Improving innovation and entrepreneurship education in higher institutions: the impact of "Mass Entrepreneurship and Innovation" education on undergraduates' entrepreneurial intentions

The exploration of the way"mass entrepreneurship and innovation"(MEI)education influences students'aspirations to become entrepreneurs has grown into an important area of analysis in studies related to higher education.This research intends to examine the consequences of MEI education on students'tendency towards entrepreneurship,and to put forward methods for augmenting the teaching of innovation and entrepreneurship in private higher educational establishments.To achieve this objective,questionnaires and semi-structured interviews were employed in the study,which involved a total of 197 students and five education experts.The statistical analysis of the questionnaire data revealed that MEI education was positively related to students'entrepreneurial intentions,and that both entrepreneurial experience and family entrepreneurial background played moderating roles in this relationship.The interview findings indicated that private universities could enhance educational reforms by designing talent training programs,developing diversified curricula,and developing more professional entrepreneurial platforms to encourage students'entrepreneurial intentions.This study offers fresh insights for improving and perfecting the mechanism of innovation and entrepreneurship education in private universities.

Cardiovascular health awareness,risk perception,behavioural intention and INTERHEART risk stratification among middle-aged adults in Malaysia

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.

CBA: multi source fusion model for fast and intelligent target intention identification

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.

KGTLIR:An Air Target Intention Recognition Model Based on Knowledge Graph and Deep Learning

As a core part of battlefield situational awareness,air target intention recognition plays an important role in modern air operations.Aiming at the problems of insufficient feature extraction and misclassification in intention recognition,this paper designs an air target intention recognition method(KGTLIR)based on Knowledge Graph and Deep Learning.Firstly,the intention recognition model based on Deep Learning is constructed to mine the temporal relationship of intention features using dilated causal convolution and the spatial relationship of intention features using a graph attention mechanism.Meanwhile,the accuracy,recall,and F1-score after iteration are introduced to dynamically adjust the sample weights to reduce the probability of misclassification.After that,an intention recognition model based on Knowledge Graph is constructed to predict the probability of the occurrence of different intentions of the target.Finally,the results of the two models are fused by evidence theory to obtain the target’s operational intention.Experiments show that the intention recognition accuracy of the KGTLIRmodel can reach 98.48%,which is not only better than most of the air target intention recognition methods,but also demonstrates better interpretability and trustworthiness.

Heterogeneous information fusion recognition method based on belief rule structure

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.

Alcohol and illicit drugs:prevalence of alcohol and illicit drug use and their predictors in young people from Argentina,Bulgaria,Chile and Romania

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.

Promoting sustainable consumption and circular economy:the intention of Vietnamese youth consumers to purchase products made from recycled plastics

Recycling is viewed as a key component in a circular economy and serves as an ideal solution for promoting sustainability.During the global plastic crisis,plastic recycling practices have been adopted worldwide,leading to the production of various products made from recycled plastics(PRP).Nevertheless,a gap persists between consumption and demand for such products,which is primarily attributed to a lack of comprehension from the consumer perspective.Given the pivotal role consumers play in the adoption of these products,this study explores consumers’intentions to purchase PRP.This is particularly significant in Vietnam,which is an emerging economy aspiring to achieve the objectives of a circular economy and sustainable development.Utilizing an integrated cognitive-emotional framework comprising the Valence Theory and the Norm Activation Model,data from 564 Vietnamese students were gathered and analyzed using structural equation modeling.The results show that awareness of consequences is a major driver of consumer purchase intentions,followed by perceived ease of application and monetary incentives.The results also indicate that health concerns have the strongest effect on purchase intention and in the negative side,meaning that the health-related risk is the primary concern for consumers during the decision-making process.This research holds substantial value for academics and managers,as it aids in the theoretical exploration and the formulation of strategies to improve consumer acceptance of PRP.

Analysis of risk factors for postoperative anxiety and depression in endometriosis patients with reproductive intention

BACKGROUND The occurrence of postoperative depression and anxiety in patients with endomet-riosis(EMS)not only causes psychological distress,but may also harm their physical health.AIM To explore the postoperative depression status,and its influencing factors,of EMS patients with reproductive intention.METHODS A total of 321 EMS patients with reproductive intent were included.Using the self-rating anxiety scale and self-rating depression scale,EMS patients with anxiety or depression were distinguished.A clinical model for predicting anxiety or depression in EMS patients was constructed and evaluated using a nomogram,receiver operating characteristic curve,and calibration curve.RESULTS The results of the single factor analysis showed that smoking,coffee,EMS stage,chronic pelvic pain,and sexual discomfort may be related to anxiety.Further,smoking,drinking,spouse,annual household income and EMS stage may be related to depression in EMS patients.Multivariate logistic regression illustrated that smoking,coffee,chronic pelvic pain and sexual discomfort may be inde-pendent risk factors for anxiety in EMS patients,while smoking,EMS stage(Phase III and Phase IV),spouse and high annual household income may be independent risk factors for depression in EMS patients.Additionally,the models used to predict the risk of anxiety or depression in EMS patients have good predictive value.CONCLUSION The anxiety and depression of EMS patients may be related to many factors.In clinical treatment,additional attention should be paid to the psychological status of EMS patients.

