Marital Status as a Moderator:Exploring the Relationship between Social Engagement and Depressive Symptoms in China’s Older Adult Population

Objective This study aims to explore the complex relationship between social engagement and depressive symptoms among older adults in China,focusing particularly on the moderating role of marital status.Methods This study used data from the latest Chinese Longitudinal Healthy Longevity Survey(CLHLS).The analysis used the latent class analysis to delineate personality clusters and hierarchical linear regression,supplemented by the PROCESS macro,to investigate the effects of social engagement and marital status on depressive symptoms.Results The analysis encompassed 7,789 respondents(mean age:82.53[s=11.20]years),with 54%female.The personality analysis categorized participants into four clusters,with the majority(77.60%)classified as Confident Idealists,who exhibited the lowest levels of depressive symptoms.Hierarchical linear regression analysis yielded several significant findings:Higher levels of social engagement were significantly associated with fewer depressive symptoms(t=-7.932,P<0.001,B=-0.463).Marital status was a significant factor;married individuals reported fewer depressive symptoms compared to their unmarried counterparts(t=-6.368,P<0.001,B=-0.750).There was a significant moderating effect of marital status on the relationship between social engagement and depressive symptoms(t=-2.092,P=0.037,B=-0.217).Conclusion This study demonstrates that,among Chinese older adults,both social engagement and marital status significantly influence depressive symptoms.Higher social engagement,particularly in other activities like doing household chores,gardening,reading newspapers or books,and playing cards or Mahjong,is associated with fewer depressive symptoms,especially among married individuals.

Re-Distributing Facial Features for Engagement Prediction with ModernTCN

Automatically detecting learners’engagement levels helps to develop more effective online teaching and assessment programs,allowing teachers to provide timely feedback and make personalized adjustments based on students’needs to enhance teaching effectiveness.Traditional approaches mainly rely on single-frame multimodal facial spatial information,neglecting temporal emotional and behavioural features,with accuracy affected by significant pose variations.Additionally,convolutional padding can erode feature maps,affecting feature extraction’s representational capacity.To address these issues,we propose a hybrid neural network architecture,the redistributing facial features and temporal convolutional network(RefEIP).This network consists of three key components:first,utilizing the spatial attention mechanism large kernel attention(LKA)to automatically capture local patches and mitigate the effects of pose variations;second,employing the feature organization and weight distribution(FOWD)module to redistribute feature weights and eliminate the impact of white features and enhancing representation in facial feature maps.Finally,we analyse the temporal changes in video frames through the modern temporal convolutional network(ModernTCN)module to detect engagement levels.We constructed a near-infrared engagement video dataset(NEVD)to better validate the efficiency of the RefEIP network.Through extensive experiments and in-depth studies,we evaluated these methods on the NEVD and the Database for Affect in Situations of Elicitation(DAiSEE),achieving an accuracy of 90.8%on NEVD and 61.2%on DAiSEE in the fourclass classification task,indicating significant advantages in addressing engagement video analysis problems.

The Importance of Stakeholder Engagement in Progressive Rehabilitation of Large-Scale Open Pit Mines—A Case Study of Lumwana Mine

Mining globally contributes to the growth of many economies of the world. Since its inception, the Zambian mining industry has contributed largely to the country’s economy. The various developments both in technology and knowledge have contributed to the scale at which mining is being done. Challenges in such a setting arise due to the socio-economic and environmental impacts of mining, which create multidimensional problems. The study investigated the importance of engaging stakeholders in progressive rehabilitation programs for large-scale open pit mines, using a case study of the Lumwana Mine and its host community, Manyama. A qualitative approach was used, and data was collected through one-on-one interviews. A combination of convenient and quota sampling was used to engage with host community leaders, professionals and academicians from various fields and institutions. Results showed that most participants had agreed that stakeholder engagement is important for progressive rehabilitation, but the challenge was that the host community and municipal council representatives were not aware of any progressive rehabilitation efforts at Lumwana Mine. This was attributed to a lack of stakeholder engagement and communication of mitigation progress activities by the Lumwana Mine. Results also revealed that the lack of environmental impact assessment regulations to compel companies to involve stakeholders throughout the entire life of the mine other than just at the pre-mining stage led to a lack of compliance and accountability in rehabilitation.

Leveraging Predictive Analytics for Strategic Corporate Communications: Enhancing Stakeholder Engagement and Crisis Management

This study explores the integration of predictive analytics in strategic corporate communications, with a specific focus on stakeholder engagement and crisis management. Our mixed-methods approach, which combines a comprehensive literature review with case studies of five multinational corporations, allows us to investigate the applications, challenges, and ethical implications of leveraging predictive models in communication strategies. While our findings reveal significant potential for enhancing personalized content delivery, real-time sentiment analysis, and proactive crisis management, we stress the need for careful consideration of challenges such as data privacy concerns and algorithmic bias. This emphasis on ethical implementation is crucial in navigating the complex landscape of predictive analytics in corporate communications. To address these issues, we propose a framework that prioritizes ethical considerations. Furthermore, we identify key areas for future research, thereby contributing to the evolving field of data-driven communication management.

