BACKGROUND Preoperative anxiety is a significant concern for patients,as it affects surgical outcomes,satisfaction,and pain perception.Although both anxiety and pain are common in surgical settings,their relationship ...BACKGROUND Preoperative anxiety is a significant concern for patients,as it affects surgical outcomes,satisfaction,and pain perception.Although both anxiety and pain are common in surgical settings,their relationship with personality traits has not been previously investigated in the Lebanese population.AIM To examine the prevalence of preoperative anxiety,pain perception,and personality traits among Lebanese surgical patients,and to assess the associations between these factors.METHODS A descriptive cross-sectional study was conducted between April 2024 and January 2025 across Lebanese hospitals.A total of 392 adult patients were recruited through convenience sampling.Data were collected using a questionnaire that included sociodemographic,clinical,and surgical variables,the Amsterdam Preoperative Anxiety and Information Scale for anxiety,the Visual Analog Scale and Numerical Pain Rating Scale for preoperative pain,and the Ten-Item Personality Inventory for personality traits.Ethical approval was obtained from the Institutional Review Boards of Makassed General Hospital and Hammoud University Medical Center.RESULTS Overall,25%of participants experienced preoperative anxiety,and 34.5%reported moderate pain.Personality assessment showed that the majority of participants had moderate extraversion(84.1%),moderate emotional stability(65.1%),high conscientiousness(61%),high agreeableness(54.1%),and moderate openness(49.2%).High conscientiousness was significantly associated with higher pain perception(P<0.05),while high emotional stability was associated with lower levels of anxiety(P<0.05).No significant association was found between preoperative anxiety and pain(P>0.05).CONCLUSION This study challenges the assumption that preoperative anxiety and pain are directly correlated and highlights the role of personality traits in shaping patient experience.These findings support the potential value of integrating psychological profiling into preoperative care and lay the groundwork for developing personalized interventions to improve patient-centered surgical outcomes.展开更多
The current study examined the roles of collective self-esteem and personal self-esteem in the relationship between national identity and subjective well-being.Participants were 583 Chinese college students(females=49...The current study examined the roles of collective self-esteem and personal self-esteem in the relationship between national identity and subjective well-being.Participants were 583 Chinese college students(females=49%;mean age=19.25±1.85 years).They completed measures of national identity,collective self-esteem,personal self-esteem,and subjective well-being.Path analysis findings result indicated national identity to influence the students’subjective wellbeing through three pathways:(1)national identity→collective self-esteem→subjective well-being,meaning higher subjective wellbeing with collective self-esteem.(2)national identity→personal self-esteem→subjective well-being,to suggest higher personal self-esteem was associated with subjective wellbeing;(3)national identity→collective selfesteem→personal self-esteem→subjective well-being.Compared to simple mediation models constructed with only personal self-esteem or collective self-esteem as a single mediating variable,the chain mediation model better explains the mediating mechanism of national identity on subjective well-being(the variance explained by the mediating variables increased by 65.38%and 59.26%,respectively).The collective self-esteem and personal self-esteem mediation is consistent with social identity theory,whereby national identity enhances collective self-evaluation,which in turn bolsters personal self-worth and subjective well-being.These findings of the current study offer new insights into how national identity affects subjective well-being in collectivistic culture.展开更多
Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories.The collaborative filtering(CF)model,which depends exclusively...Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories.The collaborative filtering(CF)model,which depends exclusively on user-item interactions,commonly encounters challenges,including the cold-start problem and an inability to effectively capture the sequential and temporal characteristics of user behavior.This paper introduces a personalized recommendation system that combines deep learning techniques with Bayesian Personalized Ranking(BPR)optimization to address these limitations.With the strong support of Long Short-Term Memory(LSTM)networks,we apply it to identify sequential dependencies of user behavior and then incorporate an attention mechanism to improve the prioritization of relevant items,thereby enhancing recommendations based on the hybrid feedback of the user and its interaction patterns.The proposed system is empirically evaluated using publicly available datasets from movie and music,and we evaluate the performance against standard recommendation models,including Popularity,BPR,ItemKNN,FPMC,LightGCN,GRU4Rec,NARM,SASRec,and BERT4Rec.The results demonstrate that our proposed framework consistently achieves high outcomes in terms of HitRate,NDCG,MRR,and Precision at K=100,with scores of(0.6763,0.1892,0.0796,0.0068)on MovieLens-100K,(0.6826,0.1920,0.0813,0.0068)on MovieLens-1M,and(0.7937,0.3701,0.2756,0.0078)on Last.fm.The results show an average improvement of around 15%across all metrics compared to existing sequence models,proving that our framework ranks and recommends items more accurately.展开更多
