We measured 324 college students’self-esteem,motivation for achievement,and learning burnout,and also we examined the inner mechanism of self-esteem’s influence on students’learning burnout.The results show:(1)Ther...We measured 324 college students’self-esteem,motivation for achievement,and learning burnout,and also we examined the inner mechanism of self-esteem’s influence on students’learning burnout.The results show:(1)There is a negative effect of self-esteem on college students’learning burnout;(2)Motivation of pursuing succeed plays partial mediating effect between self-esteem and learning burnout,but mediating effect of avoiding failure motivation between them is not significant.The study reveals that:the degree of learning burnout can be reduced by improving college students’level of self-esteem.展开更多
With the advancement of economic globalization,mobile networks and media technology are developing rapidly.Media information is manufactured and spread at any time,and people can better understand global information w...With the advancement of economic globalization,mobile networks and media technology are developing rapidly.Media information is manufactured and spread at any time,and people can better understand global information with the help of the media.In the Internet era,college students,overwhelmed by complex information and a lack of information discernment,are susceptible to indulging in the curated online world presented by others.At the same time,negative information such as false,violent,and pornographic also spread rapidly in various media.These phenomena impact students’media literacy and affect their mental health,thereby leading to learning burnout.This study analyzes the current situation of learning burnout among university students,explores the effective path of improving online education of college students,and provides a theoretical basis for reducing the burnout of college students and assisting students in developing a positive mentality.展开更多
Purpose:To investigate the degree of learning burnout and its influencing factors in medical college and university students.Methods:The Learning Burnout Scale,Attributional Style Questionnaire,and Self-Efficacy Scale...Purpose:To investigate the degree of learning burnout and its influencing factors in medical college and university students.Methods:The Learning Burnout Scale,Attributional Style Questionnaire,and Self-Efficacy Scale were used to investigate 679 medical college and university students.Results:The Learning Burnout Scale score was 61.338.28.The score for the Attributional Style Questionnaire was 0.191.18,and Self-Efficacy Scale score was 2.460.37.Selfefficacy and attributional styles were negatively correlated with learning burnout.Field of study,scholarship status,grade,and attributional style and self-efficacy total scores affected the degree of learning burnout,and explained 27%of the total variance of observed learning burnout.Conclusion:Learning burnout in students is of a moderate level.We should help and guide students according to their profession,grade,learning characteristics,and whether they have existing attributional style problems;these interventions should help to reduce learning burnout.展开更多
Objective:The objective of this study is to explore the mediating role of professional commitment of undergraduate nursing students between positive psychological capital(PsyCap)and learning burnout.Materials and Meth...Objective:The objective of this study is to explore the mediating role of professional commitment of undergraduate nursing students between positive psychological capital(PsyCap)and learning burnout.Materials and Methods:A cluster sampling method was used to survey 442 students of a bachelor’s degree in freshman to the junior in a medical college in Shaanxi Province.Results:Professional commitment was positively related to positive PsyCap(=0.487,P<0.01),while positive PsyCap and professional commitment were negatively related to learning burnout(r=−0.456,r=−0.411,P all<0.01).There are certain mediating effects of professional commitment in the relationship between positive PsyCap and learning burnout.The mediating effect value of professional commitment is−0.065,accounting for 16.08%of the total effect.Conclusion:Nursing educators in medical colleges and universities should focus on developing and training the positive PsyCap of undergraduate nursing students,thereby enhancing and stabilizing their professional commitment level and reducing the risk of learning burnout.展开更多
To analyze the psychometric performance of Learning Burnout Scale for Undergraduates(LBSU)in Guangdong province.LBSU was used to conduct the survey involving 1628 undergraduates who were selected with stratified rando...To analyze the psychometric performance of Learning Burnout Scale for Undergraduates(LBSU)in Guangdong province.LBSU was used to conduct the survey involving 1628 undergraduates who were selected with stratified random sampling from 7 colleges in Guangdong province.Cronbach’sαcoefficient and split-half reliability were used to analyze the internal consistency of the questionnaire.Convergent validity,discriminant validity and factor analysis were used to evaluate its structural validity.Ceiling and floor effect were used to analyze its sensitivity.Cronbach’sαcoefficient of the total questionnaire was 0.89 and cronbach’sαcoefficient of 3 dimensions were 0.73-0.78,which met with the requirements of the group comparison.Spearman-Brown split-half coefficient of the total questionnaire and 3 dimensions were 0.90,0.85,0.81,0.79,respectively,which also met with the requirements of the group comparison.Both the calibration success rate of convergent validity and discriminant validity of each dimension were 100%.Four components obtained from 20 items which cumulative variance contribution rate was 51.924%.The total score and score of each dimension were all normal distribution,without any floor or ceiling effect in dimensions.The psychometric performance of LBSU for assessing undergraduates in Guangdong province is valid and reliable.展开更多
Learning burnout is a common psychological problem of college students,which seriously affects college students'academic achievement and physical and mental health,wastes educational resources,and brings various h...Learning burnout is a common psychological problem of college students,which seriously affects college students'academic achievement and physical and mental health,wastes educational resources,and brings various hidden dangers to talent growth and social development.Starting from the definition of the concept of learning burnout,this paper introduces the dimension composition and measurement tools of college students'learning burnout,analyzes the influencing factors of college students'learning burnout,and puts forward the corresponding research prospects in view of the shortcomings of previous research.展开更多
