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Objective We aim to evaluate the relationship between the use of metformin and the risk of pancreatic cancer in type 2 diabetes patients. Method We systematically searched the observational studies on PubMed, Embase, Web of Science, Cochrane Library, clinicalrials.gov, and CNKI databases, extracted relevant data, combined the OR value and 95% CI using the random effect model, and conducted a sensitivity analysis, subgroup analysis, and meta-regression to evaluate the size and stability of this relationship. Result Twenty-nine studies from twenty-four articles met our inclusion criteria, including more than 2 million subjects. Overall analysis showed that compared with no use of metformin, the use of metformin could reduce the risk of pancreatic cancer in patients with type 2 diabetes (OR = 0.82, 95% CI (0.69, 0.98)). Subgroup analysis showed that compared with the use of hypoglycemic drugs, the use of metformin could reduce the risk of pancreatic cancer in patients with type 2 diabetes (OR = 0.79, 95% CI (0.66, 0.94)). However, compared with no drugs or only diet therapy, metformin users might increase the risk of pancreatic cancer (OR = 2.19, 95% CI (1.08, 4.44)). Sensitivity analysis confirmed the stability of the study, and there was no significant publication bias. Conclusion Compared with the no-use of metformin, metformin users with diabetes can reduce the risk of pancreatic cancer. More research is needed to prove it works.

In view of complex and fuzziness of geological adaptive cutterhead selection for earth pressure balance (EPB) shield, a cutterhead selection method based on BP neural network is put forward. Considering the structure characteristics of EPB shield cutterhead, typical cutterhead types are classified and summarized based on cutterhead topology structure and number of spokes. After analyzing the determinants of cutterhead selection, one-to-many mapping relation between cutterhead type and geological parameters is put forward, and then core geologic parameters related to cutterhead selection are concluded. The feasibility of using neural network method to choose the cutterhead type is analyzed, and a BP neural network training model for cutterhead selection is set up and tested in testing sample data. The result shows that the selected cutterhead and the construction cutterhead are basically consistent. The feasibility of this method is proved and it can be theoretical basis for the cutterhead structure design which will improve scientific of cutterhead selection.

We discuss how to rank fuzzy numbers using weighted distance in this paper. Basic properties of this ranking method are investigated. Further, we prove that the proposed order can be generated by a ranking index. At last, in the case of a pair of specific weights, we compute the ranking index in some common cases of practical applications.