Search results

Aiding employment is an important poverty reduction strategy in many countries’ social welfare systems, as this strategy can help empower the recipients with a better living standard, development and social inclusion. The purpose of this paper is to identify the most significant individual and systematic variables for the employment status of low-income groups in urban China.

The data of this study are drawn from “Social Policy Support System for Poverty-stricken Families in Urban and Rural China 2015” report. The Ministry of Civil Affairs of the People’s Republic of China appointed and funded the Institute of Social Science Survey (ISSS) at Peking University to deliver the related project and organize a research team to write the report. Multiple binary logistic regression analysis is adopted to identify both individual and systematic factors that affect the employment status among low-income groups in urban China.

According to the results of the binary logistic regression model, individual factors, including: gender; householder status; education; and self-rated health status, play a significant role in determining the employment status of low-income groups in urban China. Clearly, the impacts of individual factors are more influential to marginal families than to families entitled to receive Basic Living Allowance. In contrast, compared with marginal families, systematic factors are more influential to families entitled to receive Basic Living Allowance.

This study highlights the importance of precise poverty reduction strategy and the issue of “welfare dependence” among low-income groups in urban China. Policy recommendations derived from the findings are hence given, including: the promotion of family-friendly policies; the introduction of a smart healthcare system; the establishment of a Basic Living Allowance adjustment mechanism; and the provision of related social services.

Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural network (PINN), which was proposed to encode physical laws into neural networks, is a less data-demanding approach for flow field reconstruction. However, when the fluid physics is complex, it is tricky to obtain accurate solutions under the PINN framework. This study aims to propose a physics-based data-driven approach for time-averaged flow field reconstruction which can overcome the hurdles of the above methods.

A multifidelity strategy leveraging PINN and a nonlinear information fusion (NIF) algorithm is proposed. Plentiful low-fidelity data are generated from the predictions of a PINN which is constructed purely using Reynold-averaged Navier–Stokes equations, while sparse high-fidelity data are obtained by field or experimental measurements. The NIF algorithm is performed to elicit a multifidelity model, which blends the nonlinear cross-correlation information between low- and high-fidelity data.

Two experimental cases are used to verify the capability and efficacy of the proposed strategy through comparison with other widely used strategies. It is revealed that the missing flow information within the whole computational domain can be favorably recovered by the proposed multifidelity strategy with use of sparse measurement/experimental data. The elicited multifidelity model inherits the underlying physics inherent in low-fidelity PINN predictions and rectifies the low-fidelity predictions over the whole computational domain. The proposed strategy is much superior to other contrastive strategies in terms of the accuracy of reconstruction.

In this study, a physics-informed data-driven strategy for time-averaged flow field reconstruction is proposed which extends the applicability of the PINN framework. In addition, embedding physical laws when training the multifidelity model leads to less data demand for model development compared to purely data-driven methods for flow field reconstruction.

This paper attempts to explain the phenomenon that Macau has a parliament (Legislative Assembly) and mass suffrage but no political parties.

This paper reviews the development process of “parliament – mass suffrage – political party” in Hong Kong and Macau and tries to explain why Macau does not have a party using comparative research methods.

The political party development of Hong Kong and Macau was influenced by both the (former) colonial power and China, and whether there were political parties in these two regions was the result of the game between China and the (former) colonial power. China hoped to limit the development of party politics in the two regions. Since Britain felt reluctant to cooperate with China, political parties in Hong Kong developed. At the same time, Portugal chose to defer to China, which led Macau not to have a political party.

Existing studies have yet to explain why there are no political parties in Macau, and this paper is the first attempt to do so.

Library intelligence institutions, which are a kind of traditional knowledge management organization, are at the frontline of the big data revolution, in which the use of unstructured data has become a modern knowledge management resource. The paper aims to discuss this issue.

This research combined theme logic structure (TLS), artificial neural network (ANN), and ensemble empirical mode decomposition (EEMD) to transform unstructured data into a signal-wave to examine the research characteristics.

Research characteristics have a vital effect on knowledge management activities and management behavior through concentration and relaxation, and ultimately form a quasi-periodic evolution. Knowledge management should actively control the evolution of the research characteristics because the natural development of six to nine years was found to be difficult to plot.

Periodic evaluation using TLS-ANN-EEMD gives insights into journal evolution and allows journal managers and contributors to follow the intrinsic mode functions and predict the journal research characteristics tendencies.

This research mainly aims to test the impact of two leadership styles (ethical and servant leadership) on employee creativity; and to determine whether organizational citizenship behavior (OCB) mediates the relationships between them.

The paper relied on a quantitative research approach with a sample of 213 staff from public universities in the United Arab Emirates. In this paper, the partial least square approach (PLS-SEM) was employed in order to verify the proposed hypotheses.

The outcomes confirmed that OCB has a positive impact on employee creativity. Additionally, the findings indicated that ethical leadership positively affected OCB and employee creativity. It was also confirmed that servant leadership has a significant positive impact on OCB and employee creativity. Finally, the findings revealed that OCB fully mediates the linkages among servant and ethical leadership and employee creativity.

This paper acknowledges the existing gaps in the prior literature, and enables us to understand clearly about the significance of ethical as well as servant leadership in affecting employee creativity via OCB as a mediator.