Accurate and rapid defect identification based on pulsed eddy current testing plays an important role in the structural integrity and health monitoring of in-service equipment in the renewable energy system. However, in conventional data-driven defect identification methods, the signal feature extraction is time consuming and requires expert experience. To avoid the difficulty of manual feature extraction and overcome the shortcomings of the classic deep convolutional network, such as large memory and high computational cost, an intelligent defect recognition pipeline based on (...) the general Warblet transform method and optimized two-dimensional DCNN is proposed. The GWT method is used to convert the one-dimensional PECT signal to a 2D grayscale image used as the input of 2D DCNN. A compound method is proposed to optimize the baseline VGG16, a well-known DCNN, from four aspects including reducing the input size, adding batch normalization layer after every convolutional layer and fully connection layer, simplifying the FCs, and removing unimportant filters in Convs so as to reduce memory and computational costs while improving accuracy. Through a pulsed eddy current testing experiment considering interference factors including liftoff and noise, the following conclusion can be obtained. The time-frequency representation obtained by the GWT method not only has excellent ability in terms of the transient component analysis but also is less affected by the reduction of image size; the proposed optimized DCNN can accurately identify defect types without manual feature extraction. And compared to the baseline VGG16, the accuracy obtained by the optimized DCNN is improved by 7%, to about 99.58%, and the memory and computational cost are reduced by 98%. Moreover, compared with other well-known DCNNs, such as GoogLeNet, Inception V3, ResNet50, and AlexNet, the optimized network has significant advantages in terms of accuracy and computational cost, too. (shrink)

Biobanks are potential goldmines for genomics research. They have become increasingly common as a means to determine the relationship between lifestyle, environmental exposures and predisposition to genetic disease. More and more countries are developing massive national scale biobanks, including Iceland, the UK and Estonia. Now several large-scale regional and national biobanks are planned in China, such as Shanghai Biobank, which is defined as a key-element in Shanghai's twelfth five-year Development Plan of Science and Technology. It is imperative that the authors (...) who are in charge of the ethical aspect of Shanghai Biobank discuss the ethical aspects of these biobanks up front. Currently there is a great deal of heterogeneity in the approaches to informed consent taken by different countries. In the article, after briefly introducing the biobanks in China, we focus on the three most common approaches: classical informed consent, tiered consent, and one-time general (or blanket) consent, and propose a version of the latter for China, based on compelling arguments. (shrink)

The continuous increase of carbon emissions is a serious challenge all over the world, and many countries are striving to solve this problem. Since 2020, a widespread lockdown in the country to prevent the spread of COVID-19 escalated, severely restricting the movement of people and unnecessary economic activities, which unexpectedly reduced carbon emissions. This paper aims to analyze the carbon emissions data of 30 provinces in the 2020 and provide references for reducing emissions with epidemic lockdown measures. Based on the (...) method of time series visualization, we transform the time series data into complex networks to find out the hidden information in these data. We found that the lockdown would bring about a short-term decrease in carbon emissions, and most provinces have a short time point of impact, which is closely related to the level of economic development and industrial structure. The current results provide some insights into the evolution of carbon emissions under COVID-19 blockade measures and valuable insights into energy conservation and response to the energy crisis in the post-epidemic era. (shrink)

Awareness logic is a type of belief logic in which an agent's beliefs are restricted to those sentences that the agent is aware of. Awareness logic is a successful way to circumvent the problem of omniscience so that actual belief is modelled in a reasonable way. In this paper, we suggest a new method modelling awareness and actual belief by using two-dimensional logics. We show that the two-dimensional logics are flexible tools. Different types of concepts of awareness can be easily (...) modelled by this method. (shrink)