The Influence of Academic Self-Efficacy on Learning Engagement in Online Education in China:The Mediating Role of Effort

The shift towards online intelligent learning has become the norm in education and is now a fundamental part of modern educational activities.However,this new model can influence students’learning behavior and lead to changes in their approach to learning.Based on online intelligent learning,we investigated how the academic self-efficacy of nursing students affects their engagement with learning and explored the role of academic attribution as a mediator.Five hundred fifty-three nursing college students from Hebei and Hunan provinces in China participated in the online questionnaire.The results revealed that effort plays a mediating role in the relationship between academic self-efficacy and learning engagement.

Effects of Perceived Teacher Support on Student Behavioral Engagement in the Blended Learning Environment:Learning Experience as a Mediator

Blended learning(BL)has been widely adopted to improve students’academic achievements in higher education.However,its success relies mainly on student engagement,which plays an essential role in active learning and provides a rich understanding of students’experiences.The study utilized three self-designed scales-the Teacher Support Scale,Student Engagement Scale,and Student Learning Experience Scale-to gauge and examine the impact and relationship between perceived teacher support,student behavioral engagement,and the intermediary role of learning experiences.A cohort of 899 college students undertaking the obligatory College English course through BL modes across five Chinese universities actively participated by completing a comprehensive questionnaire.The results showed significant correlations between perceived teacher support,learning experience,and behavioral engagement.Perceived teacher support significantly predicted students’behavioral engagement,with socio-affective support exerting the most substantial predictive effects.All predictive effects were partially mediated by learning experience(learning mode,online resources,overall LMS-based learning,interaction with their instructor and peers,and learning outcome).The influence of perceived teacher support on behavioral engagement differed between students who reported the most positive(vs.negative)learning experiences.Suggestions for further research are offered for consideration.

MDNN:Predicting Student Engagement via Gaze Direction and Facial Expression in Collaborative Learning

Prediction of students’engagement in aCollaborative Learning setting is essential to improve the quality of learning.Collaborative learning is a strategy of learning through groups or teams.When cooperative learning behavior occurs,each student in the group should participate in teaching activities.Researchers showed that students who are actively involved in a class gain more.Gaze behavior and facial expression are important nonverbal indicators to reveal engagement in collaborative learning environments.Previous studies require the wearing of sensor devices or eye tracker devices,which have cost barriers and technical interference for daily teaching practice.In this paper,student engagement is automatically analyzed based on computer vision.We tackle the problem of engagement in collaborative learning using a multi-modal deep neural network(MDNN).We combined facial expression and gaze direction as two individual components of MDNN to predict engagement levels in collaborative learning environments.Our multi-modal solution was evaluated in a real collaborative environment.The results show that the model can accurately predict students’performance in the collaborative learning environment.

A Study of Multimodal Intelligent Adaptive Learning System and Its Pattern of Promoting Learners’Online Learning Engagement

As the field of artificial intelligence continues to evolve,so too does the application of multimodal learning analysis and intelligent adaptive learning systems.This trend has the potential to promote the equalization of educational resources,the intellectualization of educational methods,and the modernization of educational reform,among other benefits.This study proposes a construction framework for an intelligent adaptive learning system that is supported by multimodal data.It provides a detailed explanation of the system’s working principles and patterns,which aim to enhance learners’online engagement in behavior,emotion,and cognition.The study seeks to address the issue of intelligent adaptive learning systems diagnosing learners’learning behavior based solely on learning achievement,to improve learners’online engagement,enable them to master more required knowledge,and ultimately achieve better learning outcomes.

A Study of the Effects of Employee Engagement on Job Satisfaction:Mediating Effect of Labor Relations Climate

With the rapid advancement of new technologies and the transformation of the economic model on a global scale,enterprises are facing unprecedented changes in the external environment and more diversified demands from employees.Therefore,it is essential for enterprises to bolster employee engagement by enhancing employees’job satisfaction,building a stable and harmonious internal climate,and adapting to the dynamic,complex external environment.This study analyzes the positive effects of employee engagement on job satisfaction,explores the mediating effect of labor relations climate in the relationship between the two,and proposes corresponding practical countermeasures and suggestions.

