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The purpose of this paper is to use Weibo as a window to examine the Chinese netizens’ online attitudes and responses to two sets of population policy: the Selective Two-Child Policy (Phase 2) and the Universal Two-Child Policy. The population policy change from the rigid One-Child Policy to the Selective Two-Child Policy then to the Universal Two-Child Policy aroused great attention of the Chinese people.

This research uses the crawler technique to extract data on the Sina Weibo platform. Through opinion mining of Weibo posts on two sets of population policy, the Weibo users’ online opinions on the Two-Child Policy are analyzed from two perspectives: their attention intensity and sentiment tendency. The research also use the State Bureau of Statistics of China’s national population data between 2011 and 2016 to examine the Chinese people’s actual birth behaviors after implementing the two different sets of the Two-Child Policy.

The research findings indicate that the Selective Two-Child Policy (Phase 2) and the Universal Two-Child Policy are good examples of thematic public sphere of Weibo. Weibo posts on the two sets of the Two-Child Policy have undergone different opinion cycles. People from economically developed regions and populous regions have paid more attention to both sets of Two-Child Policy than their counterparts in the less developed and less populated regions. Men pay more attention to the Two-Child Policy than women do. Despite people’s huge attention to the new population policy, the population growth after the policy is not sustainable.

The new population policy alone is difficult to boost China’s population within a short period of time. The Chinese Government must provide its people with enough incentives and supporting welfare to make the population growth happen.

These findings have important implications for understanding the dynamics of online opinion formation and changing population policy in China.

As an effective mode to help small and medium enterprises (SMEs) raise working capital, supply chain finance has recently gained extensive attention. The purpose of this paper is to explore the intrinsic mechanism of how both weak and strong ties in the supply chain network impact the supply chain financing performance (SCFP) of SMEs from the perspective of the supply chain network.

Based on the extended resource-based perspective, this paper proposes a theoretical model to explain the mode in which strong ties and weak ties of SMEs in the supply chain network influence SCFP through both physical distribution flexibility and demand management flexibility. Based on data from 182 manufacturing firms in China, this paper uses multiple regression analysis to test hypotheses.

The results of this paper indicate that weak ties improve SCFP more effectively than strong ties. Furthermore, both physical distribution flexibility and demand management flexibility exert different mediating roles either between strong ties and SCFP or between weak ties and SCFP. Moreover, the effect of physical distribution flexibility and demand management flexibility on SCFP of SMEs is not reinforced.

This paper highlights the importance to expand supply chain finance research from the perspective of the supply chain network. In particular, this paper explores the poorly understood mediating effect both physical distribution flexibility and demand management flexibility exert on the relationship between network ties and the SCFP of SMEs.

To improve the speed and accuracy of turbine blade film cooling design process, the most advanced deep learning models were introduced into this study to investigate the most suitable define for prediction work. This paper aims to create a generative surrogate model that can be applied on multi-objective optimization problems.

The latest backbone in the field of computer vision (Swin-Transformer, 2021) was introduced and improved as the surrogate function for prediction of the multi-physics field distribution (film cooling effectiveness, pressure, density and velocity). The basic samples were generated by Latin hypercube sampling method and the numerical method adopt for the calculation was validated experimentally at first. The training and testing samples were calculated at experimental conditions. At last, the surrogate model predicted results were verified by experiment in a linear cascade.

The results indicated that comparing with the Multi-Scale Pix2Pix Model, the Swin-Transformer U-Net model presented higher accuracy and computing speed on the prediction of contour results. The computation time for each step of the Swin-Transformer U-Net model is one-third of the original model, especially in the case of multi-physics field prediction. The correlation index reached more than 99.2% and the first-order error was lower than 0.3% for multi-physics field. The predictions of the data-driven surrogate model are consistent with the predictions of the computational fluid dynamics results, and both are very close to the experimental results. The application of the Swin-Transformer model on enlarging the different structure samples will reduce the cost of numerical calculations as well as experiments.

The number of U-Net layers and sample scales has a proper relationship according to equation (8). Too many layers of U-Net will lead to unnecessary nonlinear variation, whereas too few layers will lead to insufficient feature extraction. In the case of Swin-Transformer U-Net model, incorrect number of U-Net layer will reduce the prediction accuracy. The multi-scale Pix2Pix model owns higher accuracy in predicting a single physical field, but the calculation speed is too slow. The Swin-Transformer model is fast in prediction and training (nearly three times faster than multi Pix2Pix model), but the predicted contours have more noise. The neural network predicted results and numerical calculations are consistent with the experimental distribution.

