Y Jiang

Systems with human-centered artificial intelligence are always as good as their ability to consider their users’ context when making decisions. Research on identifying people’s everyday activities has evolved rapidly, but little attention has been paid to recognizing both the activities themselves and the motions they make during those tasks. Automated monitoring, human-to-computer interaction, and sports analysis all benefit from Web 4.0. Every sport has gotten its move, and every move is not known to everyone. In ice hockey, every move cannot be monitored by the referee. Here, Convolution Neural Network-based Real-Time Image Processing Framework (CNN-RTIPF) is introduced to classify every move in Ice Hockey. CNN-RTIPF can reduce the challenges in monitoring the player’s move individually. The image of every move is captured and compared with the trained data in CNN. These real-time captured images are processed using a human-centered artificial intelligence system. They compared images predicted by probability calculation of the trained set of images for effective classification. Simulation analysis shows that the proposed CNN-RTIPF can classify real-time images with improved classification ratio, sensitivity, and error rate. The proposed CNN-RTIPF has been validated based on the optimization parameter for reliability. To improve the algorithm for movement identification and train the system for many other everyday activities, human-centered artificial intelligence-based Web 4.0 will continue to develop.

Preservice mathematics teachers’ accurate understanding of mathematical problem solving and its teaching is key to the performance of their professional quality. This study aims to investigate preservice mathematics teachers’ understanding of problem solving and its teaching and compares it with the understanding of in-service mathematics teachers. After surveying 326 in-service mathematics teachers, this study constructs a reliable and valid tool for the cognition of mathematical problem solving and its teaching and conducts a questionnaire survey on 26 preservice mathematics teachers. Survey results reveal that preservice mathematics teachers have a good understanding of mathematical problem solving and its teaching and are more confident in the transfer value of problem solving ability. By contrast, in-service teachers are more optimistic that problem solving requires exploration, continuous thinking, and the participation of metacognition. This article concludes that preservice mathematics teachers should focus more on the initiative and creativity of students and put students at the center of education. In addition, teacher educators should provide more teaching practice opportunities for preservice teachers. The findings also show that in-service teachers’ understanding of problem solving and its teaching is inferior to that of preservice teachers on some indicators, implying the importance of post-service training for in-service teachers.

With the global pandemic of COVID-19, it has been striking psychological burdens on individuals. Under this background, more and more people get wellbeing by watching live broadcasts. However, the psychological mechanism behind this phenomenon is still a black box. This study finds that when people watch a live broadcast and interact with anchors and other people, an interaction ritual chain is formed, and emotional energy is generated, thus making people experience and understand the meaning of the live interaction ritual chains. Under the effect of the meaning transfer model, people will generate wellbeing. Specifically, the basic meaning of live interaction drives people’s generation of wellbeing. The meanings of self-participation, self-display, self-concept, and self-renewal play a role in mediation in enhancing people’s wellbeing with the basic meaning of live broadcast interaction.

With the development of network technology, enterprises face the explosive growth of data every day. Therefore, to fully mine the value of massive data, big data analysis technology has become the key to developing the core competitiveness of enterprises. However, few empirical studies have investigated the influencing mechanism of the BDA capability of an enterprise on its operational performance. To fill this gap, this study explores how BDA technology capability influences enterprise operation performance, based on dynamic capabilities theory and resource-based theory. It proposes the key variables, including the connectivity, compatibility, and modularization of big data analysis technical capability, enterprise's operational performance, and the fit between data and tools, to establish a model and study the correlation between the variables. The results highlight the mediating role of data-tool fit in the relationships between BDA capability and the enterprise's operational performance, which is a major finding that has not been underlined in the extant literature. This study provides valuable insight for operational managers to help them in mobilizing BDA capability for enterprises' operational management and improving operational performance.

