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Fault interpretation plays an important role in the field of coal mine safety. The development of neural network gives rise to many intelligent seismic data interpretation and processing schemes based on neural network algorithm. This study ①selected the Faster R-CNN target detection algorithm more suitable for fault recognition by comparing different deep convolutional neural network target detection algorithms. ②tested AlexNet, residual network ResNet50 and ResNet101 feature extraction networks through seismic forward modeling with various geological characteristics. It is found that ResNet101 feature extraction network has better performance in fault detection. ③constructed a fault detection model based on the preferred ResNet101 feature extraction network and Faster R-CNN target detection algorithm, and detected the actual seismic data. Results show that the object detection algorithm based on deep convolutional neural network shows satisfactory generalization ability in fault detection. It could improve the fault interpretation efficiency, and has potential in application.

Compared with the variables separation method and integral-transform method, Green’s function method is much more applicable and thus of great practical significance. Based on the general steps of Green’s function method, a new method is originally raised to get Green’s function in oil and gas flow problems through underground formations. After that, the transient pressure solution is get for flow in a spherical reservoir by using this method. This case enlarged the application of Greenʼs method function, and this work can also be a good foundation for the solution of more and more complicated flow problems in oil and gas reservoirs.

Scientific evaluation of the development efficiency of the regional tourism industry has important practical significance in promoting the high-quality development of the industry. This study calculated the county tourism efficiency and total factor productivity (TFP) for 86 counties and cities in Xinjiang, China, from 2011 to 2019 based on the SBM-data envelopment analysis model and the Malmquist productivity index model, which determines the respective temporal changes and spatial differences. The factors affecting the evolution of the spatial–temporal pattern of the tourism industry efficiency were also analyzed using the geographic detector model. The results show that: (1) The average tourism efficiency of Xinjiang county from 2011 to 2019 was at a low level of 0.382. During the study period, the evolution trend of the tourism efficiency fluctuated. There was a positive spatial autocorrelation in the tourism efficiency. The difference between cold and hot spots was evident by “hot-in-the-north and cold-in-the-South” characteristics. (2) The TFP maintained an upward trend during the research period, with an average annual growth rate of 28.7%. The number of counties and cities with an increased TFP was much higher than those with a decreased TFP, and its growth was mainly attributed to the progress of technical efficiency. (3) The county tourism efficiency was mainly affected by the level of economic development, tourism resource endowment, market scale, and government policies. There were large differences in the intensity and space among various influencing factors. During the research period, the influence of the economic development level and market scale gradually weakened, and the influence of tourism resource endowment and government policies continuously strengthened.

Autism is a neurodevelopmental disorder with great uncertainty in its diagnosis. However, the existing modeling methods for autism diagnosis have not been effectively studied for the uncertainty of the diagnosis process so far. In this paper, based on TSK (Takagi-Sugeno-Kang) fuzzy system and combining the association information between functional connections, a new sparse modeling method JGSL-TSK (joint-group-sparse-learning Takagi-Sugeno-Kang) for uncertain joint group is proposed and applied to the auxiliary diagnosis of autism. Firstly, the original rs-fMRI (resting-state functional magnetic resonance imaging) data are preprocessed and extracted to obtain the reduced dimension feature matrix. Secondly, based on the TSK fuzzy system framework, the joint sparse regulari-zation term is introduced to the consequent parameter learning process from the correlation between features, so as to guide the joint selection of features within the same rule and between rules. Finally, the alternating optimization method is used to solve the model. Compared with the existing methods, this method has the advantages of strong interpretability and good classification performance. Experimental results show that this method is conducive to the auxiliary diagnosis of autism.

Objective To investigate the expression and function of the TNF signaling pathway in the early stage of orthodontic tooth movement with periodontitis and to provide evidence to study the early inflammatory response in patients with periodontitis orthodontic treatment. Methods Sixteen SD rats were randomly divided into four groups: group A--12 h of orthodontic tooth movement of the bilateral maxillary first molars in rats with periodontitis; group B--periodontitis model of the bilateral maxillary first molars without orthodontic tooth movement; group C--12 h of orthodontic tooth movement of the same teeth in rats with healthy periodontium; group D--control group without operations. The bilateral maxillary first molars and surrounding periodontal tissue of each group were collected for gene chip detection. Pathway enrichment analysis, qRT-PCR and GO (gene ontology) analysis were performed to identify differential genes involved in the TNF signaling pathway. Results Gene chip results showed that the TNF signaling pathway was significantly upregulated in group A, group B and group C (P group B > group C (P

AbstractSuperfine powders of poly (methyl methacrylate) (PMMA) have been prepared by means of an emulsion polymerization method. These have been used as templates in the synthesis of tetragonal phase mesoporous zirconia by the sol–gel method, using zirconium oxychloride and oxalic acid as raw materials. The products have been characterized by infrared spectroscopy, X-ray diffraction analysis, transmission electron microscopy, N2adsorption-desorption isotherms, and pore size distribution. The results indicate that the average pore size was found to be 3.7 nm.