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This paper aims to provide a comprehensive overview of the challenges and employee development strategies for executives and managers when managing flexible work systems.

The paper takes an employee development perspective to discuss management strategies of flexible work systems. Research findings on the effects of work flexibility through flexible work systems, the challenges and the development strategies that executives and managers can use were reviewed from multi-level perspectives.

The flexible work system is the new normal in the workplace. Lack of social and face-to-face interactions reduces employees’ social learning, jeopardizing managerial justice and weakening the culture. To remain competitive and retain talented employees, executives should reexamine their current employee development strategies and implement new strategies that fit the characteristics of flexible work systems.

To the best of the authors’ knowledge, this is the first comprehensive review of employee development strategies for flexible working arrangements. The paper provides practical guidelines and insights for executives and leaders managing employees under various flexible work systems.

Vicarious abusive supervision (VAS) has recently garnered the attention of hospitality researchers. VAS is prevalent in hospitality work settings characterized by long production chains and open operating environments. Based on the conservation of resources (CORs) theory, this study aims to examine how VAS influences hospitality employees’ work behaviours (i.e. supervisor-directed deviance, silence and helping behaviour) via affective rumination, with the moderating role of industry tenure as an individual contingency on the relationship between VAS and affective rumination.

The data were gathered from 233 restaurant frontline employees and their supervisors in Turkey. The authors tested the proposed model using partial least squares method through SmartPLS 3.

The results reveal that VAS triggers affective rumination, which, in turn, is positively related to supervisor-directed deviance and silence, and negatively related to helping behaviour. Moreover, industry tenure, as a buffer resource, significantly moderates the relationship between VAS and affective rumination.

To reduce the occurrence of VAS and mitigate its negative effects, managers should establish a work environment that embraces understanding and respect, pay attention to how they communicate with employees, implement appropriate interventions when VAS occurs and conduct stress management training and improve employees’ emotion regulation skills in ways that correspond to the employees’ industry experience.

This study advances research on VAS by offering insight into how VAS impacts employees’ work behaviours via the underlying mechanism of affective rumination through a COR lens. The findings also shed light on the salient buffering effect of industry tenure as an individual contingency.

Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.

This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.

Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.

At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.

This study aims to offer a comprehensive exploration of the potential and challenges associated with sensor fusion-based virtual reality (VR) applications in the context of enhanced physical training. The main objective is to identify key advancements in sensor fusion technology, evaluate its application in VR systems and understand its impact on physical training.

The research initiates by providing context to the physical training environment in today’s technology-driven world, followed by an in-depth overview of VR. This overview includes a concise discussion on the advancements in sensor fusion technology and its application in VR systems for physical training. A systematic review of literature then follows, examining VR’s application in various facets of physical training: from exercise, skill development and technique enhancement to injury prevention, rehabilitation and psychological preparation.

Sensor fusion-based VR presents tangible advantages in the sphere of physical training, offering immersive experiences that could redefine traditional training methodologies. While the advantages are evident in domains such as exercise optimization, skill acquisition and mental preparation, challenges persist. The current research suggests there is a need for further studies to address these limitations to fully harness VR’s potential in physical training.

The integration of sensor fusion technology with VR in the domain of physical training remains a rapidly evolving field. Highlighting the advancements and challenges, this review makes a significant contribution by addressing gaps in knowledge and offering directions for future research.

Higher education institutions (HEIs) have a great responsibility to put education for sustainable development at the centre of their work. Curricula should therefore start to incorporate the sustainable development goals (SDGs) and key competencies in sustainability, and research should be carried out to determine effective learning methods for this. This study aims to explore the usefulness of problem-based learning (PBL) approaches to train biomedical students in sustainability and to provide some recommendations for the design and implementation of new PBL-SDG scenarios.

Two PBL-SDG scenarios were designed, implemented and evaluated for 110 students of human biology degree. Learning outcomes and student perceptions of this approach were analysed through questionnaires, student productions, non-participant observation and focus groups.

The results show that the PBL-SDG scenarios effectively addressed several SDGs and sustainability competencies in a transversal, collaborative and innovative manner. According to student perceptions, the elements that contributed most to the development of these competencies were emotional involvement with the scenario, reflection on their own actions, freedom to approach the problem and tutors who empowered them with their proposals.

The PBL-SDG approach presented in this study is an example of a pedagogical strategy that can help HEIs educate their learners as key change agents. The findings of this study provide evidence for this important aspect and give guidelines and strategies to successfully designing and implementing such methodologies in biomedical education.

This paper aims to study the agent learning from expert demonstration data while incorporating reinforcement learning (RL), which enables the agent to break through the limitations of expert demonstration data and reduces the dimensionality of the agent’s exploration space to speed up the training convergence rate.

Firstly, the decay weight function is set in the objective function of the agent’s training to combine both types of methods, and both RL and imitation learning (IL) are considered to guide the agent's behavior when updating the policy. Second, this study designs a coupling utilization method between the demonstration trajectory and the training experience, so that samples from both aspects can be combined during the agent’s learning process, and the utilization rate of the data and the agent’s learning speed can be improved.

The method is superior to other algorithms in terms of convergence speed and decision stability, avoiding training from scratch for reward values, and breaking through the restrictions brought by demonstration data.

The agent can adapt to dynamic scenes through exploration and trial-and-error mechanisms based on the experience of demonstrating trajectories. The demonstration data set used in IL and the experience samples obtained in the process of RL are coupled and used to improve the data utilization efficiency and the generalization ability of the agent.

