Within the prefrontal-cingulate cortex,abnormalities in coupling between neuronal networks can disturb the emotion-cognition interactions,contributing to the development of mental disorders such as depression.Despite ...Within the prefrontal-cingulate cortex,abnormalities in coupling between neuronal networks can disturb the emotion-cognition interactions,contributing to the development of mental disorders such as depression.Despite this understanding,the neural circuit mechanisms underlying this phenomenon remain elusive.In this study,we present a biophysical computational model encompassing three crucial regions,including the dorsolateral prefrontal cortex,subgenual anterior cingulate cortex,and ventromedial prefrontal cortex.The objective is to investigate the role of coupling relationships within the prefrontal-cingulate cortex networks in balancing emotions and cognitive processes.The numerical results confirm that coupled weights play a crucial role in the balance of emotional cognitive networks.Furthermore,our model predicts the pathogenic mechanism of depression resulting from abnormalities in the subgenual cortex,and network functionality was restored through intervention in the dorsolateral prefrontal cortex.This study utilizes computational modeling techniques to provide an insight explanation for the diagnosis and treatment of depression.展开更多
In task-oriented dialogue systems, intent, emotion, and actions are crucial elements of user activity. Analyzing the relationships among these elements to control and manage task-oriented dialogue systems is a challen...In task-oriented dialogue systems, intent, emotion, and actions are crucial elements of user activity. Analyzing the relationships among these elements to control and manage task-oriented dialogue systems is a challenging task. However, previous work has primarily focused on the independent recognition of user intent and emotion, making it difficult to simultaneously track both aspects in the dialogue tracking module and to effectively utilize user emotions in subsequent dialogue strategies. We propose a Multi-Head Encoder Shared Model (MESM) that dynamically integrates features from emotion and intent encoders through a feature fusioner. Addressing the scarcity of datasets containing both emotion and intent labels, we designed a multi-dataset learning approach enabling the model to generate dialogue summaries encompassing both user intent and emotion. Experiments conducted on the MultiWoZ and MELD datasets demonstrate that our model effectively captures user intent and emotion, achieving extremely competitive results in dialogue state tracking tasks.展开更多
BACKGROUND Neck pain,a primary symptom of cervical spondylosis,affects patients'physical and mental health,reducing their quality of life.Pain and emotional state interact;however,their longitudinal interrelations...BACKGROUND Neck pain,a primary symptom of cervical spondylosis,affects patients'physical and mental health,reducing their quality of life.Pain and emotional state interact;however,their longitudinal interrelationship remains unclear.In this study,we applied a dual-trajectory model to assess how neck pain and emotional state evolve together over time and how clinical interventions,particularly acupuncture,influence these trajectories.AIM To investigate the longitudinal relationship between neck pain and emotional state in patients with cervical spondylosis.METHODS This prospective cohort study included 472 patients with cervical spondylosis from eight Chinese hospitals.Participants received acupuncture or medication and were followed up at baseline,and at 1,2,4,6,and 8 weeks.Neck pain and emotional distress were assessed using the Northwick Park Neck Pain Questionnaire(NPQ)and the affective subscale of the Short-Form McGill Pain Questionnaire(SF-MPQ),respectively.Group-based trajectory models and dual trajectory analysis were used to identify and correlate pain-emotion trajectories.Multivariate logistic regression identified predictors of group membership.RESULTS Three trajectory groups were identified for NPQ and SF-MPQ scores(low,medium,and high).Higher NPQ trajectory was associated with older age(OR=1.058,P<0.001)and was significantly reduced by acupuncture(OR=0.382,P<0.001).Similarly,acupuncture lowered the odds of high SF-MPQ trajectory membership(OR=0.336,P<0.001),while age increased it(OR=1.037,P<0.001).Dual-trajectory analysis revealed bidirectional associations:69.1%of patients with low NPQ had low SF-MPQ scores,and 42.6%of patients with high SF-MPQ also had high NPQ scores.Gender was a predictor for medium SF-MPQ trajectory(OR=1.629,P=0.094).Occupation and education levels differed significantly across the trajectory groups(P<0.05).CONCLUSION Over time,neck pain and emotional distress are closely associated in patients with cervical spondylosis.Acupuncture alleviates both outcomes significantly,while age is a risk factor.Integrated approaches to pain and emotional management are encouraged.展开更多
A cascaded projection of the Gaussian mixture model algorithm is proposed.First,the marginal distribution of the Gaussian mixture model is computed for different feature dimensions, and a number of sub-classifiers are...A cascaded projection of the Gaussian mixture model algorithm is proposed.First,the marginal distribution of the Gaussian mixture model is computed for different feature dimensions, and a number of sub-classifiers are generated using the marginal distribution model.Each sub-classifier is based on different feature sets.The cascaded structure is adopted to fuse the sub-classifiers dynamically to achieve sample adaptation ability.Secondly,the effectiveness of the proposed algorithm is verified on electrocardiogram emotional signal and speech emotional signal.Emotional data including fidgetiness,happiness and sadness is collected by induction experiments.Finally,the emotion feature extraction method is discussed,including heart rate variability, the chaotic electrocardiogram feature and utterance level static feature.The emotional feature reduction methods are studied, including principle component analysis,sequential forward selection, the Fisher discriminant ratio and maximal information coefficient.The experimental results show that the proposed classification algorithm can effectively improve recognition accuracy in two different scenarios.展开更多
According to the basic emotional theory, the artificial emotional model based on the finite state machine（FSM） was presented. In finite state machine model of emotion, the emotional space included the basic emotiona...According to the basic emotional theory, the artificial emotional model based on the finite state machine（FSM） was presented. In finite state machine model of emotion, the emotional space included the basic emotional space and the multiple emotional spaces. The emotion-switching diagram was defined and transition fimction was developed using Markov chain and linear interpolation algorithm. The simulation model was built using Stateflow toolbox and Simulink toolbox based on the Matlab platform. And the model included three subsystems： the input one, the emotion one and the behavior one. In the emotional subsystem, the responses of different personalities to the external stimuli were described by defining personal space. This model takes states from an emotional space and updates its state depending on its current state and a state of its input （also a state-emotion）. The simulation model realizes the process of switching the emotion from the neutral state to other basic emotions. The simulation result is proved to correspond to emotion-switching law of human beings.展开更多
