Objective:To investigate the interrelationship between cardiovascular health awareness,risk perception,behavioural intention,and INTERHEART risk stratification in a middle-aged adult population in Malaysia.Methods:A c...Objective:To investigate the interrelationship between cardiovascular health awareness,risk perception,behavioural intention,and INTERHEART risk stratification in a middle-aged adult population in Malaysia.Methods:A cross-sectional survey with convenience sampling was conducted during November 2022 and January 2023.Participants completed validated questionnaires assessing cardiovascular health awareness,risk perception of cardiovascular diseases,behavioural intention towards adopting healthy habits,and INTERHEART risk stratification score(IHRS)based on established risk factors.A total of 602 respondents were included in the final analysis.Data were analysed with independent t-test/one-way ANOVA or Mann-Whitney/Kruskal-Wallis to test the differences,Pearson correlation or linear regression test to analyze the association of independent and dependent variables.Results:There was a significant positive correlation between medical knowledge related to cardiovascular disease(CVD)and knowledge related to CVD risk prevention,risk perception,behavioural intention and IHRS(P<0.05,Pearson correlation).Notably,individuals with higher IHRS tended to have lower knowledge related to CVD and CVD risk prevention,risk perception,and behavioural intention.Males,laborers,active/former smokers,individuals with lower household income and educational levels,those involved in occupations not related to the healthcare sector,and those who did not receive the CVD health brochure or are unaware of health self-assessment tools are likely to have lower levels of knowledge,risk perception,and poorer behavioural intention regarding cardiovascular health(P<0.05,one-way ANOVA).While educational level,smoking status,awareness about CVD poster,self-assessment tools were repeatedly significantly associated with knowledge related to CVD and CVD risk prevention,risk perception,behavioral intention and/or IHRS(P<0.05,linear regression).Conclusions:These findings underscore the importance of promoting cardiovascular health awareness and risk perception among middle-aged adults to foster positive BI and reduce CVD risk.Tailored interventions targeting specific risk factors identified by INTERHEART may enhance risk stratification accuracy and facilitate targeted preventive strategies.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
The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interest...The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and motivations.Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation.The user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social platforms.However,a combination of multiple behaviors in profiling users has yet to be considered.This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition.This research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning.The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation.User feedback is based on online behavior and practices collected by using a survey method.The participants include both males and females from different occupation sectors and different ages.The data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics.Different techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136.Feature average is computed to identify user intent type characteristics.The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on their preferences in online content,platforms,criteria,and frequency.The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater.展开更多
Background:Alcohol and illicit drugs(AID)continue to be a major global health concern.Although preventable,AID is linked to millions of deaths annually worldwide.The situation is particularly grave for young people,wi...Background:Alcohol and illicit drugs(AID)continue to be a major global health concern.Although preventable,AID is linked to millions of deaths annually worldwide.The situation is particularly grave for young people,with AID being a major direct risk factor for disability-adjusted youth life-years lost and death.It further contributes to assaults,road crashes,accidental poisoning,and suicide,leading to long-term issues and public health concerns.Objective:This study aimed at disclosing current AID prevalence data for Argentinian,Bulgarian,Chilean and Romanian youth.It shed light on the predictors of AID in young people from those countries.Method:The study used an online survey to gather data from people aged 18 to 25(n=1,297).The survey was underpinned by the theory of planned behaviour(TPB).Predictors were investigated separately for drinking alcohol and using illicit drugs.Results:Our data revealed that across the four target countries,49%to 90%of the participants drank alcohol,and 8%to 35%used illicit drugs in the past three months.Between 20%and 91%of them intended to drink,and between 8%and 31%intended to use illicit drugs in the following three months.Our TPB model predicted statistically significant(P<0.001)amounts of variance in drinking alcohol(between 61%and 72%)and using illicit drugs(between 20.3%and 74.4%).Intention was consistent in significantly predicting both behaviours.Evidence around the predictive validity of self-efficacy,age and gender was mixed across the investigated countries.Conclusion:This research provided an update on the scarce AID epidemiological data.It also supplied evidence about what theoretically-informed measures might be useful targets of interventions in the case of Argentina,Bulgaria,Chile and Romania.This new knowledge of understanding substance abuse determinants and prevalence may help researchers and practitioners better meet young people's health prevention needs.展开更多
The recent increase in the use of artificial intelligence has led to fundamental changes in the development of training and teaching methods for executive education. However, the success of artificial intelligence in ...The recent increase in the use of artificial intelligence has led to fundamental changes in the development of training and teaching methods for executive education. However, the success of artificial intelligence in regional centers for teaching and training professions will depend on the acceptance of this technology by young executive trainees. This article discusses the potential benefits of adopting AI in executive training institutions in Morocco, specifically focusing on CRMEF Casablanca Settat. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003), this study proposes a model to identify the factors influencing the acceptance of artificial intelligence in regional centers for teaching professions and training in Morocco. To achieve this, a structural equation modeling approach was used to quantitatively describe the impact of each factor on AI adoption, utilizing data collected from 173 young executive trainees. The results indicate that perceived ease of use, perceived usefulness, trainer influence, and personal innovativeness influence the intention to use artificial intelligence. Our research provides managers of CRMEFs with a set of practical recommendations to enhance the implementation conditions of an artificial intelligence system. It aims to understand which factors should be considered in designing an artificial intelligence system within regional centers for teaching professions and training (CRMEFs).展开更多