Biobanks are potential goldmines for genomics research. They have become increasingly common as a means to determine the relationship between lifestyle, environmental exposures and predisposition to genetic disease. More and more countries are developing massive national scale biobanks, including Iceland, the UK and Estonia. Now several large-scale regional and national biobanks are planned in China, such as Shanghai Biobank, which is defined as a key-element in Shanghai's twelfth five-year Development Plan of Science and Technology. It is imperative that the authors (...) who are in charge of the ethical aspect of Shanghai Biobank discuss the ethical aspects of these biobanks up front. Currently there is a great deal of heterogeneity in the approaches to informed consent taken by different countries. In the article, after briefly introducing the biobanks in China, we focus on the three most common approaches: classical informed consent, tiered consent, and one-time general (or blanket) consent, and propose a version of the latter for China, based on compelling arguments. (shrink)

Constant conjunction theory of causation had been the dominant theory in philosophy for a long time and regained attention recently. This paper gives a logical framework of causation based on the theory. The basic idea is that causal statements are empirical, and are derived from our past experience by observing constant conjunction between objects. The logic is defined on linear time structures. A causal statement is evaluated at time points, such that its value depends on what has been in the (...) past. We first give a semantics that contains basic conditions that, we think, must hold for a concept of causation, on which we define the minimal causal logic. Then we discuss its possible extensions for various concepts of causation. Complete deductive systems are given. (shrink)

The present two-year longitudinal study aimed to examine the relationship between depression and reasoning ability in adolescents, and further investigated the modulation effect of growth mindset on this relationship. A total of 1,961 and 1,667 Chinese adolescents participated in the study for the first year and second year, respectively. The results showed that T1 depression was negatively correlated with T1 growth mindset, T1 reasoning, and T2 reasoning. Regression analysis revealed that T1 depression and the interaction between T1 depression and T1 (...) growth mindset significantly predicted T1 reasoning. After controlling for gender, age, family socioeconomic status, and T1 reasoning ability, both T1 depression and the interaction between T1 depression and T1 growth mindset still significantly predicted T2 reasoning. The simple slope analysis found that the negative correlation between depression and reasoning in the high growth mindset group was weaker than that of the low growth mindset group in both T1 and T2, suggesting that growth mindset plays a significant moderating role in the relationship between depression and reasoning. In conclusion, depression was negatively correlated with reasoning ability in adolescents, in addition, growth mindset moderated the relationship between depression and reasoning. (shrink)

In the big data era, sequencing technology has produced a large number of biological sequencing data. Different views of the cancer genome data provide sufficient complementary information to explore genetic activity. The identification of differentially expressed genes from multiview cancer gene data is of great importance in cancer diagnosis and treatment. In this paper, we propose a novel method for identifying differentially expressed genes based on tensor robust principal component analysis, which extends the matrix method to the processing of multiway (...) data. To identify differentially expressed genes, the plan is carried out as follows. First, multiview data containing cancer gene expression data from different sources are prepared. Second, the original tensor is decomposed into a sum of a low-rank tensor and a sparse tensor using TRPCA. Third, the differentially expressed genes are considered to be sparse perturbed signals and then identified based on the sparse tensor. Fourth, the differentially expressed genes are evaluated using Gene Ontology and Gene Cards tools. The validity of the TRPCA method was tested using two sets of multiview data. The experimental results showed that our method is superior to the representative methods in efficiency and accuracy aspects. (shrink)

Extant research on design thinking is subjective and limited. This manuscript combines protocol analysis and electroencephalogram to read design thoughts in the core design activities of concept generation phase. The results suggest that alpha band power had event related synchronization in the scenario task and divergent thinking occupies a dominant position. However, it had event related desynchronization in analogy and inference activities, etc., and it is stronger for mental pressure and exercised cognitive processing. In addition, the parietooccipital area differs significantly (...) from other brain areas in most design activities. This study explores the relationship of different design thinking and EEG data, which is innovative and professional in the field of design, providing a more objective data basis and evaluation method for future applied research and diverse educational practices. (shrink)

