BACKGROUND The prevalence of negative emotional states,such as anxiety and depression,has increased annually.Although personal habits are known to influence emotional regulation,the precise mechanisms underlying this ...BACKGROUND The prevalence of negative emotional states,such as anxiety and depression,has increased annually.Although personal habits are known to influence emotional regulation,the precise mechanisms underlying this relationship remain unclear.AIM To investigate emotion regulation habits impact on students negative emotions during lockdown,using the coronavirus disease 2019 pandemic as a case example.METHODS During the coronavirus disease 2019 lockdown,an online cross-sectional survey was conducted at a Chinese university.Emotional states were assessed using the Depression,Anxiety,and Stress Scale-21(DASS-21),while demographic data and emotion regulation habits were collected concurrently.Data analysis was performed using SPSS version 27.0 and includedχ^(2)-tests for intergroup comparisons,Spearman’s rank-order correlation coefficient analysis to examine associations,and stepwise linear regression modeling to explore the relationships between emotion regulation habits and emotional states.Statistical significance was set atα=0.05.RESULTS Among the 494 valid questionnaires analyzed,the prevalence rates of negative emotional states were as follows:Depression(65.0%),anxiety(69.4%),and stress(50.8%).DASS-21 scores(mean±SD)demonstrated significant symptomatology:Total(48.77±34.88),depression(16.21±12.18),anxiety(14.90±11.91),and stress(17.64±12.07).Significant positive intercorrelations were observed among all DASS-21 subscales(P<0.01).Regression analysis identified key predictors of negative emotions(P<0.05):Risk factors included late-night frequency and academic pressure,while protective factors were the frequency of parental contact and the number of same-gender friends.Additionally,compensatory spending and binge eating positively predicted all negative emotion scores(β>0,P<0.01),whereas appropriate recreational activities negatively predicted these scores(β<0,P<0.01).CONCLUSION High negative emotion prevalence occurred among confined students.Recreational activities were protective,while compensatory spending and binge eating were risk factors,necessitating guided emotion regulation.展开更多
BACKGROUND Anxiety,depression,and other negative emotions are common among patients with chronic renal failure(CRF).Analyzing the factors related to negative emotions is necessary to provide targeted nursing care.AIM ...BACKGROUND Anxiety,depression,and other negative emotions are common among patients with chronic renal failure(CRF).Analyzing the factors related to negative emotions is necessary to provide targeted nursing care.AIM To explore the correlations among life satisfaction,pleasure levels,and negative emotions in patients with CRF.METHODS One hundred patients with CRF who received therapy at the First Affiliated Hospital of Jinzhou Medical University between December 2022 and February 2025 were included.The Depression,Anxiety,and Stress Scale(DASS-21),Satisfaction with Life Scale(SWLS),and Temporal Experience of Pleasure Scale(TEPS)were used to evaluate negative emotions,life satisfaction,and pleasure level,respectively.Pearson’s correlation coefficient analyzed the correlation between life satisfaction,pleasure level,and negative emotions.Linear regression analysis identified the factors affecting negative emotions.RESULTS The average DASS-21 score among patients with CRF was 51.90±2.30,with subscale scores of 17.90±1.50 for depression,18.53±1.18 for anxiety,and 15.47±2.36 for stress,all significantly higher than the domestic norm(P<0.05).The average SWLS score was 22.17±4.90.Correlation analysis revealed a negative correlation between the SWLS and total DASS-21 scores(P<0.05),but not with the individual depression,anxiety,or stress dimensions.The average TEPS score was 67.80±8.34.TEPS scores were negatively correlated with the DASS-21 score and the stress dimension(P<0.05),but not with depression or anxiety.Linear regression analysis showed that TEPS scores significantly influenced DASS-21 scores(P<0.05).CONCLUSION Patients with CRF experience high levels of negative emotions,which are negatively correlated with life satisfaction and pleasure.Furthermore,pleasure level had an impact on negative emotions.展开更多
This study investigated the impact of problematic mobile phone use(PMPU)on emotion recognition.The PMPU levels of 150 participants were measured using the standardized SAS-SV scale.Based on the SAS-SV cutoff scores,pa...This study investigated the impact of problematic mobile phone use(PMPU)on emotion recognition.The PMPU levels of 150 participants were measured using the standardized SAS-SV scale.Based on the SAS-SV cutoff scores,participants were divided into PMPU and Control groups.These participants completed two emotion recognition experiments involving facial emotion stimuli that had been manipulated to varying emotional intensities using Morph software.Experiment 1(n=75)assessed differences in facial emotion detection accuracy.Experiment 2(n=75),based on signal detection theory,examined differences in hit and false alarm rates across emotional expressions.The results showed that PMPU users demonstrated higher recognition accuracy rates for disgust faces but lower accuracy for happy faces.This indicates a tendency among PMPU users to prioritize specific negative emotions and may have impaired perception of positive emotions.Practically,incorporating diverse emotional stimuli into PMPU intervention may help alleviate the negative emotional focus bias associated with excessive mobile devices use.展开更多
Background:The Canadian 24-h movement guidelines(24-HMG)emphasize the holistic consideration of physical activity(PA),sedentary behavior,and sleep in shaping health outcomes.This study aimed to examine the association...Background:The Canadian 24-h movement guidelines(24-HMG)emphasize the holistic consideration of physical activity(PA),sedentary behavior,and sleep in shaping health outcomes.This study aimed to examine the associations between meeting 24-HMG and emotion regulation-related indicators among children and adolescents.Methods:A total of 534 Chinese children and adolescents aged 12.94±1.10 years(49.81%males)participated in this study and completed self-report measures assessing 24-h movement behaviors,emotion regulation strategies,emotion regulation flexibility,and regulatory emotional self-efficacy.Results:Only 7.12% of theparticipants adhered to two or all three guidelines.The number of guidelines met was positively associated with the use of emotion regulation strategies,emotion regulation flexibility,and regulatory emotional self-efficacy.Compared with meeting none of the guidelines,participants whomet one ormore guidelines reported significantly better performance in these outcomes.Conclusion:Meeting 24-HMG was associated with superior emotion regulation in children and adolescents.The importance of engaging in regular PA,limiting recreational screen time,and getting enough sleep should be highlighted for fostering emotion regulation in this demographic.展开更多
