Associations between Mental Health Outcomes and Adverse Childhood Experiences and Character Strengths among University Students in Southern China

Adverse childhood experiences(ACEs)can negatively affect mental health,whereas character strengths seem to be positively correlated with mental health.Detailed information on the history of ACEs among university students in China and the extent which mental health is associated with ACEs and character strengths can contribute to the needed empirical evidence for relevant stakeholders.Objectives of this study are 1)to estimate the prevalence of ACEs among undergraduate students in Southern China;and 2)to assess the extent which mental health outcomes(positive growth,well-being,and depression)are associated with ACEs and character strengths among undergraduate students in Southern China.We conducted a self-administered survey amongfirst and second-year students at a university in Southern China and analyzed data using descriptive statistics and linear regression analyses.Among the 779 students who completed the questionnaire,283 were males,439 were females,and 57 did not indicate their gender.The prevalence of ACEs among the participants was 32.1%.History of ACEs was associated with lower positive growth score(Adjusted Beta=-0.20;95%CI=-0.37,-0.02;p=0.030)and lower well-being score(Adjusted Beta=-1.13;95%CI=-2.04,-0.23;p=0.014).Vitality domain of character strength was associated with all three mental health outcomes after adjusting for covariables(all p-values<0.001).Our studyfindings provide empirical evidence for stakeholders in university mental health.However,caveats regarding lack of temporality,selection bias,social desirability bias,and lack of generalizability should be considered in the interpretation of the studyfindings.

Research on Behavioral Characteristics of Tourism Consumption from College Students

College students have become important tourist market segments,and they are of great development potential.On the basis of survey to college students from ten universities in Guangzhou University City,their tourism consumption behaviors were discussed.Finally,some relevant recommendations to service quality improvement of tourism enterprises and development of college students market were proposed.

Optimised CNN Architectures for Handwritten Arabic Character Recognition

Handwritten character recognition is considered challenging compared with machine-printed characters due to the different human writing styles.Arabic is morphologically rich,and its characters have a high similarity.The Arabic language includes 28 characters.Each character has up to four shapes according to its location in the word(at the beginning,middle,end,and isolated).This paper proposed 12 CNN architectures for recognizing handwritten Arabic characters.The proposed architectures were derived from the popular CNN architectures,such as VGG,ResNet,and Inception,to make them applicable to recognizing character-size images.The experimental results on three well-known datasets showed that the proposed architectures significantly enhanced the recognition rate compared to the baseline models.The experiments showed that data augmentation improved the models’accuracies on all tested datasets.The proposed model outperformed most of the existing approaches.The best achieved results were 93.05%,98.30%,and 96.88%on the HIJJA,AHCD,and AIA9K datasets.

Effect of Co on Solidification Characteristics and Microstructural Transformation of Non-equilibrium Solidified Cu-Ni Alloys

Non-equilibrium solidification structures of Cu55Ni45 and Cu55Ni43Co2 alloys were prepared by the molten glass purification cycle superheating method.The variation of the recalescence phenomenon with the degree of undercooling in the rapid solidification process was investigated using an infrared thermometer.The addition of the Co element affected the evolution of the recalescence phenomenon in Cu-Ni alloys.The images of the solid-liquid interface migration during the rapid solidification of supercooled melts were captured by using a high-speed camera.The solidification rate of Cu-Ni alloys,with the addition of Co elements,was explored.Finally,the grain refinement structure with low supercooling was characterised using electron backscatter diffraction(EBSD).The effect of Co on the microstructural evolution during nonequilibrium solidification of Cu-Ni alloys under conditions of small supercooling is investigated by comparing the microstructures of Cu55Ni45 and Cu55Ni43Co2 alloys.The experimental results show that the addition of a small amount of Co weakens the recalescence behaviour of the Cu55Ni45 alloy and significantly reduces the thermal strain in the rapid solidification phase.In the rapid solidification phase,the thermal strain is greatly reduced,and there is a significant increase in the characteristic undercooling degree.Furthermore,the addition of Co and the reduction of Cu not only result in a lower solidification rate of the alloy,but also contribute to the homogenisation of the grain size.