Emotions serve various functions.The traditional emotion recognition methods are based primarily on readily accessible facial expressions,gestures,and voice signals.However,it is often challenging to ensure that these...Emotions serve various functions.The traditional emotion recognition methods are based primarily on readily accessible facial expressions,gestures,and voice signals.However,it is often challenging to ensure that these non-physical signals are valid and reliable in practical applications.Electroencephalogram(EEG)signals are more successful than other signal recognition methods in recognizing these characteristics in real-time since they are difficult to camouflage.Although EEG signals are commonly used in current emotional recognition research,the accuracy is low when using traditional methods.Therefore,this study presented an optimized hybrid pattern with an attention mechanism(FFT_CLA)for EEG emotional recognition.First,the EEG signal was processed via the fast fourier transform(FFT),after which the convolutional neural network(CNN),long short-term memory(LSTM),and CNN-LSTM-attention(CLA)methods were used to extract and classify the EEG features.Finally,the experiments compared and analyzed the recognition results obtained via three DEAP dataset models,namely FFT_CNN,FFT_LSTM,and FFT_CLA.The final experimental results indicated that the recognition rates of the FFT_CNN,FFT_LSTM,and FFT_CLA models within the DEAP dataset were 87.39%,88.30%,and 92.38%,respectively.The FFT_CLA model improved the accuracy of EEG emotion recognition and used the attention mechanism to address the often-ignored importance of different channels and samples when extracting EEG features.展开更多
BACKGROUND Acute pancreatitis(AP),as a common acute abdomen disease,has a high incidence rate worldwide and is often accompanied by severe complications.Negative emotions lead to increased secretion of stress hormones...BACKGROUND Acute pancreatitis(AP),as a common acute abdomen disease,has a high incidence rate worldwide and is often accompanied by severe complications.Negative emotions lead to increased secretion of stress hormones,elevated blood sugar levels,and enhanced insulin resistance,which in turn increases the risk of AP and significantly affects the patient's quality of life.Therefore,exploring the intervention effects of narrative nursing programs on the negative emotions of patients with AP is not only helpful in alleviating psychological stress and improving quality of life but also has significant implications for improving disease outcomes and prognosis.AIM To construct a narrative nursing model for negative emotions in patients with AP and verify its efficacy in application.METHODS Through Delphi expert consultation,a narrative nursing model for negative emotions in patients with AP was constructed.A non-randomized quasi-experimental study design was used in this study.A total of 92 patients with AP with negative emotions admitted to a tertiary hospital in Nantong City of Jiangsu Province,China from September 2022 to August 2023 were recruited by convenience sampling,among whom 46 patients admitted from September 2022 to February 2023 were included in the observation group,and 46 patients from March to August 2023 were selected as control group.The observation group received narrative nursing plan,while the control group was given with routine nursing.Self-rating anxiety scale(SAS),self-rating depression scale(SDS),positive and negative affect scale(PANAS),caring behavior scale,patient satisfaction scale and 36-item short form health survey questionnaire(SF-36)were used to evaluate their emotions,satisfaction and caring behaviors in the two groups on the day of discharge,1-and 3-month following discharge.RESULTS According to the inclusion and exclusion criteria,a total of 45 cases in the intervention group and 44 cases in the control group eventually recruited and completed in the study.On the day of discharge,the intervention group showed significantly lower scores of SAS,SDS and negative emotion(28.57±4.52 vs 17.4±4.44,P<0.001),whereas evidently higher outcomes in the positive emotion score,Caring behavior scale score and satisfaction score compared to the control group(P<0.05).Repeated measurement analysis of variance showed that significant between-group differences were found in time effect,inter-group effect and interaction effect of SAS and PANAS scores as well as in time effect and inter-group effect of SF-36 scores(P<0.05);the SF-36 scores of two groups at 3 months after discharge were higher than those at 1 month after discharge(P<0.05).CONCLUSION The application of narrative nursing protocols has demonstrated significant effectiveness in alleviating anxiety,ameliorating negative emotions,and enhancing satisfaction among patients with AP.展开更多
Panic is a common emotion when pedestrians are in danger during the actual evacuation, which can affect pedestrians a lot and may lead to fatalities as people are crushed or trampled. However, the systematic studies a...Panic is a common emotion when pedestrians are in danger during the actual evacuation, which can affect pedestrians a lot and may lead to fatalities as people are crushed or trampled. However, the systematic studies and quantitative analysis of evacuation panic, such as panic behaviors, panic evolution, and the stress responses of pedestrians with different personality traits to panic emotion are still rare. Here, combined with the theories of OCEAN(openness, conscientiousness,extroversion, agreeableness, neuroticism) model and SIS(susceptible, infected, susceptible) model, an extended cellular automata model is established by the floor field method in order to investigate the dynamics of panic emotion in the crowd and dynamics of pedestrians affected by emotion. In the model, pedestrians are divided into stable pedestrians and sensitive pedestrians according to their different personality traits in response to emotion, and their emotional state can be normal or panic. Besides, emotion contagion, emotion decay, and the influence of emotion on pedestrian movement decision-making are also considered. The simulation results show that evacuation efficiency will be reduced, for panic pedestrians may act maladaptive behaviors, thereby making the crowd more chaotic. The results further suggest that improving pedestrian psychological ability and raising the standard of management can effectively increase evacuation efficiency. And it is necessary to reduce the panic level of group as soon as possible at the beginning of evacuation. We hope this research could provide a new method to analyze crowd evacuation in panic situations.展开更多