With the rapid development of web technology,Social Networks(SNs)have become one of the most popular platforms for users to exchange views and to express their emotions.More and more people are used to commenting on a...With the rapid development of web technology,Social Networks(SNs)have become one of the most popular platforms for users to exchange views and to express their emotions.More and more people are used to commenting on a certain hot spot in SNs,resulting in a large amount of texts containing emotions.Textual Emotion Cause Extraction(TECE)aims to automatically extract causes for a certain emotion in texts,which is an important research issue in natural language processing.It is different from the previous tasks of emotion recognition and emotion classification.In addition,it is not limited to the shallow-level emotion classification of text,but to trace the emotion source.In this paper,we provide a survey for TECE.First,we introduce the development process and classification of TECE.Then,we discuss the existing methods and key factors for TECE.Finally,we enumerate the challenges and developing trend for TECE.展开更多
The Guide to Learning and Development for Children Aged 3–6 states that“a pleasant emotion is one of the important indicators of children’s physical and mental health,and it is also the foundation for learning and ...The Guide to Learning and Development for Children Aged 3–6 states that“a pleasant emotion is one of the important indicators of children’s physical and mental health,and it is also the foundation for learning and development in other areas.”Emotional health in early childhood plays a significant role in their future development.This article focuses on understanding the characteristics of young children’s emotions and the factors that affect their emotional management.Simultaneously,it proposes effective methods for families and kindergartens to jointly protect children’s emotional and mental health,providing a healthy and positive growth environment for preschool children,and jointly supporting their growth.展开更多
To the editor:Non-suicidal self-injury(NSSI)is an array of directly prepense or repetitive self-harm behaviours without suicidal intent.Individuals engage in self-injurious behaviours to reduce negative mental and cog...To the editor:Non-suicidal self-injury(NSSI)is an array of directly prepense or repetitive self-harm behaviours without suicidal intent.Individuals engage in self-injurious behaviours to reduce negative mental and cognitive states or evoke positive emotions.展开更多
Although numerousfindings show that people experience both positive and negative experiences with regards to solitude,the relationship between solitude capacity and emotional experience remains unclear.The current stud...Although numerousfindings show that people experience both positive and negative experiences with regards to solitude,the relationship between solitude capacity and emotional experience remains unclear.The current study investigated the extent to which emotion regulation may play a suppressive role in the relationship between solitude capacity and emotional experience.Questionnaires on solitude capacity,emotion regulation,and emotional experience were completed by a sample of Chinese college students(n=844;432 females;Meanage=19.79 years,SD=1.43 years).The results of the indirect effect test showed that cognitive reappraisal suppresses the prediction of solitude capacity on positive emotions,while the solitude capacity prediction of negative emotions was suppressed by both cognitive reappraisal and expressive suppression.This suggests that solitude capacity does not predict emotional experience directly,but instead is realized through an antagonistic system consisting of adaptive and nonadaptive emotion regulation strategies.Thesefindings provide cross-sectional empirical support for the ecological niche hypothesis of solitude,and are of theoretical significance in clarifying the role of internal mechanisms of solitude capacity on the human emotional experience.展开更多
Previous studies on classroom concentration were conducted from the perspective of teachers,without considering students’real feelings.This study,starting from students’feelings of classroom concentration,sent out q...Previous studies on classroom concentration were conducted from the perspective of teachers,without considering students’real feelings.This study,starting from students’feelings of classroom concentration,sent out questionnaires to undergraduates of different levels and natures of schools.Through analysis and research,it was found that teachers’emotions indirectly affect students’concentration through students’individual emotions and classroom atmosphere.On this basis,it is suggested that teachers can improve classroom concentration through the correct emotional expression.展开更多
In robotics and human-robot interaction,a robot’s capacity to express and react correctly to human emotions is essential.A significant aspect of the capability involves controlling the robotic facial skin actuators i...In robotics and human-robot interaction,a robot’s capacity to express and react correctly to human emotions is essential.A significant aspect of the capability involves controlling the robotic facial skin actuators in a way that resonates with human emotions.This research focuses on human anthropometric theories to design and control robotic facial actuators,addressing the limitations of existing approaches in expressing emotions naturally and accurately.The facial landmarks are extracted to determine the anthropometric indicators for designing the robot head and is employed to the displacement of these points to calculate emotional values using Fuzzy C-Mean(FCM).The rotating angles of skin actuators are required to account for the smaller emotions,which enhance the robot’s ability to perform emotions in reality.In addition,this study contributes a novel approach based on facial anthropometric indicators to tailor emotional expressions to diverse human characteristics,ensuring more personalized and intuitive interactions.The results demonstrated howfuzzy logic can be employed to improve a robot’s ability to express emotions,which are digitized into fuzzy values.This is also the contribution of the research,which laid the groundwork for robots that can interact with humans more intuitively and empathetically.The performed experiments demonstrated that the suitability of proposed models to conduct tasks related to human emotions with the accuracy of emotional value determination and motor angles is 0.96 and 0.97,respectively.展开更多
