Search results

The safety assessment of engineering structures under repeated variable dynamic loads such as seismic and wind loads can be considered as a dynamic shakedown problem. This paper aims to extend the stress compensation method (SCM) to perform lower bound dynamic shakedown analysis of engineering structures and a double-closed-loop iterative algorithm is proposed to solve the shakedown load.

The construction of the dynamic load vertexes is carried out to represent the loading domain of a structure under both dynamic and quasi-static load. The SCM is extended to perform lower bound dynamic shakedown analysis of engineering structures, which constructs the self-equilibrium stress field by a series of direct iteration computations. The self-equilibrium stress field is not only related to the amplitude of the repeated variable load but also related to its frequency. A novel double-closed-loop iterative algorithm is presented to calculate the dynamic shakedown load multiplier. The inner-loop iteration is to construct the self-equilibrated residual stress field based on the certain shakedown load multiplier. The outer-loop iteration is to update the dynamic shakedown load multiplier. With different combinations of dynamic load vertexes, a dynamic shakedown load domain could be obtained.

Three-dimensional examples are presented to verify the applicability and accuracy of the SCM in dynamic shakedown analysis. The example of cantilever beam under harmonic dynamic load with different frequency shows the validity of the dynamic load vertex construction method. The shakedown domain of the elbow structure varies with the frequency under the dynamic approach. When the frequency is around the resonance frequency of the structure, the area of shakedown domain would be significantly reduced.

In this study, the dynamical response of structure is treated as perfect elastoplastic. The current analysis does not account for effects such as large deformation, stochastic external load and nonlinear vibration conditions which will inevitably be encountered and affect the load capacity.

This study provides a direct method for the dynamical shakedown analysis of engineering structures under repeated variable dynamic load.

High-rise tower structures supported by side frame structure and viscous damper in chemical industry can produce plasticity under dynamic loads, such as wind and earthquake, which will heavily influence the long-term safety operation. This paper aims to systematically study the optimization design of these structures by free vibration and dynamic shakedown analysis.

The transfer matrix method and Euler–Bernoulli beam vibration are used to study the free vibration characteristic of the simplified high-rise tower structure. Then the extended stress compensation method is used to construct the self-equilibrated stress by using the dynamic load vertexes and the lower bound dynamic shakedown analysis for the structure with viscous damper. Using the proposed method, comprehensive parametric studies and optimization are performed to examine the shakedown load of high-rise tower with various supported conditions.

The numerical results show that the supported frame stiffness, attached damper or spring parameters influence the free vibration and shakedown characters of high-rise tower very much. The dynamic shakedown load is lowered down quickly with external load frequency increasing to the fundamental natural frequency of the structure under spring supported condition, while changed little with the damping connection. The optimized location and parameter of support are obtained under dynamical excitations.

In this study, the high-rise tower structure is simplified as a cantilever beam supported by a short cantilever beam and a damper under repeated dynamic load, and linear elasticity for solid is assumed for free vibration analysis. The current analysis does not account for effects such as large deformation, stochastic external load and nonlinear vibration conditions which will inevitably be encountered and affect the load capacity.

This study provides a comprehensive method for the dynamical optimization of high-rise tower structure by combining free vibration and shakedown analysis.

The purpose of this research is to investigate how the consumer’s trusting mechanism influences their behavioral adoption intention in the context of genetic testing.

Based on the technology acceptance theory and trust formation theory, the research posits and develops a comprehensive trust model by integrating trust-related factors that correlate to the consumer’s trusting beliefs and trusting intention. Survey data with 525 respondents allow to test and validate the model.

The tested model shows that technology institutional trust base, end-user’s cognitive trust base and social influence are significant determinants of trusting beliefs. The findings also reveal that mediation effects of performance expectancy and perceived risks exist in the relationship between trusting beliefs and trusting intention.

The foreseeable positive impact and rapid market growth of emerging healthcare technologies necessitate the strong need to study user acceptance. However, there is a lack of research on how consumers trust and their adoption intention of such innovations. Prior empirical evidence from different contexts and perspectives also show contradictory findings. This research extends the existing technology acceptance literature to a healthcare context, provides an improved generalized understanding of the consumer’s trusting mechanism in emerging biotechnology and discusses practical insights for regulatory authorities, healthcare institutes and medical professionals.

