Table_1_Mobile phone addiction and depressive symptoms among Chinese University students: The mediating role of sleep disturbances and the moderating role of gender.XLS

BackgroundWith the continuous updating of mobile phone functions, the phenomenon of mobile phone addiction among University students is becoming more and more serious. It is important to identify the potential risk factors for mobile phone addiction. The aim of the study was to examine whether there is a relationship between mobile phone addiction and depression symptoms in University students, and to investigate whether sleep disturbances play a mediating role between mobile phone addiction and depression symptoms, as well as the moderating role of gender.MethodsA cross-sectional study, carried out between September to December 2021, recruited 973 students (478 males) from seven comprehensive universities in western China. The Mobile Phone Addiction Index (MPAI), the Patient Health Questionnaire-9 (PHQ9), and the Pittsburgh Sleep Quality Index (PSQI) were used to complete measures of mobile phone addiction, depressive symptoms, and sleep disturbances. For statistical analyses, descriptive statistics, correlation, regression, mediation and moderated mediation analyses were used. Furthermore, we tested the mediation model and moderated mediation model using the SPSS macro PROCESS.ResultsIn this study, it was found that there were positive correlations between mobile phone addiction and depressive symptoms among Chinese University students. Mediation analyses revealed that this relationship was partially mediated by sleep disturbances, but the mediating role was not moderated by gender.ConclusionSleep disturbances have a partial mediating role in the relationship between mobile phone addiction and depressive symptoms. Our results highlight the critical role of prevention and early identification of mobile phone addiction among University students, especially those with sleep disturbances.</p

Ensemble model end different machine learning combination model comparison.

Ensemble model end different machine learning combination model comparison.</p

Hyper-parameter values of the KISM.

Coronaviruses have affected the lives of people around the world. Increasingly, studies have indicated that the virus is mutating and becoming more contagious. Hence, the pressing priority is to swiftly and accurately predict patient outcomes. In addition, physicians and patients increasingly need interpretability when building machine models in healthcare. We propose an interpretable machine framework(KISM) that can diagnose and prognose patients based on blood test datasets. First, we use k-nearest neighbors, isolated forests, and SMOTE to pre-process the original blood test datasets. Seven machine learning tools Support Vector Machine, Extra Tree, Random Forest, Gradient Boosting Decision Tree, eXtreme Gradient Boosting, Logistic Regression, and ensemble learning were then used to diagnose and predict COVID-19. In addition, we used SHAP and scikit-learn post-hoc interpretability to report feature importance, allowing healthcare professionals and artificial intelligence models to interact to suggest biomarkers that some doctors may have missed. The 10-fold cross-validation of two public datasets shows that the performance of KISM is better than that of the current state-of-the-art methods. In the diagnostic COVID-19 task, an AUC value of 0.9869 and an accuracy of 0.9787 were obtained, and ultimately Leukocytes, platelets, and Proteina C reativa mg/dL were found to be the most indicative biomarkers for the diagnosis of COVID-19. An AUC value of 0.9949 and an accuracy of 0.9677 were obtained in the prognostic COVID-19 task and Age, LYMPH, and WBC were found to be the most indicative biomarkers for identifying the severity of the patient.</div

Feature Importance (Feature_Importance_/Dataset2).

Feature Importance (Feature_Importance_/Dataset2).</p

Effect of Spacer and Mesogen in Side-Chain Liquid Crystal Elastomer Structure on Reversible Actuation Behavior

A liquid crystal elastomer (LCE) actuator is capable of displaying reversible shape change through order–disorder phase transition, and it is generally prepared by aligning the mesogens (often through mechanical stretching) and then cross-linking polymer chains. Herein, a series of four side-chain LCEs are synthesized by grafting side-group mesogens onto the middle block of the styrene–butadiene–styrene (SBS) triblock copolymer. These LCEs differ either in the length of the flexible spacer linking mesogen and chain backbone or in the mesogen used in their chemical structures. By means of polarized infrared spectroscopic and X-ray diffraction (XRD) measurements, the effects of spacer and mesogen on stretching-induced orientation of mesogens are investigated. The results show that varying the length of spacer or changing the mesogen has a profound effect on the orientation direction (parallel or perpendicular to the stretching direction), orientation degree (order parameter), and orientation stability to large strain. The characteristic orientation behaviors of the side-chain LCEs are retained in their respective actuators, i.e., stretched films subjected to photo-cross-linking and thermal equilibrium in the isotropic state, and determine their reversible actuation upon heating to the isotropic phase and cooling to the LC phase. In particular, the results confirm that in order for a side-chain LCE actuator to exhibit the unusual thermally induced auxetic-like shape change, i.e., its strip contracts in both length and width on heating and extends in both directions on cooling, the LCE must have a high and stable perpendicular orientation of mesogens that can compete with the conformational change of the main chain backbone aligned parallel to the stretching direction

Feature Importance (SHAP/Dataset2).

