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Recognition and guidance of initial welding position (IWP) is one of the most important steps of automatic welding process, also a key technology of autonomous welding process. The purpose of this paper is to advance an improved Harris Algorithm and grey scale scanning method (GSCM) to raise the precision of image processing.

Through the configuration of “single camera and double positions,” a new set of image processing algorithms is adopted to extract feature points by using the pattern of rough location and subtle extraction, so as to restructure three‐dimensional information to guide robot move to IWP in the practical welding environment.

Experiments showed that mean square errors (MSEs) in X, Y, Z‐directions for both flat butt joint and flat flange are 0.4491, 0.8178, 1.4797, and 0.5398, 0.4861, 1.1071 mm, respectively.

It has a limitation in providing guidance for only one step, and would be more accurate if fractional steps are adopted.

Guidance experiments of IWPs on oxidant tank's simulating parts are carried out, whose success rate is up to 95 percent and MSEs are 0.7407, 0.7971, and 1.3429 mm. It meets the demands of continuous and automatic welding process.

Improved Harris Algorithm and GSCM are advanced to raise the precision of image processing which influenced guidance precision most.

The purpose of this paper is to develop a kind of low cost measuring system based on binocular vision sensor to detect both the weld pool geometry and root gap simultaneously for robot welding process.

Two normal charge coupled device cameras are used for capturing clear images from two directions; one of them is used to measure the root gap and another one is used to measure the geometric parameters of the weld pool. Efforts are made from both hardware and software aspects to decrease the strong interferences in pulsed gas tungsten arc welding process, so that clear and steady images can be obtained. The grey level distribution characteristics of root gap edge and weld pool edge in images are analyzed and utilized for developing the image processing algorithms.

A solid foundation for seam tracking and penetration control of robot welding process can be established based on the binocular vision sensor.

The results show that the algorithms can extract the root gap edges and the contour of weld pool effectively, and then some geometric parameters can be calculated from the results.

The binocular vision system provides a new method for sensing of robot welding process.

The purpose of this study is to investigate the mechanism of shared leadership on team members’ innovative behavior.

Paired questionnaires were collected from 89 scientific research teams in the Beijing-Tianjin-Hebei region of China at two-time points with a time lag of 4 months. Then multilevel structural equation model method was applied to analyze the multiple mediating effects.

This study finds that: the form of shared leadership in scientific research teams of universities; shared leadership has a positive impact on team members’ innovative behavior; there are multiple mediations in the relationship including synchronization and sequence of creative self-efficacy and achievement motivation.

According to the “stimulus-organism-response” model, this paper has constructed a multi-level theoretical model that shared leadership influences individual innovation behavior and reveals the “black box” from the perspective of psychological mechanism. It not only verifies that “can-do” shapes “willing to do” but also makes up for the gap of an empirical test of the effectiveness of shared leadership in scientific research teams of universities. Besides, the formal scale of shared leadership in the Chinese situation is revised, which can provide a reference for future empirical research on shared leadership. The research conclusions provide new ideas for improving the management of scientific research teams in universities.

This paper aims to investigate the corrosion kinetics and corrosion behavior of NdFeB magnets with the addition of heavy rare earth dysprosium (Dy) for its inhibitory activity on poor corrosion resistance of NdFeB magnets.

To study the effect of dysprosium addition on corrosion behavior of NdFeB magnets and investigate its mechanism, potentiodynamic polarization, scanning electron microscopy (SEM), electrochemical impedance, energy dispersion spectrum (EDS) and scanning Kelvin probe force microscopy (SKPFM) were applied in the research. Besides, microstructures were observed by SEM equipped with EDS. Atomic force microscopy was introduced to analyze the morphology, potential image as well as the contact potential difference. The SKPFM mapping scan was applied to obtain the contact potential around Nd-rich phase at 0.1 Hz. The magnets were detected via X-ray diffraction.

Substitution of Nd with Dy led to improvement of corrosion resistance and reduced the potential difference between matrix and Nd-rich phase. Corrosion resistance is Nd-rich phase < the void < metal matrix; maximum potential difference between matrix and Nd-rich phase of Dy = 0, Dy = 3 and Dy = 6 Wt.% is 411.3, 279.4 and 255.8 mV, respectively. The corrosion rate of NdFeB magnet with 6 Wt.% Dy is about 67% of that without Dy at steady corrosion stage. The addition of Dy markedly enhanced the corrosion resistance of NdFeB magnets.

This research innovatively investigates the effect of adding heavy rare earth Dy to NdFeB permanent magnets on magnetic properties, as well as their effects on microstructure, phase structure and most importantly on corrosion resistance. Most scholars are studying the effect of element addition on magnetic properties but not on corrosion resistance. This paper creatively fills this research gap. NdFeB magnets are applied in smart cars, robotics, AI intelligence, etc. The in-depth research on corrosion resistance by adding heavy rare earths has made significant and outstanding contributions to promoting the rapid development of the rare earth industry.

For many pattern recognition problems, the relation between the sample vectors and the class labels are known during the data acquisition procedure. However, how to find the useful rules or knowledge hidden in the data is very important and challengeable. Rule extraction methods are very useful in mining the important and heuristic knowledge hidden in the original high-dimensional data. It can help us to construct predictive models with few attributes of the data so as to provide valuable model interpretability and less training times.

In this paper, a novel rule extraction method with the application of biclustering algorithm is proposed.

