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The purpose of this paper is to explore the relationship between top management team (TMT) internal social capital and strategic decision-making speed, and further explore role of TMT behavioral integration in their relationship. It reveals how TMT internal social capital impacts strategic decision-making speed.

On the basis of the social capital theory and upper echelons theory, at first, a model about TMT internal social capital and strategic decision-making speed is proposed by exploratory case study. Then, the data obtained via questionnaire from 67 TMTs by software SPSS 19.0 and AMOS 17.0 are analyzed, and the theoretical hypotheses as mentioned above are verified.

The empirical study found that different dimensions of TMT internal social capital have significant positive impact on TMT behavioral integrity; TMT behavioral integrity has significant positive impact on strategic decision-making speed; and TMT behavioral integrity as an intermediary variable played a brokering role in the relationship between TMT internal social capital and strategic decision-making speed.

The study enriches the empirical test on the relationship between TMT internal social capital and decision speed, thereby helping the authors further understand how to improve the speed of strategic decision making in TMT.

Two and one half‐dimensional (2.5D) grid maps are useful for navigation in outdoor environment or on non‐flat surface. However, little attention has been given to how to find an optimal path in a 2.5D grid map. The purpose of this paper is to develop a path‐planning method in a 2.5D grid map, which aims to provide an efficient solution to robot path planning no matter whether the robot is equipped with the prior knowledge of the environment.

A 2.5D grid representation is proposed to model non‐flat surface for mobile robots. According to the graph extracted from the 2.5D grid map, an improved searching approach derived from A* algorithm is presented for the shortest path planning. With reasonable assumption, the approach is improved for the path planning in unknown environment.

It is confirmed by experiments that the proposed planning approach provide a solution to the problem of optimal path planning in 2.5 grid maps. Furthermore, the experiment results demonstrate that our 2.5D D* method leads to more efficient dynamic path planning for navigation in unknown environment.

This paper proposes a path‐planning approach in a 2.5D grid map which is used to represent a non‐flat surface. The approach is capable of efficient navigation no matter whether the global environmental information is available at the beginning of exploration.

The aim of this paper is to explore the relationship between leadership style, top management teams' (TMTs') behavioral integration, and strategic decision‐making speed. It reveals how leadership impacts on team progress and strategic decision‐making speed.

The sample was collected from more than ten provinces/cities in China. Factor analysis and structural equation model (SEM) were used to conduct the data.

The empirical study found that leadership style has a direct positive impact on the speed of strategic decision making, and an indirect positive impact on the speed of strategic decision making through the team behavior integration. The results of SEM show both leadership style and team behavior integration have a significant impact on strategic decision‐making speed. It demonstrates that transformational leadership and transactional leadership have both direct and indirect impact on the speed of strategic decisions. Moreover, transformational leadership has a greater impact than transactional leadership on team behavior integration and strategic decision‐making speed.

The study enriches the empirical test on the relationship between leadership, team and decision speed, therefore helping us further understand how to improve the speed of strategic decision‐making.

Path planning approaches based on conventional occupancy grid maps are problematic in off‐road environment because impossible areas include not only obstacles but also landscapes like ramps and pits. The purpose of this paper is to develop a path planning method in a hybrid grid map, which aims to provide a better solution for outdoor navigation.

A hybrid vision system which consists of one stereo vision and one omnidirectional vision is adopted to provide environmental information for 2.5D grid and 2D grid mapping, respectively. An improved planning method originated from conventional D*‐based search algorithm is proposed for more efficient navigation in such hybrid grid maps.

It is confirmed by simulations and experiments that the path planning in the hybrid grid map is more efficient than that in conventional grid maps. Furthermore, it helps to guarantee a safe exploration for field and planetary robots.

This paper proposes a path planning approach in a hybrid grid map representing unstructured environment. The map consists of two different grid representations with diverse resolutions and structures, named 2.5D and 2D grids. The navigation process is expected to become efficient by reducing the replanning times and track length.

This paper aims to propose a bilateral robotic system for lower extremity hemiparesis rehabilitation. The hemiplegic patients can complete rehabilitation exercise voluntarily with the assistance of the robot. The reinforcement learning is included in the robot control system, enhancing the muscle activation of the impaired limbs (ILs) efficiently with ensuring the patients’ safety.

