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The purpose of this paper is to develop a human activity-aware adaptive shared control solution for human–robot interaction in surgical operation. Hands-on control and teleoperation are two main procedures switched frequently in teleoperated minimally invasive surgery (MIS). The detailed human activity in the procedures can be defined and recognized using the sensor information. In this paper, a novel continuous adaptive shared control method is proposed for manipulators with Cartesian impedance control in the surgical scenario.

A human activity-aware shared control solution by adjusting the weight function is introduced to achieve smooth transition among different human activities, including hands-on control and teleoperation. Instead of introducing various controllers and switching among them during the surgical procedures, the proposed solution integrated all the human activity-based controllers into a single controller and the transition among the procedures is smooth and stable. The effectiveness of the proposed control approach was verified in a lab setup environment. The results prove that the robot behavior is stable and smooth. The algorithm is feasible and can achieve a human activity-aware adaptive shared control solution for human–robot interaction in surgical operation.

Based on the experiment, the results confirm that the proposed human activity-aware adaptive shared control solution can switch the device behavior automatically using the real-time sensor information. The transition between different activities is smooth and stable.

For teleoperated surgical applications, the proposed method integrated different controllers for various human activities into a single controller by recognizing the activities using the real-time sensor information and the transition between different procedures is smooth and stable. It eases the surgical work for the surgeon and enhances the safety during the transition of control modes. The presented scheme provides a general solution to address the switching of working procedures in teleoperated MIS.

To the best of the authors’ knowledge, this paper is the first to propose human activity-aware adaptive shared control solution for human–robot interaction in surgical operations.

The purpose of this paper is to mainly center on the touchless interaction between humans and robots in the real world. The accuracy of hand pose identification and stable operation in a non-stationary environment is the main challenge, especially in multiple sensors conditions. To guarantee the human-machine interaction system’s performance with a high recognition rate and lower computational time, an adaptive sensor fusion labeling framework should be considered in surgery robot teleoperation.

In this paper, a hand pose estimation model is proposed consisting of automatic labeling and classified based on a deep convolutional neural networks (DCNN) structure. Subsequently, an adaptive sensor fusion methodology is proposed for hand pose estimation with two leap motions. The sensor fusion system is implemented to process depth data and electromyography signals capturing from Myo Armband and leap motion, respectively. The developed adaptive methodology can perform stable and continuous hand position estimation even when a single sensor is unable to detect a hand.

The proposed adaptive sensor fusion method is verified with various experiments in six degrees of freedom in space. The results showed that the clustering model acquires the highest clustering accuracy (96.31%) than other methods, which can be regarded as real gestures. Moreover, the DCNN classifier gets the highest performance (88.47% accuracy and lowest computational time) than other methods.

This study can provide theoretical and engineering guidance for hand pose recognition in surgery robot teleoperation and design a new deep learning model for accuracy enhancement.

This paper aims on the trajectory tracking of the developed six wheel-legged robot with heavy load conditions under uncertain physical interaction. The accuracy of trajectory tracking and stable operation with heavy load are the main challenges of parallel mechanism for wheel-legged robots, especially in complex road conditions. To guarantee the tracking performance in an uncertain environment, the disturbances, including the internal friction, external environment interaction, should be considered in the practical robot system.

In this paper, a fuzzy approximation-based model predictive tracking scheme (FMPC) for reliable tracking control is developed to the six wheel-legged robot, in which the fuzzy logic approximation is applied to estimate the uncertain physical interaction and external dynamics of the robot system. Meanwhile, the advanced parallel mechanism of the electric six wheel-legged robot (BIT-NAZA) is presented.

Co-simulation and comparative experimental results using the BIT-NAZA robot derived from the developed hybrid control scheme indicate that the methodology can achieve satisfactory tracking performance in terms of accuracy and stability.

This research can provide theoretical and engineering guidance for lateral stability of intelligent robots under unknown disturbances and uncertain nonlinearities and facilitate the control performance of the mobile robots in a practical system.

Snake-inspired robots are of great significance in many fields because of their great adaptability to the environment. This paper aims to systematically illustrate the research progress of snake-inspired robots according to their application environments. It classifies snake-inspired robots according to the numbers of degrees of freedom in each joint and briefly describes the modeling and control of snake-inspired robots. Finally, the application fields and future development trends of snake-inspired robots are analyzed and discussed.