The goal of the present study is to develop a more suitable inventory to evaluate the burnout of non-English majors in China,which we called it foreign language learning burnout inventory(FLLB).The operative definitio...The goal of the present study is to develop a more suitable inventory to evaluate the burnout of non-English majors in China,which we called it foreign language learning burnout inventory(FLLB).The operative definition of burnout proposed by Maslach and Jackson is used to define three dimensions(exhaustion,cynicism and reduced efficacy).The selection of items is based on the existing burnout inventories,combining with the consideration of the features showed in the studies of foreign language learning.In the research,the data obtained from 101 non-English majors and is analyzed for the validity and reliability studies studies of FLLB.Three factors explained 56%of the total variance.Factor loadings ranged from.438 to.742.Cronbach Alpha reliability coefficient for the sub-dimensions ranged from.861 to.915.The model indices emerged from the Confirmtory Factor Analysis is[GFI=.994,RMSEA=.067,[X^(2)=458.086,df=206,p<0.01]]indicated that there was a good fit.展开更多
BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are p...BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are pivotal in identifying the most suitable transplant candidates.Traditionally,scoring systems like the model for end-stage liver disease have been instrumental in this process.Nevertheless,the landscape of prognostication is undergoing a transformation with the integration of machine learning(ML)and artificial intelligence models.AIM To assess the utility of ML models in prognostication for LT,comparing their performance and reliability to established traditional scoring systems.METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines,we conducted a thorough and standardized literature search using the PubMed/MEDLINE database.Our search imposed no restrictions on publication year,age,or gender.Exclusion criteria encompassed non-English studies,review articles,case reports,conference papers,studies with missing data,or those exhibiting evident methodological flaws.RESULTS Our search yielded a total of 64 articles,with 23 meeting the inclusion criteria.Among the selected studies,60.8%originated from the United States and China combined.Only one pediatric study met the criteria.Notably,91%of the studies were published within the past five years.ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values(ranging from 0.6 to 1)across all studies,surpassing the performance of traditional scoring systems.Random forest exhibited superior predictive capabilities for 90-d mortality following LT,sepsis,and acute kidney injury(AKI).In contrast,gradient boosting excelled in predicting the risk of graft-versus-host disease,pneumonia,and AKI.CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT,marking a significant evolution in the field of prognostication.展开更多
Stroke is a leading cause of disability and mortality worldwide,necessitating the development of advanced technologies to improve its diagnosis,treatment,and patient outcomes.In recent years,machine learning technique...Stroke is a leading cause of disability and mortality worldwide,necessitating the development of advanced technologies to improve its diagnosis,treatment,and patient outcomes.In recent years,machine learning techniques have emerged as promising tools in stroke medicine,enabling efficient analysis of large-scale datasets and facilitating personalized and precision medicine approaches.This abstract provides a comprehensive overview of machine learning’s applications,challenges,and future directions in stroke medicine.Recently introduced machine learning algorithms have been extensively employed in all the fields of stroke medicine.Machine learning models have demonstrated remarkable accuracy in imaging analysis,diagnosing stroke subtypes,risk stratifications,guiding medical treatment,and predicting patient prognosis.Despite the tremendous potential of machine learning in stroke medicine,several challenges must be addressed.These include the need for standardized and interoperable data collection,robust model validation and generalization,and the ethical considerations surrounding privacy and bias.In addition,integrating machine learning models into clinical workflows and establishing regulatory frameworks are critical for ensuring their widespread adoption and impact in routine stroke care.Machine learning promises to revolutionize stroke medicine by enabling precise diagnosis,tailored treatment selection,and improved prognostication.Continued research and collaboration among clinicians,researchers,and technologists are essential for overcoming challenges and realizing the full potential of machine learning in stroke care,ultimately leading to enhanced patient outcomes and quality of life.This review aims to summarize all the current implications of machine learning in stroke diagnosis,treatment,and prognostic evaluation.At the same time,another purpose of this paper is to explore all the future perspectives these techniques can provide in combating this disabling disease.展开更多
Vascular etiology is the second most prevalent cause of cognitive impairment globally.Endothelin-1,which is produced and secreted by endothelial cells and astrocytes,is implicated in the pathogenesis of stroke.However...Vascular etiology is the second most prevalent cause of cognitive impairment globally.Endothelin-1,which is produced and secreted by endothelial cells and astrocytes,is implicated in the pathogenesis of stroke.However,the way in which changes in astrocytic endothelin-1 lead to poststroke cognitive deficits following transient middle cerebral artery occlusion is not well understood.Here,using mice in which astrocytic endothelin-1 was overexpressed,we found that the selective overexpression of endothelin-1 by astrocytic cells led to ischemic stroke-related dementia(1 hour of ischemia;7 days,28 days,or 3 months of reperfusion).We also revealed that astrocytic endothelin-1 overexpression contributed to the role of neural stem cell proliferation but impaired neurogenesis in the dentate gyrus of the hippocampus after middle cerebral artery occlusion.Comprehensive proteome profiles and western blot analysis confirmed that levels of glial fibrillary acidic protein and peroxiredoxin 6,which were differentially expressed in the brain,were significantly increased in mice with astrocytic endothelin-1 overexpression in comparison with wild-type mice 28 days after ischemic stroke.Moreover,the levels of the enriched differentially expressed proteins were closely related to lipid metabolism,as indicated by Kyoto Encyclopedia of Genes and Genomes pathway analysis.Liquid chromatography-mass spectrometry nontargeted metabolite profiling of brain tissues showed that astrocytic endothelin-1 overexpression altered lipid metabolism products such as glycerol phosphatidylcholine,sphingomyelin,and phosphatidic acid.Overall,this study demonstrates that astrocytic endothelin-1 overexpression can impair hippocampal neurogenesis and that it is correlated with lipid metabolism in poststroke cognitive dysfunction.展开更多
