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This paper aims to study the energy absorption properties of the thin-walled square tube with lateral piecewise variable thickness under axial crashing and the influence of the tube parameters on energy absorption.

In this work, the energy absorption properties of the thin-walled square tube were analyzed by theoretical, numerical and experimental approach. The numerical results are obtained based on the finite element method. The explicit formulation for predicting the mean crushing force of the tube with lateral piecewise variable thickness was derived based on Super Folding Element method. The limitation of the prediction formulation was analyzed by numerical calculation. The numerical calculation was also used to compare the energy absorption between the tube with lateral piecewise variable thickness and other tubes, and to carry out the parametric analysis.

Results indicate that the thin-walled tube with lateral piecewise variable thickness has higher energy absorption properties than the uniform thickness tubes and the tubes with lateral linear variable thickness. The thickness of the corner is the key factor for the energy absorption of the tubes. The thickness of the non-corner region is the secondary factor. Increasing the corner thickness and decreasing the non-corner thickness can make the energy absorption improved. It is also found that the prediction formulation of the mean crushing force given in this paper can quickly and accurately predict the energy absorption of the square tube.

The outcome of the present research provides a design idea to improve the energy absorption of thin-walled tube by designing cross-section thickness and gives an explicit formulation for predicting the mean crushing force quickly and accurately.

Recently, deep learning (DL) has been widely applied in various aspects of human endeavors. However, studies have shown that DL models may also be a primary cause of data leakage, which raises new data privacy concerns. Membership inference attacks (MIAs) are prominent threats to user privacy from DL model training data, as attackers investigate whether specific data samples exist in the training data of a target model. Therefore, the aim of this study is to develop a method for defending against MIAs and protecting data privacy.

One possible solution is to propose an MIA defense method that involves adjusting the model’s output by mapping the output to a distribution with equal probability density. This approach effectively preserves the accuracy of classification predictions while simultaneously preventing attackers from identifying the training data.

Experiments demonstrate that the proposed defense method is effective in reducing the classification accuracy of MIAs to below 50%. Because MIAs are viewed as a binary classification model, the proposed method effectively prevents privacy leakage and improves data privacy protection.

The method is only designed to defend against MIA in black-box classification models.

The proposed MIA defense method is effective and has a low cost. Therefore, the method enables us to protect data privacy without incurring significant additional expenses.

The purpose of this study is to identify factors that may influence Chinese customers' online shopping satisfaction, including those which are ignored by prior studies, from the perspective of total online shopping experience.

In this paper, the authors propose a model of the satisfaction process in the e‐commerce environment, identifying key constructs proposed by prior studies and developing hypotheses about which dimensions of online retailer constructs are significant predictors of online shopper satisfaction. The authors test the hypotheses through multiple regression analysis based on a survey of 1,001 online customers.

The analysis suggests that eight constructs – information quality, web site design, merchandise attributes, transaction capability, security/privacy, payment, delivery, and customer service – are strongly predictive of online shopping customer satisfaction, while the effect of response time is not significant.

This study does not control the differences across product categories; the use of self‐reported scales to measure both independent and dependent variables may imply the possibility of a common method bias for the results.

This research contributes to the study of online shopping customer satisfaction by: developing a model of the satisfaction process in the e‐commerce environment, and identifying factors that may influence Chinese customers' online shopping satisfaction including those which are ignored by prior studies.

Amid rising environmental concerns, Industry 4.0 and blockchain technology (BCT) are transforming circular economy practices and prevailing business models. Recognizing the same, the current study examines the role of advanced technology in circular practices and their impact on eco-environmental performance, which influences organizational performance. The study collects data from 185 food processing enterprises that are located in Malaysian territories. By employing CB-SEM modeling, the study provides three key findings. First, Industry 4.0 significantly improves the circular economy practices. Second, circular economy practices help to improve firms' environmental performance but did not stimulate operational performance. Third, higher eco-environmental performance significantly boosts organizational performance. This study set out the foundations for participating countries/firms that help to achieve sustainable goals through the integration of blockchain technology in circular economy practices.

Given the unique cultural-political context of China, this paper aims to investigate two research questions: What has been the development trajectory of policy-making on consumer privacy protection in China, and what factors have shaped its development over the years?

This paper adopts a historical approach and examines the development of Chinese consumer privacy policy during four periods: 1980s, 1990s, 2000s and 2010-present.

Chinese policy-making on consumer privacy protection has made steady advancement in the past few decades due to factors such as technological development, elite advocacy and emulation of other markets; however, the effects of these factors are conditioned by local forces.