Since the outbreak of COVID-19, community workers’ proactive behavior has played a noteworthy role in the crisis response. Previous research has not highlighted this group and their proactive behavior. To address this important gap, drawing upon the job demands–resources model, this study explores how red tape affects proactive behavior and investigates the mediating role of lack of goal progress and the moderating role of public service motivation in this relationship. Based on a two-wave survey with a sample of 656 community (...) workers in China, we found a negative relationship between red tape and proactive behavior. Moreover, this study showed that LOGP mediated this relationship. Contrary to our hypothesis, PSM did not moderate the relationship between LOGP and proactive behavior. These findings have important theoretical and practical implications and can better inform community work during COVID-19. (shrink)

The aim of this study was to explore the effect of scoring first on match outcomes in the Chinese Football Super League. A total of 1,116 matches in which at least one goal was scored from the 2014 to 2018 seasons were collected. Match outcomes, absolute goal differences, the minute of the first goal, match locations, and teams’ budgets were analyzed. A team’s budget was measured in terms of a team’s value at the beginning of the season, and teams were (...) clustered into two groups. A descriptive analysis was conducted, and two generalized linear models were applied. The results showed a favorable outcome for the team that scored first both in match outcome and goal difference. Regarding the teams that scored first, 66.31% won their matches, 20.70% achieved a draw, and 12.99% lost. Specifically, home teams were more likely to win and less likely to lose or draw than away teams. Home teams also had a higher likelihood of obtaining a larger goal difference. Higher budget teams were more likely to win and less likely to lose or draw than low-budget teams. Additionally, for each minute, the team scores closer to the end of the match, and the average probability of winning increased by 0.0028. These findings can guide the strategies of coaches in different match scenarios according to the match location and the opponent’s quality. (shrink)

Background:When the contagious COVID-19 spread worldwide, the frontline staff faced unprecedented excessive work pressure and expectations of all of the society.Objective:The aim was to explore healthcare workers’ stress and influencing factors when caring for COVID-19 patients from an altruistic perspective.Methods:A cross-sectional, descriptive study was conducted in a tertiary hospital during the outbreak of COVID-19 between February and March 2020 in Wuhan, the capital city of Hubei province in China. Data were collected from 1208 healthcare workers. Descriptive statistics and multiple linear (...) regression were used to analyze the data.Ethical considerations:Research ethics approval (with the code of TJ-IRB20200379) was obtained from Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology. Written informed consent was also received from participants.Results:Less than 60% of participants chose moderate or severe stress on all stressors, indicating a low stress level among healthcare workers. The main source of stress among frontline healthcare workers caring for COVID-19 patients came from the fear of being infected, the fear of family members being infected, and the discomfort caused by protective equipment. Frontline staff who were nurses, were married, and had worked more than 20 days suffered higher stress, whereas rescue staff showed lower stress.Conclusion:The healthcare workers caring for patients with COVID-19 had low stress level, although they still had the fear of being infected or uncomfortable feeling caused by personal protective equipment. A low stress level among healthcare workers indicated their professional devotion and altruism during COVID-19 epidemic. Medical institutions and the government should continue to strengthen infection prevention measures and provide more comprehensive care involving families of frontline healthcare workers, especially nurses and married staff. It will be a lesson to other countries that awaking healthcare workers’ inside motivation and providing necessary support from government and society were significant. (shrink)

With the rapid development of the Internet and the normalization of COVID-19 epidemic prevention and control, Online health communities have gradually become one of the important ways for people to obtain health information, and users have to go through a series of information processing when facing the massive amount of data. Understanding the factors influencing user information processing is necessary to promote users’ health literacy, health knowledge popularization and health behavior shaping. Based on the Heuristic-Systematic Model, Information Ecology Theory, Privacy (...) Trade-Off and Self-Efficacy Theory, we constructed a model of factors influencing user information processing in online health communities. We found that information quality and emotional support had indirect effects on heuristic and systematic information processing, and these effects were mediated by privacy concerns and self-efficacy. In our research model, systematic information processing was most positively influenced directly by self-efficacy. Privacy concerns had a direct negative correlation with both dual information processing pathways. Therefore, OHCs managers should develop relevant regulations to ensure the information quality in OHCs and improve privacy protection services to promote user information processing by improving users’ self-efficacy and reducing their privacy concerns. Providing a user-friendly and interactive environment for users is also recommended to create more emotional support, thus facilitating more systematic information processing. (shrink)

