The pre-warning of abnormal energy consumption is important for energy conservation of industrial engineering. However, related studies on the lead smelting industries which usually have a huge energy consumption are ...The pre-warning of abnormal energy consumption is important for energy conservation of industrial engineering. However, related studies on the lead smelting industries which usually have a huge energy consumption are rarely reported. Therefore, a pre-warning system was established in this study based on the intelligent prediction of energy consumption and the identification of abnormal energy consumption. A least square support vector regression (LSSVR) model optimized by the adaptive genetic algorithm was developed to predict the energy consumption in the process of lead smelting. A recurrence plots (RP) analysis and a confidence intervals (CI) analysis were conducted to quantitatively confirm the stationary degree of energy consumption and the normal range of energy consumption, respectively, to realize the identification of abnormal energy consumption. It is found the prediction accuracy of LSSVR model can exceed 90% based on the comparison between the actual and predicted data. The energy consumption is considered to be non-stationary if the correlation coefficient between the time series of periodicity and energy consumption is larger than that between the time series of periodicity and Lorenz. Additionally, the lower limit and upper limit of normal energy consumption are obtained.展开更多
Objective To determine the incidence, course, potential risk factors, and outcomes of noninfectious fever developed in patients after aortic surgery. Methods Patients who received operation for aortic aneurysm or dis...Objective To determine the incidence, course, potential risk factors, and outcomes of noninfectious fever developed in patients after aortic surgery. Methods Patients who received operation for aortic aneurysm or dissection in our center from January 2006 to January 2008 were reviewed. Patients who met one of the following criteria were excluded： having a known source of infection during hospitalization; having a preoperative oral temperature greater than or equal to 38.0℃; undertaking emergency surgery; having incomplete data. Univariate analysis was performed in patients with noninfectious postoperative fever and those without, with respect to demographics, intraoperative data, etc. Risk factors for postoperative fever were considered for the muhivariate logistic regression model if they had a P value less than 0.10 in the univariate analysis. Results Totally 463 patients undergoing aortic surgery were enrolled for full review. Among them, 345 （74.5%） patients had noninfectious postoperative fever, the other 118 （25.5%） patients didn＇t develop postoperative fever. Univariate analysis demonstrated that several risk factors were associated with the development of noninfectious postoperative fever, including weight, surgical procedure, minimum intraoperative bladder temperature, temperature upon intensive care unit （ICU） admission, discharge, and during ICU stay, as well as blood transfusion. In a further multivariate analysis, surgical site of thoracic and thoracoabdominal aorta （odds ratio： 4.861; 95% confidence interval： 3.029-5.801; P=0.004）, lower minimum intraoperative bladder temperature （odds ratio： 1.117; 95% confidence interval： 1.01-1.24; P=0.04）, and higher temperature on admission to the ICU （odds ratio： 2.57; 95% confidence interval： 1.28-5.18; P=0.008） were found to be significant predictors for noninfectious postoperative fever. No difference was found between the febrile and afebrile patients with regard to postoperative hospitalization duration （P=0.558） or total medical costs （P=0.896）. Conclusion Noninfectious postoperative fever following aortic surgery is very common and closely related with perioperative interventions.展开更多
When the population, from which the samples are extracted, is not normally distributed, or if the sample size is particularly reduced, become preferable the use of not parametric statistic test. An alternative to the ...When the population, from which the samples are extracted, is not normally distributed, or if the sample size is particularly reduced, become preferable the use of not parametric statistic test. An alternative to the normal model is the permutation or randomization model. The permutation model is nonparametric because no formal assumptions are made about the population parameters of the reference distribution, i.e., the distribution to which an obtained result is compared to determine its probability when the null hypothesis is true. Typically the reference distribution is a sampling distribution for parametric tests and a permutation distribution for many nonparametric tests. Within the regression models, it is possible to use the permutation tests, considering their ownerships of optimality, especially in the multivariate context and the normal distribution of the response variables is not guaranteed. In the literature there are numerous permutation tests applicable to the estimation of the regression models. The purpose of this study is to examine different kinds of permutation tests applied to linear models, focused our attention on the specific test statistic on which they are based. In this paper we focused our attention on permutation test of the independent variables, proposed by Oja, and other methods to effect the inference in non parametric way, in a regression model. Moreover, we show the recent advances in this context and try to compare them.展开更多
