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This study analyzes the causal effect of education on consumers' cognition and attitudes toward genetically modified (GM) foods.

The authors propose an analytical framework to clarify the role of education levels and education content in the formation of attitudes toward GM foods and utilize education reforms in China as natural experiments to test the theoretical predictions empirically. For education levels, the authors use Compulsory Education Law's implementation to construct the instrument variable. For education content, the authors utilize the revision of the biology textbook in the Eighth Curriculum Reform to implement staggered difference-in-difference estimation. The authors use two national household surveys, the China Genuine Progress indicator Survey (CGPiS) and the China Household Finance Survey (CHFS) of 2017, combined with provincial-level data of education reforms.

The education level, instrumented by the Compulsory Education Law's implementation, has an insignificant effect on consumers' cognition and attitudes toward GM foods, whereas the acquisition of formal education on genetic science, introduced by the Eighth Curriculum Reform, has a statistically significant and positive influence.

This is the first study to investigate the causal effects of education level and content on consumers' cognition and attitude toward GM foods using national representative data. It is also the first to evaluate the long-term effects of the biology textbook reform in China. The findings help open the black box of how education shapes people's preferences and attitudes and highlight the significance of formal biology education in formulating consumers' willingness to accept GM foods.

A diffusion-reaction-deformation coupled model is employed and implemented as a user-defined element (UEL) subroutine in the commercial finite element software package ABAQUS.

Chemical reaction and diffusion are treated as two distinct processes by introducing the extent of reaction and the diffusion concentration as two kinds of independent variables, for which the independent governing equations for chemical reaction and diffusion processes are obtained. Furthermore, an exponential form of chemical kinetics, instead of the linearly phenomenological relation, between the reaction rate and the chemical affinity is used to describe reaction process. As a result, complex chemical reaction can be simulated, no matter it is around or away from equilibrium.

Two numerical examples are presented, one for validation of the model and another for the modeling of the deflection of a plane caused by a chemical reaction.

1. Independent governing equations for diffusion and reaction processes are given. 2. An exponential relation between the reaction rate and its driving force is employed. 3. The UEL subroutine is used to implement the finite element procedure.

Poor bending response is a major shortcoming of lower-order elements due to excessive representation of shear stress/strain field. Advanced finite element (FE) formulations for classical elasticity enhance the bending response by either nullifying or filtering some of the symmetric shear stress/strain modes. Nevertheless, the stress/strain field in Cosserat elasticity is asymmetric; consequently any attempt to nullify or filter the anti-symmetric shear stress/strain modes may lead to failure in the constant couple-stress patch test where the anti-symmetric shear stress/strain field is linear. This paper aims at enhancing the bending response of lower-order elements for Cosserat elasticity problems.

A four-node quadrilateral and an eight-node hexahedron are formulated by hybrid-stress approach. The symmetric stress is assumed as those of Pian and Sumihara and Pian and Tong. The anti-symmetric stress components are first assumed to be completely linear in order to pass the constant couple-stress patch test. The linear modes are then constrained with respect to the prescribed body-couple via the equilibrium conditions.

Numerical tests show that the hybrid elements can strictly pass the constant couple-stress patch test and are markedly more accurate than the conventional elements as well as the incompatible elements for bending problems in Cosserat elasticity.

This paper proposes a hybrid FE formulation to improve the bending response of four-node quadrilateral and eight-node hexahedral elements for Cosserat elasticity problems without compromising the constant couple-stress patch test.

In recent years, the convolutional neural network (CNN) based deep learning approach has succeeded in data-mining the relationship between microstructures and macroscopic properties of materials. However, such CNN models usually rely heavily on a large set of labeled images to ensure the accuracy and generalization ability of the predictive models. Unfortunately, in many fields, acquiring image data is expensive and inconvenient. This study aims to propose a data augmentation technique to enhance the performance of the CNN models for linking microstructural images to the macroscopic properties of composites.