BACKGROUND Primiparas are usually at high risk of experiencing perinatal depression,which may cause prolonged labor,increased blood loss,and intensified pain,affecting maternal and fetal outcomes.Therefore,interventio...BACKGROUND Primiparas are usually at high risk of experiencing perinatal depression,which may cause prolonged labor,increased blood loss,and intensified pain,affecting maternal and fetal outcomes.Therefore,interventions are necessary to improve maternal and fetal outcomes and alleviate primiparas’negative emotions(NEs).AIM To discusses the impact of nursing responsibility in midwifery and postural and psychological interventions on maternal and fetal outcomes as well as primiparas’NEs.METHODS As participants,115 primiparas admitted to Quanzhou Maternity and Child Healthcare Hospital between May 2020 and May 2022 were selected.Among them,56 primiparas(control group,Con)were subjected to conventional midwifery and routine nursing.The remaining 59(research group,Res)were subjected to the nursing model of midwifery and postural and psychological interventions.Both groups were comparatively analyzed from the perspectives of delivery mode(cesarean,natural,or forceps-assisted),maternal and fetal outcomes(uterine inertia,postpartum hemorrhage,placental abruption,neonatal pulmonary injury,and neonatal asphyxia),NEs(Hamilton Anxiety/Depressionrating Scale,HAMA/HAMD),labor duration,and nursing satisfaction.RESULTS The Res exhibited a markedly higher natural delivery rate and nursing satisfaction than the Con.Additionally,the Res indicated a lower incidence of adverse events(e.g.,uterine inertia,postpartum hemorrhage,placental abruption,neonatal lung injury,and neonatal asphyxia)and shortened duration of various stages of labor.It also showed statistically lower post-interventional HAMA and HAMD scores than the Con and pre-interventional values.CONCLUSION The nursing model of midwifery and postural and psychological interventions increase the natural delivery rate and reduce the duration of each labor stage.These are also conducive to improving maternal and fetal outcomes and mitigating primiparas’NEs and thus deserve popularity in clinical practice.展开更多
Background:Understanding the factors that influence adolescent psychological resilience is critical for promoting mental health.This study explores the impact and mechanism of labor values on adolescent psychological ...Background:Understanding the factors that influence adolescent psychological resilience is critical for promoting mental health.This study explores the impact and mechanism of labor values on adolescent psychological resilience from the perspective of emotion regulation theory.Methods:This study conducted an in-depth analysis using the Labor Value Scale on 2691 elementary school upper-grade students,middle school students,and high school students.Results:The results show that:(1)labor values can positively predict adolescents’mental resilience;(2)cognitive reappraisal and expression inhibition play a partial mediating role in the relationship between labor values and adolescents’psychological resilience.Among them,labor values can positively predict adolescents’mental resilience through positive cognitive reappraisal,and labor values can also predict adolescents’mental resilience through expression inhibition.Conclusion:Based on the theory of emotion regulation,this study explores the direct effect of labor values on mental resilience and the mediating effect of different strategies of emotion regulation.The results of this study provide a theoretical basis for improving the mental resilience of adolescents.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
Maintaining optimal quality of life(QoL)is a pivotal for“successful aging”.Understanding how the QoL of the elderly develops and what role psychological factors play in its development will help improve QoL from a p...Maintaining optimal quality of life(QoL)is a pivotal for“successful aging”.Understanding how the QoL of the elderly develops and what role psychological factors play in its development will help improve QoL from a psychological perspective.Embedded within the lifespan theory of control,this longitudinal study aimed to(1)map the temporal trajectory of QoL among Chinese older adults,(2)examine differential effects of tripartite negative emotions(stress,anxiety,depression),and(3)test themoderating role of control strategies(goal engagement,goal disengagement,self-protection)in emotion-QoL dynamics.A prospective cohort of 345 community-dwelling older adults(Mage=83.84±8.49 years;55.1%female)completed validated measures-SF-36 for QoL,DASS-21 for negative emotions,and an adapted Control Strategies Questionnaire(CAS)-at three waves spanning 12 months.Hierarchical linear modeling(HLM)with time-nested structure analyzed intraindividual changes and interindividual differences.QoL exhibited a significant linear decline over time(β=−4.75,p<0.001).Stress(β=−14.12,p<0.001)and anxiety(β=−11.24,p<0.001)robustly predicted QoL decline,whereas depression showed no significant effect.Control strategies had divergent associations:goal engagement(β=3.51,p<0.001)and self-protection(β=2.38,p=0.015)predicted higher baseline QoL,while goal disengagement accelerated decline(β=−7.00,p<0.001;interaction with time:β=−2.46,p<0.001).Contrary to hypotheses,control strategies did not moderate emotion-QoL associations(ΔR2=0.02,p=0.21).The results showed that stress and anxiety played an important role in the QoL of the elderly.At the same time,goal engagement and self-protection were beneficial to the QoL of the elderly,while goal disengagement was not conducive to QoL and its development among the elderly.Meanwhile,the negative effect of anxiety and stress on the QoL of the elderly was not affected by the control strategies.展开更多
Emotion Model is the basis of facial expression recognition system. The constructed emotional model should not only match facial expressions with emotions, but also reflect the location relationship between different ...Emotion Model is the basis of facial expression recognition system. The constructed emotional model should not only match facial expressions with emotions, but also reflect the location relationship between different emotions. In this way, it is easy to understand the current emotion of an individual through the analysis of the acquired facial expression information. This paper constructs an improved three-dimensional model for emotion based on fuzzy theory, which corresponds to the facial features to emotions based on the basic emotions proposed by Ekman. What’s more, the three-dimensional model for motion is able to divide every emotion into three different groups which can show the positional relationship visually and quantitatively and at the same time determine the degree of emotion based on fuzzy theory.展开更多
In the face offierce competition in the social environment,mental health problems gradually get the attention of the public,in order to achieve accurate mental health data analysis,the construction of music education ...In the face offierce competition in the social environment,mental health problems gradually get the attention of the public,in order to achieve accurate mental health data analysis,the construction of music education is based on emotional tendency analysis of psychological adjustment function model.Design emotional tendency analysis of music education psychological adjustment function architecture,music teaching goal as psychological adjust-ment function architecture building orientation,music teaching content as a foundation for psychological adjust-ment function architecture and music teaching process as a psychological adjustment function architecture building,music teaching evaluation as the key of building key regulating function architecture,Establish a core literacy oriented evaluation system.Different evaluation methods were used to obtain the evaluation results.Four levels of psychological adjustment function model of music education are designed,and the psychological adjust-ment function of music education is put forward,thus completing the construction of psychological adjustment function model of music education.The experimental results show that the absolute value of the data acquisition error of the designed model is minimum,which is not more than 0.2.It is less affected by a bad coefficient and has good performance.It can quickly converge to the best state in the actual prediction process and has a strong con-vergence ability.展开更多
