Objectives This study aimed to determine the current prevalence of nurse retention in Sub-Saharan Africa(SSA),evaluate the strategies and interventions in SSA countries used to retain their nurses,and identify the key...Objectives This study aimed to determine the current prevalence of nurse retention in Sub-Saharan Africa(SSA),evaluate the strategies and interventions in SSA countries used to retain their nurses,and identify the key challenges impeding nurse retention.Methods A systematic review and meta-analysis were conducted.An electronic search was performed in August 2024 across multiple databases,including PubMed,Ovid Medline,Embase,CINAHL,Scopus,and grey literature sources.The studies were screened using Covidence,and quality assessments were conducted using the Mixed Methods Appraisal Tool.Results A total of 31 articles were included in the review.Meta-analysis revealed that the pooled nurses’retention rate in SSA was 53%(95%CI:38%–67%;I2=97%),while the pooled intention to stay(ITS)rate at work was 57%(95%CI:43%–71%;I2=99%).Subgroup analysis by region showed that the ITS rate was highest in East Africa(65%),followed by West Africa(63%),and lowest in Southern Africa(35%).Effective retention strategies included financial and non-financial incentives,increased production and training of nurses,steering students to shortage specialties,adequate rural housing,facility level improvements,availability of career and professional progression opportunities,nurses’recognition and involvement,employment terms,transparency and predictable management of human resources,supportive work environments,leadership,religious factors,and stakeholders’collaborations.Key challenges to nurses’retention include inadequate healthcare funding,governance issues,poor remuneration and working conditions,political interference,high unemployment rates,ineffective mobility management,unregulated international migration,and active recruitment by wealthier nations.Conclusions Nurse retention in SSA remains critically low.Interventions should be formulated for the above-mentioned effective improvement strategies to address these systemic challenges in order to retain nurses in SSA.展开更多
Sustainable development has become a critical global priority,and green transportation solutions,such as electric taxis,play a vital role in achieving this goal.This study examines the factors influencing students’in...Sustainable development has become a critical global priority,and green transportation solutions,such as electric taxis,play a vital role in achieving this goal.This study examines the factors influencing students’intentions to adopt electric taxi services in Hanoi,Vietnam,as a step toward sustainable urban mobility.We surveyed 573 students and ana-lyzed key determinants using reliability tests,exploratory factor analysis(EFA),and linear regression.The results indi-cate that four factors significantly influence adoption intentions:Perceived Usefulness and Sustainability,Price,Brand Awareness,and Service Quality.Among these,Perceived Usefulness and Sustainability had the strongest positive im-pact,while Service Quality exerted the weakest influence.Notably,Habit and Ease of Use&Transaction Convenience were found to be statistically insignificant in the final model.These findings provide practical implications for business-es and policymakers aiming to use electric taxi adoption.To enhance appeal,stakeholders should emphasize environ-mental benefits,competitive pricing,and brand recognition while improving service reliability.By addressing these fac-tors,electric taxi services can accelerate the transition to sustainable transportation,aligning with global climate goals and transforming urban mobility.This study offers actionable insights for encouraging greener travel behaviors among students,a key demographic for long-term sustainability impact.展开更多
Recently,wearable gait-assist robots have been evolving towards using soft materials designed for the elderly rather than individuals with disabilities,which emphasize modularization,simplification,and weight reductio...Recently,wearable gait-assist robots have been evolving towards using soft materials designed for the elderly rather than individuals with disabilities,which emphasize modularization,simplification,and weight reduction.Thus,synchronizing the robotic assistive force with that of the user’s leg movements is crucial for usability,which requires accurate recognition of the user’s gait intent.In this study,we propose a deep learning model capable of identifying not only gait mode and gait phase but also phase progression.Utilizing data from five inertial measurement units placed on the body,the proposed two-stage architecture incorporates a bidirectional long short-term memory-based model for robust classification of locomotion modes and phases.Subsequently,phase progression is estimated through 1D convolutional neural network-based regressors,each dedicated to a specific phase.The model was evaluated on a diverse dataset encompassing level walking,stair ascent and descent,and sit-to-stand activities from 10 healthy participants.The results demonstrate its ability to accurately classify locomotion phases and estimate phase progression.Accurate phase progression estimation is essential due to the age-related variability in gait phase durations,particularly evident in older adults,the primary demographic for gait-assist robots.These findings underscore the potential to enhance the assistance,comfort,and safety provided by gait-assist robots.展开更多
Background of the study:The Bangladeshi cosmetics market has witnessed significant growth in recent years,driven by changing consumer lifestyles,increased disposable incomes,and rising awareness of personal grooming.T...Background of the study:The Bangladeshi cosmetics market has witnessed significant growth in recent years,driven by changing consumer lifestyles,increased disposable incomes,and rising awareness of personal grooming.The study investigates the impact of content cues which influence on purchasing intention towards cosmetic brands in Bangladesh.Purpose:The basic purpose of this study was to evaluate the factors influencing consumers’purchasing intentions for cosmetic brands in Bangladesh.Specifically,the study explored the roles of various cosmetic-related attributes and their impact on purchasing intentions within the context of Bangladesh’s cosmetic industry.Research methods:A quantitative research approach was adopted,and data were collected through a structured survey targeting Bangladeshi consumers who frequently engage with cosmetic products.All the valid responses were analyzed using SmartPLS 4.0 to perform structural equation modeling.Research findings:The findings revealed that trust in cosmetic brands and competitive pricing significantly influence consumers’purchasing intentions,highlighting the importance of fostering trust and affordability.However,certain constructs,such as ethnocentric tendencies and concerns about ingredient safety,showed limited impact on consumers’decisions.Conclusion:This study contributes to the existing literature by offering empirical insights into the Bangladeshi context,particularly within the rapidly growing cosmetics market.The findings provide actionable recommendations for cosmetic brands aiming to strengthen their market position through trust-building initiatives,competitive pricing strategies,and educational campaigns to enhance consumer awareness.These insights are particularly relevant for marketing practitioners seeking to understand and respond to the unique dynamics of the Bangladeshi cosmetics industry.展开更多