The effective use of land in Northeast China is of great significance for ensuring national food security and regional rural revitalization.In this study,based on the survey data collected from Jilin Province,Northeas...The effective use of land in Northeast China is of great significance for ensuring national food security and regional rural revitalization.In this study,based on the survey data collected from Jilin Province,Northeast China,we analyzed the vacancy rates of rural homesteads in suburban,outer suburban,and remote villages,as well as the withdrawal intention of rural-settled farmers,urbansettled farmers,and farmers with urban and rural dual residency from vacant homesteads.From the perspective of farmers’perceptions,this study constructed a theoretical framework of the influence mechanism of their withdrawal intention and analyzed such mechanism by constructing a structural equation model.The results indicated that:1)rural homestead vacancy rates increased with distance from the village to the city.2)rural-settled farmers showed a low withdrawal intention for vacant rural homesteads,followed by urban and rural dual-residence farmers,and urban-settled farmers showed the highest withdrawal intention.3)the relative importance of the factors influencing withdrawal intention is in the following order:policy awareness>interest perception>living environment perception>family characteristics.Finally,this study discussed the reformation of the homestead system and rural homestead transition in Northeast China,which can provide policy support to increase the potential of cultivated land and promote sustainable rural development and urban-rural coordination.展开更多
Session-based Recommendation(SBR)aims to accurately recom-mend a list of items to users based on anonymous historical session sequences.Existing methods for SBR suffer from several limitations:SBR based on Graph Neura...Session-based Recommendation(SBR)aims to accurately recom-mend a list of items to users based on anonymous historical session sequences.Existing methods for SBR suffer from several limitations:SBR based on Graph Neural Network often has information loss when constructing session graphs;Inadequate consideration is given to influencing factors,such as item price,and users’dynamic interest evolution is not taken into account.A new session recommendation model called Price-aware Session-based Recommendation(PASBR)is proposed to address these limitations.PASBR constructs session graphs by information lossless approaches to fully encode the original session information,then introduces item price as a new factor and models users’price tolerance for various items to influence users’preferences.In addition,PASBR proposes a new method to encode user intent at the item category level and tries to capture the dynamic interest of users over time.Finally,PASBR fuses the multi-perspective features to generate the global representation of users and make a prediction.Specifically,the intent,the short-term and long-term interests,and the dynamic interests of a user are combined.Experiments on two real-world datasets show that PASBR can outperform representative baselines for SBR.展开更多
In this paper,a stable two-sided matching(TSM)method considering the matching intention of agents under a hesitant fuzzy environment is proposed.The method uses a hesitant fuzzy element(HFE)as its basis.First,the HFE ...In this paper,a stable two-sided matching(TSM)method considering the matching intention of agents under a hesitant fuzzy environment is proposed.The method uses a hesitant fuzzy element(HFE)as its basis.First,the HFE preference matrix is transformed into the normalized HFE preference matrix.On this basis,the distance and the projection of the normalized HFEs on positive and negative ideal solutions are calculated.Then,the normalized HFEs are transformed into agent satisfactions.Considering the stable matching constraints,a multiobjective programming model with the objective of maximizing the satisfactions of two-sided agents is constructed.Based on the agent satisfaction matrix,the matching intention matrix of two-sided agents is built.According to the agent satisfaction matrix and matching intention matrix,the comprehensive satisfaction matrix is set up.Furthermore,the multiobjective programming model based on satisfactions is transformed into a multiobjective programming model based on comprehensive satisfactions.Using the G-S algorithm,the multiobjective programming model based on comprehensive satisfactions is solved,and then the best TSM scheme is obtained.Finally,a terminal distribution example is used to verify the feasibility and effectiveness of the proposed method.展开更多
The extant literature on international immigrants has discussed migrants’entrepreneurial activities in the context of Western countries but has paid little attention to return-home entrepreneurial intention(RHEI).Rur...The extant literature on international immigrants has discussed migrants’entrepreneurial activities in the context of Western countries but has paid little attention to return-home entrepreneurial intention(RHEI).Rural migrant workers(RMWs)in China,who used to promote rural development by remittances and were characterized by similarities with early international migrants,have gradually returned to their hometowns to initiate entrepreneurial activities.Based on the structured questionnaire conducted in 2015 and 2020in Anhui Province,China,this article combines the concept of mixed embeddedness with the idea of double-layered embeddedness and analyzes the impacts of the social,economic and institutional context in RMWs’hometowns and migration destinations on RMWs’RHEI by using binary logistic regression.The article shows that the social,economic,and institutional environments of RMWs’hometowns and migration destinations have effects on their RHEI.The embeddedness in the economic and informal institutional context in RMWs’RHEI is even more important than personal characteristics.Compared with migration destinations,RMWs’hometowns exert a more influential effect on their RHEI.However,that does not mean that the role of migration destinations can be undervalued.Actually,the better the social,economic,and institutional environments of migration destinations RMWs moved into is,the higher entrepreneurial intention they will have after returning to their hometowns.The article proposes a modified framework in combination of mixed embeddedness with double-layer embeddedness and proves that it is suitable for analyzing RMWs’RHEI.The framework has important implications for strengthening China’s RMWs to return home to start their own businesses.展开更多
Rapid urbanization and population growth have triggered an increase in urban housing demand and rising housing prices,which can influence the migration intention of high-level talents.Much work within the literature h...Rapid urbanization and population growth have triggered an increase in urban housing demand and rising housing prices,which can influence the migration intention of high-level talents.Much work within the literature has focused more on the migration of the general public.However,antecedents of migration intention and the impact of housing prices on the migration of high-level talents remain unclear.Therefore,based on the push-pull theory,this study investigates the influencing factors of talent migration intention and explores the role of housing prices.This study reveals a complex mechanism underlying migration decisions by using logistic regression models and survey data of high-level talents in the Pearl River Delta(PRD)megalopolis of China.The results indicate that:1)in high house-price regions,social integration is the primary push factor,and the main factors for retaining talents are the expectation of future work and intimate family relationships;2)in medium house-price regions,the main factors that attract talents are the current salary level and close family ties;3)in low house-price regions,living convenience is a determining factor in retaining talents.This study provides a new perspective for talent mobility research and offers valuable inputs for retaining and attracting talents in different economic development regions.Findings are of great significance for formulating talent introduction policies and forming a new pattern of rational spatial docking and effective allocation of human resources.展开更多