Emotion mismatch between training and testing is one of the important factors causing the performance degradation of speaker recognition system. In our previous work, a bi-model emotion speaker recognition (BESR) meth...Emotion mismatch between training and testing is one of the important factors causing the performance degradation of speaker recognition system. In our previous work, a bi-model emotion speaker recognition (BESR) method based on virtual HD (High Different from neutral, with large pitch offset) speech synthesizing was proposed to deal with this problem. It enhanced the system performance under mismatch emotion states in MASC, while still suffering the system risk introduced by fusing the scores from the unreliable VHD model and the neutral model with equal weight. In this paper, we propose a new BESR method based on score reliability fusion. Two strategies, by utilizing identification rate and scores average relative loss difference, are presented to estimate the weights for the two group scores. The results on both MASC and EPST shows that by using the weights generated by the two strategies, the BESR method achieve a better performance than that by using the equal weight, and the better one even achieves a result comparable to that by using the best weights selected by exhaustive strategy.展开更多
Understanding people's emotions through natural language is a challenging task for intelligent systems based on Internet of Things(Io T). The major difficulty is caused by the lack of basic knowledge in emotion ex...Understanding people's emotions through natural language is a challenging task for intelligent systems based on Internet of Things(Io T). The major difficulty is caused by the lack of basic knowledge in emotion expressions with respect to a variety of real world contexts. In this paper, we propose a Bayesian inference method to explore the latent semantic dimensions as contextual information in natural language and to learn the knowledge of emotion expressions based on these semantic dimensions. Our method synchronously infers the latent semantic dimensions as topics in words and predicts the emotion labels in both word-level and document-level texts. The Bayesian inference results enable us to visualize the connection between words and emotions with respect to different semantic dimensions. And by further incorporating a corpus-level hierarchy in the document emotion distribution assumption, we could balance the document emotion recognition results and achieve even better word and document emotion predictions. Our experiment of the wordlevel and the document-level emotion predictions, based on a well-developed Chinese emotion corpus Ren-CECps, renders both higher accuracy and better robustness in the word-level and the document-level emotion predictions compared to the state-of-theart emotion prediction algorithms.展开更多
How to make machines express emotions would be instrumental in establishing acompletely new paradigm for man machine interaction. A new method for simulating and assessingartificial psychology has been developed for t...How to make machines express emotions would be instrumental in establishing acompletely new paradigm for man machine interaction. A new method for simulating and assessingartificial psychology has been developed for the research of the emotion robot. The human psychologyactivity is regarded as a Markov process. An emotion space and psychology model is constructedbased on Markov process. The conception of emotion entropy is presented to assess the artificialemotion complexity. The simulating results play up to human psychology activity. This model can alsobe applied to consumer-friendly human-computer interfaces, and interactive video etc.展开更多
As an interdisciplinary comprehensive subject involving multidisciplinary knowledge,emotional analysis has become a hot topic in psychology,health medicine and computer science.It has a high comprehensive and practica...As an interdisciplinary comprehensive subject involving multidisciplinary knowledge,emotional analysis has become a hot topic in psychology,health medicine and computer science.It has a high comprehensive and practical application value.Emotion research based on the social network is a relatively new topic in the field of psychology and medical health research.The text emotion analysis of college students also has an important research significance for the emotional state of students at a certain time or a certain period,so as to understand their normal state,abnormal state and the reason of state change from the information they wrote.In view of the fact that convolutional neural network cannot make full use of the unique emotional information in sentences,and the need to label a large number of highquality training sets for emotional analysis to improve the accuracy of the model,an emotional analysismodel using the emotional dictionary andmultichannel convolutional neural network is proposed in this paper.Firstly,the input matrix of emotion dictionary is constructed according to the emotion information,and the different feature information of sentences is combined to form different network input channels,so that the model can learn the emotion information of input sentences from various feature representations in the training process.Then,the loss function is reconstructed to realize the semi supervised learning of the network.Finally,experiments are carried on COAE 2014 and self-built data sets.The proposed model can not only extract more semantic information in emotional text,but also learn the hidden emotional information in emotional text.The experimental results show that the proposed emotion analysis model can achieve a better classification performance.Compared with the best benchmark model gram-CNN,the F1 value can be increased by 0.026 in the self-built data set,and it can be increased by 0.032 in the COAE 2014 data set.展开更多
In this paper, an emotional mathematical model and affective state probability description space of a humanoid robot are set up on the basis of psycho-dynamics＇ psychological energy and affective energy conservation ...In this paper, an emotional mathematical model and affective state probability description space of a humanoid robot are set up on the basis of psycho-dynamics＇ psychological energy and affective energy conservation law. The emotional state transferring process and hidden Markov chain algorithm of stimulating transition process are then studied. The simulation results show that the mathematical model is applicable to the authentic affective state change rule of human beings. Finally, the gait generation experiment results of control signal and electric current tracking wave-form are presented to demonstrate the validity of the proposed mathematical model.展开更多
基金supported by the Major Research Instrument Development Project of the National Natural Science Foundation of China(82327810)the Foundation of the President of Hebei University(XZJJ202202)the Hebei Province“333 talent project”(A202101058).
