Ting Zhang

This study explores the polysemy of the word “bitter” in English and ku in Chinese. It examines the similarities and differences between their semantics and identifies the cognitive mechanisms that motivate their semantic expansion. The study attempts to answer two questions: (1) What are the similarities and differences between Chinese ku and English “bitter” in terms of meaning? (2) What cognitive mechanisms motivate meaning extensions of these two words? To this end, 汉语大词典 (Chinese Dictionary), 英汉大词典 (English-Chinese Dictionary), the British National Corpus (BNC) and BLCU Corpus Center (BCC) were employed. The two dictionaries were utilised to investigate the multiple meanings of the two terms, while the BNC and the BCC were employed to identify metaphors and metonymies in sentences. Theoretically, the study is informed by Lakoff and Johnson’s conceptual metaphor and conceptual metonymy. The results showed that half of the meanings of “bitter” and ku overlapped, but there were still some differences. Their semantic expansion is motivated by both conceptual metonymy and conceptual metaphor. In addition, the words under investigation differ in the emotions expressed. English “bitter” is more negative, while Chinese ku is more positive. The present findings have important implications for education and cross-cultural communication in language.

The electroencephalogram is the most common method used to study emotions and capture electrical brain activity changes. Long short-term memory processes the temporal characteristics of data and is mostly used for emotional text and speech recognition. Since an EEG involves a time series signal, this article mainly studied the introduction of LSTM for emotional EEG recognition. First, an ALL-LSTM model with a four-layered LSTM network was established in which the average accuracy rate for emotional classification reached 86.48%. Second, four EEG characteristics were extracted via the wavelet transform using the LSTM-based sentiment classification network. The experimental results showed that the best average classification accuracy of these four features was 73.48%. This was 13% lower than in the ALL-LSTM model, indicating that inappropriate feature extraction methods could destroy the timing of EEG signals. LSTM can be used to thoroughly examine EEG signal timing and preprocessed EEG data. The accuracy and stability of the ALL-LSTM model are significantly superior to those of the WT-LSTM model. The result showed that the process of emotion generation based on EEG is sequential. Compared with EEG emotion extraction using WT, the raw EEG signal’s timing is more suitable for the LSTM network.

ObjectivesSeveral studies have examined the effects of repetitive transcranial magnetic stimulation (rTMS) on associative memory (AM) but findings were inconsistent. Here, we aimed to test whether twice-daily rTMS could significantly improve AM.MethodsIn this single-blind, sham-controlled experiment, 40 participants were randomized to receive twice-daily sham or real rTMS sessions for five consecutive days (a total of 16,000 pulses). The stimulation target in left inferior parietal lobule (IPL) exhibiting peak functional connectivity to the left hippocampus was individually defined for each participant. Participants completed both a picture-cued word association task and Stroop test at baseline and 1 day after the final real or sham rTMS session. Effects of twice-daily rTMS on AM and Stroop test performance were compared using two-way repeated measures analysis of variance with main factors Group (real vs. sham) and Time (baseline vs. post-rTMS).ResultsThere was a significant Group × Time interaction effect. AM score was significantly enhanced in the twice-daily real group after rTMS, but this difference could not survive the post hoc analysis after multiple comparison correction. Further, AM improvement in the twice-daily real group was not superior to a previously reported once-daily rTMS group receiving 8,000 pulses. Then, we combined the twice- and once-daily real groups, and found a significant Group × Time interaction effect. Post hoc analysis indicated that the AM score was significantly enhanced in the real group after multiple comparisons correction.ConclusionOur prospective experiment did not show significant rTMS effect on AM, but this effect may become significant if more participants could be recruited as revealed by our retrospective analysis.