To demonstrate how green innovation effectively occurs, this study examines the effects of green intellectual capital on GI from the perspective of green supply chain integration. Based on a natural-resource-based view and knowledge-based view, the authors constructed an intermediary model of GIC-GSCI-GI, and analyzed the effects of green absorptive ability and relationship learning ability as moderators. An empirical survey of 328 Chinese manufacturing companies was conducted. Our results indicate that three dimensions of GIC positively impact GI. The mediating effects of (...) internal and external GSCI exist in the relationship between GIC and GI. The moderating effects of GAA and RLA in these effects were also verified. Our study provides further empirical evidence for the relationship between GIC and GI, highlights the effects of companies’ internal and external abilities on GI, and suggests new ways and implementation contexts for GI. (shrink)

Type 2 diabetes mellitus is associated with cognitive impairment in many domains. There are several pieces of evidence that changes in neuronal neuropathies and metabolism have been observed in T2DM. Structural and functional MRI shows that abnormal connections and synchronization occur in T2DM brain circuits and related networks. Neuroplasticity and energy metabolism appear to be principal effector systems, which may be related to amyloid beta deposition, although there is no unified explanation that includes the complex etiology of T2DM with cognitive (...) impairment. Herein, we assume that cognitive impairment in diabetes may lead to abnormalities in neuroplasticity and energy metabolism in the brain, and those reflected to MRI structural connectivity and functional connectivity, respectively. (shrink)

Most current online distributed machine learning algorithms have been studied in a data-parallel architecture among agents in networks. We study online distributed machine learning from a different perspective, where the features about the same samples are observed by multiple agents that wish to collaborate but do not exchange the raw data with each other. We propose a distributed feature online gradient descent algorithm and prove that local solution converges to the global minimizer with a sublinear rate O 2 T. Our (...) algorithm does not require exchange of the primal data or even the model parameters between agents. Firstly, we design an auxiliary variable, which implies the information of the global features, and estimate at each agent by dynamic consensus method. Then, local parameters are updated by online gradient descent method based on local data stream. Simulations illustrate the performance of the proposed algorithm. (shrink)

This study investigates the mechanism of how sensory experiences influence visitors’ digital engagement with a destination through establishing a strong bond and identification between a destination and tourist utilizing a two-step process. First, visitors’ sensory experiences in a destination are identified through a content analysis of online review comments posted by visitors. Afterward, the effects of those sensory experiences on visitors’ digital engagement through destination dependence and identification with that destination are examined. Findings suggest that sensory experiences are critical antecedents (...) of visitors’ bond and identification with a destination. Visitors’ positive destination-related sensory experiences increase their dependence on and identification with the destination, and this dependence and identification positively influence their digital engagement behavior on social media. (shrink)

This empirical research applies cointegration in the traditional measurement method first to build directed weighted networks in the context of stock market. Then, this method is used to design the indicators and the value simulation for measuring network fluctuation and studying the dynamic evolution mechanism of stock market transaction networks as affected by price fluctuations. Finally, the topological structure and robustness of the network are evaluated. The results show that network structure stability is strong in the bull market stage and (...) weak in the bear market stage. And the convergence rate of the dynamic evolution of network fluctuation is higher in the bull market stage than in the bear market stage. (shrink)