BACKGROUND Empathetic psychological care improves mood and enhances the quality of life in critically ill patients.AIM To study the impact of combining 222-nm ultraviolet(UV)disinfection with empathetic psychological ...BACKGROUND Empathetic psychological care improves mood and enhances the quality of life in critically ill patients.AIM To study the impact of combining 222-nm ultraviolet(UV)disinfection with empathetic psychological care on emotional states,nosocomial infection rates,and quality of life in critically ill patients.METHODS A total of 202 critically ill patients admitted to Beijing Ditan Hospital(December 2023 to May 2024)were randomly assigned to control(Ctrl,n=101)or observation groups(Obs,n=101).The Ctrl group received 222-nm UV disinfection and routine care,while the Obs group received 222-nm UV disinfection with empathetic psychological care.Emotional states[Self-Rating Anxiety Scale(SAS),Self-Rating Depression Scale(SDS)],hospital infection rates,quality of life(36-Item Short Form Health Survey),and patient satisfaction were evaluated.RESULTS At baseline,there were no significant differences in SAS and SDS scores between the groups(P>0.05).Following care,both groups demonstrated reductions in SAS and SDS scores,with the Obs group exhibiting a significantly greater reduction(P<0.05).The Obs group also experienced a significantly lower overall hospital infection rate(P<0.05).Similarly,while baseline 36-Item Short Form Health Survey scores did not differ significantly between the groups(P>0.05),post-care scores improved in both groups,with a greater improvement observed in the Obs group(P<0.05).Additionally,the Obs group reported higher patient satisfaction ratings(P<0.05).CONCLUSION The combination of 222-nm UV disinfection and empathetic psychological care improves emotional states,reduces hospital infection rates,enhances the quality of life,and increases patient satisfaction among critically ill patients.展开更多
In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry problem.Recent demonstrations and applications in several fi...In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry problem.Recent demonstrations and applications in several fields,including computer games,smart homes,expression analysis,gesture recognition,surveillance films,depression therapy,patientmonitoring,anxiety,and others,have brought attention to its significant academic and commercial importance.This study emphasizes research that has only employed facial images for face expression recognition(FER),because facial expressions are a basic way that people communicate meaning to each other.The immense achievement of deep learning has resulted in a growing use of its much architecture to enhance efficiency.This review is on machine learning,deep learning,and hybrid methods’use of preprocessing,augmentation techniques,and feature extraction for temporal properties of successive frames of data.The following section gives a brief summary of assessment criteria that are accessible to the public and then compares them with benchmark results the most trustworthy way to assess FER-related research topics statistically.In this review,a brief synopsis of the subject matter may be beneficial for novices in the field of FER as well as seasoned scholars seeking fruitful avenues for further investigation.The information conveys fundamental knowledge and provides a comprehensive understanding of the most recent state-of-the-art research.展开更多
In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accurac...In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accuracy significantly declines when the data is occluded.To enhance the accuracy of gait emotion recognition under occlusion,this paper proposes a Multi-scale Suppression Graph ConvolutionalNetwork(MS-GCN).TheMS-GCN consists of three main components:Joint Interpolation Module(JI Moudle),Multi-scale Temporal Convolution Network(MS-TCN),and Suppression Graph Convolutional Network(SGCN).The JI Module completes the spatially occluded skeletal joints using the(K-Nearest Neighbors)KNN interpolation method.The MS-TCN employs convolutional kernels of various sizes to comprehensively capture the emotional information embedded in the gait,compensating for the temporal occlusion of gait information.The SGCN extracts more non-prominent human gait features by suppressing the extraction of key body part features,thereby reducing the negative impact of occlusion on emotion recognition results.The proposed method is evaluated on two comprehensive datasets:Emotion-Gait,containing 4227 real gaits from sources like BML,ICT-Pollick,and ELMD,and 1000 synthetic gaits generated using STEP-Gen technology,and ELMB,consisting of 3924 gaits,with 1835 labeled with emotions such as“Happy,”“Sad,”“Angry,”and“Neutral.”On the standard datasets Emotion-Gait and ELMB,the proposed method achieved accuracies of 0.900 and 0.896,respectively,attaining performance comparable to other state-ofthe-artmethods.Furthermore,on occlusion datasets,the proposedmethod significantly mitigates the performance degradation caused by occlusion compared to other methods,the accuracy is significantly higher than that of other methods.展开更多
BACKGROUND Emotional reactions,such as anxiety,irritability,and aggressive behavior,have attracted clinical attention as behavioral and emotional problems in preschool-age children.AIM To investigate the current statu...BACKGROUND Emotional reactions,such as anxiety,irritability,and aggressive behavior,have attracted clinical attention as behavioral and emotional problems in preschool-age children.AIM To investigate the current status of family rearing,parental stress,and behavioral and emotional problems of preschool children and to analyze the mediating effect of the current status of family rearing on parental stress and behavioral/emo-tional problems.METHODS We use convenience sampling to select 258 preschool children in the physical examination center of our hospital from October 2021 to September 2023.The children and their parents were evaluated using a questionnaire survey.Pearson's correlation was used to analyze the correlation between child behavioral and emotional problems and parental stress and family rearing,and the structural equation model was constructed to test the mediating effect.RESULTS The score for behavioral/emotional problems of 258 preschool children was(27.54±3.63),the score for parental stress was(87.64±11.34),and the score for parental family rearing was(31.54±5.24).There was a positive correlation between the behavioral and emotional problems of the children and the“hostile/mandatory”parenting style;meanwhile,showed a negative correlation with the“support/participation”parenting style(all P<0.05).The intermediary effect value between the family upbringing of parents in parental stress and children's behavior problems was 29.89%.CONCLUSION Parental family upbringing has a mediating effect between parental stress and behavioral and emotional problems of children.Despite paying attention to the behavioral and emotional problems of preschool-age children,clinical medical staff should provide correct and reasonable parenting advice to their parents to promote the mental health of preschool-age children.展开更多
