Objective： The associations between social support and burnout were explored in ICU nurses of Shanghai. Methods： We performed a cross-sectional study of 356 ICU nurses by applying random cluster sampling. Data were ...Objective： The associations between social support and burnout were explored in ICU nurses of Shanghai. Methods： We performed a cross-sectional study of 356 ICU nurses by applying random cluster sampling. Data were collected using self-reported questionnaires under the instruction of trained investigators. Data on emotional exhaustion, depersonalisation and feelings of low personal accomplishment etc. were collected, calculated and analyzed. Results： The participants had a mean age of 26.96 years （SD 4.07）. The mean value （M） and stand- ard deviation （SD） of emotional exhaustion was M=31.85, SD=8.38, those of depersonalisation was M= 11.69, SD= 5.54 and those of feelings of low personal accomplishment was M= 19. 79, SD = 7.02. The high degree of emotional exhaustion （EE）, depersonalisation （DP）, and lack of personal accomplishment （PA） were revealed to be 76.4%, 39.6%, and 94.9%, respectively. The major influencing factors of emotional exhaustion included support from co-workers（b=0. 343, t = 1.98, P=0. 049）, taking leave（b=-1. 182, t=-3. 747, P=0. 001）, requisition of work（b=-l. 41, t=-2. 369, P=0. 018）, and supervisor support（b=-0. 524, t=-3. 926, P=0. 001）. The major influencing factors of depersonalisation were support from the supervisor（b=-0. 333, t=-4. 146, P=0.001）, age（b=-0. 89, t=-2. 272, P= 0. 024） and requisition of work（b=-0. 148, t=-2. 124, P=0. 034）. There was a positive co-relationship between personal accomplishment and supervisor support. Conclusions： Supervisor support, age, and requisition of work were the major influencing factors of depersonalisation. In addition, supervisor support plays an important role in low personal accomplishment. Further research should focus on supervisor support, co-worker support, time on leave, and requisition of work associated with emotional exhaustion.展开更多
The support vector classification （SVC） was employed to make a model for classification of antifungal activities of 1-（1H-1,2,4-triazole-l-yl）-2-（2,4-difluorophenyl）-3-substituted-2-propanols triazole derivative...The support vector classification （SVC） was employed to make a model for classification of antifungal activities of 1-（1H-1,2,4-triazole-l-yl）-2-（2,4-difluorophenyl）-3-substituted-2-propanols triazole derivatives. The compounds with high antifungal activities and those with low antifungal activities were compared on the basis of the following molecular descriptors： net atomic charge on the atom N connecting with R, dipole moment and heat of formation, By using the SVC, a mathematical model was constructed, which can predict the antifungal activities of the triazole derivatives, with an accuracy of 91% on the basis of the leave-one-out cross-validation （LOOCV） test, The results indicate that the performance of the SVC model can exceed that of the principal component analysis （PCA） and K-Nearest Neighbor （KNN） models for this real world data set.展开更多
Traditional Folk relations of debit and credit have existed for thousands of years in Chinese society. In the rapidly develop of the context of mobile internet and social network, the borrowing that relies on the rela...Traditional Folk relations of debit and credit have existed for thousands of years in Chinese society. In the rapidly develop of the context of mobile internet and social network, the borrowing that relies on the relationship among people is not just a financial domain scope discussion topic. In the rapidly developing Chinese mobile Internet, a new anonymous mechanism which is based on interpersonal credit extension and evaluation ultimately form borrowing is continuously formed.?In this paper, the author researches and analyzes on what is relationship lending mechanism, the basic operation modes of relationship lending mechanism, a part of theoretical supporting and values.展开更多
In this work, support vector classification （SVC） algorithm was used to build structure-activity relationship （SAR） model of the 5-hydroxytryptamine type 3 （5-HT3 ） receptor antagonists with 26 compounds. In a b...In this work, support vector classification （SVC） algorithm was used to build structure-activity relationship （SAR） model of the 5-hydroxytryptamine type 3 （5-HT3 ） receptor antagonists with 26 compounds. In a benchmark test, SVC was compared with several techniques of machine learning currently used in the field. The prediction performance of the model was discussed on the basis of the leave-one-out cross-validation. The results show that the accuracy of prediction of SVC model was higher than those of back propagation artificial neural network （BP ANN）, K-nearest neighbor （KNN） and Fisher methods.展开更多
