Search Results (1,636)

(1) Background: The right to refuse dangerous work (RTRDW) is essential for preventing industrial accidents and protecting worker safety in Korea. However, its use remains limited in practice. This study seeks to identify the factors hindering its activation across industries such as construction, manufacturing, and services, offering a comprehensive analysis beyond previous research. (2) Methods: A survey was conducted across key industries to assess five factors—safety behavior, communication, management commitment, education and training, and education and training—using structural equation modeling (SEM) to evaluate their influence on the exercise of RTRDW. (3) Results: The SEM model showed a good fit (χ² = 1151.333, p < 0.001, TLI = 0.978, CFI = 0.984, RMSEA = 0.05). The most significant factors influencing RTRDW were safety performance behavior and communication, while ambiguous regulations, poor training, and fear of job loss discouraged its use. (4) Conclusions: To improve RTRDW activation, clearer regulations, enhanced safety education and training, stronger management commitment, and better communication are necessary. Addressing these issues can help workers confidently exercise their right to refuse dangerous work, enhancing overall workplace safety. (5) Benefits: This study provides practical strategies for policymakers and industry leaders to promote safety, empowering workers to use RTRDW effectively and contributing to a safer work environment. Full article

In China, preschool teachers significantly influence the development of young children aged 3–6 years. Understanding their mental health and its associated factors is imperative to informing policies, optimizing their mental well-being, and supporting their job performance. This study aimed to investigate the factors contributing to anxiety symptoms among preschool teachers. A sample of 393 Chinese preschool teachers (279 women, 114 men), aged 21–56, completed online questionnaires collecting their sociodemographic and work-related information and assessing anxiety symptoms, interpersonal difficulties, personality traits, perceived stress, resilience, and inner strength. Pearson correlation and multiple linear regression analyses identified significant predictors of anxiety symptoms. The prevalence of anxiety symptoms among the studied cohort was found to be 12.2%. Statistically significant factors that positively correlated with anxiety symptoms included objective work-related stress (B = 0.149, p < 0.001), interpersonal difficulties (B = 0.921, p < 0.001), perceived stress (B = 0.108, p = 0.001), and neuroticism (B = 0.071, p = 0.002). These findings highlight the urgent need for measures to reduce work-related stress and anxiety. However, when negative mental health factors were included, the effect of positive psychological factors became nullified. Positive mental health might have a moderating role in a negative mental health outcome such as anxiety. Therefore, further research is required to understand the impact of positive mental health factors more clearly. Full article

Heavy metal cadmium (II) residuals have inflicted severe damage to human health and ecosystems. It has become imperative to devise straightforward and highly selective sensing methods for the detection of Cd²⁺. In this work, a ratiometric benzothiazole-based fluorescence probe (BQFA) was effortlessly synthesized and characterized using standard optical techniques for the visual detection of Cd²⁺ with a change in color from blue to green, exhibiting a significant Stokes shift. Moreover, the binding ratio of BQFA to Cd²⁺ was established as 1:1 by the Job’s plot and was further confirmed by FT-IR and ¹HNMR titrations. The ratiometric fluorescence response via the ICT mechanism was confirmed by DFT calculations. Furthermore, the limit of detection for detecting Cd²⁺ was determined to be 68 nM. Furthermore, it is noteworthy that BQFA showed good performance in real water samples, paper strips, smartphone colorimetric identification, and cell imaging. Full article

This study considers a flexible job-shop scheduling problem where energy cost savings are the primary objective and where the classical objective of the minimization of the make-span is replaced by the satisfaction of due times for each job. An original two-level mixed-integer formulation of this optimization problem is proposed, where the processed flows of material and their timing are explicitly considered. Its exact solution is discussed, and, considering its computational complexity, a comprehensive heuristic, balancing energy performance and due time constraint satisfaction, is developed to provide acceptable solutions in polynomial time to the minimum-energy flexible job-shop scheduling problem, even when considering its dynamic environment. The proposed approach is illustrated through a small-scale example. Full article

