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This study aims to address the issue of random movement and non coordination between docking mechanisms and locking mechanisms, and proposes a comprehensive dynamic docking control architecture that integrates perception, planning, and motion control.

Firstly, the proposed dynamic docking control architecture uses laser sensors and a charge-coupled device camera to perceive the pose of the target. The sensor data are mapped to a high-dimensional potential field space and fused to reduce interference caused by detection noise. Next, a new potential function based on multi-dimensional space is developed for docking path planning, which enables the docking mechanism based on Stewart platform to rapidly converge to the target axis of the locking mechanism, which improves the adaptability and terminal docking accuracy of the docking state. Finally, to achieve precise tracking and flexible docking in the final stage, the system combines a self-impedance controller and an impedance control algorithm based on the planned trajectory.

Extensive simulations and experiments have been conducted to validate the effectiveness of the dynamic docking system and its control architecture. The results indicate that even if the target moves randomly, the system can successfully achieve accurate, stable and flexible dynamic docking.

This research can provide technical guidance and reference for docking task of unmanned vehicles under the ground conditions. It can also provide ideas for space docking missions, such as space simulator docking.

This paper aims to focus on the spatial docking task of unmanned vehicles under ground conditions. The docking task of military unmanned vehicle application scenarios has strict requirements. Therefore, how to design a docking robot mechanism to achieve accurate docking between vehicles has become a challenge.

In this paper, first, the docking mechanism system is described, and the inverse kinematics model of the docking robot based on Stewart is established. Second, the genetic algorithm-based optimization method for multiobjective parameters of parallel mechanisms including workspace volume and mechanism flexibility is proposed to solve the problem of multiparameter optimization of parallel mechanism and realize the docking of unmanned vehicle space flexibility. The optimization results verify that the structural parameters meet the design requirements. Besides, the static and dynamic finite element analysis are carried out to verify the structural strength and dynamic performance of the docking robot according to the stiffness, strength, dead load and dynamic performance of the docking robot. Finally, taking the docking robot as the experimental platform, experiments are carried out under different working conditions, and the experimental results verify that the docking robot can achieve accurate docking tasks.

Experiments on the docking robot that the proposed design and optimization method has a good effect on structural strength and control accuracy. The experimental results verify that the docking robot mechanism can achieve accurate docking tasks, which is expected to provide technical guidance and reference for unmanned vehicles docking technology.

This research can provide technical guidance and reference for spatial docking task of unmanned vehicles under the ground conditions. It can also provide ideas for space docking missions, such as space simulator docking.

The purpose of this paper is that the modular mobile robots reformed the multimachine joint mode to achieve obstacle-crossing, climbing and other multifunctional inspection in unstructured environment under the connection of the cone–hole docking mechanism.

An arc-shaped docking cone head with a posture-maintaining spring and two arc-shaped connecting rods that formed a ring round hole were designed to achieve large tolerance docking. Before active locking, the coordination between structures was used to achieve passive locking, which mitigated the docking impact of modular robots in unstructured environment. Using the locking ring composed of the two arc-shaped connecting rods, open-loop and closed-loop motion characteristics were obtained through the mutual motion of the connecting rod and the sliding block to achieve active locking, which not only ensured high precision docking, but also achieved super docking stability.

The cone–hole docking mechanism had the docking tolerance performance of position deviation of 6mm and pitch deviation of 8° to achieve docking of six degrees of freedom (6-DOF), which had a load capacity of 230 N to achieve super docking stability. Under the connection of the cone–hole docking mechanism, the modular mobile robots reformed the multimachine joint mode to achieve obstacle-crossing, climbing and other multifunctional inspection in unstructured environment.

Based on mechanical analysis of universal models, a cone–hole docking mechanism combining active and passive functions, six-dimensional constraints could be implemented, was proposed in this paper. The characteristics of the posture-maintaining spring in the cone docking head and the compression spring at the two ends of two arc-shaped connecting rods were used to achieve docking with large tolerance. Passive locking and active locking modules were designed, mitigating impact load and the locking did not require power to maintain, which not only ensured high precision docking, but also achieved super docking stability.

In the single-cargo market, the ordinary market share analysis method has been the representative tool for revealed competitiveness analysis. This paper develops and employs an applied market share index called the additive market share (AMS). Data are collected from 8 major ports for the 2001-2013 period. In comparison to the results of an ordinary market share analysis, there has been some fluctuation of the role of the Port of Singapore as the regional hub port though it still maintain its position. There are substitutable relationships between Singapore and the Malaysian ports in Malacca Strait. In Malacca Strait, Port Klang still leads the market in spite of the construction of PTP.

People's conciliation is a grass‐roots effort for conflict management in China. It settles civil disputes without characterizing them as conflicts between blameworthy adversaries. It does not take legal effect. But with support from the people's court and government, it is generally honored as an institution to maintain and promote mutual confidence and reciprocal relations among rural villagers, urban residents, and work unit employees. This paper attempts to provide a comprehensive description and analysis of people's conciliation. The origin, development, and organization are approached in relation to conciliation in Chinese history as well as other forms of intervention such as self‐conducted, lawyer‐assisted, administrative, and judicial conciliation. Case variety, principle, prohibition, strategy, procedure, and conciliator training are examined Illustrative cases are provided. Ideological and institutional aspects are analyzed in light of Maoism, political economy, culture and community, and public attitude in China.

Although user behaviors in social network service (SNS) have been well studied in prior literature, most of these studies focus on those behaviors with relatively deep user engagement such as information disclosure, while the underlying mechanisms that explain users’ shallow engagement behaviors (e.g. Like behavior) have been rarely discussed. To fill this research gap, the purpose of this paper is to propose and empirically test a research model to identify the antecedents of Like behavior.