Objective:This study aims to identify the prevalence of bullying in the workplace and to examine its association with turnover(TO)intention and secondary traumatic stress(STS)among Jordanian nurses employed in Emergen...Objective:This study aims to identify the prevalence of bullying in the workplace and to examine its association with turnover(TO)intention and secondary traumatic stress(STS)among Jordanian nurses employed in Emergency Departments(EDs)and critical care units(CCUs).Nurses employed in the EDs and CCUs are exposed to high levels of bullying behaviors that may contribute to STS,leading to high rates of TO.Methods:A descriptive cross-sectional design was used.A sample of 150 Jordanian nurses working in CCUs and EDs completed the study.Data collection was performed using the Demographical Questionnaire,the Negative Acts Questionnaire-Revised(NAQ-R),ProQOL scale,and TO scale.The IBM SPSS software was used to analyze data.Results:About 10.7%and 89.3%were categorized as“occasionally bullied”and“victims of workplace bullying”subsequently.Workplace bullying was positively associated with TO intention(r=0.46,P<0.001)and STS(r=0.36,P<0.001).TO was positively associated with STS(r=0.36,P<0.001).Bullying was a unique significant predictor of TO intention(t=4.59,B=0.34,P<0.001)and STS(t=4.15,B=0.34,P<0.001).Conclusions:Bullying behavior has negative adversarial effects on TO and the experience of STS.The prevalence of bullying behavior in the EDs and CCUs remains high despite the increasing awareness of its negative impacts.Healthcare organizations should put systems in place to ensure that zero-tolerance policy are monitored in terms of the effectiveness of its implementation.展开更多
This study utilized a sequential mediating model to examine the role of motivation to learn and transfer selfefficacy in the relationships between perceived content validity,mentoring function,continuous learning work...This study utilized a sequential mediating model to examine the role of motivation to learn and transfer selfefficacy in the relationships between perceived content validity,mentoring function,continuous learning work culture and intention to transfer learning.The sample comprized 429 final-year apprentices in Guangdong province,China(females=69.9%,Engineering&Medicine=69%,mean age=20.99,SD=1.60).The apprentices completed standardized measures of motivation to learn,transfer self-efficacy perceived content validity,mentoring function,and continuous learning work culture.Structural equation modeling was used to analyze the data.Results showed perceived content validity,mentoring function,continuous learning culture to predict intention to transfer learning.Of these factors,perceived content validity was the strongest predictor of intention to transfer learning.Of these factors,perceived content validity was the most influential predictor of intention to transfer learning.The motivation to learn and transfer self-efficacy sequentially mediated the relationship between mentoring function and intention to learning transfer to be stronger than by either alone.Although perceived content validity and continuous learning culture exhibited no significant direct effects on intention to transfer learning,they demonstrated positive indirect associations with intention to transfer via motivation to learn and transfer self-efficacy.These study findings extend the applications of the learning transfer framework to individuals undergoing apprenticeship training which also would apply to other a long-term work-based learning programs.展开更多
To address the issue of neglecting scenarios involving joint operations and collaborative drone swarm operations in air combat target intent recognition.This paper proposes a transfer learning-based intention predicti...To address the issue of neglecting scenarios involving joint operations and collaborative drone swarm operations in air combat target intent recognition.This paper proposes a transfer learning-based intention prediction model for drone formation targets in air combat.This model recognizes the intentions of multiple aerial targets by extracting spatial features among the targets at each moment.Simulation results demonstrate that,compared to classical intention recognition models,the proposed model in this paper achieves higher accuracy in identifying the intentions of drone swarm targets in air combat scenarios.展开更多
Today, most people know that physical activity(PA) is beneficial for their health ^(1,2)and aspire to engage in regular PA.^(3,4)However, despite their awareness of the importance of PA, it is evident that the transit...Today, most people know that physical activity(PA) is beneficial for their health ^(1,2)and aspire to engage in regular PA.^(3,4)However, despite their awareness of the importance of PA, it is evident that the transition from intention to action is challenging-a situation that has important public health implications. According to the World Health Organization,^(5)1 person dies every 6 s worldwide from causes related to physical inactivity, which underscores the urgency of addressing this situation.展开更多
High complexity and uncertainty of air combat pose significant challenges to target intention prediction.Current interpolation methods for data pre-processing and wrangling have limitations in capturing interrelations...High complexity and uncertainty of air combat pose significant challenges to target intention prediction.Current interpolation methods for data pre-processing and wrangling have limitations in capturing interrelationships among intricate variable patterns.Accordingly,this study proposes a Mogrifier gate recurrent unit-D(Mog-GRU-D)model to address the com-bat target intention prediction issue under the incomplete infor-mation condition.The proposed model directly processes miss-ing data while reducing the independence between inputs and output states.A total of 1200 samples from twelve continuous moments are captured through the combat simulation system,each of which consists of seven dimensional features.To bench-mark the experiment,a missing valued dataset has been gener-ated by randomly removing 20%of the original data.Extensive experiments demonstrate that the proposed model obtains the state-of-the-art performance with an accuracy of 73.25%when dealing with incomplete information.This study provides possi-ble interpretations for the principle of target interactive mecha-nism,highlighting the model’s effectiveness in potential air war-fare implementation.展开更多