Surface electromyography(sEMG)is widely used for analyzing and controlling lower limb assisted exoskeleton robots.Behavior intention recognition based on sEMG is of great significance for achieving intelligent prosthe...Surface electromyography(sEMG)is widely used for analyzing and controlling lower limb assisted exoskeleton robots.Behavior intention recognition based on sEMG is of great significance for achieving intelligent prosthetic and exoskeleton control.Achieving highly efficient recognition while improving performance has always been a significant challenge.To address this,we propose an sEMG-based method called Enhanced Residual Gate Network(ERGN)for lower-limb behavioral intention recognition.The proposed network combines an attention mechanism and a hard threshold function,while combining the advantages of residual structure,which maps sEMG of multiple acquisition channels to the lower limb motion states.Firstly,continuous wavelet transform(CWT)is used to extract signals features from the collected sEMG data.Then,a hard threshold function serves as the gate function to enhance signals quality,with an attention mechanism incorporated to improve the ERGN’s performance further.Experimental results demonstrate that the proposed ERGN achieves extremely high accuracy and efficiency,with an average recognition accuracy of 98.41%and an average recognition time of only 20 ms-outperforming the state-of-the-art research significantly.Our research provides support for the application of lower limb assisted exoskeleton robots.展开更多
Electrical and electronic waste(e-waste)is a growing challenge,matching the widespread boom in the use of information and communication technology.Opposite to an alarming increasing amount of e-waste,a low rate of con...Electrical and electronic waste(e-waste)is a growing challenge,matching the widespread boom in the use of information and communication technology.Opposite to an alarming increasing amount of e-waste,a low rate of consumer engagement in ensuring the proper disposal of such materials intensifies the pressure on the exist‐ing e-waste crisis.To deal with this thorny problem,it is of great interest to grasp consumers’disposal and re‐cycling behavioral intentions.Therefore,this study attempts to understand complementary perspectives around consumers’e-waste recycling intention based on the integration of the valence theory and the norm activation theory.Four data mining models using classification and prediction-based algorithms,namely Chi squared automatic interaction detector(CHAID),Neural network,Discriminant analysis,and Quick,unbiased,efficient statistical tree(QUEST),were employed to analyze a set of the 398 data collected in Vietnam.The re‐sults revealed that the social support value is by far the most critical predictor,followed by the utilitarian value,task difficulty,and monetary risk.It is also noteworthy that the awareness of consequences,education background,the ascription of responsibility,and age were also ranked as critical affecting factors.The lowest influential predictors found in this study were income and gender.In addition,a comparison was made in terms of the classification performance of the four utilized data mining techniques.Based on several evalua‐tion measurements(confusion matrix,accuracy,precision,recall,specificity,F-measure,ROC curve,and AUC),the aggregated results suggested that CHAID and Neural network performed the best.The findings of this research are expected to assist policymakers and future researchers in updating all information surround‐ing consumer behavioral intention-related topics focusing on e-waste.Furthermore,the adoption of data min‐ing algorithms for prediction is another insight of this study,which may shed the light on data mining applica‐tions in such environmental studies in the future.展开更多
BACKGROUND The frequency and content of follow-up strategies remain controversial for colorectal cancer(CRC),and scheduled follow-ups have limited value.AIM To compare intensive and conventional follow-up strategies f...BACKGROUND The frequency and content of follow-up strategies remain controversial for colorectal cancer(CRC),and scheduled follow-ups have limited value.AIM To compare intensive and conventional follow-up strategies for the prognosis of non-metastatic CRC treated with curative intent using a meta-analysis.METHODS PubMed,Embase,and the Cochrane Library databases were systematically searched for potentially eligible randomized controlled trials(RCTs)from inception until April 2023.The Cochrane risk of bias was used to assess the methodological quality of the included studies.The hazard ratio,relative risk,and 95%confidence interval were used to calculate survival and categorical data,and pooled analyses were performed using the random-effects model.Additional exploratory analyses were performed for sensitivity,subgroups,and publication bias.RESULTS Eighteen RCTs involving 8533 patients with CRC were selected for the final analysis.Intensive follow-up may be superior to conventional follow-up in improving overall survival,but this difference was not statistically significant.Moreover,intensive follow-up was associated with an increased incidence of salvage surgery compared to conventional follow-up.In addition,there was no significant difference in the risk of recurrence between intensive and conventional follow-up strategies,whereas intensive follow-up was associated with a reduced risk of interval recurrence compared to conventional follow-up.Finally,the effects of intensive and conventional follow-up strategies differed when stratified by tumor location and follow-up duration.CONCLUSION Intensive follow-up may have a beneficial effect on the overall survival of patients with non-metastatic CRC treated with curative intent.展开更多
For autonomous vehicles and driver assist systems,path planning and collision avoidance algorithms benefit from accurate predictions of future location of other vehicles and intent of their drivers.In the literature,t...For autonomous vehicles and driver assist systems,path planning and collision avoidance algorithms benefit from accurate predictions of future location of other vehicles and intent of their drivers.In the literature,the algorithms that provide driver intent belong to two categories:those that use physics based models with some type of filtering,and machine learning based approaches.In this paper we employ barrier functions(BF)to decide driver intent.BFs are typically used to prove safety by establishing forward invariance of an admissible set.Here,we decide if the“target”vehicle is violating one or more possibly fictitious(i.e.,non-physical)barrier constraints determined based on the context provided by the road geometry.The algorithm has a very small computational footprint and better false positive and negative rates than some of the alternatives.The predicted intent is then used by a control barrier function(CBF)based collision avoidance system to prevent unnecessary interventions,for either an autonomous or human-driven vehicle.展开更多
