This case study describes the care provided to a female patient with borderline personality disorder (BPD) who presented to the emergency department (ED). While people with borderline personality disorder use emergenc...This case study describes the care provided to a female patient with borderline personality disorder (BPD) who presented to the emergency department (ED). While people with borderline personality disorder use emergency services frequently, clinicians often face difficulties when providing medical and behavioral services to these patients. It may be difficult for nurse practitioners to determine if a patient with BPD who presents to the ED in crisis should be admitted, medicated, observed, or discharged. Self-harm is frequently confused with suicide attempts, which can result in unnecessary hospitalizations. This case study seeks to examine the proper management and difficulties encountered by healthcare providers in managing crises involving individuals with BPD in ED settings. The case study underscores the significance of thorough evaluation, recognition of BPD characteristics, active engagement in treatment, the therapeutic alliance, and the emphasis on interpersonal connections and stressors alongside the utilization of psychopharmacology.展开更多
A personalized outfit recommendation has emerged as a hot research topic in the fashion domain.However,existing recommendations do not fully exploit user style preferences.Typically,users prefer particular styles such...A personalized outfit recommendation has emerged as a hot research topic in the fashion domain.However,existing recommendations do not fully exploit user style preferences.Typically,users prefer particular styles such as casual and athletic styles,and consider attributes like color and texture when selecting outfits.To achieve personalized outfit recommendations in line with user style preferences,this paper proposes a personal style guided outfit recommendation with multi-modal fashion compatibility modeling,termed as PSGNet.Firstly,a style classifier is designed to categorize fashion images of various clothing types and attributes into distinct style categories.Secondly,a personal style prediction module extracts user style preferences by analyzing historical data.Then,to address the limitations of single-modal representations and enhance fashion compatibility,both fashion images and text data are leveraged to extract multi-modal features.Finally,PSGNet integrates these components through Bayesian personalized ranking(BPR)to unify the personal style and fashion compatibility,where the former is used as personal style features and guides the output of the personalized outfit recommendation tailored to the target user.Extensive experiments on large-scale datasets demonstrate that the proposed model is efficient on the personalized outfit recommendation.展开更多
Objectives:To explore the efficacy and safety of virtual reality(VR)in relieving negative emotions in patients with breast cancer with different personalities.Methods:A randomized controlled trial was conducted.Betwee...Objectives:To explore the efficacy and safety of virtual reality(VR)in relieving negative emotions in patients with breast cancer with different personalities.Methods:A randomized controlled trial was conducted.Between April 2023 and October 2023,we enrolled patients with breast cancer treated in the Department of Breast Cancer and Oncology at Sun Yat-Sen Memorial Hospital,Sun Yat-Sen University,Guangdong Province.The patients were randomly divided into an intervention group(n=118)and a control group(n=119)using block randomization.The intervention group received the VR intervention 3-5 times over 5±2 weeks using natural landscapes with music or relaxation guidance,and the duration of each VR intervention was 15±3 min.The control group received routine nursing care,including disease education and psychological counseling.Patients were assessed using the Type D Scale,Positive and Negative Affect Scale,and Distress Thermometer,and adverse events during the intervention were recorded.Results:Overall,85 patients completed the study(44 in the intervention group and 41 in the control group).Patients with Type D personalities showed more negative emotions[25.0(21.5,27.5)vs.19.0(16.0,24.0),P=0.001]and distressed attitudes[4.0(2.0,5.0)vs.3.0(1.0,4.0),P=0.020]with fewer positive emotions(27.2±5.6 vs.31.0±5.9,P=0.014)than those with non-Type D personalities.Total population analysis revealed no significant differences between the groups.However,in the subgroup analysis,patients with Type D personalities in the intervention group showed greater relief from negative emotions than those in the control group[median difference,-5.0(-9.0,-2.5)vs.-2.0(-4.0,2.0),P=0.046].No significant differences were found between groups of patients with non-Type D personality traits.The proportion of adverse events was not significantly different between groups(P=0.110).Conclusions:Breast cancer patients with Type D personalities suffer more severe negative emotions and distress,and more attention should be paid to them.VR intervention significantly and safely reduced negative emotions in patients with Type D personalities.展开更多
Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’devices without sharing private data.It trains a global model through collaboration between clients a...Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’devices without sharing private data.It trains a global model through collaboration between clients and the server.However,the presence of data heterogeneity can lead to inefficient model training and even reduce the final model’s accuracy and generalization capability.Meanwhile,data scarcity can result in suboptimal cluster distributions for few-shot clients in centralized clustering tasks,and standalone personalization tasks may cause severe overfitting issues.To address these limitations,we introduce a federated learning dual optimization model based on clustering and personalization strategy(FedCPS).FedCPS adopts a decentralized approach,where clients identify their cluster membership locally without relying on a centralized clustering algorithm.Building on this,FedCPS introduces personalized training tasks locally,adding a regularization term to control deviations between local and cluster models.This improves the generalization ability of the final model while mitigating overfitting.The use of weight-sharing techniques also reduces the computational cost of central machines.Experimental results on MNIST,FMNIST,CIFAR10,and CIFAR100 datasets demonstrate that our method achieves better personalization effects compared to other personalized federated learning methods,with an average test accuracy improvement of 0.81%–2.96%.Meanwhile,we adjusted the proportion of few-shot clients to evaluate the impact on accuracy across different methods.The experiments show that FedCPS reduces accuracy by only 0.2%–3.7%,compared to 2.1%–10%for existing methods.Our method demonstrates its advantages across diverse data environments.展开更多