Objective The effects of prolonged exposure to persistently elevated atmospheric pollutants,commonly termed air pollution waves,on fertility intentions remain inadequately understood.This study aims to investigate the...Objective The effects of prolonged exposure to persistently elevated atmospheric pollutants,commonly termed air pollution waves,on fertility intentions remain inadequately understood.This study aims to investigate the association between particulate matter(PM)exposure and fertility intentions.Methods In this nationwide cross-sectional study,we analyzed data from 10,747 participants(5496 females and 5251 males).PM waves were defined as periods lasting 3‒6 consecutive days during which the daily average concentrations exceeded China’s Ambient Air Quality Standards Grade II thresholds(PM2.5>75μg/m3 and PM10>150μg/m3).We employed multivariate logistic regression models to assess the association between exposure to PM waves and fertility intentions.Results Significant inverse associations were detected between exposure to PM2.5 wave events(characterized by concentrations exceeding 75μg/m3 for durations of 4‒6 days,P<0.05)and PM10 wave events(defined as concentrations exceeding 150μg/m3 for 6 consecutive days,P<0.05)and fertility intentions among females.In contrast,neither the PM2.5 wave nor the PM10 wave events demonstrated statistically significant correlations with fertility intentions in males(P>0.05 for all comparisons).The potentially susceptible subgroup was identified as females aged 20–30 years.Conclusions Our results provide the first evidence that PM2.5 and PM10 waves are associated with a reduction in female fertility intentions,offering critical insights for the development of public health policies and strategies aimed at individual protection.展开更多
This study is based on the Extended Theory of Planned Behavior(ETPB)and focuses on the elderly population in the main urban area of Chongqing to explore their intentions and influencing factors regarding health and we...This study is based on the Extended Theory of Planned Behavior(ETPB)and focuses on the elderly population in the main urban area of Chongqing to explore their intentions and influencing factors regarding health and wellness tourism behavior.Data was collected through questionnaire surveys and field research,and SPSS 26.0 and Amos 29.0 software were used for reliability,validity analysis,and structural equation modeling testing.The study shows that behavioral attitude,perceived behavior control,and the context of health and wellness tourism have a significant positive impact on the elderly’s intentions regarding health and wellness tourism behavior,while the influence of subjective norms is not significant.In addition,subjective norms have a significant positive effect on behavioral attitudes and perceived behavioral control.Based on the research conclusions,suggestions are made to strengthen the behavioral intention of elderly tourists to participate in health and wellness tourism.展开更多
In the contemporary digital landscape,the proliferation of information has led to an increasing diversity of channels through which consumers obtain information,resulting in a gradual transformation of shopping habits...In the contemporary digital landscape,the proliferation of information has led to an increasing diversity of channels through which consumers obtain information,resulting in a gradual transformation of shopping habits.Consumers now frequently rely on external sources to make well-informed purchasing decisions,leading to the emergence of live shopping as a prominent avenue for gathering product information and completing transactions.E-commerce live streaming has experienced rapid growth,leveraging its ability to generate traffic and capture consumer attention.The integration of content and live streaming not only meets users’psychological needs but also facilitates seamless communication between buyers and sellers.From the perspective of content marketing typologies,this paper examines content marketing across three key dimensions:informational content,entertainment content,and emotional content.It further explores the impact of content marketing on consumers’purchase intentions within the context of e-commerce live streaming.展开更多
The consultation intention of emergency decision-makers in urban rail transit(URT)is input into the emergency knowledge base in the form of domain questions to obtain emergency decision support services.This approach ...The consultation intention of emergency decision-makers in urban rail transit(URT)is input into the emergency knowledge base in the form of domain questions to obtain emergency decision support services.This approach facilitates the rapid collection of complete knowledge and rules to form effective decisions.However,the current structured degree of the URT emergency knowledge base remains low,and the domain questions lack labeled datasets,resulting in a large deviation between the consultation outcomes and the intended objectives.To address this issue,this paper proposes a question intention recognition model for the URT emergency domain,leveraging knowledge graph(KG)and data enhancement technology.First,a structured storage of emergency cases and emergency plans is realized based on KG.Subsequently,a comprehensive question template is developed,and the labeled dataset of emergency domain questions in URT is generated through the KG.Lastly,data enhancement is applied by prompt learning and the NLP Chinese Data Augmentation(NLPCDA)tool,and the intention recognition model combining Generalized Auto-regression Pre-training for Language Understanding(XLNet)and Recurrent Convolutional Neural Network for Text Classification(TextRCNN)is constructed.Word embeddings are generated by XLNet,context information is further captured using Bidirectional Long Short-Term Memory Neural Network(BiLSTM),and salient features are extracted with Convolutional Neural Network(CNN).Experimental results demonstrate that the proposed model can enhance the clarity of classification and the identification of domain questions,thereby providing supportive knowledge for emergency decision-making in URT.展开更多
A survey was conducted among 616 students from six private undergraduate universities in Hefei City,Anhui Province.Using Likert 5-point scale measurements and SPSS 27.0 for data analysis,the study revealed that studen...A survey was conducted among 616 students from six private undergraduate universities in Hefei City,Anhui Province.Using Likert 5-point scale measurements and SPSS 27.0 for data analysis,the study revealed that students with prior entrepreneurial experience demonstrated lower satisfaction with university entrepreneurship education,and their past entrepreneurial attempts showed a negative correlation with future entrepreneurial intentions.However,high-quality entrepreneurship education exhibited a positive mediating role in this relationship.This study reveals the intrinsic mechanisms through which personal experiences and entrepreneurship education influence college students'entrepreneurial intentions,providing theoretical references for higher education institutions to optimize entrepreneurship education programs and enhance students'entrepreneurial motivations.These findings will contribute to the national strategy of"promoting employment through entrepreneurship".展开更多
This study investigates the adoption of carbon footprint tracking apps(CFAs)among Thai millennials,a critical element in addressing climate change.CFAs have yet to gain significant traction among users despite offerin...This study investigates the adoption of carbon footprint tracking apps(CFAs)among Thai millennials,a critical element in addressing climate change.CFAs have yet to gain significant traction among users despite offering personalized missions.Employing an extended Technology Acceptance Model(TAM)framework,we examine factors influencing CFA adoption intentions based on a sample of 30 environmentally conscious Thai millennials.Our findings indicate that perceived ease of use and enjoyment are crucial drivers of CFA adoption.Trust significantly impacts perceived usefulness,while enjoyment influences perceived ease of use.The study underscores the importance of user experience(UX)and enjoyment in driving adoption,highlighting the need for intuitive interfaces and engaging features.This research provides comprehensive insights into CFA adoption in Thailand by integrating TAM with external trust and perceived enjoyment factors.These findings offer valuable guidance for app developers,policymakers,and marketers,emphasizing the critical role of user experience and fun in fostering widespread CFA adoption.We discuss implications for stakeholders and suggest directions for future research,including larger-scale studies and cross-cultural comparisons within Southeast Asia.This research contributes to SDG 13(Climate Action)and SDG 12(Responsible Consumption and Production).展开更多