文摘Within the prefrontal-cingulate cortex,abnormalities in coupling between neuronal networks can disturb the emotion-cognition interactions,contributing to the development of mental disorders such as depression.Despite this understanding,the neural circuit mechanisms underlying this phenomenon remain elusive.In this study,we present a biophysical computational model encompassing three crucial regions,including the dorsolateral prefrontal cortex,subgenual anterior cingulate cortex,and ventromedial prefrontal cortex.The objective is to investigate the role of coupling relationships within the prefrontal-cingulate cortex networks in balancing emotions and cognitive processes.The numerical results confirm that coupled weights play a crucial role in the balance of emotional cognitive networks.Furthermore,our model predicts the pathogenic mechanism of depression resulting from abnormalities in the subgenual cortex,and network functionality was restored through intervention in the dorsolateral prefrontal cortex.This study utilizes computational modeling techniques to provide an insight explanation for the diagnosis and treatment of depression.
基金funded by the Science and Technology Foundation of Chongqing EducationCommission(GrantNo.KJQN202301153)the ScientificResearch Foundation of Chongqing University of Technology(Grant No.2021ZDZ025)the Postgraduate Innovation Foundation of Chongqing University of Technology(Grant No.gzlcx20243524).
文摘In task-oriented dialogue systems, intent, emotion, and actions are crucial elements of user activity. Analyzing the relationships among these elements to control and manage task-oriented dialogue systems is a challenging task. However, previous work has primarily focused on the independent recognition of user intent and emotion, making it difficult to simultaneously track both aspects in the dialogue tracking module and to effectively utilize user emotions in subsequent dialogue strategies. We propose a Multi-Head Encoder Shared Model (MESM) that dynamically integrates features from emotion and intent encoders through a feature fusioner. Addressing the scarcity of datasets containing both emotion and intent labels, we designed a multi-dataset learning approach enabling the model to generate dialogue summaries encompassing both user intent and emotion. Experiments conducted on the MultiWoZ and MELD datasets demonstrate that our model effectively captures user intent and emotion, achieving extremely competitive results in dialogue state tracking tasks.
基金Supported by 2022 Chinese Medicine Scientific Research Project of Hebei Administration of Traditional Chinese Medicine,No.20221572025 Annual Scientific Research Project of Higher Education Institutions in Hebei Province,No.QN2025654.
文摘BACKGROUND Neck pain,a primary symptom of cervical spondylosis,affects patients'physical and mental health,reducing their quality of life.Pain and emotional state interact;however,their longitudinal interrelationship remains unclear.In this study,we applied a dual-trajectory model to assess how neck pain and emotional state evolve together over time and how clinical interventions,particularly acupuncture,influence these trajectories.AIM To investigate the longitudinal relationship between neck pain and emotional state in patients with cervical spondylosis.METHODS This prospective cohort study included 472 patients with cervical spondylosis from eight Chinese hospitals.Participants received acupuncture or medication and were followed up at baseline,and at 1,2,4,6,and 8 weeks.Neck pain and emotional distress were assessed using the Northwick Park Neck Pain Questionnaire(NPQ)and the affective subscale of the Short-Form McGill Pain Questionnaire(SF-MPQ),respectively.Group-based trajectory models and dual trajectory analysis were used to identify and correlate pain-emotion trajectories.Multivariate logistic regression identified predictors of group membership.RESULTS Three trajectory groups were identified for NPQ and SF-MPQ scores(low,medium,and high).Higher NPQ trajectory was associated with older age(OR=1.058,P<0.001)and was significantly reduced by acupuncture(OR=0.382,P<0.001).Similarly,acupuncture lowered the odds of high SF-MPQ trajectory membership(OR=0.336,P<0.001),while age increased it(OR=1.037,P<0.001).Dual-trajectory analysis revealed bidirectional associations:69.1%of patients with low NPQ had low SF-MPQ scores,and 42.6%of patients with high SF-MPQ also had high NPQ scores.Gender was a predictor for medium SF-MPQ trajectory(OR=1.629,P=0.094).Occupation and education levels differed significantly across the trajectory groups(P<0.05).CONCLUSION Over time,neck pain and emotional distress are closely associated in patients with cervical spondylosis.Acupuncture alleviates both outcomes significantly,while age is a risk factor.Integrated approaches to pain and emotional management are encouraged.
基金The National Natural Science Foundation of China(No.61231002,61273266,51075068,61271359)Doctoral Fund of Ministry of Education of China(No.20110092130004)
文摘A cascaded projection of the Gaussian mixture model algorithm is proposed.First,the marginal distribution of the Gaussian mixture model is computed for different feature dimensions, and a number of sub-classifiers are generated using the marginal distribution model.Each sub-classifier is based on different feature sets.The cascaded structure is adopted to fuse the sub-classifiers dynamically to achieve sample adaptation ability.Secondly,the effectiveness of the proposed algorithm is verified on electrocardiogram emotional signal and speech emotional signal.Emotional data including fidgetiness,happiness and sadness is collected by induction experiments.Finally,the emotion feature extraction method is discussed,including heart rate variability, the chaotic electrocardiogram feature and utterance level static feature.The emotional feature reduction methods are studied, including principle component analysis,sequential forward selection, the Fisher discriminant ratio and maximal information coefficient.The experimental results show that the proposed classification algorithm can effectively improve recognition accuracy in two different scenarios.
