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In this study, a model test system of shield driving adjacent to pile is realized to study the response of surrounding rock and pile induced by an advanced shield tunnel in silty clay layer in a coastal area. The function of automatic pushing and the synchronous grouting of the shield were partially simulated. Similar materials of lining, soft rock and pile, were developed. A total of 33 sets of orthogonal model tests were designed to study the progressive failure caused by excavation in silty clay and the change in stratum displacement and soil pressure; the displacement and internal force response of the pile were also analyzed. The following conclusions can be given: (1) the best grouting filling coefficient range of the shield test for the case study is 1.4 to 1.6; (2) the fitting of the experimental data to obtain the settlement through the width parameter suggests that the K value of the surrounding rock is decreased by 0.075 at least, and the K value increases with the increase in pile-tunnel distance and decreases with the increase in pile depth; and (3) the weak surrounding rock in the tunnel system can be divided into five zones according to the displacement and pressure change in soft surrounding rock and the pile response in the case study. The key condition control factor was also determined.

We survey expressions of the effective modal volume, Veff, commonly used in the literature for nanoscale photonic and plasmonic cavities. We apply different expressions of Veff to several canonical cavities designed for nanoscale near-infrared light sources, including metallo-dielectric and coaxial geometries. We develop a metric for quantifying the robustness of different Veff expressions to the different cavities and materials studied. We conclude that no single expression for Veff is universally applicable. Several expressions yield nearly identical results for cavities with well-confined photonic-type modes. For cavities with poor confinement and a low quality factor, however, expressions using the proper normalization method need to be implemented to adequately describe the diverging behavior of their effective modal volume. The results serve as a practical guideline for mode analysis of nanoscale optical cavities, which show promise for future sensing, communication, and computing platforms.

Football is one of the most popular sports in the world, arousing a wide range of research topics related to its off- and on-the-pitch performance. The extraction of football entities from football news helps to construct sports frameworks, integrate sports resources, and timely capture the dynamics of the sports through visual text mining results, including the connections among football players, football clubs, and football competitions, and it is of great convenience to observe and analyze the developmental tendencies of football. Therefore, in this paper, we constructed a 1000,000-word Chinese corpus in the field of football and proposed a BiLSTM-based model for named entity recognition. The ALBERT-BiLSTM combination model of deep learning is used for entity extraction of football textual data. Based on the BiLSTM model, we introduced ALBERT as a pre-training model to extract character and enhance the generalization ability of word embedding vectors. We then compared the results of two different annotation schemes, BIO and BIOE, and two deep learning models, ALBERT-BiLSTM-CRF and ALBERT BiLSTM. It was verified that the BIOE tagging was superior than BIO, and the ALBERT-BiLSTM model was more suitable for football datasets. The precision, recall, and F-Score of the model were 85.4%, 83.47%, and 84.37%, correspondingly.