This paper creates a generative surrogate model that can be applied on multi-objective optimization problems. The generative adversarial networks using new backbone is chosen to adjust the output from single contour to multi-physics fields, which will generate more results simultaneously than traditional surrogate models and reduce the time-cost. And it is more applicable to multi-objective spatial optimization algorithms. The Swin-Transformer surrogate model is three times faster to computation speed than the Multi Pix2Pix model. In the prediction results of multi-physics fields, the prediction results of the Swin-Transformer model are more accurate.

In recent years, terrorist attacks have gradually become one of the important factors endangering social security. In this context, this research aims to propose methods and principles which can be utilized to make effective evacuation plans to reduce casualties in terrorist attacks.

By analyzing the statistical data of terrorist attack videos, this paper proposes an extended cellular automaton (CA) model and simulates the panic evacuation of the pedestrians in the terrorist attack.

The main findings are as follows. (1) The panic movement of pedestrians leads to the dispersal of the crowd and the increase in evacuation time. (2) Most deaths occur in the early stage of crowd evacuation while pedestrians gather without perceiving the risk. (3) There is a trade-off between escaping from the room and avoidance of attackers for pedestrians. Appropriate panic contagion enables pedestrians to respond more quickly to risks. (4) Casualties are mainly concentrated in complex terrains, e.g. walls, corners, obstacles, exits, etc. (5) The initial position of the attackers has a significant effect on the crowd evacuation. The evacuation efficiency should be reduced if the attacker starts the attack from the exit or corners.

In this research, the concept of “focus region” is proposed to depict the different reactions of pedestrians to danger and the effects of the attacker’s motion (especially the attack strategies of attackers) are classified. Additionally, the influences on pedestrians by direct and indirect panic sources are studied.

The purpose of this study is to analyze the dynamic changes of the arms trade network not only from the network structure but also the influence mechanism from the aspects of the economy, politics, security, strategy and transaction costs.

The study employs the Temporal Exponential Random Graph Model and the Separable Temporal Exponential Random Graph Model to analyze the endogenous network structure effect, the attribute effect and the exogenous network effect of 47 major arms trading countries from 2015 to 2020.

The results show that the international arms trade market is unevenly distributed, and there are great differences in military technology. There is a fixed hierarchical structure in the arms trade, but the rise of emerging countries is expected to break this situation. In international arms trade relations, economic forces dominate, followed by political, security and strategic factors.

Economic and political factors play an important role in the arms trade. Therefore, countries should strive to improve their economic strength and military technology. Also, countries should increase political mutual trust and gain a foothold in the industrial chain of arms production to enhance their military power.

The contribution of this paper is to analyze the special trade area of arms trade from a dynamic network perspective by incorporating economic, political, security, strategic and transaction cost factors together into the TERGM and STERGM models.

The purpose of this paper is to analyze the crowd stampede risk mechanism from the perspective of systems thinking.

Causal loop diagram is drawn to outline the non-linear interactions among complex factors across the whole system and dissect the contributory factors of crowd stampede accident. To systematically construct the theoretical framework and find fundamental solutions, co-word analysis with Citespace is used to get the critical data. An agent-based simulation using Pathfinder is conducted to develop a spatial model for the Shanghai Stampede Accident that happened in 2014.

The causal loop diagram is formed to not only illustrate the symptomatic solutions with a quick fix but also dissect the fundamental solutions through an underlying systemic analysis. The simulation shows that crowd stampede experiences an interactive process of accumulation, trigger, delay, break and diffusion of risk factors within the crowd system. A linkage effect among the multidimensional characters of individuals and the system accelerates the stampede risk deterioration. There exists delay of the result of effect from the deep-level measure.

A top-down approach is offered to policymakers for crowd stampede risk protocol design and synergic emergency control that may reduce the risk of the stampede.

In this study, SDFT paradigm is proposed as the critical solution for the crowd stampede accident. In addition, a chain effect of energy and a linkage effect within the crowd system is illustrated for in-depth understanding of crowd stampede risk.

