This study aimed to explore the motivations, limitations, and aging-related outcomes associated with participation in the artificial intelligence (AI)–based Smart Senior Center Project. In-depth interviews were conducted with a purposeful sample of older adults from local senior centers in Korea, and data were analyzed using a phenomenological approach. The senior centers incorporated various AI technologies into health programs, and participants were invited to share their experiences and perceptions of these programs. Analysis revealed 3 overarching themes and 11 subthemes. The first theme reflected participants’ motivations such as pursuing health improvement, expanding social relationships, and seeking enjoyment. The second theme aligned with the optimal aging model, highlighting elements of physical and cognitive maintenance, active social engagement, and enhanced well-being. The third theme addressed program limitations, including fear of digital adoption, dissatisfaction with the lack of program diversity, and challenges in achieving interactive communication compared with face-to-face settings. These findings demonstrate that, although the program facilitated meaningful aspects of optimal aging, its effectiveness was constrained by digital literacy gaps and implementation barriers. Balancing these positive outcomes with the identified limitations points to the need for broadened program diversity, digital literacy support, and hybrid delivery models. Through these improvements, the AI-based Smart Senior Center Project holds considerable potential to contribute to the optimal aging of older adults.
The Industrial Internet of Things (IIoT) integrates intelligent sensing, communication, and analytics into industrial environments, including manufacturing, energy, and critical infrastructure. While IIoT enables predictive maintenance and cross-site optimization of modern industrial control systems, such as those in manufacturing and energy, it also introduces significant privacy and confidentiality risks due to the sensitivity of operational data. Contrastive learning, a self-supervised representation learning paradigm, has recently emerged as a promising approach for privacy-preserving analytics by reducing reliance on labeled data and raw data sharing. Although contrastive learning-based privacy-preserving techniques have been explored in the Internet of Things (IoT) domain, this paper offers a comprehensive review of these techniques specifically for privacy preservation in Industrial Internet of Things (IIoT) systems. It emphasizes the unique characteristics of industrial data, system architectures, and various application scenarios. Additionally, the paper discusses solutions and open challenges and outlines future research directions.
Sequential recommender infers users' evolving psychological motivations from historical interactions to recommend the next preferred items. Most existing methods compress recent behaviors into a single vector and optimize it toward a single observed target item, but lack explicit modeling of psychological motivation shift. As a result, they struggle to uncover the distributional patterns across different shift degrees and to capture collaborative knowledge that is sensitive to psychological motivation shift. We propose a general framework, the Sequential Recommender System Based on User Psychological Motivation, to enhance sequential recommenders with psychological motivation shift-aware user modeling. Specifically, the Psychological Motivation Shift Assessment quantitatively measures psychological motivation shift; guided by PMSA, the Shift Information Construction models dynamically evolving multi-level shift states, and the Psychological Motivation Shift-driven Information Decomposition decomposes and regularizes representations across shift levels. Moreover, the Psychological Motivation Shift Information Matching strengthens collaborative patterns related to psychological motivation shift to learn more discriminative user representations. Extensive experiments on three public benchmarks show that SRSUPM consistently outperforms representative baselines on diverse sequential recommender tasks.
Orientation: This study examines the phenomenon of blue-collar workers’ struggles in organisations in various sectors in developing countries, such as Indonesia, to achieve happiness at work (HAW) with the challenges of a tiring work life.
Research purpose: This study aims to investigate the role of work-life integration (WLI) in promoting HAW and moderation of work stress. The study population consists of blue-collar workers in various sectors in big cities in Indonesia.
Motivation for the study: The study was carried out to supplement research in the field of WLI and HAW in blue-collar workers and the importance of understanding how WLI impacts HAW.
Research approach/design and method: The research sample technique is purposive sampling, obtaining a sample size of 1885. Research data collection is done using a questionnaire instrument. Data analysis uses partial least squares.
Main findings: The study’s results indicate that WLI, with its main dimension of WLI, has a positive impact on HAW on blue-collar workers in big cities in Indonesia. Furthermore, work stress can weaken the positive impact of WLI on HAW.
Practical/managerial implications: This study contributes to the insight of employers to form employee-friendly policies by considering the integrated work focus between work life and personal life, which will have an impact on increasing work happiness, especially for blue-collar workers.