Introduction: Also known as maternal burnout syndrome, maternal burnout is a state of physical, emotional and mental exhaustion generated by prolonged stress in the family environment. It is experienced by women in th...Introduction: Also known as maternal burnout syndrome, maternal burnout is a state of physical, emotional and mental exhaustion generated by prolonged stress in the family environment. It is experienced by women in their role as mothers. Those affected can develop psychological disorders, sleep disturbances, etc., all of which impair their day-to-day lives, and thus their maternal role. The repercussions affect both the child and other family members. Objective: The aim of the present study was to investigate maternal burnout among female users of public and private health facilities in the commune of Parakou in 2023. Methods: Descriptive cross-sectional study was conducted from December 2022 to July 2023 among all mothers using public and private health facilities in the commune of Parakou. All healthy mothers with at least one biological or adoptive child fully dependent on them and living at home, who came for a consultation in one of the health facilities or for an appointment at the Expanded Program on Immunization (EPI) and gave their free and informed consent. Sampling was done for non-exhaustive convenience were included in the study. Burnout was assessed using the Parental Burnout Assessment (PBA) scale. Results: A total of 888 mothers meeting the inclusion criteria were surveyed. The prevalence of burnout calculated using the Parental Burnout Assessment (PBA) scale was 6.19%. The risk factors for maternal burnout were poor relationships with family and friends (OR = 8.90;p = 0.045), moderate (OR = 11.71;p = 0.020) and severe depression (OR = 40.85;p = 0.001), followed by the presence of repeated nocturnal awakening (OR = 5.14;p = 0.014). Conclusion: This is a subject that is almost never discussed in African society, but whose reality is revealed by the present study, which provided statistical data on maternal burnout. From now on, the risk of burnout will no longer be discussed solely in the family context. It will also need to be explored within the family unit to prevent its deleterious consequences for children and adults alike.展开更多
The visions of Industry 4.0 and 5.0 have reinforced the industrial environment.They have also made artificial intelligence incorporated as a major facilitator.Diagnosing machine faults has become a solid foundation fo...The visions of Industry 4.0 and 5.0 have reinforced the industrial environment.They have also made artificial intelligence incorporated as a major facilitator.Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure,and thus timely maintenance can ensure safe operations.Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model,which typically involves two datasets.In response to the availability of multiple datasets,this paper proposes using selective and adaptive incremental transfer learning(SA-ITL),which fuses three algorithms,namely,the hybrid selective algorithm,the transferability enhancement algorithm,and the incremental transfer learning algorithm.It is a selective algorithm that enables selecting and ordering appropriate datasets for transfer learning and selecting useful knowledge to avoid negative transfer.The algorithm also adaptively adjusts the portion of training data to balance the learning rate and training time.The proposed algorithm is evaluated and analyzed using ten benchmark datasets.Compared with other algorithms from existing works,SA-ITL improves the accuracy of all datasets.Ablation studies present the accuracy enhancements of the SA-ITL,including the hybrid selective algorithm(1.22%-3.82%),transferability enhancement algorithm(1.91%-4.15%),and incremental transfer learning algorithm(0.605%-2.68%).These also show the benefits of enhancing the target model with heterogeneous image datasets that widen the range of domain selection between source and target domains.展开更多
Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM...Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance.Since,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by SM.This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic outbreaks.DL has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation results.In recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM analysis.This paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM analysis.Finally,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.展开更多
The evaluation of disease severity through endoscopy is pivotal in managing patients with ulcerative colitis,a condition with significant clinical implications.However,endoscopic assessment is susceptible to inherent ...The evaluation of disease severity through endoscopy is pivotal in managing patients with ulcerative colitis,a condition with significant clinical implications.However,endoscopic assessment is susceptible to inherent variations,both within and between observers,compromising the reliability of individual evaluations.This study addresses this challenge by harnessing deep learning to develop a robust model capable of discerning discrete levels of endoscopic disease severity.To initiate this endeavor,a multi-faceted approach is embarked upon.The dataset is meticulously preprocessed,enhancing the quality and discriminative features of the images through contrast limited adaptive histogram equalization(CLAHE).A diverse array of data augmentation techniques,encompassing various geometric transformations,is leveraged to fortify the dataset’s diversity and facilitate effective feature extraction.A fundamental aspect of the approach involves the strategic incorporation of transfer learning principles,harnessing a modified ResNet-50 architecture.This augmentation,informed by domain expertise,contributed significantly to enhancing the model’s classification performance.The outcome of this research endeavor yielded a highly promising model,demonstrating an accuracy rate of 86.85%,coupled with a recall rate of 82.11%and a precision rate of 89.23%.展开更多
When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized environment.FL allows ...When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized environment.FL allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third parties.This paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data sources.By virtue of FL,models can be learned from all such distributed data sources while preserving data privacy.The aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software frameworks.Furthermore,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL landscape.The ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.展开更多