This review explores the use of agent-based modeling(ABM)within the framework of study human emotion and cognition in the context of its ability to simulate complex social interactions,adaptive changes,and evolutionar...This review explores the use of agent-based modeling(ABM)within the framework of study human emotion and cognition in the context of its ability to simulate complex social interactions,adaptive changes,and evolutionary processes.By representing agents and their defined environments with probabilistic interactions,ABM allows the assessment of the effects of individual behavior at the micro level on the greater social phenomena at the macro level.The review looks into the applications of ABM in portraying some of the key components of emotions and cognition-empathy,cooperation,decision making,and emotional transmission-and analyzes the problems including scalability,empirical validation,and description of sensitive emotional states.The most important conclusion is that merging ABM with information neurobiological data and artificial intelligence(AI)techniques would allow for deepening the interactions within the system and enhancing its responsiveness to stimuli.This review highlights approaches that aim to exploit the ABM methodology more fully and integrates methods from biology,neuroscience,and engineering.This integration could contribute to our understanding of the human behavior evolution and adaptation within systems relevant to policymaking,healthcare,and education.展开更多
In order to effectively conduct emotion recognition from spontaneous, non-prototypical and unsegmented speech so as to create a more natural human-machine interaction; a novel speech emotion recognition algorithm base...In order to effectively conduct emotion recognition from spontaneous, non-prototypical and unsegmented speech so as to create a more natural human-machine interaction; a novel speech emotion recognition algorithm based on the combination of the emotional data field （EDF） and the ant colony search （ACS） strategy, called the EDF-ACS algorithm, is proposed. More specifically, the inter- relationship among the turn-based acoustic feature vectors of different labels are established by using the potential function in the EDF. To perform the spontaneous speech emotion recognition, the artificial colony is used to mimic the turn- based acoustic feature vectors. Then, the canonical ACS strategy is used to investigate the movement direction of each artificial ant in the EDF, which is regarded as the emotional label of the corresponding turn-based acoustic feature vector. The proposed EDF-ACS algorithm is evaluated on the continueous audio）＇visual emotion challenge （AVEC） 2012 dataset, which contains the spontaneous, non-prototypical and unsegmented speech emotion data. The experimental results show that the proposed EDF-ACS algorithm outperforms the existing state-of-the-art algorithm in turn-based speech emotion recognition.展开更多
The rapid growth of digital data necessitates advanced natural language processing(NLP)models like BERT(Bidi-rectional Encoder Representations from Transformers),known for its superior performance in text classificati...The rapid growth of digital data necessitates advanced natural language processing(NLP)models like BERT(Bidi-rectional Encoder Representations from Transformers),known for its superior performance in text classification.However,BERT’s size and computational demands limit its practicality,especially in resource-constrained settings.This research compresses the BERT base model for Bengali emotion classification through knowledge distillation(KD),pruning,and quantization techniques.Despite Bengali being the sixth most spoken language globally,NLP research in this area is limited.Our approach addresses this gap by creating an efficient BERT-based model for Bengali text.We have explored 20 combinations for KD,quantization,and pruning,resulting in improved speedup,fewer parameters,and reduced memory size.Our best results demonstrate significant improvements in both speed and efficiency.For instance,in the case of mBERT,we achieved a 3.87×speedup and 4×compression ratio with a combination of Distil+Prune+Quant that reduced parameters from 178 to 46 M,while the memory size decreased from 711 to 178 MB.These results offer scalable solutions for NLP tasks in various languages and advance the field of model compression,making these models suitable for real-world applications in resource-limited environments.展开更多
Emotion recognition plays a crucial role in various fields and is a key task in natural language processing (NLP). The objective is to identify and interpret emotional expressions in text. However, traditional emotion...Emotion recognition plays a crucial role in various fields and is a key task in natural language processing (NLP). The objective is to identify and interpret emotional expressions in text. However, traditional emotion recognition approaches often struggle in few-shot cross-domain scenarios due to their limited capacity to generalize semantic features across different domains. Additionally, these methods face challenges in accurately capturing complex emotional states, particularly those that are subtle or implicit. To overcome these limitations, we introduce a novel approach called Dual-Task Contrastive Meta-Learning (DTCML). This method combines meta-learning and contrastive learning to improve emotion recognition. Meta-learning enhances the model’s ability to generalize to new emotional tasks, while instance contrastive learning further refines the model by distinguishing unique features within each category, enabling it to better differentiate complex emotional expressions. Prototype contrastive learning, in turn, helps the model address the semantic complexity of emotions across different domains, enabling the model to learn fine-grained emotions expression. By leveraging dual tasks, DTCML learns from two domains simultaneously, the model is encouraged to learn more diverse and generalizable emotions features, thereby improving its cross-domain adaptability and robustness, and enhancing its generalization ability. We evaluated the performance of DTCML across four cross-domain settings, and the results show that our method outperforms the best baseline by 5.88%, 12.04%, 8.49%, and 8.40% in terms of accuracy.展开更多
Speech Emotion Recognition(SER)has received widespread attention as a crucial way for understanding human emotional states.However,the impact of irrelevant information on speech signals and data sparsity limit the dev...Speech Emotion Recognition(SER)has received widespread attention as a crucial way for understanding human emotional states.However,the impact of irrelevant information on speech signals and data sparsity limit the development of SER system.To address these issues,this paper proposes a framework that incorporates the Attentive Mask Residual Network(AM-ResNet)and the self-supervised learning model Wav2vec 2.0 to obtain AM-ResNet features and Wav2vec 2.0 features respectively,together with a cross-attention module to interact and fuse these two features.The AM-ResNet branch mainly consists of maximum amplitude difference detection,mask residual block,and an attention mechanism.Among them,the maximum amplitude difference detection and the mask residual block act on the pre-processing and the network,respectively,to reduce the impact of silent frames,and the attention mechanism assigns different weights to unvoiced and voiced speech to reduce redundant emotional information caused by unvoiced speech.In the Wav2vec 2.0 branch,this model is introduced as a feature extractor to obtain general speech features(Wav2vec 2.0 features)through pre-training with a large amount of unlabeled speech data,which can assist the SER task and cope with data sparsity problems.In the cross-attention module,AM-ResNet features and Wav2vec 2.0 features are interacted with and fused to obtain the cross-fused features,which are used to predict the final emotion.Furthermore,multi-label learning is also used to add ambiguous emotion utterances to deal with data limitations.Finally,experimental results illustrate the usefulness and superiority of our proposed framework over existing state-of-the-art approaches.展开更多
