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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.

Drawing upon person–supervisor fit theory, a model is developed to illustrate how leader–member trait mindfulness (in)congruence may impact leader–member exchange (LMX) and how such trait mindfulness (in)congruence can indirectly influence taking charge.

Polynomial regression and response surface methodology are used to analyze 237 valid matched leader–member dyads.

LMX increases as leaders' and members' trait mindfulness become more aligned; LMX is higher when leader–member dyads are congruent at high levels (vs low levels). In the case of incongruence, LMX is higher when the member's trait mindfulness exceeds that of the leader. Furthermore, the relationship between leader–member trait mindfulness (in)congruence and taking charge is mediated by LMX.

The joint and interactive role of high trait mindfulness in leader–member dyads can help them to generate high-quality interpersonal exchange, as well as to cope with challenges posed by present and future changes.

The linear, nonlinear, simultaneous and interactive effects of dyadic trait mindfulness expand previous research, clarifying that the evaluation of leader–member congruence and incongruence at various degrees, and for various patterns of trait mindfulness, is more informative than examining the direct effect alone.

With the rapid development of railway-intelligent video technology, scene understanding is becoming more and more important. Semantic segmentation is a major part of scene understanding. There is an urgent need for an algorithm with high accuracy and real-time to meet the current railway requirements for railway identification. In response to this demand, this paper aims to explore a variety of models, accurately locate and segment important railway signs based on the improved SegNeXt algorithm, supplement the railway safety protection system and improve the intelligent level of railway safety protection.

This paper studies the performance of existing models on RailSem19 and explores the defects of each model through performance so as to further explore an algorithm model dedicated to railway semantic segmentation. In this paper, the authors explore the optimal solution of SegNeXt model for railway scenes and achieve the purpose of this paper by improving the encoder and decoder structure.

This paper proposes an improved SegNeXt algorithm: first, it explores the performance of various models on railways, studies the problems of semantic segmentation on railways and then analyzes the specific problems. On the basis of retaining the original excellent MSCAN encoder of SegNeXt, multiscale information fusion is used to further extract detailed features such as multihead attention and mask, solving the problem of inaccurate segmentation of current objects by the original SegNeXt algorithm. The improved algorithm is of great significance for the segmentation and recognition of railway signs.

The model constructed in this paper has advantages in the feature segmentation of distant small objects, but it still has the problem of segmentation fracture for the railway, which is not completely segmented. In addition, in the throat area, due to the complexity of the railway, the segmentation results are not accurate.

The identification and segmentation of railway signs based on the improved SegNeXt algorithm in this paper is of great significance for the understanding of existing railway scenes, which can greatly improve the classification and recognition ability of railway small object features and can greatly improve the degree of railway security.

This article introduces an enhanced version of the SegNeXt algorithm, which aims to improve the accuracy of semantic segmentation on railways. The study begins by investigating the performance of different models in railway scenarios and identifying the challenges associated with semantic segmentation on this particular domain. To address these challenges, the proposed approach builds upon the strong foundation of the original SegNeXt algorithm, leveraging techniques such as multi-scale information fusion, multi-head attention, and masking to extract finer details and enhance feature representation. By doing so, the improved algorithm effectively resolves the issue of inaccurate object segmentation encountered in the original SegNeXt algorithm. This advancement holds significant importance for the accurate recognition and segmentation of railway signage.

For decades, conflict resolution (CR) educators working cross-culturally have struggled with a fundamental dilemma – whether to offer western, evidence-based approaches through a top-down (prescriptive) training process or to use a bottom-up (elicitive) strategy that builds on local cultural knowledge of effective in situ conflict management. This study aims to explore which conditions that prompted experienced CR instructors to use more prescriptive or elicitive approaches to such training in a foreign culture and the implications for training outcomes.

There are two parts to this study. First, the authors conducted a literature review to identify basic conditions that might be conducive to conducting prescriptive or elicitive cross-cultural CR training. The authors then tested the identified conditions in a survey with experienced CR instructors to identify different conditions that afforded prescriptive or elicitive approaches. Exploratory factor analysis and regression were used to assess which conditions determined whether a prescriptive or elicitive approach produced better outcomes.

In general, although prescriptive methods were found to be more efficient, elicitive methods produced more effective, culturally appropriate, sustainable and culturally sensitive training. Results revealed a variety of instructor, participant and contextual factors that influenced whether a prescriptive or elicitive approach was applied and found to be more suitable.

This study used empirical survey data with practicing experts to provide insight and guidance into when to use different approaches to CC-CR training effectively.

Career adaptability is emerging as an important research area in today's uncertain, volatile world of work created by the COVID-19 pandemic. The present study focuses on career adaptability research post-COVID-19 by scientifically capturing the literature evolution, hotspots and future trends using bibliometric analysis.

The Scopus database, due to its vast and quality literature, was used to search the papers from the period 2020 to 2023. Bibliometric data were extracted and analyzed from the relevant literature. For further scientific mapping, VOSviewer and Biblioshiny software tools were used.

Findings of the analysis suggest a positive research trend related to career adaptability research post-Covid. Keyword analysis revealed noteworthy clusters and important themes. Bibliometric visual networks regarding authors, sources, citations, future themes, etc. are also presented from the 441 analyzed publications with comprehensive interpretation.

The literature for carrying out the bibliometric analysis was confined to the Scopus database. Other databases in combination with different software can be used for future niche research. From the analysis, future research avenues and practical interventions are presented which have significant implications for future researchers, career counselors and managers.

The study summarizes the recent literature on career adaptability in the aftermath of the pandemic and makes a novel contribution to the existing literature. A reliable study has been provided by the authors using the scientific bibliometric technique. The study highlights emerging research trends post the pandemic. The results are concluded with further suggestions which can guide future research related to the topic.