To date, most studies of consumer privacy issues have focused on Western countries, especially the European Union and the USA. A better understanding of how consumer privacy policy has developed in China provides important lessons on the promotion of consumer privacy protection in other developing countries.

The study aims to understand how cultural route heritage is conceptualized and managed in China by systematically reviewing the research literature on Chinese cultural route heritage (CRH). The study intends to inspire further discussion on the theoretical and practical development of cultural routes since the development is still at a liminal stage in China.

A total of 253 research articles related to Chinese cultural rote heritage from major Chinese and English research databases China National Knowledge Infrastructure (CNKI), Web of Science (WOS) and Scopus have been comprehensively identified and reviewed for the purpose of the study.

Four major themes of research on Chinese CRH have been identified: conceptual evaluation, list of the routes and characteristics of the routes, conservation and utilization. The results revealed that China has very rich resources in CRH, many of which were formed a long time ago, which exist across vast geographic regions and have assumed multiple functions and undergone dynamic reciprocal exchanges among diverse cultures and ethnicities.

The paper summarizes some major obstacles faced by CRH in China and proposes a strategic model to address the need for a more sustainable development of CRH in the Chinese context.

The paper offers a comprehensive overview of CRH in China and discusses practical issues in management and development of heritage great in size, number and complexity.

Grey modeling technique is an important element of grey system theory, and academic articles applied to agricultural science research have been published since 1985, proving the broad applicability and effectiveness of the technique from different aspects and providing a new means to solve agricultural science problems. The analysis of the connotation and trend of the application of grey modeling technique in agricultural science research contributes to the enrichment of grey technique and the development of agricultural science in multiple dimensions.

Based on the relevant literature selected from China National Knowledge Infrastructure, the Web of Science, SpiScholar and other databases in the past 37 years (1985–2021), this paper firstly applied the bibliometric method to quantitatively visualize and systematically analyze the trend of publication, productive author, productive institution, and highly cited literature. Then, the literature is combed by the application of different grey modeling techniques in agricultural science research, and the literature research progress is systematically analyzed.

The results show that grey model technology has broad prospects in the field of agricultural science research. Agricultural universities and research institutes are the main research forces in the application of grey model technology in agricultural science research, and have certain inheritance. The application of grey model technology in agricultural science research has wide applicability and precise practicability.

By analyzing and summarizing the application trend of grey model technology in agricultural science research, the research hotspot, research frontier and valuable research directions of grey model technology in agricultural science research can be more clearly grasped.

Past studies on housework and marital studies seldom considered the possible endogeneity between these two factors. This chapter analyses data of the Women’s Status Survey 2010 to investigate the association between satisfaction with family status and housework participation in dual-earner married couples in China. The authors examine the association by ordinary least squares (OLS) regression models and structural equation models (SEM), taking into account of time constraints, economic resources and other demographic characteristics. Results suggest that both men and women are less satisfied with their family status if they share more housework than their partners, after controlling for household income, relative economic contribution, educational qualifications and other factors. Moreover, relative housework contribution is associated more consistently and significantly with satisfaction with family status than absolute housework time. In SEM, the authors include a correlated error term between housework time and satisfaction in the models to take endogeneity between these factors into account. For both urban men and women, relative contribution of housework, but not absolute time of housework, is still negatively associated with family status satisfaction.

In multidisciplinary design optimization (MDO), if the relationships between design variables and some output parameters, which are important performance constraints, are complex implicit problems, plenty of time should be spent on computationally expensive simulations to identify whether the implicit constraints are satisfied with the given design variables during the optimization iteration process. The purpose of this paper is to propose an ensemble of surrogates-based analytical target cascading (ESATC) method to tackle such MDO engineering design problems with reduced computational cost and high optimization accuracy.

Different surrogate models are constructed based on the sample point sets obtained by Latin hypercube sampling (LHS) method. Then, according to the error metric of each surrogate model, the repeated ensemble of surrogates is constructed to approximate the implicit objective functions and constraints. Under the framework of analytical target cascading (ATC), the MDO problem is decomposed into several optimization subproblems and the function of analysis module of each subproblem is simulated by repeated ensemble of surrogates, working together to find the optimum solution.

The proposed method shows better modeling accuracy and robustness than other individual surrogate model-based ATC method. A numerical benchmark problem and an industrial case study of the structural design of a super heavy vertical lathe machine tool are utilized to demonstrate the accuracy and efficiency of the proposed method.

This paper integrates a repeated ensemble method with ATC strategy to construct the ESATC framework which is an effective method to solve MDO problems with implicit constraints and black-box objectives.