Recognizing target intent is crucial for making decisions on the battlefield.However,the imperfect and ambiguous character of battlefield situations challenges the validity and causation analysis of classical intent r...Recognizing target intent is crucial for making decisions on the battlefield.However,the imperfect and ambiguous character of battlefield situations challenges the validity and causation analysis of classical intent recognition techniques.Facing with the challenge,a target intention causal analysis paradigm is proposed by combining with an Intervention Retrieval(IR)model and a Hybrid Intention Recognition(HIR)model.The target data acquired by the sensors are modelled as Basic Probability Assignments(BPAs)based on evidence theory to create uncertain datasets.Then,the HIR model is utilized to recognize intent for a tested sample from uncertain datasets.Finally,the intervention operator under the evidence structure is utilized to perform attribute intervention on the tested sample.Data retrieval is performed in the sample database based on the IR model to generate the intention distribution of the pseudo-intervention samples to analyze the causal effects of individual sample attributes.The simulation results demonstrate that our framework successfully identifies the target intention under the evidence structure and goes further to analyze the causal impact of sample attributes on the target intention.展开更多
Objective:This study aimed to review the relationship between job embeddedness and turnover intentions among nurses and explore the effects of the Job Embeddedness Scale,number of years in the career,education,and mar...Objective:This study aimed to review the relationship between job embeddedness and turnover intentions among nurses and explore the effects of the Job Embeddedness Scale,number of years in the career,education,and marital status on this relationship.Methods:The review was conducted by searching the China Knowledge Resource Integrated Database(CNKI),Weipu Database(CQVIP),China Biology Medicine(CBM),Wanfang Database,PubMed,Web of Science,Embase,CINAHL,and APA-PsycNet for articles on nurses’job embeddedness and turnover from intention up to March 2024.The research quality was evaluated using the Agency for Healthcare Research and Quality(AHRQ)assessment criteria.The review protocol has been registered on PROSPERO[CRD42023483947].Results:The results of this review included 47 studies consisting of 15,742 nurses from seven countries worldwide.A moderate negative correlation was found between job embeddedness and turnover intention(r=0.487).Furthermore,on-the-job embeddedness(r=0.527)was more negatively associated with turnover intention than off-the-job embeddedness(r=0.234).The highest negative correlation was found between sacrifice and turnover intention(r=0.460),while the lowest was for the link(r=0.185).Furthermore,the relationship between job embeddedness and its dimensions with turnover intention was affected by different job embeddedness scales,number of years in the career,education,and marital status(P<0.05).Conclusion:This systematic review and meta-analysis analyzed the relationships between nurses’job embeddedness,dimensions,and turnover intention.Meanwhile,subgroup analysis and meta-regression explored the factors influencing these relationships.It is an important reference for nurse managers to promote nurse retention.展开更多
How to mine valuable information from massive multisource heterogeneous data and identify the intention of aerial targets is a major research focus at present. Aiming at the longterm dependence of air target intention...How to mine valuable information from massive multisource heterogeneous data and identify the intention of aerial targets is a major research focus at present. Aiming at the longterm dependence of air target intention recognition, this paper deeply explores the potential attribute features from the spatiotemporal sequence data of the target. First, we build an intelligent dynamic intention recognition framework, including a series of specific processes such as data source, data preprocessing,target space-time, convolutional neural networks-bidirectional gated recurrent unit-atteneion (CBA) model and intention recognition. Then, we analyze and reason the designed CBA model in detail. Finally, through comparison and analysis with other recognition model experiments, our proposed method can effectively improve the accuracy of air target intention recognition,and is of significance to the commanders’ operational command and situation prediction.展开更多
To solve the problem that the existing situation awareness research focuses on multi-sensor data fusion,but the expert knowledge is not fully utilized,a heterogeneous informa-tion fusion recognition method based on be...To solve the problem that the existing situation awareness research focuses on multi-sensor data fusion,but the expert knowledge is not fully utilized,a heterogeneous informa-tion fusion recognition method based on belief rule structure is proposed.By defining the continuous probabilistic hesitation fuzzy linguistic term sets(CPHFLTS)and establishing CPHFLTS distance measure,the belief rule base of the relationship between feature space and category space is constructed through information integration,and the evidence reasoning of the input samples is carried out.The experimental results show that the proposed method can make full use of sensor data and expert knowledge for recognition.Compared with the other methods,the proposed method has a higher correct recognition rate under different noise levels.展开更多
Next point-of-interest(POI)recommendation has been applied by many internet companies to enhance the user travel experience.Recent research advocates deep-learning methods to model long-term check-in sequences and min...Next point-of-interest(POI)recommendation has been applied by many internet companies to enhance the user travel experience.Recent research advocates deep-learning methods to model long-term check-in sequences and mine mobility patterns of people to improve recommendation performance.Existing approaches model general user preferences based on historical check-ins and can be termed as preference pattern models.The preference pattern is different from the intention pattern,in that it does not emphasize the user mobility pattern of revisiting POIs,which is a common behavior and kind of intention for users.An effective module is needed to predict when and where users will repeat visits.In this paper,we propose a Spatio-Temporal Intention Learning Self-Attention Network(STILSAN)for next POI recommendation.STILSAN employs a preference-intention module to capture the user’s long-term preference and recognizes the user’s intention to revisit some specific POIs at a specific time.Meanwhile,we design a spatial encoder module as a pretrained model for learning POI spatial feature by simulating the spatial clustering phenomenon and the spatial proximity of the POIs.Experiments are conducted on two real-world check-in datasets.The experimental results demonstrate that all the proposed modules can effectively improve recommendation accuracy and STILSAN yields outstanding improvements over the state-of-the-art models.展开更多
