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The density peak clustering algorithm (DP) is proposed to identify cluster centers by two parameters, i.e. ρ value (local density) and δ value (the distance between a point and another point with a higher ρ value). According to the center-identifying principle of the DP, the potential cluster centers should have a higher ρ value and a higher δ value than other points. However, this principle may limit the DP from identifying some categories with multi-centers or the centers in lower-density regions. In addition, the improper assignment strategy of the DP could cause a wrong assignment result for the non-center points. This paper aims to address the aforementioned issues and improve the clustering performance of the DP.

First, to identify as many potential cluster centers as possible, the authors construct a point-domain by introducing the pinhole imaging strategy to extend the searching range of the potential cluster centers. Second, they design different novel calculation methods for calculating the domain distance, point-domain density and domain similarity. Third, they adopt domain similarity to achieve the domain merging process and optimize the final clustering results.

The experimental results on analyzing 12 synthetic data sets and 12 real-world data sets show that two-stage density peak clustering based on multi-strategy optimization (TMsDP) outperforms the DP and other state-of-the-art algorithms.

The authors propose a novel DP-based clustering method, i.e. TMsDP, and transform the relationship between points into that between domains to ultimately further optimize the clustering performance of the DP.

The purpose of this article is the effect of doping minor Ni on the microstructure evolution of a Sn-xNi (x = 0, 0.05 and 0.1 wt.%)/Ni (Poly-crystal/Single-crystal abbreviated as PC Ni/SC Ni) solder joint during reflow and aging treatment. Results showed that the intermetallic compounds (IMCs) of the interfacial layer of Sn-xNi/PC Ni joints were Ni₃Sn₄ phase, while the IMCs of Sn-xNi/SC Ni joints were NiSn₄ phase. After the reflow process and thermal aging of different joints, the growth behavior of interfacial layer was different due to the different mechanism of element diffusion of the two substrates. The PC Ni substrate mainly provided Ni atoms through grain boundary diffusion. The Ni₃Sn₄ phase of the Sn0.05Ni/PC Ni joint was finer, and the diffusion flux of Sn and Ni elements increased, so the Ni₃Sn₄ layer of this joint was the thickest. The SC Ni substrate mainly provided Ni atoms through the lattice diffusion. The Sn0.1Ni/SC Ni joint increases the number of Ni atoms at the interface due to the doping of 0.1Ni (wt.%) elements, so the joint had the thickest NiSn₄ layer.

The effects of doping minor Ni on the microstructure evolution of an Sn-xNi (x = 0, 0.05 and 0.1 Wt.%)/Ni (Poly-crystal/Single-crystal abbreviated as PC Ni/SC Ni) solder joint during reflow and aging treatment was investigated in this study.

Results showed that the intermetallic compounds (IMCs) of the interfacial layer of Sn-xNi/PC Ni joints were Ni₃Sn₄ phase, while the IMCs of Sn-xNi/SC Ni joints were NiSn₄ phase. After the reflow process and thermal aging of different joints, the growth behavior of the interfacial layer was different due to the different mechanisms of element diffusion of the two substrates.

In this study, the effect of doping Ni on the growth and formation mechanism of IMCs of the Sn-xNi/Ni (single-crystal) solder joints (x = 0, 0.05 and 0.1 Wt.%) was investigated.

The purpose of this paper is to study the participation behaviors in the context of crowdsourcing projects from the perspective of gamification.

This paper first proposed a model to depict the effect of four categories of game elements on three types of motivation based upon several motivation theories, which may, in turn, influence user participation. Then, 5 × 2 between-subject Web experiments were designed for collecting data and validating this model.

Game elements which provide participants with rewards and recognitions or remind participants of the completion progress of their tasks may positively influence the extrinsic motivation, whereas game elements which can help create a fantasy scene may strengthen intrinsic motivation. Besides, recognition-kind and progress-kind game elements may trigger the internalization of extrinsic motivation. In addition, when a task is of high complexity, the effects from game elements on extrinsic motivation and intrinsic motivation will be less prominent, whereas the internalization of extrinsic motivation may benefit from the increase of task complexity.

