BACKGROUND In contrast to many Western countries,China has maintained its large psychiatric hospitals.The prevalence and clinical characteristics of coronavirus disease 2019(COVID-19)in inpatients with schizophrenia(S...BACKGROUND In contrast to many Western countries,China has maintained its large psychiatric hospitals.The prevalence and clinical characteristics of coronavirus disease 2019(COVID-19)in inpatients with schizophrenia(SCZ)are unclear.AIM To assess the prevalence of COVID-19 among inpatients with SCZ and compare the infected to uninfected SCZ patients in a Wuhan psychiatric hospital.METHODS We retrospectively collected demographic characteristics and clinical profiles of all SCZ patients with COVID-19 at Wuhan’s Youfu Hospital.RESULTS Among the 504 SCZ patients,84 had COVID-19,and we randomly sampled 174 who were uninfected as a comparison group.The overall prevalence of COVID-19 in SCZ patients was 16.7%.Among the 84 SCZ patients with confirmed COVID-19,the median age was 54 years and 76.2%were male.The most common symptom was fever(82%),and less common symptoms were cough(31%),poor appetite(20%),and fatigue(16%).Compared with SCZ patients without COVID-19,those with COVID-19 were older(P=0.006)and significantly lighter(P=0.002),and had more comorbid physical diseases(P=0.001).Surprisingly,those infected were less likely to be smokers(<0.001)or to be treated with dozapine(P=0.03).Further logistic regression showed that smoking[odds ratio(OR)=5.61],clozapine treated(OR=2.95),and male(OR=3.48)patients with relatively fewer comorbid physical diseases(OR=0.098)were at a lower risk for COVID-19.SCZ patients with COVID-19 presented primarily with fever,but only one-third had a cough,which might otherwise be the most common mode of transmission between individuals.CONCLUSION Two unexpected protective factors for COVID-19 among SCZ inpatients are smoking and dozapine treatment.展开更多
Air channeling in the annulus between the casing and the cement sheath and/or between the cement sheath and formation is the main factor affecting the safe operation of natural gas wells at high temperatures and press...Air channeling in the annulus between the casing and the cement sheath and/or between the cement sheath and formation is the main factor affecting the safe operation of natural gas wells at high temperatures and pressures.Prevention of this problem requires,in general,excellent anti-channeling performances of the cement sheath.Three methods to predict such anti-channeling performances are proposed here,which use the weightless pressure of cement slurry,the permeability of cement stone and the volume expansion rate of cement sheath as input parameters.Guided by this approach,the anti-channeling performances of the cement slurry are evaluated by means of indoor experiments,and the cement slurry is optimized accordingly.The results show that the dangerous transition time of the cement slurry with optimized dosage of admixture is only 76 min,the permeability of cement stone is 0.005 md,the volume shrinkage at final setting is only 0.72%,and the anti-channeling performances are therefore maximized.The effective utilization of the optimized cement slurry in some representative wells(LD10–1-A1 and LD10–1-A2 in LD10–1 gas field)is also discussed.展开更多
Water resources are an indispensable and valuable resource for human survival and development.Water quality predicting plays an important role in the protection and development of water resources.It is difficult to pr...Water resources are an indispensable and valuable resource for human survival and development.Water quality predicting plays an important role in the protection and development of water resources.It is difficult to predictwater quality due to its random and trend changes.Therefore,amethod of predicting water quality which combines Auto Regressive Integrated Moving Average(ARIMA)and clusteringmodelwas proposed in this paper.By taking thewater qualitymonitoring data of a certain river basin as a sample,thewater quality Total Phosphorus(TP)index was selected as the prediction object.Firstly,the sample data was cleaned,stationary analyzed,and white noise analyzed.Secondly,the appropriate parameters were selected according to the Bayesian Information Criterion(BIC)principle,and the trend component characteristics were obtained by using ARIMA to conduct water quality predicting.Thirdly,the relationship between the precipitation and the TP index in themonitoring water field was analyzed by the K-means clusteringmethod,and the random incremental characteristics of precipitation on water quality changes were calculated.Finally,by combining with the trend component characteristics and the random incremental characteristics,the water quality prediction results were calculated.Compared with the ARIMA water quality prediction method,experiments showed that the proposed method has higher accuracy,and its Mean Absolute Error(MAE),Mean Square Error(MSE),and Mean Absolute Percentage Error(MAPE)were respectively reduced by 44.6%,56.8%,and 45.8%.展开更多
At present,water pollution has become an important factor affecting and restricting national and regional economic development.Total phosphorus is one of the main sources of water pollution and eutrophication,so the p...At present,water pollution has become an important factor affecting and restricting national and regional economic development.Total phosphorus is one of the main sources of water pollution and eutrophication,so the prediction of total phosphorus in water quality has good research significance.This paper selects the total phosphorus and turbidity data for analysis by crawling the data of the water quality monitoring platform.By constructing the attribute object mapping relationship,the correlation between the two indicators was analyzed and used to predict the future data.Firstly,the monthly mean and daily mean concentrations of total phosphorus and turbidity outliers were calculated after cleaning,and the correlation between them was analyzed.Secondly,the correlation coefficients of different times and frequencies were used to predict the values for the next five days,and the data trend was predicted by python visualization.Finally,the real value was compared with the predicted value data,and the results showed that the correlation between total phosphorus and turbidity was useful in predicting the water quality.展开更多
文摘BACKGROUND In contrast to many Western countries,China has maintained its large psychiatric hospitals.The prevalence and clinical characteristics of coronavirus disease 2019(COVID-19)in inpatients with schizophrenia(SCZ)are unclear.AIM To assess the prevalence of COVID-19 among inpatients with SCZ and compare the infected to uninfected SCZ patients in a Wuhan psychiatric hospital.METHODS We retrospectively collected demographic characteristics and clinical profiles of all SCZ patients with COVID-19 at Wuhan’s Youfu Hospital.RESULTS Among the 504 SCZ patients,84 had COVID-19,and we randomly sampled 174 who were uninfected as a comparison group.The overall prevalence of COVID-19 in SCZ patients was 16.7%.Among the 84 SCZ patients with confirmed COVID-19,the median age was 54 years and 76.2%were male.The most common symptom was fever(82%),and less common symptoms were cough(31%),poor appetite(20%),and fatigue(16%).Compared with SCZ patients without COVID-19,those with COVID-19 were older(P=0.006)and significantly lighter(P=0.002),and had more comorbid physical diseases(P=0.001).Surprisingly,those infected were less likely to be smokers(<0.001)or to be treated with dozapine(P=0.03).Further logistic regression showed that smoking[odds ratio(OR)=5.61],clozapine treated(OR=2.95),and male(OR=3.48)patients with relatively fewer comorbid physical diseases(OR=0.098)were at a lower risk for COVID-19.SCZ patients with COVID-19 presented primarily with fever,but only one-third had a cough,which might otherwise be the most common mode of transmission between individuals.CONCLUSION Two unexpected protective factors for COVID-19 among SCZ inpatients are smoking and dozapine treatment.
