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This paper aims to introduce a custom-designed integrated nucleic acid detection polymerase chain reaction (PCR) instrument for clinical detection applications.

The PCR instrument can make rapid, sensitive, low-cost and quantitative molecular diagnosis compared with the current routine test flow from the pipette, series reagent to RT-PCR by manual manipulation. By integrating the multichannel automatic pipetting module, heat amplification module and real-time fluorescence detection module for the first time, the custom-designed integrated nucleic acid detection PCR instrument can achieve sample collection, subpackage, mixing, extracting, measuring and result presentation.

The multichannel automatic pipetting module was assembled with an accuracy of 0.4% (2 microliters) for accuracy measurement. Besides, the accuracy and sensitivity of nucleic acid using integrated low-cost nucleic acid detection PCR instruments were checked with COV-2019 virus (staining method) and African swine fever virus (probe method) under different concentrations.

Because of its high cost, complex system and bulky laboratory settings, including sample subpackage, mixing, extracting, measuring and finally result in presentation, the current nucleic acid detection system is not suitable for field operation and disease diagnosis in remote areas. The group independently designed and assembled an integrated low-cost multichannel nucleic acid detection PCR instrument, including a multichannel automatic pipetting module, a heat amplification module and a real-time fluorescence detection module.

The above equipment showed better reliability compared with commercial qPCR. These results can lay the foundation for functional, fast and low-cost PCR equipment for trace measurements.

The incentive cost of enterprises increases significantly with the rapid growth of the social commerce (SC) market. In this context, enterprises need to develop the optimal strategy to improve incentive effectiveness and reduce cost. Different types of consumers’ responses to incentives bring different values to enterprises. Hence, this paper proposes the social commerce value network (SCVN) to help enterprises study the contributions of different types of consumers to the network.

Based on the graphical evaluation and review technique (GERT), the authors construct the social commerce value GERT (i.e. SCV-GERT) network and design three progressive experiments for estimating the value contributions of “network stage”, “consumer type”, and “resource type” to the SCVN under the same incentives. The authors initialize the SCV-GERT model with consumer data in SC and distinguish the most valuable consumers by adjusting the incentive parameters.

The results show that the SCV-GERT model can well describe the value flow of SCVN. The incentive on forwarding consumers brings the greatest value gain to the SCVN, and social trust contributes the most to forwarding consumers.

Under the guidance of the results, platforms and enterprises in SC can select the optimal type of consumers who bring the maximum network value so as to improve the effectiveness of incentive strategy and reduce marketing costs. A four-level incentive system should be established according to the ranking of the corresponding value gains: forwarding consumers > agent consumers > commenting consumers > potential consumers. Enterprises also need to find ways to improve the social resource investments of consumers participating in SC.

This paper investigates the incentive problem in SC grounded in the SCVN and uses the GERT method to construct the SCV-GERT model, which is the first attempt to introduce GERT into the SC context. This study also makes up for the lack of comparative research on different types of consumers in SC and can provide support for enterprises’ customer relationship management and marketing decisions.

This study aims to take advantage of the unprecedented anti-corruption campaign launched in China in December 2012 and examine the effect of improved public governance on tunneling.

This study uses a sample of Shanghai and Shenzhen Stock Exchange listed companies from 2010 to 2014 and conduct regression analyses to investigate the effect of improved public governance attributed to the anti-corruption campaign on tunneling.

This study finds that the level of tunneling decreased significantly after the anti-corruption campaign, suggesting that increased public governance effectively curbs tunneling. Cross-sectional results show that this mitigating effect is more pronounced for non-SOE firms, especially non-SOE firms with political connections, firms audited by non-Big 8 auditors, firms with a large divergence between control rights and cash flow rights and firms located in areas with lower marketization.

This study highlights the importance of anti-corruption initiatives in improving public governance and in turn reducing tunneling. This study provides important implications for many other emerging economies to improve public governance.

This study contributes to the literature on the role of public governance in constraining corporate agency problems and advances the understanding of the economic consequences of China's anti-corruption campaign in the context of tunneling.