文摘BACKGROUND The prevalence of negative emotional states,such as anxiety and depression,has increased annually.Although personal habits are known to influence emotional regulation,the precise mechanisms underlying this relationship remain unclear.AIM To investigate emotion regulation habits impact on students negative emotions during lockdown,using the coronavirus disease 2019 pandemic as a case example.METHODS During the coronavirus disease 2019 lockdown,an online cross-sectional survey was conducted at a Chinese university.Emotional states were assessed using the Depression,Anxiety,and Stress Scale-21(DASS-21),while demographic data and emotion regulation habits were collected concurrently.Data analysis was performed using SPSS version 27.0 and includedχ^(2)-tests for intergroup comparisons,Spearman’s rank-order correlation coefficient analysis to examine associations,and stepwise linear regression modeling to explore the relationships between emotion regulation habits and emotional states.Statistical significance was set atα=0.05.RESULTS Among the 494 valid questionnaires analyzed,the prevalence rates of negative emotional states were as follows:Depression(65.0%),anxiety(69.4%),and stress(50.8%).DASS-21 scores(mean±SD)demonstrated significant symptomatology:Total(48.77±34.88),depression(16.21±12.18),anxiety(14.90±11.91),and stress(17.64±12.07).Significant positive intercorrelations were observed among all DASS-21 subscales(P<0.01).Regression analysis identified key predictors of negative emotions(P<0.05):Risk factors included late-night frequency and academic pressure,while protective factors were the frequency of parental contact and the number of same-gender friends.Additionally,compensatory spending and binge eating positively predicted all negative emotion scores(β>0,P<0.01),whereas appropriate recreational activities negatively predicted these scores(β<0,P<0.01).CONCLUSION High negative emotion prevalence occurred among confined students.Recreational activities were protective,while compensatory spending and binge eating were risk factors,necessitating guided emotion regulation.
文摘BACKGROUND Anxiety,depression,and other negative emotions are common among patients with chronic renal failure(CRF).Analyzing the factors related to negative emotions is necessary to provide targeted nursing care.AIM To explore the correlations among life satisfaction,pleasure levels,and negative emotions in patients with CRF.METHODS One hundred patients with CRF who received therapy at the First Affiliated Hospital of Jinzhou Medical University between December 2022 and February 2025 were included.The Depression,Anxiety,and Stress Scale(DASS-21),Satisfaction with Life Scale(SWLS),and Temporal Experience of Pleasure Scale(TEPS)were used to evaluate negative emotions,life satisfaction,and pleasure level,respectively.Pearson’s correlation coefficient analyzed the correlation between life satisfaction,pleasure level,and negative emotions.Linear regression analysis identified the factors affecting negative emotions.RESULTS The average DASS-21 score among patients with CRF was 51.90±2.30,with subscale scores of 17.90±1.50 for depression,18.53±1.18 for anxiety,and 15.47±2.36 for stress,all significantly higher than the domestic norm(P<0.05).The average SWLS score was 22.17±4.90.Correlation analysis revealed a negative correlation between the SWLS and total DASS-21 scores(P<0.05),but not with the individual depression,anxiety,or stress dimensions.The average TEPS score was 67.80±8.34.TEPS scores were negatively correlated with the DASS-21 score and the stress dimension(P<0.05),but not with depression or anxiety.Linear regression analysis showed that TEPS scores significantly influenced DASS-21 scores(P<0.05).CONCLUSION Patients with CRF experience high levels of negative emotions,which are negatively correlated with life satisfaction and pleasure.Furthermore,pleasure level had an impact on negative emotions.
基金supported by the National Social Science Fund of China(Grant Number:20BSH134).
文摘This study investigated the impact of problematic mobile phone use(PMPU)on emotion recognition.The PMPU levels of 150 participants were measured using the standardized SAS-SV scale.Based on the SAS-SV cutoff scores,participants were divided into PMPU and Control groups.These participants completed two emotion recognition experiments involving facial emotion stimuli that had been manipulated to varying emotional intensities using Morph software.Experiment 1(n=75)assessed differences in facial emotion detection accuracy.Experiment 2(n=75),based on signal detection theory,examined differences in hit and false alarm rates across emotional expressions.The results showed that PMPU users demonstrated higher recognition accuracy rates for disgust faces but lower accuracy for happy faces.This indicates a tendency among PMPU users to prioritize specific negative emotions and may have impaired perception of positive emotions.Practically,incorporating diverse emotional stimuli into PMPU intervention may help alleviate the negative emotional focus bias associated with excessive mobile devices use.
基金supported by Zhejiang Provincial Social Science Funding(22NDJC050YB).
文摘Background:The Canadian 24-h movement guidelines(24-HMG)emphasize the holistic consideration of physical activity(PA),sedentary behavior,and sleep in shaping health outcomes.This study aimed to examine the associations between meeting 24-HMG and emotion regulation-related indicators among children and adolescents.Methods:A total of 534 Chinese children and adolescents aged 12.94±1.10 years(49.81%males)participated in this study and completed self-report measures assessing 24-h movement behaviors,emotion regulation strategies,emotion regulation flexibility,and regulatory emotional self-efficacy.Results:Only 7.12% of theparticipants adhered to two or all three guidelines.The number of guidelines met was positively associated with the use of emotion regulation strategies,emotion regulation flexibility,and regulatory emotional self-efficacy.Compared with meeting none of the guidelines,participants whomet one ormore guidelines reported significantly better performance in these outcomes.Conclusion:Meeting 24-HMG was associated with superior emotion regulation in children and adolescents.The importance of engaging in regular PA,limiting recreational screen time,and getting enough sleep should be highlighted for fostering emotion regulation in this demographic.