Scalability and information personal privacy are vital for training and deploying large-scale deep learning models.Federated learning trains models on exclusive information by aggregating weights from various devices ...Scalability and information personal privacy are vital for training and deploying large-scale deep learning models.Federated learning trains models on exclusive information by aggregating weights from various devices and taking advantage of the device-agnostic environment of web browsers.Nevertheless,relying on a main central server for internet browser-based federated systems can prohibit scalability and interfere with the training process as a result of growing client numbers.Additionally,information relating to the training dataset can possibly be extracted from the distributed weights,potentially reducing the privacy of the local data used for training.In this research paper,we aim to investigate the challenges of scalability and data privacy to increase the efficiency of distributed training models.As a result,we propose a web-federated learning exchange(WebFLex)framework,which intends to improve the decentralization of the federated learning process.WebFLex is additionally developed to secure distributed and scalable federated learning systems that operate in web browsers across heterogeneous devices.Furthermore,WebFLex utilizes peer-to-peer interactions and secure weight exchanges utilizing browser-to-browser web real-time communication(WebRTC),efficiently preventing the need for a main central server.WebFLex has actually been measured in various setups using the MNIST dataset.Experimental results show WebFLex’s ability to improve the scalability of federated learning systems,allowing a smooth increase in the number of participating devices without central data aggregation.In addition,WebFLex can maintain a durable federated learning procedure even when faced with device disconnections and network variability.Additionally,it improves data privacy by utilizing artificial noise,which accomplishes an appropriate balance between accuracy and privacy preservation.展开更多
Aim:This study aims to establish an artificial intelligence model,ThyroidNet,to diagnose thyroid nodules using deep learning techniques accurately.Methods:A novel method,ThyroidNet,is introduced and evaluated based on...Aim:This study aims to establish an artificial intelligence model,ThyroidNet,to diagnose thyroid nodules using deep learning techniques accurately.Methods:A novel method,ThyroidNet,is introduced and evaluated based on deep learning for the localization and classification of thyroid nodules.First,we propose the multitask TransUnet,which combines the TransUnet encoder and decoder with multitask learning.Second,we propose the DualLoss function,tailored to the thyroid nodule localization and classification tasks.It balances the learning of the localization and classification tasks to help improve the model’s generalization ability.Third,we introduce strategies for augmenting the data.Finally,we submit a novel deep learning model,ThyroidNet,to accurately detect thyroid nodules.Results:ThyroidNet was evaluated on private datasets and was comparable to other existing methods,including U-Net and TransUnet.Experimental results show that ThyroidNet outperformed these methods in localizing and classifying thyroid nodules.It achieved improved accuracy of 3.9%and 1.5%,respectively.Conclusion:ThyroidNet significantly improves the clinical diagnosis of thyroid nodules and supports medical image analysis tasks.Future research directions include optimization of the model structure,expansion of the dataset size,reduction of computational complexity and memory requirements,and exploration of additional applications of ThyroidNet in medical image analysis.展开更多
Machine learning(ML) is well suited for the prediction of high-complexity,high-dimensional problems such as those encountered in terminal ballistics.We evaluate the performance of four popular ML-based regression mode...Machine learning(ML) is well suited for the prediction of high-complexity,high-dimensional problems such as those encountered in terminal ballistics.We evaluate the performance of four popular ML-based regression models,extreme gradient boosting(XGBoost),artificial neural network(ANN),support vector regression(SVR),and Gaussian process regression(GP),on two common terminal ballistics’ problems:(a)predicting the V50ballistic limit of monolithic metallic armour impacted by small and medium calibre projectiles and fragments,and(b) predicting the depth to which a projectile will penetrate a target of semi-infinite thickness.To achieve this we utilise two datasets,each consisting of approximately 1000samples,collated from public release sources.We demonstrate that all four model types provide similarly excellent agreement when interpolating within the training data and diverge when extrapolating outside this range.Although extrapolation is not advisable for ML-based regression models,for applications such as lethality/survivability analysis,such capability is required.To circumvent this,we implement expert knowledge and physics-based models via enforced monotonicity,as a Gaussian prior mean,and through a modified loss function.The physics-informed models demonstrate improved performance over both classical physics-based models and the basic ML regression models,providing an ability to accurately fit experimental data when it is available and then revert to the physics-based model when not.The resulting models demonstrate high levels of predictive accuracy over a very wide range of projectile types,target materials and thicknesses,and impact conditions significantly more diverse than that achievable from any existing analytical approach.Compared with numerical analysis tools such as finite element solvers the ML models run orders of magnitude faster.We provide some general guidelines throughout for the development,application,and reporting of ML models in terminal ballistics problems.展开更多
Underwater image enhancement aims to restore a clean appearance and thus improves the quality of underwater degraded images.Current methods feed the whole image directly into the model for enhancement.However,they ign...Underwater image enhancement aims to restore a clean appearance and thus improves the quality of underwater degraded images.Current methods feed the whole image directly into the model for enhancement.However,they ignored that the R,G and B channels of underwater degraded images present varied degrees of degradation,due to the selective absorption for the light.To address this issue,we propose an unsupervised multi-expert learning model by considering the enhancement of each color channel.Specifically,an unsupervised architecture based on generative adversarial network is employed to alleviate the need for paired underwater images.Based on this,we design a generator,including a multi-expert encoder,a feature fusion module and a feature fusion-guided decoder,to generate the clear underwater image.Accordingly,a multi-expert discriminator is proposed to verify the authenticity of the R,G and B channels,respectively.In addition,content perceptual loss and edge loss are introduced into the loss function to further improve the content and details of the enhanced images.Extensive experiments on public datasets demonstrate that our method achieves more pleasing results in vision quality.Various metrics(PSNR,SSIM,UIQM and UCIQE) evaluated on our enhanced images have been improved obviously.展开更多
基金Kashi University Research Project on High-Quality Economic and Social Development in Southern Xinjiang,“Research on Online Presence,Participation and sense of Gain of Southern Xinjiang College Students in Online Course Learning under the Background of Epidemic”(Project number:NFG2202)Kashi University Graduate Education Teaching Reform research project“New Flipped Classroom Research Based on OBE Concept-Teaching Reform Based on Graduate Talent Training”(Project number:KD2022J007).