Investigation of the Acceptability of Cultured Meat in University Students

Background: Over the past 20 years, cultured meat has drawn a lot of public attention as a potential solution to issues with animal husbandry, including inadequate use of natural sources, improper animal welfare practices, and possible risks to public health and safety. The novel method of producing meat through culture reduces the need for animals to produce muscle fiber, thereby obviating the necessity for animal slaughter. Apart from its ethical advantages, cultured meat presents a possible way to fulfill the expanding need for food among growing populations. The purpose of this research was to find out whether Turkish students would be willing to pay for and accept cultured meat. Technique: Method: 371 university students who willingly consented to fill out a questionnaire and provide demographic data make up the research sample. Questions from previous studies on the acceptability of cultured meat were compiled to create the survey. The research’s data collection took place in March and April of 2022. The research was completed in June 2022 after the data had been processed and analyzed. Results: The results showed that the majority of participants were female and had omnivorous eating habits. Based on the results of the Bonferroni correction test, students with a higher intention to purchase and consume cultured meat were those who received economics and business education. Students with two years of university education had a higher overall survey score than those with four years of education (p < 0.05). Furthermore, it is discovered that there is a negative correlation between the participants’ ages and their Factor 2 (using cultured meat as an alternative to industrial meat) and Factor 3 (consuming and purchasing it) section points (r = -109, p = 0.036) (r = -0.121, p = 0.019). In conclusion, university students generally have a negative outlook on health-related issues, such as eating cultured meat as an alternative.

Comparison of vergence mechanisms between university students with good and poor sleep quality

AIM:To compare the vergence mechanisms between good and poor sleepers in university students.METHODS:A total of 64 university students were recruited in this study.The validated Malay version of Pittsburgh Sleep Quality Index questionnaire(PSQI-M)was used to measure the participants’sleep quality over the past month.Participants were categorized as good sleepers(n=32)and poor sleepers(n=32)based on the PSQI-M scores.Heterophoria and fusional vergences were measured at distance and near.Mann-Whitney U test was used to compare heterophoria,negative fusional vergence(NFV),and positive fusional vergence(PFV)at distance and near between good and poor sleepers.Spearman correlation analysis was used to study the relationship between PSQI-M score and PFV at distance.RESULTS:Both distance and near heterophorias were not significantly different between good and poor sleepers(P>0.05).There was a difference in distance PFV(P<0.05)between good and poor sleepers,but not in distance NFV,near NFV,and near PFV(P>0.05).Distance PFV was negatively correlated with PSQI-M score(rs=-0.33,P<0.05).CONCLUSION:University students with poor sleep quality demonstrates a reduced ability to maintain fusion with increasing convergence demand at distance.Sleep quality assessment during binocular vision examination in university students is recommended.

The Impact of Social Media on Dietary Behaviors and Body Image of College Students: A Qualitative Approach

Background: The use of social media platforms for health and nutrition information has become popular among college students. Although social media made information readily accessible in different formats, nutritional misinformation promoted by influencers and non-experts caused negative impact on diet behavior and perception of body image. Previous research indicated that extensive use of social media was positively linked to disordered eating behaviors. Social media platforms like Facebook and Instagram that allow users to follow celebrities intensified exposure to influencers’ messages and images and resulted in negative moods and body dissatisfaction. Objective: This paper aims to explore the impact of social media on college students’ dietary behaviors and body image. Participants: 18 undergraduate students from a public university in the Southern United States were recruited through a mass email. Methods: A cross-sectional qualitative study of three focus groups was conducted. The focus groups were based on guiding open-ended questions. Atlas.ti was used to code and analyze the data using inductive and deductive codes. Results: Three main themes were identified. The conditions theme included elements that explain why and how social media influences the participants’ actions. The actions theme included eating behavior, physical activity, and dietary supplement intake. The consequences theme describes anticipated or actual outcomes of actions such as body image and ideal weight. Conclusions: Social media has had a negative influence on diet behaviors and a positive influence on physical activity. Evidence-based nutrition and weight management information is needed to thwart potential misinformation.

Instance Segmentation of Characters Recognized in Palmyrene Aramaic Inscriptions

This study presents a single-class and multi-class instance segmentation approach applied to ancient Palmyrene inscriptions,employing two state-of-the-art deep learning algorithms,namely YOLOv8 and Roboflow 3.0.The goal is to contribute to the preservation and understanding of historical texts,showcasing the potential of modern deep learning methods in archaeological research.Our research culminates in several key findings and scientific contributions.We comprehensively compare the performance of YOLOv8 and Roboflow 3.0 in the context of Palmyrene character segmentation—this comparative analysis mainly focuses on the strengths and weaknesses of each algorithm in this context.We also created and annotated an extensive dataset of Palmyrene inscriptions,a crucial resource for further research in the field.The dataset serves for training and evaluating the segmentation models.We employ comparative evaluation metrics to quantitatively assess the segmentation results,ensuring the reliability and reproducibility of our findings and we present custom visualization tools for predicted segmentation masks.Our study advances the state of the art in semi-automatic reading of Palmyrene inscriptions and establishes a benchmark for future research.The availability of the Palmyrene dataset and the insights into algorithm performance contribute to the broader understanding of historical text analysis.