基金partially supported by the National Natural Science Foundation of China under Grant No.62372121the Ministry of education of Humanities and Social Science project under Grant No.20YJAZH118+1 种基金the National Key Research and Development Program of China under Grant No.2020YFB1005804the MOE Project at Center for Linguistics and Applied Linguistics,Guangdong University of Foreign Studies。
文摘With the rapid development of web technology,Social Networks(SNs)have become one of the most popular platforms for users to exchange views and to express their emotions.More and more people are used to commenting on a certain hot spot in SNs,resulting in a large amount of texts containing emotions.Textual Emotion Cause Extraction(TECE)aims to automatically extract causes for a certain emotion in texts,which is an important research issue in natural language processing.It is different from the previous tasks of emotion recognition and emotion classification.In addition,it is not limited to the shallow-level emotion classification of text,but to trace the emotion source.In this paper,we provide a survey for TECE.First,we introduce the development process and classification of TECE.Then,we discuss the existing methods and key factors for TECE.Finally,we enumerate the challenges and developing trend for TECE.
文摘The Guide to Learning and Development for Children Aged 3–6 states that“a pleasant emotion is one of the important indicators of children’s physical and mental health,and it is also the foundation for learning and development in other areas.”Emotional health in early childhood plays a significant role in their future development.This article focuses on understanding the characteristics of young children’s emotions and the factors that affect their emotional management.Simultaneously,it proposes effective methods for families and kindergartens to jointly protect children’s emotional and mental health,providing a healthy and positive growth environment for preschool children,and jointly supporting their growth.
基金the Innovation 2030-Major Project of Brain Science and Brain-lnspired Intelligence Technology(2021ZD0200600)Shanghai Science and Technology Committee(22YF1439100,YDZX20213100001003)National Natural Science Foundation of China(82201678).
文摘To the editor:Non-suicidal self-injury(NSSI)is an array of directly prepense or repetitive self-harm behaviours without suicidal intent.Individuals engage in self-injurious behaviours to reduce negative mental and cognitive states or evoke positive emotions.
基金supported by grants from the Doctoral Research Project of Yan’an University(2003-205040349)the 2022 General Special Scientific Research Plan Project of the Shaanxi Provincial Department of Education(YDZZYB23-40)the Social Science Foundation of Shaanxi Province(2023P013 and 2024P028).
文摘Although numerousfindings show that people experience both positive and negative experiences with regards to solitude,the relationship between solitude capacity and emotional experience remains unclear.The current study investigated the extent to which emotion regulation may play a suppressive role in the relationship between solitude capacity and emotional experience.Questionnaires on solitude capacity,emotion regulation,and emotional experience were completed by a sample of Chinese college students(n=844;432 females;Meanage=19.79 years,SD=1.43 years).The results of the indirect effect test showed that cognitive reappraisal suppresses the prediction of solitude capacity on positive emotions,while the solitude capacity prediction of negative emotions was suppressed by both cognitive reappraisal and expressive suppression.This suggests that solitude capacity does not predict emotional experience directly,but instead is realized through an antagonistic system consisting of adaptive and nonadaptive emotion regulation strategies.Thesefindings provide cross-sectional empirical support for the ecological niche hypothesis of solitude,and are of theoretical significance in clarifying the role of internal mechanisms of solitude capacity on the human emotional experience.
基金2024 Guangxi Higher Education Undergraduate Teaching Reform Project“OBE-Guided,Digitally Empowered‘Hadoop Big Data Development Technology’Course Ideological and Political Construction Innovation Exploration and Practice”(2024JGA396)。
文摘Previous studies on classroom concentration were conducted from the perspective of teachers,without considering students’real feelings.This study,starting from students’feelings of classroom concentration,sent out questionnaires to undergraduates of different levels and natures of schools.Through analysis and research,it was found that teachers’emotions indirectly affect students’concentration through students’individual emotions and classroom atmosphere.On this basis,it is suggested that teachers can improve classroom concentration through the correct emotional expression.
基金funded by the University of Economics Ho Chi Minh City-UEH,Vietnam.
文摘In robotics and human-robot interaction,a robot’s capacity to express and react correctly to human emotions is essential.A significant aspect of the capability involves controlling the robotic facial skin actuators in a way that resonates with human emotions.This research focuses on human anthropometric theories to design and control robotic facial actuators,addressing the limitations of existing approaches in expressing emotions naturally and accurately.The facial landmarks are extracted to determine the anthropometric indicators for designing the robot head and is employed to the displacement of these points to calculate emotional values using Fuzzy C-Mean(FCM).The rotating angles of skin actuators are required to account for the smaller emotions,which enhance the robot’s ability to perform emotions in reality.In addition,this study contributes a novel approach based on facial anthropometric indicators to tailor emotional expressions to diverse human characteristics,ensuring more personalized and intuitive interactions.The results demonstrated howfuzzy logic can be employed to improve a robot’s ability to express emotions,which are digitized into fuzzy values.This is also the contribution of the research,which laid the groundwork for robots that can interact with humans more intuitively and empathetically.The performed experiments demonstrated that the suitability of proposed models to conduct tasks related to human emotions with the accuracy of emotional value determination and motor angles is 0.96 and 0.97,respectively.
基金Supported by Beijing Ditan Hospital Affiliated to Capital Medical University“Sailing Plan”,No.DTQH-202405.