This paper according to the logic of the “digital access divide--digital capability divide--digital outcome divide” aims to systematically discuss the impact of the digital divide on individual happiness in China, accounting for the variations that exist across different groups, as well as the corresponding mechanisms.

This paper presents cross-sectional analyses of the relationship between the digital divide and individual happiness in China. The analyses are based on data from the Chinese General Social Survey 2017, which academic institutions run on the Chinese Mainland. This database contains information on respondents' Internet access, skills and consequences of use, which can measure the digital divide of Chinese individuals at three levels.

First, individual happiness declined when they experienced the digital access divide in China. For the digital capability divide, the lower the usage skills, the more individual happiness declined. When analyzing the digital outcome divide, the greater the negative consequences, the more individual happiness declined. Second, the impacts of digital access, capability and outcome divide vary according to age, gender, education degrees, hukou, region and sub-dimensions. Third, the digital access and capability divide reduce individuals' happiness by lowering their self-rated social and economic status, whereas the digital outcome divide reduce individual happiness by lowering their fairness perception and social trust.

The authors believe that this is the first study to examine the impact and its variations among different groups of the three-level digital divide on individual happiness, as well as its mechanisms.

I study a series of restructuring activities undertaken by Guoco Group Limited in recent years and the implications on minority shareholders. The divestment of Dao Heng Bank Group to DBS Group reaped substantial benefits for Guoco, including an enormous cash reserves to fund future investments. However, the cash hoard was not implemented to the best use by Guoco’s managers. Subsequently, Guoco was involved in a number of share buybacks schemes. The share‐buybacks met strong resistance from the minority shareholders and eventually forced out the second largest shareholders. Guoco was also engaged in related party transaction involving its subsidiaries in the property development business. Overall, I find evidences suggesting that corporate restructuring activities enhance the controlling owner’s grip on the group at the expense of the minority shareholders.

The purpose of this paper is to identify and synthesize major streams of research on quality of student experience in higher education, to present an agenda for future research.

The paper presents a systematic review of research published in high-quality journals during the period 2000 to 2014 in the areas of quality of student experience and higher education.

Findings highlight current research trends on the quality of student experience in higher education. Results show five prevailing research streams: exploration of learning experience; exploration of student experience; gender differences in assessment of higher education experience; improvement in quality of student experience; and student satisfaction with higher education experience.

The identification of the five research streams provides the basis for a synthesis of key issues identified within each research stream. In addition, the identification of purposes and limitations in existing research supports attempts to address issues of the quality of student experiences in higher education.

Literature currently portrays the quality of student experience as a student-centric idea. Together with the purposes and limitations identified in existing research, the paper proposes an agenda for future research that increases the variety of research streams to provide a deeper understanding of the student experience and to enhance the delivery of quality in higher education.

The findings contribute to the research scene by providing important insights in terms of the current trends and focus of existing research in the area of quality of student experiences in higher education.

In 2013, Chinese president Xi Jinping proposed the concept of “One Belt and One Road” economic cooperation. “The Belt and Road Initiative (B&R)” is the short of “The Silk Road Economic Belt” and the “21st-century Maritime Silk Road,” which has got a series of remarkable achievements and worldwide attentions in past five years such as Asian Infrastructure Investment Bank, China–Pakistan Economic Corridor, B&R Forum for International Cooperation, etc. Especially, cross-border EC has greatly strengthened the trade links between countries along the way, which is a rare chance for Chinese Export-oriented Cross-border EC’s rapid growth. Thus, the authors take DHgate.com as a typical example to do a big data analysis. This chapter analyzes vast data from 2013 to 2017 about seven kinds of commodities including Fashion accessories, Jewelry, Sports & Outdoors, Security & Surveillances, Car accessories, Watches, and Hair & Styling by using data mining related software and algorithms. The authors do some monthly sale charts and find a few counter-intuitive but useful conclusions such as by taking association analysis, the study shows that sports products and jewelry products have strong association rules. In addition, for potential products (such as Fashion accessories and Jewelry), although their sales have a certain shock, the overall selling line keep rising. It is possible to put forward some practical suggestions for Chinese Export-oriented Cross-border EC that actively respond to the One Belt One Road Initiative based on these analysis results.