AIM To investigate the specific biomarkers and potential pathogenesis of colorectal cancer-related ischemic stroke(CRCIS).METHODS A retrospective study was conducted on CRCIS patients(colorectal cancer patients with i...AIM To investigate the specific biomarkers and potential pathogenesis of colorectal cancer-related ischemic stroke(CRCIS).METHODS A retrospective study was conducted on CRCIS patients(colorectal cancer patients with ischemic stroke without conventional stroke risk factors) registered at seven centers between January 2007 and December 2017. Clinical data and laboratory and imaging findings were compared with age-and sex-matched patients with colorectal cancer(CRC) without ischemic stroke that were admitted to the same hospital during the same period. Univariate and multivariate analyses were performed to analyze the independent risk factors for CRCIS. A receiver operator characteristic curve was configured to calculate the optimal cut-off value of the products of the independent risk factors for CRCIS. RESULTS A total of 114 CRCIS patients and 114 CRC patients were included. Multiple lesions in multiple vascular territories were common in CRCIS patients(71, 62.28%). The levels of plasma D-dimer, carcinoembryonic antigen(CEA), cancer antigen 125, and neutrophil count were significantly higher in CRCIS patients than in CRC patients. Multiple logistic regression analysis revealed that plasma D-dimer levels [odds ratio(OR) = 1.002, 95% confidence interval(CI): 1.001-1.003, P < 0.001], CEA levels(OR = 1.011, 95%CI: 1.006-1.015, P < 0.001), and neutrophil count levels(OR = 1.626, 95%CI: 1.268-2.087, P < 0.001) were independent risk factors for CRCIS. In addition, receiver operator characteristic curve revealed that the area under curve for the products of plasma D-dimer, CEA, and neutrophil count was 0.889 ± 0.022(95%CI: 0.847-0.932, P < 0.001), and the optimal cut-off value for the product was 252.06, which was called the CRCIS Index, with a sensitivity of 86.0% and specificity of 79.8%.CONCLUSION Hypercoagulability induced by elevated CEA and neutrophils may be an important cause of CRCIS. The CRCIS index, which serves as a biomarker of CRCIS, needs further study.展开更多
This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 199...This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity.Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased.SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices(i.e.,farmland density and temporal change).The GWR model has smooth process when constructing the farmland spatial model.The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations.The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious.The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors.展开更多
A novel systematic quality monitoring and prediction method based on Fisher discriminant analysis (FDA) and kernel regression is proposed. The FDA method is first used for quality monitoring. If the process is un-der ...A novel systematic quality monitoring and prediction method based on Fisher discriminant analysis (FDA) and kernel regression is proposed. The FDA method is first used for quality monitoring. If the process is un-der normal condition, then kernel regression is further used for quality prediction and estimation. If faults have oc-curred, the contribution plot in the fault feature direction is used for fault diagnosis. The proposed method can ef-fectively detect the fault and has better ability to predict the response variables than principle component regression (PCR) and partial least squares (PLS). Application results to the industrial fluid catalytic cracking unit (FCCU) show the effectiveness of the proposed method.展开更多
Problem of goal-setting is related to the fundamental principles of informationology and general, rather than specific, aproaches, which enable a more adequate appraisal of certain managerial decisions in terms of the...Problem of goal-setting is related to the fundamental principles of informationology and general, rather than specific, aproaches, which enable a more adequate appraisal of certain managerial decisions in terms of their efficiency. In article this problem is considered and from positions of informationology is formulated and on the basis of a method of full mathematical induction the Law of positive dynamics of the Universe is proved. This law establishes preference of the positive purposes in all processes happening in the Universe. Thus, the goal-setting which is carried out from positions of the described Law is the important world outlook prerequisite searching, identification and development of strategic alternatives of purposeful social innovations.展开更多
基金Project(2015SK1002) supported by Key Projects of Hunan Province Science and Technology Plan,China
文摘The pre-warning of abnormal energy consumption is important for energy conservation of industrial engineering. However, related studies on the lead smelting industries which usually have a huge energy consumption are rarely reported. Therefore, a pre-warning system was established in this study based on the intelligent prediction of energy consumption and the identification of abnormal energy consumption. A least square support vector regression (LSSVR) model optimized by the adaptive genetic algorithm was developed to predict the energy consumption in the process of lead smelting. A recurrence plots (RP) analysis and a confidence intervals (CI) analysis were conducted to quantitatively confirm the stationary degree of energy consumption and the normal range of energy consumption, respectively, to realize the identification of abnormal energy consumption. It is found the prediction accuracy of LSSVR model can exceed 90% based on the comparison between the actual and predicted data. The energy consumption is considered to be non-stationary if the correlation coefficient between the time series of periodicity and energy consumption is larger than that between the time series of periodicity and Lorenz. Additionally, the lower limit and upper limit of normal energy consumption are obtained.
