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The purpose of this paper is to apply an analysis of complex networks to empirically research international agricultural commodity trade and countries’ trading relations. The structure of global agricultural commodity trade is quantitatively described and analysed.

Based on statistical physics and graph theory, the research paradigm of a complex network, which has sprung up in the last decade, provides us with new global perspective to discuss the topic of international trade, especially agricultural commodity trade. In this paper, the authors engage in the issue of countries’ positions in international agricultural commodity trade using the latest complex network theories. The authors at first time introduce the improved bootstrap percolation to simulate cascading influences following the breaking down of bilateral agricultural commodity trade relations.

On a mid-level structure, countries are classified into three communities that reflect the structure of the “core/periphery” using the weighted extremal optimisation algorithm and the coarse graining process. On a micro-level, countries’ rankings are provided with the aid of network’s node centralities, which presents world agricultural commodity trade as a closed, imbalanced, diversified and multi-polar development.

The authors at first time introduce the improved bootstrap percolation to simulate cascading influences following the breaking down of bilateral agricultural commodity trade relations.

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.

Collaboration is a common phenomenon in human society. The best way of collaborations can make the group achieve the best interests. Because of the low cost and high repeatability of simulation, it is a good method to explore the best way of collaborations by means of simulation. The traditional simulation is difficult to adapt to the crowd intelligence network simulation, so the crowd collaborations simulation is proposed.

In this paper, the atomic swarm intelligence unit and collective swarm intelligence unit are proposed to represent the behavior of individuals and groups in physical space and the interaction between them.

To explore the best collaboration mode of the group, a framework of crowd collaborations simulation is proposed, which decomposes the big goal into the small goals by constructing the cooperation chain and analyzes the cooperation results and feeds them back to the next simulation.

Two kinds of swarm intelligence units are used to represent the simulated individuals in the group, and the pattern is used to represent individual behavior. It is suitable for the simulation of collaboration problems in various types and situations.

Inspired by the development of eco-friendly flexible electronics, this paper aims to present a series of paper-based electronics drawn by pencils, which can be used as favorable sensing elements in daily life.

Pencil traces are deposited on the porous surface of Xerox paper by the mechanical exfoliation during writing process, which can be used as basic components to construct functional electronics for daily sensing applications. By changing pencil grade, the obtained traces can work as conductive wires, electrodes, resistors and piezoresistive gauges.

The experimental results confirm their practical applications in sensing several daily activities, including finger motion, touching and the temperature of water in paper cup. Moreover, the used electronics can be easily handled and recycled.

The shortage in functionality, reliability and performance consistency induced by manual operation is an evident challenge, which makes the pencil-on-paper devices more suitable to work as a temporary solution to satisfying the demands from emergency circumstances.

The pencil-on-paper devices, motivated by the electroconductibility and piezoresistivity of pencil trace, can be explored as sensing prototypes in detecting daily activities. Meantime, their advances in easy accessibility, rapid fabrication, low cost and eco-fitness endow them excellent capacity of meeting the “on-site, real-time” demands.

With the development of the modern economy, vehicles are no longer a luxury for people, which greatly facilitate people’s daily life, but at the same time bring traffic congestion. How to relieve traffic congestion and improve its capacity is a hot research area. This paper aims to propose a new simulation framework for crowd transportations to ease traffic congestion.

This paper establishes related simulation models such as vehicles, traffic lights and advisers. Then the paper describes their relationships, gives their interaction mechanism and solidifies the above into a software implementation framework.

This paper proposes a simulation framework for crowd transportations.

In this framework, traffic lights are used as a control method to control the road network and road conditions are used as an Affecter to influence individual behavior. The vehicle passing rate is defined by the correlation between endowment and the start time of the traffic lights. In this framework, members are related, dynamically adjusted according to road conditions and dynamically optimized member decisions. The optimal path is dynamic and real-time adjustments are made for each step forward. It is different from the traditional optimal path in which there is only one fixed one and it is different from the macroscopic optimal path that does not exist.

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).

The differential magnetic gradient tensor system is usually constructed from the three-axis magnetic sensor array. While the effects of measurement error, sensor performance and baseline distance on localization performance of such systems have been widely reported, the research about the effect of spatial design of sensor array is less presented. This paper aims to provide a spatial design method of sensor array and corresponding optimization strategy to localization based on magnetic tensor gradient to get the optimum design of the sensor array. Based on the results of simulation, magnetic localization systems constructed from the proposed array and the traditional array have been built to carry out a localization experiment. The results of experiment have verified the effectiveness of magnetic localization based on the proposed array.