基金Project(2006AA04Z201) supported by the National High-Tech Research and Development Program of China
文摘According to the basic emotional theory, the artificial emotional model based on the finite state machine（FSM） was presented. In finite state machine model of emotion, the emotional space included the basic emotional space and the multiple emotional spaces. The emotion-switching diagram was defined and transition fimction was developed using Markov chain and linear interpolation algorithm. The simulation model was built using Stateflow toolbox and Simulink toolbox based on the Matlab platform. And the model included three subsystems： the input one, the emotion one and the behavior one. In the emotional subsystem, the responses of different personalities to the external stimuli were described by defining personal space. This model takes states from an emotional space and updates its state depending on its current state and a state of its input （also a state-emotion）. The simulation model realizes the process of switching the emotion from the neutral state to other basic emotions. The simulation result is proved to correspond to emotion-switching law of human beings.
基金This work was supported by the National Nature Science Foundation of China(No.61503423,H.P.Jiang).The URL is http://www.nsfc.gov.cn/.
文摘Emotions serve various functions.The traditional emotion recognition methods are based primarily on readily accessible facial expressions,gestures,and voice signals.However,it is often challenging to ensure that these non-physical signals are valid and reliable in practical applications.Electroencephalogram(EEG)signals are more successful than other signal recognition methods in recognizing these characteristics in real-time since they are difficult to camouflage.Although EEG signals are commonly used in current emotional recognition research,the accuracy is low when using traditional methods.Therefore,this study presented an optimized hybrid pattern with an attention mechanism(FFT_CLA)for EEG emotional recognition.First,the EEG signal was processed via the fast fourier transform(FFT),after which the convolutional neural network(CNN),long short-term memory(LSTM),and CNN-LSTM-attention(CLA)methods were used to extract and classify the EEG features.Finally,the experiments compared and analyzed the recognition results obtained via three DEAP dataset models,namely FFT_CNN,FFT_LSTM,and FFT_CLA.The final experimental results indicated that the recognition rates of the FFT_CNN,FFT_LSTM,and FFT_CLA models within the DEAP dataset were 87.39%,88.30%,and 92.38%,respectively.The FFT_CLA model improved the accuracy of EEG emotion recognition and used the attention mechanism to address the often-ignored importance of different channels and samples when extracting EEG features.
文摘BACKGROUND Acute pancreatitis(AP),as a common acute abdomen disease,has a high incidence rate worldwide and is often accompanied by severe complications.Negative emotions lead to increased secretion of stress hormones,elevated blood sugar levels,and enhanced insulin resistance,which in turn increases the risk of AP and significantly affects the patient's quality of life.Therefore,exploring the intervention effects of narrative nursing programs on the negative emotions of patients with AP is not only helpful in alleviating psychological stress and improving quality of life but also has significant implications for improving disease outcomes and prognosis.AIM To construct a narrative nursing model for negative emotions in patients with AP and verify its efficacy in application.METHODS Through Delphi expert consultation,a narrative nursing model for negative emotions in patients with AP was constructed.A non-randomized quasi-experimental study design was used in this study.A total of 92 patients with AP with negative emotions admitted to a tertiary hospital in Nantong City of Jiangsu Province,China from September 2022 to August 2023 were recruited by convenience sampling,among whom 46 patients admitted from September 2022 to February 2023 were included in the observation group,and 46 patients from March to August 2023 were selected as control group.The observation group received narrative nursing plan,while the control group was given with routine nursing.Self-rating anxiety scale(SAS),self-rating depression scale(SDS),positive and negative affect scale(PANAS),caring behavior scale,patient satisfaction scale and 36-item short form health survey questionnaire(SF-36)were used to evaluate their emotions,satisfaction and caring behaviors in the two groups on the day of discharge,1-and 3-month following discharge.RESULTS According to the inclusion and exclusion criteria,a total of 45 cases in the intervention group and 44 cases in the control group eventually recruited and completed in the study.On the day of discharge,the intervention group showed significantly lower scores of SAS,SDS and negative emotion(28.57±4.52 vs 17.4±4.44,P<0.001),whereas evidently higher outcomes in the positive emotion score,Caring behavior scale score and satisfaction score compared to the control group(P<0.05).Repeated measurement analysis of variance showed that significant between-group differences were found in time effect,inter-group effect and interaction effect of SAS and PANAS scores as well as in time effect and inter-group effect of SF-36 scores(P<0.05);the SF-36 scores of two groups at 3 months after discharge were higher than those at 1 month after discharge(P<0.05).CONCLUSION The application of narrative nursing protocols has demonstrated significant effectiveness in alleviating anxiety,ameliorating negative emotions,and enhancing satisfaction among patients with AP.
基金the National Natural Science Foundation of China (Grant Nos. 71790613 and 72091512)the Science and Technology Innovation Program of Hunan Province, China (Grant No. 2020SK2004)。
文摘Panic is a common emotion when pedestrians are in danger during the actual evacuation, which can affect pedestrians a lot and may lead to fatalities as people are crushed or trampled. However, the systematic studies and quantitative analysis of evacuation panic, such as panic behaviors, panic evolution, and the stress responses of pedestrians with different personality traits to panic emotion are still rare. Here, combined with the theories of OCEAN(openness, conscientiousness,extroversion, agreeableness, neuroticism) model and SIS(susceptible, infected, susceptible) model, an extended cellular automata model is established by the floor field method in order to investigate the dynamics of panic emotion in the crowd and dynamics of pedestrians affected by emotion. In the model, pedestrians are divided into stable pedestrians and sensitive pedestrians according to their different personality traits in response to emotion, and their emotional state can be normal or panic. Besides, emotion contagion, emotion decay, and the influence of emotion on pedestrian movement decision-making are also considered. The simulation results show that evacuation efficiency will be reduced, for panic pedestrians may act maladaptive behaviors, thereby making the crowd more chaotic. The results further suggest that improving pedestrian psychological ability and raising the standard of management can effectively increase evacuation efficiency. And it is necessary to reduce the panic level of group as soon as possible at the beginning of evacuation. We hope this research could provide a new method to analyze crowd evacuation in panic situations.