Knowledge of Hospital Acquired Infections (HAIs) among Medical Students in a Tertiary Hospital in Jos North Local Government Area, Plateau State, Nigeria

Background: Hospital Acquired Infections (HAIs) remain a common cause of death, functional disability, emotional suffering and economic burden among hospitalized patients. Knowledge of HAIs is important in its prevention and control. This study seeks to assess the knowledge of Hospital Acquired Infections (HAIs) among medical students in a Tertiary Hospital in Jos North Local Government Area, Plateau State, Nigeria. Methods: This was a descriptive cross-sectional study done in October 2019 among clinical medical students using a Multistage sampling technique. Data was collected using a self-administered structured questionnaire and analyzed using the IBM SPSS 20 (Statistical Package for the Social Sciences). Ethical approval was granted by Bingham University Teaching Hospital, Ethics Committee, Jos, Plateau State. Results: A total of 219 students in the clinical arm of the College of Medicine and Health Sciences were selected. A higher proportion (97.7%) of respondents knew about Hospital Acquired Infections and 85.4% knew that Hospital Acquired infections occur in the hospital, and (86.3%) considered patients contagious with half (58.9%) considered patients as the most important source of HAIs, followed by care givers (13.2%), then doctors including medical students and interns (10.0%) and lastly nurses (8.7%). The majority of respondents (70.8%) considered Surgical Wound Infections to be the most commonly occurring HAI, followed by UTIs (69.9%), RTIs (61.2%), BSIs (37.0%) and others (0.9%). The clinical thermometer was the instrument that most commonly transmits HAIs (82.6%), then followed by stethoscope (62.1%), white coats (53.9%), and blood pressure cuff (51.1%). Most respondents knew the infectious substances, like blood (96.3%), nasal discharge (82.6%), saliva (85.3%), and faeces (79.4%) transmitted HAIs, 72.6% of the respondents said that they were aware of the recommended hand washing techniques by WHO. Conclusion: The majority of students 91.3% had good knowledge while 8.7% had poor knowledge of HAIs. Lower classes had more respondents with poor knowledge. This finding was statistically significant (p = 0.002, Chi-square 12.819). Students are encouraged to keep up the level of knowledge they have about HAIs. These students can help improve the knowledge of those whose knowledge level is low. Government and NGOs should support sponsorship for capacity-building events targeted at HAIs for healthcare workers and medical students.

Knowledge of Risk Factors for Hypertension among University Students in Northwestern Tanzania: A Cross-Sectional Study

Introduction: Obesity, excessive alcohol use, cigarette smoking, a lack of physical activity, stress, and an unhealthy diet are modifiable risk factors linked to hypertension. Non-modifiable risk factors for hypertension include older age and a family history of hypertension. The purpose of this study was to assess university students’ knowledge of hypertension risk factors. Methods: This was a cross-sectional study conducted at St. Augustine University of Tanzania. A pre-tested, semi-structured questionnaire was used to collect data. Out of a total score of eight, a score of four or more was considered good knowledge, and a score of less than four was considered poor. All sociodemographic characteristics were included in logistic regression to calculate the adjusted odds ratio. Results: A total of 390 undergraduate students participated in this study. Most of the participants 266 (68.2%) identified stress as a risk factor for hypertension. The median (IQR) knowledge score was 2 (2 - 3). Overall, only 43 (11.0%) of the participants had good knowledge of risk factors for hypertension. However, none of the sociodemographic factors were associated with a good level of knowledge of risk factors for hypertension. Conclusion: Our findings highlight poor knowledge of modifiable and non-modifiable risk factors for hypertension among university students in northwestern Tanzania. Only stress and older age were known by more than half of the students as risk factors for hypertension. To reduce the burden of hypertension, it is crucial for prevention and control programs to target improving university students’ knowledge of risk factors for hypertension.

