To address the poor performance of commonly used intelligent optimization algorithms in solving location problems—specifically regarding effectiveness,efficiency,and stability—this study proposes a novel location al...To address the poor performance of commonly used intelligent optimization algorithms in solving location problems—specifically regarding effectiveness,efficiency,and stability—this study proposes a novel location allocation method for the delivery sites to deliver daily necessities during epidemic quarantines.After establishing the optimization objectives and constraints,we developed a relevant mathematical model based on the collected data and utilized traditional intelligent optimization algorithms to obtain Pareto optimal solutions.Building on the characteristics of these Pareto front solutions,we introduced an improved clustering algorithm and conducted simulation experiments using data from Changchun City.The results demonstrate that the proposed algorithm outperforms traditional intelligent optimization algorithms in terms of effectiveness,efficiency,and stability,achieving reductions of approximately 12%and 8%in time and labor costs,respectively,compared to the baseline algorithm.展开更多
Graphitized carbon foams(GFms)were prepared using mesophase pitch(MP)as a raw material by foaming(450℃),pre-oxidation(320℃),carbonization(1000℃)and graphitization(2800℃).The differences in structure and properties...Graphitized carbon foams(GFms)were prepared using mesophase pitch(MP)as a raw material by foaming(450℃),pre-oxidation(320℃),carbonization(1000℃)and graphitization(2800℃).The differences in structure and properties of GFms prepared from different MP precursors pretreated by ball milling or liquid phase extraction were investigated and compared,and semi-quantitative calculations were conducted on the Raman and FTIR spectra of samples at each preparation stage.Semi-quantitat-ive spectroscopic analysis provided detailed information on the structure and chemical composition changes of the MP and GFm de-rived from it.Combined with microscopic observations,the change from precursor to GFm was analyzed.The results showed that ball milling concentrated the distribution of aromatic molecules in the pitch,which contributed to uniform foaming to give a GFm with a uniform pore distribution and good properties.Liquid phase extraction helped remove light components while retaining large aromatics to form graphitic planes with the largest average size during post-treatment to produce a GFm with the highest degree of graphitization and the fewest open pores,giving the best compression resistance(2.47 MPa),the highest thermal conductivity(64.47 W/(m·K))and the lowest electrical resistance(13.02μΩ·m).Characterization combining semi-quantitative spectroscopic ana-lysis with microscopic observations allowed us to control the preparation of the MP-derived GFms.展开更多
Objective Clinical medical record data associated with hepatitis B-related acute-on-chronic liver failure(HBV-ACLF)generally have small sample sizes and a class imbalance.However,most machine learning models are desig...Objective Clinical medical record data associated with hepatitis B-related acute-on-chronic liver failure(HBV-ACLF)generally have small sample sizes and a class imbalance.However,most machine learning models are designed based on balanced data and lack interpretability.This study aimed to propose a traditional Chinese medicine(TCM)diagnostic model for HBV-ACLF based on the TCM syndrome differentiation and treatment theory,which is clinically interpretable and highly accurate.Methods We collected medical records from 261 patients diagnosed with HBV-ACLF,including three syndromes:Yang jaundice(214 cases),Yang-Yin jaundice(41 cases),and Yin jaundice(6 cases).To avoid overfitting of the machine learning model,we excluded the cases of Yin jaundice.After data standardization and cleaning,we obtained 255 relevant medical records of Yang jaundice and Yang-Yin jaundice.To address the class imbalance issue,we employed the oversampling method and five machine learning methods,including logistic regression(LR),support vector machine(SVM),decision tree(DT),random forest(RF),and extreme gradient boosting(XGBoost)to construct the syndrome diagnosis models.This study used precision,F1 score,the area under the receiver operating characteristic(ROC)curve(AUC),and accuracy as model evaluation metrics.The model with the best classification performance was selected to extract the diagnostic rule,and its clinical significance was thoroughly analyzed.Furthermore,we proposed a novel multiple-round stable rule extraction(MRSRE)method to obtain a stable rule set of features that can exhibit the model’s clinical interpretability.Results The precision of the five machine learning models built using oversampled balanced data exceeded 0.90.Among these models,the accuracy of RF classification of syndrome types was 0.92,and the mean F1 scores of the two categories of Yang jaundice and Yang-Yin jaundice were 0.93 and 0.94,respectively.Additionally,the AUC was 0.98.The extraction rules of the RF syndrome differentiation model based on the MRSRE method revealed that the common features of Yang jaundice and Yang-Yin jaundice were wiry pulse,yellowing of the urine,skin,and eyes,normal tongue body,healthy sublingual vessel,nausea,oil loathing,and poor appetite.The main features of Yang jaundice were a red tongue body and thickened sublingual vessels,whereas those of Yang-Yin jaundice were a dark tongue body,pale white tongue body,white tongue coating,lack of strength,slippery pulse,light red tongue body,slimy tongue coating,and abdominal distension.This is aligned with the classifications made by TCM experts based on TCM syndrome differentiation and treatment theory.Conclusion Our model can be utilized for differentiating HBV-ACLF syndromes,which has the potential to be applied to generate other clinically interpretable models with high accuracy on clinical data characterized by small sample sizes and a class imbalance.展开更多
基金National Natural Science Foundation of China(62202477)。
文摘To address the poor performance of commonly used intelligent optimization algorithms in solving location problems—specifically regarding effectiveness,efficiency,and stability—this study proposes a novel location allocation method for the delivery sites to deliver daily necessities during epidemic quarantines.After establishing the optimization objectives and constraints,we developed a relevant mathematical model based on the collected data and utilized traditional intelligent optimization algorithms to obtain Pareto optimal solutions.Building on the characteristics of these Pareto front solutions,we introduced an improved clustering algorithm and conducted simulation experiments using data from Changchun City.The results demonstrate that the proposed algorithm outperforms traditional intelligent optimization algorithms in terms of effectiveness,efficiency,and stability,achieving reductions of approximately 12%and 8%in time and labor costs,respectively,compared to the baseline algorithm.