Currently, the coronavirus disease 2019 pandemic experienced by the international community has increased the usage frequency of borderless, highly personalized social media platforms of all age groups. Analyzing and modeling texts sent through social media online can reveal the characteristics of the psychological dynamic state and living conditions of social media users during the pandemic more extensively and comprehensively. This study selects the Sina Weibo platform, which is highly popular in China and analyzes the subjective well-being of Weibo users during the COVID-19 pandemic in combination with the machine learning classification algorithm. The study first invokes the SWB classification model to classify the SWB level of original texts released by 1,322 Weibo active users during the COVID-19 pandemic and then combines the latent growth curve model and the latent growth mixture model to investigate the developmental trend and heterogeneity characteristics of the SWB of Weibo users after the COVID-19 outbreak. The results present a downward trend and then an upward trend of the SWB of Weibo users during the pandemic as a whole. There was a significant correlation between the initial state and the development rate of the SWB after the COVID-19 outbreak. LGMM results show that there were two heterogeneous classes of the SWB after the COVID-19 outbreak, and the development rate of the SWB of the two classes was significantly different. The larger class showed a slow growth, while the smaller class showed a rapid growth. Furthermore, the slope means across the two classes were significantly different. Therefore, the individuals with a higher growth rate of SWB exhibited stronger adaptability to the changes in their living environments. These results could help to formulate effective interventions on the mental health level of the public after the public health emergency outbreak.

Previous research studies of traffic networks are mainly based on planar networks and less considered the influence of multilayer networks, which illustrate and represent different appropriate urban traffic modes. Development of rail and road networks is inseparable from the development of a prosperous urban area; thus, research on multilayer networks has scientific potential and fulfils a real need. In this paper, a framework of complex network based integrated multilayer urban growth and optimisation model is proposed, to analyse the complex relationships between the traffic network structure, the population growth, and the urban land-use. The innovation of this paper is the combination of the traffic complex multilayer networks and the “Four Step Model”. With the multiobjective, multilayer network coevolution and optimisation model, a more efficient traffic network layout was generated based on different land-use, population density, and travel speed scenarios. Then, this paper has proved that the proposed CNIMUGOM can save the traffic network construction investment, reduce the travel cost, make the urban traffic network more efficient, and decrease the total traffic flow amount. This research has connected the recent complex multilayer network related study and traditional urban economic model based study. The findings of the study afford to improve the current land-use and traffic integrated models and can provide traffic network planning suggestions for urban agglomeration development.

This paper investigates the integrity of financial analysts by examining their recommendation responses to large quarterly earnings surprises. Although there is no significant difference in recommendation changes between affiliated and unaffiliated analysts in response to positive earnings surprises, affiliated analysts are more reluctant than unaffiliated analysts to downgrade stock recommendations in response to negative earnings surprises. The evidence implies that conflicts of interest undermine the integrity of financial analysts. We further examine the effects of reputation concern and the Global Research Analyst Settlement as informal and formal mechanisms, on restoring analysts’ integrity. The results show that the positive bias in recommendations remains prevalent for affiliated analysts from reputable investment banks and for the postreform period. Finally, evidence from market reactions suggests that investors fail to notice that analysts’ integrity is compromised by conflicts of interest and are misled by affiliated analysts.

The completeness of the modal logic S4 for all topological spaces as well as for the real line , the n-dimensional Euclidean space and the segment etc. was proved by McKinsey and Tarski in 1944. Several simplified proofs contain gaps. A new proof presented here combines the ideas published later by G. Mints and M. Aiello, J. van Benthem, G. Bezhanishvili with a further simplification. The proof strategy is to embed a finite rooted Kripke structure for S4 into a subspace of the Cantor space which in turn encodes . This provides an open and continuous map from onto the topological space corresponding to . The completeness follows as S4 is complete with respect to the class of all finite rooted Kripke structures

The completeness of the modal logic S4 for all topological spaces as well as for the real line, the n-dimensional Euclidean space and the segment etc. was proved by McKinsey and Tarski in 1944. Several simplified proofs contain gaps. A new proof presented here combines the ideas published later by G. Mints and M. Aiello, J. van Benthem, G. Bezhanishvili with a further simplification. The proof strategy is to embed a finite rooted Kripke structure for S4 into a subspace of the Cantor space which in turn encodes. This provides an open and continuous map from onto the topological space corresponding to. The completeness follows as S4 is complete with respect to the class of all finite rooted Kripke structures.