This study identifies the distinctions between post behavior and Like behavior and develops a research model of Like behavior by emphasizing the role of sense of presence from the perspective of symbolic interactionism. The model is tested through a survey with 479 users of WeChat (a popular SNS tool in China). Structural equation modeling, SmartPLS in particular, is used for data analysis.

Three value perceptions, namely cognitive value, hedonic value and social value, are found to be positively associated with Like intention, and sense of presence is found to affect Like intention both directly and indirectly via the three value perceptions.

The research model is tested based on a specific SNS in China, so whether the conclusions can be applied to other research contexts should be further examined in future research. This study identifies the distinctions between post behavior and Like behavior and suggests to view the Like behavior from the perspective of symbolic interactionism.

The paper outlines ways to effectively promote SNS users’ Like behaviors by enhancing the functions related to three value perceptions, especially by enriching the ways that facilitate interpersonal interactions.

This paper is one of the first to distinguish Like behavior from post behavior in SNS, propose and empirically test a research model of Like behavior. In particular, this paper strengthens the important role of sense of presence from the perspective of symbolic interactionism which has rarely been investigated in prior studies.

The purpose of this study is to analyze from multiple perspectives, so as to form an effective massive open online course (MOOC)personalized recommendation method to help learners efficiently obtain MOOC resources.

This study introduced ontology construction technology and a new semantic association algorithm to form a new MOOC resource personalized recommendation idea. On the one hand, by constructing a learner model and a MOOC resource ontology model, based on the learner’s characteristics, the learner’s MOOC resource learning preference is predicted, and a recommendation list is formed. On the other hand, the semantic association algorithm is used to calculate the correlation between the MOOC resources to be recommended and the learners’ rated resources and predict the learner’s learning preferences to form a recommendation list. Finally, the two recommendation lists were comprehensively analyzed to form the final MOOC resource personalized recommendation list.

The semantic association algorithm based on hierarchical correlation analysis and attribute correlation analysis introduced in this study can effectively analyze the semantic similarity between MOOC resources. The hybrid recommendation method that introduces ontology construction technology and performs semantic association analysis can effectively realize the personalized recommendation of MOOC resources.

This study has formed an effective method for personalized recommendation of MOOC resources, solved the problems existing in the personalized recommendation that is, the recommendation relies on the learner’s rating of the resource, the recommendation is specialized, and the knowledge structure of the recommended resource is static, and provides a new idea for connecting MOOC learners and resources.

While the usage of digital technology can bring many operational improvements for firms, it is unclear whether it can effectively improve firm resilience to deal with supply chain disruptions caused by emergencies such as COVID-19. From a dynamic capability perspective, this study aims to investigate how digital technology usage can improve firm resilience in a rapidly changing and turbulent environment.

Based on the survey sample of 237 Chinese firms, the stepwise regression approach was used to examine the proposed research hypotheses.

The empirical evidence shows that digital technology usage has a U-shaped effect on firm resilience, and that effect is fully achieved by first affecting market acuity and then promoting resource reconfiguration. Moreover, the authors further found that the U-shaped association between digital technology usage and firm resilience is derived from the U-shaped association between digital technology usage and market acuity.

This study enriches the resilience literature by revealing the mechanism of digital technology usage’s effects rather than focusing on the role of specific digital technologies. This study also provides guidance for firms to develop effective digital technology usage strategies.

The purpose of this study is to develop an intelligent tutoring system (ITS) for programming learning based on information tutoring feedback (ITF) to provide real-time guidance and feedback to self-directed learners during programming problem-solving and to improve learners’ computational thinking.

By analyzing the mechanism of action of ITF on the development of computational thinking, an ITF strategy and corresponding ITS acting on the whole process of programming problem-solving were developed to realize the evaluation of programming problem-solving ideas based on program logic. On the one hand, a lexical and syntactic analysis of the programming problem solutions input by the learners is performed and presented with a tree-like structure. On the other hand, by comparing multiple algorithms, it is implemented to compare the programming problem solutions entered by the learners with the answers and analyze the gaps to give them back to the learners to promote the improvement of their computational thinking.

This study clarifies the mechanism of the role of ITF-based ITS in the computational thinking development process. Results indicated that the ITS designed in this study is effective in promoting students’ computational thinking, especially for low-level learners. It also helped to improve students’ learning motivation, and reducing cognitive load, while there’s no significant difference among learners of different levels.

This study developed an ITS based on ITF to address the problem of learners’ difficulty in obtaining real-time guidance in the current programming problem-solving-based computational thinking development, providing a good aid for college students’ independent programming learning.

The purpose of this paper is to propose a new algorithm chaotic pigeon-inspired optimization (CPIO), which can effectively improve the computing efficiency of the basic Itti’s model for saliency-based detection. The CPIO algorithm and relevant applications are aimed at air surveillance for target detection.

To compare the improvements of the performance on Itti’s model, three bio-inspired algorithms including particle swarm optimization (PSO), brain storm optimization (BSO) and CPIO are applied to optimize the weight coefficients of each feature map in the saliency computation.

According to the experimental results in optimized Itti’s model, CPIO outperforms PSO in terms of computing efficiency and is superior to BSO in terms of searching ability. Therefore, CPIO provides the best overall properties among the three algorithms.

The algorithm proposed in this paper can be extensively applied for fast, accurate and multi-target detections in aerial images.

CPIO algorithm is originally proposed, which is very promising in solving complicated optimization problems.