The aim of this study was to analyze the clinical data, discharge rate, and fatality rate of COVID‐19 patients for clinical help. The clinical data of COVID‐19 patients from December 2019 to February ...2020 were retrieved from four databases. We statistically analyzed the clinical symptoms and laboratory results of COVID‐19 patients and explained the discharge rate and fatality rate with a single‐arm meta‐analysis. The available data of 1994 patients in 10 literatures were included in our study. The main clinical symptoms of COVID‐19 patients were fever (88.5%), cough (68.6%), myalgia or fatigue (35.8%), expectoration (28.2%), and dyspnea (21.9%). Minor symptoms include headache or dizziness (12.1%), diarrhea (4.8%), nausea and vomiting (3.9%). The results of the laboratory showed that the lymphocytopenia (64.5%), increase of C‐reactive protein (44.3%), increase of lactic dehydrogenase (28.3%), and leukocytopenia (29.4%) were more common. The results of single‐arm meta‐analysis showed that the male took a larger percentage in the gender distribution of COVID‐19 patients 60% (95% CI 0.54, 0.65), the discharge rate of COVID‐19 patients was 52% (95% CI 0.34,0.70), and the fatality rate was 5% (95% CI 0.01,0.11).
Research Highlights
Our study explored the clinical and epidemiological characteristics of COVID‐19 patients, and proposed the need to focus on other systemic symptoms such as the gastrointestinal tract firstly, which could be of use for clinical work.
In this letter, a deep learning framework for direction of arrival (DOA) estimation is developed. We first show that the columns of the array covariance matrix can be formulated as under-sampled ...noisy linear measurements of the spatial spectrum. Then, a deep convolution network (DCN) that learns the inverse transformation from large training dataset is introduced. In contrast to traditional sparsity-inducing methods with computationally complex iterations, the proposed DCN-based framework could efficiently obtain DOA estimates in near real time. Moreover, the utilization of the sparsity prior improves DOA estimation performance compared to existing deep learning based methods. Simulation results have demonstrated the superiority of the proposed method in both DOA estimation precision and computation efficiency especially when SNR is low.
The scalable production of nanomaterials‐based electronic components with mechanically compliable form factors not only provides interesting research topics but also ushers in exciting opportunities ...for wearable applications in consumer electronics, healthcare, human–machine interface, etc. Wearable nanosystems consist of components such as thin‐film transistors, flexible sensors, energy harvesters, and energy storage devices. Despite the increased interests and efforts in nanotechnology‐enabled wearables, reducing the manufacturing and assembly costs while improving the performance at the device and system level remains a major technological challenge. The inkjet printing process has emerged as a potential economic method for nanomanufacturing functional devices. Here, the authors review the recent advances in inkjet‐printed wearable nanodevices and provide an in‐depth discussion focusing on the materials, manufacturing process, integration, performance issues, and potential applications for inkjet‐printed self‐powered wearable devices. The authors compile a comprehensive list of the reported flexible devices with the corresponding materials employed. Finally, they discuss the challenges and opportunities associated with related topics.
The scalable production of nanomaterials‐based electronic components with mechanically compliable form factors not only provides interesting research topics but also ushers in exciting opportunities for wearable applications in consumer electronics, healthcare, human–machine interface, etc. Inkjet printing of multifunctional materials holds promise for implementing diversified self‐powered wearable devices for a broad range of human‐integrated applications.
This paper explores the cognitive attention mechanism in mental activities, analyzes the learning emotion contained therein as well as the information of thinking activity that carries out the whole ...cognitive process of mental learning, and obtains the information construction data of the three-dimensional interest model after the estimation of the learner’s head gesture and the recognition of dynamic expression. A conditional random forest model is built using multimodal information fusion technology, and a natural smile detection method is proposed based on it. After training, it realizes the information estimation of the learner’s head pose data, generates the smiley face classifier based on conditional random forest, and determines the teaching decision boundary using K-Means clustering. To analyze the psychological personalized teaching results of the model, an empirical study is conducted through a controlled experiment. The experimental results show that the learning efficiency of the model on the CelebA dataset and SMILEsmileD dataset is improved, the accuracy rate is stabilized at 95% after the number of iterations 10, and the model’s performance is superior. The majority of the students in the experiment have a mastery of psychological knowledge that is around 0.85, and there is a significant positive correlation, and personalized teaching has a more significant effect.
To effectively promote the integration of red culture and Civic Education courses in higher education institutions, we aim to spread red culture, pass on red genes, and enrich and enhance the ...specific contents of Civic Education in higher education institutions. Based on the LightGBM model and then combined with the Civic Education Red Culture, this paper constructs two examples of evaluation indexes for the application of Civic Education Integrated Red Culture in teaching courses, i.e., teaching course foundation construction and teaching course effect evaluation and also conducts a comparison experiment for the performance of this paper’s model with four other models. The results show that the four performance indicators of the LightGBM model are higher than those of the other four models, with values of 0.1239, 0.0326, 0.1364, and 0.4625 for each evaluation indicator. 21.40%, 30.42%, 26.36%, and 21.82%. The overall evaluation of the teaching course effect indicators is 90.73% above C grade, among which the average percentages of A, B, C, and D indicators are 43.94%, 27.83%, 18.96%, and 9.27%, respectively, which shows that the teaching course evaluation of Civic and Political Education integrating red culture is very good. Based on the LightGBM model, Civic and Political Education integrating red culture can be well applied in teaching courses, and Civic and Political Education relying on red culture can be given a new life. In the future, we will continue to explore the same points of red and ideological and political education and promote the synergistic development of ideological and political education and red culture.
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