Cardiovascular diseases are the leading cause of death,requiring innovative approaches for prevention,diagnosis,and treatment.Personalized medicine customizes interventions according to individual characteristics,with...Cardiovascular diseases are the leading cause of death,requiring innovative approaches for prevention,diagnosis,and treatment.Personalized medicine customizes interventions according to individual characteristics,with artificial intelligence(AI)playing a key role in analyzing complex data to improve diagnostic accuracy,predict outcomes,and optimize therapies.AI can identify patterns in imaging and biomarkers,facilitating the earlier detection of medical conditions.Wearable devices and health applications facilitate continuous monitoring and personalized care.Emerging fields such as digital Chinese medicine offer additional perspectives by integrating traditional diagnostic principles with modern digital tools,contributing to holistic and individualized cardiovascular care.This study examines the advancements and challenges in personalized cardiovascular medicine,highlighting the need to address issues such as data privacy,algorithmic bias,and accessibility to promote the equitable application of personalized medicine.展开更多
Objective: To explore the effect of Health Action Process Approach (HAPA) theory in patients with type D personality psoriasis. Methods: A total of 66 patients with type D personality psoriasis admitted to the dermato...Objective: To explore the effect of Health Action Process Approach (HAPA) theory in patients with type D personality psoriasis. Methods: A total of 66 patients with type D personality psoriasis admitted to the dermatology department of a top-three hospital in Jingzhou City from November 2022 to July 2023 were selected and divided into control group and test group with 33 cases in each group by random number table method. The control group received routine health education, and the experimental group received health education based on the HAPA theory. Chronic disease self-efficacy scale, hospital anxiety and depression scale and skin disease quality of life scale were used to evaluate the effect of intervention. Results: After 3 months of intervention, the scores of self-efficacy in experimental group were higher than those in control group (P P Conclusion: Health education based on the theory of HAPA can enhance the self-efficacy of patients with type D personality psoriasis, relieve negative emotions and improve their quality of life.展开更多
Objective:The combination of science and art in nursing is essential for providing effective care.Since art is inherent and a part of human personality traits,it is believed that nurses’personality traits are importa...Objective:The combination of science and art in nursing is essential for providing effective care.Since art is inherent and a part of human personality traits,it is believed that nurses’personality traits are important to achieve this cohesive combination in nursing.Accordingly,this study was conducted to determine the relationship between nurses’personality traits and the esthetics of nursing care.Methods:A cross-sectional descriptive design was employed.Study participants that consisted of 95 nurses and 285 patients from health centers in Iran were selected by convenience sampling method.Measures included the five-factor personality questionnaires(NEO-FFI)scale and Esthetics of Nursing Care Scale(ENCS).Results:The findings indicated a significant relationship between neuroticism(r=−0.149,P=0.028)and extraversion traits(r=0.136,P=0.045)of nurses in esthetics nursing care.In this study,no significant relationship was found between the personality traits and esthetics of nursing care using nurses’demographic information.Conclusions:The esthetics of nursing care depends on nurse personality traits.Since the art of nursing complements the expected care,it is suggested that nursing managers pay attention to the personality traits of nurses in planning to provide effective care.展开更多
The requirement for precise detection and recognition of target pedestrians in unprocessed real-world imagery drives the formulation of person search as an integrated technological framework that unifies pedestrian de...The requirement for precise detection and recognition of target pedestrians in unprocessed real-world imagery drives the formulation of person search as an integrated technological framework that unifies pedestrian detection and person re-identification(Re-ID).However,the inherent discrepancy between the optimization objectives of coarse-grained localization in pedestrian detection and fine-grained discriminative learning in Re-ID,combined with the substantial performance degradation of Re-ID during joint training caused by the Faster R-CNN-based branch,collectively constitutes a critical bottleneck for person search.In this work,we propose a cascaded person searchmodel(SeqXt)based on SeqNet and ConvNeXt that adopts a sequential end-to-end network as its core architecture,artfully integrates the design logic of the two-stepmethod and one-step method framework,and concurrently incorporates the two-step method’s advantage in efficient subtask handling while preserving the one-step method’s efficiency in end-toend training.Firstly,we utilize ConvNeXt-Base as the feature extraction module,which incorporates part of the design concept of Transformer,enhances the consideration of global context information,and boosts feature discrimination through an implicit self-attention mechanism.Secondly,we introduce prototype-guided normalization for calibrating the feature distribution,which leverages the archetype features of individual identities to calibrate the feature distribution and thereby prevents features from being overly inclined towards frequently occurring IDs,notably improving the intra-class compactness and inter-class separability of person identities.Finally,we put forward an innovative loss function named the Dynamic Online Instance Matching Loss Function(DOIM),which employs the hard sample assistantmethod to adaptively update the lookup table(LUT)and the circular queue(CQ)and aims to further enhance the distinctiveness of features between classes.Experimental results on the public datasets CUHK-SYSU and PRWand the private dataset UESTC-PS show that the proposed method achieves state-of-the-art results.展开更多
This study investigated the political,economic,social,and cultural environment perceptions on international students that define their acculturation and health related quality of life.Participants were 117 internation...This study investigated the political,economic,social,and cultural environment perceptions on international students that define their acculturation and health related quality of life.Participants were 117 international students from 32 countries attending a Chinese university(females=43%,mean age=21.17 years,SD=4.45 years).They reported on their acculturation to China and physical and psychological well-being.Results from t-tests and correlation analyses indicate political liberals had more positive attitudes toward China than the conservatives,and higher self-reported physical and psychological results.Higher scores on the“interdependence”dimension of self-construal,as well as the“extraversion”and“emotional stability”dimensions of personality traits,were associated with more positive views of China and better health outcomes.These findings are consistent with Berry’s framework for acculturation,which posits that individual-level variables are related to cultural adaptation,and that cultural adaptation is associated with improved physical and mental health.International student offices at host universities should implement comprehensive support programs,including language assistance,cultural orientation,and social integration initiatives to effectively enhance the health related quality of life of international students.展开更多