As a sub-realm of Language Teacher Psychology, teachers’ professional agency has gained significant attention from educational practitioners and teachers. The aim is to better discern teachers’ professional development and teaching effectiveness with a view to ensuring the quality of language teaching. International literature concerning teachers’ professional agency has noted a shift from knowledge training to vocational development in relation to teachers’ experience in decision making. Yet, little research so far has scrutinized this specific issue in Chinese university settings, and the real picture of teacher agency needs further exploration. Besides, the multidimensional and complex nature of agency identifies the overwhelming research work in understanding its contents in detail based on the previous perspectives from individuals, society, and time. To this end, the ecological understanding of professional agency reframed the theoretical basis of this study, prone to explore how teachers’ experience could be examined in relation to individual capacity, resources, and structural and contextual variables. The study was conducted in Chinese university settings in response to the research gap related to understanding professional agency. Quantitative and qualitative data from questionnaires, interviews, and classroom observations with 116 university teachers in China demonstrated that the enactment of professional agency rested on the interplay between temporal contexts, teacher capacity, and beliefs, especially in the instructional community. The findings revealed that university teachers at different stages of career development manifested variability in exercising their professional agency in relation to adapting or adopting existing teaching concepts, methods, or approaches. Mounting evidence revealed some enablers and constraints in relation to formative assessment, time impact, classroom interaction, and school culture. Emphasizing the interaction between individuals’ ability and their engagement with the professional environment, the findings provide insights into theoretical implications associated with ecological theory and enhance the practical discussion about promoting professional development for novice, mid-career, and veteran English teachers at the university level.

Color harmony is the focus of many researchers in the field of art and design, and its research results have been widely used in artistic creation and design activities. With the development of signal processing and artificial intelligence technology, new ideas and methods are provided for color harmony theory and color harmony calculation. In this article, psychological experimental methods and information technology are combined to design and quantify the 16-dimensional physical features of multiple colors, including multi-color statistical features and multi-color contrast features. Eighty-four subjects are invited to give a 5-level score on the degree of color harmony for 164 multi-color materials selected from the screenshots of film and television scenes. Based on the multi-color physical features and the subjective evaluation experiment, the correlation analysis is firstly carried out, which shows that the overall lightness, difference of the color tones, number of multiple colors, lightness contrast, color tone contrast, and cool/warm contrast are significantly correlated with color harmony. On the other hand, the regression prediction model and classification prediction model of color harmony are constructed based on machine learning algorithms. In terms of regression prediction model, the prediction accuracy of linear models is higher than that of nonlinear models, with 63.9% as the highest, indicating that the multi-color physical features can explain color harmony well. In terms of classification prediction model, the Random Forest has the best prediction performance, with an accuracy of 80.2%.

Behavioral studies have demonstrated differences in the effect of constrained retrieval of semantic vs. non-semantic information on the encoding of foils. However, the impact of recognition on foils between semantic and non-semantic trials remains unclear. This study thus examines the roles of recognition—familiarity and recollection—in constrained retrieval for foils. We applied the event-related brain potentials data of new/old effects to elucidate the neural mechanisms underlying the “foil effect.” Participants encoded semantic and non-semantic tasks, were tested in a blocked memory task with new words presented as foils, and performed a surprise recognition task involving foils and completely new words. Behavioral results showed better recognition performance regarding reaction times and accuracy by hit and correct reject for semantic vs. non-semantic trials in Phase 2. Conversely, inferior recognition performance in reaction times and accuracy by hit and correct reject was noted for semantic vs. non-semantic foils in Phase 3. ERP results showed more positive Frontal N400 for hit in non-semantic trials, more positive late positive component for correct rejects in semantic trials in Phase 2, and more positive LPC for hits in both semantic and non-semantic trials only in Phase 3. Through dual-processing theory, we prove that different task types in constrained retrieval depend on different retrieval processes. Particularly, familiarity may be applied more often in non-semantic trials, and recollection in semantic trials. The difference in processes between semantic and non-semantic trials during constrained retrieval affects incidental encoding of foils.

BackgroundThe cognitive outcomes induced by subthalamic nucleus deep brain stimulation (STN-DBS) remain unclear, especially in PD patients with mild cognitive impairment (MCI). This study explored the cognitive effects of STN-DBS in PD patients with MCI.MethodsThis was a retrospective cohort study that included 126 PD patients who underwent STN-DBS; all patients completed cognitive and motor assessments before and at least 6 months after surgery. Cognitive changes were mainly evaluated by the Montreal cognitive assessment (MoCA) scale and the seven specific MoCA domains, including visuospatial/executive function, naming, attention, language, abstract, delayed recall, and orientation. Motor improvement was evaluated by the UPDRS-III. Cognitive changes and motor improvements were compared between PD-MCI and normal cognitive (NC) patients. Logistic regression analyses were performed to explore predictors of post-operative cognitive change.ResultsAt the time of surgery, 61.90% of the included PD patients had MCI. Compared with the PD-MCI group, the PD-NC group had a significantly higher proportion of cases with post-operative cognitive decline during follow-up of up to 36 months (mean 17.34 ± 10.61 months), mainly including in global cognitive function, visuospatial/executive function and attention. Covariate-adjusted binary logistic regression analyses showed that pre-operative global cognitive status was an independent variable for post-operative cognitive decline. We also found that pre-operative cognitive specific function could predict its own decline after STN-DBS, except for the naming and orientation domains.ConclusionPD-MCI patients are at a lower risk of cognitive decline after STN-DBS compared with PD-NC patients.