Remanufacturing, which benefits the environment and saves resources, is attracting increasing attention. Disassembly is arguably the most critical step in the remanufacturing of end-of-life (EoL) products. Human–robot collaborative disassembly as a flexible semi-automated approach can increase productivity and relieve people of tedious, laborious, and sometimes hazardous jobs. Task allocation in human–robot collaborative disassembly involves methodically assigning disassembly tasks to human operators or robots. However, the schemes for task allocation in recent studies have not been sufficiently refined and the issue of component placement after disassembly has not been fully addressed in recent studies. This paper presents a method of task allocation and sequence planning for human–robot collaborative disassembly of EoL products. The adopted criteria for human–robot disassembly task allocation are introduced. The disassembly of each component includes dismantling and placing. The performance of a disassembly plan is evaluated according to the time, cost, and utility value. A discrete Bees Algorithm using genetic operators is employed to optimise the generated human–robot collaborative disassembly solutions. The proposed task allocation and sequence planning method is validated in two case studies involving an electric motor and a power battery from an EoL vehicle. The results demonstrate the feasibility of the proposed method for planning and optimising human–robot collaborative disassembly solutions. Full article

Objectives: This study aims to examine the mediating effect of resilience and the moderating effect of school organizational culture on the relationship between job stress and burnout among school health teachers during COVID-19. Methods: The participants of the study were 223 school health teachers. The data collected included the Korean version of the Connor-Davidson Resilience Scale (K-CD-RISC), Job Stress Scale, Maslach Burnout Inventory (MBI), and School Organizational Culture Scale. Data analysis was performed using SPSS/WIN 25.0 software. Results: There was a significant positive correlation between job stress and burnout among school health teachers. Conversely, both resilience and school organizational culture were negatively correlated with burnout. The mediating effect of resilience on the relationship between job stress and burnout was significant. However, the moderating effect of school organizational culture was not significant. Conclusions: To prevent burnout in school health teachers, it is necessary to develop policy alternatives aimed at reducing job stress and to implement psychological and emotional support measures to improve resilience. Full article

With the leapfrog development of information and communication technology and the intensification of external competition among enterprises, after-hours work connectivity through communication devices has become a new norm in the workplace. While it offers certain conveniences, the constant connectivity it entails also imposes significant pressure on employees. How to comprehensively understand and rationally treat after-hours work connectivity has become an issue that organizations need to pay great attention to. Based on conservation of resources theory, this study analyzed 407 questionnaires to explore the “double-edged sword” effect of after-hours work connectivity on employee performance and analyzed the moderating effect of the psychological contract. The results indicate the following: (1) Proactive pathway: after-hours work connectivity promotes employees’ job crafting behaviors toward approach-oriented adjustments, thereby enhancing job performance. (2) Passive pathway: after-hours work connectivity encourages employees’ job crafting behaviors toward avoidance-oriented adjustments, leading to decreased job performance. (3) The psychological contract positively moderates the relationship between after-hours work connectivity and approach-oriented job crafting and negatively moderates the relationship between after-hours work connectivity and avoidance-oriented job crafting, regulating both the positive and negative coping pathways. The research findings contribute to assisting organizations in adopting a dialectical perspective towards and effectively utilizing after-hours work connectivity. This aids in achieving a balance between organizational effectiveness and employee well-being, seeking a mutually beneficial work paradigm, and providing managerial recommendations to promote sustainable organizational development. Full article

Energy-efficient human–robot collaboration poses significant challenges to the sustainable operation of production systems. Therefore, our work proposes novel robotic edge intelligence to address the issue. First, robotic edge intelligence is proposed to fully utilize the embedded computing capabilities of edge robots, and the state transition diagrams are developed for jobs, humans, and robots, respectively. Second, a multi-objective model is designed for the energy-efficient human–robot scheduling problem to evaluate the production performance and energy efficiency as a whole. Third, a heuristic algorithm is developed to search for the optimal solutions based on an artificial plant community, which is lightweight enough to be run on edge robots. Finally, a benchmark data set is developed, and a series of benchmark experiments are implemented to test the proposed system. The results demonstrate that the proposed method can effectively enhance energy efficiency and production performance with satisfying solution performance. Full article

