Computational screening of O‐functional MXenes for electrocatalytic ammonia synthesis

The nitrogen reduction reaction(NRR)using new and efficient electrocatalysts is a promising al‐ternative to the traditional Haber‐Bosch process.Nevertheless,it remains a challenge to design efficient catalysts with improved catalytic performance.Herein,various O‐functional MXenes were investigated as NRR catalysts by a combination of density functional theory calculations and least absolute shrinkage and selection operator(LASSO)regression.Nb_(3)C_(2)O_(X) has been regarded as a promising catalyst for the NRR because of its stability,activity,and selectivity.The poten‐tial‐determining step is*NH_(2) hydrogenation to*NH3 with a limiting potential of-0.45 V.Further‐more,via LASSO regression,the descriptors and equations fitting the relationship between the properties of O‐functional MXenes and NRR activity have been proposed.This work not only pro‐vides a rational design strategy for catalysts but also provides machine learning data for further investigation.

Identification and validation of prognostic factors in patients with COVID-19: A retrospective study based on artificial intelligence algorithms

Background:Novel coronavirus disease 2019(COVID-19)is an ongoing global pandemic with high mortality.Although several studies have reported different risk factors for mortality in patients based on traditional analytics,few studies have used artificial intelligence(AI)algorithms.This study investigated prognostic factors for COVID-19 patients using AI methods.Methods:COVID-19 patients who were admitted in Wuhan Infectious Diseases Hospital from December 29,2019 to March 2,2020 were included.The whole cohort was randomly divided into training and testing sets at a 6:4 ratio.Demographic and clinical data were analyzed to identify predictors of mortality using least absolute shrinkage and selection operator(LASSO)regression and LASSO-based artificial neural network(ANN)models.The predictive performance of the models was evaluated using receiver operating characteristic(ROC)curve analysis.Results:A total of 1145 patients(610 male,53.3%)were included in the study.Of the 1145 patients,704 were assigned to the training set and 441 were assigned to the testing set.The median age of the patients was 57 years(range:47-66 years).Severity of illness,age,platelet count,leukocyte count,prealbumin,C-reactive protein(CRP),total bilirubin,Acute Physiology and Chronic Health Evaluation(APACHE)II score,and Sequential Organ Failure Assessment(SOFA)score were identified as independent prognostic factors for mortality.Incorporating these nine factors into the LASSO regression model yielded a correct classification rate of 0.98,with area under the ROC curve(AUC)values of 0.980 and 0.990 in the training and testing cohorts,respectively.Incorporating the same factors into the LASSO-based ANN model yielded a correct classification rate of 0.990,with an AUC of 0.980 in both the training and testing cohorts.Conclusions:Both the LASSO regression and LASSO-based ANN model accurately predicted the clinical outcome of patients with COVID-19.Severity of illness,age,platelet count,leukocyte count,prealbumin,CRP,total bilirubin,APACHE II score,and SOFA score were identified as prognostic factors for mortality in patients with COVID-19.