Personalized nursing is a necessary means to improve the satisfaction of emergency pediatric nursing.It can enhance the responsiveness of nursing services,strengthen the emotional connection between nurses and patient...Personalized nursing is a necessary means to improve the satisfaction of emergency pediatric nursing.It can enhance the responsiveness of nursing services,strengthen the emotional connection between nurses and patients,and provide a theoretical basis for clinical practice.Therefore,in the context of the new era,it is necessary to deeply analyze the essence and connotation of personalized nursing,and analyze the existing deficiencies in current emergency pediatric personalized nursing,so as to develop effective improvement plans.Research shows that personalized nursing can significantly improve the satisfaction of emergency pediatric nursing,largely avoid nursing risks,and has strong clinical application value.This article summarizes and explores the research on the influence of personalized nursing on improving the satisfaction of emergency pediatric nursing,and puts forward corresponding views.展开更多
Changing a person’s posture and low resolution are the key challenges for person re-identification(ReID)in various deep learning applications.In this paper,we introduce an innovative architecture using a dual attenti...Changing a person’s posture and low resolution are the key challenges for person re-identification(ReID)in various deep learning applications.In this paper,we introduce an innovative architecture using a dual attention network that includes an attentionmodule and a joint measurement module of spatial-temporal information.The proposed approach can be classified into two main tasks.Firstly,the spatial attention feature map is formed by aggregating features in the spatial dimension.Additionally,the same operation is carried out on the channel dimension to formchannel attention featuremaps.Therefore,the receptive field size is adjusted adaptively tomitigate the changing person posture issue.Secondly,we use a joint measurement method for the spatial-temporal information to fully harness the data,and it can also naturally integrate the information into the visual features of supervised ReID and hence overcome the low resolution problem.The experimental results indicate that our proposed algorithm markedly improves the accuracy in addressing changing human postures and low-resolution issues compared with contemporary leading techniques.The proposed method shows superior outcomes on widely recognized benchmarks,which are the Market-1501,MSMT17,and DukeMTMC-reID datasets.Furthermore,the proposed algorithmattains a Rank-1 accuracy of 97.4% and 94.9% mAP(mean Average Precision)on the Market-1501 dataset.Moreover,it achieves a 94.2% Rank-1 accuracy and 91.8% mAP on the DukeMTMC-reID dataset.展开更多
Social media outlets deliver customers a medium for communication,exchange,and expression of their thoughts with others.The advent of social networks and the fast escalation of the quantity of data have created opport...Social media outlets deliver customers a medium for communication,exchange,and expression of their thoughts with others.The advent of social networks and the fast escalation of the quantity of data have created opportunities for textual evaluation.Utilising the user corpus,characteristics of social platform users,and other data,academic research may accurately discern the personality traits of users.This research examines the traits of consumer personalities.Usually,personality tests administered by psychological experts via interviews or self-report questionnaires are costly,time-consuming,complex,and labour-intensive.Currently,academics in computational linguistics are increasingly focused on predicting personality traits from social media data.An individual’s personality comprises their traits and behavioral habits.To address this distinction,we propose a novel LSTMapproach(BERT-LIWC-LSTM)that simultaneously incorporates users’enduring and immediate personality characteristics for textual personality recognition.Long-termPersonality Encoding in the proposed paradigmcaptures and represents persisting personality traits.Short-termPersonality Capturing records changing personality states.Experimental results demonstrate that the designed BERT-LIWC-LSTM model achieves an average improvement in accuracy of 3.41% on the Big Five dataset compared to current methods,thereby justifying the efficacy of encoding both stable and dynamic personality traits simultaneously through long-and short-term feature interaction.展开更多
Diabetes mellitus(DM)comprises distinct subtypes-including type 1 DM,type 2 DM,and gestational DM-all characterized by chronic hyperglycemia and sub-stantial morbidity.Conventional diagnostic and therapeutic strategie...Diabetes mellitus(DM)comprises distinct subtypes-including type 1 DM,type 2 DM,and gestational DM-all characterized by chronic hyperglycemia and sub-stantial morbidity.Conventional diagnostic and therapeutic strategies often fall short in addressing the complex,multifactorial nature of DM.This review ex-plores how multi-omics integration enhances our mechanistic understanding of DM and informs emerging personalized therapeutic approaches.We consolidated genomic,transcriptomic,proteomic,metabolomic,and microbiomic data from major databases and peer-reviewed publications(2015-2025),with an emphasis on clinical relevance.Multi-omics investigations have identified convergent mole-cular networks underlyingβ-cell dysfunction,insulin resistance,and diabetic complications.The combination of metabolomics and microbiomics highlights critical interactions between metabolic intermediates and gut dysbiosis.Novel biomarkers facilitate early detection of DM and its complications,while single-cell multi-omics and machine learning further refine risk stratification.By dissecting DM heterogeneity more precisely,multi-omics integration enables targeted in-terventions and preventive strategies.Future efforts should focus on data har-monization,ethical considerations,and real-world validation to fully leverage multi-omics in addressing the global DM burden.展开更多
Objective:To investigate the distribution of health literacy(HL)levels and the association of HL with proactive personality in patients with permanent colostomy.Methods:A cross-sectional study was conducted to measure...Objective:To investigate the distribution of health literacy(HL)levels and the association of HL with proactive personality in patients with permanent colostomy.Methods:A cross-sectional study was conducted to measure proactive personality and HL using validated scales.A total of 172 patients with permanent colostomy were selected from January 2021 to May 2022 in Yantai City,China.Descriptive statistics,t-test,ANOVA,Pearson correlation analysis,and multiple linear regression analysis techniques were used.Results:The results obtained from the study showed that the HL status of the participants was moderate.The correlation between participants’total HL scores and proactive personality scores was 0.417(P-value<0.001).In addition,HL showed statistically significant differences according to education level,place of residence,profession,and average monthly household income.Conclusions:This study showed that patients with higher proactive personality scores had higher HL.The key stakeholders require several positive strategies to improve the HL of patients with permanent colostomy by cultivating their proactive personalities,and these important policies will help to improve patient health and quality of life.展开更多