Autonomous underwater vehicles are widely used to accomplish various missions in the complex marine environment; the design of a control system for AUVs is particularly difficult due to the high nonlinearity, variations in hydrodynamic coefficients, and external force from ocean currents. In this paper, we propose a controller based on deep reinforcement learning in a simulation environment for studying the control performance of the vectored thruster AUV. RL is an important method of artificial intelligence that can learn behavior through trial-and-error (...) interactions with the environment, so it does not need to provide an accurate AUV control model that is very hard to establish. The proposed RL algorithm only uses the information that can be measured by sensors inside the AUVs as the input parameters, and the outputs of the designed controller are the continuous control actions, which are the commands that are set to the vectored thruster. Moreover, a reward function is developed for deep RL controller considering different factors which actually affect the control accuracy of AUV navigation control. To confirm the algorithm’s effectiveness, a series of simulations are carried out in the designed simulation environment, which is a method to save time and improve efficiency. Simulation results prove the feasibility of the deep RL algorithm applied to the control system for AUV. Furthermore, our work also provides an optional method for robot control problems to deal with improving technology requirements and complicated application environments. (shrink)

Despite the fact that human daily emotions are co-occurring by nature, most neuroscience studies have primarily adopted a univariate approach to identify the neural representation of emotion (emotion experience within a single emotion category) without adequate consideration of the co-occurrence of different emotions (emotion experience across different emotion categories simultaneously). To investigate the neural representations of multivariate emotion experience, this study employed the inter-situation representational similarity analysis (RSA) method. Researchers used an EEG dataset of 78 participants who watched 28 video (...) clips and rated their experience on eight emotion categories. The EEG-based electrophysiological representation was extracted as the power spectral density (PSD) feature per channel in the five frequency bands. The inter-situation RSA method revealed significant correlations between the multivariate emotion experience ratings and PSD features in the Alpha and Beta bands, primarily over the frontal and parietal-occipital brain regions. The study found the identified EEG representations to be reliable with sufficient situations and participants. Moreover, through a series of ablation analyses, the inter-situation RSA further demonstrated the stability and specificity of the EEG representations for multivariate emotion experience. These findings highlight the importance of adopting a multivariate perspective for a comprehensive understanding of the neural representation of human emotion experience. (shrink)

Long distance travel is an unusual activity for humans. The economical cabin environment during the long haul flights causes discomfort and even stress for many passengers. In-flight video and music systems are commonly available to improve the comfort level of the passengers. However, current in-flight music systems do not explore how the content can be used to reduce passengers stress. Most of these systems are designed and implemented assuming a homogeneous passenger group that has similar tastes and desires. In this (...) paper, we present a heart rate controlled in-flight music recommendation system for reducing the stress during air travel. The system recommends personalized music playlists to the passengers and attempts to keep their heart rate in a normal range with these playlists. Experiments in a simulated long haul flight cabin environment find that the passengers’ stress can indeed be significantly reduced through listening to the recommended music playlists. (shrink)

Long distance travel is an unusual activity for humans. The economical cabin environment during the long haul flights causes discomfort and even stress for many passengers. In-flight video and music systems are commonly available to improve the comfort level of the passengers. However, current in-flight music systems do not explore how the content can be used to reduce passengers stress. Most of these systems are designed and implemented assuming a homogeneous passenger group that has similar tastes and desires. In this (...) paper, we present a heart rate controlled in-flight music recommendation system for reducing the stress during air travel. The system recommends personalized music playlists to the passengers and attempts to keep their heart rate in a normal range with these playlists. Experiments in a simulated long haul flight cabin environment find that the passengers’ stress can indeed be significantly reduced through listening to the recommended music playlists. (shrink)

Whether and how the differentiated social cognition of the elderly affects their self-rated health has not been deeply discussed. Based on social cognition theory and Chinese situation, this study constructs the social cognitive dimension of Chinese elderly including social attitude, class identity and perception of social relations. Using the data from Chinese General Social Survey in 2017, this study screens out 1,728 elderly people aged 60 and over, and discusses the impact mechanism of social cognition on self-rated health of Chinese (...) elderly people aged 60 and over by the construction of structural equation model and mediation effect test method. The results show that social attitude, class identity and perceptions of social relationships have significantly positive effects on the self-rated health levels of elderly individuals. Among them, the path coefficient of social attitude to self-rated health was 0.049, the path coefficient of class identity to self-rated health was 0.171, and the path coefficient of social relationship perception to self-rated health was 0.248; both class identity and perception of social relationship have significant mediating effects on elderly self-rated health through social engagement. This study shows that social cognition rarely studied in existing literature has significant effects on the self-rated health of older adults, providing fruitful insights for enhancing the self-rated health level of elderly individuals. (shrink)