文摘We measured 324 college students’self-esteem,motivation for achievement,and learning burnout,and also we examined the inner mechanism of self-esteem’s influence on students’learning burnout.The results show:(1)There is a negative effect of self-esteem on college students’learning burnout;(2)Motivation of pursuing succeed plays partial mediating effect between self-esteem and learning burnout,but mediating effect of avoiding failure motivation between them is not significant.The study reveals that:the degree of learning burnout can be reduced by improving college students’level of self-esteem.
文摘With the advancement of economic globalization,mobile networks and media technology are developing rapidly.Media information is manufactured and spread at any time,and people can better understand global information with the help of the media.In the Internet era,college students,overwhelmed by complex information and a lack of information discernment,are susceptible to indulging in the curated online world presented by others.At the same time,negative information such as false,violent,and pornographic also spread rapidly in various media.These phenomena impact students’media literacy and affect their mental health,thereby leading to learning burnout.This study analyzes the current situation of learning burnout among university students,explores the effective path of improving online education of college students,and provides a theoretical basis for reducing the burnout of college students and assisting students in developing a positive mentality.
文摘Purpose:To investigate the degree of learning burnout and its influencing factors in medical college and university students.Methods:The Learning Burnout Scale,Attributional Style Questionnaire,and Self-Efficacy Scale were used to investigate 679 medical college and university students.Results:The Learning Burnout Scale score was 61.338.28.The score for the Attributional Style Questionnaire was 0.191.18,and Self-Efficacy Scale score was 2.460.37.Selfefficacy and attributional styles were negatively correlated with learning burnout.Field of study,scholarship status,grade,and attributional style and self-efficacy total scores affected the degree of learning burnout,and explained 27%of the total variance of observed learning burnout.Conclusion:Learning burnout in students is of a moderate level.We should help and guide students according to their profession,grade,learning characteristics,and whether they have existing attributional style problems;these interventions should help to reduce learning burnout.
基金the Shaanxi Provincial Administration of Traditional Chinese Medicine Fund(No.:2019‑ZZ‑ZC010).
文摘Objective:The objective of this study is to explore the mediating role of professional commitment of undergraduate nursing students between positive psychological capital(PsyCap)and learning burnout.Materials and Methods:A cluster sampling method was used to survey 442 students of a bachelor’s degree in freshman to the junior in a medical college in Shaanxi Province.Results:Professional commitment was positively related to positive PsyCap(=0.487,P<0.01),while positive PsyCap and professional commitment were negatively related to learning burnout(r=−0.456,r=−0.411,P all<0.01).There are certain mediating effects of professional commitment in the relationship between positive PsyCap and learning burnout.The mediating effect value of professional commitment is−0.065,accounting for 16.08%of the total effect.Conclusion:Nursing educators in medical colleges and universities should focus on developing and training the positive PsyCap of undergraduate nursing students,thereby enhancing and stabilizing their professional commitment level and reducing the risk of learning burnout.
文摘To analyze the psychometric performance of Learning Burnout Scale for Undergraduates(LBSU)in Guangdong province.LBSU was used to conduct the survey involving 1628 undergraduates who were selected with stratified random sampling from 7 colleges in Guangdong province.Cronbach’sαcoefficient and split-half reliability were used to analyze the internal consistency of the questionnaire.Convergent validity,discriminant validity and factor analysis were used to evaluate its structural validity.Ceiling and floor effect were used to analyze its sensitivity.Cronbach’sαcoefficient of the total questionnaire was 0.89 and cronbach’sαcoefficient of 3 dimensions were 0.73-0.78,which met with the requirements of the group comparison.Spearman-Brown split-half coefficient of the total questionnaire and 3 dimensions were 0.90,0.85,0.81,0.79,respectively,which also met with the requirements of the group comparison.Both the calibration success rate of convergent validity and discriminant validity of each dimension were 100%.Four components obtained from 20 items which cumulative variance contribution rate was 51.924%.The total score and score of each dimension were all normal distribution,without any floor or ceiling effect in dimensions.The psychometric performance of LBSU for assessing undergraduates in Guangdong province is valid and reliable.
文摘Learning burnout is a common psychological problem of college students,which seriously affects college students'academic achievement and physical and mental health,wastes educational resources,and brings various hidden dangers to talent growth and social development.Starting from the definition of the concept of learning burnout,this paper introduces the dimension composition and measurement tools of college students'learning burnout,analyzes the influencing factors of college students'learning burnout,and puts forward the corresponding research prospects in view of the shortcomings of previous research.
文摘The goal of the present study is to develop a more suitable inventory to evaluate the burnout of non-English majors in China,which we called it foreign language learning burnout inventory(FLLB).The operative definition of burnout proposed by Maslach and Jackson is used to define three dimensions(exhaustion,cynicism and reduced efficacy).The selection of items is based on the existing burnout inventories,combining with the consideration of the features showed in the studies of foreign language learning.In the research,the data obtained from 101 non-English majors and is analyzed for the validity and reliability studies studies of FLLB.Three factors explained 56%of the total variance.Factor loadings ranged from.438 to.742.Cronbach Alpha reliability coefficient for the sub-dimensions ranged from.861 to.915.The model indices emerged from the Confirmtory Factor Analysis is[GFI=.994,RMSEA=.067,[X^(2)=458.086,df=206,p<0.01]]indicated that there was a good fit.