The purpose of this systematic review was to explore the benefits and challenges in the implementation of simulation-based education (SBE) in the classroom and clinical settings in sub-Saharan Africa. The objectives of this systematic review were to identify the benefits of utilising SBE in the classroom and clinical practice in sub-Saharan Africa and to assess the challenges in the implementation of SBE in the classroom and clinical practice in sub-Saharan Africa.

Five databases were searched for existing English literature (Medline, CINAHL and Science Direct), including grey literature on the subject. Out of 26 eligible studies conducted in sub-Saharan Africa between 2014 and 2021, six studies that used mixed-methods design were included. Hawker et al.’s framework was used to assess the quality of the studies. Quantitative data were presented using descriptive and inferential statistics in the form of means and standard deviations while qualitative data were analysed and presented thematically.

Quantitative findings showed that participants rated SBE highly in terms of teaching (93.2%), learning (91.4%) and skill acquisition (88.6%). SBE improved the clinical skill competency from 30% at baseline to 75% at the end. On the other hand, qualitative findings yielded themes namely: improved confidence and competence; knowledge acquisition and critical thinking; motivation and supervision; independent, self-paced learning; simulation equipment and work schedules; and planning and delivery of simulation activity. Pedagogical skills, competence and confidence are some of the elements that determine the feasibility of implementing SBE in the classroom and clinical settings.

SBE could help to bridge the gap between theory and practice and improve the quality of care provided by nurses. Simulation-based training is effective in improving the clinical skills of midwives and increasing their confidence in providing care. However, SBE trainees require motivation and close supervision in classroom settings if simulation is to be successfully implemented in sub-Saharan Africa. Furthermore, careful planning of scenarios, students briefing and reading of content prior to implementation facilitate effective simulation.

While there may be a lack of literature on the use of SBE for training nurses and midwives in the developing world, there is growing evidence that it can be an effective way to improve clinical skills and quality of care. However, there are also significant challenges to implementing simulation-based training in resource-limited settings, and more research is needed to understand how best to address these challenges. This study fills this gap in the literature.

The purpose of this study is to explore the potential contribution of the metaverse to improve training and development as a function of human resource development (HRD) perspective. The authors explore the benefits and challenges of the metaverse and introduce cases of companies using the metaverse in training.

A narrative literature review was conducted to collect information on the metaverse in training. The authors reviewed peer- and non-peer-reviewed articles, book chapters, white papers, corporate websites and blogs and business magazines.

A total of 75 articles were reviewed, including 14 cases, which were summarized to demonstrate how companies are applying metaverse technology in training contexts. For a more in-depth review, three cases were selected and summarized in terms of context, process and outcomes.

The metaverse is an emergent topic in HRD. It has the potential to revolutionize the functions of training and development through the combination of advanced technologies, including virtual reality, augmented reality and mixed reality. This article is the foundational attempt to provide a comprehensive summary of existing literature and case studies that highlight the potential of the metaverse in training within the context of HRD.

Security education, training and awareness (SETA) programs are the key to addressing “people problems” in information systems (IS) security. Contrary to studies using conventional methods, the present study leveraged an “event” lens and dimensionalized employees' perceptions into three sub-dimensions: perceived novelty, perceived disruption and perceived criticality. Moreover, this research went a step further by examining how pedagogical and communication approaches to a SETA program affect employees' perceptions of the program. This study then investigated whether – and if so, how – these approaches impact employees' perceptions of the SETA program and their subsequent commitment to it.

Utilizing a factorial-based scenario survey, this study empirically tested a model of the above relationships via covariance-based structural equation modeling.

The results of this research showed that pedagogical approaches were more effective than communication approaches and that employees' perceptions of the SETA program accounted for a large variance in their commitment to SETA.

First, this research deepens understanding of the protection of information assets by elaborating on the different approaches that organizations can take to encourage employees' commitment to SETA. Second, the study enriches the SETA literature by theorizing a SETA program as an organizational “event”, which represents a major shift from the conventional approach. Third, the study adds to the theoretical knowledge of the event lens by extending it to the SETA context and investigating the relationship among three event strength components.

Limited by the types of sensors, the state information available for musculoskeletal robots with highly redundant, nonlinear muscles is often incomplete, which makes the control face a bottleneck problem. The aim of this paper is to design a method to improve the motion performance of musculoskeletal robots in partially observable scenarios, and to leverage the ontology knowledge to enhance the algorithm’s adaptability to musculoskeletal robots that have undergone changes.

A memory and attention-based reinforcement learning method is proposed for musculoskeletal robots with prior knowledge of muscle synergies. First, to deal with partially observed states available to musculoskeletal robots, a memory and attention-based network architecture is proposed for inferring more sufficient and intrinsic states. Second, inspired by muscle synergy hypothesis in neuroscience, prior knowledge of a musculoskeletal robot’s muscle synergies is embedded in network structure and reward shaping.

Based on systematic validation, it is found that the proposed method demonstrates superiority over the traditional twin delayed deep deterministic policy gradients (TD3) algorithm. A musculoskeletal robot with highly redundant, nonlinear muscles is adopted to implement goal-directed tasks. In the case of 21-dimensional states, the learning efficiency and accuracy are significantly improved compared with the traditional TD3 algorithm; in the case of 13-dimensional states without velocities and information from the end effector, the traditional TD3 is unable to complete the reaching tasks, while the proposed method breaks through this bottleneck problem.

In this paper, a novel memory and attention-based reinforcement learning method with prior knowledge of muscle synergies is proposed for musculoskeletal robots to deal with partially observable scenarios. Compared with the existing methods, the proposed method effectively improves the performance. Furthermore, this paper promotes the fusion of neuroscience and robotics.