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In the context of globalization, the reconstruction of material spaces and the evolution of social relations have accelerated the loss of emotions in traditional villages. Intangible heritage can play an important role in the emotional maintenance of traditional villages as an emotional carrier for its residents. Previous studies have been less involved in the synergistic relationship between the interactive game of power subjects and the evolution of emotions in the practice of intangible heritage. Particularly, research on the evolution of the emotional exchange mode is insufficient. Taking the Dong minority chorus of Huangdu Village as an example, this study adopts qualitative research methods, such as semi-structured interviews and participant observation, to construct an analytical framework of "daily life practice-emotional exchange", and to deeply explore the evolution process and motivational mechanism of the emotional exchange mode in the daily life practice of traditional village residents. The study found that: 1) According to the changes in the subject and relationship, motive and mode, resource and situation, perception and experience of emotional exchange in the natural, livelihood, institutional, and spiritual dimensions, the daily life practice of Huangdu Village can be divided into four stages: primitive equilibrium, passive compromising, active resisting, and regenerating. 2) In the process of intangible cultural heritage practices, the manipulation of capital and the suppression of power have broken the original balance of Huangdu Village, and the division of power and status among subjects has squeezed the living spaces of local residents, forcing them to become involved in power struggles. They resist the control of the "other" by means of physical empowerment and restatement of the local subjectivity, and ultimately strike a balance of power within the village. In the daily practice of intangible cultural heritage, the mode of residents' emotional exchange changes from reciprocity to general negotiation and production modes. 3) Emotional exchanges in traditional villages are produced during power struggles between residents and other subjects. When power is balanced, residents master the discourse of intangible cultural heritage and produce positive emotions such as attachment and belonging. When residents are suppressed by power and capital, they gradually lose discourse and produce negative emotions, such as a sense of crisis and separation. 4) The evolution of emotional exchange modes in the daily practices of traditional village residents is systematic. The evolution of the outer system pushes the kernel system to adapt, and the driving, pulling, and supporting forces promote the synergistic evolution of "daily life―emotional exchange―intangible cultural heritage practice" in Huangdu village. The evolution of emotional exchange patterns during the practice of the Dong minority chorus in Huangdu Village was an inevitable process for reconstructing the cultural subjectivity of local residents in the context of tourism development. Exploring emotional exchange patterns at different stages of daily life practices can help understand the developmental law of traditional villages and provide useful references for its emotional governance and sustainable development.

The value discovery of data is the core of industrial internet,but for historical reasons,there are many information islands exist in enterprises.The identifier is the key technology for solving information isolated island and industrial big data gathering.Also,the identifier is the basis technology of understanding industrial big data.Several key elements in the application of identifier in industrial internet were analyzed,and some research ideas for the future study of industrial big data’s understanding were given.

By reviewing and studying relevant historical materials, this study presents the advanced and fruitful clinical teaching in Peking Union Medical College Hospital(PUMCH) nearly a hundred years ago. It proved that the clinical teaching of PUMCH had reached the internationally advanced level in 1925. The bed-side teaching, case discussion, multidisciplinary joint ward round, interdisciplinary teaching, and resident training system still have practical significance presently. As it turns out, many of the teachers who were passionate about teaching and students who were actively involved in teaching went on to become medical masters. The spirit of taking teaching as an honor, responsibility and mission has been passed on and deeply rooted in the spirit of PUMCH and has remained unchanged for a century.

Habaqila site is located in the west of Habaqila Village, Yuzhoudi Town, Keshketeng banner, Inner Mongolia Autonomous Region. In 2020, the Frontier Archaeological Research Center of Jilin University, together with the Institute of Cultural Heritage and History of Science and Technology, University of Science and Technology Beijing and the Institute of Cultural Relics and Archaeology of Inner Mongolia Autonomous Region, conducted archaeological excavations, at the same time, the surrounding area of the site was systematically investigated. The sites A, B and C of the Habaqila Site, the Xibeigou, Yihewomente and Shangjuzi Sites were investigated. The relics can be dated from Hongshan Culture, Xiaoheyan Culture, Xiquegou Type, Xiajiadian Upper Culture and Yan, Qin and Han Dynasties. The focus of this investigation is the metallurgical relics of the Xiquegou Type and the upper culture period of Xiajiadian.