A Cognitive Study on Politeness Intention Processing and Its Association with Pragmatic Failure in Cross-Cultural Communication

Although a large number of studies have focused on various aspects of politeness,very little is known about how politeness intention is activated cognitively during verbal communication.The present study aims to explore the cognitive mechanism of politeness intention processing,and how it is related to pragmatic failure during cross-cultural communication.Using 30 Chinese EFL university students who were instructed to finish a probe word judgment task with 96 virtual scenarios,the results indicate that within both mono-and cross-cultural contexts,the response time in the experimental scenarios was significantly slower than that of the filler scenarios.This suggests that politeness intention was activated while understanding the surface meaning of the conversation;however,the EFL learners could not completely avoid the negative transfer of their native politeness conventions when they were comprehending the conversational intention of the target language.Furthermore,no significant differences in response time were found between the groups with high and low English pragmatic competence,illustrating that transferring the pragmatic rules and principles into cross-cultural communication skills was more cognitively demanding.Overall,this study adds to the literature on politeness research and provides some implications for foreign language pragmatic instructions.

Understanding factors affecting non-participants’interest in community-supported agriculture

Community-supported agriculture(CSA)has emerged as a viable solution for addressing the agricultural challenges faced by countries like Indonesia.This study uses the wellestablished unified theory of acceptance and use of technology(UTAUT2)model to examine the interest in CSA of potential customers in Indonesia.A standardized questionnaire was distributed to 1200 respondents,and the data were analyzed using structural equation model-partial least square(SEM-PLS)in SmartPLS 4.0 software.The results capture potential CSA consumer interest and will help to improve CSA development strategies in Indonesia.The model explains 44.4%of customers’intentions,and identifies performance expectancy as the decisive factor in customers’willingness to participate in CSA.Performance expectancy(0.292),hedonic motivation(0.262),social influence(0.259),and facilitating conditions(0.086)positively influence customers’interest in participating in a CSA program.The adoption of CSA programs by both farmers and customers could be increased by implementing regulations that provide tax incentives and subsidies,offering training on sustainable farming practices,facilitating the establishment of distribution channels,and establishing guidelines for fair price and quality standards.This study shows the high potential for the implementation of CSA in Indonesia.It could also be used as a foundation for the development of new policies regarding sustainable agriculture markets in Indonesia.

Measuring Cognitive and Psychosocial Accessibility to Modern Contraception: A Comprehensive Framework

Nowadays, more than ever, the improvement of access to family planning (FP) has become an international goal. What constitutes access to FP? Current human rights-based contraceptive guidelines indicate that access begins as soon as women or couples express a desire to avoid pregnancy and their risk of unintended pregnancy is established. However, few studies have sought to define and measure cognitive and psychosocial access to contraception. To propose a comprehensive framework for the cognitive and psychosocial accessibility of contraception, we critically analyzed the literature on attitudes toward FP. The main dimensions that emerged were knowledge about FP, fear of side effects, approval of contraception, and contraceptive agency. We then identified and adjusted some questions that can capture these dimensions more comprehensively. As a result, we developed a questionnaire module comprising 15 questions, which was integrated into the 6th round of the PMA2020 survey in Burkina Faso in 2019. This research highlighted that previous studies have collected separate dimensions of contraceptive access, and the psychosocial dimension tended to be neglected. Our results demonstrate that it is possible to collect comprehensive data on cognitive and psychosocial dimensions of access to family planning.

Perspectives and Experiences of Education Stakeholders: A Quantitative Study on the Adoption of Artificial Intelligence in Executive Training Using Structural Equation Modeling

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).

The Impact of E-Commerce Live Broadcasts on Consumers’Purchase Intention

E-commerce live broadcast has an important influence on consumers’purchase intention.The three dimensions of live broadcast content in the broadcast room are the number of comments,product quality,and live content as independent variables.A theoretical model is constructed with perceived value and risk as intermediaries and the consumers’purchase intention as the dependent variable,and corresponding hypotheses are put forward.We designed the scale,collected relevant data,and tested the model hypothesis using Statistical Package for Social Sciences(SPSS)and Analysis of Moment Structure(AMOS)software.The study found that the number of comments and product quality had a significant impact on perceived value,perceived risk,and consumers’purchase intention.From this conclusion,it is suggested that businesses should control the number of comments,strengthen the product quality of comments,and distinguish the repetition degree of the content of live broadcasts.