Since the outbreak of COVID-19,tourists have been increasingly concerned over various risks of international travel,while knowledge of the pandemic appears to vary significantly.In addition,as travel restrictions cont...Since the outbreak of COVID-19,tourists have been increasingly concerned over various risks of international travel,while knowledge of the pandemic appears to vary significantly.In addition,as travel restrictions continue to impact adversely on international tourism,tourism efforts should be placed more on the domestic markets.Via structural equation modeling,this study unearthed different risk factors impacting Korean travelers’choices of alternative local destinations in the post-pandemic era.In addition,this study extended the goal-directed behavior framework with the acquisition of perceived risk and knowledge of COVID-19,which was proven to hold a sig-nificantly superior explanatory power of tourists’decisions of local alternatives over foreign countries during the COVID-19 pandemic.Furthermore,desire was found to play an imminent mediating role in the conceptual mod-el,maximizing the impact of perceived risk on travel intentions.Henceforth,this research offers meaningful the-oretical implication as thefirst empirical study to deepen the goal-directed behaviour framework with perceived risk and knowledge in the context of post-COVID-19 era.It also serves as insightful knowledge for Korean tour-ism authorities and practitioners to understand local tourists’decision-making processes and tailor effective recovery strategy for domestic tourism.展开更多
Traditional monitoring systems that are used in shopping malls or com-munity management,mostly use a remote control to monitor and track specific objects;therefore,it is often impossible to effectively monitor the enti...Traditional monitoring systems that are used in shopping malls or com-munity management,mostly use a remote control to monitor and track specific objects;therefore,it is often impossible to effectively monitor the entire environ-ment.Whenfinding a suspicious person,the tracked object cannot be locked in time for tracking.This research replaces the traditionalfixed-point monitor with the intelligent drone and combines the image processing technology and automatic judgment for the movements of the monitored person.This intelligent system can effectively improve the shortcomings of low efficiency and high cost of the traditional monitor system.In this article,we proposed a TIMT(The Intel-ligent Monitoring and Tracking)algorithm which can make the drone have smart surveillance and tracking capabilities.It combined with Artificial Intelligent(AI)face recognition technology and the OpenPose which is able to monitor the phy-sical movements of multiple people in real time to analyze the meaning of human body movements and to track the monitored intelligently through the remote con-trol interface of the drone.This system is highly agile and could be adjusted immediately to any angle and screen that we monitor.Therefore,the system couldfind abnormal conditions immediately and track and monitor them automatically.That is the system can immediately detect when someone invades the home or community,and the drone can automatically track the intruder to achieve that the two significant shortcomings of the traditional monitor will be improved.Experimental results show that the intelligent monitoring and tracking drone sys-tem has an excellent performance,which not only dramatically reduces the num-ber of monitors and the required equipment but also achieves perfect monitoring and tracking.展开更多
The prevalence of mental health problems in both Malaysian and global workplaces has significantly increased due to the presence of the coronavirus disease(COVID-19)pandemic,globalization,technology advancement in Indu...The prevalence of mental health problems in both Malaysian and global workplaces has significantly increased due to the presence of the coronavirus disease(COVID-19)pandemic,globalization,technology advancement in Industry 4.0,and other contributing factors.The pervasiveness of the issue poses a huge challenge to improving the occupational safety and health(OSH)of workers in various industries,especially in the digital industry.The emergence of the innovative industry is evident mainly due to the rapid development of Industry 4.0 and the rele-vant demands of multiple businesses in the digital transformation.Nonetheless,limited studies and academic dis-cussions were conducted on the OSH topic of digital employees.Hence,the current study serves tofill the existing gap and provide academic contributions by scrutinising the perceptions of digital workers regarding their work-place well-being,mental health literacy,and the impression of employing e-mental health.The objectives of this study are:1)To examine the mental health literacy and workplace wellness of digital workers,2)to explore the e-mental health usage intention and actual e-mental health utilization,and 3)to identify digital workers’feedback on e-mental health.In the current context,e-mental health focuses on three dimensions,namely,1)“health in our hand(HIOH)”,2)“interacting for health(IFH)”,and 3)“data enabling health(DEH)”.The present study employed an online cross-sectional survey and received 326 digital workers’completed responses.Variables,such as“mental health literacy(MHL)”,“workplace wellness(WW)”,and e-mental health intention and usage were explicated by analysing the data through descriptive statistics.The study results indicated a moderate to a high level of the MHL and the WW.More than half of the workers possessed a high intention level to employ e-mental health,with the HIOH dimension being the most prevalent domain.Nevertheless,the actual e-mental health usage was very low,owing to the online resources being a new concept amongst digital employees.Numerous confounding factors also existed in affecting the low usage,such as privacy concerns,data security levels,and health verification issues.In addition,the mental health issue has not been openly and widely discussed in Malay-sian workplaces due to stigmatisation.As such,the currentfindings could provide additional insights into the OSH literature;it could serve as a guideline for the OSH decision-makers,employers,and eHealth developers when establishing a feasible framework for the practical adoption of e-mental health services by digital workers.展开更多
To meet the needs of today’s library users,institutions are developing library mobile apps(LMAs),as their libraries are increasingly intelligent and rely on deep learning.This paper explores the influencing factors a...To meet the needs of today’s library users,institutions are developing library mobile apps(LMAs),as their libraries are increasingly intelligent and rely on deep learning.This paper explores the influencing factors and differences in the perception of LMAs at different time points after a user has downloaded an LMA.A research model was constructed based on the technology acceptance model.A questionnaire was designed and distributed twice to LMA users with an interval of three months to collect dynamic data.The analysis was based on structural equation modeling.The empirical results show that the perceived ease of use,the perceived usefulness,the social influence,and the facilitating conditions affected the users’behavioral intention,but their impacts were different at different times.As the usage time increases,the technology acceptance model is still universal for understanding the user perception of LMA.In addition,two extended variables(social impact and convenience)also affect the user’s behavior intention.User behavior is dynamic and changed over time.This study is important both theoretically and practically,as the results could be used to improve the service quality of LMAs and reduce the loss rate of users.Its findings may help the designers and developers of LMAs to optimize them from the perspective of a user and improve the service experience by providing a deeper understanding of the adoption behavior of information systems by LMA users.展开更多
基金This work was supported by the Ministry of Higher Education(MOHE),Malaysia,through Fundamental Research Grant Scheme(FRGS/1/2022/SKK10/UTAR/02/1)Universiti Tunku Abdul Rahman,Malaysia,through UTAR-Research Grant(IPSR/RMC/UTARRF/2021-C2/L08)MBBS Community Health Project(2022/2023),UTAR.