This study proposes a learner profile framework based on multi-feature fusion,aiming to enhance the precision of personalized learning recommendations by integrating learners’static attributes(e.g.,demographic data a...This study proposes a learner profile framework based on multi-feature fusion,aiming to enhance the precision of personalized learning recommendations by integrating learners’static attributes(e.g.,demographic data and historical academic performance)with dynamic behavioral patterns(e.g.,real-time interactions and evolving interests over time).The research employs Term Frequency-Inverse Document Frequency(TF-IDF)for semantic feature extraction,integrates the Analytic Hierarchy Process(AHP)for feature weighting,and introduces a time decay function inspired by Newton’s law of cooling to dynamically model changes in learners’interests.Empirical results demonstrate that this framework effectively captures the dynamic evolution of learners’behaviors and provides context-aware learning resource recommendations.The study introduces a novel paradigm for learner modeling in educational technology,combining methodological innovation with a scalable technical architecture,thereby laying a foundation for the development of adaptive learning systems.展开更多
Deep phenotyping and genetic characterization of individuals are fundamental to assessing the metabolic status and determining nutrition-specific requirements.This study aimed to ascertain the utmost effectiveness of ...Deep phenotyping and genetic characterization of individuals are fundamental to assessing the metabolic status and determining nutrition-specific requirements.This study aimed to ascertain the utmost effectiveness of personalized interventions by aligning dietary adjustments with both the genotype and metabolotype of individuals.Therefore,we assessed here the usefulness of a polygenic score(PGS)characterizing a potential pro-inflammatory profile(PGSi)as a nutrigenetic tool to discern individuals from the Danish PREVENTOMICS cohort that could better respond to precision nutrition(PN)plans,specifically targeted at counteracting the low-grade inflammatory profile typically found in obesity.The cohort followed a PN plan to counteract the pro-inflammatory profile(PNi group)or generic dietary recommendations(Control)for 10 weeks.PGSi was applied for genetic stratification(Low/High).The effects of the intervention on anthropometrics and biomarkers related to inflammatory profile and carbohydrate metabolism were assessed.Around 30%of subjects had a high genetic predisposition to pro-inflammatory status(high-PGSi).These individuals demonstrated the most effective response to the dietary plan,experiencing improved body composition,with significant decreases in body weight(∆:-4.84%;P=0.039)and body fat(∆:-4.86%;P=0.007),and beneficial changes in pro-and anti-inflammatory biomarkers,with significant increases in IL-10(∆:71.3%;P=0.025)and decreases in TNF-α(∆:-3.0%;P=0.048),CRP(∆:-31.1%),ICAM1(∆:-5.8%),and MCP1(∆:-4.2%)circulating levels,compared to low-PGSi individuals.Both phenotypic and genetic stratification contributed to a better understanding of metabolic heterogeneity in response to diet.This approach allows for refinement of the prediction of individual requirements and potentially for better management of obesity.展开更多
With the continuous advancement of artificial intelligence(AI)technology,personalized learning systems are increasingly applied in higher education.Particularly within STEM(Science,Technology,Engineering,and Mathemati...With the continuous advancement of artificial intelligence(AI)technology,personalized learning systems are increasingly applied in higher education.Particularly within STEM(Science,Technology,Engineering,and Mathematics)education,AI demonstrates significant advantages through adaptive learning pathways,instant feedback,and individualized resource allocation.However,current research predominantly focuses on the technical architecture and application effectiveness of such systems,with insufficient exploration of how AI-enabled personalized learning systems influence university students’learning motivation and academic achievement through educational psychological mechanisms.This paper adopts an educational psychology perspective to construct a causal mechanism model linking“learning motivation-learning behavior-academic achievement.”Findings indicate that AI-powered personalized learning systems enhance learning autonomy,boost self-efficacy,and optimize feedback mechanisms.These effects collectively stimulate university students’learning motivation in STEM disciplines,thereby promoting academic achievement.Building upon empirical research,this paper proposes implications for educational practice and policy formulation,emphasizing the necessity of advancing higher education reform through the dual influence of technology and psychological mechanisms.展开更多
A brain-computer interface(BCI)based on motor imagery(MI)provides additional control pathways by decoding the intentions of the brain.MI ability has great intra-individual variability,and the majority of MI-BCI system...A brain-computer interface(BCI)based on motor imagery(MI)provides additional control pathways by decoding the intentions of the brain.MI ability has great intra-individual variability,and the majority of MI-BCI systems are unable to adapt to this variability,leading to poor training effects.Therefore,prediction of MI ability is needed.In this study,we propose an MI ability predictor based on multi-frequency EEG features.To validate the performance of the predictor,a video-guided paradigm and a traditional MI paradigm are designed,and the predictor is applied to both paradigms.The results demonstrate that all subjects achieved>85%prediction precision in both applications,with a maximum of 96%.This study indicates that the predictor can accurately predict the individuals’MI ability in different states,provide the scientific basis for personalized training,and enhance the effect of MI-BCI training.展开更多
Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure ...Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.展开更多
文摘This case study describes the care provided to a female patient with borderline personality disorder (BPD) who presented to the emergency department (ED). While people with borderline personality disorder use emergency services frequently, clinicians often face difficulties when providing medical and behavioral services to these patients. It may be difficult for nurse practitioners to determine if a patient with BPD who presents to the ED in crisis should be admitted, medicated, observed, or discharged. Self-harm is frequently confused with suicide attempts, which can result in unnecessary hospitalizations. This case study seeks to examine the proper management and difficulties encountered by healthcare providers in managing crises involving individuals with BPD in ED settings. The case study underscores the significance of thorough evaluation, recognition of BPD characteristics, active engagement in treatment, the therapeutic alliance, and the emphasis on interpersonal connections and stressors alongside the utilization of psychopharmacology.