Intellect is an important personality trait, especially with regard to the prediction and explanation of intellectual performance, such as occupational or academic success. However, much less is known about the development of Intellect. I present results from a longitudinal study spanning eight years to investigate changes in Intellect during a critical period: the transition from school to vocation. The study is based on a large and heterogeneous sample with up to 1964 participants. Using a facet approach, I investigate predictors of longitudinal trajectories theoretically derived from construct definition, including subjective and objective attributes of education and profession; attitudes regarding the malleability of personality traits; as well as personality traits beyond Intellect, especially intelligence. Results reveal some support for the social investment principle according to neo-socioanalytic theory, as epistemic job demands and epistemic leisure activities predicted the increase in Intellect over time. The study contributes to our understanding of the development of personality traits related to intellectual achievement, including important internal and external predictors of longitudinal trajectories. Full article

This study examined the relationship between cognitive ability (IQ) and emotional intelligence (EQ) in predicting a range of different performance metrics from a call centre environment. In all, 303 call centre staff completed multi-dimensional measures of both EQ and IQ. We also had recorded nine performance data measures for each individual over a 12-month period. There were a few significant correlations with IQ (4/35) and a few more with EQ (4/28), though all EQ measures were related to “Errors Made over the year”. The performance metric that had most correlates was Average Handling Time (AHT) relating to speed of working. The number of errors an employee made was significantly positively correlated with all four EQ factors. Correlational and Structural Equation Model (SEM) analysis highlighted the importance of analysing performance metrics as distinct variables, finding contradictory evidence in the sense that some individual difference factors correlated positively with some and negatively with other outcome measures. The results are discussed in relation to the theoretical implications for researchers interested in analysing call centre performance, and also practical implications for organisations with call centres. Full article

High-performance computing (HPC) systems are becoming increasingly important as contemporary exascale applications with demand extensive computational and data processing capability. To optimize these systems, efficient scheduling of HPC applications is important. In particular, because I/O is a shared resource among applications and is becoming more important due to the emergence of big data, it is possible to improve performance by considering the architecture of HPC systems and scheduling jobs based on I/O resource requirements. In this paper, we propose a scheduling scheme that prioritizes HPC applications based on their I/O requirements. To accomplish this, our scheme analyzes the IOPS of scheduled applications by examining their execution history. Then, it schedules the applications at pre-configured intervals based on their expected IOPS to maximize the available IOPS across the entire system. Compared to the existing first-come first-served (FCFS) algorithm, experimental results using real-world HPC log data show that our scheme reduces total execution time by 305 h and decreases costs by USD 53 when scheduling 10,000 jobs utilizing public cloud resources. Full article

The high-quality development of the manufacturing industry necessitates accelerating its transformation towards high-end, intelligent, and green development. Considering logistics resource constraints, the impact of dynamic disturbance events on production, and the need for energy-efficient production, the integrated scheduling of production equipment and automated guided vehicles (AGVs) in a flexible job shop environment is investigated in this study. Firstly, a static model for the integrated scheduling of production equipment and AGVs (ISPEA) is developed based on mixed-integer programming, which aims to optimize the maximum completion time and total production energy consumption (EC). In recent years, reinforcement learning, including deep reinforcement learning (DRL), has demonstrated significant advantages in handling workshop scheduling issues with sequential decision-making characteristics, which can fully utilize the vast quantity of historical data accumulated in the workshop and adjust production plans in a timely manner based on changes in production conditions and demand. Accordingly, a DRL-based approach is introduced to address the common production disturbances in emergency order insertions. Combined with the characteristics of the ISPEA problem and an event-driven strategy for handling dynamic events, four types of agents, namely workpiece selection, machine selection, AGV selection, and target selection agents, are set up, which refine workshop production status characteristics as observation inputs and generate rules for selecting workpieces, machines, AGVs, and targets. These agents are trained offline using the QMIX multi-agent reinforcement learning framework, and the trained agents are utilized to solve the dynamic ISPEA problem. Finally, the effectiveness of the proposed model and method is validated through a comparison of the solution performance with other typical optimization algorithms for various cases. Full article