文摘Objective To determine the incidence, course, potential risk factors, and outcomes of noninfectious fever developed in patients after aortic surgery. Methods Patients who received operation for aortic aneurysm or dissection in our center from January 2006 to January 2008 were reviewed. Patients who met one of the following criteria were excluded： having a known source of infection during hospitalization; having a preoperative oral temperature greater than or equal to 38.0℃; undertaking emergency surgery; having incomplete data. Univariate analysis was performed in patients with noninfectious postoperative fever and those without, with respect to demographics, intraoperative data, etc. Risk factors for postoperative fever were considered for the muhivariate logistic regression model if they had a P value less than 0.10 in the univariate analysis. Results Totally 463 patients undergoing aortic surgery were enrolled for full review. Among them, 345 （74.5%） patients had noninfectious postoperative fever, the other 118 （25.5%） patients didn＇t develop postoperative fever. Univariate analysis demonstrated that several risk factors were associated with the development of noninfectious postoperative fever, including weight, surgical procedure, minimum intraoperative bladder temperature, temperature upon intensive care unit （ICU） admission, discharge, and during ICU stay, as well as blood transfusion. In a further multivariate analysis, surgical site of thoracic and thoracoabdominal aorta （odds ratio： 4.861; 95% confidence interval： 3.029-5.801; P=0.004）, lower minimum intraoperative bladder temperature （odds ratio： 1.117; 95% confidence interval： 1.01-1.24; P=0.04）, and higher temperature on admission to the ICU （odds ratio： 2.57; 95% confidence interval： 1.28-5.18; P=0.008） were found to be significant predictors for noninfectious postoperative fever. No difference was found between the febrile and afebrile patients with regard to postoperative hospitalization duration （P=0.558） or total medical costs （P=0.896）. Conclusion Noninfectious postoperative fever following aortic surgery is very common and closely related with perioperative interventions.
文摘When the population, from which the samples are extracted, is not normally distributed, or if the sample size is particularly reduced, become preferable the use of not parametric statistic test. An alternative to the normal model is the permutation or randomization model. The permutation model is nonparametric because no formal assumptions are made about the population parameters of the reference distribution, i.e., the distribution to which an obtained result is compared to determine its probability when the null hypothesis is true. Typically the reference distribution is a sampling distribution for parametric tests and a permutation distribution for many nonparametric tests. Within the regression models, it is possible to use the permutation tests, considering their ownerships of optimality, especially in the multivariate context and the normal distribution of the response variables is not guaranteed. In the literature there are numerous permutation tests applicable to the estimation of the regression models. The purpose of this study is to examine different kinds of permutation tests applied to linear models, focused our attention on the specific test statistic on which they are based. In this paper we focused our attention on permutation test of the independent variables, proposed by Oja, and other methods to effect the inference in non parametric way, in a regression model. Moreover, we show the recent advances in this context and try to compare them.