Objective:To explore the clinical effect of personalized nutritional support in elderly women with gestational diabetes(GDM),and explore its impact on the incidence of maternal complications and pregnancy outcomes.Met...Objective:To explore the clinical effect of personalized nutritional support in elderly women with gestational diabetes(GDM),and explore its impact on the incidence of maternal complications and pregnancy outcomes.Methods:A total of 90 elderly pregnant women with gestational diabetes who were delivered in our hospital from January 2023 to January 2024 were selected as the research objects.They were randomly divided into an observation group and a control group,with 45 cases in each group.The control group only received routine pregnancy care and basic nutrition guidance,while the observation group received personalized nutrition support on this basis.Compare the blood glucose control,incidence of pregnancy complications,pregnancy outcomes,and neonatal outcomes between two groups of parturient.Result:After intervention,the fasting blood glucose(FPG),2-hour postprandial blood glucose(2hPG),and glycated hemoglobin(HbA1c)of the observation group were significantly lower than those of the control group,and the differences were statistically significant(p<0.05);The incidence of complications such as gestational hypertension syndrome,polyhydramnios,premature rupture of membranes,and postpartum hemorrhage in the observation group was significantly lower than that in the control group,and the difference was statistically significant(p<0.05);The cesarean section rate in the observation group was significantly lower than that in the control group,and the incidence of adverse neonatal outcomes such as fetal distress,macrosomia,neonatal asphyxia,and neonatal hypoglycemia in the observation group was significantly lower than that in the control group,with statistical significance(p<0.05).Conclusion:Individualized nutritional support for elderly women with gestational diabetes can effectively improve the level of maternal blood sugar control,reduce the incidence of complications during pregnancy,and improve the outcome of pregnancy and neonatal outcomes,which is of high clinical value.展开更多
The support vector machine,a widely used binary classification method,may expose sensitive information during training.To address this,the authors propose a personalized differential privacy method that extends differ...The support vector machine,a widely used binary classification method,may expose sensitive information during training.To address this,the authors propose a personalized differential privacy method that extends differential privacy.Specifically,the authors introduce personalized differentially private support vector machines to meet different individuals'privacy requirements,using a reweighting strategy and the Laplace mechanism.Theoretical analysis demonstrates that the proposed methods simultaneously satisfy the requirements of personalized differential privacy and ensure model prediction accuracy at these privacy levels.Extensive experiments demonstrate that the proposed methods outperform the existing methods.展开更多
Latest digital advancements have intensified the necessity for adaptive,data-driven and socially-centered learning ecosystems.This paper presents the formulation of a cross-platform,innovative,gamified and personalize...Latest digital advancements have intensified the necessity for adaptive,data-driven and socially-centered learning ecosystems.This paper presents the formulation of a cross-platform,innovative,gamified and personalized Learning Ecosystem,which integrates 3D/VR environments,as well as machine learning algorithms,and business intelligence frameworks to enhance learner-centered education and inferenced decision-making.This Learning System makes use of immersive,analytically assessed virtual learning spaces,therefore facilitating real-time monitoring of not just learning performance,but also overall engagement and behavioral patterns,via a comprehensive set of sustainability-oriented ESG-aligned Key Performance Indicators(KPIs).Machine learning models support predictive analysis,personalized feedback,and hybrid recommendation mechanisms,whilst dedicated dashboards translate complex educational data into actionable insights for all Use Cases of the System(Educational Institutions,Educators and Learners).Additionally,the presented Learning System introduces a structured Mentoring and Consulting Subsystem,thence reinforcing human-centered guidance alongside automated intelligence.The Platform’s modular architecture and simulation-centered evaluation approach actively support personalized,and continuously optimized learning pathways.Thence,it exemplifies a mature,adaptive Learning Ecosystem,supporting immersive technologies,analytics,and pedagogical support,hence,contributing to contemporary digital learning innovation and sociotechnical transformation in education.展开更多
