A novel combined intelligent algorithm prediction model for the risk of the coal and gas outburst.

The mechanism of coal and gas outburst disasters is perplexing, and the evaluation methods of outburst disasters based on various sensitive indicators often have some imprecision and fuzziness. With the concept of accurate and intelligent mining in coal mines proposed in China, selecting quantifiable parameters for machine learning risk prediction can avoid the deviation caused by human subjectivity, and improve the accuracy of coal and gas outburst prediction. Aiming at the shortcomings of the support vector machine (SVM) such as low noise resistance and being prone to be influenced by parameters easily, this research proposed a prediction method based on a grey wolf optimizer to optimize the support vector machine (GWO-SVM). To coordinate the global and local optimization ability of the GWO, Tent Chaotic Mapping and DLH strategies were introduced to improve the optimization ability of the GWO and reduce the local optimal probability. The improved prediction model IGWO-SVM was used to predict the coal and gas outburst. The results showed that this model has faster training speed and higher classification prediction accuracy than the SVM and GWO-SVM models, which the accuracy rate reaching 100%. Finally, to obtain the correlation between the parameters of the coal and gas outburst prediction parameters, the random forest algorithm was used for training, and the three parameters with the highest feature importance were selected to rebuild the data set for machine learning. The accuracy of the IGWO-SVM outburst prediction model based on Random Forest was still 100%. Therefore, even if some prediction parameters are missing, the outburst can still be effectively predicted by using the RF-IGWO-SVM model, which is beneficial for the model application and underground safety management.

Asprosin aggravates vascular endothelial dysfunction via disturbing mitochondrial dynamics in obesity models.

OBJECTIVE: The aim of the study was to investigate the contribution of asprosin (ASP), a fasting-induced hormone involved in metabolic disorders, to vascular endothelial dysfunction in obesity models. METHODS: Primary rat thoracic aortic endothelial cells treated with palmitic acid and mice fed with a high-fat diet (HFD) were used as the obesity models. The role and mechanism of ASP in endothelial dysfunction were investigated by the means of morphologic, functional, and genetic analysis. RESULTS: ASP aggravated the endothelial dysfunction induced by either palmitic acid in vitro or an HFD in vivo, characterized as the impairment of endothelium-dependent vasodilation, reduction of nitric oxide levels, elevation of malondialdehyde levels, and inhibition of phosphoinositide 3-kinase-AKT-endothelial nitric oxide synthase signaling. However, adipose conditional knockout of ASP or ASP neutralization significantly alleviated the endothelial dysfunction induced by an HFD. Mechanistically, ASP enhanced mitochondrial fission, and inhibition of the fission through knockdown of dynamin-related protein 1 (a fission-hallmark factor) rescued the endothelial dysfunction and the disturbance to mitochondrial dynamics induced by ASP. CONCLUSIONS: The findings demonstrate that ASP causes and even exacerbates vascular endothelial dysfunction through promoting mitochondrial fission in obesity, suggesting that ASP can act as an early predictive marker of blood vessel dysfunction and become a novel potential therapeutic target for obesity-related cardiovascular diseases.