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Hybrid and virtual work settings offer greater flexibility and autonomy, yet they also have the paradoxical effect of weakening the connection of employees to each other and their identification with the organization. The purpose of this article is to discuss how to manage this paradox effectively.

Leveraging structural adaptation theory, the authors discuss hybrid and virtual work as one of five dimensions of team interdependence that collectively determine the tightness of coupling between team members.

The authors propose that the introduction of virtual and hybrid work can lead to a lower sense of belonging and identification with the organization that would need to be counteracted by respective increases in team interdependence in one or several of the remaining dimensions of team interdependence.

The authors apply research on team interdependence to develop a series of practical interventions that can address the Great Resignation. These interventions seek to enhance employees' experiences of belongingness after the shift to virtual and hybrid work. In doing so, the authors provide a toolkit that organizations can leverage to improve their employees' experiences in a post-COVID-19 workplace.

Polishing is a crucial process in mechanical manufacturing. The use of industrial robots to automate polishing is an inevitable trend in future developments. However, current robotic polishing tools are too large to reach inside deep holes or grooves in workpieces. This study aims to use a pneumatic artificial muscle (PAM) as the actuator and designs a force-controlled end-effector to reach inside the deep and narrow areas in the workpiece.

This approach first addresses the challenge of converting the tensile force generated by the PAM into pushing force through mechanism design. In addition, a dynamics model of the end-effector was established based on the three-element model of the PAM. A combined control strategy was proposed to enhance force control accuracy and adaptability during the polishing process.

Experiments were conducted on a robotic platform equipped with the proposed end-effector. The experimental results demonstrate that the end-effector can polish the inner end face of holes or grooves with diameters as small as 80 mm and depths reaching 200 mm. By implementing the combined control strategies, the target force tracking error was reduced by 48.66% compared to the use of the PID controller alone.

A new force-controlled end-effector based on the PAM is designed for robotic polishing. According to the experimental result, this end-effector can polish not only the outer surfaces of the workpiece but also the internal surfaces of workpieces with deep holes or grooves specifically. By using the combined control strategy proposed in this paper, the end-effector significantly improves force control precision and polishing quality.

The main objective of this article is to visualize the structure and trends of pro-environmental behavior (PEB) between 1999 and 2023 through mapping and in-depth analysis. The aim is to analyze PEB, which has received considerable academic attention in recent years due to its key role in the conservation of the environment and the protection of local communities in tourist destinations. This paper provides an important summary of the recent research that has explored the role that tourists have in protecting the environment through PEB.

This study presents a visual analysis of 2005 scholarly articles between the years 1999 and 2023 related to PEB. Using the knowledge mapping based on VOSviewer it presents the current status of research, which includes the analysis of citation analysis, co-citation analysis, co-citation network and longitudinal analysis.

PEB is an emerging topic due to its relevance to protecting the environment in the context of travel. The citation and co-citation analysis show the relevance of the behavior of tourists with regard to protecting the environment. The co-word analysis highlights the current significance of research concerning green hotels and the destination image of environmentally responsible destinations.

This study sheds light on the current research progress of PEB in the context of tourism through a comprehensive analysis (citation, co-citation and co-word). In addition, we provide theories and factors that have been previously used to study PEB in the context of tourism. The findings contribute to a broad and diverse understanding of the concept of PEB, which can provide important insights for policymakers in formulating management strategies and policies aimed at reducing environmental impacts in destinations.

This paper aims to solve the problem between detection efficiency and performance in grasp commodities rapidly. A fast detection and grasping method based on improved faster R-CNN is purposed and applied to the mobile manipulator to grab commodities on the shelf.

To reduce the time cost of algorithm, a new structure of neural network based on faster R CNN is designed. To select the anchor box reasonably according to the data set, the data set-adaptive algorithm for choosing anchor box is presented; multiple models of ten types of daily objects are trained for the validation of the improved faster R-CNN. The proposed algorithm is deployed to the self-developed mobile manipulator, and three experiments are designed to evaluate the proposed method.

The result indicates that the proposed method is successfully performed on the mobile manipulator; it not only accomplishes the detection effectively but also grasps the objects on the shelf successfully.

The proposed method can improve the efficiency of faster R-CNN, maintain excellent performance, meet the requirement of real-time detection, and the self-developed mobile manipulator can accomplish the task of grasping objects.

The purpose of the study is to address concerns regarding the subjectivity and imprecision of decision-making in table tennis refereeing by developing and enhancing a sensor node system. This system is designed to accurately detect the points on the table tennis table where balls collide. The study introduces the twined-reinforcement chimp optimization (TRCO) framework, which combines two novel approaches to optimize the distribution of sensor nodes. The main goal is to reduce the number of sensor units required while maintaining high accuracy in determining the locations of ball collisions, with error margins significantly below the critical 3.5 mm cutoff. Through complex optimization procedures, the study aims to improve the efficiency and reliability of decision-making in table tennis refereeing by leveraging sensor technology.