基金Shanghai Frontier Science Research Center for Modern Textiles,Donghua University,ChinaOpen Project of Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment,Zhengzhou University of Light Industry,China(No.IM202303)National Key Research and Development Program of China(No.2019YFB1706300)。
文摘A personalized outfit recommendation has emerged as a hot research topic in the fashion domain.However,existing recommendations do not fully exploit user style preferences.Typically,users prefer particular styles such as casual and athletic styles,and consider attributes like color and texture when selecting outfits.To achieve personalized outfit recommendations in line with user style preferences,this paper proposes a personal style guided outfit recommendation with multi-modal fashion compatibility modeling,termed as PSGNet.Firstly,a style classifier is designed to categorize fashion images of various clothing types and attributes into distinct style categories.Secondly,a personal style prediction module extracts user style preferences by analyzing historical data.Then,to address the limitations of single-modal representations and enhance fashion compatibility,both fashion images and text data are leveraged to extract multi-modal features.Finally,PSGNet integrates these components through Bayesian personalized ranking(BPR)to unify the personal style and fashion compatibility,where the former is used as personal style features and guides the output of the personalized outfit recommendation tailored to the target user.Extensive experiments on large-scale datasets demonstrate that the proposed model is efficient on the personalized outfit recommendation.
基金supported by a project of the National Natural Science Foundation of China:Research on the integration of artificial intelligence and virtual reality technology to promote psychological rehabilitation of breast cancer patients with different personalities(project approval no.82073408).
文摘Objectives:To explore the efficacy and safety of virtual reality(VR)in relieving negative emotions in patients with breast cancer with different personalities.Methods:A randomized controlled trial was conducted.Between April 2023 and October 2023,we enrolled patients with breast cancer treated in the Department of Breast Cancer and Oncology at Sun Yat-Sen Memorial Hospital,Sun Yat-Sen University,Guangdong Province.The patients were randomly divided into an intervention group(n=118)and a control group(n=119)using block randomization.The intervention group received the VR intervention 3-5 times over 5±2 weeks using natural landscapes with music or relaxation guidance,and the duration of each VR intervention was 15±3 min.The control group received routine nursing care,including disease education and psychological counseling.Patients were assessed using the Type D Scale,Positive and Negative Affect Scale,and Distress Thermometer,and adverse events during the intervention were recorded.Results:Overall,85 patients completed the study(44 in the intervention group and 41 in the control group).Patients with Type D personalities showed more negative emotions[25.0(21.5,27.5)vs.19.0(16.0,24.0),P=0.001]and distressed attitudes[4.0(2.0,5.0)vs.3.0(1.0,4.0),P=0.020]with fewer positive emotions(27.2±5.6 vs.31.0±5.9,P=0.014)than those with non-Type D personalities.Total population analysis revealed no significant differences between the groups.However,in the subgroup analysis,patients with Type D personalities in the intervention group showed greater relief from negative emotions than those in the control group[median difference,-5.0(-9.0,-2.5)vs.-2.0(-4.0,2.0),P=0.046].No significant differences were found between groups of patients with non-Type D personality traits.The proportion of adverse events was not significantly different between groups(P=0.110).Conclusions:Breast cancer patients with Type D personalities suffer more severe negative emotions and distress,and more attention should be paid to them.VR intervention significantly and safely reduced negative emotions in patients with Type D personalities.
基金supported by the Foundation of President of Hebei University(XZJJ202303).