文摘Objective:To investigate the interrelationship between cardiovascular health awareness,risk perception,behavioural intention,and INTERHEART risk stratification in a middle-aged adult population in Malaysia.Methods:A cross-sectional survey with convenience sampling was conducted during November 2022 and January 2023.Participants completed validated questionnaires assessing cardiovascular health awareness,risk perception of cardiovascular diseases,behavioural intention towards adopting healthy habits,and INTERHEART risk stratification score(IHRS)based on established risk factors.A total of 602 respondents were included in the final analysis.Data were analysed with independent t-test/one-way ANOVA or Mann-Whitney/Kruskal-Wallis to test the differences,Pearson correlation or linear regression test to analyze the association of independent and dependent variables.Results:There was a significant positive correlation between medical knowledge related to cardiovascular disease(CVD)and knowledge related to CVD risk prevention,risk perception,behavioural intention and IHRS(P<0.05,Pearson correlation).Notably,individuals with higher IHRS tended to have lower knowledge related to CVD and CVD risk prevention,risk perception,and behavioural intention.Males,laborers,active/former smokers,individuals with lower household income and educational levels,those involved in occupations not related to the healthcare sector,and those who did not receive the CVD health brochure or are unaware of health self-assessment tools are likely to have lower levels of knowledge,risk perception,and poorer behavioural intention regarding cardiovascular health(P<0.05,one-way ANOVA).While educational level,smoking status,awareness about CVD poster,self-assessment tools were repeatedly significantly associated with knowledge related to CVD and CVD risk prevention,risk perception,behavioral intention and/or IHRS(P<0.05,linear regression).Conclusions:These findings underscore the importance of promoting cardiovascular health awareness and risk perception among middle-aged adults to foster positive BI and reduce CVD risk.Tailored interventions targeting specific risk factors identified by INTERHEART may enhance risk stratification accuracy and facilitate targeted preventive strategies.
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.
文摘The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and motivations.Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation.The user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social platforms.However,a combination of multiple behaviors in profiling users has yet to be considered.This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition.This research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning.The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation.User feedback is based on online behavior and practices collected by using a survey method.The participants include both males and females from different occupation sectors and different ages.The data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics.Different techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136.Feature average is computed to identify user intent type characteristics.The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on their preferences in online content,platforms,criteria,and frequency.The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater.
文摘Background:Alcohol and illicit drugs(AID)continue to be a major global health concern.Although preventable,AID is linked to millions of deaths annually worldwide.The situation is particularly grave for young people,with AID being a major direct risk factor for disability-adjusted youth life-years lost and death.It further contributes to assaults,road crashes,accidental poisoning,and suicide,leading to long-term issues and public health concerns.Objective:This study aimed at disclosing current AID prevalence data for Argentinian,Bulgarian,Chilean and Romanian youth.It shed light on the predictors of AID in young people from those countries.Method:The study used an online survey to gather data from people aged 18 to 25(n=1,297).The survey was underpinned by the theory of planned behaviour(TPB).Predictors were investigated separately for drinking alcohol and using illicit drugs.Results:Our data revealed that across the four target countries,49%to 90%of the participants drank alcohol,and 8%to 35%used illicit drugs in the past three months.Between 20%and 91%of them intended to drink,and between 8%and 31%intended to use illicit drugs in the following three months.Our TPB model predicted statistically significant(P<0.001)amounts of variance in drinking alcohol(between 61%and 72%)and using illicit drugs(between 20.3%and 74.4%).Intention was consistent in significantly predicting both behaviours.Evidence around the predictive validity of self-efficacy,age and gender was mixed across the investigated countries.Conclusion:This research provided an update on the scarce AID epidemiological data.It also supplied evidence about what theoretically-informed measures might be useful targets of interventions in the case of Argentina,Bulgaria,Chile and Romania.This new knowledge of understanding substance abuse determinants and prevalence may help researchers and practitioners better meet young people's health prevention needs.
文摘The recent increase in the use of artificial intelligence has led to fundamental changes in the development of training and teaching methods for executive education. However, the success of artificial intelligence in regional centers for teaching and training professions will depend on the acceptance of this technology by young executive trainees. This article discusses the potential benefits of adopting AI in executive training institutions in Morocco, specifically focusing on CRMEF Casablanca Settat. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003), this study proposes a model to identify the factors influencing the acceptance of artificial intelligence in regional centers for teaching professions and training in Morocco. To achieve this, a structural equation modeling approach was used to quantitatively describe the impact of each factor on AI adoption, utilizing data collected from 173 young executive trainees. The results indicate that perceived ease of use, perceived usefulness, trainer influence, and personal innovativeness influence the intention to use artificial intelligence. Our research provides managers of CRMEFs with a set of practical recommendations to enhance the implementation conditions of an artificial intelligence system. It aims to understand which factors should be considered in designing an artificial intelligence system within regional centers for teaching professions and training (CRMEFs).