The study employs a design methodology focused on developing a sensor array system to enhance decision-making in table tennis refereeing. It introduces the twined-reinforcement chimp optimization (TRCO) framework, combining dual adaptive weighting strategies and a stochastic approach for optimization. By meticulously engineering the sensor array and utilizing complex optimization procedures, the study aims to improve the accuracy of detecting ball collisions on the table tennis table. The methodology aims to reduce the number of sensor units required while maintaining high precision, ultimately enhancing the reliability of decision-making in the sport.

The optimization research study yielded promising outcomes, showcasing a substantial reduction in the number of sensor units required from the initial count of 60 to a more practical 49. The sensor array system demonstrated excellent accuracy in identifying the locations of ball collisions, with error margins significantly below the critical 3.5 mm cutoff. Through the implementation of the twined-reinforcement chimp optimization (TRCO) framework, which integrates dual adaptive weighting strategies and a stochastic approach, the study achieved its goal of enhancing the efficiency and reliability of decision-making in table tennis refereeing.

This study introduces novel contributions to the field of table tennis refereeing by pioneering the development and optimization of a sensor array system. The innovative twined-reinforcement chimp optimization (TRCO) framework, integrating dual adaptive weighting strategies and a stochastic approach, sets a new standard for sensor node distribution in sports technology. By substantially reducing the number of sensor units required while maintaining high accuracy in detecting ball collisions, this research offers practical solutions to address the inherent subjectivity and imprecision in decision-making processes. The study’s originality lies in its meticulous design methodology and complex optimization procedures, offering significant value to the field of sports technology and officiating.

Students pursuing different professional courses at the higher education level during 2021–2022 saw the first-time occurrence of a pandemic in the form of coronavirus disease 2019 (COVID-19), and their mental health was affected. Many works are available in the literature to assess mental health severity. However, it is necessary to identify the affected students early for effective treatment.

Predictive analytics, a part of machine learning (ML), helps with early identification based on mental health severity levels to aid clinical psychologists. As a case study, engineering and medical course students were comparatively analysed in this work as they have rich course content and a stricter evaluation process than other streams. The methodology includes an online survey that obtains demographic details, academic qualifications, family details, etc. and anxiety and depression questions using the Hospital Anxiety and Depression Scale (HADS). The responses acquired through social media networks are analysed using ML algorithms – support vector machines (SVMs) (robust handling of health information) and J48 decision tree (DT) (interpretability/comprehensibility). Also, random forest is used to identify the predictors for anxiety and depression.

The results show that the support vector classifier produces outperforming results with classification accuracy of 100%, 1.0 precision and 1.0 recall, followed by the J48 DT classifier with 96%. It was found that medical students are affected by anxiety and depression marginally more when compared with engineering students.

The entire work is dependent on the social media-displayed online questionnaire, and the participants were not met in person. This indicates that the response rate could not be evaluated appropriately. Due to the medical restrictions imposed by COVID-19, which remain in effect in 2022, this is the only method found to collect primary data from college students. Additionally, students self-selected themselves to participate in this survey, which raises the possibility of selection bias.

The responses acquired through social media networks are analysed using ML algorithms. This will be a big support for understanding the mental issues of the students due to COVID-19 and can taking appropriate actions to rectify them. This will improve the quality of the learning process in higher education in Oman.

Furthermore, this study aims to provide recommendations for mental health screening as a regular practice in educational institutions to identify undetected students.

Comparing the mental health issues of two professional course students is the novelty of this work. This is needed because both studies require practical learning, long hours of work, etc.

The purpose of this paper is to develop and validate an instrument intended to measure servant leadership behavior in the Chinese hospitality industry.

After reviewing the literature, a scale of nine dimensions with 81 items was generated and then subjected to exploratory factor analysis (EFA) using a sample of 600 participants from a polytechnic college and hospitality industry, resulting in 6-factor-33-item solution. The derived measure was then shortened to 24 items by using item response theory (IRT). Drawing on the data from 440 respondents in the hospitality industry, this 6-factor-24 item measure was subsequently validated with confirmatory factor analysis (CFA) and the test of construct validity.

Difference in factors has been found between this instrument and western-developed scales. This study resulted in 6-dimension-24-items scale. These dimensions were named integrity, self-sacrifice, building community, empowering people, emotional healing and visioning. This servant leadership scale was demonstrated to have good internal consistency reliability and strong construct validity.

This is the first study that used IRT as a statistic tool to shorten a servant leadership scale and also this study provided additional support to cultural psychology theory.