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High-power electric arc heaters are the only viable test equipments for heating air to high temperatures (2000–10 000 K) in a few minutes for experimental ground studies of hypersonic flight vehicle thermal protection systems. With the expansion of the exploration of outer space and the rapid development of hypersonic vehicles, more powerful arc heaters are urgently required to meet the demands of the aerospace industry. However, the design of high-power arc heaters faces many technical obstacles such as the need for high structural strength, the need for high-efficiency heat-transfer for cooling, the problem of high internal temperature flow, and the electromagnetic plasma coupling problem. To address these problems, this paper has discussed an optimization scheme, in order to improve the reliability and security of a high-power arc heater in extreme conditions, and at the same time improve the thermal efficiency of the arc heater, and extend the service life of an arc heater. [ABSTRACT FROM AUTHOR]

Background: Modernizing medical education by using artificial intelligence and other new technologies to improve the clinical thinking ability of medical students is an important research topic in recent years. Prominent medical universities are actively conducting research and exploration in this area. In particular, given the shortage of human resources, the need to maintain social distancing to prevent the spread of the epidemics, and the increase in the cost of medical education, it is critical to harness online learning to promote medical education. A virtual case learning system that uses natural language processing technology to process and present a hospital's real medical records and evaluate student responses can effectively improve medical students' clinical thinking abilities.Objective: The purpose of this study is to develop a virtual case system, AIteach, based on actual complete hospital medical records and natural language processing technology, and achieve clinical thinking ability improvement through a contactless, self-service, trial-and-error system application.Methods: Case extraction is performed on a hospital's case data center and the best-matching cases are produced through natural language processing, word segmentation, synonym conversion, and sorting. A standard clinical questioning data module, virtual case data module, and student learning difficulty module are established to achieve simulation. Students can view the objective examination and inspection data of actual cases, including details of the consultation and physical examination, and automatically provide their learning response via a multi-dimensional evaluation system. In order to assess the changes in students' clinical thinking after using AIteach, 15 medical graduate students were subjected to two simulation tests before and after learning through the virtual case system. The tests, which included the full-process case examination of cases having the same difficulty level, examined core clinical thinking test points such as consultation, physical examination, and disposal, and generated multi-dimensional evaluation indicators (rigor, logic, system, agility, and knowledge expansion). Thus, a complete and credible evaluation system is developed.Results: The AIteach system used an internal and external double-cycle learning model. Students collect case information through online inquiries, physical examinations, and other means, analyze the information for feedback verification, and generate their detailed multi-dimensional clinical thinking after learning. The feedback report can be evaluated and its knowledge gaps analyzed. Such learning based on real cases is in line with traditional methods of disease diagnosis and treatment, and addresses the practical difficulties in reflecting actual disease progression while keeping pace with recent research. Test results regarding short-term learning showed that the average score (P < 0.01) increased from 69.87 to 85.6, the five indicators of clinical thinking evaluation improved, and there was obvious logical improvement, reaching 47%.Conclusion: By combining real cases and natural language processing technology, AIteach can provide medical students (including undergraduates and postgraduates) with an online learning tool for clinical thinking training. Virtual case learning helps students to cultivate clinical thinking abilities even in the absence of clinical tutor, such as during pandemics or natural disasters. [ABSTRACT FROM AUTHOR]

High silicon oxygen/phenolic aldehyde (VSF/PR) ablative heat-proof material is widely used as resin-based heat-proof system for long-term aircraft. Understanding the generation and flow process of the surface droplets during high temperature is key to improving the thermal stability, and prior to which, a direct visualization of the surface evolution is required. Here we use optical imaging technique to in-situ and real time record the surface evolution of a flat plate subjected to thermal ablation at 1700 °C in wind tunnel. Based on the experimental results processed using image processing method and by combining the fluid theory in high temperature wind tunnel, we explained the distribution of the surface temperature and surface liquid droplets, the velocity field, and the movement of the liquid droplets on the plate surface. Results show that the distribution of the liquid droplets was correlated to the temperature distribution. A verification experiment for the merging process of two liquid droplets shows that droplet with high viscosity would generate a great friction force and cause the droplet to move forward in a rolling manner. The chasing and merging process of the liquid droplets with high viscosity is rather an entrainment process than a spontaneous merging process normally induced by surface tension. These findings help to understand the flow behavior of the liquid droplets during thermal ablation. [ABSTRACT FROM AUTHOR]