Contribution/value-add: Work-life integration for blue-collar workers is a very significant transformation, where work life and personal life are not contradictory but mutually reinforcing, giving rise to a positive psychological effect that has an impact on higher happiness.
Bernardus Aris Ferdinan, Robertus Adi Nugroho, Jonathan Edward Pangemana
Introduction/Main Objectives: This study aims to examine the role of job stress and employee engagement of millennial workers mediated by job crafting and moderated by empathetic leadership. Background Problems: Leadership is one of the factors that will affect workforce management. The right leadership is seen to help reduce job stress and increase employee engagement. Novelty: Many studies have attempted to examine the relationship between job stress and employee engagement. Gap research, based on the inconsistency of the results of the research that has been done, is the basis for this study. The existing inconsistency makes this research add the role of job crafting and empathic leadership. Research Methods: This study involved 304 non-managerial workers in Surabaya City, Indonesia, who belong to the Millennial generation category. Finding/Results: The results obtained are that job stress has no effect on job crafting. Empathetic leadership moderates job stress and job crafting. Job crafting does not affect employee engagement. Job stress has a negative effect on employee engagement. Job crafting does not mediate the relationship between job stress and employee engagement, and empathetic leadership moderates the relationship between job stress and employee engagement. Conclusion: Practical implications, leaders need to determine the right leadership model according to the context of workers.
Recommender systems have generated tremendous value for both users and businesses, drawing significant attention from academia and industry alike. However, due to practical constraints, academic research remains largely confined to offline dataset optimizations, lacking access to real user data and large-scale recommendation platforms. This limitation reduces practical relevance, slows technological progress, and hampers a full understanding of the key challenges in recommender systems. In this survey, we provide a systematic review of industrial recommender systems and contrast them with their academic counterparts. We highlight key differences in data scale, real-time requirements, and evaluation methodologies, and we summarize major real-world recommendation scenarios along with their associated challenges. We then examine how industry practitioners address these challenges in Transaction-Oriented Recommender Systems and Content-Oriented Recommender Systems, a new classification grounded in item characteristics and recommendation objectives. Finally, we outline promising research directions, including the often-overlooked role of user decision-making, the integration of economic and psychological theories, and concrete suggestions for advancing academic research. Our goal is to enhance academia's understanding of practical recommender systems, bridge the growing development gap, and foster stronger collaboration between industry and academia.
Snigdha Sinha, Manoj Kumar Sharma, Gitanjali Narayanan
et al.
Gaming as an increasingly common phenomenon and is frequently used as a coping mechanism during difficult life situations, such as grief and bereavement. This coping usually takes place as an avoidant, escapist strategy, which can lead to problematic or excessive gaming, with harmful outcomes. The aim of this study was to review and collate findings from literature on gaming use as an escapist coping strategy during difficult life situations. A systematic review was carried out with the use of the databases such as PubMed, Google Scholar, Research Gate, and APA PsycNet, where English language studies published in peer-reviewed journals were perused. The full text of the studies selected were then reviewed and suitability for the present study was checked with the eligibility criteria. Out of the 35 texts fully reviewed, 18 studies were finalised that fit the eligibility criteria, and out of these, three themes were derived – “Problematic online gaming and difficult life situations”, “Problematic online gaming, coping and escapism ”, and “Types of gaming”, which demonstrated the psychosocial perspective on gaming used as a coping mechanism for difficult situations. This association is further affected by the type of gaming, whether Massive Multi-player Online Role Playing Games (MMORPG) or otherwise. The aetiology (life situations as a causative factor) and manifestation (gaming used for coping), along with the type of gaming were significantly affected by personal and social factors. The study showed findings that support the view of gaming as an escapist coping strategy in difficult life situations. It is therefore important to look into provisions for better social support and individuals’ psychosocial environment while evolving interventions that enhance coping among gamers.
We study the optimal allocation of prizes in rank-order tournaments with loss averse agents. Prize sharing becomes increasingly optimal with loss aversion because more equitable prizes reduce the marginal psychological cost of anticipated losses. Furthermore, loss aversion can boost effort if prizes are sufficiently equitable, but otherwise effort declines with loss aversion. Overall, these results give credence to more equitable allocations of competitive rewards. A win-win scenario is where optimal prizes are equitable even under loss neutrality, in which case the principal benefits from agents' loss aversion.