文摘BACKGROUND Primiparas are usually at high risk of experiencing perinatal depression,which may cause prolonged labor,increased blood loss,and intensified pain,affecting maternal and fetal outcomes.Therefore,interventions are necessary to improve maternal and fetal outcomes and alleviate primiparas’negative emotions(NEs).AIM To discusses the impact of nursing responsibility in midwifery and postural and psychological interventions on maternal and fetal outcomes as well as primiparas’NEs.METHODS As participants,115 primiparas admitted to Quanzhou Maternity and Child Healthcare Hospital between May 2020 and May 2022 were selected.Among them,56 primiparas(control group,Con)were subjected to conventional midwifery and routine nursing.The remaining 59(research group,Res)were subjected to the nursing model of midwifery and postural and psychological interventions.Both groups were comparatively analyzed from the perspectives of delivery mode(cesarean,natural,or forceps-assisted),maternal and fetal outcomes(uterine inertia,postpartum hemorrhage,placental abruption,neonatal pulmonary injury,and neonatal asphyxia),NEs(Hamilton Anxiety/Depressionrating Scale,HAMA/HAMD),labor duration,and nursing satisfaction.RESULTS The Res exhibited a markedly higher natural delivery rate and nursing satisfaction than the Con.Additionally,the Res indicated a lower incidence of adverse events(e.g.,uterine inertia,postpartum hemorrhage,placental abruption,neonatal lung injury,and neonatal asphyxia)and shortened duration of various stages of labor.It also showed statistically lower post-interventional HAMA and HAMD scores than the Con and pre-interventional values.CONCLUSION The nursing model of midwifery and postural and psychological interventions increase the natural delivery rate and reduce the duration of each labor stage.These are also conducive to improving maternal and fetal outcomes and mitigating primiparas’NEs and thus deserve popularity in clinical practice.
基金supported by Scientific Research Fund of Hunan Provincial EducationDepartment(23B1133):How Labor Affects Moral Development:Based on the perspective of mixed research methods.
文摘Background:Understanding the factors that influence adolescent psychological resilience is critical for promoting mental health.This study explores the impact and mechanism of labor values on adolescent psychological resilience from the perspective of emotion regulation theory.Methods:This study conducted an in-depth analysis using the Labor Value Scale on 2691 elementary school upper-grade students,middle school students,and high school students.Results:The results show that:(1)labor values can positively predict adolescents’mental resilience;(2)cognitive reappraisal and expression inhibition play a partial mediating role in the relationship between labor values and adolescents’psychological resilience.Among them,labor values can positively predict adolescents’mental resilience through positive cognitive reappraisal,and labor values can also predict adolescents’mental resilience through expression inhibition.Conclusion:Based on the theory of emotion regulation,this study explores the direct effect of labor values on mental resilience and the mediating effect of different strategies of emotion regulation.The results of this study provide a theoretical basis for improving the mental resilience of adolescents.
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.
文摘Maintaining optimal quality of life(QoL)is a pivotal for“successful aging”.Understanding how the QoL of the elderly develops and what role psychological factors play in its development will help improve QoL from a psychological perspective.Embedded within the lifespan theory of control,this longitudinal study aimed to(1)map the temporal trajectory of QoL among Chinese older adults,(2)examine differential effects of tripartite negative emotions(stress,anxiety,depression),and(3)test themoderating role of control strategies(goal engagement,goal disengagement,self-protection)in emotion-QoL dynamics.A prospective cohort of 345 community-dwelling older adults(Mage=83.84±8.49 years;55.1%female)completed validated measures-SF-36 for QoL,DASS-21 for negative emotions,and an adapted Control Strategies Questionnaire(CAS)-at three waves spanning 12 months.Hierarchical linear modeling(HLM)with time-nested structure analyzed intraindividual changes and interindividual differences.QoL exhibited a significant linear decline over time(β=−4.75,p<0.001).Stress(β=−14.12,p<0.001)and anxiety(β=−11.24,p<0.001)robustly predicted QoL decline,whereas depression showed no significant effect.Control strategies had divergent associations:goal engagement(β=3.51,p<0.001)and self-protection(β=2.38,p=0.015)predicted higher baseline QoL,while goal disengagement accelerated decline(β=−7.00,p<0.001;interaction with time:β=−2.46,p<0.001).Contrary to hypotheses,control strategies did not moderate emotion-QoL associations(ΔR2=0.02,p=0.21).The results showed that stress and anxiety played an important role in the QoL of the elderly.At the same time,goal engagement and self-protection were beneficial to the QoL of the elderly,while goal disengagement was not conducive to QoL and its development among the elderly.Meanwhile,the negative effect of anxiety and stress on the QoL of the elderly was not affected by the control strategies.
文摘Emotion Model is the basis of facial expression recognition system. The constructed emotional model should not only match facial expressions with emotions, but also reflect the location relationship between different emotions. In this way, it is easy to understand the current emotion of an individual through the analysis of the acquired facial expression information. This paper constructs an improved three-dimensional model for emotion based on fuzzy theory, which corresponds to the facial features to emotions based on the basic emotions proposed by Ekman. What’s more, the three-dimensional model for motion is able to divide every emotion into three different groups which can show the positional relationship visually and quantitatively and at the same time determine the degree of emotion based on fuzzy theory.
