BACKGROUND Liver transplantation aims to increase the survival of patients with end-stage liver diseases and improve their quality of life.The number of organs available for transplantation is lower than the demand.To...BACKGROUND Liver transplantation aims to increase the survival of patients with end-stage liver diseases and improve their quality of life.The number of organs available for transplantation is lower than the demand.To provide fair organ distribution,predictive mortality scores have been developed.AIM To compare the Acute Physiology and Chronic Health Evaluation IV(APACHE IV),balance of risk(BAR),and model for end-stage liver disease(MELD)scores as predictors of mortality.METHODS Retrospective cohort study,which included 283 adult patients in the postoperative period of deceased donor liver transplantation from 2014 to 2018.RESULTS The transplant recipients were mainly male,with a mean age of 58.1 years.Donors were mostly male,with a mean age of 41.6 years.The median cold ischemia time was 3.1 hours,and the median intensive care unit stay was 5 days.For APACHE IV,a mean of 59.6 was found,BAR 10.7,and MELD 24.2.The 28-day mortality rate was 9.5%,and at 90 days,it was 3.5%.The 28-day mortality prediction for APACHE IV was very good[area under the curve(AUC):0.85,P<0.001,95%CI:0.76-0.94],P<0.001,BAR(AUC:0.70,P<0.001,95%CI:0.58–0.81),and MELD(AUC:0.66,P<0.006,95%CI:0.55-0.78),P<0.008.At 90 days,the data for APACHE IV were very good(AUC:0.80,P<0.001,95%CI:0.71–0.90)and moderate for BAR and MELD,respectively,(AUC:0.66,P<0.004,95%CI:0.55–0.77),(AUC:0.62,P<0.026,95%CI:0.51–0.72).All showed good discrimination between deaths and survivors.As for the best value for liver transplantation,it was significant only for APACHE IV(P<0.001).CONCLUSION The APACHE IV assessment score was more accurate than BAR and MELD in predicting mortality in deceased donor liver transplant recipients.展开更多
Intemational Vehicle Emissions （IVE） model funded by U.S. Environmental Protection Agency （USEPA） is designed to estimate emissions from motor vehicles in developing countries. In this study, the IVE model was eva...Intemational Vehicle Emissions （IVE） model funded by U.S. Environmental Protection Agency （USEPA） is designed to estimate emissions from motor vehicles in developing countries. In this study, the IVE model was evaluated by utilizing a dataset available from the remote sensing measurements on a large number of vehicles at five different sites in Hangzhou, China, in 2004 and 2005. Average fuel-based emission factors derived from the remote sensing measurements were compared with corresponding emission factors derived from IVE calculations for urban, hot stabilized condition. The results show a good agreement between the two methods for gasoline passenger cars＇ HC emission for all 1VE subsectors and technology classes. In the case of CO emissions, the modeled results were reasonably good, although systematically underestimate the emissions by almost 12%-50% for different technology classes. However, the model totally overestimated NOx emissions. The IVE NOx emission factors were 1.5-3.5 times of the remote sensing measured ones. The IVE model was also evaluated for light duty gasoline truck, heavy duty gasoline vehicles and motor cycles. A notable result was observed that the decrease in emissions from technology class State II to State I were overestimated by the IVE model compared to remote sensing measurements for all the three pollutants. Finally, in order to improve emission estimation, the adjusted base emission factors from local studies are strongly recommended to be used in the IVE model.展开更多
To well describe the Ti（IV）-catalyzed H2O2/O3 reaction in aqueous solution, a kinetic model was established based on its mechanism. This model was then validated by the experiments of acetic acid degradation in aque...To well describe the Ti（IV）-catalyzed H2O2/O3 reaction in aqueous solution, a kinetic model was established based on its mechanism. This model was then validated by the experiments of acetic acid degradation in aqueous solution. It was found that the correlation coefficient of fittings was higher than 0.970. Three key operating factors affecting organic degradation in the Ti（IV）-catalyzed H2O2/O3 process were studied, including Ti（IV） concentration, dissolved ozone concentration and initial H2O2 concentration. Furthermore, some experiments were conducted to determine the rate constant for dissolved ozone decomposition initiated by Ti2O52＋. The rate constant measured is almost in accord with the data analyzed by this kinetic model. The goodness of fittings demonstrated that this model could well describe the kinetics of the Ti（IV）-catalyzed H2O2/O3 reaction mathematically and chemically. Therefore, this kinetic model can provide some useful information to optimize the parameters in ozonation of water containing certain pollutants.展开更多