PurposeGross motor skills and inhibitory control which are both development in preschool stage is significant for preschooler to healthy growth. However, the evidence of relationship between them in preschoolers are still insufficient, most of studies only focus on youth. Thus, the aim of this research is to examine the association between GMS and IC in preschool children.MethodsThis cross-sectional study used baseline data from a previous intervention study of preschoolers conducted in 2018. GMS were assessed by using the Test for Gross (...) Motor Development in preschoolers, which includes two subtests of locomotor and object control skills. Total GMS is calculated from the sum of these two subtests. The Fish Flanker task was used to evaluate both accuracy and reaction time of IC. Multivariate linear regression models were established to analyze the relationships between GMS and IC.ResultsA total of 123 preschool-age children were included in the final analysis. After adjusting for confounders, GMS, locomotor, and object control skills were all negatively related with reaction time of IC.ConclusionThere was a significant negative correlation between gross motor skills and the reaction time of inhibitory control in preschool children. Further research is needed to verify this finding in prospective and experimental studies. (shrink)

This study examines the feature of reasoning talk used by 37 Chinese families at the dinner table across three generations with the background of co-parenting and in consideration of different communicative contexts. Drawing upon Hasan’s semantic framework, reasons were mainly coded as logical or social types. We categorize the communicative context of reasoning talk into contextualized and decontextualized topics. When the proportion of social reasoning was found slightly higher than that of logical reasoning, the families’ reasoning talk account for only (...) 3.85% of the total language. Specifically, the count of mothers’ total reasoning talk was significantly above other family members, while there were no significant differences among the other participants. The effect of the communicative contexts on family members’ social reasoning was found. The reasoning talk grounded on local rules and coercive power occurred significantly more frequently in contextualized than decontextualized context. A higher rate of local-rule grounded reasoning talk of all family members appeared in contextualized than decontextualized context, and this gap was particularly obvious among mothers. These findings reveal the significant role of mothers in family communications and confirm the pedagogical values of decontextualized communicative context for promoting children’s learning opportunities at the dinner table. (shrink)

Grey prediction models have been widely used in various fields of society due to their high prediction accuracy; accordingly, there exists a vast majority of grey models for equidistant sequences; however, limited research is focusing on nonequidistant sequence. The development of nonequidistant grey prediction models is very slow due to their complex modeling mechanism. In order to further expand the grey system theory, a new nonequidistant grey prediction model is established in this paper. To further improve the prediction accuracy of (...) the NEGM model, the background values of the improved nonequidistant grey model are optimized based on Simpson formula, which is abbreviated as INEGM. Meanwhile, to verify the validity of the proposed model, this model is applied in two real-world cases in comparison with three other benchmark models, and the modeling results are evaluated through several commonly used indicators. The results of two cases show that the INEGM model has the best prediction performance among these competitive models. (shrink)

Based on the quarterly data of mutual funds in China from the fourth quarter of 2004 to the fourth quarter of 2019, this paper constructs a series of complex bipartite networks based on the overlapped portfolios of mutual funds and then explores the influences of fund network position on mutual fund’s investment behavior and performance. This paper finds that a mutual fund with shorter information transmission path to other entities in the fund network or with stronger ties with those entities (...) in important information positions will achieve better investment performance. However, a stronger mediating role over the potential information flow of the fund network cannot help a mutual fund increase performance. The empirical results also indicate that a mutual fund holding stock portfolios with high valuation difficulties caused by the market or fundamental information uncertainty will achieve better investment performance, while holding hard-to-value portfolios caused by limited public information will reduce the performance of the fund. Furthermore, high closeness centrality or eigenvector centrality can help mutual funds deal with the disclose problems of public information, thus reducing the likelihood of a mutual fund holding hard-to-value portfolios caused by limited public information to achieve worse performance. Eigenvector centrality brings information advantages about company fundamentals, so it is easier for a mutual fund with high eigenvector centrality to profit from holding hard-to-value portfolios caused by the fundamental information uncertainty. The conclusions of this paper can enhance our understanding of the fund network and its information mechanism and shed new light on mutual fund’s information advantages and related asset allocation strategies. (shrink)

