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This paper aims to present the design and experimental tests of an active circulating cooling system for the Experimental Advanced Superconducting Tokamak in-vessel inspection manipulator, which will help the current manipulator prototype to achieve a full-scale in-vessel high temperature environment compatibility.

The high-temperature effects and heat transfer conditions of the manipulator under in-vessel environment were analyzed. An active circulating cooling system was designed and implemented on the manipulator prototype. A simulative in-vessel inspection task in a high temperature environment of 100°C was carried out to evaluate the performance of the active circulating cooling system.

The proposed active circulating cooling system was proved effective in helping the manipulator prototype to achieve its basic in-vessel inspection capability in a high temperature environment. The active circulating cooling system performance can be further improved considering the cooling structure coefficient differences in different manipulator parts.

For the first time, the active circulating cooling system was implemented and tested on a full-scale of the in-vessel inspection manipulator. The experimental data of the temperature distribution inside the manipulator and the operating status of the circulating system were helpful to evaluate the current active circulating cooling system design and provided effective guidance for improving the overall system performance.

This paper aims to propose a novel method to identify the parameters of robotic manipulators using the torque exerted by the robot joint motors (measured by current sensors).

Previous studies used additional sensors like force sensor and inertia measurement unit, or additional payload mounted on the end-effector to perform parameter identification. The settings of these previous works were complicated. They could only identify part of the parameters. This paper uses the torque exerted by each joint while performing Fourier periodic excited trajectories. It divides the parameters into a linear part and a non-linear part, and uses linear least square (LLS) parameter estimation and dual-swarm-based particle swarm optimization (DPso) to compute the linear and non-linear parts, respectively.

The settings are simpler and can identify the dynamic parameters, the viscous friction coefficients and the Coulomb friction coefficients of two joints at the same time. A SIASUN 7-Axis Flexible Robot is used to experimentally validate the proposal. Comparison between the predicted torque values and ground-truth values of the joints confirms the effectiveness of the method.

The proposed method identifies two joints at the same time with satisfying precision and high efficiency. The identification errors of joints do not accumulate.

The aim of this paper is to implement direct teaching of industrial manipulators using current sensors. The traditional way to implement teaching is either to use a teaching pedant, which is time consuming, or use force sensors, which increases system cost. To overcome these disadvantages, a novel method is explored in the paper by using current sensors installed at joints as torque observers.

The method uses current sensors installed at each joint of a manipulator as torque observers and estimates external forces from differences between joint-driven torque computed based on the values of current sensors and commanded values of motor-driven torque. The joint-driven torque is computed by cancelling out both pre-calibrated gravity and friction resistance (compensation). Also, to make the method robust, the paper presents a strategy to detect unexpected slowly drifts and zero external forces and stop the robot in those situations.

Experimental results demonstrated that compensating the joint torques using both pre-calibrated gravity and friction resistance has performance comparable to a force sensor installed on the end effector of a manipulator. It is possible to implement satisfying direct teaching without using force sensors on 7 degree of freedom manipulators with large mass and friction resistance.

The main contribution of the paper is that the authors cancel out both pre-calibrated gravity and friction resistance to improve the direct teaching using only current sensors; they develop methods to avoid unsafe situations like slow drifts. The method will benefit industrial manipulators, especially those with large mass and friction resistance, to realize flexible and reliable direct teaching.

This paper aims to present a different cooling method (water cooling) to protect all the mechanical/electrical components for Tokamak in-vessel inspection manipulator. The method is demonstrated effective through high temperature experiment, which provides an economical and robust approach for manipulators to work normally under high temperature.

The design of cooling system uses spiral copper tube structure, which is versatile for all types of key components of manipulator, including motors, encoders, drives and vision systems. Besides, temperature sensors are set at different positions of the manipulator to display temperature data to construct a close-loop feedback control system with cooling components.

The cooling system for the whole inspection manipulator working under high temperature is effective. Using insulation material such as rubber foam as component coating can significantly reduce the environmental heat transferred to cooling system.

Compared with nitrogen gas cooling applied in robotic protection design, although it is of less interest in prior research, water cooling method proves to be effective and economical through our high temperature experiment. This paper also presents an energetic analysis method to probe into the global process of water cooling and to evaluate the cooling system.

Given the progressive rise of online health communities (OHC) that have predominantly changed health delivery services, healthcare organizations still face tremendous challenges of low patient participation and lack of high-quality contribution to OHC. Prior scholars indicated that inducing patient value co-creation behavior (VCB) is substantially beneficial for the sustainable growth of OHCs. However, what drives patients' behavior to co-create value is still unknown. To fill this important gap, this study used the service-dominant logic of value co-creation theory and face (mianzi in Chinese) literature to discover how patient co-creation attitude (CA) affects patient VCB. Also, this study aimed to explore the joint mechanism of how face gain (FG) and face loss (FL) impact patients' VCB in OHCs.

The survey data of 322 patients actively using OHC in China were analyzed via partial least squares structural equation model (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA).

The results revealed that patient CA positively influences VCB, that is participation behavior (PB) and citizenship behavior (CB). Face gain (FG) strengthens the impact of CA and patient PB and CB, whereas face loss (FL) weakens the impact of CA and patient PB and CB. Furthermore, the fsQCA findings signify the robustness of the study model.

This study explores the multifaceted mechanism of patient value co-creation in OHC and discloses the crucial role of face for the first time. Further, the novel findings of this study provide a robust framework for advancing the understanding of important drivers of patient VCBs that significantly helps healthcare service providers and OHC managers to sustain OHCs.