This paper summarizes the research progress of snake-inspired robots and clarifies the requirements of snake-inspired robots for self-adaptive environments and multi-functional tasks. By equipping various sensors and tool modules, snake-inspired robots are developed from fixed-point operation in a single environment to autonomous operation in an amphibious environment. Finally, it is pointed out that snake-inspired robots will be developed in terms of rigid and flexible deformable structure, long endurance and multi-function and intelligent autonomous control.

Inspired by the modular and reconfigurable concepts of biological snakes, snake-inspired robots are well adapted to unknown and changing environments. Therefore, snake-inspired robots will be widely used in industrial, military, medical, post-disaster search and rescue applications. Snake-inspired robots have become a hot research topic in the field of bionic robots.

This paper summarizes the research status of snake-inspired robots, which facilitates the reader to be a comprehensive and systematic understanding of the research progress of snake-inspired robots. This helps the reader to gain inspiration from biological perspectives.

This study investigates how a firm's structural embeddedness, the structural position in a supply network that consists of major customers, influences the acquisition of supplier trade credit. Specifically, this study examines how network interconnectedness, network integration and network independence of a firm affect its ability to acquire supplier trade credit.

This study utilizes financial data from Compustat to build a longitudinal dataset of manufacturing firms from 1998 to 2013. Customer segment disclosure data are used to construct firm-level network variables. A fixed effect regression approach is used for estimation.

The study results show that network interconnectedness is negatively associated with supplier trade credit, while network integration is positively associated with supplier trade credit. Network independence does not influence the extent of supplier trade credit. The post hoc analysis shows that the effects of the hypothesized factors vary under different product categories and credit ratings.

This study broadens the supply chain finance literature by showing how a firm's embedded network structural position can influence its ability to obtain supplier trade credit.

The purpose of this study is to examine the effects of three strategic environmental options on reducing greenhouse gas (GHG) emissions. Namely, we examine the effects of pollution prevention and waste management (PPWM) practices, green supply chain (GSC) practices, and outsourcing on reducing local and supply chain GHG emissions.

Using ASSET4 and deploying first-differencing fixed-effects panel data models, the study conducts a large-scale empirical examination on the effects of these focal strategic environmental options on GHG emissions.

This study finds that PPWM practices reduce local GHG emissions and that GSC practices reduce supply chain GHG emissions. The results also show that outsourcing does not reduce local GHG emissions and has an adverse effect on supply chain GHG emissions.

The study findings indicate that environmental practices are effective in reducing GHG emissions. However, they are effective only in their corresponding domain. Further, outsourcing is not a viable strategic option, and managers should be mindful of its undesired environmental consequences.

Firms undertake strategic environmental options, such as implementing environmental practices and reallocating production activities, to improve their environmental performance. Nevertheless, the effectiveness of these options on reducing GHG emissions has not been thoroughly examined.

China’s expanded rice imports offer a profitable business opportunity for both domestic and international grain marketers. The purpose of this paper is to analyze the impact of select variables on Chinese consumers’ rice choices, specifically focusing on country-of-origin, price, organic, brand, freshness, and taste. The study concludes with suggestions for domestic and international rice marketers to help them develop more efficient rice marketing plans.

This study developed a conditional logit model to analyze survey data gathered from Chongqing and Chengdu, two of China’s largest rice consumption cities.

Chinese consumers are price sensitive in their rice choices. Country-of-origin is the most imperative factor affecting rice selection for lower food expense consumers but branded rice attracts the attention of higher food expense consumers. Furthermore, these higher food expense consumers are willing to pay a small premium of $0.22 for a pound of organic rice.

A general trend in demand for organic rice from higher food expense consumers was identified, and this trend predicts a profitable market for organic rice sellers. However, these research findings are geographically limited and may only represent a consumption trend from the two sampled cities rather than all of China.

The study concludes with meaningful recommendations to rice marketers to aid in developing profitable market entrance strategies to China.

The Technium initiative started in 2001 with an initial Business and Innovation Centre established in the Swansea docklands area. Early success of this first Technium building led to the concept being rapidly proliferated into a pan-Wales network of primarily sector-focussed centres. Although the Welsh Government withdrew its support for the Technium network initiative in 2010, the individual centres continued under a range of ownerships and the historic initiative of continued interest, particularly with respect to regional policy.

A vibrant policy and practice debate subsequently emerged together with strident media comment. Lack of coherence between Technium Centres and weaknesses in monitoring systems meant this debate has been poorly informed. This case study helps address the evidence deficit within this debate by revisiting the initial Technium Swansea initiative and its subsequent development.