Criminals targeting and exploiting older adults in online environments are of great concern. This study systematically retrieved and analyzed articles on the psychological characteristics of older adult victims of online fraud. First, we found that there was no evidence that older adults were more prevalent than other individuals of other ages among online fraud victims, and current researchers have focused more on why older adults are easy targets for fraud. Second, research on psychological factors of older adults' susceptibility to online fraud has mainly focused on cognitive function, trust traits, and other personality traits, such as social loneliness, the Big Five personality traits, and self-control. Among them, most researchers claim that the cyber-cheating of older adults may be due to a decline in their cognitive function. However, there has not been a consensus on how cognitive function and physical and mental conditions affect older people who are cheated. Third, techniques and experience may be related to the susceptibility of older adults to fraud, and these studies have also not yet generated a consensus supported by reliable data. Based on the above research uncertainties, we propose that fraud prevention and control strategies for older adults should be applied with caution.

Orphans and vulnerable children fall under the category of children who are at risk of exposure to more stressful circumstances and receive less social assistance compared to other children. This study aims to investigate the impact of group counseling based on social intervention and psychological therapy on social support and the perceived stress growth of orphans and vulnerable children. In one special educational needs school in Nanning, China, the SSGPC was developed and implemented. Using the social support and post-stress growth scales, the researchers investigated the effects of SSGPC on orphans and vulnerable children. Twenty-seven orphans and vulnerable children between the ages of nine and 12 were arbitrarily assigned to the experimental and control groups. A pre-test post-test method of quasi-experimental design was applied, with 13 participants in the experimental group and 14 in the control group. The results revealed that the intervention group had significantly higher scores for social support and post-stress growth than the control group. The SSGPC had significantly improved the levels of social support for orphans. The findings indicated that the SSGPC provided an effective way to improve social support and post-stress growth of orphans and vulnerable children.

As the aging population problem intensifies, many emerging economies are caught in labor shortage and rising labor costs, thus improving the corporate labor investment efficiency is crucial for these countries. In this context, we take China as an example to explore the influence of the current booming digital finance on corporate LIE. This paper, which enriches the existing literature, is one of the few studies that explores the link between macroeconomic policies and firms’ LIE. Our research adopts the baseline methodology of ordinary least squares regression, and the data comprise 23,503 observations for Chinese A-share listed businesses from 2011 to 2020. In addition, we use fixed effects regression, instrumental variables method and substitution of independent variables to deal with endogeneity and test the robustness. The outcomes suggest that DF may significantly increase corporate LIE. Further results from the path mechanism study suggest that DF could alleviate financing constraints and optimize human capital structure, both of which have a favorable effect on the LIE. Last but not least, the heterogeneity results imply that DF can more effectively encourage LIE of firms in economically underdeveloped regions and of private nature. The study recommends that emerging economies should pay attention to strengthening regulation to avoid financial risks while vigorously promoting DF. In addition, enhancing the level of human capital and optimizing human capital allocation are also essential.

The present study investigated the effects of L1-L2 congruency, collocation type, and restriction on L2 collocational processing. Advanced Chinese learners of English and native English-speaking controls performed an online acceptability judgment task to investigate how advanced L2 learners processed congruent (sharing the same meaning and structure in L1 language) collocations and English-only (not equivalent in L1 construction) collocations with the same node (right) word and a different collocate (left). The experimental materials included verb-noun (VN), adjective-noun (AN) collocations, free (less fixed), and restricted (more fixed) collocations chosen from BNC. The results revealed that (i) The non-native speakers were sensitive to L1-L2 congruency, but the native speakers were not. (ii) The native speakers were sensitive to collocation restriction, whereas the non-native speakers were not. These results lend initial support to the mapping hypothesis and open choice principle of L2 collocational processing for Chinese English learners.

