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The inverse analysis of the deformation moduli of high arch dams based on displacement monitoring data is essential for structural safety assessment. In traditional inverse analysis methods, the deformation moduli are identified based on the single‐objective optimization and the hydrostatic component derived from the statistical model. This type of method has two main shortcomings: First, it treats the essential multi‐objective optimization problem as a single‐objective problem; second, the extracted hydrostatic component may be biased due to the multicollinearity of variables in the statistical model. This paper presents a methodology for the inverse analysis of the deformation moduli of high arch dams under a multi‐objective optimization strategy. The methodology employs empirical mode decomposition to extract the aging component from displacement monitoring data. Then, thermomechanical analysis is used to reconstruct the remaining hydrostatic and temperature components, thereby avoiding the biases encountered in solving the statistical model. The adaptive polynomial chaos expansion method is embedded in the NSGA‐III algorithm to establish and solve multi‐objective functions in the inverse analysis. Additionally, a composite decision index considering errors and test information is proposed to determine acceptable deformation moduli from the Pareto solution set. A high arch dam is selected to illustrate this methodology with static and dynamic monitoring data. The results show that the identified deformation moduli have errors of 3.8% and 7.2% in displacement and acceleration, respectively. The proposed methodology can yield deformation modulus values that are more consistent with the physical implications than those of the single‐objective optimization method. [ABSTRACT FROM AUTHOR]

Background: Bladder cancer is the second most common malignant tumor in the urogenital system. The research investigated the prognostic role of immune‐related long non‐coding RNA (lncRNA) in bladder cancer. Methods: We extracted 411 bladder cancer samples from The Cancer Genome Atlas database. Single‐sample gene set enrichment analysis was employed to assess the immune cell infiltration of these samples. We recognized differentially expressed lncRNAs between tumors and paracancerous tissues, and differentially expressed lncRNAs between the high and low immune cell infiltration groups. Venn diagram analysis detected differentially expressed lncRNAs that intersected the above groups. LncRNAs with prognostic significance were identified by regression analysis. Multivariate Cox analysis was used to establish the risk score model. Then we established and evaluated the nomogram. Additionally, we performed gene set enrichment analysis to explore the potential functions of the screened lncRNAs in tumor pathogenesis. Results: Three hundred and twenty differentially expressed lncRNAs were recognized. We randomly divided patients into the training data set and the testing data set at a 2: 1 ratio. In the training data set, 9 immune‐related lncRNAs with prognostic significance were identified. The risk score model was constructed to classify patients as high‐ and low‐risk cohorts. Patients in the low‐risk cohort had better survival outcomes than those in the high‐risk cohort. The nomogram was established based on the indicators including age, gender, tumor‐node‐metastases stage, and risk score. The model's predictive performance was confirmed by the receiver operating characteristic curve analysis, concordance index method, calibration curve, and decision curve analysis. The testing data set also achieved similar results. Bioinformatics analysis suggested that the 9‐lncRNA signature was involved in the modulation of various immune responses, antigen processing and presentation, and T cell receptor signaling pathway. Conclusions: Our study uncovered the prognostic value of immune‐related lncRNAs for bladder cancer and showed that they may regulate tumor pathogenesis in various ways. [ABSTRACT FROM AUTHOR]

Frequency response analysis (FRA) is a widely used approach for detecting winding faults in a transformer. Appropriate and quantitative FRA features will help to improve the accuracy of fault diagnosis. In this study, a novel FRA interpretation including new image features is proposed based on the image processing technique. First, winding faults of different windings are simulated in a test autotransformer and the FRA curves are measured under various faults. Then frequency region division method and image processing technique are first applied to the measured FRA curves. The area ratio and centroid deviation in different frequency regions are calculated through a novel algorithm. Finally, the image features are used as the inputs to support vector machine model. Additionally, three different parametric optimisation algorithm are compared during the training process. The results show that the particle swarm optimisation and image feature exhibit best performance for identifying winding faults. [ABSTRACT FROM AUTHOR]

The windings of a traction transformer are always be threatened by the fluctuation of the load. Frequency response analysis (FRA) is regarded as the most effective method to diagnose the winding deformation. Appropriate FRA modelling method may help to predict typical winding faults for large transformers. The existing model usually simplify dozens of winding disks into several disks, it is applicable when analysing small displacement. However, when the disks between windings are seriously misplaced, the conventional model may not be suitable. This study presents an improved lumped parameter circuit model considering intersection capacitances and mutual inductance between windings. The feasibility of the proposed model is verified based on a 10 kV test transformer. Finally, the model is applied for a 220 kV traction transformer, the variation of parameters and features are analysed for axial displacement of different windings. The results show that the axial displacement may lead to increase of resonance frequency and decrease of amplitude. The low‐frequency band (1–30 kHz) may be regarded as visible features for diagnosing axial displacement of H winding and the middle frequency band (50–200 kHz) may be a characteristic band for diagnosing axial displacement of T winding. [ABSTRACT FROM AUTHOR]