To address the uncontrollable risks associated with the overreliance on ship operators\' driving in current ship safety braking methods, this study aims to reduce the impact of operator fatigue on navigation safety. Firstly, this study established a human-ship-environment monitoring system with functional and technical architecture, emphasizing the investigation of a ship braking model that integrates brain fatigue monitoring using electroencephalography (EEG) to reduce braking safety risks during navigation. Subsequently, the Stroop task experiment was employed to induce fatigue responses in drivers. By utilizing principal component analysis (PCA) to reduce dimensionality across multiple channels of the data acquisition device, this study extracted centroid frequency (CF) and power spectral entropy (PSE) features from channels 7 and 10. Additionally, a correlation analysis was conducted between these features and the Fatigue Severity Scale (FSS), a five-point scale for assessing fatigue severity in the subjects. This study established a model for scoring driver fatigue levels by selecting the three features with the highest correlation and utilizing ridge regression. The human-ship-environment monitoring system and fatigue prediction model proposed in this study, combined with the ship braking model, achieve a safer and more controllable ship braking process. By real-time monitoring and prediction of driver fatigue, appropriate measures can be taken in a timely manner to ensure navigation safety and driver health.

The aim of this study was to investigate the geographical spatial distribution of creatine kinase isoenzyme (CK-MB) in order to provide a scientific basis for clinical examination. The reference values of CK-MB of 8697 healthy adults in 137 cities in China were collected by reading a large number of literates. Moran index was used to determine the spatial relationship, and 24 factors were selected, which belonged to terrain, climate, and soil indexes. Correlation analysis was conducted between CK-MB and geographical factors to determine significance, and 9 significance factors were extracted. Based on R language to evaluate the degree of multicollinearity of the model, CK-MB Ridge model, Lasso model, and PCA model were established, through calculating the relative error to choose the best model PCA, testing the normality of the predicted values, and choosing the disjunctive kriging interpolation to make the geographical distribution. The results show that CK-MB reference values of healthy adults were generally correlated with latitude, annual sunshine duration, annual mean relative humidity, annual precipitation amount, and annual range of air temperature and significantly correlated with annual mean air temperature, topsoil gravel content, topsoil cation exchange capacity in clay, and topsoil cation exchange capacity in silt. The geospatial distribution map shows that on the whole, it is higher in the north and lower in the south, and gradually increases from the southeast coastal area to the northwest inland area. If the geographical factors are obtained in a location, the CK-MB model can be used to predict the CK-MB of healthy adults in the region, which provides a reference for us to consider regional differences in clinical diagnosis.

COVID-19 has made the entire society pay more attention to medical students training. Medicine development is inseparable from the spirit of innovation, focusing on cultivating medical students\' innovative awareness and improving entrepreneurship education performance, which has an irreplaceable effect on both the students themselves and the society. This study is based on the ridge regression model to study the driving factors of the entrepreneurship education performance of medical students. Compared with traditional multiple regression, it can improve the consistency of parameter estimation and obtain more realistic results. Based on a large sample of empirical survey data of 24,677 medical students in China, this study analyzed the driving factors of the entrepreneurship education performance of medical students and found that medical students of different genders have differences in entrepreneurship education performance; the digital economy impacts entrepreneurship education performance of medical students; entrepreneurship course, entrepreneurship faculty, entrepreneurship competition, entrepreneurship practice, and entrepreneurship policy have a driving effect on the entrepreneurship education performance of medical students. Meanwhile, the impact of entrepreneurship policy is the most obvious, followed by entrepreneurship practice and entrepreneurship competition, followed by entrepreneurship course and entrepreneurship faculty.

A study on 70 acute lymphoblastic leukemia (ALL) children (age ≤16 years) treated with high-dose methotrexate (HD-MTX) in Sichuan Provincial People\'s Hospital was conducted. The aim of the study was to establish a risk-scoring model to predict HD-MTX-induced liver injury, considering gene polymorphisms\' effects. Data screening was performed through t-test, chi-square test, and ridge regression, and six predictors were identified: age, MTRR_AA, MTRR_AG, SLCO1B1_11045879_CC, albumin_1 day before MTX administration, and IBIL_1 day before MTX administration (p < 0.1). Then, the risk-scoring model was established by ridge regression and evaluated the prediction performance. In a training cohort (n = 49), the area under the curve (AUC) was 0.76, and metrics including accuracy, precision, sensitivity, specificity, positive predictive value, and negative predictive value were promising (0.86, 0.81, 0.76, 0.91, 0.81, 0.88, respectively). In a test cohort (n = 21), the AUC was 0.62 and negative predictive value was 0.80; other evaluation metrics were not satisfactory, possibly due to the limited sample size. Ultimately, the risk scores were stratified into three groups based on their distributions: low- (≤48), medium- (49-89), and high-risk (>89) groups. This study could provide knowledge for the prediction of HD-MTX-induced liver injury and reference for the clinical medication.

Accurate prediction of municipal solid waste (MSW) generation is necessary for choosing appropriate waste treatment methods and for planning the distribution of disposal facilities. In this study, a hybrid model was established to forecast MSW generation through the combination of the ridge regression and GM(1,N) models. The hybrid model is multivariate and involves total urban population, total retail sales of social consumer goods, per capita consumption expenditure of urban areas, tourism, and college graduation. Compared with the constituent models alone, the hybrid model yields higher accuracy, with a mean absolute percentage error (MAPE) of only 2.59%. Through weight allocation and optimal treatment of residuals, the hybrid model also balances the growth trends of the individual models, making the prediction curve smoother. The model coefficients and correlation analysis show that population, economics, and educational factors are influential for waste generation. MSW output in Hangzhou will gradually increase in the future, and is expected to reach 5.12 million tons in 2021. Results can help decision makers to develop the measures and policies of waste management in the future.