Domain Adaptation for Bearing Fault Diagnosis Based on SimAM and Adaptive Weighting Strategy.

Domain adaptation techniques are crucial for addressing the discrepancies between training and testing data distributions caused by varying operational conditions in practical bearing fault diagnosis. However, transfer fault diagnosis faces significant challenges under complex conditions with dispersed data and distinct distribution differences. Hence, this paper proposes CWT-SimAM-DAMS, a domain adaptation method for bearing fault diagnosis based on SimAM and an adaptive weighting strategy. The proposed scheme first uses Continuous Wavelet Transform (CWT) and Unsharp Masking (USM) for data preprocessing, and then feature extraction is performed using the Residual Network (ResNet) integrated with the SimAM module. This is combined with the proposed adaptive weighting strategy based on Joint Maximum Mean Discrepancy (JMMD) and Conditional Adversarial Domain Adaption Network (CDAN) domain adaptation algorithms, which minimizes the distribution differences between the source and target domains more effectively, thus enhancing domain adaptability. The proposed method is validated on two datasets, and experimental results show that it improves the accuracy of bearing fault diagnosis.

Ginsenoside Rg3 suppresses the growth of gemcitabine-resistant pancreatic cancer cells by upregulating lncRNA-CASC2 and activating PTEN signaling.

Pancreatic cancer is one of the most fatal malignancies with high mortality. Gemcitabine (GEM)-based chemotherapy is the most important treatment. However, the development of GEM resistance leads to chemotherapy failure. Previous studies demonstrated the anticancer activity of ginsenoside Rg3 in a variety of carcinomas through modulating multiple signaling pathways. In the present study, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide assay, colony formation assay, flow cytometry apoptosis assay, Western blotting assay, xenograft experiment, and immunohistochemistry assay were performed in GEM-resistant pancreatic cancer cell lines. Ginsenoside Rg3 inhibited the viability of GEM-resistant pancreatic cancer cells in a time-dependent and concentration-dependent manner through induction of apoptosis. The level of long noncoding RNA cancer susceptibility candidate 2 (CASC2) and PTEN expression was upregulated by the ginsenoside Rg3 treatment, and CASC2/PTEN signaling was involved in the ginsenoside Rg3-induced cell growth suppression and apoptosis in GEM-resistant pancreatic cancer cells. Ginsenoside Rg3 could be an effective anticancer agent for chemoresistant pancreatic cancer.