文摘Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’devices without sharing private data.It trains a global model through collaboration between clients and the server.However,the presence of data heterogeneity can lead to inefficient model training and even reduce the final model’s accuracy and generalization capability.Meanwhile,data scarcity can result in suboptimal cluster distributions for few-shot clients in centralized clustering tasks,and standalone personalization tasks may cause severe overfitting issues.To address these limitations,we introduce a federated learning dual optimization model based on clustering and personalization strategy(FedCPS).FedCPS adopts a decentralized approach,where clients identify their cluster membership locally without relying on a centralized clustering algorithm.Building on this,FedCPS introduces personalized training tasks locally,adding a regularization term to control deviations between local and cluster models.This improves the generalization ability of the final model while mitigating overfitting.The use of weight-sharing techniques also reduces the computational cost of central machines.Experimental results on MNIST,FMNIST,CIFAR10,and CIFAR100 datasets demonstrate that our method achieves better personalization effects compared to other personalized federated learning methods,with an average test accuracy improvement of 0.81%–2.96%.Meanwhile,we adjusted the proportion of few-shot clients to evaluate the impact on accuracy across different methods.The experiments show that FedCPS reduces accuracy by only 0.2%–3.7%,compared to 2.1%–10%for existing methods.Our method demonstrates its advantages across diverse data environments.
文摘Cardiovascular diseases are the leading cause of death,requiring innovative approaches for prevention,diagnosis,and treatment.Personalized medicine customizes interventions according to individual characteristics,with artificial intelligence(AI)playing a key role in analyzing complex data to improve diagnostic accuracy,predict outcomes,and optimize therapies.AI can identify patterns in imaging and biomarkers,facilitating the earlier detection of medical conditions.Wearable devices and health applications facilitate continuous monitoring and personalized care.Emerging fields such as digital Chinese medicine offer additional perspectives by integrating traditional diagnostic principles with modern digital tools,contributing to holistic and individualized cardiovascular care.This study examines the advancements and challenges in personalized cardiovascular medicine,highlighting the need to address issues such as data privacy,algorithmic bias,and accessibility to promote the equitable application of personalized medicine.
文摘Objective: To explore the effect of Health Action Process Approach (HAPA) theory in patients with type D personality psoriasis. Methods: A total of 66 patients with type D personality psoriasis admitted to the dermatology department of a top-three hospital in Jingzhou City from November 2022 to July 2023 were selected and divided into control group and test group with 33 cases in each group by random number table method. The control group received routine health education, and the experimental group received health education based on the HAPA theory. Chronic disease self-efficacy scale, hospital anxiety and depression scale and skin disease quality of life scale were used to evaluate the effect of intervention. Results: After 3 months of intervention, the scores of self-efficacy in experimental group were higher than those in control group (P P Conclusion: Health education based on the theory of HAPA can enhance the self-efficacy of patients with type D personality psoriasis, relieve negative emotions and improve their quality of life.
文摘Objective:The combination of science and art in nursing is essential for providing effective care.Since art is inherent and a part of human personality traits,it is believed that nurses’personality traits are important to achieve this cohesive combination in nursing.Accordingly,this study was conducted to determine the relationship between nurses’personality traits and the esthetics of nursing care.Methods:A cross-sectional descriptive design was employed.Study participants that consisted of 95 nurses and 285 patients from health centers in Iran were selected by convenience sampling method.Measures included the five-factor personality questionnaires(NEO-FFI)scale and Esthetics of Nursing Care Scale(ENCS).Results:The findings indicated a significant relationship between neuroticism(r=−0.149,P=0.028)and extraversion traits(r=0.136,P=0.045)of nurses in esthetics nursing care.In this study,no significant relationship was found between the personality traits and esthetics of nursing care using nurses’demographic information.Conclusions:The esthetics of nursing care depends on nurse personality traits.Since the art of nursing complements the expected care,it is suggested that nursing managers pay attention to the personality traits of nurses in planning to provide effective care.
基金supported by the major science and technology special projects of Xinjiang(No.2024B03041)the scientific and technological projects of Kashgar(No.KS2024024).
文摘The requirement for precise detection and recognition of target pedestrians in unprocessed real-world imagery drives the formulation of person search as an integrated technological framework that unifies pedestrian detection and person re-identification(Re-ID).However,the inherent discrepancy between the optimization objectives of coarse-grained localization in pedestrian detection and fine-grained discriminative learning in Re-ID,combined with the substantial performance degradation of Re-ID during joint training caused by the Faster R-CNN-based branch,collectively constitutes a critical bottleneck for person search.In this work,we propose a cascaded person searchmodel(SeqXt)based on SeqNet and ConvNeXt that adopts a sequential end-to-end network as its core architecture,artfully integrates the design logic of the two-stepmethod and one-step method framework,and concurrently incorporates the two-step method’s advantage in efficient subtask handling while preserving the one-step method’s efficiency in end-toend training.Firstly,we utilize ConvNeXt-Base as the feature extraction module,which incorporates part of the design concept of Transformer,enhances the consideration of global context information,and boosts feature discrimination through an implicit self-attention mechanism.Secondly,we introduce prototype-guided normalization for calibrating the feature distribution,which leverages the archetype features of individual identities to calibrate the feature distribution and thereby prevents features from being overly inclined towards frequently occurring IDs,notably improving the intra-class compactness and inter-class separability of person identities.Finally,we put forward an innovative loss function named the Dynamic Online Instance Matching Loss Function(DOIM),which employs the hard sample assistantmethod to adaptively update the lookup table(LUT)and the circular queue(CQ)and aims to further enhance the distinctiveness of features between classes.Experimental results on the public datasets CUHK-SYSU and PRWand the private dataset UESTC-PS show that the proposed method achieves state-of-the-art results.
