X. Wang

Chinese international students who studied in the United States received “double stigmatization” from American and Chinese authorities because of the “political othering” tactic during COVID-19. The research used a phenomenological approach to examine why and how specifically the transnational identity of Chinese international students in the United States shifted during the double stigmatization. The researcher conducted a total of three rounds of interviews with 15 Chinese international students who studied in the United States and returned to China between 2018 and 2020, which culminated in 45 interviews through a longitudinal study to probe the transnational identities of this population before and during the double stigmatization; the study also examined how the mindsponge mechanism worked during the identity shifts and the interplay among stigmatization, transnational identity shifts, and the mindsponge mechanism. The study concluded that before COVID-19, Chinese international students had been stigmatized in both China and the United States.

China has a longstanding tradition of stressing the values of harmony and coherence, and Chinese society has often been portrayed as a culture in which conflict avoidance is viewed more positively than direct confrontation and argumentation. In order to evaluate the validity of this claim, this paper sketches Chinese people’s feelings and understandings about interpersonal arguing by reporting results of a data collection in China, using measures of argumentativeness, verbal aggressiveness, argument frames, and personalization of conflict. These results were compared to those from a US sample. Chinese and US data differed in complex ways, but did not show Chinese respondents to be more avoidant. The Chinese correlations among variables were a reasonable match to expectations based on Western argumentation theories, although they did not replicate the US results precisely. The paper offers evidence that Chinese respondents had a more sophisticated understanding of interpersonal arguing than their US counterparts, and were more sensitive to the constructive possibilities of face-to-face disagreement

This paper presents a theoretical framework of the circular shift network coding system through the study of nonmultiple clustered interval music performance style conversion and the analysis of music conversion by using circular shift topology, and a series of basic research results of circular shift network coding is obtained under this framework. It reveals the essential connection between scalar network coding based on finite domain and cyclic shift network coding, designs a solution construction algorithm for cyclic shift network coding under multicast network, and portrays the multicast capacity of cyclic shift network coding. It overcomes the problem that the piano roll-curtain representation cannot distinguish between a single long note and multiple consecutive notes of the same pitch, describes musical information more comprehensively, extracts musical implicit style from the note matrix based on autoencoder, and better eliminates the potential influence of musical content on musical performance style. A two-way recurrent neural network based on the gated recurrent unit is used to extract a sequence of note feature vectors of different styles, and a one-dimensional convolutional neural network is used to predict the intensity of the extracted note feature vector sequence for a specific style, which better learns the intensity variation of different styles of MIDI music.

Online social network services have brought a kind of new lifestyle to the world that is parallel to people’s daily offline activities. Social network analysis provides a useful perspective on a range of social computing applications. Social interaction on the Web includes both positive and negative relationships, which is certainly important to social networks. The authors of this article found that the accuracy of the signs of links in the underlying social networks can be predicted. The trust that other users impart on a node is an important attribute of networks. In this article, the authors present a model to compute the prestige of nodes in a trust-based network. The model is based on the idea that trustworthy nodes weigh more. To fulfill this task, the authors first attempt to infer the attitude of one user toward another by predicting signed edges in networks. Then, the authors propose an algorithm to compute the prestige and trustworthiness where the edge weight denotes the trust score. To prove the algorithm’s effectiveness, the authors conducted experiments on the public dataset. Theoretical analysis and experimental results show that this method is efficient and effective.

A novel data-driven model-free adaptive control approach is first proposed by combining the advantages of model-free adaptive control and data-driven optimal iterative learning control, and then its stability and convergence analysis is given to prove algorithm stability and asymptotical convergence of tracking error. Besides, the parameters of presented approach are adaptively adjusted with fuzzy logic to determine the occupied proportions of MFAC and DDOILC according to their different control performances in different control stages. Lastly, the proposed fuzzy DDMFAC approach is applied to the control of particle quality in drug development phase of spray fluidized-bed granulation process, and its control effect is compared with MFAC and DDOILC and their fuzzy forms, in which the parameters of MFAC and DDOILC are adaptively adjusted with fuzzy logic. The effectiveness of the presented FDDMFAC approach is verified by a series of simulations.

In recent years, with the continuous improvement of urban public transportation capacity, citizens’ travel has become more and more convenient, but there are still some potential problems, such as morning and evening peak congestion, imbalance between the supply and demand of vehicles and passenger flow, emergencies, and social local passenger flow surged due to special circumstances such as activities and inclement weather. If you want to properly guide the local passenger flow and make a reasonable deployment of operating buses, it is necessary to grasp the changing law of public transportation short-term passenger flow. This paper builds a short-term passenger flow prediction model for urban public transportation based on the idea of integrated learning. The goal is to use the integrated model to accurately predict the short-term passenger flow of urban public transportation, using Multivariable Linear Regression, K-Nearest Neighbor, eXtreme Gradient Boosting, and Gated Recurrent Unit as the four seed models, and then use regression algorithm to integrate the model and predict the passenger flow, station boarding and landing, and cross-sectional passenger flow data of the typical representative line 428 in the “Huitian Area” of Beijing from January 1, 2020, to May 31, 2020. Finally, the prediction results of the submodels are compared with those of the integrated model to verify the superiority of the integrated model. The research results of this paper can enrich the short-term passenger flow forecasting system of urban public transportation and provide effective data support and scientific basis for the passenger flow, vehicle management, and dispatch of urban public transportation.