BackgroundExercise is one of the effective ways to improve cognition. Different forms of exercises, such as aerobic exercise, resistance exercise, and coordination exercise, have different effects on the improvement of cognitive impairment. In recent years, exergames based on Non-Immersive Virtual Reality have been widely used in entertainment and have gradually been applied to clinical rehabilitation. However, the mechanism of NIVR-Exergames on improving motor cognition has not been clarified. Therefore, the aim of this study is to find whether NIVR-Exergames result in a better neural response mechanism to improve the area of the cerebral cortex related to motor cognition under functional near-infrared spectroscopy dynamic monitoring in comparison with resistance exercise.MethodsA cross-over study design was adopted in this study, and 15 healthy young subjects were randomly divided into group A and group B according to a computerized digital table method. Task 1 was an NIVR-Exergame task, and Task 2 was resistance band stretching. Group A first performed Task 1, rested for 30 min, and then performed Task 2. Group B had the reverse order. The fNIRS test was synchronized in real time during exercise tasks, and heart rate measurements, blood pressure measurements, and 2-back task synchronization fNIRS tests were performed at baseline, Post-task 1, and Post-task 2. The primary outcomes were beta values from the general linear model in different regions of interest, and the secondary outcomes were heart rate, blood pressure, reaction time of 2-back, and accuracy rate of 2-back.ResultsThe activation differences of Task 1 and Task 2 in the right premotor cortex and the left PMC were statistically significant. There were statistically significant differences in the activation of the right supplementary motor area, left dorsolateral prefrontal cortex, left and right PMC between baseline and Post-task 1. The differences in systolic pressure between the two groups at three time points among women were statistically significant.ConclusionIn this study, we found that NIVR-Exergames combined with motor and challenging cognitive tasks can promote the activation of SMA, PMC and DLPFC in healthy young people compared with resistance exercise alone, providing compelling preliminary evidence of the power for the rehabilitation of motor and cognitive function in patients with central nervous system diseases.

Previous researchers have found that head-down bed rest will affect the emotional state of individuals, and negative emotions such as anxiety are closely related to attention bias. The present study adopted the dot-probe task to evaluate the effects of 15-days of −6° HDBR on the attention bias of threatening stimulus in 17 young men, which was completed before, during, after the bed rest. In addition, self-report inventories were conducted to record emotional changes. The results showed that the participants’ negative affect scores on HDBR-8 were significantly lower than the HDBR-1 in PANAS while there was no significant difference on positive affect scores and anxiety scores in SAI. And the results showed that at the Pre-HDBR, HDBT-1, HDBR-15, Post-HDBR, the response speed to threatening stimulus was faster than neutral stimulus, but no statistical significance. However, reaction time of threatening stimulus is significantly longer than neutral stimulus in the HDBR-8, indicating that HDBR may have an effect on the participants’ attention bias, and this effect is manifested as attention avoidance.

In this paper, we mainly investigate the existence of states based on the Glivenko theorem in bounded semihoops, which are building blocks for the algebraic semantics for relevant fuzzy logics. First, we extend algebraic formulations of the Glivenko theorem to bounded semihoops and give some characterizations of Glivenko semihoops and regular semihoops. The category of regular semihoops is a reflective subcategory of the category of Glivenko semihoops. Moreover, by means of the negative translation term, we characterize the Glivenko variety. Then we show that the regular semihoop of regular elements of a free algebra in the variety of Glivenko semihoops is free in the corresponding variety of regular semihoops. Similar results are derived for the semihoop of dense elements of free Glivenko semihoops. Finally, we give a purely algebraic method to check the existence of states on Glivenko semihoops. In particular, we prove that a bounded semihoop has Bosbach states if and only if it has a divisible filter, and a bounded semihoop has Riečan states if and only if it has a semi-divisible filter.

The inspiration for this collective writing project began with a digital conference entitled ‘Knowledge Socialism, COVID-19 and the New Reality of Education’ held at Beijing Normal University. In this conference and through this article, multiple researchers spread across six continents have engaged in the collaborative task of outlining emerging innovations and alternative contingencies towards education, international collaboration, and digital reform in this time of global crisis. Trends associated with digital education, knowledge openness, peer production, and collective intelligence as articulated by Michael A. Peters’ conception of Knowledge Socialism are given careful analysis and exploration. Some of the members of this collective endeavor to identify problems, others, begin to draw boxes around potential solutions. Overall, this article engages with real world challenges and innovations that look beyond dominant neoliberal trends in the knowledge economy to build bridges toward novel possibilities in this era of rapid digital change.