基金the Guangxi Natural Science Foundation,No.2015GXNSFAA139228 and No.2016GXNSFAA380281Guangxi Medical and Health and Appropriate Technology Development and Promotion Application Project,No.S201660+1 种基金Innovation Project of Guangxi Graduate Education,No.YCSW2018105National Key Research and Development Program,No.2018YFC1311300
文摘AIM To investigate the specific biomarkers and potential pathogenesis of colorectal cancer-related ischemic stroke(CRCIS).METHODS A retrospective study was conducted on CRCIS patients(colorectal cancer patients with ischemic stroke without conventional stroke risk factors) registered at seven centers between January 2007 and December 2017. Clinical data and laboratory and imaging findings were compared with age-and sex-matched patients with colorectal cancer(CRC) without ischemic stroke that were admitted to the same hospital during the same period. Univariate and multivariate analyses were performed to analyze the independent risk factors for CRCIS. A receiver operator characteristic curve was configured to calculate the optimal cut-off value of the products of the independent risk factors for CRCIS. RESULTS A total of 114 CRCIS patients and 114 CRC patients were included. Multiple lesions in multiple vascular territories were common in CRCIS patients(71, 62.28%). The levels of plasma D-dimer, carcinoembryonic antigen(CEA), cancer antigen 125, and neutrophil count were significantly higher in CRCIS patients than in CRC patients. Multiple logistic regression analysis revealed that plasma D-dimer levels [odds ratio(OR) = 1.002, 95% confidence interval(CI): 1.001-1.003, P < 0.001], CEA levels(OR = 1.011, 95%CI: 1.006-1.015, P < 0.001), and neutrophil count levels(OR = 1.626, 95%CI: 1.268-2.087, P < 0.001) were independent risk factors for CRCIS. In addition, receiver operator characteristic curve revealed that the area under curve for the products of plasma D-dimer, CEA, and neutrophil count was 0.889 ± 0.022(95%CI: 0.847-0.932, P < 0.001), and the optimal cut-off value for the product was 252.06, which was called the CRCIS Index, with a sensitivity of 86.0% and specificity of 79.8%.CONCLUSION Hypercoagulability induced by elevated CEA and neutrophils may be an important cause of CRCIS. The CRCIS index, which serves as a biomarker of CRCIS, needs further study.
基金Under the auspices of National Natural Science Foundation of China(No.40601073,41101192,41201571)Fundamental Research Funds for the Central Universities(No.2011PY112,2011QC041,2011QC091)Huazhong Agricultural University Scientific&Technological Self-innovation Foundation(No.2011SC21)
文摘This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity.Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased.SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices(i.e.,farmland density and temporal change).The GWR model has smooth process when constructing the farmland spatial model.The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations.The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious.The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors.
基金Supported by the National Natural Science Foundation of China (60504033)the Open Project of State Key Laboratory of Industrial Control Technology in Zhejiang University (0708004)
文摘A novel systematic quality monitoring and prediction method based on Fisher discriminant analysis (FDA) and kernel regression is proposed. The FDA method is first used for quality monitoring. If the process is un-der normal condition, then kernel regression is further used for quality prediction and estimation. If faults have oc-curred, the contribution plot in the fault feature direction is used for fault diagnosis. The proposed method can ef-fectively detect the fault and has better ability to predict the response variables than principle component regression (PCR) and partial least squares (PLS). Application results to the industrial fluid catalytic cracking unit (FCCU) show the effectiveness of the proposed method.
文摘Problem of goal-setting is related to the fundamental principles of informationology and general, rather than specific, aproaches, which enable a more adequate appraisal of certain managerial decisions in terms of their efficiency. In article this problem is considered and from positions of informationology is formulated and on the basis of a method of full mathematical induction the Law of positive dynamics of the Universe is proved. This law establishes preference of the positive purposes in all processes happening in the Universe. Thus, the goal-setting which is carried out from positions of the described Law is the important world outlook prerequisite searching, identification and development of strategic alternatives of purposeful social innovations.