文摘This study investigated the political,economic,social,and cultural environment perceptions on international students that define their acculturation and health related quality of life.Participants were 117 international students from 32 countries attending a Chinese university(females=43%,mean age=21.17 years,SD=4.45 years).They reported on their acculturation to China and physical and psychological well-being.Results from t-tests and correlation analyses indicate political liberals had more positive attitudes toward China than the conservatives,and higher self-reported physical and psychological results.Higher scores on the“interdependence”dimension of self-construal,as well as the“extraversion”and“emotional stability”dimensions of personality traits,were associated with more positive views of China and better health outcomes.These findings are consistent with Berry’s framework for acculturation,which posits that individual-level variables are related to cultural adaptation,and that cultural adaptation is associated with improved physical and mental health.International student offices at host universities should implement comprehensive support programs,including language assistance,cultural orientation,and social integration initiatives to effectively enhance the health related quality of life of international students.
文摘Personalized nursing is a necessary means to improve the satisfaction of emergency pediatric nursing.It can enhance the responsiveness of nursing services,strengthen the emotional connection between nurses and patients,and provide a theoretical basis for clinical practice.Therefore,in the context of the new era,it is necessary to deeply analyze the essence and connotation of personalized nursing,and analyze the existing deficiencies in current emergency pediatric personalized nursing,so as to develop effective improvement plans.Research shows that personalized nursing can significantly improve the satisfaction of emergency pediatric nursing,largely avoid nursing risks,and has strong clinical application value.This article summarizes and explores the research on the influence of personalized nursing on improving the satisfaction of emergency pediatric nursing,and puts forward corresponding views.
基金supported by the Young Doctoral Research Initiation Fund Project of Harbin University“Research on Wood Recognition Methods Based on Deep Learning Fusion Model”(Project no.HUDF2022110)the Self-Funded Project of Harbin Science and Technology Plan“Research on Computer Vision Recognition Technology of Wood Species Based on Transfer Learning FusionModel”(Project no.ZC2022ZJ010027)the Fundamental Research Funds for the Central Universities(2572017PZ10).
文摘Changing a person’s posture and low resolution are the key challenges for person re-identification(ReID)in various deep learning applications.In this paper,we introduce an innovative architecture using a dual attention network that includes an attentionmodule and a joint measurement module of spatial-temporal information.The proposed approach can be classified into two main tasks.Firstly,the spatial attention feature map is formed by aggregating features in the spatial dimension.Additionally,the same operation is carried out on the channel dimension to formchannel attention featuremaps.Therefore,the receptive field size is adjusted adaptively tomitigate the changing person posture issue.Secondly,we use a joint measurement method for the spatial-temporal information to fully harness the data,and it can also naturally integrate the information into the visual features of supervised ReID and hence overcome the low resolution problem.The experimental results indicate that our proposed algorithm markedly improves the accuracy in addressing changing human postures and low-resolution issues compared with contemporary leading techniques.The proposed method shows superior outcomes on widely recognized benchmarks,which are the Market-1501,MSMT17,and DukeMTMC-reID datasets.Furthermore,the proposed algorithmattains a Rank-1 accuracy of 97.4% and 94.9% mAP(mean Average Precision)on the Market-1501 dataset.Moreover,it achieves a 94.2% Rank-1 accuracy and 91.8% mAP on the DukeMTMC-reID dataset.
文摘Social media outlets deliver customers a medium for communication,exchange,and expression of their thoughts with others.The advent of social networks and the fast escalation of the quantity of data have created opportunities for textual evaluation.Utilising the user corpus,characteristics of social platform users,and other data,academic research may accurately discern the personality traits of users.This research examines the traits of consumer personalities.Usually,personality tests administered by psychological experts via interviews or self-report questionnaires are costly,time-consuming,complex,and labour-intensive.Currently,academics in computational linguistics are increasingly focused on predicting personality traits from social media data.An individual’s personality comprises their traits and behavioral habits.To address this distinction,we propose a novel LSTMapproach(BERT-LIWC-LSTM)that simultaneously incorporates users’enduring and immediate personality characteristics for textual personality recognition.Long-termPersonality Encoding in the proposed paradigmcaptures and represents persisting personality traits.Short-termPersonality Capturing records changing personality states.Experimental results demonstrate that the designed BERT-LIWC-LSTM model achieves an average improvement in accuracy of 3.41% on the Big Five dataset compared to current methods,thereby justifying the efficacy of encoding both stable and dynamic personality traits simultaneously through long-and short-term feature interaction.
文摘Diabetes mellitus(DM)comprises distinct subtypes-including type 1 DM,type 2 DM,and gestational DM-all characterized by chronic hyperglycemia and sub-stantial morbidity.Conventional diagnostic and therapeutic strategies often fall short in addressing the complex,multifactorial nature of DM.This review ex-plores how multi-omics integration enhances our mechanistic understanding of DM and informs emerging personalized therapeutic approaches.We consolidated genomic,transcriptomic,proteomic,metabolomic,and microbiomic data from major databases and peer-reviewed publications(2015-2025),with an emphasis on clinical relevance.Multi-omics investigations have identified convergent mole-cular networks underlyingβ-cell dysfunction,insulin resistance,and diabetic complications.The combination of metabolomics and microbiomics highlights critical interactions between metabolic intermediates and gut dysbiosis.Novel biomarkers facilitate early detection of DM and its complications,while single-cell multi-omics and machine learning further refine risk stratification.By dissecting DM heterogeneity more precisely,multi-omics integration enables targeted in-terventions and preventive strategies.Future efforts should focus on data har-monization,ethical considerations,and real-world validation to fully leverage multi-omics in addressing the global DM burden.