Improved Behavioural Engagement of Students through Low-stakes Online Tests and Immediate Dialogic Feedback

The engagement of students is a recognised challenge for teachers.Technology offers some practical student engagement tools,and this paper examines the use of low-stakes online tests and immediate dialogic feedback to improve behavioural engagement.The academic exploration of low-stakes tests and dialogic feedback has been extensive,and they are credible teaching tools.In this study,we explore the learning benefit of their combination.Postgraduate engineering students’self-reported and learning analytics data shows conclusive evidence of improved behavioural engagement.We measured a 500%increase in the Learning Management System(LMS)page views on the days when we ran the low-stakes tests(each worth 2%of the marks for the subject)and engaged in immediate dialogic feedback.To interpret these results,we draw on theories of behavioural engagement,low-stakes tests,and feedback.We conclude that the combination of low-stakes tests and immediate feedback improves student behavioural engagement.

Current Situation and Improvement Strategies of Teaching Engagement by Teachers in Higher Medical Colleges Under the Background of“New Medical Education”

The“New Medical Education”is a new requirement for the development of medical education in the new era.As a key and core element of the construction of the“New Medical Education,”the engagement in teaching work of university teachers is a key issue that determines the level of teaching development of university teachers and affects the quality of talent cultivation in universities.Using the literature review method,this study analyzed 39 relevant literatures retrieved from China National Knowledge Infrastructure(CNKI)from 1997 to 2023.The concept,connotation,current situation,influencing factors,and improvement strategies of teaching engagement by university teachers were reviewed and summarized.The results showed that the issue of teaching engagement by university teachers has not received high attention in the field of higher education,especially in higher medical education,which has not yet been reported.Although the overall number of literature is not large,some of the conclusions have important implications for the next research on the influencing factors and mechanisms of double-qualified teacher engagement in higher medical colleges.

Analysis of Students’ Preferences and Engagement with Mobile Games: A Study of Game Assets and Colour Impact

Mobile games are gaining in popularity among students, although the factors driving engagement remain poorly understood. This study, conducted among 378 students studying for a Bachelor’s degree in Computer and Digital Sciences at the Université Virtuelle de Côte d’Ivoire, explores the influence of game assets and colours on player engagement. Using an online questionnaire, the study identified students’ gaming preferences and their perception of colours. The results show that the key elements most favoured by students are graphics, sound design, the game system, narrative, interactivity and accessibility. What’s more, colour plays a decisive role in their preferences and commitment to educational mobile games.

Predictive roles of organizational and personal factors in work engagement among nurses

Objective:To determine the work engagement levels of hospital nurses and the predictive roles that nurses’personal and organizational characteristics play.Methods:A cross-sectional design was employed in this study,which included 549 nurses working in the Central Philippines.Nurses were selected through purposive sampling,and a self-repor t questionnaire outlining personal and organizational characteristics was employed.The Utrecht Work Engagement Scale(UWES)was applied to gather data about nurse engagement.Results:The dedication score was the highest among the three dimensions of engagement while the vigor score was the lowest.Overall,Filipino nurses experienced an average level of work engagement.We observed significant relationships between a nurse’s age,years of experience in nursing,years in their present unit,and their position at work and their levels of engagement,vigor,sense of dedication,and absorption.Hospital capacity was a negative predictor of vigor,dedication,absorption,and overall work engagement.Fur ther,the type of nursing contract was a negative predictor of vigor and dedication.The type of hospital and the nurse’s position were positive predictors of vigor,dedication,and absorption.Conclusions:Filipino nurses were satisfactorily engaged at work.A number of nurses’personal and organizational characteristics play a predictive role in their work engagement.Thus,nursing administrators must suppor t,develop,and implement activities and measures that engage nurses at work.

Developing Engagement in the Learning Management System Supported by Learning Analytics

Learning analytics is an emerging technique of analysing student par-ticipation and engagement.The recent COVID-19 pandemic has significantly increased the role of learning management systems(LMSs).LMSs previously only complemented face-to-face teaching,something which has not been possible between 2019 to 2020.To date,the existing body of literature on LMSs has not analysed learning in the context of the pandemic,where an LMS serves as the only interface between students and instructors.Consequently,productive results will remain elusive if the key factors that contribute towards engaging students in learning are notfirst identified.Therefore,this study aimed to perform an exten-sive literature review with which to design and develop a student engagement model for holistic involvement in an LMS.The required data was collected from an LMS that is currently utilised by a local Malaysian university.The model was validated by a panel of experts as well as discussions with students.It is our hope that the result of this study will help other institutions of higher learning determine factors of low engagement in their respective LMSs.

A Case Study of Employees＇ Engagement Practice in Corporate Responsibility Program

Organizational corporate social responsibility is a good practice by organizations. Organizations engagement in corporate social responsibility programs may benefit the organization and stakeholders. The focus of this paper is to explore the extent of employees＇ engagement in the organization＇s corporate responsibility programs by utilizing Unerman＇s （2007） stakeholders engagement framework. The main research question is how employees engage themselves in corporate social responsibility programs. This study employs a case study research design approach. The findings have shown that there is empowerment between employees and the management in the corporate social responsibility programs. Their involvement appears from their feedbacks and views in the surveys as well as discussion with regards to corporate responsibility programs. All in all, employees＇ engagement is seen as an important practice in the organization, particularly in planning, implementing, monitoring, and reporting the corporate responsibility program.