Purpose: Predictively diagnosing infectious diseases helps in providing better treatment and enhances the prevention and control of such diseases. This study uses actual data from a hospital. A multiple infectious disease diagnostic model (MIDDM) is designed for conducting multi-classification of infectious diseases so as to assist in clinical infectious-disease decision-making.Methods: Based on actual hospital medical records of infectious diseases from December 2012 to December 2020, a deep learning model for multi-classification research on infectious diseases is constructed. The data includes 20,620 cases covering seven types of infectious diseases, including outpatients and inpatients, of which training data accounted for 80%, i.e., 16,496 cases, and test data accounted for 20%, i.e., 4124 cases. Through the auto-encoder, data normalization and sparse data densification processing are carried out to improve the model training effect. A residual network and attention mechanism are introduced into the MIDDM model to improve the performance of the model.Result: MIDDM achieved improved prediction results in diagnosing seven kinds of infectious diseases. In the case of similar disease diagnosis characteristics and similar interference factors, the prediction accuracy of disease classification with more sample data is significantly higher than the prediction accuracy of disease classification with fewer sample data. For instance, the training data for viral hepatitis, influenza, and hand foot and mouth disease were 2954, 3924, and 3015 respectively and the corresponding test accuracy rates were 99.86%, 98.47%, and 97.31%. There is less training data for syphilis, infectious diarrhea, and measles, i.e., 1208, 575, and 190 respectively and the corresponding test accuracy rates were noticeably lower, i.e., 83.03%, 87.30%, and42.11%. We also compared the MIDDM model with the models used in other studies. Using the same input data, taking viral hepatitis as an example, the accuracy of MIDDM is 99.44%, which is significantly higher than that of XGBoost (96.19%), Decision tree (90.13%), Bayesian method (85.19%), and logistic regression (91.26%). Other diseases were also significantly better predicted by MIDDM than by these three models.Conclusion: The application of the MIDDM model to multi-class diagnosis and prediction of infectious diseases can improve the accuracy of infectious-disease diagnosis. However, these results need to be further confirmed via clinical randomized controlled trials. [ABSTRACT FROM AUTHOR]

To predict complex flow, including transition on the windward side and massive separation on the leeward side, a Reynolds averaged Navier Stokes-Large Eddy Simulation (RANS-LES) hybrid model based on the three-equation k - ω - γ transition model (RANS-LES-Tr) was developed. Then, it was applied to simulate the following three cases, such as the low-speed flow past a cylinder only with the first transition mode, the low-speed prolate spheroid flow at a high angle of attack (AoA) with both the first and crossflow modes; and hypersonic flow past the "Orion" capsule with the first, second and crossflow modes. RANS-LES-Tr shows much better agreements with available measurements, such as skin friction and heat flux on the windward side, than RANS-LES based on the full turbulence model. [ABSTRACT FROM AUTHOR]

• A new DDES-Tr, based on the three-equation k-ω-γ transition/turbulence integrated model, was proposed through the similar way with the typical DDES. • A modification of the length scale was introduced to ensure the models worked in their own interest regions. • DDES-Tr model can not only resolve the massive separation on the leeward side, but also model the transition on the windward side. The hypersonic flow at high angle of attack past the Orion capsule was predicted by a delayed detached eddy simulation (DDES), based on a three-equation k-ω-γ transition/turbulence integrated model (Tr). The computational results of the mean and instantaneous heat rates by DDES-Tr are compared with the available measurements and DDES-FT (full turbulence). As expected, the massive separation on the leeward side can be well predicted by both DDES models, but DDES-FT cannot model the transition and over-predicts the heat flux on the windward side. Fortunately, the DDES-Tr model can not only resolve the massive separation on the leeward side, but also model the transition on the windward side. Furthermore, the contributions of the first, second, and crossflow modes on the transition are quantitatively explored. Although the freestream is hypersonic, the second Mack mode is very weak owing to the low supersonic speed after the strong bow shock wave. The transition is mainly dominated by the first and rarely by the crossflow mode. [ABSTRACT FROM AUTHOR]