This study aims to investigate the relationships between radical innovation, market forces and political/business relationships in China by combining social capital theory and contingent theory. The paper focuses on how two types of managerial ties (i.e. business and political ties) impact firms’ capacity for radical innovation. It also examines the different moderating effects of market forces (i.e. demand uncertainty, technological turbulence and competitive intensity) on the linkage of managerial ties with radical innovation in the Chinese transitional context.

A systematic literature review on managerial ties, radical innovation and market forces in emerging markets provides the theoretical foundation of our conceptual model and hypothesis. Using a survey sample of 119 Chinese firms, the authors conduct a regression analysis on the theoretical model and hypotheses.

The results show that business ties have an inverted U-shape effect on radical innovation, while political ties have a positive impact on radical innovation. Furthermore, the market forces in transitional economies (i.e. demand uncertainty, technological turbulence and competitive intensity) have different moderating effects on the relationships between two types of managerial ties and Chinese firms’ radical innovation.

This study adopts its data set from the Chinese context. It would be necessary to replicate this research in other transitional economies because of specific differences between China and other transitional economies.

Findings from our study indicate that firms which wish to succeed in radical innovation may need to adapt their tie-based strategies according to different market settings.

The paper is original in its comparative investigation of the effect of business ties and political ties on radical innovation in contingent transitional market environments using a combination of social capital and contingent theories.

Climate change, deforestation and hydropower dams are contributing to environmental change in the Lower Mekong River region, the combined effects of which are felt by many rural Cambodians. How people perceive and manage the effects of environmental change will influence future adaptation strategies. The objective of this research was to investigate whether the use of a low-cost, explicitly spatial method (participatory mapping) can help identify locally relevant opportunities and challenges to climate change adaptation in small, flood-prone communities. Four villages along the banks of the Mekong River in Kratie Province, Cambodia, were the subject of this research. To identify perceived environmental hazards and adaptive responses, eight workshops were conducted using focus-group interviews and participatory mapping. The communities’ responses highlight the evolving nature of environmental hazards, as droughts increase in perceived importance while the patterns of wet season flooding were also perceived to be changing. The attribution of the drivers of these hazards was strongly skewed towards local factors such as deforestation and less towards regional or global drivers affecting the hydrology of the Mekong and climate patterns. Combining participatory mapping with focus-group interviews allowed a greater depth of understanding of the vulnerabilities and opportunities available to communities than reliance on a single qualitative method. The study highlights the potential for a bottom-up transfer of information to strengthen existing climate change policies and tailor adaptation plans to local conditions.

The COVID-19 has become a global pandemic, which has caused large number of deaths and huge economic losses. These losses are not only caused by the virus but also by the related rumors. Nowadays, online social media are quite popular, where billions of people express their opinions and propagate information. Rumors about COVID-19 posted on online social media usually spread rapidly; it is hard to analyze and detect rumors only by artificial processing. The purpose of this paper is to propose a novel model called the Topic-Comment-based Rumor Detection model (TopCom) to detect rumors as soon as possible.

The authors conducted COVID-19 rumor detection from Sina Weibo, one of the most widely used Chinese online social media. The authors constructed a dataset about COVID-19 from January 1 to June 30, 2020 with a web crawler, including both rumor and non-rumors. The rumor detection task is regarded as a binary classification problem. The proposed TopCom model exploits the topical memory networks to fuse latent topic information with original microblogs, which solves the sparsity problems brought by short-text microblogs. In addition, TopCom fuses comments with corresponding microblogs to further improve the performance.

Experimental results on a publicly available dataset and the proposed COVID dataset have shown superiority and efficiency compared with baselines. The authors further randomly selected microblogs posted from July 1–31, 2020 for the case study, which also shows the effectiveness and application prospects for detecting rumors about COVID-19 automatically.

The originality of TopCom lies in the fusion of latent topic information of original microblogs and corresponding comments with DNNs-based models for the COVID-19 rumor detection task, whose value is to help detect rumors automatically in a short time.