This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble lear...This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.展开更多
Nuclear level density(NLD) is a characteristic property of many-body quantum mechanical systems.NLDs are of special importance to make statistical calculations in reactor studies and various theoretical and experiment...Nuclear level density(NLD) is a characteristic property of many-body quantum mechanical systems.NLDs are of special importance to make statistical calculations in reactor studies and various theoretical and experimental nuclear physics and engineering applications.In this study,we have investigated a set of particle states in distinct rotational and vibrational bands to calculate nuclear level density parameters and the NLDs of accessible states of some deformed Dy radionuclides using a collective model approach,which included different excitation bands of the observed nuclear spectra.The method used assumes equidistant spacing of collective coupled state bands of the considered nuclei.The results of the calculated NLD have been compared with the experimental and compiled data obtained by the Oslo group,shell model Monte Carlo,Hartree-Fock-Bogoliubov + combinatorial approach,Bardeen-Cooper-Schrieffer approach and are in a good agreement.展开更多
This article, in order to improve the assembly of the high-pressure spool, presents an assembly variation identification method achieved by response surface method （RSM）-based model updating using IV-optimal designs...This article, in order to improve the assembly of the high-pressure spool, presents an assembly variation identification method achieved by response surface method （RSM）-based model updating using IV-optimal designs. The method involves screening out non-relevant assembly parameters using IV-optimal designs and the preload of the joints is chosen as the input features and modal frequency is the only response feature. Emphasis is placed on the construction of response surface models including the interactions between the bolted joints by which the non-linear relationship between the assembly variation caused by the changes ofpreload and the output frequency variation is established. By achieving an optimal process of selected variables in the model, assembly variation can be identified. With a case study of the laboratory bolted disks as an example, the proposed method is verified and it gives enough accuracy in variation identification. It has been observed that the first-order response surface models considering the interactions between the bolted joints based on the IV-optimal criterion are adequate for assembly purposes.展开更多
BACKGROUND Stage IV pancreatic cancer(PC)has a poor prognosis and lacks individualized prognostic tools.Current survival prediction models are limited,and there is a need for more accurate,personalized methods.The Sur...BACKGROUND Stage IV pancreatic cancer(PC)has a poor prognosis and lacks individualized prognostic tools.Current survival prediction models are limited,and there is a need for more accurate,personalized methods.The Surveillance,Epidemiology,and End Results(SEER)database offers a valuable resource for studying large patient cohorts,yet machine learning-based nomograms for stage IV PC prognosis remain underexplored.This study hypothesizes that a machine learning-based nomogram can predict cancer-specific survival(CSS)and overall survival(OS)with high accuracy in stage IV PC patients.AIM To construct and validate a machine learning-based nomogram for predicting survival in stage IV PC patients using real-world data.METHODS Clinical data from stage IV PC patients diagnosed via pathology from 2000 to 2019 INTRODUCTION Pancreatic cancer(PC)is a significant human health issue and,by 2025,is projected to surpass breast cancer as the third leading cause of cancer-related deaths[1].In the United States,an estimated 66440 new cases and 51750 deaths due to PC were reported in 2024.PC is often asymptomatic in its early stages,with more than half of patients presenting with distant organ metastasis at the time of initial diagnosis[2].Consequently,the prognosis is very poor,with a 5-year relative survival rate of only 12.8%[2]In clinical practice,considerable heterogeneity in survival outcomes has been observed among patients with stage IV PC,highlighting the need for an individualized survival prediction tool for this population.Nomograms,which are visual tools incorporating multiple prognostic factors to predict patient survival,aid in person-alized treatment planning and clinical decision-making and are widely used in cancer prognosis evaluation[3-6].Machine learning,a core technique within artificial intelligence,employs algorithms to analyze data,learn from patterns,and predict real-world events with high accuracy,and is increasingly applied in health assessment,medical decision-making,prognosis,and personalized treatment[7-9].This study leverages the large sample size and comprehensive clinical data from the United State Surveillance,Epidemiology,and End Results(SEER)database to develop a prognostic nomogram for stage IV PC patients using machine learning,with the aim of providing individualized prognostic assessments to improve clinical decision-making.展开更多
