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As a key component of proton exchange membrane (PEM) fuel cell, gas diffusion layer (GDL) plays an important role in mass transport process. In this study, the layered setting method is used to cut the GDL along the thickness direction, and the porosity of different layers is set to make it have porosity distribution along the thickness direction. The influence of five porosity distributions of GDL on the internal mass transport process and performance of fuel cell is studied. It is found that the porosity gradient and distribution shape of the GDL affects the effective diffusion coefficient of the reactant gas, and larger porosity gradient leads to poorer uniformity of the gas diffusion coefficient distribution, which is not conducive to the internal mass transport of fuel cell. When the porosity gradient of the GDL is smaller, the gas diffusion process occurs more uniformly, the electrochemical reaction rate is higher, and the performance of the fuel cell is better. In addition, the inverted "V" shaped porosity distribution along the thickness direction of the GDL is more conducive to the reactant gas transport and fuel cell performance than the "V" shaped porosity distribution. [ABSTRACT FROM AUTHOR]

The Corona Virus Disease 2019 has a great impact on public health and public psychology. People stay at home for a long time and rarely go out. With the improvement of the epidemic situation, people began to go to different places to check in. To maintain public mental health, it is necessary to propose a point‐of‐interest (POI) prediction model which can mine users' interests. However, the current techniques suffer from lower precision during prediction and the practical value is poor, which is due to the sparse data of users' check‐in. Faced with this challenge, we propose an attention‐based bidirectional gated recurrent unit (GRU) model for POI category prediction (ABG_poic). We regard the user's POI category as the user's interest preference because the fuzzy POI category is easier to reflect the user's interest than the POI. This method can alleviate the data sparsity, and protect users' location privacy. Since users' preferences are variable, we utilize a bidirectional GRU to capture the dynamic dependence of users' check‐ins. Furthermore, since the neural network is similar to a "black box" in feature learning, the decision‐making stage is opaque. Thus, we combine the attention mechanism with bidirectional GRU to selectively focus on historical check‐in records, which can improve the interpretability of the model. Considering the time impact on users' check‐in, we utilize the time sliding window in the ABG_poic model. Experiments on two data sets demonstrate that our ABG_poic outperforms the comparison models for POI category prediction on sparse check‐in data. [ABSTRACT FROM AUTHOR]

Background: The vital‐sign monitoring strategy of patients with acute aortic dissection in the emergency department is mainly based on traditional experience. This study attempts to explore the significance of the national early warning score (NEWS) in monitoring the condition of patients with acute aortic dissection during emergency observation and to provide evidence for emergency nurses in optimal and scientific monitoring of patients. Methods: The case–control method was used to continuously enrol patients with acute aortic dissection who had been in the emergency department; the STROBE checklist was used in this process. Based on patients' clinical deterioration, they were divided into two groups: clinical deterioration and non‐clinical deterioration. The NEWS at each time point was compared by independent‐samples t‐test, and the predictive power of NEWS was evaluated according to the area under the receiver operating characteristic curve. Results: A total of 290 patients with acute aortic dissection were included: 46 patients showed clinical deterioration and 244 did not. There were significant differences in the NEW scores of the two groups at admission time and at 12, 8, 4 and 0.5 h before clinical deterioration. The NEW scores of the clinical deterioration group showed an upward trend, while the non‐clinical deterioration group showed a relatively stable trend. The NEWS can be used to predict the occurrence of clinical deterioration earlier at 4 h before clinical deterioration. Simultaneously, the patient's respiration rate and SpO2 had better predictive performance than other vital signs. Conclusion: The NEWS can be used to triage patients with acute aortic dissection admitted to the emergency department. Continuous use of the NEWS for monitoring can play a vital role in early warning of clinical deterioration in patients with acute aortic dissection. In clinical care, attention should also be paid when patients with acute aortic dissection have abnormal respiration rate and SpO2. [ABSTRACT FROM AUTHOR]

In this study, we aimed to identify the main innovation factors that affect the economic performance of emerging countries. We analyzed data on 21 innovation-related variables in six emerging Asian countries (China, Indonesia, Malaysia, the Philippines, Thailand and Vietnam) from 1990 to 2008. Using principal component analysis (PCA), we identified six factors that explained up to 90.6% of the variation of all of the variables. Taking the least absolute shrinkage and selection operator clustering approach to variable selection, along with the other classification and estimation methods, we explored the effects of the six identified factors on economic growth. Research and development expenditure and contract enforcement (cost) had negative effects on economic growth, whereas gross enrolment ratio (tertiary), primary-school pupil–teacher ratio (inverse), paved roads and number of registered carrier departures worldwide had positive effects on economic growth. [ABSTRACT FROM AUTHOR]