基金Under the auspices of Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA28100405,XDA28020403)National Natural Science Foundation of China (No. 41771179, 42071275)+1 种基金Talents Training Project Supported by the Central Government for the Reform and Development of Local UniversitiesYoung Scientist Group Project of Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences (No. 2022QNXZ02)
文摘The effective use of land in Northeast China is of great significance for ensuring national food security and regional rural revitalization.In this study,based on the survey data collected from Jilin Province,Northeast China,we analyzed the vacancy rates of rural homesteads in suburban,outer suburban,and remote villages,as well as the withdrawal intention of rural-settled farmers,urbansettled farmers,and farmers with urban and rural dual residency from vacant homesteads.From the perspective of farmers’perceptions,this study constructed a theoretical framework of the influence mechanism of their withdrawal intention and analyzed such mechanism by constructing a structural equation model.The results indicated that:1)rural homestead vacancy rates increased with distance from the village to the city.2)rural-settled farmers showed a low withdrawal intention for vacant rural homesteads,followed by urban and rural dual-residence farmers,and urban-settled farmers showed the highest withdrawal intention.3)the relative importance of the factors influencing withdrawal intention is in the following order:policy awareness>interest perception>living environment perception>family characteristics.Finally,this study discussed the reformation of the homestead system and rural homestead transition in Northeast China,which can provide policy support to increase the potential of cultivated land and promote sustainable rural development and urban-rural coordination.
文摘Session-based Recommendation(SBR)aims to accurately recom-mend a list of items to users based on anonymous historical session sequences.Existing methods for SBR suffer from several limitations:SBR based on Graph Neural Network often has information loss when constructing session graphs;Inadequate consideration is given to influencing factors,such as item price,and users’dynamic interest evolution is not taken into account.A new session recommendation model called Price-aware Session-based Recommendation(PASBR)is proposed to address these limitations.PASBR constructs session graphs by information lossless approaches to fully encode the original session information,then introduces item price as a new factor and models users’price tolerance for various items to influence users’preferences.In addition,PASBR proposes a new method to encode user intent at the item category level and tries to capture the dynamic interest of users over time.Finally,PASBR fuses the multi-perspective features to generate the global representation of users and make a prediction.Specifically,the intent,the short-term and long-term interests,and the dynamic interests of a user are combined.Experiments on two real-world datasets show that PASBR can outperform representative baselines for SBR.
基金supported by the National Natural Science Foundation of China (Grant No.71861015)the Humanities and Social Science Foundation of the Ministry of Education of China (Grant No.18YJA630047)the Distinguished Young Scholar Talent of Jiangxi Province (Grant No.20192BCBL23008).
文摘In this paper,a stable two-sided matching(TSM)method considering the matching intention of agents under a hesitant fuzzy environment is proposed.The method uses a hesitant fuzzy element(HFE)as its basis.First,the HFE preference matrix is transformed into the normalized HFE preference matrix.On this basis,the distance and the projection of the normalized HFEs on positive and negative ideal solutions are calculated.Then,the normalized HFEs are transformed into agent satisfactions.Considering the stable matching constraints,a multiobjective programming model with the objective of maximizing the satisfactions of two-sided agents is constructed.Based on the agent satisfaction matrix,the matching intention matrix of two-sided agents is built.According to the agent satisfaction matrix and matching intention matrix,the comprehensive satisfaction matrix is set up.Furthermore,the multiobjective programming model based on satisfactions is transformed into a multiobjective programming model based on comprehensive satisfactions.Using the G-S algorithm,the multiobjective programming model based on comprehensive satisfactions is solved,and then the best TSM scheme is obtained.Finally,a terminal distribution example is used to verify the feasibility and effectiveness of the proposed method.
基金Under the auspices of National Natural Science Foundation of China (No.42071152)。
文摘The extant literature on international immigrants has discussed migrants’entrepreneurial activities in the context of Western countries but has paid little attention to return-home entrepreneurial intention(RHEI).Rural migrant workers(RMWs)in China,who used to promote rural development by remittances and were characterized by similarities with early international migrants,have gradually returned to their hometowns to initiate entrepreneurial activities.Based on the structured questionnaire conducted in 2015 and 2020in Anhui Province,China,this article combines the concept of mixed embeddedness with the idea of double-layered embeddedness and analyzes the impacts of the social,economic and institutional context in RMWs’hometowns and migration destinations on RMWs’RHEI by using binary logistic regression.The article shows that the social,economic,and institutional environments of RMWs’hometowns and migration destinations have effects on their RHEI.The embeddedness in the economic and informal institutional context in RMWs’RHEI is even more important than personal characteristics.Compared with migration destinations,RMWs’hometowns exert a more influential effect on their RHEI.However,that does not mean that the role of migration destinations can be undervalued.Actually,the better the social,economic,and institutional environments of migration destinations RMWs moved into is,the higher entrepreneurial intention they will have after returning to their hometowns.The article proposes a modified framework in combination of mixed embeddedness with double-layer embeddedness and proves that it is suitable for analyzing RMWs’RHEI.The framework has important implications for strengthening China’s RMWs to return home to start their own businesses.
基金Under the auspices of National Natural Science Foundation of China (No. 72274143) and (No.71874127)
文摘Rapid urbanization and population growth have triggered an increase in urban housing demand and rising housing prices,which can influence the migration intention of high-level talents.Much work within the literature has focused more on the migration of the general public.However,antecedents of migration intention and the impact of housing prices on the migration of high-level talents remain unclear.Therefore,based on the push-pull theory,this study investigates the influencing factors of talent migration intention and explores the role of housing prices.This study reveals a complex mechanism underlying migration decisions by using logistic regression models and survey data of high-level talents in the Pearl River Delta(PRD)megalopolis of China.The results indicate that:1)in high house-price regions,social integration is the primary push factor,and the main factors for retaining talents are the expectation of future work and intimate family relationships;2)in medium house-price regions,the main factors that attract talents are the current salary level and close family ties;3)in low house-price regions,living convenience is a determining factor in retaining talents.This study provides a new perspective for talent mobility research and offers valuable inputs for retaining and attracting talents in different economic development regions.Findings are of great significance for formulating talent introduction policies and forming a new pattern of rational spatial docking and effective allocation of human resources.