文摘BACKGROUND Liver transplantation aims to increase the survival of patients with end-stage liver diseases and improve their quality of life.The number of organs available for transplantation is lower than the demand.To provide fair organ distribution,predictive mortality scores have been developed.AIM To compare the Acute Physiology and Chronic Health Evaluation IV(APACHE IV),balance of risk(BAR),and model for end-stage liver disease(MELD)scores as predictors of mortality.METHODS Retrospective cohort study,which included 283 adult patients in the postoperative period of deceased donor liver transplantation from 2014 to 2018.RESULTS The transplant recipients were mainly male,with a mean age of 58.1 years.Donors were mostly male,with a mean age of 41.6 years.The median cold ischemia time was 3.1 hours,and the median intensive care unit stay was 5 days.For APACHE IV,a mean of 59.6 was found,BAR 10.7,and MELD 24.2.The 28-day mortality rate was 9.5%,and at 90 days,it was 3.5%.The 28-day mortality prediction for APACHE IV was very good[area under the curve(AUC):0.85,P<0.001,95%CI:0.76-0.94],P<0.001,BAR(AUC:0.70,P<0.001,95%CI:0.58–0.81),and MELD(AUC:0.66,P<0.006,95%CI:0.55-0.78),P<0.008.At 90 days,the data for APACHE IV were very good(AUC:0.80,P<0.001,95%CI:0.71–0.90)and moderate for BAR and MELD,respectively,(AUC:0.66,P<0.004,95%CI:0.55–0.77),(AUC:0.62,P<0.026,95%CI:0.51–0.72).All showed good discrimination between deaths and survivors.As for the best value for liver transplantation,it was significant only for APACHE IV(P<0.001).CONCLUSION The APACHE IV assessment score was more accurate than BAR and MELD in predicting mortality in deceased donor liver transplant recipients.
基金Project supported by the Natural Science Foundation of ZhejiangProvince China (No. Y506126).
文摘Intemational Vehicle Emissions （IVE） model funded by U.S. Environmental Protection Agency （USEPA） is designed to estimate emissions from motor vehicles in developing countries. In this study, the IVE model was evaluated by utilizing a dataset available from the remote sensing measurements on a large number of vehicles at five different sites in Hangzhou, China, in 2004 and 2005. Average fuel-based emission factors derived from the remote sensing measurements were compared with corresponding emission factors derived from IVE calculations for urban, hot stabilized condition. The results show a good agreement between the two methods for gasoline passenger cars＇ HC emission for all 1VE subsectors and technology classes. In the case of CO emissions, the modeled results were reasonably good, although systematically underestimate the emissions by almost 12%-50% for different technology classes. However, the model totally overestimated NOx emissions. The IVE NOx emission factors were 1.5-3.5 times of the remote sensing measured ones. The IVE model was also evaluated for light duty gasoline truck, heavy duty gasoline vehicles and motor cycles. A notable result was observed that the decrease in emissions from technology class State II to State I were overestimated by the IVE model compared to remote sensing measurements for all the three pollutants. Finally, in order to improve emission estimation, the adjusted base emission factors from local studies are strongly recommended to be used in the IVE model.
基金supported by the National Natural Science Foundation of China (No. 50578146, 20876151,21176225)the Natural Science Foundation of Zhejiang Province, China (No. Y5080178)
文摘To well describe the Ti（IV）-catalyzed H2O2/O3 reaction in aqueous solution, a kinetic model was established based on its mechanism. This model was then validated by the experiments of acetic acid degradation in aqueous solution. It was found that the correlation coefficient of fittings was higher than 0.970. Three key operating factors affecting organic degradation in the Ti（IV）-catalyzed H2O2/O3 process were studied, including Ti（IV） concentration, dissolved ozone concentration and initial H2O2 concentration. Furthermore, some experiments were conducted to determine the rate constant for dissolved ozone decomposition initiated by Ti2O52＋. The rate constant measured is almost in accord with the data analyzed by this kinetic model. The goodness of fittings demonstrated that this model could well describe the kinetics of the Ti（IV）-catalyzed H2O2/O3 reaction mathematically and chemically. Therefore, this kinetic model can provide some useful information to optimize the parameters in ozonation of water containing certain pollutants.
