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Applications of robotic systems in agriculture, forestry and high-altitude work will enter a new and huge stage in the near future. For these application fields, climbing robots have attracted much attention and have become one central topic in robotic research. The purpose of this paper is to propose an energy-optimal motion planning method for climbing robots that are applied in an outdoor environment.

First, a self-designed climbing robot named Climbot is briefly introduced. Then, an energy-optimal motion planning method is proposed for Climbot with simultaneous consideration of kinematic constraints and dynamic constraints. To decrease computing complexity, an acceleration continuous trajectory planner and a path planner based on spatial continuous curve are designed. Simulation and experimental results indicate that this method can search an energy-optimal path effectively.

Climbot can evidently reduce energy consumption when it moves along the energy-optimal path derived by the method used in this paper.

Only one step climbing motion planning is considered in this method.

With the proposed motion planning method, climbing robots applied in an outdoor environment can commit more missions with limit power supply. In addition, it is also proved that this motion planning method is effective in a complicated obstacle environment with collision-free constraint.

The main contribution of this paper is that it establishes a two-planner system to solve the complex motion planning problem with kinodynamic constraints.

Recognizing every worker's working status instead of only describing the existing construction activities in static images or videos as most computer vision-based approaches do; identifying workers and their activities simultaneously; establishing a connection between workers and their behaviors.

Taking a reinforcement processing area as a research case, a new method for recognizing each different worker's activity through the position relationship of objects detected by Faster R-CNN is proposed. Firstly, based on four workers and four kinds of high-frequency activities, a Faster R-CNN model is trained. Then, by inputting the video into the model, with the coordinate of the boxes at each moment, the status of each worker can be judged.

The Faster R-CNN detector shows a satisfying performance with an mAP of 0.9654; with the detected boxes, a connection between the workers and activities is established; Through this connection, the average accuracy of activity recognition reached 0.92; with the proposed method, the labor consumption of each worker can be viewed more intuitively on the visualization graphics.

With this proposed method, the visualization graphics generated will help managers to evaluate the labor consumption of each worker more intuitively. Furthermore, human resources can be allocated more efficiently according to the information obtained. It is especially suitable for some small construction scenarios, in which the recognition model can work for a long time after it is established. This is potentially beneficial for the healthy operation of the entire project, and can also have a positive indirect impact on structural health and safety.

This paper aims to investigate how chief executive officer (CEO) responsible leadership impacts corporate social responsibility (CSR) and organization performance by considering diverse organizational climates (including ethical, service and initiative climates) as mediators and CEO founder status as a moderator.

This study analyzed survey data from 212 service organizations in China with structural equation modeling.

The results clearly established that CEO responsible leadership played a crucial role in augmenting both CSR and organization performance by shaping positive organizational climates. Notably, CEO responsible leadership significantly fostered ethical, service and initiative climates. Furthermore, an ethical climate promoted CSR and organization performance, whereas service and initiative climates specifically enhanced organization performance. Additionally, responsible CEOs with founder status exhibited a higher propensity for enhancing ethical, service and initiative climates within service organizations.

Service organizations should take measures to build CEO responsible leadership, especially for CEOs with founder status. Furthermore, service organizations should motivate employees to reach consensus on ethical conducts, superior service and proactive approach to work.

First, the findings on CEO responsible leadership’s effects on CSR and organization performance extend the research on responsible leadership outcomes. Second, this paper adds to responsible leadership literature through exploring the mediating effects of ethical, service and initiative climates. Finally, the finding on the moderating role of founder CEOs offers a novel perspective regarding the boundary condition of the effects of CEO responsible leadership.

The purpose of this paper is to explore the effecting mechanism of relational embeddedness on technological innovation performance in the context of China.

By probing into the related theories and five exploratory case studies of Chinese manufacturing firms, this paper establishes a conceptual model about the effects of relational embeddedness on technological innovation performance and proposes nine hypotheses. The authors then investigate 228 Chinese manufacturing firms by questionnaires, and testify the hypotheses and conceptual model by structural equation modeling.

Chinese firm's relational embeddedness in the international manufacturing network has a positive effect on its technological innovation performance through explorative learning. Specifically, trust, information sharing and joint problem solving are beneficial to new knowledge acquisition and application, and then to the improvement of technological innovation performance.

This paper mainly focuses on bilateral relations among firms, regardless of the influence of network structure. Future research can extend to multilateral relations as well.

The paper builds up linkages among theories of network resources, organizational learning and technological innovation to open the black‐box of how relational embeddedness acts on technological innovation. It is a supplement to the existing research on inter‐firm network theories in developing countries.

Employee engagement has been identified as a prevalent issue affecting higher education institutions, particularly since the emergence of COVID-19. Therefore, this study aims to investigate the factors contributing to employee engagement in Islamic higher education institutions in the context of Malaysia.

A quantitative research design was used for this study, and a survey questionnaire was used to collect data from 340 staff members of Islamic higher education institutions in Malaysia. The proposed hypotheses underwent testing through the statistical technique of structural equation modelling, using statistical package for the social sciences (SPSS) and analysis of moment structures (AMOS).