To choose the most significant biclusters from the huge number of detected biclusters, a specially modified information entropy calculation method is also provided. It will be shown that all of the important knowledge is in practice hidden in these biclusters.

The novelty of the new method lies in the detected biclusters can be conveniently translated into if-then rules. It provides an intuitively explainable and comprehensive approach to extract rules from high-dimensional data while keeping high classification accuracy.

Simulation is a well-known technique for using computers to imitate or simulate the operations of various kinds of real-world facilities or processes. The facility or process of interest is usually called a system, and to study it scientifically, we often have to make a set of assumptions about how it works. These assumptions, which usually take the form of mathematical or logical relationships, constitute a model that is used to gain some understanding of how the corresponding system behaves, and the quality of these understandings essentially depends on the credibility of given assumptions or models, known as VV&A (verification, validation and accreditation). The main purpose of this paper is to present an in-depth theoretical review and analysis for the application of VV&A in large-scale simulations.

After summarizing the VV&A of related research studies, the standards, frameworks, techniques, methods and tools have been discussed according to the characteristics of large-scale simulations (such as crowd network simulations).

The contributions of this paper will be useful for both academics and practitioners for formulating VV&A in large-scale simulations (such as crowd network simulations).

This paper will help researchers to provide support of a recommendation for formulating VV&A in large-scale simulations (such as crowd network simulations).

This paper aims to examine the effects of perceived authenticity at an independent, full-service mainstream ethnic restaurant and the moderating effects of diners’ cultural familiarity and cultural motivation on the influence of perceived authenticity on perceived value and behavioral intention.

A total of 417 self-administered questionnaires were collected from customers of an independent, full-service Italian restaurant in southeastern USA. The data analysis was performed using structural equation modeling.

Restaurant authenticity has a positive influence on perceived value. Respondents who are more familiar with and interested in Italian culture and food tend to attach more value to the restaurant authenticity. Respondents tend to use authenticity to convey quality judgment of the restaurant.

First, this study advances previous literature on dining authenticity by incorporating cultural familiarity and cultural motivation. Second, this study extends the theoretical framework of perceived quality of ethnic restaurants by connecting authenticity perceptions and quality assessment.

Results suggest that the managers at independent, full-service mainstream ethnic restaurants should focus on the restaurants’ environment and atmospheric authenticity, especially for customers who possess cultural familiarity and cultural motivation, while also ensuring the quality of food and service.

This study makes an initial attempt at studying the role of authenticity in a mainstream ethnic restaurant context and adds to the knowledge of restaurant authenticity from the perspectives of cultural familiarity, cultural motivation and perceived quality.

This paper aims to investigate the influence of religiosity on the relationship between abusive supervision and deviant work behavior (DWB). This paper examines whether the desire for revenge mediates the relationship between abusive supervision and DWB and whether the strength of this relationship is moderated by religiosity.

The proposed relationships were examined by collecting primary data from 350 employees using multistage sampling procedures. Hayes’ Process Macro was used to analyze the proposed moderated-mediation model of abusive supervision, DWB, religiosity and desire for revenge.

The analytical findings indicate that an employee’s desire for revenge mediates the relationship between abusive supervision and DWB. However, religiosity moderates the indirect effect of abusive supervision (mediated by the desire for revenge) on DWB.

To the best of the authors’ knowledge, this is the first study that explores the role of individual-level religiosity in mitigating the harmful effects of abusive supervision on deviant behavior and revenge in the workplace.

The objective of this chapter is to provide a first assessment on the evolution of spatial distribution of foreign firms in China.

We examine the overall changes in the location of foreign firms in China over the period 1999–2009. Then, we distinguish two time periods, 1998–2001 and 2002–2009 so as to analyze whether foreign firms’ agglomeration across regions has changed significantly after the China’s entry into the WTO (2001) and the first launch of the Chinese government policies to develop western internal areas.

Our analysis suggests that foreign-invested enterprises (FIEs) with higher foreign capital shares are more geographically clustered in coastal regions than other enterprises with lower foreign capital shares. This group with the highest intensity of foreign involvement in firm capital also experienced the most relevant changes over the decade of our analysis becoming more localized between the core-periphery divide (coastal provinces and the rest of mainland China).

The main limitation refers to poor data availability, data matching problems, and measurement errors in the database used, as highlighted by Nie, Jiang, and Yang (2012).

A general analysis of location patterns and the role of public policies may inform foreign companies in their entry strategy in the Chinese market.

Very few studies have explored location patterns with detailed geographical data and, at the same time, with data disaggregated by foreign ownership shares.

This paper aims to evaluate the early effects of the pandemic of coronavirus disease 2019 (COVID-19) and accompanying stay-at-home orders on restaurant demand in US counties.

The following two sets of daily restaurant demand data were collected for each US county: foot traffic data and card transaction data. A two-way fixed-effects panel data model was used to estimate daily restaurant demand from February 1 to April 30, 2020.

Results show that a 1% increase in daily new COVID-19 cases led to a 0.0556% decrease in daily restaurant demand, while stay-at-home orders were collectively associated with a 3.25% drop in demand. The extent of these declines varied across counties; ethnicity, political ideology, eat-in habits and restaurant diversity were found to moderate the effects of the COVID-19 pandemic and stay-at-home orders.

These results characterize the regional restaurant industry’s resilience to COVID-19 and identify particularly vulnerable areas that may require pubic policies and managerial strategies for intervention.

This study represents a pioneering attempt to investigate the economic impact of COVID-19 on restaurant businesses.