文摘Objectives This study aimed to determine the current prevalence of nurse retention in Sub-Saharan Africa(SSA),evaluate the strategies and interventions in SSA countries used to retain their nurses,and identify the key challenges impeding nurse retention.Methods A systematic review and meta-analysis were conducted.An electronic search was performed in August 2024 across multiple databases,including PubMed,Ovid Medline,Embase,CINAHL,Scopus,and grey literature sources.The studies were screened using Covidence,and quality assessments were conducted using the Mixed Methods Appraisal Tool.Results A total of 31 articles were included in the review.Meta-analysis revealed that the pooled nurses’retention rate in SSA was 53%(95%CI:38%–67%;I2=97%),while the pooled intention to stay(ITS)rate at work was 57%(95%CI:43%–71%;I2=99%).Subgroup analysis by region showed that the ITS rate was highest in East Africa(65%),followed by West Africa(63%),and lowest in Southern Africa(35%).Effective retention strategies included financial and non-financial incentives,increased production and training of nurses,steering students to shortage specialties,adequate rural housing,facility level improvements,availability of career and professional progression opportunities,nurses’recognition and involvement,employment terms,transparency and predictable management of human resources,supportive work environments,leadership,religious factors,and stakeholders’collaborations.Key challenges to nurses’retention include inadequate healthcare funding,governance issues,poor remuneration and working conditions,political interference,high unemployment rates,ineffective mobility management,unregulated international migration,and active recruitment by wealthier nations.Conclusions Nurse retention in SSA remains critically low.Interventions should be formulated for the above-mentioned effective improvement strategies to address these systemic challenges in order to retain nurses in SSA.
文摘Sustainable development has become a critical global priority,and green transportation solutions,such as electric taxis,play a vital role in achieving this goal.This study examines the factors influencing students’intentions to adopt electric taxi services in Hanoi,Vietnam,as a step toward sustainable urban mobility.We surveyed 573 students and ana-lyzed key determinants using reliability tests,exploratory factor analysis(EFA),and linear regression.The results indi-cate that four factors significantly influence adoption intentions:Perceived Usefulness and Sustainability,Price,Brand Awareness,and Service Quality.Among these,Perceived Usefulness and Sustainability had the strongest positive im-pact,while Service Quality exerted the weakest influence.Notably,Habit and Ease of Use&Transaction Convenience were found to be statistically insignificant in the final model.These findings provide practical implications for business-es and policymakers aiming to use electric taxi adoption.To enhance appeal,stakeholders should emphasize environ-mental benefits,competitive pricing,and brand recognition while improving service reliability.By addressing these fac-tors,electric taxi services can accelerate the transition to sustainable transportation,aligning with global climate goals and transforming urban mobility.This study offers actionable insights for encouraging greener travel behaviors among students,a key demographic for long-term sustainability impact.
基金supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI)funded by the Ministry of Health&Welfare,Republic of Korea(Grant Number:RS-2022-KH129263).
文摘Recently,wearable gait-assist robots have been evolving towards using soft materials designed for the elderly rather than individuals with disabilities,which emphasize modularization,simplification,and weight reduction.Thus,synchronizing the robotic assistive force with that of the user’s leg movements is crucial for usability,which requires accurate recognition of the user’s gait intent.In this study,we propose a deep learning model capable of identifying not only gait mode and gait phase but also phase progression.Utilizing data from five inertial measurement units placed on the body,the proposed two-stage architecture incorporates a bidirectional long short-term memory-based model for robust classification of locomotion modes and phases.Subsequently,phase progression is estimated through 1D convolutional neural network-based regressors,each dedicated to a specific phase.The model was evaluated on a diverse dataset encompassing level walking,stair ascent and descent,and sit-to-stand activities from 10 healthy participants.The results demonstrate its ability to accurately classify locomotion phases and estimate phase progression.Accurate phase progression estimation is essential due to the age-related variability in gait phase durations,particularly evident in older adults,the primary demographic for gait-assist robots.These findings underscore the potential to enhance the assistance,comfort,and safety provided by gait-assist robots.
基金Correspondence concerning this article should be addressed to Meher Neger,Comilla University,Comilla,Bangladesh.
文摘Background of the study:The Bangladeshi cosmetics market has witnessed significant growth in recent years,driven by changing consumer lifestyles,increased disposable incomes,and rising awareness of personal grooming.The study investigates the impact of content cues which influence on purchasing intention towards cosmetic brands in Bangladesh.Purpose:The basic purpose of this study was to evaluate the factors influencing consumers’purchasing intentions for cosmetic brands in Bangladesh.Specifically,the study explored the roles of various cosmetic-related attributes and their impact on purchasing intentions within the context of Bangladesh’s cosmetic industry.Research methods:A quantitative research approach was adopted,and data were collected through a structured survey targeting Bangladeshi consumers who frequently engage with cosmetic products.All the valid responses were analyzed using SmartPLS 4.0 to perform structural equation modeling.Research findings:The findings revealed that trust in cosmetic brands and competitive pricing significantly influence consumers’purchasing intentions,highlighting the importance of fostering trust and affordability.However,certain constructs,such as ethnocentric tendencies and concerns about ingredient safety,showed limited impact on consumers’decisions.Conclusion:This study contributes to the existing literature by offering empirical insights into the Bangladeshi context,particularly within the rapidly growing cosmetics market.The findings provide actionable recommendations for cosmetic brands aiming to strengthen their market position through trust-building initiatives,competitive pricing strategies,and educational campaigns to enhance consumer awareness.These insights are particularly relevant for marketing practitioners seeking to understand and respond to the unique dynamics of the Bangladeshi cosmetics industry.
