and suitable for direct inclusion in abstracting services as a self-contained article. It should not exceed 200 words. It should summarise the general scope and also state the main results obtained, methods used, the value of the work and the conclusions drawn. No figure numbers, table numbers, references or displayed mathematical expressions should be included. The abstract should be included in both the Manuscript Central submission step (Step 1) and the submitted paper. Accurate traffic flow prediction is crucial for modern traffic control and management, particularly under adverse conditions such as severe weather, accidents, holidays, and prolonged construction. These scenarios often lead to abnormal traffic patterns, increasing congestion risks, reducing road capacity, deteriorating travel experiences, and escalating safety hazards. This study proposes a situation prediction model based on the Kernel-KNN algorithm, based on the radar-vision integrated machine, and the nonlinear mapping relationship between speed and traffic situation is combined. incorporating the nonlinear relationship between speed and traffic conditions. Implemented in MATLAB, the model determines optimal parameter ranges and validates its effectiveness. Results indicate that prediction accuracy yielding stable accuracy with a Mean Absolute Error (MAE) of 5.28, Mean Absolute Percentage Error (MAPE) of 4.55, and Root Mean Square Error (RMSE) of 3.96. Additionally, single-day speed prediction results demonstrate strong alignment with actual values, further verifying the model efficacy.