文摘Objective:To investigate the distribution of health literacy(HL)levels and the association of HL with proactive personality in patients with permanent colostomy.Methods:A cross-sectional study was conducted to measure proactive personality and HL using validated scales.A total of 172 patients with permanent colostomy were selected from January 2021 to May 2022 in Yantai City,China.Descriptive statistics,t-test,ANOVA,Pearson correlation analysis,and multiple linear regression analysis techniques were used.Results:The results obtained from the study showed that the HL status of the participants was moderate.The correlation between participants’total HL scores and proactive personality scores was 0.417(P-value<0.001).In addition,HL showed statistically significant differences according to education level,place of residence,profession,and average monthly household income.Conclusions:This study showed that patients with higher proactive personality scores had higher HL.The key stakeholders require several positive strategies to improve the HL of patients with permanent colostomy by cultivating their proactive personalities,and these important policies will help to improve patient health and quality of life.
基金This work is supported by the Ministry of Education of Humanities and Social Science projects in China(No.20YJCZH124)Guangdong Province Education and Teaching Reform Project No.640:Research on the Teaching Practice and Application of Online Peer Assessment Methods in the Context of Artificial Intelligence.
文摘This study proposes a learner profile framework based on multi-feature fusion,aiming to enhance the precision of personalized learning recommendations by integrating learners’static attributes(e.g.,demographic data and historical academic performance)with dynamic behavioral patterns(e.g.,real-time interactions and evolving interests over time).The research employs Term Frequency-Inverse Document Frequency(TF-IDF)for semantic feature extraction,integrates the Analytic Hierarchy Process(AHP)for feature weighting,and introduces a time decay function inspired by Newton’s law of cooling to dynamically model changes in learners’interests.Empirical results demonstrate that this framework effectively captures the dynamic evolution of learners’behaviors and provides context-aware learning resource recommendations.The study introduces a novel paradigm for learner modeling in educational technology,combining methodological innovation with a scalable technical architecture,thereby laying a foundation for the development of adaptive learning systems.
基金supported through the European Union’s Horizon 2020 Research and Innovation Program(818318)。
文摘Deep phenotyping and genetic characterization of individuals are fundamental to assessing the metabolic status and determining nutrition-specific requirements.This study aimed to ascertain the utmost effectiveness of personalized interventions by aligning dietary adjustments with both the genotype and metabolotype of individuals.Therefore,we assessed here the usefulness of a polygenic score(PGS)characterizing a potential pro-inflammatory profile(PGSi)as a nutrigenetic tool to discern individuals from the Danish PREVENTOMICS cohort that could better respond to precision nutrition(PN)plans,specifically targeted at counteracting the low-grade inflammatory profile typically found in obesity.The cohort followed a PN plan to counteract the pro-inflammatory profile(PNi group)or generic dietary recommendations(Control)for 10 weeks.PGSi was applied for genetic stratification(Low/High).The effects of the intervention on anthropometrics and biomarkers related to inflammatory profile and carbohydrate metabolism were assessed.Around 30%of subjects had a high genetic predisposition to pro-inflammatory status(high-PGSi).These individuals demonstrated the most effective response to the dietary plan,experiencing improved body composition,with significant decreases in body weight(∆:-4.84%;P=0.039)and body fat(∆:-4.86%;P=0.007),and beneficial changes in pro-and anti-inflammatory biomarkers,with significant increases in IL-10(∆:71.3%;P=0.025)and decreases in TNF-α(∆:-3.0%;P=0.048),CRP(∆:-31.1%),ICAM1(∆:-5.8%),and MCP1(∆:-4.2%)circulating levels,compared to low-PGSi individuals.Both phenotypic and genetic stratification contributed to a better understanding of metabolic heterogeneity in response to diet.This approach allows for refinement of the prediction of individual requirements and potentially for better management of obesity.
文摘With the continuous advancement of artificial intelligence(AI)technology,personalized learning systems are increasingly applied in higher education.Particularly within STEM(Science,Technology,Engineering,and Mathematics)education,AI demonstrates significant advantages through adaptive learning pathways,instant feedback,and individualized resource allocation.However,current research predominantly focuses on the technical architecture and application effectiveness of such systems,with insufficient exploration of how AI-enabled personalized learning systems influence university students’learning motivation and academic achievement through educational psychological mechanisms.This paper adopts an educational psychology perspective to construct a causal mechanism model linking“learning motivation-learning behavior-academic achievement.”Findings indicate that AI-powered personalized learning systems enhance learning autonomy,boost self-efficacy,and optimize feedback mechanisms.These effects collectively stimulate university students’learning motivation in STEM disciplines,thereby promoting academic achievement.Building upon empirical research,this paper proposes implications for educational practice and policy formulation,emphasizing the necessity of advancing higher education reform through the dual influence of technology and psychological mechanisms.
基金supported by the Natural Science Foundation of Hebei Province(F2024202019)the National Natural Science Foundation of China(32201072).
文摘A brain-computer interface(BCI)based on motor imagery(MI)provides additional control pathways by decoding the intentions of the brain.MI ability has great intra-individual variability,and the majority of MI-BCI systems are unable to adapt to this variability,leading to poor training effects.Therefore,prediction of MI ability is needed.In this study,we propose an MI ability predictor based on multi-frequency EEG features.To validate the performance of the predictor,a video-guided paradigm and a traditional MI paradigm are designed,and the predictor is applied to both paradigms.The results demonstrate that all subjects achieved>85%prediction precision in both applications,with a maximum of 96%.This study indicates that the predictor can accurately predict the individuals’MI ability in different states,provide the scientific basis for personalized training,and enhance the effect of MI-BCI training.
基金supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(Grant No.2022D01B 187)。
文摘Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.