Microstructures of composites are synthesized using discrete element simulations and Potts kinetic Monte Carlo simulations. Macroscopic properties such as the elastic modulus, Poisson's ratio, shear modulus, coefficient of thermal expansion, and triple-phase boundary length density are extracted on representative volume elements. The CNN model is trained using the 3D microstructural images as inputs and corresponding macroscopic properties as the labels. The comparison of the predictive performance of the CNN models with and without data augmentation treatment are compared.

The comparison between the prediction performance of CNN models with and without data augmentation showed that the former reduced the weighted mean absolute percentage error (WMAPE) for the prediction from 5.1627% to 1.7014%. This significant reduction signifies that the proposed data augmentation method can effectively enhance the generalization ability and robustness of CNN models.

This study demonstrates that data augmentation is beneficial for solving the problems of model overfitting, data scarcity, and sample imbalance for CNN-based deep learning tasks at a low cost. By developing more and advanced data augmentation techniques, deep learning accelerated homogenization will boost the multi-scale computational mechanics and materials.

Although the literature has highlighted that a firm’s board is critical for firm innovation, the impact of board characteristics on firm innovation has always been examined separately, leading to inconclusive research results. Based on the complexity theory, this paper incorporates four board characteristics, including board size, board ownership, board independence and CEO duality, to examine the impact of the combinations of different board characteristics on firm innovation through qualitative comparative analysis.

Using the panel data of listed manufacturing firms in China from 2007 to 2022, this paper conducted the fuzzy set qualitative comparative analysis to test the proposed hypotheses.

The research results show that no single board characteristic can explain firm innovation, as board size, board ownership, board independence and CEO duality can lead to either positive or negative firm innovation. Moreover, firm innovation depends on a complex combination of board characteristics.

This paper makes the following contributions: Firstly, this paper advances the firm innovation literature by extending the role of board characteristics on firm innovation, thereby offering a new way to model firm innovation in terms of board characteristics. Secondly, this paper provides a more comprehensive account of the role of a firm’s board by integrating agency theory and resource dependence theory. Thirdly, this paper also identifies a promising avenue for further research in the field of corporate governance: the investigation of other contingency contexts in which the effect of board characteristics may be observed, with the aim of further increasing the understanding of board functioning.

This study examines the mediating roles of the three dimensions of business intelligence (sensing capability, transforming capability and driving capability) in the relationship between the three dimensions of big data analytics capability (big data analytics management, technology and talent capabilities), and radical innovation among Chinese manufacturing enterprises.

A theoretical framework was developed using the resource-based view. The hypothesis was tested using empirical survey data from 326 Chinese manufacturing enterprises.

Empirical results show that, in the Chinese manufacturing context, business intelligence sensing capability, business intelligence transforming capability and business intelligence driving capability positively mediate the impact of big data analytics capability on radical innovation.

The results offer managerial guidance for leaders to properly use big data analytics capability, business intelligence and radical innovation as well as offering theoretical insight for future research in the manufacturing industry’s radical innovation.

This is among the first studies to examine three dimensions of big data analytics capability on the manufacturing industry’s radical innovation by considering the mediating role of three dimensions of business intelligence.

This study aims to offer insights regarding the impact of emotional conflict on innovation behavior. This study also explores the boundary conditions by examining the moderating effects of leader-member exchange (LMX) and team-member exchange (TMX) on the relationship between emotional conflict and innovation behavior.

This study used a questionnaire survey to collect data in China. Hypotheses were tested using hierarchical regression analysis. To test for inverted U-shaped relationship between emotional conflict and innovation behavior, the authors computed the squared term for emotional conflict. To investigate moderating roles of LMX and TMX, the authors carried out an interaction term between the main effect variables (emotional conflict and emotional conflict²) and the moderating variables (LMX and TMX).

The empirical findings indicated that emotional conflict had an inverted U-shaped relationship with innovation behavior. Furthermore, LMX and TMX moderated the inverted U-shaped relationship between the emotional conflict and innovation behavior in such a way that the inverted U-shaped relationship was flatter in high-quality LMX and TMX than in low-quality LMX and TMX. That is to say, LMX and TMX may dampen the positive effects of lower levels of emotional conflict on innovation behavior; yet, it may also weaken the negative effects of higher levels of emotional conflict on innovation behavior.

