Characterization of unknown groundwater contaminant sources is an important but difficult step in effective groundwater management. The difficulties arise mainly due to the time of contaminant detection which usually ...Characterization of unknown groundwater contaminant sources is an important but difficult step in effective groundwater management. The difficulties arise mainly due to the time of contaminant detection which usually happens a long time after the start of contaminant source(s) activities. Usually, limited information is available which also can be erroneous. This study utilizes Self-Organizing Map (SOM) and Gaussian Process Regression (GPR) algorithms to develop surrogate models that can approximate the complex flow and transport processes in a contaminated aquifer. The important feature of these developed surrogate models is that unlike the previous methods, they can be applied independently of any linked optimization model solution for characterizing of unknown groundwater contaminant sources. The performance of the developed surrogate models is evaluated for source characterization in an experimental contaminated aquifer site within the heterogeneous sand aquifer, located at the Botany Basin, New South Wales, Australia. In this study, the measured contaminant concentrations and hydraulic conductivity values are assumed to contain random errors. Simulated responses of the aquifer to randomly specified contamination stresses as simulated by using a three-dimensional numerical simulation model are utilized for initial training of the surrogate models. The performance evaluation results obtained by using different surrogate models are also compared. The evaluation results demonstrate the different capabilities of the developed surrogate models. These capabilities lead to development of an efficient methodology for source characterization based on utilizing the trained and tested surrogate models in an inverse mode. The obtained results are satisfactory and show the potential applicability of the SOM and GPR-based surrogate models for unknown groundwater contaminant source characterization in an inverse mode.展开更多
Objective A diagnostic model was established to discriminate infectious diseases from non-infectious diseases. Methods The clinical data of patients with fever of unknown origin(FUO) hospitalized in Xiangya Hospital C...Objective A diagnostic model was established to discriminate infectious diseases from non-infectious diseases. Methods The clinical data of patients with fever of unknown origin(FUO) hospitalized in Xiangya Hospital Central South University, from January, 2006 to April, 2011 were retrospectively analyzed. Patients enrolled were divided into two groups. The first group was used to develop a diagnostic model: independent variables were recorded and considered in a logistic regression analysis to identify infectious and non-infectious diseases(αin = 0.05, αout = 0.10). The second group was used to evaluate the diagnostic model and make ROC analysis.Results The diagnostic rate of 143 patients in the first group was 87.4%, the diagnosis included infectious disease(52.4%), connective tissue diseases(16.8%), neoplastic disease(16.1%) and miscellaneous(2.1%). The diagnostic rate of 168 patients in the second group was 88.4%, and the diagnosis was similar to the first group. Logistic regression analysis showed that decreased white blood cell count(WBC < 4.0×109/L), higher lactate dehydrogenase level(LDH > 320 U/L) and lymphadenectasis were independent risk factors associated with non-infectious diseases. The odds ratios were 14.74, 5.84 and 5.11(P ≤ 0.01), respectively. In ROC analysis, the sensitivity and specificity of the positive predictive values was 62.1% and 89.1%, respectively, while that of negative predicting values were 75% and 81.7%, respectively(AUC = 0.76, P = 0.00).Conclusions The combination of WBC < 4.0×109/L, LDH > 320 U/L and lymphadenectasis may be useful in discriminating infectious diseases from non-infectious diseases in patients hospitalized as FUO.展开更多
文摘Characterization of unknown groundwater contaminant sources is an important but difficult step in effective groundwater management. The difficulties arise mainly due to the time of contaminant detection which usually happens a long time after the start of contaminant source(s) activities. Usually, limited information is available which also can be erroneous. This study utilizes Self-Organizing Map (SOM) and Gaussian Process Regression (GPR) algorithms to develop surrogate models that can approximate the complex flow and transport processes in a contaminated aquifer. The important feature of these developed surrogate models is that unlike the previous methods, they can be applied independently of any linked optimization model solution for characterizing of unknown groundwater contaminant sources. The performance of the developed surrogate models is evaluated for source characterization in an experimental contaminated aquifer site within the heterogeneous sand aquifer, located at the Botany Basin, New South Wales, Australia. In this study, the measured contaminant concentrations and hydraulic conductivity values are assumed to contain random errors. Simulated responses of the aquifer to randomly specified contamination stresses as simulated by using a three-dimensional numerical simulation model are utilized for initial training of the surrogate models. The performance evaluation results obtained by using different surrogate models are also compared. The evaluation results demonstrate the different capabilities of the developed surrogate models. These capabilities lead to development of an efficient methodology for source characterization based on utilizing the trained and tested surrogate models in an inverse mode. The obtained results are satisfactory and show the potential applicability of the SOM and GPR-based surrogate models for unknown groundwater contaminant source characterization in an inverse mode.
文摘Objective A diagnostic model was established to discriminate infectious diseases from non-infectious diseases. Methods The clinical data of patients with fever of unknown origin(FUO) hospitalized in Xiangya Hospital Central South University, from January, 2006 to April, 2011 were retrospectively analyzed. Patients enrolled were divided into two groups. The first group was used to develop a diagnostic model: independent variables were recorded and considered in a logistic regression analysis to identify infectious and non-infectious diseases(αin = 0.05, αout = 0.10). The second group was used to evaluate the diagnostic model and make ROC analysis.Results The diagnostic rate of 143 patients in the first group was 87.4%, the diagnosis included infectious disease(52.4%), connective tissue diseases(16.8%), neoplastic disease(16.1%) and miscellaneous(2.1%). The diagnostic rate of 168 patients in the second group was 88.4%, and the diagnosis was similar to the first group. Logistic regression analysis showed that decreased white blood cell count(WBC < 4.0×109/L), higher lactate dehydrogenase level(LDH > 320 U/L) and lymphadenectasis were independent risk factors associated with non-infectious diseases. The odds ratios were 14.74, 5.84 and 5.11(P ≤ 0.01), respectively. In ROC analysis, the sensitivity and specificity of the positive predictive values was 62.1% and 89.1%, respectively, while that of negative predicting values were 75% and 81.7%, respectively(AUC = 0.76, P = 0.00).Conclusions The combination of WBC < 4.0×109/L, LDH > 320 U/L and lymphadenectasis may be useful in discriminating infectious diseases from non-infectious diseases in patients hospitalized as FUO.