A review of foreign researches on influential factors affecting students＇ engagement in English classroom

Students＇ active engagement constitutes the core of the process of learning and teaching in the student-oriented classroom. The paper centers on a review of foreign researches on influential factors affecting students＇ engagement in English classroom. It is expected to figure out the relevant factors in order to promote students＇ active engagement.

On Fashion’s Political Engagement in Modern Society

In class society,fashion is identified as a sign of class position and social status,while in modern democratic society fashion’s political connotation and significance have greatly changed and is mainly displayed from the following three aspects:1)as a way of social protest from vulnerable social groups by being symbol of being an independent and free subject.However,the resistance or protest represented by fashion is no more than something ritual.2)as a way of identity and subjectivity construction,both individual and national;3)as embodiment of fashion leadership and a way of incorporation.Generally to say,fashion has played an important role in every aspect above.However,there is still a tension between them and we must manage carefully to keep a balance.

Parametric Analysis of the End Face Engagement Worm Gear

A novel specific type of worm drive, so-called end face engagement worm gear（EFEWD）, is originally presented to minimize or overcome the gear backlash. Different factors, including the three different types, contact curves, tooth profile, lubrication angle and the induced normal curvature are taken into account to investigate the meshing characteristics and create the profile of a novel specific type of worm drive through mathematical models and theoretical analysis. The tooth of the worm wheel is very specific with the sine-shaped tooth which is located at the alveolus of the worm and the tooth profile of a worm is generated by the meshing movement of the worm wheel with the sine-shaped tooth, but just the end face of the worm（with three different typical meshing types） is adapted to meshing, and therefore an extraordinary manufacturing methods is used to generate the profile of the end face engagement worm. The research results indicates that the bearing contacts of the generated conjugate hourglass worm gear set are in line contacts, with certain advantages of no-backlash, high precision and high operating efficiency over other gears and gear systems besides the end face engagement worm gear drive may improve bearing contact, reduce the level of transmission errors and lessen the sensitivity to errors of alignment. Also, the end face engagement worm can be easily made with superior meshing and lubrication performance compared with the conventional techniques. In particular, the meshing and lubrication performance of the end face engagement worm gear by using the end face to meshing can be increased over 10% and 7%, respectively. This investigate is expect to provide a new insight on the design of the future no-backlash worm drive for industry.

Optimal Design for an End Face Engagement Worm Gear with Multiple Worm-wheel Meshing

To solve the problem for lacking a special mechanical transmission that could provide multiple outputs with high transmission efficiency and good lubrication in the modem industrial, a novel worm gear, named end face engagement worm gear, with multiple worm-wheel meshing is proposed for the first time. The essential parameters for the worm gear are optimized to enhance lubrication and meshing properties. Moreover, analysis of variance（ANOVA） is applied to determine the optimum levels and to determine the influence of parameters. The ANOVA results show that the novel end face engagement worm gear with multiple worm wheels provides high lubrication（the lubrication angle is more than 89~） and meshing performance（the induce normal curvature is less than 0.0002 mm ＇）. The interaction between center distance and roller slant distance most strongly influences the lubrication angle（contributed 51.6%）, followed by the parameters of center distance（contributed 25.0%）, roller slant distance（contributed 16.4%）, tooth angle of gear, gear ratio, and roller radius. In addition, roller radius most strongly influences the induced normal curvature（contributed 39.4%）, followed by roller slant distance（contributed 15.2%）, tooth angle of the gear（contributed 9.0%）, center distance, and gear ratio. The proposed worm gear helps to enrich the no-backlash high precision worm drive and the optimal design method can provide a useful reference on performance improvement of other worm gear.

Formation and adjustment of manned/unmanned combat aerial vehicle cooperative engagement system

Manned combat aerial vehicles （MCAVs）, and un-manned combat aerial vehicles （UCAVs） together form a cooper-ative engagement system to carry out operational mission, whichwill be a new air engagement style in the near future. On the basisof analyzing the structure of the MCAV/UCAV cooperative engage-ment system, this paper divides the unique system into three hi-erarchical levels, respectively, i.e., mission level, task-cluster leveland task level. To solve the formation and adjustment problem ofthe latter two levels, three corresponding mathematical modelsare established. To solve these models, three algorithms calledquantum artificial bee colony （QABC） algorithm, greedy strategy（GS） and two-stage greedy strategy （TSGS） are proposed. Finally,a series of simulation experiments are designed to verify the effec-tiveness and superiority of the proposed algorithms.