Multi-criteria group decision-making method for green supplier selection based on distributed interval variables

Addressing the multi-criteria group decision making problem with interval attribute values and attribute weights, this paper proposes a decision method based on attribute distribution information. The selection of green suppliers is taken as an example for decision analysis. First, in the case of group decision-making, the quantitative values of the evaluation attributes of green suppliers are imputed by decision-makers, and the relevant distributions are constructed for each attribute. Next, combined with the ranges of attribute values, the random interval values are used to describe the information represented by each attribute to overcome the loss caused by the aggregation of individual expert information into group information. We then propose the distributed interval weighted arithmetic average (DIWAA) operator and corresponding operation rules, which realizes the fusion of qualitative data and quantitative judgment. Thus, the proposed approach allows ensuring reasonable results of the multi-criteria analysis. We also construct a ranking method for alternatives based on distributed interval comprehensive scores. Finally, we verify the feasibility and effectiveness of the proposed method for the task of green supplier selection through numerical experiments

Pulse Diagnosis Signals Analysis of Fatty Liver Disease and Cirrhosis Patients by Using Machine Learning

Objective. To compare the signals of pulse diagnosis of fatty liver disease (FLD) patients and cirrhosis patients. Methods. After collecting the pulse waves of patients with fatty liver disease, cirrhosis patients, and healthy volunteers, we do pretreatment and parameters extracting based on harmonic fitting, modeling, and identification by unsupervised learning Principal Component Analysis (PCA) and supervised learning Least squares Regression (LS) and Least Absolute Shrinkage and Selection Operator (LASSO) with cross-validation step by step for analysis. Results. There is significant difference between the pulse diagnosis signals of healthy volunteers and patients with FLD and cirrhosis, and the result was confirmed by 3 analysis methods. The identification accuracy of the 1st principal component is about 75% without any classification formation by PCA, and supervised learning’s accuracy (LS and LASSO) was even more than 93% when 7 parameters were used and was 84% when only 2 parameters were used. Conclusion. The method we built in this study based on the combination of unsupervised learning PCA and supervised learning LS and LASSO might offer some confidence for the realization of computer-aided diagnosis by pulse diagnosis in TCM. In addition, this study might offer some important evidence for the science of pulse diagnosis in TCM clinical diagnosis

DEAR1 Is a Dominant Regulator of Acinar Morphogenesis and an Independent Predictor of Local Recurrence-Free Survival in Early-Onset Breast Cancer

Ann Killary and colleagues describe a new gene that is genetically altered in breast tumors, and that may provide a new breast cancer prognostic marker