基金supported by Shandong Provincial Social Science Planning Research Project“Research on Inheritance and Innovation of Shandong Wooden Clappers Culture”(20CCXJ26).
文摘In the face offierce competition in the social environment,mental health problems gradually get the attention of the public,in order to achieve accurate mental health data analysis,the construction of music education is based on emotional tendency analysis of psychological adjustment function model.Design emotional tendency analysis of music education psychological adjustment function architecture,music teaching goal as psychological adjust-ment function architecture building orientation,music teaching content as a foundation for psychological adjust-ment function architecture and music teaching process as a psychological adjustment function architecture building,music teaching evaluation as the key of building key regulating function architecture,Establish a core literacy oriented evaluation system.Different evaluation methods were used to obtain the evaluation results.Four levels of psychological adjustment function model of music education are designed,and the psychological adjust-ment function of music education is put forward,thus completing the construction of psychological adjustment function model of music education.The experimental results show that the absolute value of the data acquisition error of the designed model is minimum,which is not more than 0.2.It is less affected by a bad coefficient and has good performance.It can quickly converge to the best state in the actual prediction process and has a strong con-vergence ability.
文摘Emotion mismatch between training and testing is one of the important factors causing the performance degradation of speaker recognition system. In our previous work, a bi-model emotion speaker recognition (BESR) method based on virtual HD (High Different from neutral, with large pitch offset) speech synthesizing was proposed to deal with this problem. It enhanced the system performance under mismatch emotion states in MASC, while still suffering the system risk introduced by fusing the scores from the unreliable VHD model and the neutral model with equal weight. In this paper, we propose a new BESR method based on score reliability fusion. Two strategies, by utilizing identification rate and scores average relative loss difference, are presented to estimate the weights for the two group scores. The results on both MASC and EPST shows that by using the weights generated by the two strategies, the BESR method achieve a better performance than that by using the equal weight, and the better one even achieves a result comparable to that by using the best weights selected by exhaustive strategy.
基金supported in part by the National Natural Science Foundation of China(NSFC)Key Program(61573094)Fundamental Research Funds for the Central Universities(N140402001)
文摘Understanding people's emotions through natural language is a challenging task for intelligent systems based on Internet of Things(Io T). The major difficulty is caused by the lack of basic knowledge in emotion expressions with respect to a variety of real world contexts. In this paper, we propose a Bayesian inference method to explore the latent semantic dimensions as contextual information in natural language and to learn the knowledge of emotion expressions based on these semantic dimensions. Our method synchronously infers the latent semantic dimensions as topics in words and predicts the emotion labels in both word-level and document-level texts. The Bayesian inference results enable us to visualize the connection between words and emotions with respect to different semantic dimensions. And by further incorporating a corpus-level hierarchy in the document emotion distribution assumption, we could balance the document emotion recognition results and achieve even better word and document emotion predictions. Our experiment of the wordlevel and the document-level emotion predictions, based on a well-developed Chinese emotion corpus Ren-CECps, renders both higher accuracy and better robustness in the word-level and the document-level emotion predictions compared to the state-of-theart emotion prediction algorithms.
基金This work was financially supported by the National Natural Science Foundation of China (No.69975002).
文摘How to make machines express emotions would be instrumental in establishing acompletely new paradigm for man machine interaction. A new method for simulating and assessingartificial psychology has been developed for the research of the emotion robot. The human psychologyactivity is regarded as a Markov process. An emotion space and psychology model is constructedbased on Markov process. The conception of emotion entropy is presented to assess the artificialemotion complexity. The simulating results play up to human psychology activity. This model can alsobe applied to consumer-friendly human-computer interfaces, and interactive video etc.
基金This paper was supported by the 2018 Science and Technology Breakthrough Project of Henan Provincial Science and Technology Department(No.182102310694).
文摘As an interdisciplinary comprehensive subject involving multidisciplinary knowledge,emotional analysis has become a hot topic in psychology,health medicine and computer science.It has a high comprehensive and practical application value.Emotion research based on the social network is a relatively new topic in the field of psychology and medical health research.The text emotion analysis of college students also has an important research significance for the emotional state of students at a certain time or a certain period,so as to understand their normal state,abnormal state and the reason of state change from the information they wrote.In view of the fact that convolutional neural network cannot make full use of the unique emotional information in sentences,and the need to label a large number of highquality training sets for emotional analysis to improve the accuracy of the model,an emotional analysismodel using the emotional dictionary andmultichannel convolutional neural network is proposed in this paper.Firstly,the input matrix of emotion dictionary is constructed according to the emotion information,and the different feature information of sentences is combined to form different network input channels,so that the model can learn the emotion information of input sentences from various feature representations in the training process.Then,the loss function is reconstructed to realize the semi supervised learning of the network.Finally,experiments are carried on COAE 2014 and self-built data sets.The proposed model can not only extract more semantic information in emotional text,but also learn the hidden emotional information in emotional text.The experimental results show that the proposed emotion analysis model can achieve a better classification performance.Compared with the best benchmark model gram-CNN,the F1 value can be increased by 0.026 in the self-built data set,and it can be increased by 0.032 in the COAE 2014 data set.
基金supported by National High Technology Research and Development Program of China (863 Program)(No.2007AA04Z218)
文摘In this paper, an emotional mathematical model and affective state probability description space of a humanoid robot are set up on the basis of psycho-dynamics＇ psychological energy and affective energy conservation law. The emotional state transferring process and hidden Markov chain algorithm of stimulating transition process are then studied. The simulation results show that the mathematical model is applicable to the authentic affective state change rule of human beings. Finally, the gait generation experiment results of control signal and electric current tracking wave-form are presented to demonstrate the validity of the proposed mathematical model.