基金funded by the CNOOC Scientific Research Project“Study of cementing key properties and its matching technology of LD-10 gas field”(Grant No.CCL2019ZJFN1227).
文摘Air channeling in the annulus between the casing and the cement sheath and/or between the cement sheath and formation is the main factor affecting the safe operation of natural gas wells at high temperatures and pressures.Prevention of this problem requires,in general,excellent anti-channeling performances of the cement sheath.Three methods to predict such anti-channeling performances are proposed here,which use the weightless pressure of cement slurry,the permeability of cement stone and the volume expansion rate of cement sheath as input parameters.Guided by this approach,the anti-channeling performances of the cement slurry are evaluated by means of indoor experiments,and the cement slurry is optimized accordingly.The results show that the dangerous transition time of the cement slurry with optimized dosage of admixture is only 76 min,the permeability of cement stone is 0.005 md,the volume shrinkage at final setting is only 0.72%,and the anti-channeling performances are therefore maximized.The effective utilization of the optimized cement slurry in some representative wells(LD10–1-A1 and LD10–1-A2 in LD10–1 gas field)is also discussed.
基金funded by the National Natural Science Foundation of China(No.51775185),Natural Science Foundation of Hunan Province(2022JJ90013)Scientific Research Fund of Hunan Province Education Department(18C0003)+1 种基金Research project on teaching reform in colleges and universities of Hunan Province Education Department(20190147)Hunan Normal University University-Industry Cooperation.This work is implemented at the 2011 Collaborative Innovation Center for Development and Utilization of Finance and Economics Big Data Property,Universities of Hunan Province,Open project,Grant Number 20181901CRP04.
文摘Water resources are an indispensable and valuable resource for human survival and development.Water quality predicting plays an important role in the protection and development of water resources.It is difficult to predictwater quality due to its random and trend changes.Therefore,amethod of predicting water quality which combines Auto Regressive Integrated Moving Average(ARIMA)and clusteringmodelwas proposed in this paper.By taking thewater qualitymonitoring data of a certain river basin as a sample,thewater quality Total Phosphorus(TP)index was selected as the prediction object.Firstly,the sample data was cleaned,stationary analyzed,and white noise analyzed.Secondly,the appropriate parameters were selected according to the Bayesian Information Criterion(BIC)principle,and the trend component characteristics were obtained by using ARIMA to conduct water quality predicting.Thirdly,the relationship between the precipitation and the TP index in themonitoring water field was analyzed by the K-means clusteringmethod,and the random incremental characteristics of precipitation on water quality changes were calculated.Finally,by combining with the trend component characteristics and the random incremental characteristics,the water quality prediction results were calculated.Compared with the ARIMA water quality prediction method,experiments showed that the proposed method has higher accuracy,and its Mean Absolute Error(MAE),Mean Square Error(MSE),and Mean Absolute Percentage Error(MAPE)were respectively reduced by 44.6%,56.8%,and 45.8%.
基金the National Natural Science Foundation of China(No.51775185)Natural Science Foundation of Hunan Province(No.2022JJ90013)+1 种基金Intelligent Environmental Monitoring Technology Hunan Provincial Joint Training Base for Graduate Students in the Integration of Industry and Education,and Hunan Normal University University-Industry Cooperation.the 2011 Collaborative Innovation Center for Development and Utilization of Finance and Economics Big Data Property,Universities of Hunan Province,Open Project,Grant Number 20181901CRP04.
文摘At present,water pollution has become an important factor affecting and restricting national and regional economic development.Total phosphorus is one of the main sources of water pollution and eutrophication,so the prediction of total phosphorus in water quality has good research significance.This paper selects the total phosphorus and turbidity data for analysis by crawling the data of the water quality monitoring platform.By constructing the attribute object mapping relationship,the correlation between the two indicators was analyzed and used to predict the future data.Firstly,the monthly mean and daily mean concentrations of total phosphorus and turbidity outliers were calculated after cleaning,and the correlation between them was analyzed.Secondly,the correlation coefficients of different times and frequencies were used to predict the values for the next five days,and the data trend was predicted by python visualization.Finally,the real value was compared with the predicted value data,and the results showed that the correlation between total phosphorus and turbidity was useful in predicting the water quality.