Online health communities (OHCs) have been recognized as emerging platforms on the Internet used for health purposes. Despite its emergence, developing a successful OHC is still a challenge. Prior studies identified that value co-creation behavior (VCB) of members is an essential factor for sustaining OHCs; however, little is known about how members’ behavior drives to co-create value? Therefore, this study aims to discover the inclusive mechanism for members’ VCB in OHCs.

The authors develop the study model and hypothesis based on the service-dominant logic of value co-creation theory and social support (SS) literature. The survey data of 608 active OHCs users in China were analyzed using partial least squares structural equation modeling (PLS-SEM).

The results revealed that SS positively affects members’ VCBs. Ethical aspects; Trust and ethical interaction (EI) partially mediate their relationships. In addition, community members’ current health status (CHS) negatively moderates the relationships between SS and VCB. From the findings, it becomes evident that only SS is not enough; developing an ethical environment in OHCs, i.e. trust and ethically rich interactions among members, significantly helps OHCs to promote co-creation. Also, the negative moderation of CHS findings provides novel insights when cramming health conditions.

Exploring the complex mechanism of co-creation in OHC, the authors illustrate the potential of service-dominant logic to create new theoretical insight for healthcare and provide the framework of co-creation with ethics for the first time. This will extend the application of ethics in healthcare services and offer a robust platform from which the understanding of drivers of members’ VCB can be advanced in the OHC context.

Interest in reverse logistics (RL) as a critical component of supply chain management (SCM) is gaining more traction with both practitioners and academics. Because of RL’s growing strategic importance, it is imperative to conduct a timely and comprehensive literature review and to identify associated opportunities for future research. The paper aims to discuss these issues.

In this paper, the researchers conduct an extensive bibliometric analysis of published academic articles on RL for the period of 1992-2015. Specifically, the CiteSpace software is utilized to conduct document co-citation analysis and burst detection analysis on 912 selected RL articles and their 22,642 references.

This research identifies the most influential RL research publications/citations in each of the five periods and their research contribution. Using co-citation analysis, the authors are able to identify and illustrate major research themes, knowledge groups, and future research opportunities in the RL field.

In contrast to existing literature review studies in the logistics field, the study uses impact factor as a key article selection criterion. The influential articles identified in this process well represent the core literature and RL body of knowledge and have important implications for future research.

Prior research demonstrated that China's Outward FDI (OFDI) is aimed at sustaining long-term economic growth by promoting industrialization and technological upgrading in the country. However, empirical evidence on the effectiveness of this strategy remains scarce. This study intends to fill this gap by exploiting endogenous changes in industrial productivity stemming from OFDI to examine if China's new strategy to spur OFDI is economically beneficial for the industries involved.

The authors employed the two-step system-GMM and pooled mean group approaches on a panel dataset of 18 Chinese industries over the 2004–2017 period. The industrial sectors are further classified into the state dominated and non-state dominated ones to evaluate whether the productivity growth impact of OFDI varies by the level of ownership structure. Besides, the dataset is further decomposed into the ex ante and ex-post BRI era to test if this initiative has altered the underlying relationship.

The results provide robust evidence that China's OFDI through reverse spillover effects promotes productivity growth in the domestic industries, and such productivity gains are greater for the non-state dominated industries, and the OFDI in the BRI era. The findings suggest that OFDI can act as a catch-up strategy to release excess capacity and acquire technology and smart business practices.

This study is the first attempt to highlight the reverse productivity spillovers associated with OFDI at the industrial level. The study's findings guide the government officials and the practitioners of foreign investment to better understand the implications of their investment projects in terms of technology improvements and to optimize market opportunities.

As the global economy expands, it would seem to be in the interests of the major professional sport leagues of the US to move into new markets, especially China, one of the fastest growing and largest in the world. In order to sell effectively in this market, it is vital to gain an understanding of the potential fan base. To explore national differences in fandom, a survey was completed by sports fans in both China and the US to assess which sports participants followed and which media they used, to identify fan motives and their feelings about expansion. This study determined that there are clear differences between Chinese and Americans. While many of the Chinese were fans of American sports, they tended to follow individual athletes rather than teams and had relatively low fan identity but high levels of fan motivation. Motives also varied by country, with aesthetics and affiliation the primary motives among the Chinese sample. These distinctions can be utilised to create marketing strategies.

The purpose of this paper is to establish the grey‐weighted relationship prediction pattern of the soil organic matter content spectral inversion under the uncertainties between soil organic matter contents and spectral characteristics and the theory of grey system.

At first, according to grey‐weighted distance, a new grey relationship model is presented. Second, in order to make full use of the information of grey relationship sequences, the maximum grey relationship discrimination principle is improved and then the soil organic matter content spectral inversion pattern is put forward based on weighted grey recognition theory. A numeric example of Hengshan County in Shanxi Province is also computed in the last part of the paper.

The results are convincing: not only that soil organic matter content spectral inversion pattern based on the weighted grey recognition theory is valid, but also the model's prediction accuracy is higher; the sample's average prediction accuracy is 94.917 per cent.

The method exposed in the paper can be used at soil organic matter content hyper‐spectral inversion and even for other similar forecast problems.

The paper succeeds in realising both prediction pattern and application of soil organic matter content hyper‐spectral inversion by using the newest developed theories: weighted grey recognition theory.