基金the University of Transport Technology under the project entitled“Application of Machine Learning Algorithms in Landslide Susceptibility Mapping in Mountainous Areas”with grant number DTTD2022-16.
文摘This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.
文摘Nuclear level density(NLD) is a characteristic property of many-body quantum mechanical systems.NLDs are of special importance to make statistical calculations in reactor studies and various theoretical and experimental nuclear physics and engineering applications.In this study,we have investigated a set of particle states in distinct rotational and vibrational bands to calculate nuclear level density parameters and the NLDs of accessible states of some deformed Dy radionuclides using a collective model approach,which included different excitation bands of the observed nuclear spectra.The method used assumes equidistant spacing of collective coupled state bands of the considered nuclei.The results of the calculated NLD have been compared with the experimental and compiled data obtained by the Oslo group,shell model Monte Carlo,Hartree-Fock-Bogoliubov + combinatorial approach,Bardeen-Cooper-Schrieffer approach and are in a good agreement.
文摘This article, in order to improve the assembly of the high-pressure spool, presents an assembly variation identification method achieved by response surface method （RSM）-based model updating using IV-optimal designs. The method involves screening out non-relevant assembly parameters using IV-optimal designs and the preload of the joints is chosen as the input features and modal frequency is the only response feature. Emphasis is placed on the construction of response surface models including the interactions between the bolted joints by which the non-linear relationship between the assembly variation caused by the changes ofpreload and the output frequency variation is established. By achieving an optimal process of selected variables in the model, assembly variation can be identified. With a case study of the laboratory bolted disks as an example, the proposed method is verified and it gives enough accuracy in variation identification. It has been observed that the first-order response surface models considering the interactions between the bolted joints based on the IV-optimal criterion are adequate for assembly purposes.
基金Supported by Mianyang Health and Health Committee 2023 Scientific Research Project,No.202309Chengdu University of Traditional Chinese Medicine University-Hospital Joint Innovation Fund,No.LH202402010Mianyang Chinese Medicine Association 2024 Traditional Chinese Medicine Inheritance and Innovation Science and Technology Project,No.MYSZYYXH-202426.
文摘BACKGROUND Stage IV pancreatic cancer(PC)has a poor prognosis and lacks individualized prognostic tools.Current survival prediction models are limited,and there is a need for more accurate,personalized methods.The Surveillance,Epidemiology,and End Results(SEER)database offers a valuable resource for studying large patient cohorts,yet machine learning-based nomograms for stage IV PC prognosis remain underexplored.This study hypothesizes that a machine learning-based nomogram can predict cancer-specific survival(CSS)and overall survival(OS)with high accuracy in stage IV PC patients.AIM To construct and validate a machine learning-based nomogram for predicting survival in stage IV PC patients using real-world data.METHODS Clinical data from stage IV PC patients diagnosed via pathology from 2000 to 2019 INTRODUCTION Pancreatic cancer(PC)is a significant human health issue and,by 2025,is projected to surpass breast cancer as the third leading cause of cancer-related deaths[1].In the United States,an estimated 66440 new cases and 51750 deaths due to PC were reported in 2024.PC is often asymptomatic in its early stages,with more than half of patients presenting with distant organ metastasis at the time of initial diagnosis[2].Consequently,the prognosis is very poor,with a 5-year relative survival rate of only 12.8%[2]In clinical practice,considerable heterogeneity in survival outcomes has been observed among patients with stage IV PC,highlighting the need for an individualized survival prediction tool for this population.Nomograms,which are visual tools incorporating multiple prognostic factors to predict patient survival,aid in person-alized treatment planning and clinical decision-making and are widely used in cancer prognosis evaluation[3-6].Machine learning,a core technique within artificial intelligence,employs algorithms to analyze data,learn from patterns,and predict real-world events with high accuracy,and is increasingly applied in health assessment,medical decision-making,prognosis,and personalized treatment[7-9].This study leverages the large sample size and comprehensive clinical data from the United State Surveillance,Epidemiology,and End Results(SEER)database to develop a prognostic nomogram for stage IV PC patients using machine learning,with the aim of providing individualized prognostic assessments to improve clinical decision-making.
