L Qin

Hormonal changes across the menstrual cycle have been shown to influence reward-related motivation and impulsive behaviors. Here, to compare neural mechanisms of cognitive impulse control during the mid-luteal phase (LP) versus during the late follicular phase (FP), we employed functional magnetic resonance imaging (fMRI) with an event-related monetary delay discounting (EMDD) behavioral task (study 1) and then employed resting state (RS)-fMRI (study 2). The imaging data were analyzed and related to behavior-associated neural activation. In study 1, women in the late FP showed a preference for sooner rewards while exhibiting enhanced activity in the dorsal striatum (DS), whereas women in the mid-LP showed a preference for delayed, higher-value rewards with enhanced activity in the dorsolateral prefrontal cortex (dlPFC). Discount rate (transformed k) in the EMDD task correlated with functional connectivity (FC) of the DS-dlPFC circuitry, consistent with the possibility that DS-dlPFC circuitry may regulate impulsivity. In study 2, the right dlPFC was significantly more active in mid-LP than in late FP, and this effect was sensitive to estradiol (E2) and relative E2 levels during mid-LP. The magnitude of DS-dlPFC FC correlated negatively with psychometric impulsivity scores during the late FP. These results replicated findings from study 1 and further indicated that relative E2 levels may play an important role in increasing cognitive control during the mid-LP, which provides new insight into the treatment of conditions characterized by hyper-impulsivity, such as obsessive-compulsive disorder, Parkinson disease, and attention deficit hyperactivity disorder. In conclusion, our results suggest that cyclical gonadal hormones affect cognitive control on impulsive behavior periodically, possibility via DS-dlPFC circuitry.

School violence research has mainly focused on the impact on students. Very few studies, even fewer from a cross-cultural perspective, have examined the relationships between school violence and teacher professional engagement, and the role played by teacher self-efficacy and school climate related factors. The present study utilizes a SEM research methodology to analyze the 2013 TALIS data. The purpose is to understand and compare the relationships in four different cultural contexts; the U.S., England, South Korea, and Mexico. Results indicate, on average, that the significant and negative impacts of school violence on teacher professional engagement are partly mediated by teacher self-efficacy. The negativity of school violence is significantly alleviated by enhancing participation among school stakeholders and improving teacher–student relationships. The relationships among the factors apply across all four cultural systems, though, the effects of factors and variables vary to a degree. The paper also discusses other relevant issues and differences as well as their implications.

Information on age-related differences in affective meanings of words is widely used by researchers to study emotions, word recognition, attention, memory, and text-based sentiment analysis. To date, no Chinese affective norms for older adults are available although Chinese as a spoken language has the largest population in the world. This article presents the first large-scale age-related affective norms for 2,061 four-character Chinese words (AANC). Each word in this database has rating values in the four dimensions, namely, valence, arousal, dominance, and familiarity. We found that older adults tended to perceive positive words as more arousing and less controllable and evaluate negative words as less arousing and more controllable than younger adults did. This indicates that the positivity effect is reliable for older adults who show a processing bias toward positive vs. negative words. Our AANC database supplies valuable information for researchers to study how emotional characteristics of words influence the cognitive processes and how this influence evolves with age. This age-related difference study on affective norms not only provides a tool for cognitive science, gerontology, and psychology in experimental studies but also serves as a valuable resource for affective analysis in various natural language processing applications.

In order to exchange knowledge, humans need to share a common lexicon of words as well as to access the world models underlying that lexicon. What is a natural process for a human turns out to be an extremely hard task for a machine: computers can’t represent knowledge as effectively as humans do, which hampers, for example, meaning disambiguation and communication. Applied ontologies and NLP have been developed to face these challenges. Integrating ontologies with (possibly multilingual) lexical resources is an essential requirement to make human language understandable by machines, and also to enable interoperability and computability across information systems and, ultimately, in the Web. This book explores recent advances in the integration of ontologies and lexical resources, including questions such as building the required infrastructure (e.g., the Semantic Web) and different formalisms, methods and platforms for eliciting, analyzing and encoding knowledge contents (e.g., multimedia, emotions, events, etc.). The contributors look towards next-generation technologies, shifting the focus from the state of the art to the future of Ontologies and Lexical Resources. This work will be of interest to research scientists, graduate students, and professionals in the fields of knowledge engineering, computational linguistics, and semantic technologies.

With the expansion of the digital business line, the network flow behind the digital power grid is also exploding. To prevent network congestion, this article proposes a novel network flow forecasting model, which is composed of variational mode decomposition, GRU-xgboost block, and a forecasting adjustment block, to grasp the changing patterns and trends of network flow in advance, and to formulate reasonable and effective flow management strategies and meet the requirements of users for network service quality. The network flow series in power grid enterprise always contain complex patterns and outliers, and VMD is applied to adaptively process complex net flow time series into several subseries with simpler patterns. A GRU-xgboost block is designed to reconstruct the features of historical series. Then, xgboost model is applied to generate predictions for all decomposed subsignals. For the final predictions, we design a forecasting adjustment block to further remove the influence of random noise. Finally, the empirical results show the superior performance of the proposed model on network flow forecasting task.

ObjectiveTo revise and test the Chinese version of the Sports Emotional Intelligence Scale in sports situations.Materials and methodsAfter pretesting 112 college students, 832 college students were formally tested, and item analysis, validity test, internal consistency reliability analysis, and calibration validity and equivalence test of the Chinese version of the SEIS were performed. The Chinese version of the SEIS had 14 items with four dimensions, with a cumulative variance contribution of 57.812 percent; the four-factor measurement model fit well. The internal consistency coefficients of the Chinese version of the SEIS ranged from 0.824 to 0. 905, and the split-half reliability ranged from 0.807 to 0.891; the correlations between the total SEIS score and its dimensional scores and the calibration variables were significantly correlated, ranging from 0.255 to 0.603. The gender measure equivalence test was valid, and the mean difference of potential variables across gender groups was not significant in the potential mean analysis.ConclusionThe Chinese version of the SEIS has good reliability and validity, and it is appropriate for assessing emotional intelligence in sports situations.