This research can be extended in several ways. First, future research can investigate the impact mechanism of emotional conflict on innovation behavior. Second, future research can analyze other types of moderators at different levels. The last but not the least, future research can test the results using heterogeneous samples. Despite these potential limitations, this study provides an elaborate understanding of the conflict–creativity relationship by outlining the inverted U-shaped relationship between emotional conflict and innovation behavior under the LMX and TMX contexts, which can make important contributions to the conflict management literature.

The findings of this study offer some guidance on how to stimulate innovation behavior through emotional conflict. It suggests that managers should maintain the emotional conflict at the moderate level. Furthermore, managers can strengthen the LMX and TMX to avoid the negative effects of high levels of emotional conflict, and several practices are provided as well.

This study develops an exhaustive understanding of the conflict–creativity relationship by figuring the curvilinear relationship between emotional conflict and innovation behavior, which is the response to the call of Posthuma to focus on the outcomes of conflict management. The findings further provide an empirical evidence of the conceptual argument that the consequences of conflict depend on the situational context by pointing out the important contingency factors of LMX and TMX.

This paper aims to study the mechanism of hydrogen embrittlement in 12Cr2Mo1R(H) steel, which will help to provide valuable information for the subsequent hydrogen embrittlement research of this kind of steel, so as to optimize the processing technology and take more appropriate measures to prevent hydrogen damage.

The hydrogen diffusion coefficient of 12Cr2Mo1R(H) steel was measured by the hydrogen permeation technique of double electrolytic cells. Moreover, the influence of hydrogen traps in the material and experimental temperature on hydrogen diffusion behavior was discussed. The first-principles calculations based on density functional theory were used to study the occupancy of H atoms in the bcc-Fe cell, the diffusion path and the interaction with vacancy defects.

The results revealed that the logarithm of the hydrogen diffusion coefficient of the material has a linear relationship with the reciprocal of temperature and the activation energy of hydrogen atom diffusion in 12Cr2Mo1R(H) steel is 23.47 kJ/mol. H atoms stably exist in the nearly octahedral interstices in the crystal cell with vacancies. In addition, the solution of Cr/Mo alloy atom does not change the lowest energy path of H atom, but increases the diffusion activation energy of hydrogen atom, thus hindering the diffusion of hydrogen atom. Cr/Mo and vacancy have a synergistic effect on inhibiting the diffusion of H atoms in α-Fe.

This article combines experiments with first-principles calculations to explore the diffusion behavior of hydrogen in 12Cr2Mo1R(H) steel from the macroscopic and microscopic perspectives, which will help to establish a calculation model with complex defects in the future.

The purpose of this paper is to propose a sub-pixel calibration method for a microassembly system with coaxial alignment function (MSCA) because traditional sub-pixel calibration approaches cannot be used in this system.

The in-house microassembly system comprises a six degrees of freedom (6-DOF) large motion serial robot with microgrippers, a hexapod 6-DOF precision alignment worktable and a vision system whose optical axis of the microscope is parallel with the horizontal plane. A prism with special coating is fixed in front of the objective lens; thus, two parts’ Figures, namely the images of target and base part, can be acquired simultaneously. The relative discrepancy between the two parts can be calculated from image plane coordinate instead of calculating space transformation matrix. Therefore, the traditional calibration method cannot be applied in this microassembly system. An improved calibration method including the check corner detection solves the distortion coefficient conversely. This new way can detect the corner at sub-pixel accuracy. The experiment proves that the assembly accuracy of the coaxial microassembly system which has been calibrated by the new method can reach micrometer level.

The calibration results indicate that solving the distortion conversely could improve the assembly accuracy of MSCA.

The paper provides certain calibration methodological guidelines for devices with 2 dimensions or 2.5 dimensions, such as microelectromechanical systems devices, using MSCA.