The construction time of concrete structures in hydraulic tunnels is long, the construction environment is complex, and there are many influencing factors. The requirements for construction quality are high, not only to meet the strength requirements, but also to meet the design requirements of erosion resistance, crack resistance, and seepage resistance according to its specific operating environment. Therefore, evaluating the construction quality of concrete structures in hydraulic tunnels is of great significance. Considering the randomness and fuzziness of factors affecting the construction quality of concrete structures in hydraulic tunnels, this paper proposes a comprehensive evaluation model based on Combined Weighting (CWM) and Uncertainty Measurement Theory (UM). The IAHP method and CRITIC method are used to determine the subjective and objective weights of evaluation indicators, combined weighting is based on the principle of minimum entropy, and the UM method is used to evaluate the construction quality level. Finally, taking a hydraulic tunnel as an example, its construction quality grade is calculated to be III according to the evaluation model proposed in this paper, which matches with the engineering reality, and a comparative study is made with the mixture element topology theory at the same time. It is verified that the evaluation model can scientifically and reasonably evaluate the construction quality level of concrete structures in hydraulic tunnels.

Classifying network flow subsequences in an Industrial IoT gateway is an effective method for identifying Industrial IoT faults. This paper proposes a subsequence classification algorithm SSGBUL-IKNN based on unsupervised learning encoding to address the problem of low classification accuracy. Firstly, we built a network module based on unsupervised learning to encode the flow sequence of the Industrial IoT gateway. In the encoding module, a subsequence calibration algorithm controls the increasing error during the coding process. Then, the specific fault type is obtained through an improved KNN classification algorithm based on the indication distance source from the integrated coding sequences converted by multi-dimensional network flow sequences. The test result on three Industrial IoT datasets of a sewage treatment plant shows that the accuracy of SSGBUL-IKNN in this paper exceeds 92%, significantly higher than the algorithm DTW and TSF. It reaches the classification requirements of Industrial IoT fault diagnosis.

Rock materials failures are accompanied by the co-existence of various failure mechanisms, in-cluding rock fracturing, shearing, and compaction yield. These mechanisms manifest macro-scopically as multiple failure modes and nonlinear strength characteristics related to stress levels. Considering the limitations of current rock mechanics strength theories, which are primarily derived from single failure mechanisms, this study evaluates the applicability of alternative strength theories. Based on the extensional-strain criterion and the PMC (Paul-Mohr-Coulomb) model, a piecewise linear strength model was proposed that is suitable for analyzing multiple failure mechanisms in rocks and revealing the intrinsic mechanisms of multi-mechanism rock material failure. A multiple failure mechanism strength model in the form of inequalities was proposed, using the generalized shear stress, mean stress, and stress Lode angle as parameters. Strength tests conducted on sandstone and granite rock material samples under different stress conditions revealed distinct piecewise linear strength characteristics for both rock types, vali-dating the rationality and applicability of the multiple failure mechanism model. The findings construct a multi-mechanism failure model for rocks, providing enhanced predictive capabilities and aiding in the prevention of rock structural failures.

Verticillium wilt (VW) is a soil borne fungal diseases caused by Verticillium dahliae Kleb, and lead to serious damage to cotton production annually in the world. In our previous study, a transmembrane protein 214 protein (TMEM214) gene associated with VW resistance was map-based cloned from Gossypium barbadense (G. barbadense). TMEM214 proteins are a kind of transmembrane protein, but their function in plants is rarely studied. To reveal the function of TMEM214s in VW resistance, all six TMEM214s were cloned from G. barbadense in this study. These genes were named as GbTMEM214-1, GbTMEM214-4 and GbTMEM214-7 according to their location on the chromosomes, and the encoded proteins are all located on cell membrane. TMEM214 genes were all induced by Verticillium dahliae inoculation and showed significant differences between resistant and susceptible varieties, but the expression patterns of GbTMEM214s under different hormone treatments were significantly different. Virus-induced gene silencing analysis showed the resistance to VW of GbTMEM214s-silenced lines decreased significantly, which further proves the important role of GbTMEM214s in the resistance to Verticillium dahliae. Our study provides an insight into the involvement of GbTMEM214s in VW resistance, which was helpful to better understand the disease resistance mechanism of plants.