The authors focus on the localization of the magnetic target based on magnetic gradient by using three-axis magnetic sensor array and combine a design method with corresponding optimization strategy to get the optimum design of the sensor array.

This paper provides an array design and optimization method for magnetic target localization based on magnetic gradient to improve the localization performance.

In this paper, the authors focus on the magnetic localization based on magnetic gradient by using three-axis magnetic sensors and study the effect of the spatial design of sensor array on localization performance.

Despite the recent extending research on knowledge hiding, there is still scant research on social stressor phenomena-related contextual antecedent factors and new cognitive mechanisms of knowledge hiding behaviors. To shed new light on this unexplored gap, this research explores the multi-level moderated mediation model that examines how and when negative gossip experienced by targets in the workplace induces their knowledge hiding from coworkers drawing from the lens of social learning and cognitive theories. More specifically, this study aims to evaluate the relationship between negative workplace gossip and knowledge hiding via moral disengagement, and this mediation effect is also moderated by team relational conflict as a novel boundary condition.

This study collected multi-wave 338 employees’ data from 68 teams of cross-sectional industries in China, which were nested within teams. The collected nested nature data were analyzed by employing multi-level analysis based on hierarchical linear modeling.

The results suggested that negative workplace gossip first triggers moral disengagement and thereby, leads to knowledge hiding. Furthermore, the direct positive association between negative workplace gossip and moral disengagement was strengthened by increasing intra-team relational conflict. In addition, the mediation effect of moral disengagement between negative workplace gossip and knowledge hiding was also strengthened through increasing intra-team relational conflict.

This study first empirically examines the multi-level model using a new underlying mechanism (moral disengagement) and team-level boundary condition (relational conflict) and enriches the current literature on knowledge management and workplace gossip. Theoretical and practical implications of these findings and future research lines are also discussed, which will facilitate practitioners and academicians to curb counterproductive knowledge behavior.

The coronavirus disease 2019 (COVID-19) pandemic has greatly impacted society and academic life and research practices. This study is an attempt to comprehend whether a global emergency of COVID-19 pandemic has an impact on researcher international collaboration. The authors analyze the research collaboration before and after the beginning of the COVID-19 pandemic to understand how scientists collaborated within their own nation's borders and beyond.

The authors analyze the research collaboration before and after the beginning of the COVID-19 pandemic to understand how scientists collaborated within their own nation's borders and beyond. The authors collected a dataset of research publications published in journals in the research area of business and economics and indexed in the WoS Core Collection database by researchers from 11 countries (Austria, Denmark, Greece, Indonesia, Iran, Ireland, Korea (South), Mexico, Pakistan, Romania and Vietnam). In total, 14,824 publication records were considered for the literature analysis. This study presented the scientometric analysis of these publications using bibliometric, statistical, factor analysis and network analysis methods. The results are evaluated and interpreted in the context of the Hofstede's model of cultural dimensions. The results of this study provide evidence to research management to properly allocate their efforts to improve the researcher cooperation during the ongoing COVID-19 pandemic and to overcome its negative outcomes in the years to come.

The results of our study show that uncertainty avoidance as the cultural factor defined by the Hofstede's model has significantly influenced the properties of research collaboration networks in the domain of business and economics. Uncertainty avoidance focuses on how cultures adapt to changes and cope with uncertainty, while the global COVID-19 pandemic introduced a lot of change and uncertainty all levels of society around the world.

The study exclusively examines 14,824 research outputs which have been indexed in the WoS Core Collection database from 2019 till 15 November 2020 and only covered one research area (business economics). Thus, documents published in any other different channels and sources which are not covered in WoS are excluded from this study. The authors have analyzed the publications from just 11 countries, which represent a small part of the global research output. Also, the Hofstede’s cultural dimensions model is not a unique way to study cultural characteristics at the national level.

The results of this study will provide evidence to research management to properly allocate their efforts to improve the researcher cooperation during the still ongoing COVID-19 pandemic and to overcome its negative outcomes in the years to come.

Considering the global impact and social distress due to the outbreak of COVID-19 pandemic, this study is significant in the present scenario for identifying the changes in the characteristics of research collaboration networks of 11 diverse (in terms of geographical distribution and cultural differences in terms of the Hofstede’s cultural dimensions model) countries between 2019 (the year before COVID-19) and 2020 (the year of COVID-19), which has not been done before.