文摘BACKGROUND Preoperative anxiety is a significant concern for patients,as it affects surgical outcomes,satisfaction,and pain perception.Although both anxiety and pain are common in surgical settings,their relationship with personality traits has not been previously investigated in the Lebanese population.AIM To examine the prevalence of preoperative anxiety,pain perception,and personality traits among Lebanese surgical patients,and to assess the associations between these factors.METHODS A descriptive cross-sectional study was conducted between April 2024 and January 2025 across Lebanese hospitals.A total of 392 adult patients were recruited through convenience sampling.Data were collected using a questionnaire that included sociodemographic,clinical,and surgical variables,the Amsterdam Preoperative Anxiety and Information Scale for anxiety,the Visual Analog Scale and Numerical Pain Rating Scale for preoperative pain,and the Ten-Item Personality Inventory for personality traits.Ethical approval was obtained from the Institutional Review Boards of Makassed General Hospital and Hammoud University Medical Center.RESULTS Overall,25%of participants experienced preoperative anxiety,and 34.5%reported moderate pain.Personality assessment showed that the majority of participants had moderate extraversion(84.1%),moderate emotional stability(65.1%),high conscientiousness(61%),high agreeableness(54.1%),and moderate openness(49.2%).High conscientiousness was significantly associated with higher pain perception(P<0.05),while high emotional stability was associated with lower levels of anxiety(P<0.05).No significant association was found between preoperative anxiety and pain(P>0.05).CONCLUSION This study challenges the assumption that preoperative anxiety and pain are directly correlated and highlights the role of personality traits in shaping patient experience.These findings support the potential value of integrating psychological profiling into preoperative care and lay the groundwork for developing personalized interventions to improve patient-centered surgical outcomes.
文摘The current study examined the roles of collective self-esteem and personal self-esteem in the relationship between national identity and subjective well-being.Participants were 583 Chinese college students(females=49%;mean age=19.25±1.85 years).They completed measures of national identity,collective self-esteem,personal self-esteem,and subjective well-being.Path analysis findings result indicated national identity to influence the students’subjective wellbeing through three pathways:(1)national identity→collective self-esteem→subjective well-being,meaning higher subjective wellbeing with collective self-esteem.(2)national identity→personal self-esteem→subjective well-being,to suggest higher personal self-esteem was associated with subjective wellbeing;(3)national identity→collective selfesteem→personal self-esteem→subjective well-being.Compared to simple mediation models constructed with only personal self-esteem or collective self-esteem as a single mediating variable,the chain mediation model better explains the mediating mechanism of national identity on subjective well-being(the variance explained by the mediating variables increased by 65.38%and 59.26%,respectively).The collective self-esteem and personal self-esteem mediation is consistent with social identity theory,whereby national identity enhances collective self-evaluation,which in turn bolsters personal self-worth and subjective well-being.These findings of the current study offer new insights into how national identity affects subjective well-being in collectivistic culture.
基金funded by Soonchunhyang University,Grant Number 20250029。
文摘Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories.The collaborative filtering(CF)model,which depends exclusively on user-item interactions,commonly encounters challenges,including the cold-start problem and an inability to effectively capture the sequential and temporal characteristics of user behavior.This paper introduces a personalized recommendation system that combines deep learning techniques with Bayesian Personalized Ranking(BPR)optimization to address these limitations.With the strong support of Long Short-Term Memory(LSTM)networks,we apply it to identify sequential dependencies of user behavior and then incorporate an attention mechanism to improve the prioritization of relevant items,thereby enhancing recommendations based on the hybrid feedback of the user and its interaction patterns.The proposed system is empirically evaluated using publicly available datasets from movie and music,and we evaluate the performance against standard recommendation models,including Popularity,BPR,ItemKNN,FPMC,LightGCN,GRU4Rec,NARM,SASRec,and BERT4Rec.The results demonstrate that our proposed framework consistently achieves high outcomes in terms of HitRate,NDCG,MRR,and Precision at K=100,with scores of(0.6763,0.1892,0.0796,0.0068)on MovieLens-100K,(0.6826,0.1920,0.0813,0.0068)on MovieLens-1M,and(0.7937,0.3701,0.2756,0.0078)on Last.fm.The results show an average improvement of around 15%across all metrics compared to existing sequence models,proving that our framework ranks and recommends items more accurately.