BackgroundBilateral cerebral palsy is the most common type of CP in children and is often accompanied by different degrees of communication impairment. Several studies have attempted to identify children at high risk for communication impairment. However, most prediction factors are qualitative and subjective and may be influenced by rater bias. Individualized objective diagnostic and/or prediction methods are still lacking, and an effective method is urgently needed to guide clinical diagnosis and treatment. The aim of this study is to develop and (...) validate an objective, individual-based model for the prediction of communication impairment in children with BCP by the time they enter school.MethodsA multicenter prospective cohort study will be conducted in four Chinese hospitals. A total of 178 children with BCP will undergo advanced brain magnetic resonance imaging at baseline. At school entry, communication performance will be assessed by a communication function classification system. Three-quarters of children with BCP will be allocated as a training cohort, whereas the remaining children will be allocated as a test cohort. Multivariate lesion- and connectome-based approaches, which have shown good predictive ability of language performance in stroke patients, will be applied to extract features from MR images for each child with BCP. Multiple machine learning models using extracted features to predict communication impairment for each child with BCP will be constructed using data from the training cohort and externally validated using data from the test cohort. Prediction accuracy across models in the test cohort will be statistically compared.DiscussionThe findings of the study may lead to the development of several translational tools that can individually predict communication impairment in children newly diagnosed with BCP to ensure that these children receive early, targeted therapeutic intervention before they begin school.Trial registrationThe study has been registered with the Chinese Clinical Trial Registry. (shrink)

ObjectiveThe study aimed to enhance the learning motivation of college physical education students and improve their learning outcomes. Based on the perspective of the self-determination theory, this study explores the influence of “Small Private Online Course + flipped classroom” teaching on the learning motivation of students majoring in physical education and profoundly analyzes the influencing factors and promotion paths of learning motivation using this model.Materials and methodsA total of four classes of physical education majors in a university were selected and (...) randomly divided into an experimental group and a control group. The experimental group received “SPOC + flipped classroom” teaching, the control group received traditional teaching. Before and after the 16-week intervention, learning motivation, teacher support perception, basic psychological need satisfaction, and academic emotions of the 64 students were measured, and the data were analyzed by repeated-measures analysis of variance and partial least square regression.Results The instructional intervention reduced non-regulation, external regulation, and introjected regulation, while increased identified regulation, intrinsic regulation, and self-determination levels in the students. The levels of non-regulation, external regulation, identified regulation, and self-determination were also significantly different from those of the control group. After the intervention, the scores of support for autonomy, support for competence, support for relatedness, and need for relatedness in the experimental group were significantly higher than those in the control group. Support for autonomy, support for competence, support for relatedness, need for competence and need for relatedness positively predicted the self-determination level, and intrinsic regulation and identified regulation negatively predicted non-regulation, external regulation, and introjected regulation.Conclusion“SPOC + flipped classroom” teaching has a positive impact on students’ learning motivation of basketball skills and promotes students’ motivation autonomy. The improvement of support for autonomy, support for competence, support for relatedness, need for competence, and need for relatedness may be related to the improvement of learning motivation of college students majoring in Physical Education. “SPOC + flipped classroom” teaching enables students to obtain more demand satisfaction by giving them more demand support, while demand support and demand satisfaction can promote the internalization of learning motivation so that students can maintain high autonomy motivation. (shrink)