文摘BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are pivotal in identifying the most suitable transplant candidates.Traditionally,scoring systems like the model for end-stage liver disease have been instrumental in this process.Nevertheless,the landscape of prognostication is undergoing a transformation with the integration of machine learning(ML)and artificial intelligence models.AIM To assess the utility of ML models in prognostication for LT,comparing their performance and reliability to established traditional scoring systems.METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines,we conducted a thorough and standardized literature search using the PubMed/MEDLINE database.Our search imposed no restrictions on publication year,age,or gender.Exclusion criteria encompassed non-English studies,review articles,case reports,conference papers,studies with missing data,or those exhibiting evident methodological flaws.RESULTS Our search yielded a total of 64 articles,with 23 meeting the inclusion criteria.Among the selected studies,60.8%originated from the United States and China combined.Only one pediatric study met the criteria.Notably,91%of the studies were published within the past five years.ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values(ranging from 0.6 to 1)across all studies,surpassing the performance of traditional scoring systems.Random forest exhibited superior predictive capabilities for 90-d mortality following LT,sepsis,and acute kidney injury(AKI).In contrast,gradient boosting excelled in predicting the risk of graft-versus-host disease,pneumonia,and AKI.CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT,marking a significant evolution in the field of prognostication.
文摘Stroke is a leading cause of disability and mortality worldwide,necessitating the development of advanced technologies to improve its diagnosis,treatment,and patient outcomes.In recent years,machine learning techniques have emerged as promising tools in stroke medicine,enabling efficient analysis of large-scale datasets and facilitating personalized and precision medicine approaches.This abstract provides a comprehensive overview of machine learning’s applications,challenges,and future directions in stroke medicine.Recently introduced machine learning algorithms have been extensively employed in all the fields of stroke medicine.Machine learning models have demonstrated remarkable accuracy in imaging analysis,diagnosing stroke subtypes,risk stratifications,guiding medical treatment,and predicting patient prognosis.Despite the tremendous potential of machine learning in stroke medicine,several challenges must be addressed.These include the need for standardized and interoperable data collection,robust model validation and generalization,and the ethical considerations surrounding privacy and bias.In addition,integrating machine learning models into clinical workflows and establishing regulatory frameworks are critical for ensuring their widespread adoption and impact in routine stroke care.Machine learning promises to revolutionize stroke medicine by enabling precise diagnosis,tailored treatment selection,and improved prognostication.Continued research and collaboration among clinicians,researchers,and technologists are essential for overcoming challenges and realizing the full potential of machine learning in stroke care,ultimately leading to enhanced patient outcomes and quality of life.This review aims to summarize all the current implications of machine learning in stroke diagnosis,treatment,and prognostic evaluation.At the same time,another purpose of this paper is to explore all the future perspectives these techniques can provide in combating this disabling disease.
基金financially supported by the National Natural Science Foundation of China,No.81303115,81774042 (both to XC)the Pearl River S&T Nova Program of Guangzhou,No.201806010025 (to XC)+3 种基金the Specialty Program of Guangdong Province Hospital of Chinese Medicine of China,No.YN2018ZD07 (to XC)the Natural Science Foundatior of Guangdong Province of China,No.2023A1515012174 (to JL)the Science and Technology Program of Guangzhou of China,No.20210201 0268 (to XC),20210201 0339 (to JS)Guangdong Provincial Key Laboratory of Research on Emergency in TCM,Nos.2018-75,2019-140 (to JS)
文摘Vascular etiology is the second most prevalent cause of cognitive impairment globally.Endothelin-1,which is produced and secreted by endothelial cells and astrocytes,is implicated in the pathogenesis of stroke.However,the way in which changes in astrocytic endothelin-1 lead to poststroke cognitive deficits following transient middle cerebral artery occlusion is not well understood.Here,using mice in which astrocytic endothelin-1 was overexpressed,we found that the selective overexpression of endothelin-1 by astrocytic cells led to ischemic stroke-related dementia(1 hour of ischemia;7 days,28 days,or 3 months of reperfusion).We also revealed that astrocytic endothelin-1 overexpression contributed to the role of neural stem cell proliferation but impaired neurogenesis in the dentate gyrus of the hippocampus after middle cerebral artery occlusion.Comprehensive proteome profiles and western blot analysis confirmed that levels of glial fibrillary acidic protein and peroxiredoxin 6,which were differentially expressed in the brain,were significantly increased in mice with astrocytic endothelin-1 overexpression in comparison with wild-type mice 28 days after ischemic stroke.Moreover,the levels of the enriched differentially expressed proteins were closely related to lipid metabolism,as indicated by Kyoto Encyclopedia of Genes and Genomes pathway analysis.Liquid chromatography-mass spectrometry nontargeted metabolite profiling of brain tissues showed that astrocytic endothelin-1 overexpression altered lipid metabolism products such as glycerol phosphatidylcholine,sphingomyelin,and phosphatidic acid.Overall,this study demonstrates that astrocytic endothelin-1 overexpression can impair hippocampal neurogenesis and that it is correlated with lipid metabolism in poststroke cognitive dysfunction.