文摘BACKGROUND Empathetic psychological care improves mood and enhances the quality of life in critically ill patients.AIM To study the impact of combining 222-nm ultraviolet(UV)disinfection with empathetic psychological care on emotional states,nosocomial infection rates,and quality of life in critically ill patients.METHODS A total of 202 critically ill patients admitted to Beijing Ditan Hospital(December 2023 to May 2024)were randomly assigned to control(Ctrl,n=101)or observation groups(Obs,n=101).The Ctrl group received 222-nm UV disinfection and routine care,while the Obs group received 222-nm UV disinfection with empathetic psychological care.Emotional states[Self-Rating Anxiety Scale(SAS),Self-Rating Depression Scale(SDS)],hospital infection rates,quality of life(36-Item Short Form Health Survey),and patient satisfaction were evaluated.RESULTS At baseline,there were no significant differences in SAS and SDS scores between the groups(P>0.05).Following care,both groups demonstrated reductions in SAS and SDS scores,with the Obs group exhibiting a significantly greater reduction(P<0.05).The Obs group also experienced a significantly lower overall hospital infection rate(P<0.05).Similarly,while baseline 36-Item Short Form Health Survey scores did not differ significantly between the groups(P>0.05),post-care scores improved in both groups,with a greater improvement observed in the Obs group(P<0.05).Additionally,the Obs group reported higher patient satisfaction ratings(P<0.05).CONCLUSION The combination of 222-nm UV disinfection and empathetic psychological care improves emotional states,reduces hospital infection rates,enhances the quality of life,and increases patient satisfaction among critically ill patients.
文摘In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry problem.Recent demonstrations and applications in several fields,including computer games,smart homes,expression analysis,gesture recognition,surveillance films,depression therapy,patientmonitoring,anxiety,and others,have brought attention to its significant academic and commercial importance.This study emphasizes research that has only employed facial images for face expression recognition(FER),because facial expressions are a basic way that people communicate meaning to each other.The immense achievement of deep learning has resulted in a growing use of its much architecture to enhance efficiency.This review is on machine learning,deep learning,and hybrid methods’use of preprocessing,augmentation techniques,and feature extraction for temporal properties of successive frames of data.The following section gives a brief summary of assessment criteria that are accessible to the public and then compares them with benchmark results the most trustworthy way to assess FER-related research topics statistically.In this review,a brief synopsis of the subject matter may be beneficial for novices in the field of FER as well as seasoned scholars seeking fruitful avenues for further investigation.The information conveys fundamental knowledge and provides a comprehensive understanding of the most recent state-of-the-art research.
基金supported by the National Natural Science Foundation of China(62272049,62236006,62172045)the Key Projects of Beijing Union University(ZKZD202301).
文摘In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accuracy significantly declines when the data is occluded.To enhance the accuracy of gait emotion recognition under occlusion,this paper proposes a Multi-scale Suppression Graph ConvolutionalNetwork(MS-GCN).TheMS-GCN consists of three main components:Joint Interpolation Module(JI Moudle),Multi-scale Temporal Convolution Network(MS-TCN),and Suppression Graph Convolutional Network(SGCN).The JI Module completes the spatially occluded skeletal joints using the(K-Nearest Neighbors)KNN interpolation method.The MS-TCN employs convolutional kernels of various sizes to comprehensively capture the emotional information embedded in the gait,compensating for the temporal occlusion of gait information.The SGCN extracts more non-prominent human gait features by suppressing the extraction of key body part features,thereby reducing the negative impact of occlusion on emotion recognition results.The proposed method is evaluated on two comprehensive datasets:Emotion-Gait,containing 4227 real gaits from sources like BML,ICT-Pollick,and ELMD,and 1000 synthetic gaits generated using STEP-Gen technology,and ELMB,consisting of 3924 gaits,with 1835 labeled with emotions such as“Happy,”“Sad,”“Angry,”and“Neutral.”On the standard datasets Emotion-Gait and ELMB,the proposed method achieved accuracies of 0.900 and 0.896,respectively,attaining performance comparable to other state-ofthe-artmethods.Furthermore,on occlusion datasets,the proposedmethod significantly mitigates the performance degradation caused by occlusion compared to other methods,the accuracy is significantly higher than that of other methods.
基金Supported by the Shijiazhuang Science and Technology Research and Development Program,No.221460383.
文摘BACKGROUND Emotional reactions,such as anxiety,irritability,and aggressive behavior,have attracted clinical attention as behavioral and emotional problems in preschool-age children.AIM To investigate the current status of family rearing,parental stress,and behavioral and emotional problems of preschool children and to analyze the mediating effect of the current status of family rearing on parental stress and behavioral/emo-tional problems.METHODS We use convenience sampling to select 258 preschool children in the physical examination center of our hospital from October 2021 to September 2023.The children and their parents were evaluated using a questionnaire survey.Pearson's correlation was used to analyze the correlation between child behavioral and emotional problems and parental stress and family rearing,and the structural equation model was constructed to test the mediating effect.RESULTS The score for behavioral/emotional problems of 258 preschool children was(27.54±3.63),the score for parental stress was(87.64±11.34),and the score for parental family rearing was(31.54±5.24).There was a positive correlation between the behavioral and emotional problems of the children and the“hostile/mandatory”parenting style;meanwhile,showed a negative correlation with the“support/participation”parenting style(all P<0.05).The intermediary effect value between the family upbringing of parents in parental stress and children's behavior problems was 29.89%.CONCLUSION Parental family upbringing has a mediating effect between parental stress and behavioral and emotional problems of children.Despite paying attention to the behavioral and emotional problems of preschool-age children,clinical medical staff should provide correct and reasonable parenting advice to their parents to promote the mental health of preschool-age children.
文摘This review explores the use of agent-based modeling(ABM)within the framework of study human emotion and cognition in the context of its ability to simulate complex social interactions,adaptive changes,and evolutionary processes.By representing agents and their defined environments with probabilistic interactions,ABM allows the assessment of the effects of individual behavior at the micro level on the greater social phenomena at the macro level.The review looks into the applications of ABM in portraying some of the key components of emotions and cognition-empathy,cooperation,decision making,and emotional transmission-and analyzes the problems including scalability,empirical validation,and description of sensitive emotional states.The most important conclusion is that merging ABM with information neurobiological data and artificial intelligence(AI)techniques would allow for deepening the interactions within the system and enhancing its responsiveness to stimuli.This review highlights approaches that aim to exploit the ABM methodology more fully and integrates methods from biology,neuroscience,and engineering.This integration could contribute to our understanding of the human behavior evolution and adaptation within systems relevant to policymaking,healthcare,and education.