This study may uncover the motivation mechanism of several different kinds of game elements, which may help to find which game elements are more effective in enhancing engagement and participation in crowdsourcing projects. Besides, as task complexity is used as a moderator, one may be able to identify whether task complexity is able to influence the effects from game elements on motivations. Last, but not the least, this study will indicate the interrelationship between game elements, individual motivation and user participation, which can be adapted by other scholars.

Expanding the research on traditional history of economic ideology into the research on the history of economics composed of three elements – history of ideology, history of policies and events – is a new idea for researching the history of socialist political economy with Chinese characteristics. The start of the history of socialist political economy with Chinese characteristics is consistent with that of the Sinicization of Marxist political economy and can be dated from at least 1917.

The key point of the research on the history of ideologies of the socialist political economy with Chinese characteristics is to treat the relationship between theory and people properly, i.e. we should not neglect the effect brought out by the economists on theory construction while we attach importance to the theoretical contribution of the leaders and leading group of the Communist Party of China (CPC).

For the research on the history of economic policies of socialist political economy with Chinese characteristics, on the one hand, we should clarify the relationship among ideologies, strategies and policies; on the other hand, we should not evade the summarization of lessons from history.

Besides presenting the development route of socialist political economy with Chinese characteristics under competition, the research on the events in the history of socialist political economy with Chinese characteristics should also help develop the socialist political economy with Chinese characteristics.

The purpose of this paper is to provide a guiding framework for studying the travel patterns of PT users. The combination of public transit (PT) users’ travel data and user profiling (UP) technology to draw a portrait of PT users can effectively understand users’ travel patterns, which is important to help optimize the scheduling of PT operations and planning of the network.

To achieve the purpose, the paper presents a three-level classification method to construct the labeling framework. A station area attribute mining method based on the term frequency-inverse document frequency weighting algorithm is proposed to determine the point of interest attributes of user travel stations, and the spatial correlation patterns of user travel stations are calculated by Moran’s Index. User travel feature labels are extracted from travel data containing Beijing PT data for one consecutive week.

In this paper, a universal PT user labeling system is obtained and some related methods are conducted including four categories of user-preferred travel area patterns mining and a station area attribute mining method. In the application of the Beijing case, a precise exploration of the spatiotemporal characteristics of PT users is conducted, resulting in the final Beijing PTUP system.

This paper combines UP technology with big data analysis techniques to study the travel patterns of PT users. A user profile label framework is constructed, and data visualization, statistical analysis and K-means clustering are applied to extract specific labels instructed by this system framework. Through these analytical processes, the user labeling system is improved, and its applicability is validated through the analysis of a Beijing PT case.

The study explores how Taiwan’s electronic health data systems can be used to build algorithms that reduce or eliminate medical errors and to advance precision medicine.

This study is a narrative review of the literature.

The body of medical knowledge has grown far too large for human clinicians to parse. In theory, electronic health records could augment clinical decision-making with electronic clinical decision support systems (CDSSs). However, computer scientists and clinicians have made remarkably little progress in building CDSSs, because health data tend to be siloed across many different systems that are not interoperable and cannot be linked using common identifiers. As a result, medicine in the USA is often practiced inconsistently with poor adherence to the best preventive and clinical practices. Poor information technology infrastructure contributes to medical errors and waste, resulting in suboptimal care and tens of thousands of premature deaths every year. Taiwan’s national health system, in contrast, is underpinned by a coordinated system of electronic data systems but remains underutilized. In this paper, the authors present a theoretical path toward developing artificial intelligence (AI)-driven CDSS systems using Taiwan’s National Health Insurance Research Database. Such a system could in theory not only optimize care and prevent clinical errors but also empower patients to track their progress in achieving their personal health goals.

While research teams have previously built AI systems with limited applications, this study provides a framework for building global AI-based CDSS systems using one of the world’s few unified electronic health data systems.