文摘Introduction: Also known as maternal burnout syndrome, maternal burnout is a state of physical, emotional and mental exhaustion generated by prolonged stress in the family environment. It is experienced by women in their role as mothers. Those affected can develop psychological disorders, sleep disturbances, etc., all of which impair their day-to-day lives, and thus their maternal role. The repercussions affect both the child and other family members. Objective: The aim of the present study was to investigate maternal burnout among female users of public and private health facilities in the commune of Parakou in 2023. Methods: Descriptive cross-sectional study was conducted from December 2022 to July 2023 among all mothers using public and private health facilities in the commune of Parakou. All healthy mothers with at least one biological or adoptive child fully dependent on them and living at home, who came for a consultation in one of the health facilities or for an appointment at the Expanded Program on Immunization (EPI) and gave their free and informed consent. Sampling was done for non-exhaustive convenience were included in the study. Burnout was assessed using the Parental Burnout Assessment (PBA) scale. Results: A total of 888 mothers meeting the inclusion criteria were surveyed. The prevalence of burnout calculated using the Parental Burnout Assessment (PBA) scale was 6.19%. The risk factors for maternal burnout were poor relationships with family and friends (OR = 8.90;p = 0.045), moderate (OR = 11.71;p = 0.020) and severe depression (OR = 40.85;p = 0.001), followed by the presence of repeated nocturnal awakening (OR = 5.14;p = 0.014). Conclusion: This is a subject that is almost never discussed in African society, but whose reality is revealed by the present study, which provided statistical data on maternal burnout. From now on, the risk of burnout will no longer be discussed solely in the family context. It will also need to be explored within the family unit to prevent its deleterious consequences for children and adults alike.
文摘The visions of Industry 4.0 and 5.0 have reinforced the industrial environment.They have also made artificial intelligence incorporated as a major facilitator.Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure,and thus timely maintenance can ensure safe operations.Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model,which typically involves two datasets.In response to the availability of multiple datasets,this paper proposes using selective and adaptive incremental transfer learning(SA-ITL),which fuses three algorithms,namely,the hybrid selective algorithm,the transferability enhancement algorithm,and the incremental transfer learning algorithm.It is a selective algorithm that enables selecting and ordering appropriate datasets for transfer learning and selecting useful knowledge to avoid negative transfer.The algorithm also adaptively adjusts the portion of training data to balance the learning rate and training time.The proposed algorithm is evaluated and analyzed using ten benchmark datasets.Compared with other algorithms from existing works,SA-ITL improves the accuracy of all datasets.Ablation studies present the accuracy enhancements of the SA-ITL,including the hybrid selective algorithm(1.22%-3.82%),transferability enhancement algorithm(1.91%-4.15%),and incremental transfer learning algorithm(0.605%-2.68%).These also show the benefits of enhancing the target model with heterogeneous image datasets that widen the range of domain selection between source and target domains.
基金authors are thankful to the Deanship of Scientific Research at Najran University for funding this work,under the Research Groups Funding Program Grant Code(NU/RG/SERC/12/27).
文摘Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance.Since,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by SM.This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic outbreaks.DL has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation results.In recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM analysis.This paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM analysis.Finally,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.
文摘The evaluation of disease severity through endoscopy is pivotal in managing patients with ulcerative colitis,a condition with significant clinical implications.However,endoscopic assessment is susceptible to inherent variations,both within and between observers,compromising the reliability of individual evaluations.This study addresses this challenge by harnessing deep learning to develop a robust model capable of discerning discrete levels of endoscopic disease severity.To initiate this endeavor,a multi-faceted approach is embarked upon.The dataset is meticulously preprocessed,enhancing the quality and discriminative features of the images through contrast limited adaptive histogram equalization(CLAHE).A diverse array of data augmentation techniques,encompassing various geometric transformations,is leveraged to fortify the dataset’s diversity and facilitate effective feature extraction.A fundamental aspect of the approach involves the strategic incorporation of transfer learning principles,harnessing a modified ResNet-50 architecture.This augmentation,informed by domain expertise,contributed significantly to enhancing the model’s classification performance.The outcome of this research endeavor yielded a highly promising model,demonstrating an accuracy rate of 86.85%,coupled with a recall rate of 82.11%and a precision rate of 89.23%.
基金the R&D&I,Spain grants PID2020-119478GB-I00 and,PID2020-115832GB-I00 funded by MCIN/AEI/10.13039/501100011033.N.Rodríguez-Barroso was supported by the grant FPU18/04475 funded by MCIN/AEI/10.13039/501100011033 and by“ESF Investing in your future”Spain.J.Moyano was supported by a postdoctoral Juan de la Cierva Formación grant FJC2020-043823-I funded by MCIN/AEI/10.13039/501100011033 and by European Union NextGenerationEU/PRTR.J.Del Ser acknowledges funding support from the Spanish Centro para el Desarrollo Tecnológico Industrial(CDTI)through the AI4ES projectthe Department of Education of the Basque Government(consolidated research group MATHMODE,IT1456-22)。
文摘When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized environment.FL allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third parties.This paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data sources.By virtue of FL,models can be learned from all such distributed data sources while preserving data privacy.The aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software frameworks.Furthermore,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL landscape.The ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.
基金This work has been funded by King Saud University,Riyadh,Saudi Arabia,through Researchers Supporting Project Number(RSPD2024R857).