The case study provides an insight into what can realistically be expected of such initiatives in the short, medium and long terms, with realistic time-horizons for ‘success’ and the role of learning for knowledge-based development in similar initiatives and regions.

Introduction: Traditional recruitment system relied heavily on the applicants’ curriculum vitae (CV). This system, besides becoming redundant, has proved to be a futile exercise leading to the hiring of candidates that eventually turn out to be ‘misfits’. CVs were the only source of candidates’ data available for the recruiters a few years back. Face-to-face interviews was considered to be the ultimate solution for hiring suitable candidates. However, evidence suggests that interview scores and job performances do not complement each other. Advancement in artificial intelligence (AI) has introduced several techniques in the recruitment process.

Purpose: This chapter underscores the drawbacks of the traditional recruitment process. Evidence suggests that the traditional recruitment process is prone to subjectivity and is time-consuming. Surprisingly, despite the disadvantages, the integration of AI into the recruitment process is still slow. This chapter highlights the need to harness AI and the advantage technology could bring to the recruitment process. Some of the techniques that are garnering attention and widely used by organisations, such as chatbots, gamification, virtual employment interviews, and resume screening are described to enable the readers to understand with less effort. Chatbots and gamification techniques are described through process flow charts. We also describe the various types of interviews that could be conducted through virtual platforms and the modality by which the resume screening technique operates. Today, we are at a juncture wherein it is pertinent to acknowledge the superiority of technology-driven processes over traditional ones. This chapter will help the readers to understand the modus operandi to implement chatbots, gamification, virtual interviews and online resume screening techniques besides their advantages.

Scope: Although chatbots, resume screening, virtual interviews, and gamification are used in other areas, too, such as training and development, marketing, etc., in this chapter, we restrict solely to employee recruitment processes.

Methodology: Scoping review is used to examine the existing literature from various databases such as Google Scholar, IEEE, Proquest, Emerald, Elsevier, and JSTOR databases are used for extracting relevant articles.

Findings: Automation and analytics in recruitment and selection remove bias which is otherwise increasingly found in manual hiring processes. Also, previous studies have observed that candidates engage in impression management tactics in traditional face-to-face interviews. However, through automated recruitment processes, the influence of these tactics can be eliminated. AI-based virtual interviews reduce human bias. It also helps recruiters to hire talents across the globe. Gamification improves the candidate’s perception of the work and work environments. Through gamified techniques, the recruiters can understand whether a candidate possesses the required job skills. Chatbots are an interactive technique that can respond to interviewees’ queries. Resume screening techniques can save the recruiter’s time by screening and selecting the most appropriate candidates from a large pool. Hence, the chosen candidates alone can be referred to the next stage of the recruitment cycle. AI improves the efficiency of the recruitment process. It reduces mundane tasks. It saves time for the human resources (HR) team.

The present research aims to revisit the relationships between buyer dependence on suppliers, relational norms between two parties in a buyer-supplier dyad, and a buyer's tendency to either engage in opportunistic behaviors or comply with a supplier's request as an exception condition. The authors adopt a supplier's perspective to examine the supplier's anticipation of the buyer's behaviors.

Based on the original studies conducted by Joshi and Arnold, the authors extend both works using a similar methodology but with a different data sample. The previously validated buyer-supplier relationship supply disruption scenario presented in the original studies is rewritten from a supplier's perspective to examine the supplier's anticipation of the buyer's behaviors. Subjects are asked to assume the role of an account manager within the key supplier firm for an electronic equipment manufacturer and to respond to how they deal with the supplier's expectation of how the buying firm may behave in terms of compliance and opportunism.

The results show that buyer dependence is positively related to buyer compliance behaviors and that this relationship holds irrespective of the buyer's or supplier's perspective on the supply disruption scenario and irrespective of professional or student subjects. Other findings include the contingency of the moderating effect of relational norms on the link between dependence and buyer compliance on various factors, and the existence of a boundary condition for the moderation effect of relational norms on the link between dependence and buyer opportunism.

The study should prove valuable to academics and professionals alike. It reinforces the notion that buyers that are more dependent and reliant on suppliers are more willing to comply with supplier's needs. Also, it considers the possibility that supply chain agents from both the buyer and supplier sides may value the effect of relational norms differently, with suppliers perceiving that relational norms have more of a direct influence on a buyer's behavior. Lastly, the replication study extends the understanding of the generalizability of the original studies.