文摘Objective:To explore the clinical effect of personalized nutritional support in elderly women with gestational diabetes(GDM),and explore its impact on the incidence of maternal complications and pregnancy outcomes.Methods:A total of 90 elderly pregnant women with gestational diabetes who were delivered in our hospital from January 2023 to January 2024 were selected as the research objects.They were randomly divided into an observation group and a control group,with 45 cases in each group.The control group only received routine pregnancy care and basic nutrition guidance,while the observation group received personalized nutrition support on this basis.Compare the blood glucose control,incidence of pregnancy complications,pregnancy outcomes,and neonatal outcomes between two groups of parturient.Result:After intervention,the fasting blood glucose(FPG),2-hour postprandial blood glucose(2hPG),and glycated hemoglobin(HbA1c)of the observation group were significantly lower than those of the control group,and the differences were statistically significant(p<0.05);The incidence of complications such as gestational hypertension syndrome,polyhydramnios,premature rupture of membranes,and postpartum hemorrhage in the observation group was significantly lower than that in the control group,and the difference was statistically significant(p<0.05);The cesarean section rate in the observation group was significantly lower than that in the control group,and the incidence of adverse neonatal outcomes such as fetal distress,macrosomia,neonatal asphyxia,and neonatal hypoglycemia in the observation group was significantly lower than that in the control group,with statistical significance(p<0.05).Conclusion:Individualized nutritional support for elderly women with gestational diabetes can effectively improve the level of maternal blood sugar control,reduce the incidence of complications during pregnancy,and improve the outcome of pregnancy and neonatal outcomes,which is of high clinical value.
基金supported by the National Key R&D Program of China under Grant No.2023YFA1008702the National Natural Science Foundation of China under Grant No.12571300。
文摘The support vector machine,a widely used binary classification method,may expose sensitive information during training.To address this,the authors propose a personalized differential privacy method that extends differential privacy.Specifically,the authors introduce personalized differentially private support vector machines to meet different individuals'privacy requirements,using a reweighting strategy and the Laplace mechanism.Theoretical analysis demonstrates that the proposed methods simultaneously satisfy the requirements of personalized differential privacy and ensure model prediction accuracy at these privacy levels.Extensive experiments demonstrate that the proposed methods outperform the existing methods.
文摘Latest digital advancements have intensified the necessity for adaptive,data-driven and socially-centered learning ecosystems.This paper presents the formulation of a cross-platform,innovative,gamified and personalized Learning Ecosystem,which integrates 3D/VR environments,as well as machine learning algorithms,and business intelligence frameworks to enhance learner-centered education and inferenced decision-making.This Learning System makes use of immersive,analytically assessed virtual learning spaces,therefore facilitating real-time monitoring of not just learning performance,but also overall engagement and behavioral patterns,via a comprehensive set of sustainability-oriented ESG-aligned Key Performance Indicators(KPIs).Machine learning models support predictive analysis,personalized feedback,and hybrid recommendation mechanisms,whilst dedicated dashboards translate complex educational data into actionable insights for all Use Cases of the System(Educational Institutions,Educators and Learners).Additionally,the presented Learning System introduces a structured Mentoring and Consulting Subsystem,thence reinforcing human-centered guidance alongside automated intelligence.The Platform’s modular architecture and simulation-centered evaluation approach actively support personalized,and continuously optimized learning pathways.Thence,it exemplifies a mature,adaptive Learning Ecosystem,supporting immersive technologies,analytics,and pedagogical support,hence,contributing to contemporary digital learning innovation and sociotechnical transformation in education.