To better predict the lateral displacements of diaphragm walls during deep excavation, a long short-term memory (LSTM) multi-step prediction model is developed in this paper based on the LSTM algorithm. First, the multi-output strategy of multi-step prediction model is discussed. Then, the construction method of the LSTM multi-step prediction model is introduced in detail, and the two hyperparameters, i.e., the space and time dimensions of the model input set, are explored to improve the prediction accuracy of the model. Finally, the errors between the predicted values and the field monitoring data are analyzed based on an excavation project buried in water-rich sandy strata. The analysis results of three typical monitoring points indicate that the LSTM prediction model is characterized by solid generalization ability, and the relevant algorithm is practically helpful for improving and optimizing deformation prediction methods of deep excavation.

Conforms to: doi:10.34847/cocoon.49aefa90-8c1f-3ba8-a099-0ebefc6a2aa7 Enregistrement des interactions de base en chinois

Control of the disturbed displacement of adjacent tunnel during excavation is a significant issue for design and construction. Based on the multi-objective optimization method, the multi-type monitoring data in the excavation of the excavation are integrated, the key soil parameters are inverted and identified, and the time effect of the tunnel displacement is quantified and corrected. A dynamic multi-objective optimization method with adaptive infill criterion (DMO-AIC) is proposed to improve the updating efficiency of dynamic surrogate models. The proposed method takes into account the computational redundancy of dynamic surrogate models in engineering optimization, and designs an adaptive point-adding discrimination strategy, which can autonomously identify invalid updates of surrogate models on the optimization path. The results show that the proposed DMO-AIC significantly reduces the invocations of the black-box model during optimization while ensuring the good search performance and the convergence speed of the algorithm. The improved computational efficiency of DMO-AIC is helpful for the application of dynamic surrogate models in engineering optimization. The results of the virtual numerical example show that DMO-AIC can predict and update multiple model responses during excavation, such as wall deflections and tunnel displacements. The engineering practice of Shanghai Bund 596 excavation indicates that the time effect is properly updated, and the staged vertical displacements of the adjacent tunnel are accurately predicted.

The soil unloading effect caused by the adjacent excavation will influence both the uplift and the deformation of the adjacent existing tunnel, and even interfere with the normal operation of the tunnel. A simplified calculation method for the longitudinal deformation of the underlying tunnel caused by foundation pit excavation is proposed. The tunnel is simplified into an infinitely long Euler-Bernoulli beam resting on a three-parameter Kerr foundation model. The difference method is combined with the boundary conditions at both ends of the tunnel to obtain the longitudinal deformation difference decomposition of the tunnel. The accuracy of the proposed method is proved by comparing it with the finite element simulation method and some cases study. Compared with the tunnel simplified as the Euler-Bernoulli beam which is placed upon the existing Pasternak foundation model, the Kerr foundation model has more advantages. As the elastic modulus of soil mass and the depth of tunnel axis increase, the longitudinal deflection and the inner force of the tunnel will decrease. The inner force of the tunnel will increase with the increment of the stiffness of the tunnel.

[Purpose/Significance] By jointly mining multiple diaries related to National South-west Associated University (NSAU), a social network graph of NSAU that integrates information from multiple sources is constructed. The aim is to discover more potential social relationships through joint mining of multiple diaries, and break through the limitations of single diary social network mining. [Method/Process] Using multiple diaries related to NSAU from 1938 to 1941 as corpus, Python program is used to count co-occurrence relationships of characters, and Gephi is used to construct multi-diary social network graph. Through social network analysis methods, the network topology features, character centrality features and character group features based on modularity and K-core are analyzed and discussed. [Result/Conclusion] Compared with independent diary mining, multi-diary social network joint mining showed more obvious network structure features, more decentralized and rich social relationship information, which can reveal more hidden social relationships, and has good application value in the field of digital humanities.

Aiming at the problem that the basic assumption of distant supervision was too strong and easy to produce noise data,a model of the person entity relation extraction which could automatically filter the training data generated by distant supervision was proposed.For training data generation,the data produced by distant supervision would be filtered by multiple instance learning and the method of TF-IDF-based relation keyword detecting,which tried to make the training data has the manual annotation quality.Furthermore,the model combined lexical and syntactic features to extract the effective relation feature vector from two angles of words and semantics for classifier.The experiment results on large scale real-world datasets show that the proposed model outperforms other relation extraction methods which based on distant supervision.