文摘Objective:This study aims to identify the prevalence of bullying in the workplace and to examine its association with turnover(TO)intention and secondary traumatic stress(STS)among Jordanian nurses employed in Emergency Departments(EDs)and critical care units(CCUs).Nurses employed in the EDs and CCUs are exposed to high levels of bullying behaviors that may contribute to STS,leading to high rates of TO.Methods:A descriptive cross-sectional design was used.A sample of 150 Jordanian nurses working in CCUs and EDs completed the study.Data collection was performed using the Demographical Questionnaire,the Negative Acts Questionnaire-Revised(NAQ-R),ProQOL scale,and TO scale.The IBM SPSS software was used to analyze data.Results:About 10.7%and 89.3%were categorized as“occasionally bullied”and“victims of workplace bullying”subsequently.Workplace bullying was positively associated with TO intention(r=0.46,P<0.001)and STS(r=0.36,P<0.001).TO was positively associated with STS(r=0.36,P<0.001).Bullying was a unique significant predictor of TO intention(t=4.59,B=0.34,P<0.001)and STS(t=4.15,B=0.34,P<0.001).Conclusions:Bullying behavior has negative adversarial effects on TO and the experience of STS.The prevalence of bullying behavior in the EDs and CCUs remains high despite the increasing awareness of its negative impacts.Healthcare organizations should put systems in place to ensure that zero-tolerance policy are monitored in terms of the effectiveness of its implementation.
基金funded by Hanshan Normal University School-Level Research Initiation Program(grant numbers QD202244QD2024207)+3 种基金the Guangdong Higher Education Society’s“Fourteenth Five-Year”Plan 2024 Higher Education Research(grant number 24GYB43)the 2024 Guangdong Provincial Undergraduate Teaching Quality and Teaching Reform Engineering Project:Excellence Program for Cultivating Publicly-Funded Pre-service Teachers for Primary Education in the Context of Rural Revitalizationthe Hanshan Normal University Guangdong East Regional Education Collaborative Innovation Research Centerfunded by these sources.
文摘This study utilized a sequential mediating model to examine the role of motivation to learn and transfer selfefficacy in the relationships between perceived content validity,mentoring function,continuous learning work culture and intention to transfer learning.The sample comprized 429 final-year apprentices in Guangdong province,China(females=69.9%,Engineering&Medicine=69%,mean age=20.99,SD=1.60).The apprentices completed standardized measures of motivation to learn,transfer self-efficacy perceived content validity,mentoring function,and continuous learning work culture.Structural equation modeling was used to analyze the data.Results showed perceived content validity,mentoring function,continuous learning culture to predict intention to transfer learning.Of these factors,perceived content validity was the strongest predictor of intention to transfer learning.Of these factors,perceived content validity was the most influential predictor of intention to transfer learning.The motivation to learn and transfer self-efficacy sequentially mediated the relationship between mentoring function and intention to learning transfer to be stronger than by either alone.Although perceived content validity and continuous learning culture exhibited no significant direct effects on intention to transfer learning,they demonstrated positive indirect associations with intention to transfer via motivation to learn and transfer self-efficacy.These study findings extend the applications of the learning transfer framework to individuals undergoing apprenticeship training which also would apply to other a long-term work-based learning programs.
文摘To address the issue of neglecting scenarios involving joint operations and collaborative drone swarm operations in air combat target intent recognition.This paper proposes a transfer learning-based intention prediction model for drone formation targets in air combat.This model recognizes the intentions of multiple aerial targets by extracting spatial features among the targets at each moment.Simulation results demonstrate that,compared to classical intention recognition models,the proposed model in this paper achieves higher accuracy in identifying the intentions of drone swarm targets in air combat scenarios.
基金supported by The Shenzhen Educational Research Funding(zdzb2014)The Shenzhen Science and Technology Innovation Commission(202307313000096)+4 种基金The Social Science Foundation from the China's Ministry of Education(23YJA880093)The Post-Doctoral Fellowship(2022M711174)The National Center for Mental Health(Z014)BC is supported by the Chaires de recherche Rennes Métropole(23C 0909)SM is supported by the National Insti-tutes of Health(R01AG72445).
文摘Today, most people know that physical activity(PA) is beneficial for their health ^(1,2)and aspire to engage in regular PA.^(3,4)However, despite their awareness of the importance of PA, it is evident that the transition from intention to action is challenging-a situation that has important public health implications. According to the World Health Organization,^(5)1 person dies every 6 s worldwide from causes related to physical inactivity, which underscores the urgency of addressing this situation.
基金supported by the Aeronautical Science Foundation of China(2020Z023053002).
文摘High complexity and uncertainty of air combat pose significant challenges to target intention prediction.Current interpolation methods for data pre-processing and wrangling have limitations in capturing interrelationships among intricate variable patterns.Accordingly,this study proposes a Mogrifier gate recurrent unit-D(Mog-GRU-D)model to address the com-bat target intention prediction issue under the incomplete infor-mation condition.The proposed model directly processes miss-ing data while reducing the independence between inputs and output states.A total of 1200 samples from twelve continuous moments are captured through the combat simulation system,each of which consists of seven dimensional features.To bench-mark the experiment,a missing valued dataset has been gener-ated by randomly removing 20%of the original data.Extensive experiments demonstrate that the proposed model obtains the state-of-the-art performance with an accuracy of 73.25%when dealing with incomplete information.This study provides possi-ble interpretations for the principle of target interactive mecha-nism,highlighting the model’s effectiveness in potential air war-fare implementation.
基金supported by grants from the National Key Research and Development Program of China(No.2023YFC2705700)Guangdong Basic and Applied Basic Research Foundation(No.2024A1515012355)+1 种基金the Shenzhen Science and Technology Program(No.JCYJ20220530140609020)the Scientific Research Project of Wuhan Municipal Health Commission(No.WX21Q36).