文摘Objective： The associations between social support and burnout were explored in ICU nurses of Shanghai. Methods： We performed a cross-sectional study of 356 ICU nurses by applying random cluster sampling. Data were collected using self-reported questionnaires under the instruction of trained investigators. Data on emotional exhaustion, depersonalisation and feelings of low personal accomplishment etc. were collected, calculated and analyzed. Results： The participants had a mean age of 26.96 years （SD 4.07）. The mean value （M） and stand- ard deviation （SD） of emotional exhaustion was M=31.85, SD=8.38, those of depersonalisation was M= 11.69, SD= 5.54 and those of feelings of low personal accomplishment was M= 19. 79, SD = 7.02. The high degree of emotional exhaustion （EE）, depersonalisation （DP）, and lack of personal accomplishment （PA） were revealed to be 76.4%, 39.6%, and 94.9%, respectively. The major influencing factors of emotional exhaustion included support from co-workers（b=0. 343, t = 1.98, P=0. 049）, taking leave（b=-1. 182, t=-3. 747, P=0. 001）, requisition of work（b=-l. 41, t=-2. 369, P=0. 018）, and supervisor support（b=-0. 524, t=-3. 926, P=0. 001）. The major influencing factors of depersonalisation were support from the supervisor（b=-0. 333, t=-4. 146, P=0.001）, age（b=-0. 89, t=-2. 272, P= 0. 024） and requisition of work（b=-0. 148, t=-2. 124, P=0. 034）. There was a positive co-relationship between personal accomplishment and supervisor support. Conclusions： Supervisor support, age, and requisition of work were the major influencing factors of depersonalisation. In addition, supervisor support plays an important role in low personal accomplishment. Further research should focus on supervisor support, co-worker support, time on leave, and requisition of work associated with emotional exhaustion.
基金Project supported by the National Natural Science Foundation of China （Grant Nos.20373040, 20503015）
文摘The support vector classification （SVC） was employed to make a model for classification of antifungal activities of 1-（1H-1,2,4-triazole-l-yl）-2-（2,4-difluorophenyl）-3-substituted-2-propanols triazole derivatives. The compounds with high antifungal activities and those with low antifungal activities were compared on the basis of the following molecular descriptors： net atomic charge on the atom N connecting with R, dipole moment and heat of formation, By using the SVC, a mathematical model was constructed, which can predict the antifungal activities of the triazole derivatives, with an accuracy of 91% on the basis of the leave-one-out cross-validation （LOOCV） test, The results indicate that the performance of the SVC model can exceed that of the principal component analysis （PCA） and K-Nearest Neighbor （KNN） models for this real world data set.
文摘Traditional Folk relations of debit and credit have existed for thousands of years in Chinese society. In the rapidly develop of the context of mobile internet and social network, the borrowing that relies on the relationship among people is not just a financial domain scope discussion topic. In the rapidly developing Chinese mobile Internet, a new anonymous mechanism which is based on interpersonal credit extension and evaluation ultimately form borrowing is continuously formed.?In this paper, the author researches and analyzes on what is relationship lending mechanism, the basic operation modes of relationship lending mechanism, a part of theoretical supporting and values.
基金Supported by the Science and Technological Fund of Anhui Province for Outstanding Youth （1108085J02）, the National Natural Science Foundation of Anhui Province （K J2010A090）
基金Project supported by National Natural Science Foundation of China（ Grant No. 20373040）
文摘In this work, support vector classification （SVC） algorithm was used to build structure-activity relationship （SAR） model of the 5-hydroxytryptamine type 3 （5-HT3 ） receptor antagonists with 26 compounds. In a benchmark test, SVC was compared with several techniques of machine learning currently used in the field. The prediction performance of the model was discussed on the basis of the leave-one-out cross-validation. The results show that the accuracy of prediction of SVC model was higher than those of back propagation artificial neural network （BP ANN）, K-nearest neighbor （KNN） and Fisher methods.