The role of employee behavior in organizations and their interaction with management is crucial in advancing the economic progress of workers. This study examines the impact of employee behavior and management practices on organizational performance and economic progress, using advanced artificial intelligence techniques to explore complex relationships and provide evidence-based strategies for sustainable workforce development. The research analyzes critical aspects such as job satisfaction, motivation, participation, and communication to uncover the underlying mechanisms that contribute to economic development. It recognizes the dynamic relationship between employees and management, confirming the central role of effective leadership, communication, and teamwork in achieving positive results. The study emphasizes that harmonious cooperation between employees and management is necessary to create a favorable work environment that contributes to the economic development of workers. It utilizes an artificial neural network (ANN) to better understand the interdependencies between different parameters and their effects within the framework of this ongoing project. The results contribute to the existing body of knowledge by providing practical implications for organizations seeking to optimize the employee–employer relationship and increase the overall workforce productivity. By understanding the complex dynamics between employee behavior and management practices, organizations can create a supportive environment that maximizes employee potential and contributes to sustainable economic growth. The findings demonstrate an accuracy of over 70%, indicating that enhancing job satisfaction and communication can significantly improve employee participation, productivity, and overall organizational performance. Full article

The work environment in healthcare institutions, especially in psychiatric hospitals, plays a crucial role in shaping the experiences and efficacy of nurses’ performance. This environment is influenced by various factors such as facility design, resource availability, workplace culture, support systems, and interpersonal dynamics. Understanding the intricate dynamics of the work environment in psychiatric hospitals is essential for ensuring the provision of high-quality mental healthcare services and enhancing the overall quality of life for both patients and healthcare providers, including nurses. However, the work environment of psychiatric nurses in the eastern region of Saudi Arabia is still inadequately understood. Objective: This study aimed to examine the work environment of nurses working in psychiatric hospitals in the eastern region of Saudi Arabia. Method: A cross-sectional research design was employed on a sample of 346 nurses using a non-probability convenience sampling technique. The survey method was adopted with the Practice Environment Scale of the Nursing Work Index (revised, Arabic version). Results: The study found a significant association between psychiatric nurses’ work environment and their socio-demographic characteristics. Demographic factors, such as age and years of experience, were identified as influencing factors of nurses’ perceptions of their work environment. Younger nurses and those with fewer years of experience reported greater job satisfaction when their work environment was positive. Conclusions: This study underscores the critical importance of maintaining a supportive work environment for psychiatric nurses due to its possible direct influence on their job satisfaction, work performance, quality of life, and overall well-being. Tailoring interventions to address demographic variations in perceptions of the work environment can enhance the well-being of nurses and improve the quality of care provided to psychiatric patients. These findings contribute to the body of knowledge on psychiatric nursing and have clinical implications for healthcare institutions that aim to optimize their work environments and retain a skilled and satisfied nursing workforce. Full article

Digital transformation is a process in which the latest technologies are used in various business fields to keep pace with continuous changes. It involves the strategic and profound integration of digital technologies into an organization’s core business operations, processes, and models. In this study, a quantitative approach was used to study the impact of DT adoption on public sector transformational change projects in the United Arab Emirates (UAE). The diffusion of innovation theory (DIT) and the unified theory of acceptance and use of technology model (UTAUT) were used in the factor analysis. This study highlights that digital transformation initiatives in the UAE have benefited from a strategic alignment with government initiatives, such as AI and blockchain strategies. However, public sector organizations face challenges, such as the high costs of technology adoption and cybersecurity risks during integration with legacy systems. The significance of social influence, including elements like use behavior and behavioral intention, was identified as essential for digital transformation, suggesting the importance of technology in job performance. Similarly, digital transformation projects improve IT competence and reduce resistance to change among leaders and individuals. The findings underscore the importance of investing in infrastructure and continuous IT training to sustain digital transformation. More studies are required across specific sectors to further explore the impact and scalability of DT initiatives in the UAE public sector. Full article