文摘Objective The effects of prolonged exposure to persistently elevated atmospheric pollutants,commonly termed air pollution waves,on fertility intentions remain inadequately understood.This study aims to investigate the association between particulate matter(PM)exposure and fertility intentions.Methods In this nationwide cross-sectional study,we analyzed data from 10,747 participants(5496 females and 5251 males).PM waves were defined as periods lasting 3‒6 consecutive days during which the daily average concentrations exceeded China’s Ambient Air Quality Standards Grade II thresholds(PM2.5>75μg/m3 and PM10>150μg/m3).We employed multivariate logistic regression models to assess the association between exposure to PM waves and fertility intentions.Results Significant inverse associations were detected between exposure to PM2.5 wave events(characterized by concentrations exceeding 75μg/m3 for durations of 4‒6 days,P<0.05)and PM10 wave events(defined as concentrations exceeding 150μg/m3 for 6 consecutive days,P<0.05)and fertility intentions among females.In contrast,neither the PM2.5 wave nor the PM10 wave events demonstrated statistically significant correlations with fertility intentions in males(P>0.05 for all comparisons).The potentially susceptible subgroup was identified as females aged 20–30 years.Conclusions Our results provide the first evidence that PM2.5 and PM10 waves are associated with a reduction in female fertility intentions,offering critical insights for the development of public health policies and strategies aimed at individual protection.
基金Chongqing University of Science and Technology 2024 Postgraduate Innovation Program(YKJCX2420803)。
文摘This study is based on the Extended Theory of Planned Behavior(ETPB)and focuses on the elderly population in the main urban area of Chongqing to explore their intentions and influencing factors regarding health and wellness tourism behavior.Data was collected through questionnaire surveys and field research,and SPSS 26.0 and Amos 29.0 software were used for reliability,validity analysis,and structural equation modeling testing.The study shows that behavioral attitude,perceived behavior control,and the context of health and wellness tourism have a significant positive impact on the elderly’s intentions regarding health and wellness tourism behavior,while the influence of subjective norms is not significant.In addition,subjective norms have a significant positive effect on behavioral attitudes and perceived behavioral control.Based on the research conclusions,suggestions are made to strengthen the behavioral intention of elderly tourists to participate in health and wellness tourism.
文摘In the contemporary digital landscape,the proliferation of information has led to an increasing diversity of channels through which consumers obtain information,resulting in a gradual transformation of shopping habits.Consumers now frequently rely on external sources to make well-informed purchasing decisions,leading to the emergence of live shopping as a prominent avenue for gathering product information and completing transactions.E-commerce live streaming has experienced rapid growth,leveraging its ability to generate traffic and capture consumer attention.The integration of content and live streaming not only meets users’psychological needs but also facilitates seamless communication between buyers and sellers.From the perspective of content marketing typologies,this paper examines content marketing across three key dimensions:informational content,entertainment content,and emotional content.It further explores the impact of content marketing on consumers’purchase intentions within the context of e-commerce live streaming.
基金supported in part by the National Natural Science Foundation of China.The funding numbers 62433005,62272036,62132003,and 62173167.
文摘The consultation intention of emergency decision-makers in urban rail transit(URT)is input into the emergency knowledge base in the form of domain questions to obtain emergency decision support services.This approach facilitates the rapid collection of complete knowledge and rules to form effective decisions.However,the current structured degree of the URT emergency knowledge base remains low,and the domain questions lack labeled datasets,resulting in a large deviation between the consultation outcomes and the intended objectives.To address this issue,this paper proposes a question intention recognition model for the URT emergency domain,leveraging knowledge graph(KG)and data enhancement technology.First,a structured storage of emergency cases and emergency plans is realized based on KG.Subsequently,a comprehensive question template is developed,and the labeled dataset of emergency domain questions in URT is generated through the KG.Lastly,data enhancement is applied by prompt learning and the NLP Chinese Data Augmentation(NLPCDA)tool,and the intention recognition model combining Generalized Auto-regression Pre-training for Language Understanding(XLNet)and Recurrent Convolutional Neural Network for Text Classification(TextRCNN)is constructed.Word embeddings are generated by XLNet,context information is further captured using Bidirectional Long Short-Term Memory Neural Network(BiLSTM),and salient features are extracted with Convolutional Neural Network(CNN).Experimental results demonstrate that the proposed model can enhance the clarity of classification and the identification of domain questions,thereby providing supportive knowledge for emergency decision-making in URT.
基金Supported by Key Research Project of Anhui Xinhua University(2022 University-Level)from the Student Quality Education Research Center(IFQE202202)Provincial-Level Quality Engineering Project of Anhui Provincial Department of Education for Higher Education Institutions(2022-JYXM671).
文摘A survey was conducted among 616 students from six private undergraduate universities in Hefei City,Anhui Province.Using Likert 5-point scale measurements and SPSS 27.0 for data analysis,the study revealed that students with prior entrepreneurial experience demonstrated lower satisfaction with university entrepreneurship education,and their past entrepreneurial attempts showed a negative correlation with future entrepreneurial intentions.However,high-quality entrepreneurship education exhibited a positive mediating role in this relationship.This study reveals the intrinsic mechanisms through which personal experiences and entrepreneurship education influence college students'entrepreneurial intentions,providing theoretical references for higher education institutions to optimize entrepreneurship education programs and enhance students'entrepreneurial motivations.These findings will contribute to the national strategy of"promoting employment through entrepreneurship".
文摘This study investigates the adoption of carbon footprint tracking apps(CFAs)among Thai millennials,a critical element in addressing climate change.CFAs have yet to gain significant traction among users despite offering personalized missions.Employing an extended Technology Acceptance Model(TAM)framework,we examine factors influencing CFA adoption intentions based on a sample of 30 environmentally conscious Thai millennials.Our findings indicate that perceived ease of use and enjoyment are crucial drivers of CFA adoption.Trust significantly impacts perceived usefulness,while enjoyment influences perceived ease of use.The study underscores the importance of user experience(UX)and enjoyment in driving adoption,highlighting the need for intuitive interfaces and engaging features.This research provides comprehensive insights into CFA adoption in Thailand by integrating TAM with external trust and perceived enjoyment factors.These findings offer valuable guidance for app developers,policymakers,and marketers,emphasizing the critical role of user experience and fun in fostering widespread CFA adoption.We discuss implications for stakeholders and suggest directions for future research,including larger-scale studies and cross-cultural comparisons within Southeast Asia.This research contributes to SDG 13(Climate Action)and SDG 12(Responsible Consumption and Production).