文摘Graphitized carbon foams(GFms)were prepared using mesophase pitch(MP)as a raw material by foaming(450℃),pre-oxidation(320℃),carbonization(1000℃)and graphitization(2800℃).The differences in structure and properties of GFms prepared from different MP precursors pretreated by ball milling or liquid phase extraction were investigated and compared,and semi-quantitative calculations were conducted on the Raman and FTIR spectra of samples at each preparation stage.Semi-quantitat-ive spectroscopic analysis provided detailed information on the structure and chemical composition changes of the MP and GFm de-rived from it.Combined with microscopic observations,the change from precursor to GFm was analyzed.The results showed that ball milling concentrated the distribution of aromatic molecules in the pitch,which contributed to uniform foaming to give a GFm with a uniform pore distribution and good properties.Liquid phase extraction helped remove light components while retaining large aromatics to form graphitic planes with the largest average size during post-treatment to produce a GFm with the highest degree of graphitization and the fewest open pores,giving the best compression resistance(2.47 MPa),the highest thermal conductivity(64.47 W/(m·K))and the lowest electrical resistance(13.02μΩ·m).Characterization combining semi-quantitative spectroscopic ana-lysis with microscopic observations allowed us to control the preparation of the MP-derived GFms.
基金Key research project of Hunan Provincial Administration of Traditional Chinese Medicine(A2023048)Key Research Foundation of Education Bureau of Hunan Province,China(23A0273).
文摘Objective Clinical medical record data associated with hepatitis B-related acute-on-chronic liver failure(HBV-ACLF)generally have small sample sizes and a class imbalance.However,most machine learning models are designed based on balanced data and lack interpretability.This study aimed to propose a traditional Chinese medicine(TCM)diagnostic model for HBV-ACLF based on the TCM syndrome differentiation and treatment theory,which is clinically interpretable and highly accurate.Methods We collected medical records from 261 patients diagnosed with HBV-ACLF,including three syndromes:Yang jaundice(214 cases),Yang-Yin jaundice(41 cases),and Yin jaundice(6 cases).To avoid overfitting of the machine learning model,we excluded the cases of Yin jaundice.After data standardization and cleaning,we obtained 255 relevant medical records of Yang jaundice and Yang-Yin jaundice.To address the class imbalance issue,we employed the oversampling method and five machine learning methods,including logistic regression(LR),support vector machine(SVM),decision tree(DT),random forest(RF),and extreme gradient boosting(XGBoost)to construct the syndrome diagnosis models.This study used precision,F1 score,the area under the receiver operating characteristic(ROC)curve(AUC),and accuracy as model evaluation metrics.The model with the best classification performance was selected to extract the diagnostic rule,and its clinical significance was thoroughly analyzed.Furthermore,we proposed a novel multiple-round stable rule extraction(MRSRE)method to obtain a stable rule set of features that can exhibit the model’s clinical interpretability.Results The precision of the five machine learning models built using oversampled balanced data exceeded 0.90.Among these models,the accuracy of RF classification of syndrome types was 0.92,and the mean F1 scores of the two categories of Yang jaundice and Yang-Yin jaundice were 0.93 and 0.94,respectively.Additionally,the AUC was 0.98.The extraction rules of the RF syndrome differentiation model based on the MRSRE method revealed that the common features of Yang jaundice and Yang-Yin jaundice were wiry pulse,yellowing of the urine,skin,and eyes,normal tongue body,healthy sublingual vessel,nausea,oil loathing,and poor appetite.The main features of Yang jaundice were a red tongue body and thickened sublingual vessels,whereas those of Yang-Yin jaundice were a dark tongue body,pale white tongue body,white tongue coating,lack of strength,slippery pulse,light red tongue body,slimy tongue coating,and abdominal distension.This is aligned with the classifications made by TCM experts based on TCM syndrome differentiation and treatment theory.Conclusion Our model can be utilized for differentiating HBV-ACLF syndromes,which has the potential to be applied to generate other clinically interpretable models with high accuracy on clinical data characterized by small sample sizes and a class imbalance.