This paper aims to examine whether and how females on the board of directors affect US-listed companies’ merger and acquisition (M&A) decisions. Specifically, the paper concerns the impact of females in the boardroom on the likelihood and type of M&A deals (i.e. foreign vs domestic acquisitions and listed vs unlisted acquisitions).

Archival data of M&A deals using a sample of 17,899 firm-year observations of the US public companies from 2012 to 2018 are collected and examined using probit and logit models.

This paper offers three main results supporting the propositions of the behavioral consistency theory. First, female directors are negatively associated with the likelihood of making the acquisition. Second, female directors are positively associated with acquiring domestic rather than foreign targets. Third, female directors are positively associated with acquiring listed rather than unlisted targets.

The findings provide additional evidence-based insights into the debate about diversity on boards with the aim of informing policy and offering practical recommendations for the effective implementation of gender diversity on the boards of companies.

Overall, consistent with the premise of behavioral theory, the results expand the literature on gender diversity by augmenting the argument that females’ behavior in corporate policies is viewed as opposition to change and a tendency toward risk aversion and thus, influences companies’ strategic investment decisions, such as M&A.

In the context of a retailer with an international supply network, this paper develops theories of (a) how both stability and strong ties in an international supply network combine to yield a resource base that enables the development of flexible relationships with suppliers, (b) how stability and relationship flexibility in the international supply network of a retail reseller may in turn be driven both by the international diversity and by the density of the retailer’s supply network in a product category, and (c) how both the international diversity and the density of a retailer’s supply network may directly affect the likelihood of a retailer developing flexible relationships with its supplier. In doing so, this paper develops and presents six hypotheses and discusses some approaches to measurement of the underlying constructs and testing the hypothesized effects.

This study empirically investigates how ambidextrous innovations and their balancing affect the supply chain financing performance (SCFP) of small and medium-sized enterprises (SMEs), based on signaling theory. Moreover, this study explores the moderating effect of the breadth and depth of digital technology deployment on the relationship between ambidextrous innovations and the SCFP of SMEs.

A mixed-methods design is used, including a qualitative study and a quantitative study. Qualitative data have been collected from six multi-cases in different industries. Questionnaire data have been collected from 259 SMEs in China, and a multiple regression model is used to verify the research hypotheses.

The findings indicate that, in supply chain financing, both exploitative innovation and exploratory innovation are helpful in improving the SCFP of SMEs. For resource-constrained SMEs, a relative balance between exploitative innovation and exploratory innovation can help improve SCFP. The breadth of digital technology deployment can strengthen the relationship between exploitative innovation and SCFP, while the depth of digital technology deployment can weaken the relationship between exploratory innovation and SCFP. In addition, increasing the depth of digital technology deployment strengthens the positive correlation between the relative balance of ambidextrous innovations and SCFP.

To effectively obtain supply chain financing, SMEs can either concentrate their limited resources on a single type of innovation or use relative balance strategies to simultaneously pursue two innovations. In addition, in the process of obtaining supply chain financing by ambidextrous innovations, SMEs should appropriately deploy digital technologies.

This study first deconstructs the impact mechanism of ambidextrous innovation capabilities on SCFP based on signaling theory, and then discusses the balancing effect of ambidextrous innovations on SCFP in the cases of resource-constrained SMEs. This study also goes further and finds the negative moderating effect of digital technology deployment in the process of supply chain financing.

The purpose of this paper is to investigate the effect of lanthana (La₂O₃) on the mechanical and tribological properties of polyimide (PI).

The mechanical and tribological properties of PI nanocomposites filled with La₂O₃ were studied by molecular dynamic simulations to explore the deep mechanisms from an atomic or molecular view.

The results showed that the hardness of the PI matrix increased after La₂O₃ modification with a decrease of 72.4% nanoindentation depth. Besides, the friction coefficient of PI decreased by 72.2% after filling La₂O_3, and the shear deformation was largely reduced under the same conditions. The adsorption effect of La₂O₃ on the PI molecular, which reduced the atomic relative concentration, velocity, interaction with counterpart Fe layer and the temperature rise in the frictional interface, contributed to the improvement of the mechanical and tribological performance.

This study reveals the friction and wear mechanism of PI composites filled with rare earth oxide at the nanoscale.