The results indicate that training and development, trustworthiness, workplace spirituality, reward and recognition, management support and job autonomy significantly contribute to employee engagement in Islamic higher education institutions in Malaysia.

This study is limited to the staff of Islamic higher education institutions in Malaysia. A comparative cross-cultural research approach may be preferred for a more comprehensive understanding. Therefore, future researchers are encouraged to consider this limitation when investigating the factors contributing to employee engagement in Islamic higher education institutions, particularly to confirm the cogency of our findings.

The findings provide valuable insights into the workforce factors that play key roles in developing a highly engaged workforce in Islamic higher education institutions. This study contributes to the enrichment of the literature in this specific area of study.

This paper seeks to address issues of sustainability in the rapid urbanization in China with examination of knowledge management factors in the creation of new Chinese cities.

A case study was undertaken at Zhengdong New District along with semi‐structured interview mixed with evaluation and content analyses of successful knowledge management factors as the analytical approach.

This study explores the ongoing revolution of building new cities and towns in China, and highlights the importance of knowledge‐based development in achieving sustainable development. Following the establishment of the theory and model of the knowledge city, it explores the features of knowledge city in practice. Focusing on the case study of Zhengdong New District, Zhengzhou, Henan Province, it examines the factors of knowledge management in the creation of the new city. However, it is believed that the strategic development plan was made following the principles of sustainability. Furthermore, it has been accepted that the strategic plan reflexes the framework and sustains various features of the knowledge city, which could be seen as the embryo of knowledge city in China. In the meantime, it has to be recognised that the outcome of the evaluation of Zhengdong New District, which has been discussed in this paper, is merely the audit of what is currently happening in the first phase of the project and reflexes the current issues, and might give impact to the implementation of municipal government strategy in the future.

This paper concludes that, while the local authority is endeavouring to build a physically modern city, it might have overlooked the importance of using knowledge management principles as a tool to promote social, cultural, and environmental sustainability. Yet the analysis in this paper demonstrates that it is not impossible to use knowledge management framework as a tool to assist policy makers governing the creation of a new city in a sustainable way. The discussion in this paper is expected to be thought‐provoking in a holistic understanding of the theoretical perspective of knowledge city and further research into this field in the Chinese context.

Online employee reviews have emerged as a crucial information source for business managers to evaluate employee behavior and firm performance. The purpose of this paper is to test the relationship between employee personality traits, derived from online employee reviews and job satisfaction and turnover behavior at the individual level.

The authors apply text-mining techniques to extract personality traits from online employee reviews on Indeed.com based on the Big Five theory. They also apply a machine learning classification algorithm to demonstrate that incorporating personality traits can significantly enhance employee turnover prediction accuracy.

Personality traits such as agreeableness, conscientiousness and openness are positively associated with job satisfaction, while extraversion and neuroticism are negatively related to job satisfaction. Moreover, the impact of personality traits on overall job satisfaction is stronger for former employees than for current employees. Personality traits are significantly linked to employee turnover behavior, with a one-unit increase in the neuroticism score raising the probability of an employee becoming a former employee by 0.6%.

These findings have implications for firm managers looking to gain insights into employee online review behavior and improve firm performance. Online employee review websites are recommended to include the identified personality traits.

This study identifies employee personality traits from automated analysis of employee-generated data and verifies their relationship with employee satisfaction and employee turnover, providing new insights into the development of human resources in the era of big data.

The purpose of this study is to investigate the heat resistance and heat-resistant oxygen aging of 4-nitrophthalonitrile-etherified cardanol-phenol-formaldehyde (PPCF) to further use and develop the resin as the matrix resin of high-temperature resistant adhesives and coatings.

PPCF resin was synthesized by 4-nitrophthalonitrile and cardanol-phenol-formaldehyde (PCF). The structures of PPCF and PCF were investigated by Fourier transform infrared, differential scanning calorimetry and proton nuclear magnetic resonance. In addition, the heat resistance and processability of PPCF and PCF resins were studied by dynamic mechanical analysis, thermogravimetric analysis, scanning electronic microscopy (SEM), X-ray diffraction (XRD) techniques and rheological studies.

The results reveal that PPCF forms a cross-linked network at a lower temperature. PPCF resin has excellent resistance under thermal aging in an air atmosphere and that it still had a certain residual weight after aging at 500°C for 2 h, whereas the PCF resin is completely decomposed.

4-Nitrophthalonitrile was introduced into PCF resin, and XRD and SEM were used to investigate the high temperature residual carbon rate and heat-resistant oxygen aging properties of PPCF and PCF resins.

The purpose of this paper is to predict quantitative level of stray current leaking to the buried metallic structure by establishing convolution neural network (CNN) model.

First, corrosion experimental system of buried metallic structure is established. The research object of this paper is the polarization potential within 110 min, CNN model is used to predict the quantitative level of stray current leakage using the data from corrosion experimental system further. Finally, results are compared with the method using BP neural network.

Results show that the CNN model has better predictive effect and shorter prediction time than the BP model, the accuracy of which is 82.5507 per cent, and the prediction time is shortened by more than 10 times.

The established model can be used to forecast the level of stray current leakage in the subway system effectively, which provides a new theoretical basis for evaluating the stray current corrosion hazard of buried metallic structure.