Several functional magnetic resonance imaging studies have demonstrated abnormalities in static intra- and interhemispheric functional connectivity among diverse brain regions in patients with major depressive disorder. However, the dynamic changes in intra- and interhemispheric functional connectivity patterns in patients with MDD remain unclear. Fifty-eight first-episode, drug-naive patients with MDD and 48 age-, sex-, and education level-matched healthy controls underwent resting-state fMRI. Whole-brain functional connectivity, analyzed using the functional connectivity density approach, was decomposed into ipsilateral and contralateral functional connectivity. We computed the intra- and interhemispheric dynamic FCD using a sliding window analysis to capture the dynamic patterns of functional connectivity. The temporal variability in functional connectivity was quantified as the variance of the dFCD over time. In addition, intra- and interhemispheric static FCD patterns were calculated. Associations between the dFCD variance and sFCD in abnormal brain regions and the severity of depressive symptoms were analyzed. Compared to HCs, patients with MDD showed lower interhemispheric dFCD variability in the inferior/middle frontal gyrus and decreased sFCD in the medial prefrontal cortex/anterior cingulate cortex and posterior cingulate cortex/precuneus in both intra- and interhemispheric comparisons. No significant correlations were found between any abnormal dFCD variance or sFCD at the intra- and interhemispheric levels and the severity of depressive symptoms. Our results suggest intra- and interhemispheric functional connectivity alterations in the dorsolateral prefrontal cortex and default mode network regions involved in cognition, execution and emotion. Furthermore, our study emphasizes the essential role of altered interhemispheric communication dynamics in the DLPFC in patients with MDD. These findings contribute to our understanding of the pathophysiology of MDD.

Compared to a charging scheduling and management problem characterized by predetermined trip assignment, this study takes bus and driver scheduling into account, and mealtime windows must be guaranteed as one of the major labor regulations. A discretized mixed-integer linear programming model is developed based on a single electric bus route. We aim to obtain fast and high-quality global solutions for this problem, and the model can be easily executed by bus operators by directly invoking an available optimization solver such as IBM ILOG CPLEX. We test our model on a real round-trip bus route. Numerical experiments show that CPLEX takes approximately 6 sec to obtain an optimal solution. The model can not only reasonably arrange daily trips for each electric bus and driver but also effectively determine the optimal charging schedule and management for an electric bus line. Besides, we analyze the sensitivity of the key parameters in the model. With the increase in the drivers’ maximum workload, the drivers’ average idle time decreases by approximately 11.25%. The objective value decreases by approximately 38.71% and 40.04% with increases in the battery capacity and fleet size, respectively, and the objective value increases by approximately 30.06% with the decrease in the initial battery driving range. In addition, we compare the effectiveness of our time discretization modeling method in solving the same case study to that from other similar studies, and the validity of our method can be verified by the calculation time. We also compare the computational efficiency of CPLEX in solving the same case study problem with and without implementing valid inequalities, and the computational efficiency of the valid inequality method is greatly improved. Finally, through the testing of a multiline network, the potential application of the model to a large-scale traffic network is verified.

In this paper, a three-dimensional chaotic system with a line equilibrium is studied, in which a single nonbifurcation parameter is used to control the amplitude and frequency. A variety of chaotic signals can be modified using the amplitude-frequency control switch. The realization of circuit simulation based on multisim further verifies the theoretical analysis. Finally, the method for encrypting color images is tested, and the process performance is valued. It shows that the novel chaotic oscillation has a promising application prospect in image encryption.

This paper uses the 5-five-minute high-frequency data of energy-listed companies in China's A-share market to extract the jump of energy stock prices and build a dynamic stock price jump complex network. Then, we analyze the clustering effect of the complex network. The research shows that the energy stock price jump is an important part of stock price volatility, and the complex network of energy stock jump risk has obvious time-varying characteristics. However, the infection problem of stock price jump risks needs specific analysis. China's coal industry has an important influence on the development of China's energy industry. According to the clustering analysis results of the network community, the clustering effect of the network community has time-varying characteristics. After October 2017, the clustering effect of the jumping risk of the coal industry and the new energy industry is obvious. The risk contagion within the new energy industry community is a key point for the development of the new energy industry.