The Emotional Intelligence and the Associated Factors among Nursing Students

Introduction: Emotional intelligence, or the capacity to cope one’s emotions, makes it simpler to form good connections with others and do caring duties. Nursing students can enroll a health team in a helpful and beneficial way with the use of emotional intelligence. Nurses who can identify, control, and interpret both their own emotions and those of their patients provide better patient care. The purpose of this study was to assess the emotional intelligence and to investigate the relationship and differences between emotional intelligence and demographic characteristics of nursing students. Methods: A cross-sectional study was carried out on 381 nursing students. Data collection was completed by “Schutte Self Report Emotional Intelligence Test”. Data were analyzed with the Statistical Package for Social Science. An independent t test, ANOVA, and Pearson correlation, multiple linear regression were used. Results: The results revealed that the emotional intelligence mean was 143.1 ± 21.6 (ranging from 33 to 165), which is high. Also, the analysis revealed that most of the participants 348 (91.3%) had higher emotional intelligence level. This finding suggests that nursing students are emotionally intelligent and may be able to notice, analyze, control, manage, and harness emotion in an adaptive manner. Also, academic year of nursing students was a predictor of emotional intelligence. Furthermore, there was positive relationship between the age and emotional intelligence (p < 0.05). The students’ ability to use their EI increased as they rose through the nursing grades. Conclusion: This study confirmed that the emotional intelligence score of the nursing students was high. Also, academic year of nursing students was a predictor of emotional intelligence. In addition, a positive relationship was confirmed between the emotional intelligence and age of nursing students. .

The Impact of Sleep Patterns on Academic Performance and Social Integration among Medical Students

Introduction: The impact of sleep on student life is crucial, particularly for those in demanding fields such as medicine. This study examines the relationships between sleep patterns, academic performance, and social integration among medical students, who often face irregular sleep cycles and sleep deprivation due to rigorous academic demands. Aim: This study aims to assess how sleep issues affect academic achievement and social relationships among medical students. Method: Data were collected from 215 medical students through surveys and academic records. Quantitative data provided insights into sleep quality and academic performance, while qualitative interviews explored the effects of sleep on social interactions. Results: The findings revealed a significant correlation between sleep quality and academic performance, with students reporting better sleep hygiene achieving higher grades. Qualitative data indicated that poor sleep negatively impacts social interactions, leading to feelings of isolation and reduced social participation. Conclusions: The study highlights the importance of promoting good sleep practices in medical schools to enhance academic success and social well-being. Interventions aimed at improving sleep quality may help reduce burnout and improve overall well-being among medical students. Future research should focus on longitudinal studies to better understand the long-term effects of sleep on academic and social outcomes in this population.

Study of Factors Associated with the Practice of Vaccination against COVID-19 among Medical Science Students in Dakar (Senegal)

Introduction: Almost a year after the start of the COVID-19 pandemic, Senegal embarked on a vaccination campaign to protect its population. The objective of this study was to identify the factors influencing the COVID-19 vaccination practices within a health training university in Dakar. Methodology: This was a cross-sectional, descriptive and analytical study carried out from October 21 2021 to February 21 2022 among students at the Elhadj Ibrahima Niass private University in Dakar, Senegal. A 28-question online questionnaire was sent to all students enrolled at the university during this period. Results: Of the 576 students who responded, the average age was 22.60 years, with females predominating (57.81%). Medical students accounted for 66.14% of participants, pharmacy 22.40% and dental surgery 11.46%. Of these, 42.01% were bachelor’s students, 29.51% master’s students and 28.47% doctoral students. Vaccination coverage was 50.35%. Students who considered COVID-19 to be very dangerous were more likely to be vaccinated (OR = 5.05 [2.24-11.9]). Those with poor knowledge of vaccines were less likely to be vaccinated (OR = 0.07 [0.03-0.18]), as were those with poor knowledge of contraindications to vaccination (OR = 0.49 [0.28-0.86]). No association was found between vaccination status and socio-demographic or educational characteristics. Conclusion: In view of the importance of knowledge about COVID-19 vaccination, our results suggest that it is important to involve health students in the Ministry of Health’s awareness-raising strategies, because their support is necessary for better public awareness.