Trust beyond Technology Algorithms: A Theoretical Exploration of Consumer Trust and Behavior in Technological Consumption and AI Projects

In an era dominated by artificial intelligence (AI), establishing customer confidence is crucial for the integration and acceptance of AI technologies. This interdisciplinary study examines factors influencing customer trust in AI systems through a mixed-methods approach, blending quantitative analysis with qualitative insights to create a comprehensive conceptual framework. Quantitatively, the study analyzes responses from 1248 participants using structural equation modeling (SEM), exploring interactions between technological factors like perceived usefulness and transparency, psychological factors including perceived risk and domain expertise, and organizational factors such as leadership support and ethical accountability. The results confirm the model, showing significant impacts of these factors on consumer trust and AI adoption attitudes. Qualitatively, the study includes 35 semi-structured interviews and five case studies, providing deeper insight into the dynamics shaping trust. Key themes identified include the necessity of explainability, domain competence, corporate culture, and stakeholder engagement in fostering trust. The qualitative findings complement the quantitative data, highlighting the complex interplay between technology capabilities, human perceptions, and organizational practices in establishing trust in AI. By integrating these findings, the study proposes a novel conceptual model that elucidates how various elements collectively influence consumer trust in AI. This model not only advances theoretical understanding but also offers practical implications for businesses and policymakers. The research contributes to the discourse on trust creation and decision-making in technology, emphasizing the need for interdisciplinary efforts to address societal challenges associated with technological advancements. It lays the groundwork for future research, including longitudinal, cross-cultural, and industry-specific studies, to further explore consumer trust in AI.

某三级专科医院护士人格特质与离职意愿的相关性

目的调查上海市某三级专科医院护士离职意愿现状,探讨护士人格特质、工作支持和工作满意度与离职意愿的关系及人格特质对离职意愿的影响。方法2022年1—2月对上海市某三级专科医院的全体护士进行调查,采用自编社会人口学资料调查表、中国大五人格问卷简式版量表、工作支持量表、工作满意度量表、离职意愿量表调查。用SPSS 24.0对数据进行统计学分析,用t检验和方差分析进行单因素组间比较,采用分层逐步回归分析离职意愿的影响因素。结果共回收473份问卷。受调查护士离职意愿总分为(14.03±3.76)分;大五人格各维度得分从高到低依次为宜人性(35.72±5.71)分、严谨性(34.13±6.03)分、开放性(29.94±6.64)分、外向性(28.29±6.27)分、神经质(24.84±7.58)分;工作支持得分为(71.05±15.34)分、工作满意度得分为(30.60±6.26)分。不同年龄、职称、工作年限、夜班频次以及有无编制的护士的离职意愿得分差异有统计学意义(P<0.05)。Pearson相关分析结果显示:神经质与离职意愿呈正相关;严谨性、宜人性、外向性、工作支持、工作满意度与离职意愿呈负相关。分层逐步回归分析结果显示,宜人性能够解释护士离职意愿0.7%的变异。结论上海市某三级专科医院护士离职意愿水平较高。护士的人格特质、工作支持和工作满意度会影响护士的离职意愿,具有宜人性的个体表现出较低的离职意愿。

基于期望确认模型的智能预问诊患者持续使用意愿研究

目的探究患者对智能预问诊的持续使用意愿及其影响因素,为系统的管理优化提供参考。方法基于期望确认理论构建模型并提出假设,对172位使用智能预问诊的用户进行代填式问卷调查。数据利用偏最小二乘法结构方程模型进行分析。结果模型中各因素对患者持续使用意愿的解释程度为75.5%。满意度(P<0.001)、感知有用性(P<0.01)对患者的持续使用意愿有正向影响,期望确认度对满意度(P<0.001)和感知有用性(P<0.001)均有正向影响。结论在建设和推进智能预问诊的过程中,患者的持续使用意愿是系统有效实施和可持续发展的重要保障。关注患者的用户体验、完善优化系统功能、适度宣传有助于提升患者的感知有用性和满意度,从而使其愿意持续使用智能预问诊。

What Drives Migrants Back to Set up Firms?Return-home Entrepreneurial Intention of Rural Migrant Workers in China

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.

An Efficient Method for Identifying Lower Limb Behavior Intentions Based on Surface Electromyography

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.

Withdrawal Intention of Farmers from Vacant Rural Homesteads and Its Influencing Mechanism in Northeast China: A Case Study of Jilin Province

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.

Modeling Price-Aware Session-Based Recommendation Based on Graph Neural Network

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.