文摘Scalability and information personal privacy are vital for training and deploying large-scale deep learning models.Federated learning trains models on exclusive information by aggregating weights from various devices and taking advantage of the device-agnostic environment of web browsers.Nevertheless,relying on a main central server for internet browser-based federated systems can prohibit scalability and interfere with the training process as a result of growing client numbers.Additionally,information relating to the training dataset can possibly be extracted from the distributed weights,potentially reducing the privacy of the local data used for training.In this research paper,we aim to investigate the challenges of scalability and data privacy to increase the efficiency of distributed training models.As a result,we propose a web-federated learning exchange(WebFLex)framework,which intends to improve the decentralization of the federated learning process.WebFLex is additionally developed to secure distributed and scalable federated learning systems that operate in web browsers across heterogeneous devices.Furthermore,WebFLex utilizes peer-to-peer interactions and secure weight exchanges utilizing browser-to-browser web real-time communication(WebRTC),efficiently preventing the need for a main central server.WebFLex has actually been measured in various setups using the MNIST dataset.Experimental results show WebFLex’s ability to improve the scalability of federated learning systems,allowing a smooth increase in the number of participating devices without central data aggregation.In addition,WebFLex can maintain a durable federated learning procedure even when faced with device disconnections and network variability.Additionally,it improves data privacy by utilizing artificial noise,which accomplishes an appropriate balance between accuracy and privacy preservation.
基金supported by MRC,UK (MC_PC_17171)Royal Society,UK (RP202G0230)+8 种基金BHF,UK (AA/18/3/34220)Hope Foundation for Cancer Research,UK (RM60G0680)GCRF,UK (P202PF11)Sino-UK Industrial Fund,UK (RP202G0289)LIAS,UK (P202ED10,P202RE969)Data Science Enhancement Fund,UK (P202RE237)Fight for Sight,UK (24NN201)Sino-UK Education Fund,UK (OP202006)BBSRC,UK (RM32G0178B8).
文摘Aim:This study aims to establish an artificial intelligence model,ThyroidNet,to diagnose thyroid nodules using deep learning techniques accurately.Methods:A novel method,ThyroidNet,is introduced and evaluated based on deep learning for the localization and classification of thyroid nodules.First,we propose the multitask TransUnet,which combines the TransUnet encoder and decoder with multitask learning.Second,we propose the DualLoss function,tailored to the thyroid nodule localization and classification tasks.It balances the learning of the localization and classification tasks to help improve the model’s generalization ability.Third,we introduce strategies for augmenting the data.Finally,we submit a novel deep learning model,ThyroidNet,to accurately detect thyroid nodules.Results:ThyroidNet was evaluated on private datasets and was comparable to other existing methods,including U-Net and TransUnet.Experimental results show that ThyroidNet outperformed these methods in localizing and classifying thyroid nodules.It achieved improved accuracy of 3.9%and 1.5%,respectively.Conclusion:ThyroidNet significantly improves the clinical diagnosis of thyroid nodules and supports medical image analysis tasks.Future research directions include optimization of the model structure,expansion of the dataset size,reduction of computational complexity and memory requirements,and exploration of additional applications of ThyroidNet in medical image analysis.
文摘Machine learning(ML) is well suited for the prediction of high-complexity,high-dimensional problems such as those encountered in terminal ballistics.We evaluate the performance of four popular ML-based regression models,extreme gradient boosting(XGBoost),artificial neural network(ANN),support vector regression(SVR),and Gaussian process regression(GP),on two common terminal ballistics’ problems:(a)predicting the V50ballistic limit of monolithic metallic armour impacted by small and medium calibre projectiles and fragments,and(b) predicting the depth to which a projectile will penetrate a target of semi-infinite thickness.To achieve this we utilise two datasets,each consisting of approximately 1000samples,collated from public release sources.We demonstrate that all four model types provide similarly excellent agreement when interpolating within the training data and diverge when extrapolating outside this range.Although extrapolation is not advisable for ML-based regression models,for applications such as lethality/survivability analysis,such capability is required.To circumvent this,we implement expert knowledge and physics-based models via enforced monotonicity,as a Gaussian prior mean,and through a modified loss function.The physics-informed models demonstrate improved performance over both classical physics-based models and the basic ML regression models,providing an ability to accurately fit experimental data when it is available and then revert to the physics-based model when not.The resulting models demonstrate high levels of predictive accuracy over a very wide range of projectile types,target materials and thicknesses,and impact conditions significantly more diverse than that achievable from any existing analytical approach.Compared with numerical analysis tools such as finite element solvers the ML models run orders of magnitude faster.We provide some general guidelines throughout for the development,application,and reporting of ML models in terminal ballistics problems.
基金supported in part by the National Key Research and Development Program of China(2020YFB1313002)the National Natural Science Foundation of China(62276023,U22B2055,62222302,U2013202)+1 种基金the Fundamental Research Funds for the Central Universities(FRF-TP-22-003C1)the Postgraduate Education Reform Project of Henan Province(2021SJGLX260Y)。
文摘Underwater image enhancement aims to restore a clean appearance and thus improves the quality of underwater degraded images.Current methods feed the whole image directly into the model for enhancement.However,they ignored that the R,G and B channels of underwater degraded images present varied degrees of degradation,due to the selective absorption for the light.To address this issue,we propose an unsupervised multi-expert learning model by considering the enhancement of each color channel.Specifically,an unsupervised architecture based on generative adversarial network is employed to alleviate the need for paired underwater images.Based on this,we design a generator,including a multi-expert encoder,a feature fusion module and a feature fusion-guided decoder,to generate the clear underwater image.Accordingly,a multi-expert discriminator is proposed to verify the authenticity of the R,G and B channels,respectively.In addition,content perceptual loss and edge loss are introduced into the loss function to further improve the content and details of the enhanced images.Extensive experiments on public datasets demonstrate that our method achieves more pleasing results in vision quality.Various metrics(PSNR,SSIM,UIQM and UCIQE) evaluated on our enhanced images have been improved obviously.
