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Taking a running tunnel of Shanghai Railway Airport connecting line as a demonstration application project, the synchronous technology combining shield tunneling with segment assembling is proposed based on the active control on the oil pressure of the shield thrust system, which can solve the problem of the long construction period produced by a single shield machine employed in a long-distance shield tunnel project. The principle is to make full use of the extra stroke of propulsion cylinders generated by the axial insertion of the key block to assemble segments, and the theoretical operation time of a single ring can be reduced by 31.6%. Then, a large model test platform for this synchronization technology is established to verify its feasibility and reliability, and the redistribution method of the missing thrust force is introduced during the synchronous process, which is verified by the model test. The test results show that the actuators of the shield machine respond quickly, and the errors of the cylinder pressures and total thrust force of the propulsion system are controlled within ±2%. The attitude deviation of the shield machine is controlled within ±6 mm, and the error range of the driving speed is -2 to 4 mm/min. The segments are safe with the designed overburden thickness of 33 m, and the compressive safety coefficient reaches 1.68.

This article is aimed to study the parameter identification of the ExpARX system. To overcome the computational complexity associated with a large number of feature parameters, a parameter separation scheme based on the different features of the identification model is introduced. In terms of the phenomenon that the coupling parameters lead to the inability of algorithms, a separable synchronous interactive estimation method is introduced to eliminate the coupling parameters and perform parameter estimation in accordance with the hierarchical principle. For the purpose of achieving high‐accuracy performance and reducing complexity, a separable synchronous gradient iterative algorithm is derived by means of gradient search. In order to improve the identification accuracy, a separable synchronous multi‐innovation gradient iterative algorithm is proposed by introducing the multi‐innovation identification theory. In order to improve the convergence speed, a separable synchronous multi‐innovation conjugate gradient iterative algorithm is proposed by introducing the conjugate gradient theory. Finally, a simulation example and a real‐life example of piezoelectric ceramics are used to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Background: Lacrimal gland enlargement is a common pathological change in patients with thyroid eye disease (TED). Tear fluid has emerged as a new source of diagnostic biomarkers, but tear-based diagnostic biomarkers for TED with high efficacy are still lacking. Objective: We aim to investigate genes associated with TED-associated lacrimal gland lesions. Additionally, we seek to identify potential biomarkers for diagnosing TED in tear fluid. Methods: We obtained two expression profiling datasets related to TED lacrimal gland samples from the Gene Expression Omnibus (GEO). Subsequently, we combined the two separate datasets and conducted differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) on the obtained integrated dataset. The genes were employed for Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The genes were intersected with the secretory proteins profile to get the potential proteins in the tear fluid. Machine learning techniques were then employed to identify optimal biomarkers and develop a diagnostic nomogram for predicting TED. Finally, gene set enrichment analysis (GSEA) and immune infiltration analysis were conducted on screened hub genes to further elucidate their potential mechanisms in TED. Results: In our analysis of the integrated TED dataset, we identified 2,918 key module genes and 157 differentially expressed genes and finally obtained 84 lacrimal-associated key genes. Enrichment analysis disclosed that these 84 genes primarily pertain to endoplasmic reticulum organization. After intersecting with the secretory proteins, 13 lacrimal gland-associated secretory protein genes (LaSGs) were identified. The results from machine learning indicated the substantial diagnostic value of dyslexia associated gene (KIAA0319) and peroxiredoxin4 (PRDX4) in TED-associated lacrimal gland lesions. The two hub genes were chosen as candidate biomarkers in tear fluid and employed to establish a diagnostic nomogram. Furthermore, single-gene GSEA results and immune cell infiltration analysis unveiled immune dysregulation in the lacrimal gland of TED, with KIAA0319 and PRDX4 showing significant associations with infiltrating immune cells. Conclusions: We uncovered the distinct pathophysiology of TED-associated lacrimal gland enlargement compared to TED-associated orbital adipose tissue enlargement. We have demonstrated the endoplasmic reticulum-related pathways involved in TED-associated lacrimal gland lesions and established a diagnostic nomogram for TED utilizing KIAA0319 and PRDX4 through integrated bioinformatics analysis. This contribution offers novel insights for non-invasive, prospective diagnostic approaches in the context of TED. [ABSTRACT FROM AUTHOR]

The fluid–solid coupling effect is an important factor which cannot be ignored to study the surface subsidence of deep coal seam mining in the area with abundant underground water. To study the influence of boundary permeability characteristics on surface subsidence in deep near‐horizontal coal seam mining under the effect of fluid–solid coupling, the pore water pressure field, vertical stress field, surface subsidence, and vertical displacement of a rock seam during deep mining under different permeability boundary conditions were analyzed based on fluid–solid coupling theory, taking the 3−1501 working face of Erdos Hongqinghe coal mine as an example. The results revealed that the permeability characteristics of different hydraulic boundaries affected the pore water pressure, vertical stress in the rock layer, and surface subsidence during deep mining. Moreover, the trends of pore water pressure, vertical stress, and surface subsidence of a fixed‐head permeability boundary were largely the same as those under impermeable boundary conditions, but the calculated results of the fixed‐head permeability boundary were lower than those for the impermeable boundary at the same depth. The maximum surface subsidence for the fixed‐head permeable boundary condition and the impermeable boundary condition was 879 and 925 mm, respectively, which were 239 and 285 mm higher than those obtained when the fluid–solid coupling effect was neglected (an increase of 37.3% and 44.5%, respectively). The field monitoring trend for the subsidence basin aligns closely with the subsidence basin trend under the influence of fluid–solid coupling. Considering the constant head permeable boundary conditions in the context of fluid–solid coupling yields accurate surface subsidence results. The results have provided a theoretical basis for the analysis and prediction of coal mining surface subsidence considering the fluid–solid coupling effect. [ABSTRACT FROM AUTHOR]

Objective of this research was to create a machine learning algorithm that could recognize high-risk factors for gastric cancer recurrence and anticipate the correlation between gastric cancer recurrence and Helicobacter pylori (H. pylori) infection. Patients and Methods: This investigation comprised 1234 patients diagnosed with gastric cancer, and 37 characteristic variables were obtained. Four machine learning algorithms, namely, extreme gradient boosting (XGBoost), random forest (RF), k-nearest neighbor algorithm (KNN), and multilayer perceptron (MLP), were implemented to develop the models. The k-fold cross-validation technique was utilized to perform internal validation of the four models, while independent datasets were employed for external validation of the models. Results: In contrast to the other machine learning models, the XGBoost algorithm demonstrated superior predictive ability regarding high-risk factors for gastric cancer recurrence. The outcomes of Shapley additive explanation (SHAP) analysis revealed that tumor invasion depth, tumor lymph node metastasis, H. pylori infection, postoperative carcinoembryonic antigen (CEA), tumor size, and tumor number were risk elements for gastric cancer recurrence in patients, with H. pylori infection being the primary high-risk factor. Conclusion: Out of the four machine learning models, the XGBoost algorithm exhibited superior performance in predicting the recurrence of gastric cancer. In addition, machine learning models can help clinicians identify key prognostic factors that are clinically meaningful for the application of personalized patient monitoring and immunotherapy. [ABSTRACT FROM AUTHOR]