基金The National Natural Science Foundation of China(No.61231002,61273266,61571106)the Foundation of the Department of Science and Technology of Guizhou Province(No.[2015]7637)
文摘In order to effectively conduct emotion recognition from spontaneous, non-prototypical and unsegmented speech so as to create a more natural human-machine interaction; a novel speech emotion recognition algorithm based on the combination of the emotional data field （EDF） and the ant colony search （ACS） strategy, called the EDF-ACS algorithm, is proposed. More specifically, the inter- relationship among the turn-based acoustic feature vectors of different labels are established by using the potential function in the EDF. To perform the spontaneous speech emotion recognition, the artificial colony is used to mimic the turn- based acoustic feature vectors. Then, the canonical ACS strategy is used to investigate the movement direction of each artificial ant in the EDF, which is regarded as the emotional label of the corresponding turn-based acoustic feature vector. The proposed EDF-ACS algorithm is evaluated on the continueous audio）＇visual emotion challenge （AVEC） 2012 dataset, which contains the spontaneous, non-prototypical and unsegmented speech emotion data. The experimental results show that the proposed EDF-ACS algorithm outperforms the existing state-of-the-art algorithm in turn-based speech emotion recognition.
文摘The rapid growth of digital data necessitates advanced natural language processing(NLP)models like BERT(Bidi-rectional Encoder Representations from Transformers),known for its superior performance in text classification.However,BERT’s size and computational demands limit its practicality,especially in resource-constrained settings.This research compresses the BERT base model for Bengali emotion classification through knowledge distillation(KD),pruning,and quantization techniques.Despite Bengali being the sixth most spoken language globally,NLP research in this area is limited.Our approach addresses this gap by creating an efficient BERT-based model for Bengali text.We have explored 20 combinations for KD,quantization,and pruning,resulting in improved speedup,fewer parameters,and reduced memory size.Our best results demonstrate significant improvements in both speed and efficiency.For instance,in the case of mBERT,we achieved a 3.87×speedup and 4×compression ratio with a combination of Distil+Prune+Quant that reduced parameters from 178 to 46 M,while the memory size decreased from 711 to 178 MB.These results offer scalable solutions for NLP tasks in various languages and advance the field of model compression,making these models suitable for real-world applications in resource-limited environments.
基金supported by the ScientificResearch and Innovation Team Program of Sichuan University of Science and Technology(No.SUSE652A006)Sichuan Key Provincial Research Base of Intelligent Tourism(ZHYJ22-03)In addition,it is also listed as a project of Sichuan Provincial Science and Technology Programme(2022YFG0028).
文摘Emotion recognition plays a crucial role in various fields and is a key task in natural language processing (NLP). The objective is to identify and interpret emotional expressions in text. However, traditional emotion recognition approaches often struggle in few-shot cross-domain scenarios due to their limited capacity to generalize semantic features across different domains. Additionally, these methods face challenges in accurately capturing complex emotional states, particularly those that are subtle or implicit. To overcome these limitations, we introduce a novel approach called Dual-Task Contrastive Meta-Learning (DTCML). This method combines meta-learning and contrastive learning to improve emotion recognition. Meta-learning enhances the model’s ability to generalize to new emotional tasks, while instance contrastive learning further refines the model by distinguishing unique features within each category, enabling it to better differentiate complex emotional expressions. Prototype contrastive learning, in turn, helps the model address the semantic complexity of emotions across different domains, enabling the model to learn fine-grained emotions expression. By leveraging dual tasks, DTCML learns from two domains simultaneously, the model is encouraged to learn more diverse and generalizable emotions features, thereby improving its cross-domain adaptability and robustness, and enhancing its generalization ability. We evaluated the performance of DTCML across four cross-domain settings, and the results show that our method outperforms the best baseline by 5.88%, 12.04%, 8.49%, and 8.40% in terms of accuracy.
基金supported by Chongqing University of Posts and Telecommunications Ph.D.Innovative Talents Project(Grant No.BYJS202106)Chongqing Postgraduate Research Innovation Project(Grant No.CYB21203).
文摘Speech Emotion Recognition(SER)has received widespread attention as a crucial way for understanding human emotional states.However,the impact of irrelevant information on speech signals and data sparsity limit the development of SER system.To address these issues,this paper proposes a framework that incorporates the Attentive Mask Residual Network(AM-ResNet)and the self-supervised learning model Wav2vec 2.0 to obtain AM-ResNet features and Wav2vec 2.0 features respectively,together with a cross-attention module to interact and fuse these two features.The AM-ResNet branch mainly consists of maximum amplitude difference detection,mask residual block,and an attention mechanism.Among them,the maximum amplitude difference detection and the mask residual block act on the pre-processing and the network,respectively,to reduce the impact of silent frames,and the attention mechanism assigns different weights to unvoiced and voiced speech to reduce redundant emotional information caused by unvoiced speech.In the Wav2vec 2.0 branch,this model is introduced as a feature extractor to obtain general speech features(Wav2vec 2.0 features)through pre-training with a large amount of unlabeled speech data,which can assist the SER task and cope with data sparsity problems.In the cross-attention module,AM-ResNet features and Wav2vec 2.0 features are interacted with and fused to obtain the cross-fused features,which are used to predict the final emotion.Furthermore,multi-label learning is also used to add ambiguous emotion utterances to deal with data limitations.Finally,experimental results illustrate the usefulness and superiority of our proposed framework over existing state-of-the-art approaches.