Coronaviruses have affected the lives of people around the world. Increasingly, studies have indicated that the virus is mutating and becoming more contagious. Hence, the pressing priority is to swiftly and accurately predict patient outcomes. In addition, physicians and patients increasingly need interpretability when building machine models in healthcare. We propose an interpretable machine framework(KISM) that can diagnose and prognose patients based on blood test datasets. First, we use k-nearest neighbors, isolated forests, and SMOTE to pre-process the original blood test datasets. Seven machine learning tools Support Vector Machine, Extra Tree, Random Forest, Gradient Boosting Decision Tree, eXtreme Gradient Boosting, Logistic Regression, and ensemble learning were then used to diagnose and predict COVID-19. In addition, we used SHAP and scikit-learn post-hoc interpretability to report feature importance, allowing healthcare professionals and artificial intelligence models to interact to suggest biomarkers that some doctors may have missed. The 10-fold cross-validation of two public datasets shows that the performance of KISM is better than that of the current state-of-the-art methods. In the diagnostic COVID-19 task, an AUC value of 0.9869 and an accuracy of 0.9787 were obtained, and ultimately Leukocytes, platelets, and Proteina C reativa mg/dL were found to be the most indicative biomarkers for the diagnosis of COVID-19. An AUC value of 0.9949 and an accuracy of 0.9677 were obtained in the prognostic COVID-19 task and Age, LYMPH, and WBC were found to be the most indicative biomarkers for identifying the severity of the patient.</div

Feature Importance (Feature_Importance_/ Dataset1).

Feature Importance (Feature_Importance_/ Dataset1).</p

Feature Importance (SHAP/ Dataset1).

Coronaviruses have affected the lives of people around the world. Increasingly, studies have indicated that the virus is mutating and becoming more contagious. Hence, the pressing priority is to swiftly and accurately predict patient outcomes. In addition, physicians and patients increasingly need interpretability when building machine models in healthcare. We propose an interpretable machine framework(KISM) that can diagnose and prognose patients based on blood test datasets. First, we use k-nearest neighbors, isolated forests, and SMOTE to pre-process the original blood test datasets. Seven machine learning tools Support Vector Machine, Extra Tree, Random Forest, Gradient Boosting Decision Tree, eXtreme Gradient Boosting, Logistic Regression, and ensemble learning were then used to diagnose and predict COVID-19. In addition, we used SHAP and scikit-learn post-hoc interpretability to report feature importance, allowing healthcare professionals and artificial intelligence models to interact to suggest biomarkers that some doctors may have missed. The 10-fold cross-validation of two public datasets shows that the performance of KISM is better than that of the current state-of-the-art methods. In the diagnostic COVID-19 task, an AUC value of 0.9869 and an accuracy of 0.9787 were obtained, and ultimately Leukocytes, platelets, and Proteina C reativa mg/dL were found to be the most indicative biomarkers for the diagnosis of COVID-19. An AUC value of 0.9949 and an accuracy of 0.9677 were obtained in the prognostic COVID-19 task and Age, LYMPH, and WBC were found to be the most indicative biomarkers for identifying the severity of the patient.</div

The interpretable model workflow used by KISM.

Coronaviruses have affected the lives of people around the world. Increasingly, studies have indicated that the virus is mutating and becoming more contagious. Hence, the pressing priority is to swiftly and accurately predict patient outcomes. In addition, physicians and patients increasingly need interpretability when building machine models in healthcare. We propose an interpretable machine framework(KISM) that can diagnose and prognose patients based on blood test datasets. First, we use k-nearest neighbors, isolated forests, and SMOTE to pre-process the original blood test datasets. Seven machine learning tools Support Vector Machine, Extra Tree, Random Forest, Gradient Boosting Decision Tree, eXtreme Gradient Boosting, Logistic Regression, and ensemble learning were then used to diagnose and predict COVID-19. In addition, we used SHAP and scikit-learn post-hoc interpretability to report feature importance, allowing healthcare professionals and artificial intelligence models to interact to suggest biomarkers that some doctors may have missed. The 10-fold cross-validation of two public datasets shows that the performance of KISM is better than that of the current state-of-the-art methods. In the diagnostic COVID-19 task, an AUC value of 0.9869 and an accuracy of 0.9787 were obtained, and ultimately Leukocytes, platelets, and Proteina C reativa mg/dL were found to be the most indicative biomarkers for the diagnosis of COVID-19. An AUC value of 0.9949 and an accuracy of 0.9677 were obtained in the prognostic COVID-19 task and Age, LYMPH, and WBC were found to be the most indicative biomarkers for identifying the severity of the patient.</div

Comparison of different machine learning model methods.

Comparison of different machine learning model methods.</p