Session-based recommendation aims to predict the user's next preference based on the user's recent activities. Although most existing studies consider the global characteristics of items, they only learn the global characteristics of items based on a single connection relationship, which cannot fully capture the complex transformation relationship between items. We believe that multiple relationships between items in learning sessions can improve the performance of session recommendation tasks and the scalability of recommendation models. At the same time, high-quality global features of the item help to explore the potential common preferences of users.

This work proposes a session-based recommendation method with a multi-relation global context–enhanced network to capture this global transition relationship. Specifically, we construct a multi-relation global item graph based on a group of sessions, use a graded attention mechanism to learn different types of connection relations independently and obtain the global feature of the item according to the multi-relation weight.

We did related experiments on three benchmark datasets. The experimental results show that our proposed model is superior to the existing state-of-the-art methods, which verifies the effectiveness of our model.

First, we construct a multi-relation global item graph to learn the complex transition relations of the global context of the item and effectively mine the potential association of items between different sessions. Second, our model effectively improves the scalability of the model by obtaining high-quality item global features and enables some previously unconsidered items to make it onto the candidate list.

The purpose of this paper is the research of a novel gesture-based dual-robot collaborative interaction interface, which achieves the gesture recognition when both hands overlap. This paper designs a hybrid-sensor gesture recognition platform to detect the both-hand data for dual-robot control.

This paper uses a combination of Leap Motion and PrimeSense in the vertical direction, which detects both-hand data in real time. When there is occlusion between hands, each hand is detected by one of the sensors, and a quaternion-based algorithm is used to realize the conversion of two sensors corresponding to different coordinate systems. When there is no occlusion, the data are fused by a self-adaptive weight fusion algorithm. Then the collision detection algorithm is used to detect the collision between robots to ensure safety. Finally, the data are transmitted to the dual robots.

This interface is implemented on a dual-robot system consisting of two 6-DOF robots. The dual-robot cooperative experiment indicates that the proposed interface is feasible and effective, and it takes less time to operate and has higher interaction efficiency.

A novel gesture-based dual-robot collaborative interface is proposed. It overcomes the problem of gesture occlusion in two-hand interaction with low computational complexity and low equipment cost. The proposed interface can perform a long-term stable tracking of the two-hand gestures even if there is occlusion between the hands. Meanwhile, it reduces the number of hand reset to reduce the operation time. The proposed interface achieves a natural and safe interaction between the human and the dual robot.

This paper aims to investigate the corrosion rate, surface morphology and composition of corrosion products of 20# seamless steel in aqueous CO₂ solution under stratified gas-liquid two-phase flow condition. The development of a corrosion products layer has also been discussed.

The following methods were used: weight loss method, scanning electron microscopy, energy-dispersive X-ray spectroscopy and X-ray diffraction.

The corrosion rate curve presents an irregular zigzag change trend with a gradual increase in time. The peak value of the corrosion rate appears when the corrosion time is 4 h and 8 h. The corrosion products layer is composed of two sub-layers: the inner dense layer that is about 6 µm thick and the outer loose layer that is about 9 µm thick when the corrosion time is 8 h. The main corrosion product are FeCO₃ and Fe₂O₃.

The atomic ratio of Fe/C/O is relatively stable for the inner dense layer, but changes in thickness for the outer loose layer. There is a densification stage after a loose corrosion products layer forms, and it is periodic.

An accurate detection of overhead ground wire under open surroundings with varying illumination is the premise of reliable line grasping with the off-line arm when the inspection robot cross obstacle automatically. This paper aims to propose an improved approach which is called adaptive homomorphic filter and supervised learning (AHSL) for overhead ground wire detection.

First, to decrease the influence of the varying illumination caused by the open work environment of the inspection robot, the adaptive homomorphic filter is introduced to compensation the changing illumination. Second, to represent ground wire more effectively and to extract more powerful and discriminative information for building a binary classifier, the global and local features fusion method followed by supervised learning method support vector machine is proposed.

Experiment results on two self-built testing data sets A and B which contain relative older ground wires and relative newer ground wire and on the field ground wires show that the use of the adaptive homomorphic filter and global and local feature fusion method can improve the detection accuracy of the ground wire effectively. The result of the proposed method lays a solid foundation for inspection robot grasping the ground wire by visual servo.

