Search results

The purpose of this paper is to review land/real estate registration practice in Hong Kong, and make an in-depth comparison with Mainland China and finally provide helpful suggestions for the government.

Research methods including document analysis/review and comparative study are used in this paper.

The main findings focus on the problems existing in the mainland, including narrow query subject, single way of query, limited query time, and lacking of incentive mechanism. Helpful suggestions for real estate registration system in Mainland China are offered based on the comparative study.

The unified registration system can improve the efficiency of administrative institutions to ensure an open and transparent environment of property right registration, which helps prevent the relevant departments from abusing administrative power and harming the interests of obligees. The findings of this research can serve as a useful reference for policy makers to improve the unified registration system in China.

The registration system/mechanism determines the efficiency and effectiveness of real estate/land market. However, land registration and query in some countries such as Mainland China have institutional problems which hinder the sustained and healthy development of the real estate industry. The value of this paper is to propose constructive suggestions for such countries/regions by comparing and learning from a good model.

Community question answering (CQA) platforms play a significant role in knowledge dissemination and information retrieval. Expert recommendation can assist users by helping them find valuable answers efficiently. Existing works mainly use content and user behavioural features for expert recommendation, and fail to effectively leverage the correlation across multi-dimensional features.

To address the above issue, this work proposes a multi-dimensional feature fusion-based method for expert recommendation, aiming to integrate features of question–answerer pairs from three dimensions, including network features, content features and user behaviour features. Specifically, network features are extracted by first learning user and tag representations using network representation learning methods and then calculating questioner–answerer similarities and answerer–tag similarities. Secondly, content features are extracted from textual contents of questions and answerer generated contents using text representation models. Thirdly, user behaviour features are extracted from user actions observed in CQA platforms, such as following and likes. Finally, given a question–answerer pair, the three dimensional features are fused and used to predict the probability of the candidate expert answering the given question.

The proposed method is evaluated on a data set collected from a publicly available CQA platform. Results show that the proposed method is effective compared with baseline methods. Ablation study shows that network features is the most important dimensional features among all three dimensional features.

This work identifies three dimensional features for expert recommendation in CQA platforms and conducts a comprehensive investigation into the importance of features for the performance of expert recommendation. The results suggest that network features are the most important features among three-dimensional features, which indicates that the performance of expert recommendation in CQA platforms is likely to get improved by further mining network features using advanced techniques, such as graph neural networks. One broader implication is that it is always important to include multi-dimensional features for expert recommendation and conduct systematic investigation to identify the most important features for finding directions for improvement.

This work proposes three-dimensional features given that existing works mostly focus on one or two-dimensional features and demonstrate the effectiveness of the newly proposed features.

This paper aims to propose a double-sided switched reluctance linxear generator (DSRLG) exclusively for wave power generation. The initial dimensions are given through design experience and principles. To ameliorate comprehensive performance of the DSRLG, the multi-objective optimization design is processed.

The multi-objective optimization design of the DSRLG is processed by adopting a modified entropy technique for order of preference by similarity to ideal solution (TOPSIS) algorithm. First, sensitivity analyzes on geometric parameters of the DSRLG are conducted to determine several pivotal geometric parameters as optimization variables. Then, the multi-objective optimization is conducted on the basis of initial dimensions. After determination of synthetical evaluation value of each structure parameter, the best dimension scheme of the DSRLG is concluded.

After verification by finite element method simulation and dynamic simulation, the final dimension scheme proves to perform better than the initial scheme. Finally, experiments are conducted to verify the accuracy of both the stable finite element DSRLG model and dynamic simulation system model so that the conclusion of this paper proves to be reliable and compelling.

This paper proposes an improved structure of the DSRLG, which is superior for wave power generation. Meanwhile, a novel modified entropy TOPSIS algorithm is applied to the field of electrical machine multi-objective optimal design for the first time.

User engagement is critical for online health Q&A communities. Financial incentives, which vary across different communities and reward schemes, are expected to motivate such contribution behaviors. Even though financial incentives have been extensively examined in prior studies, the impact of newly designed contingent financial incentives of a new pay-for-answer reward scheme has not been empirically examined in any online health Q&A community. Given this research gap, our study aims to perform an exploratory investigation of the effects of contingent financial incentives on user engagement in terms of knowledge contribution and social interactions.

Based on expectancy-value theory and equity theory, a research model was developed to reflect the influences of contingent financial incentives on user engagement. A unique dataset was gathered from a large online health Q&A community utilizing this contingent financial incentive reward structure, and the Heckman selection model was applied using a two-step procedure to test these hypotheses. Possible endogeneity issues were also addressed in the robustness check.

Our results demonstrate that the effect of contingent financial incentives on answer quantity and quality is quadratic. Additionally, our study reveals that this contingent financial incentive enhances both comment and emotional interactions among users.

Our study enriches the literature on financial incentives, knowledge contribution and user engagement by revealing the nuanced effects of financial incentives within a novel pay-for-answer scheme. This study also offers significant implications for practitioners involved in online community incentive design.