A Method for Detecting and Recognizing Yi Character Based on Deep Learning

Aiming at the challenges associated with the absence of a labeled dataset for Yi characters and the complexity of Yi character detection and recognition,we present a deep learning-based approach for Yi character detection and recognition.In the detection stage,an improved Differentiable Binarization Network(DBNet)framework is introduced to detect Yi characters,in which the Omni-dimensional Dynamic Convolution(ODConv)is combined with the ResNet-18 feature extraction module to obtain multi-dimensional complementary features,thereby improving the accuracy of Yi character detection.Then,the feature pyramid network fusion module is used to further extract Yi character image features,improving target recognition at different scales.Further,the previously generated feature map is passed through a head network to produce two maps:a probability map and an adaptive threshold map of the same size as the original map.These maps are then subjected to a differentiable binarization process,resulting in an approximate binarization map.This map helps to identify the boundaries of the text boxes.Finally,the text detection box is generated after the post-processing stage.In the recognition stage,an improved lightweight MobileNetV3 framework is used to recognize the detect character regions,where the original Squeeze-and-Excitation(SE)block is replaced by the efficient Shuffle Attention(SA)that integrates spatial and channel attention,improving the accuracy of Yi characters recognition.Meanwhile,the use of depth separable convolution and reversible residual structure can reduce the number of parameters and computation of the model,so that the model can better understand the contextual information and improve the accuracy of text recognition.The experimental results illustrate that the proposed method achieves good results in detecting and recognizing Yi characters,with detection and recognition accuracy rates of 97.5%and 96.8%,respectively.And also,we have compared the detection and recognition algorithms proposed in this paper with other typical algorithms.In these comparisons,the proposed model achieves better detection and recognition results with a certain reliability.

Attitude and willingness on gamete donation among medical students:An experience from a state university in Sri Lanka

Objective:To assess the attitude and willingness of medical students of the Faculty of Medicine,University of Jaffna,regarding gamete donation.Methods:An institutional-based descriptive cross-sectional study was conducted at the Faculty of Medicine,University of Jaffna,from September 2022 to May 2023 among undergraduate medical students who gave their voluntary participation.A self-administered questionnaire was used as a study instrument to collect data regarding their attitude and willingness toward gamete donation.Results:A total of 345 participants were recruited and their sociodemographic data revealed that 56.8%of the participants were female,62.3%aged between 26 and 30 years,and 92.2%were unmarried.Many of them received information regarding gamete donations during their clinical appointments.Over half(67.8%)of them showed a negative attitude towards gamete donation.Regarding willingness,only 39.7%of participants had a positive approach for being a gamete donor;among them,84.7%preferred anonymous donations.Religion and ethnicity had a significant influence on their attitudes and willingness.In addition,male was also found to be more willing to donate gametes.Conclusions:Most medical students have negative views about gamete donation.Imparting awareness and knowledge of assisted reproductive technology and gamete donation within medical students'sociocultural and ethical backgrounds might facilitate a change in attitude towards gamete donation amongst future medical practitioners.

Preparedness,knowledge,and perception of nursing students about climate change and its impact on human health in India

Higher education plays an important role in all aspects of sustainability.Infusing climate change and sustainability into the curriculum is important to prepare future health-care professionals for climate change and sustainability actions to build resilient health systems.In this study,we evaluated the preparedness,knowledge,and perception regarding climate change and its impact on human health among Indian nursing students.A quantitative descriptive survey was conducted among 644 undergraduate nursing students studying in three institutes in the southern,northern,and western parts of India.We used a structured online questionnaire with established validity and reliability to collect data.The data were analyzed using Statistical Package for the Social Sciences version 26 software.The results showed that more than half of the students reported experiencing excessive increase(362;56.2%)and decrease(374;58.1%)in temperature in recent years.Though more than three quarters of the students(494;76.7%)accepted the general truth that climate change is mainly caused by human activities,only less than half of the students(309;48.0%)perceived the impact of climate change to be high.Three quarters of the students(483;75.0%)had a high level of knowledge regarding climate change.The students who had a high level of knowledge about climate change exerted a high perceived impact of climate change(χ^(2)=75.47;P<0.01).More than half of the students(50.9%)felt that they needed information about climate change to be included in the nursing curriculum,and only one sixth of the students(16.1%)were highly confident about engaging in climate change-related conversation with patients.Most of the students who participated in the study had basic knowledge about climate change and its impact on human health.However,there is an observed gap between knowledge and their preparedness and confidence to engage in climate change actions.Hence,we propose to strengthen the nursing curriculum by integrating various curricular and co-curricular activities related to climate change,so that future nurses are empowered to become climate change advocates.