Gabriele Valvano, Antonino Agostino, Giovanni De Magistris
et al.
Training supervised deep neural networks that perform defect detection and segmentation requires large-scale fully-annotated datasets, which can be hard or even impossible to obtain in industrial environments. Generative AI offers opportunities to enlarge small industrial datasets artificially, thus enabling the usage of state-of-the-art supervised approaches in the industry. Unfortunately, also good generative models need a lot of data to train, while industrial datasets are often tiny. Here, we propose a new approach for reusing general-purpose pre-trained generative models on industrial data, ultimately allowing the generation of self-labelled defective images. First, we let the model learn the new concept, entailing the novel data distribution. Then, we force it to learn to condition the generative process, producing industrial images that satisfy well-defined topological characteristics and show defects with a given geometry and location. To highlight the advantage of our approach, we use the synthetic dataset to optimise a crack segmentor for a real industrial use case. When the available data is small, we observe considerable performance increase under several metrics, showing the method's potential in production environments.
Sotiris Michaelides, Stefan Lenz, Thomas Vogt
et al.
The industrial landscape is undergoing a significant transformation, moving away from traditional wired fieldbus networks to cutting-edge 5G mobile networks. This transition, extending from local applications to company-wide use and spanning multiple factories, is driven by the promise of low-latency communication and seamless connectivity for various devices in industrial settings. However, besides these tremendous benefits, the integration of 5G as the communication infrastructure in industrial networks introduces a new set of risks and threats to the security of industrial systems. The inherent complexity of 5G systems poses unique challenges for ensuring a secure integration, surpassing those encountered with any technology previously utilized in industrial networks. Most importantly, the distinct characteristics of industrial networks, such as real-time operation, required safety guarantees, and high availability requirements, further complicate this task. As the industrial transition from wired to wireless networks is a relatively new concept, a lack of guidance and recommendations on securely integrating 5G renders many industrial systems vulnerable and exposed to threats associated with 5G. To address this situation, in this paper, we summarize the state-of-the-art and derive a set of recommendations for the secure integration of 5G into industrial networks based on a thorough analysis of the research landscape. Furthermore, we identify opportunities to utilize 5G to enhance security and indicate remaining challenges, identifying future academic directions.
Marta Kwiatkowska, Gethin Norman, David Parker
et al.
Game theory provides an effective way to model strategic interactions among rational agents. In the context of formal verification, these ideas can be used to produce guarantees on the correctness of multi-agent systems, with a diverse range of applications from computer security to autonomous driving. Psychological games (PGs) were developed as a way to model and analyse agents with belief-dependent motivations, opening up the possibility to model how human emotions can influence behaviour. In PGs, players' utilities depend not only on what actually happens (which strategies players choose to adopt), but also on what the players had expected to happen (their belief as to the strategies that would be played). Despite receiving much attention in fields such as economics and psychology, very little consideration has been given to their applicability to problems in computer science, nor to practical algorithms and tool support. In this paper, we start to bridge that gap, proposing methods to solve PGs and implementing them within PRISM-games, a formal verification tool for stochastic games. We discuss how to model these games, highlight specific challenges for their analysis and illustrate the usefulness of our approach on several case studies, including human behaviour in traffic scenarios.
Sheng-Ju Chan, Thi Xuan Nong, Thi Thanh Truc Nguyen
By integrating self-determination theory and perceived risk theory, the current research proposes a new model to predict students’ online learning adoption during an emergency situation such as the COVID-19 pandemic. More specifically, it is aimed at exploring how online communication self-efficacy, online learning belonging, and perceived risk predict students’ online learning adoption. A printed questionnaire was developed to collect data from 487 Vietnamese students using a quota sampling method. After missing data and outliers were removed, 450 questionnaires were found to be usable for data analysis. SMARTPLS version 3.2.2 was employed to analyze PLS-SEM and test the proposed hypotheses. The study found that online communication self-efficacy and perceived risk both have direct effects on students’ online learning adoption as well as indirect effects through the partial mediating role of online learning belonging. Our study also explored that perceived risk does not play a moderation in the association between online learning belonging and students’ online learning adoption. These findings fill important gaps in the literature and provide some implications for academicians, governments, educators, and parents in fostering students’ adoption of online learning.