基金partially supported by the National Natural Science Foundation of China(No.62173272)。
文摘Recognizing target intent is crucial for making decisions on the battlefield.However,the imperfect and ambiguous character of battlefield situations challenges the validity and causation analysis of classical intent recognition techniques.Facing with the challenge,a target intention causal analysis paradigm is proposed by combining with an Intervention Retrieval(IR)model and a Hybrid Intention Recognition(HIR)model.The target data acquired by the sensors are modelled as Basic Probability Assignments(BPAs)based on evidence theory to create uncertain datasets.Then,the HIR model is utilized to recognize intent for a tested sample from uncertain datasets.Finally,the intervention operator under the evidence structure is utilized to perform attribute intervention on the tested sample.Data retrieval is performed in the sample database based on the IR model to generate the intention distribution of the pseudo-intervention samples to analyze the causal effects of individual sample attributes.The simulation results demonstrate that our framework successfully identifies the target intention under the evidence structure and goes further to analyze the causal impact of sample attributes on the target intention.
基金sponsor from the Academic Research Funding of Macao Polytechnic University(Grant number RP/AE-06/2022).
文摘Objective:This study aimed to review the relationship between job embeddedness and turnover intentions among nurses and explore the effects of the Job Embeddedness Scale,number of years in the career,education,and marital status on this relationship.Methods:The review was conducted by searching the China Knowledge Resource Integrated Database(CNKI),Weipu Database(CQVIP),China Biology Medicine(CBM),Wanfang Database,PubMed,Web of Science,Embase,CINAHL,and APA-PsycNet for articles on nurses’job embeddedness and turnover from intention up to March 2024.The research quality was evaluated using the Agency for Healthcare Research and Quality(AHRQ)assessment criteria.The review protocol has been registered on PROSPERO[CRD42023483947].Results:The results of this review included 47 studies consisting of 15,742 nurses from seven countries worldwide.A moderate negative correlation was found between job embeddedness and turnover intention(r=0.487).Furthermore,on-the-job embeddedness(r=0.527)was more negatively associated with turnover intention than off-the-job embeddedness(r=0.234).The highest negative correlation was found between sacrifice and turnover intention(r=0.460),while the lowest was for the link(r=0.185).Furthermore,the relationship between job embeddedness and its dimensions with turnover intention was affected by different job embeddedness scales,number of years in the career,education,and marital status(P<0.05).Conclusion:This systematic review and meta-analysis analyzed the relationships between nurses’job embeddedness,dimensions,and turnover intention.Meanwhile,subgroup analysis and meta-regression explored the factors influencing these relationships.It is an important reference for nurse managers to promote nurse retention.
基金supported by the National Natural Science Foundation of China (61502523)。
文摘How to mine valuable information from massive multisource heterogeneous data and identify the intention of aerial targets is a major research focus at present. Aiming at the longterm dependence of air target intention recognition, this paper deeply explores the potential attribute features from the spatiotemporal sequence data of the target. First, we build an intelligent dynamic intention recognition framework, including a series of specific processes such as data source, data preprocessing,target space-time, convolutional neural networks-bidirectional gated recurrent unit-atteneion (CBA) model and intention recognition. Then, we analyze and reason the designed CBA model in detail. Finally, through comparison and analysis with other recognition model experiments, our proposed method can effectively improve the accuracy of air target intention recognition,and is of significance to the commanders’ operational command and situation prediction.
基金This work was supported by the Youth Foundation of National Science Foundation of China(62001503)the Special Fund for Taishan Scholar Project(ts 201712072).
文摘To solve the problem that the existing situation awareness research focuses on multi-sensor data fusion,but the expert knowledge is not fully utilized,a heterogeneous informa-tion fusion recognition method based on belief rule structure is proposed.By defining the continuous probabilistic hesitation fuzzy linguistic term sets(CPHFLTS)and establishing CPHFLTS distance measure,the belief rule base of the relationship between feature space and category space is constructed through information integration,and the evidence reasoning of the input samples is carried out.The experimental results show that the proposed method can make full use of sensor data and expert knowledge for recognition.Compared with the other methods,the proposed method has a higher correct recognition rate under different noise levels.
基金supported by Chongqing Technology Innovation and Application Development Project[grant number cstc2021jscx-dxwtBX0023]funding from Chongqing Changan Automobile Co.,Ltd.,Dongfeng Motor Corporation,and Dongfeng Changxing Tech Co.,Ltd.
文摘Next point-of-interest(POI)recommendation has been applied by many internet companies to enhance the user travel experience.Recent research advocates deep-learning methods to model long-term check-in sequences and mine mobility patterns of people to improve recommendation performance.Existing approaches model general user preferences based on historical check-ins and can be termed as preference pattern models.The preference pattern is different from the intention pattern,in that it does not emphasize the user mobility pattern of revisiting POIs,which is a common behavior and kind of intention for users.An effective module is needed to predict when and where users will repeat visits.In this paper,we propose a Spatio-Temporal Intention Learning Self-Attention Network(STILSAN)for next POI recommendation.STILSAN employs a preference-intention module to capture the user’s long-term preference and recognizes the user’s intention to revisit some specific POIs at a specific time.Meanwhile,we design a spatial encoder module as a pretrained model for learning POI spatial feature by simulating the spatial clustering phenomenon and the spatial proximity of the POIs.Experiments are conducted on two real-world check-in datasets.The experimental results demonstrate that all the proposed modules can effectively improve recommendation accuracy and STILSAN yields outstanding improvements over the state-of-the-art models.