Frequency and associated factors of accommodation and non-strabismic binocular vision dysfunction among medical university students

AIM:To investigate the frequency and associated factors of accommodation and non-strabismic binocular vision dysfunction among medical university students.METHODS:Totally 158 student volunteers underwent routine vision examination in the optometry clinic of Guangxi Medical University.Their data were used to identify the different types of accommodation and nonstrabismic binocular vision dysfunction and to determine their frequency.Correlation analysis and logistic regression were used to examine the factors associated with these abnormalities.RESULTS:The results showed that 36.71%of the subjects had accommodation and non-strabismic binocular vision issues,with 8.86%being attributed to accommodation dysfunction and 27.85%to binocular abnormalities.Convergence insufficiency(CI)was the most common abnormality,accounting for 13.29%.Those with these abnormalities experienced higher levels of eyestrain(χ2=69.518,P<0.001).The linear correlations were observed between the difference of binocular spherical equivalent(SE)and the index of horizontal esotropia at a distance(r=0.231,P=0.004)and the asthenopia survey scale(ASS)score(r=0.346,P<0.001).Furthermore,the right eye's SE was inversely correlated with the convergence of positive and negative fusion images at close range(r=-0.321,P<0.001),the convergence of negative fusion images at close range(r=-0.294,P<0.001),the vergence facility(VF;r=-0.234,P=0.003),and the set of negative fusion images at far range(r=-0.237,P=0.003).Logistic regression analysis indicated that gender,age,and the difference in right and binocular SE did not influence the emergence of these abnormalities.CONCLUSION:Binocular vision abnormalities are more prevalent than accommodation dysfunction,with CI being the most frequent type.Greater binocular refractive disparity leads to more severe eyestrain symptoms.

Fireworks Optimization with Deep Learning-Based Arabic Handwritten Characters RecognitionModel

Handwritten character recognition becomes one of the challenging research matters.More studies were presented for recognizing letters of various languages.The availability of Arabic handwritten characters databases was confined.Almost a quarter of a billion people worldwide write and speak Arabic.More historical books and files indicate a vital data set for many Arab nationswritten in Arabic.Recently,Arabic handwritten character recognition(AHCR)has grabbed the attention and has become a difficult topic for pattern recognition and computer vision(CV).Therefore,this study develops fireworks optimizationwith the deep learning-based AHCR(FWODL-AHCR)technique.Themajor intention of the FWODL-AHCR technique is to recognize the distinct handwritten characters in the Arabic language.It initially pre-processes the handwritten images to improve their quality of them.Then,the RetinaNet-based deep convolutional neural network is applied as a feature extractor to produce feature vectors.Next,the deep echo state network(DESN)model is utilized to classify handwritten characters.Finally,the FWO algorithm is exploited as a hyperparameter tuning strategy to boost recognition performance.Various simulations in series were performed to exhibit the enhanced performance of the FWODL-AHCR technique.The comparison study portrayed the supremacy of the FWODL-AHCR technique over other approaches,with 99.91%and 98.94%on Hijja and AHCD datasets,respectively.

Using ChatGPT to promote college students’participation in physical activities and its effect on mental health

As one of the most famous large language models,ChatGPT has great potential for application in physical education.It can provide personalized exercise plans,a variety of exercise options,and interactive support.The integration of ChatGPT into the teaching process can promote college students’participation in physical activities and improve their mental health while expanding the traditional teaching environment and promoting the reform of traditional teaching methods.However,the application of ChatGPT faces challenges and obstacles in physical education.To make full use of ChatGPT in physical education,it can be combined with wearable devices and sports equipment to enhance the efficiency of interactions with users.Relevant policies are urgently needed to avoid the improper use of users’data.

Audio2AB:Audio-driven collaborative generation of virtual character animation

Background Considerable research has been conducted in the areas of audio-driven virtual character gestures and facial animation with some degree of success.However,few methods exist for generating full-body animations,and the portability of virtual character gestures and facial animations has not received sufficient attention.Methods Therefore,we propose a deep-learning-based audio-to-animation-and-blendshape(Audio2AB)network that generates gesture animations and ARK it's 52 facial expression parameter blendshape weights based on audio,audio-corresponding text,emotion labels,and semantic relevance labels to generate parametric data for full-body animations.This parameterization method can be used to drive full-body animations of virtual characters and improve their portability.In the experiment,we first downsampled the gesture and facial data to achieve the same temporal resolution for the input,output,and facial data.The Audio2AB network then encoded the audio,audio-corresponding text,emotion labels,and semantic relevance labels,and then fused the text,emotion labels,and semantic relevance labels into the audio to obtain better audio features.Finally,we established links between the body,gestures,and facial decoders and generated the corresponding animation sequences through our proposed GAN-GF loss function.Results By using audio,audio-corresponding text,and emotional and semantic relevance labels as input,the trained Audio2AB network could generate gesture animation data containing blendshape weights.Therefore,different 3D virtual character animations could be created through parameterization.Conclusions The experimental results showed that the proposed method could generate significant gestures and facial animations.