A bilateral leader–follower robotic system is constructed for lower extremity hemiparesis rehabilitation, where the leader robot interacts with the healthy limb (HL) and the follow robot is worn by the IL. The therapeutic training is transferred from the HL to the IL with the assistance of the robot, and the IL follows the motion trajectory prescribed by the HL, which is called the mirror therapy. The model reference adaptive impedance control is used for the leader robot, and the reinforcement learning controller is designed for the follower robot. The reinforcement learning aims to increase the muscle activation of the IL and ensure that its motion can be mastered by the HL for safety. An asynchronous algorithm is designed by improving experience relay to run in parallel on multiple robotic platforms to reduce learning time.

Through clinical tests, the lower extremity hemiplegic patients can rehabilitate with high efficiency using the robotic system. Also, the proposed scheme outperforms other state-of-the-art methods in tracking performance, muscle activation, learning efficiency and rehabilitation efficacy.

Using the aimed robotic system, the lower extremity hemiplegic patients with different movement abilities can obtain better rehabilitation efficacy.

Social manufacturing has emerged. It aims to integrate the manufacturing resources of micro- and small-scale manufacturing enterprises (MSMEs) and help MSMEs cope with the dynamic, service-oriented and personalized market demands. In social manufacturing, MSMEs cooperate with each other through manufacturing resource sharing. However, because MSMEs are distributed and decentralized, the efficiency of establishing reliable cooperation between MSMEs is relatively low. Therefore, this paper presents a blockchain-driven cyber-credit evaluation system (BCCES) to implement distributed cyber-credit evaluation. BCCES can provide reliable cyber-credit for distributed MSMEs without the trusted third party. This can improve the efficiency of establishing reliable cooperation among unauthentic MSMEs.

The paper proposes a BCCES to evaluate MSMEs' cyber-credit in decentralized environment. In BCCES, a cyber-credit evaluation model is proposed by improving set pair analysis (SPA) method, and cyber-credit smart contract and distributed consensus mechanism are designed according to the runtime logic of distributed cyber-credit evaluation.

The results confirmed that BCCES is feasible and effective to implement cyber-credit evaluation without the trusted third party. With the advantages of blockchain, BCCES can automatically realize cyber-credit evaluation through smart contract and distributed consensus. At the same time, BCCES can evaluate the real-time cyber-credit of MSMEs based on their latest service evaluation. In addition, we can design corresponding smart contracts according to actual requirements, which makes blockchain applicable to different distributed scenarios.

The paper combines blockchain and SPA to implement cyber-credit evaluation in social manufacturing and provides a new feasible idea for cyber-credit evaluation without the trusted third party. This can also provide MSMEs a reference of applying blockchain to other distributed scenarios through combining smart contract and different algorithms.

The foundations of internally driven discipline are similar to self-directed learning (SDL). This study examines the effect of cultural orientation and SDL on the online performance of college students. It investigates how college students pursue SDL while maintaining a collectivist cultural orientation in their learning experience. It explains why students prefer SDL to learning constrained by an externally enforced discipline.

The explanatory sequential mixed-method design uses a quantitative method, followed by qualitative enquiry. The research was conducted in an undergraduate non-credit online course in China.

The findings show that cultural orientation has no impact on students' online performance, while SDL abilities are positively related to it. When fully mediated by SDL, a horizontal-collectivist culture has a positive effect on students' online performance.

Data were collected in a non-credit online college course, where the final assessment used a peer-rating approach and team members shared the same final score. This scoring method may not fully reflect each student's online performance.

The findings suggest that, when considering cultural influence on student performance, researchers should consider learning contexts, including educational level and learning mode. This study validates that colleges should focus on ability and skill development that enhance internal motivation to improve students' online performance, rather than focussing on their beliefs.

This paper introduces evidence to support the impact of culture on college students' online performance, showing that SDL abilities can drive performance.

In recent years, deep learning and extended reality (XR) technologies have gained popularity in the built environment, especially in construction engineering and management. A significant amount of research efforts has been thus dedicated to the automation of construction-related activities and visualization of the construction process. The purpose of this study is to investigate potential research opportunities in the integration of deep learning and XR technologies in construction engineering and management.

This study presents a literature review of 164 research articles published in Scopus from 2006 to 2021, based on strict data acquisition criteria. A mixed review method, consisting of a scientometric analysis and systematic review, is conducted in this study to identify research gaps and propose future research directions.

The proposed research directions can be categorized into four areas, including realism of training simulations; integration of visual and audio-based classification; automated hazard detection in head-mounted displays (HMDs); and context awareness in HMDs.

This study contributes to the body of knowledge by identifying the necessity of integrating deep learning and XR technologies in facilitating the construction engineering and management process.