This method AHSL has achieved 80.8 per cent detection accuracy on data set A which contains relative older ground wires and 85.3 per cent detection accuracy on data set B which contains relative newer ground wires, and the field experiment shows that the robot can detect the ground wire accurately. The performance achieved by proposed method is the state of the art under open environment with varying illumination.

The existing literature documents mixed evidence toward the association between corporate social responsibility (CSR) and corporate tax planning (e.g., Davis, Guenther, Krull, & Williams, 2016; Hoi, Wu, & Zhang, 2013). In this study, I aim to identify a causal relationship between CSR and tax planning, leveraging the staggered adoptions of constituency statutes in US states, which is a plausibly exogenous shock to firms' emphasis on their social responsibility. In general, the statutes permit firm directors to consider the interests of all constituents when making business decisions, including those who benefit from firms paying their fair share of income taxes. Thus, the adoption of the statutes raises the importance of firms' social responsibility in paying income taxes. Employing a staggered difference-in-differences (DiD) method, I find that firms incorporated in states that have adopted constituency statutes exhibit significantly higher effective tax rates (ETRs) based on current tax expense. This causal relationship suggests that managers, with the legitimacy to consider the social impact of tax avoidance, become less aggressive in tax planning. I further find that the effect of adoption is stronger for financially unconstrained firms and firms in retail businesses, where the demand (cost) for tax avoidance is lower (higher). Finally, I show that my main results are driven by firms located in states with a high sense of social responsibility and firms with high levels of tax avoidance prior to the adoption. Overall, the findings in this chapter contribute to the literature by delineating a negative causal relationship between CSR and tax avoidance and identifying a positive social impact brought by the passage of constituency legislation.

Goal orientation shapes employees’ approach to and interpretation of workplace aspects such as supervisors’ behavior. However, research has not fully examined the effect of goal orientation as an antecedent of abusive supervision. Drawing from victim precipitation theory, this study aims to fill this research gap by investigating how employees’ goal orientation influences their perception of abusive supervision.

Two studies were conducted to test the hypotheses. In Study 1, 181 employees in 45 departments participated in the survey, and multilevel confirmatory factor analysis, two-level path model and polynomial regression were used. In Study 2, 108 working adults recruited from a professional online survey platform participated in a two-wave time-lagged survey. Confirmatory factor analysis, hierarchical linear regression and polynomial regression were used.

This study found that employees’ learning goal orientation was negatively related to their perception of abusive supervision. In contrast, performance-avoidance goal orientation was positively related to their perception of abusive supervision, whereas performance-approach goal orientation was unrelated to this perception. Moreover, employees’ perception of abusive supervision was greater when learning and performance-approach goal orientation alignment occurred at lower rather than higher levels, and when performance-avoidance and performance-approach goal orientation alignment occurred at higher rather than lower levels.

This research identified two novel victim traits as antecedents of abusive supervision – employees’ learning goal orientation and performance-avoidance goal orientation. Furthermore, adopting a multiple goal perspective, the authors examined the combined effects of goal orientation on employees’ perception of abusive supervision.

Institutional actors are critical allies for grassroots movements, but few studies have examined their effects and variations within the non-democratic context. This chapter argues that while institutional allies are heavily constrained and unlikely to give open endorsement to grassroot activists, some institutional activists indirectly facilitate movement mobilization and favorable outcomes in the process of advancing their own political agendas. Drawing upon in-depth interviews conducted in 2008 and 2012, I illustrate this argument by examining the Anti-PX Movement – a landmark grassroots environmental movement against a chemical plant – in Xiamen, China. I find that the environmental institutional actors were constrained and divided, yet some still fostered opportunities for movement mobilization and in turn exploited the opportunity created by the protesters to pursue their policy interests, thus facilitating positive movement outcomes. As long as the claims are not politically subversive to the authoritarian rule, this type of tacit and tactical interaction between institutional activists within the state and grassroot activists on the street is conducive to promoting progressive policy changes.