基金The Research and the Development Fund of the Institute of Environmental Friendly Materials and Occupational Health,Anhui University of Science and Technology,Grant/Award Number:ALW2022YF06Academic Support Project for Top-Notch Talents in Disciplines(Majors)of Colleges and Universities in Anhui Province,Grant/Award Number:gxbjZD2021052+1 种基金The University Synergy Innovation Program of Anhui Province,Grant/Award Number:GXXT-2022-053Anhui Province Key R&D Program of China,Grant/Award Number:2022i01020015.
文摘Surface electromyography(sEMG)is widely used for analyzing and controlling lower limb assisted exoskeleton robots.Behavior intention recognition based on sEMG is of great significance for achieving intelligent prosthetic and exoskeleton control.Achieving highly efficient recognition while improving performance has always been a significant challenge.To address this,we propose an sEMG-based method called Enhanced Residual Gate Network(ERGN)for lower-limb behavioral intention recognition.The proposed network combines an attention mechanism and a hard threshold function,while combining the advantages of residual structure,which maps sEMG of multiple acquisition channels to the lower limb motion states.Firstly,continuous wavelet transform(CWT)is used to extract signals features from the collected sEMG data.Then,a hard threshold function serves as the gate function to enhance signals quality,with an attention mechanism incorporated to improve the ERGN’s performance further.Experimental results demonstrate that the proposed ERGN achieves extremely high accuracy and efficiency,with an average recognition accuracy of 98.41%and an average recognition time of only 20 ms-outperforming the state-of-the-art research significantly.Our research provides support for the application of lower limb assisted exoskeleton robots.
文摘Electrical and electronic waste(e-waste)is a growing challenge,matching the widespread boom in the use of information and communication technology.Opposite to an alarming increasing amount of e-waste,a low rate of consumer engagement in ensuring the proper disposal of such materials intensifies the pressure on the exist‐ing e-waste crisis.To deal with this thorny problem,it is of great interest to grasp consumers’disposal and re‐cycling behavioral intentions.Therefore,this study attempts to understand complementary perspectives around consumers’e-waste recycling intention based on the integration of the valence theory and the norm activation theory.Four data mining models using classification and prediction-based algorithms,namely Chi squared automatic interaction detector(CHAID),Neural network,Discriminant analysis,and Quick,unbiased,efficient statistical tree(QUEST),were employed to analyze a set of the 398 data collected in Vietnam.The re‐sults revealed that the social support value is by far the most critical predictor,followed by the utilitarian value,task difficulty,and monetary risk.It is also noteworthy that the awareness of consequences,education background,the ascription of responsibility,and age were also ranked as critical affecting factors.The lowest influential predictors found in this study were income and gender.In addition,a comparison was made in terms of the classification performance of the four utilized data mining techniques.Based on several evalua‐tion measurements(confusion matrix,accuracy,precision,recall,specificity,F-measure,ROC curve,and AUC),the aggregated results suggested that CHAID and Neural network performed the best.The findings of this research are expected to assist policymakers and future researchers in updating all information surround‐ing consumer behavioral intention-related topics focusing on e-waste.Furthermore,the adoption of data min‐ing algorithms for prediction is another insight of this study,which may shed the light on data mining applica‐tions in such environmental studies in the future.
文摘BACKGROUND The frequency and content of follow-up strategies remain controversial for colorectal cancer(CRC),and scheduled follow-ups have limited value.AIM To compare intensive and conventional follow-up strategies for the prognosis of non-metastatic CRC treated with curative intent using a meta-analysis.METHODS PubMed,Embase,and the Cochrane Library databases were systematically searched for potentially eligible randomized controlled trials(RCTs)from inception until April 2023.The Cochrane risk of bias was used to assess the methodological quality of the included studies.The hazard ratio,relative risk,and 95%confidence interval were used to calculate survival and categorical data,and pooled analyses were performed using the random-effects model.Additional exploratory analyses were performed for sensitivity,subgroups,and publication bias.RESULTS Eighteen RCTs involving 8533 patients with CRC were selected for the final analysis.Intensive follow-up may be superior to conventional follow-up in improving overall survival,but this difference was not statistically significant.Moreover,intensive follow-up was associated with an increased incidence of salvage surgery compared to conventional follow-up.In addition,there was no significant difference in the risk of recurrence between intensive and conventional follow-up strategies,whereas intensive follow-up was associated with a reduced risk of interval recurrence compared to conventional follow-up.Finally,the effects of intensive and conventional follow-up strategies differed when stratified by tumor location and follow-up duration.CONCLUSION Intensive follow-up may have a beneficial effect on the overall survival of patients with non-metastatic CRC treated with curative intent.
文摘For autonomous vehicles and driver assist systems,path planning and collision avoidance algorithms benefit from accurate predictions of future location of other vehicles and intent of their drivers.In the literature,the algorithms that provide driver intent belong to two categories:those that use physics based models with some type of filtering,and machine learning based approaches.In this paper we employ barrier functions(BF)to decide driver intent.BFs are typically used to prove safety by establishing forward invariance of an admissible set.Here,we decide if the“target”vehicle is violating one or more possibly fictitious(i.e.,non-physical)barrier constraints determined based on the context provided by the road geometry.The algorithm has a very small computational footprint and better false positive and negative rates than some of the alternatives.The predicted intent is then used by a control barrier function(CBF)based collision avoidance system to prevent unnecessary interventions,for either an autonomous or human-driven vehicle.
