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Due to the huge impact of temperature on mechanical and tribological properties of polymer composites, polyimide composites reinforced with glass fiber and aramid fiber were fabricated. The composites were treated by the alternate temperature from-50 °C to 150 °C with 200 cycles. The comparation of the test results were analyzed and discussed before and after alternate temperature treatment.

Cement-stabilized soil retaining wall structures are becoming increasingly popular for high-speed railway lines. However, the widely used Portland cement is heavily associated with carbon dioxide (CO2) emissions. Additionally, the rising cost and scarcity of sand traditionally used as fill material is becoming a concern. Besides, the geomechanical performances of the cement-stabilized soil retaining walls using three-dimensional (3-D) finite element method (FEM) numerical analysis have seldom been investigated. To address these issues, this study investigates the geomechanical performances of geocell-reinforced soil (GRS) backfilled with magnesia-based cement-stabilized marine fill using the nonlinear stress-strain relationship and 3-D FEM models as an alternative to the traditional earth GRS walls. The study finds satisfactory results regarding stability, deformation, and settlement. Only nominal facing reinforcements may be required as the magnesia cement mechanically stabilizes the fill. Moreover, the strength and stiffness properties improve with increased curing duration. The safety factors and observed displacements were within acceptable limits. However, the critical region for the cement-stabilized marine fill GRS wall showing a rupture zone was identified at the toe of the structure. Finally, the study developed a load-displacement model for settlement predictions and uncertainty interpretations, and explained the strength development and deformation mechanisms. The results suggest that cement-stabilized soil retaining walls using magnesia-based cement could be a viable alternative to traditional earth GRS walls, while mitigating environmental concerns and reducing costs.

The aim of this systematic review and meta-analysis was to determine the performance of magnetic resonance imaging (MRI) in the diagnosis of bowel inflammation and disease activity in Crohn's disease (CD).MEDLINE, Embase and Web of Science databases of biomedical literature were systematically searched to identify studies that investigated the diagnostic accuracy of MRI in diagnosing bowel inflammation and disease activity in CD by comparing it with reference standards. Quality Assessment of Diagnostic Accuracy Studies-2 tool was used to assess study quality. The summary sensitivity and specificity were estimated using the bivariate model, and hierarchical summary receiver operating characteristic (HSROC) parameters were calculated and plotted.Of 5492 citations of interest, 34 articles contained the diagnostic accuracy data. Of these, results for the small bowel and the colorectum were reported separately in 19 studies and jointly by 21 studies. The meta-analytic summary sensitivity and specificity under the bivariate model were 90.9% (95% CI, 85.8%-94.2%) and 90.2% (95% CI, 81.9%-95.0%), respectively. The sensitivities and specificities of individual studies ranged from 55% to 100% and 51% to 100%, respectively. Substantial heterogeneity was observed in both sensitivity (IAlthough the meta-analytic summary accuracy of MRI was high for the diagnosis of bowel inflammation in CD, the summary estimates might be unreliable due to the presence of high heterogeneity.

Discovering causal relationships among observed variables is an important research focus in data mining. Existing causal discovery approaches are mainly based on constraint-based methods and functional causal models (FCMs). However, the constraint-based method cannot identify the Markov equivalence class and the functional causal models cannot identify the complex interrelationships when multiple variables affect one variable. To address the two aforementioned problems, we propose a new graph structure Causal Star Graph (CSG) and a corresponding framework Causal Discovery via Causal Star Graphs (CD-CSG) to divide a causal directed acyclic graph into multiple CSGs for causal discovery. In this framework, we also propose a generalized learning in CSGs based on a variational approach to learn the representative intermediate variable of CSG’s non-central variables. Through the generalized learning in CSGs, the asymmetry in the forward and backward model of CD-CSG can be found to identify the causal directions in the directed acyclic graphs. We further divide the CSGs into three categories and provide the causal identification principle under each category in our proposed framework. Experiments using synthetic data show that the causal relationships between variables can be effectively identified with CD-CSG and the accuracy of CD-CSG is higher than the best existing model. By applying CD-CSG to real-world data, our proposed method can greatly augment the applicability and effectiveness of causal discovery.

The Archie model is the foundation for calculating oil saturation, but limitations exist when the model is used to calculate oil saturation in the water-flooded layer. In the process of water injection, the dynamic change in oil saturation will be caused by the different degrees of water flooding and the properties of the injected water. Under the dynamic condition of water flooding, the Archie model is not suitable for calculating the oil saturation of water-flooded layers. By combining dynamic and static methods, a “double-ratio” model of the same sedimentary facies layer in the later development stage was established: [Formula: see text]. Based on the parameters of rock resistivity and formation water resistivity, an improved Archie model for calculating oil saturation in water-flooded layers of the same sedimentary facies was established. The interpretation of the actual data of the Zhenwu oilfield in Jiangsu, China finds that the average relative error between the calculation result and the core analysis result is 5.46%. The calculation result is reasonable, which offers a scientific basis for predicting the remaining oil distributions. The computational results have been validated by real data sets. This improved mode can provide experience-based guidance for the calculation of the remaining oil saturation of the water-flooded layer in the same sedimentary interpretation layer.

Taekwondo, as an Olympic sport, has always been attached to great importance by all sectors of society, but the physiological demand of this sport is still relatively short of research. Therefore, there is still a lack of theoretical support for the actual needs of Taekwondo athletes for special training and physical training. Through searching the previous literature, the research found that (1) there is a lack of research on the aerobic capacity of Taekwondo athletes; (2) Athletes need a lot of reserves and the ability to quickly synthesize ATP, have the ability to tolerate lactic acid, and the aerobic ability to quickly eliminate lactic acid for post-competition recovery; (3) Aerobic training should be arranged in the training class or added at the end of each class; (4) LDH level can be one of the factors to evaluate the level of Taekwondo athletes.