За результатами проведеного дослідження визначено особливості військових цінностей у військовослужбовців-учасників бойових дій, у яких спостерігаються посттравматичні стресові реакції. У дослідженні прийняли участь військовослужбовці-учасники бойових дій, яких було поділено на дві групи: військовослужбовці, які мають ознаки посттравматичних стресових реакцій – 61 особа; та військовослужбовці, які не мають виражених посттравматичних стресових реакцій – 277 осіб. Вік учасників дослідження від 20 до 55 років. Визначено, що військові цінності не дозволяють надійно передбачити посттравматичні стресові реакції у військовослужбовців-учасників бойових дій, проте не можна нехтувати їх роллю у регулюванні взаємин військовослужбовців, які мають ознаки посттравматичних стресових реакцій. У військовослужбовців, які мають ознаки посттравматичного реагування, в ієрархії цінностей є виражена векторність від тріади сміливості, як уявлення про гідне поводження військовослужбовця, до тріади усвідомлення власної нестачі сил для здійснення протидії, власної ненадійності для оточуючих. Психологічна травматизація значно вплинула на структуру військових цінностей військовослужбовців, зробивши їх ненадійною ланкою військової команди. Так, їх структура цінностей, яка має регулювати професійну діяльність, відбиває їх зосередженість на травматичних аспектах – для них реалізація сміливості є виходом за межі уявлень про власні можливості, а професійність асоціюється із здоланням труднощів. Вони вважають, що пережитий досвід дозволяє їм керуватися власними уявленнями про обов’язок перед військовою командою, діяти на свій розсуд. Відсутність в структурі цінностей військовослужбовців з ознаками посттравматичних стресових реакцій на відміну від військовослужбовців, у яких після участі в бойових діях збережене психічне здоров’я, фактору, що дозволяє бути відповідним змінам, вірогідно також посилює травматичний ефект дії бойових стресорів.
Recommender system (RS) is an established technology with successful applications in social media, e-commerce, entertainment, and more. RSs are indeed key to the success of many popular APPs, such as YouTube, Tik Tok, Xiaohongshu, Bilibili, and others. This paper explores the methodology for improving modern industrial RSs. It is written for experienced RS engineers who are diligently working to improve their key performance indicators, such as retention and duration. The experiences shared in this paper have been tested in some real industrial RSs and are likely to be generalized to other RSs as well. Most contents in this paper are industry experience without publicly available references.
Using as a central instrument a new database, resulting from a compilation of historical administrative records, which covers the period 1974-2010, we can have new evidence on how industrial companies used tax benefits, and claim that these are decisive for the investment decision of the Uruguayan industrial companies during that period. The aforementioned findings served as a raw material to also affirm that the incentives to increase investment are factors that positively influence the level of economic activity and exports, and negatively on the unemployment rate.
Real-time predictive modelling with desired accuracy is highly expected in industrial artificial intelligence (IAI), where neural networks play a key role. Neural networks in IAI require powerful, high-performance computing devices to operate a large number of floating point data. Based on stochastic configuration networks (SCNs), this paper proposes a new randomized learner model, termed stochastic configuration machines (SCMs), to stress effective modelling and data size saving that are useful and valuable for industrial applications. Compared to SCNs and random vector functional-link (RVFL) nets with binarized implementation, the model storage of SCMs can be significantly compressed while retaining favourable prediction performance. Besides the architecture of the SCM learner model and its learning algorithm, as an important part of this contribution, we also provide a theoretical basis on the learning capacity of SCMs by analysing the model's complexity. Experimental studies are carried out over some benchmark datasets and three industrial applications. The results demonstrate that SCM has great potential for dealing with industrial data analytics.
Jennifer M. Cavallari, Rick Laguerre, Jacqueline M. Ferguson
et al.