基金supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2020S1A5A2A01046684).
文摘Since the outbreak of COVID-19,tourists have been increasingly concerned over various risks of international travel,while knowledge of the pandemic appears to vary significantly.In addition,as travel restrictions continue to impact adversely on international tourism,tourism efforts should be placed more on the domestic markets.Via structural equation modeling,this study unearthed different risk factors impacting Korean travelers’choices of alternative local destinations in the post-pandemic era.In addition,this study extended the goal-directed behavior framework with the acquisition of perceived risk and knowledge of COVID-19,which was proven to hold a sig-nificantly superior explanatory power of tourists’decisions of local alternatives over foreign countries during the COVID-19 pandemic.Furthermore,desire was found to play an imminent mediating role in the conceptual mod-el,maximizing the impact of perceived risk on travel intentions.Henceforth,this research offers meaningful the-oretical implication as thefirst empirical study to deepen the goal-directed behaviour framework with perceived risk and knowledge in the context of post-COVID-19 era.It also serves as insightful knowledge for Korean tour-ism authorities and practitioners to understand local tourists’decision-making processes and tailor effective recovery strategy for domestic tourism.
文摘Traditional monitoring systems that are used in shopping malls or com-munity management,mostly use a remote control to monitor and track specific objects;therefore,it is often impossible to effectively monitor the entire environ-ment.Whenfinding a suspicious person,the tracked object cannot be locked in time for tracking.This research replaces the traditionalfixed-point monitor with the intelligent drone and combines the image processing technology and automatic judgment for the movements of the monitored person.This intelligent system can effectively improve the shortcomings of low efficiency and high cost of the traditional monitor system.In this article,we proposed a TIMT(The Intel-ligent Monitoring and Tracking)algorithm which can make the drone have smart surveillance and tracking capabilities.It combined with Artificial Intelligent(AI)face recognition technology and the OpenPose which is able to monitor the phy-sical movements of multiple people in real time to analyze the meaning of human body movements and to track the monitored intelligently through the remote con-trol interface of the drone.This system is highly agile and could be adjusted immediately to any angle and screen that we monitor.Therefore,the system couldfind abnormal conditions immediately and track and monitor them automatically.That is the system can immediately detect when someone invades the home or community,and the drone can automatically track the intruder to achieve that the two significant shortcomings of the traditional monitor will be improved.Experimental results show that the intelligent monitoring and tracking drone sys-tem has an excellent performance,which not only dramatically reduces the num-ber of monitors and the required equipment but also achieves perfect monitoring and tracking.
基金This research is supported by the Malaysia Ministry of Higher Education’s Fundamental Research Grant Scheme(FRGS)[FRGS/1/2019/SS09/MMU/02/3]MMUE/190073 led by the second author.
文摘The prevalence of mental health problems in both Malaysian and global workplaces has significantly increased due to the presence of the coronavirus disease(COVID-19)pandemic,globalization,technology advancement in Industry 4.0,and other contributing factors.The pervasiveness of the issue poses a huge challenge to improving the occupational safety and health(OSH)of workers in various industries,especially in the digital industry.The emergence of the innovative industry is evident mainly due to the rapid development of Industry 4.0 and the rele-vant demands of multiple businesses in the digital transformation.Nonetheless,limited studies and academic dis-cussions were conducted on the OSH topic of digital employees.Hence,the current study serves tofill the existing gap and provide academic contributions by scrutinising the perceptions of digital workers regarding their work-place well-being,mental health literacy,and the impression of employing e-mental health.The objectives of this study are:1)To examine the mental health literacy and workplace wellness of digital workers,2)to explore the e-mental health usage intention and actual e-mental health utilization,and 3)to identify digital workers’feedback on e-mental health.In the current context,e-mental health focuses on three dimensions,namely,1)“health in our hand(HIOH)”,2)“interacting for health(IFH)”,and 3)“data enabling health(DEH)”.The present study employed an online cross-sectional survey and received 326 digital workers’completed responses.Variables,such as“mental health literacy(MHL)”,“workplace wellness(WW)”,and e-mental health intention and usage were explicated by analysing the data through descriptive statistics.The study results indicated a moderate to a high level of the MHL and the WW.More than half of the workers possessed a high intention level to employ e-mental health,with the HIOH dimension being the most prevalent domain.Nevertheless,the actual e-mental health usage was very low,owing to the online resources being a new concept amongst digital employees.Numerous confounding factors also existed in affecting the low usage,such as privacy concerns,data security levels,and health verification issues.In addition,the mental health issue has not been openly and widely discussed in Malay-sian workplaces due to stigmatisation.As such,the currentfindings could provide additional insights into the OSH literature;it could serve as a guideline for the OSH decision-makers,employers,and eHealth developers when establishing a feasible framework for the practical adoption of e-mental health services by digital workers.
文摘To meet the needs of today’s library users,institutions are developing library mobile apps(LMAs),as their libraries are increasingly intelligent and rely on deep learning.This paper explores the influencing factors and differences in the perception of LMAs at different time points after a user has downloaded an LMA.A research model was constructed based on the technology acceptance model.A questionnaire was designed and distributed twice to LMA users with an interval of three months to collect dynamic data.The analysis was based on structural equation modeling.The empirical results show that the perceived ease of use,the perceived usefulness,the social influence,and the facilitating conditions affected the users’behavioral intention,but their impacts were different at different times.As the usage time increases,the technology acceptance model is still universal for understanding the user perception of LMA.In addition,two extended variables(social impact and convenience)also affect the user’s behavior intention.User behavior is dynamic and changed over time.This study is important both theoretically and practically,as the results could be used to improve the service quality of LMAs and reduce the loss rate of users.Its findings may help the designers and developers of LMAs to optimize them from the perspective of a user and improve the service experience by providing a deeper understanding of the adoption behavior of information systems by LMA users.
