Fault Diagnosis of the Rolling Bearing by a Multi-Task Deep Learning Method Based on a Classifier Generative Adversarial Network.

Accurate fault diagnosis is essential for the safe operation of rotating machinery. Recently, traditional deep learning-based fault diagnosis have achieved promising results. However, most of these methods focus only on supervised learning and tend to use small convolution kernels non-effectively to extract features that are not controllable and have poor interpretability. To this end, this study proposes an innovative semi-supervised learning method for bearing fault diagnosis. Firstly, multi-scale dilated convolution squeeze-and-excitation residual blocks are designed to exact local and global features. Secondly, a classifier generative adversarial network is employed to achieve multi-task learning. Both unsupervised and supervised learning are performed simultaneously to improve the generalization ability. Finally, supervised learning is applied to fine-tune the final model, which can extract multi-scale features and be further improved by implicit data augmentation. Experiments on two datasets were carried out, and the results verified the superiority of the proposed method.

Advancements on the impact of hydroxychloroquine in systemic lupus erythematosus.

Hydroxychloroquine (HCQ) has gained significant attention as a therapeutic option for systemic lupus erythematosus (SLE) because of its multifaceted mechanism of action. It is a lipophilic, lysosomotropic drug, that easily traverses cell membranes and accumulates in lysosomes. Once accumulated, HCQ alkalizes lysosomes within the cytoplasm, thereby disrupting their function and interfering with processes like antigen presentation. Additionally, HCQ has shown potential in modulating T-cell responses, inhibiting cytokine production, and influencing Toll-like receptor signaling. Its immunomodulatory effects have generated interest in its application for autoimmune disorders. Despite its established efficacy, uncertainties persist regarding the optimal therapeutic concentrations and their correlation with adverse effects such as retinal toxicity. Therefore, standardized dosing and monitoring guidelines are crucial. In this study, we provide a comprehensive review of the mechanisms, efficacy, dosing variations, and retinal toxicity profiles of HCQ, which are essential to optimize SLE treatment protocols and ensure patient safety.

Bioconversion of apple pomace into a multienzyme bio-feed by two mixed strains of Aspergillus niger in solid state fermentation

Apple pomace is a wasted resource produced in large quantities and its deposit has caused serious environmental problems, so it is significance to make the full utilization of the residues. The objectives of this work were to produce multienzyme bio-feed, biodegrade the anti-nutritional factors such as pectin and tannins in apple pomace, and obtain the nutritional enrichment of the fermented substrate. The mixture of apple pomace and cottonseed powder (1:1, w/w), supplemented with 1 percent (w/w) (NH4)2SO4 and 0.1 percent (w/w) KH2PO4, was proved to be the optimum medium for the mixed strains of Aspergillus niger M2 and M3 (2:1, w/w). The activities of pectinase, proteinase and cellulase achieved 21168 u/g, 3585 u/g and 1208u/g, and the biodegradation rates of pectin and tannins reached 99.0 percent and 66.1 percent, respectively, when 0.4 percent(w/w) of the test fungiwere inoculated and incubated at 30ºC for 48 hrs in solid state fermentation. The utilization of apple pomace in the paper can be served as a model for the similar waste recycling.