Abstract Background Working time characteristics have been used to link work schedule features to health impairment; however, extant working time exposure assessments are narrow in scope. Prominent working time frameworks suggest that a broad range of schedule features should be assessed to best capture non-standard schedules. The purpose of this study was to develop a multi-dimensional scale that assesses working time exposures and test its reliability and validity for full-time workers with non-standard schedules. Methods A cross-sectional study was conducted using full-time, blue-collar worker population samples from three industries - transportation (n = 174), corrections (n = 112), and manufacturing (n = 99). Using a multi-phased approach including the review of scientific literature and input from an advisory panel of experts, the WorkTime Scale (WTS) was created and included multiple domains to characterize working time (length, time of day, intensity, control, predictability, and free time). Self-report surveys were distributed to workers at their workplace during company time. Following a comprehensive scale development procedure (Phase 1), exploratory factor analysis (EFA) (Phase 2) and, confirmatory factor analysis (CFA) (Phase 3; bivariate correlations were used to identify the core components of the WTS and assess the reliability and validity (Phase 4) in three samples. Results Phase 1 resulted in a preliminary set of 21 items that served as the basis for the quantitative analysis of the WTS. Phase 2 used EFA to yield a 14-item WTS measure with two subscales (“Extended and Irregular Work Days (EIWD)” and “Lack of Control (LOC)”). Phase 3 used CFA to confirm the factor structure of the WTS, and its subscales demonstrated good internal consistency: alpha coefficients were 0.88 for the EIWD factor and 0.76–0.81 for the LOC factor. Phase 4 used bivariate correlations to substantiate convergent, discriminant, and criterion (predictive) validities. Conclusions The 14-item WTS with good reliability and validity is an effective tool for assessing working time exposures in a variety of full-time jobs with non-standard schedules.
Khalid M Alshamrani,1– 3 Abdulkader A Alkenawi,1– 3 Reham Kaifi,1– 3 Shaza Alsharif,1– 3 Abdulaziz S Merdah,1 Wael E Munshi,1 Ahmed K Alattas,1 Majid Althaqafy,1– 3 Abdulaziz A Qurashi,4 Walaa M Alsharif,4 Ali S Alshareef1– 3 1College of Applied Medical Sciences, King Saud Bin Abdulaziz University for Health Sciences, Jeddah, Saudi Arabia; 2King Abdullah International Medical Research Center, Jeddah, Saudi Arabia; 3Ministry of the National Guard - Health Affairs, Jeddah, Saudi Arabia; 4Diagnostic Radiology Technology, College of Applied Medical Sciences, Taibah University, Madinah, Saudi ArabiaCorrespondence: Khalid M Alshamrani, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, P. O. Box 9515, Internal Mail Code 6610, Jeddah, 21423, Saudi Arabia, Tel +966 12-2266666 Ext. 46396, Email alshamranik@ksau-hs.edu.saBackground: The novel Coronavirus Disease 2019 (COVID-19) pandemic has posed unprecedented new stressors and challenges to the applied health sciences’ education. This study explored the prevalence of burnout among Saudi radiological sciences students at King Saud bin Abdulaziz University for Health Sciences during the COVID-19 pandemic.Methods: A cross-sectional study was conducted between November and December 2020 among 176-Saudi radiological sciences students, using the 16-item questionnaire of Maslach Burnout Inventory-General Survey for Students and through non-probability convenient sampling technique. The 16 items of the questionnaire were scored on a 7-point frequency rating scale ranging from 0 (never) to 6 (every day) and consisted of three distinct burnout dimensions/subscales: a) emotional exhaustion (5-items), cynicism (5-items), and professional efficacy (6-items). The means of individual items that make up each scale of burnout were calculated, and statistical analysis was performed using the Mann–Whitney U-test.Results/Observations/Findings: From the 176-radiological sciences students approached, 96 (54.5%) completed the questionnaire. The percentage of students who were at moderate to high risk of burnout was 70.8% for emotional exhaustion, 75% for cynicism, and 74% for professional efficacy subscales. Emotional exhaustion was significantly higher among fourth-year students (P = 0.042), than third-year students. Cynicism was significantly higher among fourth-year female students (P = 0.035), than third-year female students. The professional efficacy was significantly lower among fourth-year female students (P = 0.007) than males.Conclusion: Our study shows 73.3% moderate to high burnout rates among Saudi radiological sciences students during the COVID-19 pandemic. Burnout increases as students advance to the fourth year. A block/modular curriculum structure for fourth-year courses may be necessary to reduce burnout among fourth-year students. Academic counseling can ease students’ emotional stress and reduce burnout risk.Keywords: burnout syndrome, COVID-19 pandemic, psychological resilience, health education, Kingdom of Saudi Arabia, radiologic technology