Numerical Simulation on Forced Convection Cooling of Horizontal Ionic Wind with Multi-electrodes

Enhancement ofheat transfer plays an important role in the cooling of electronic or refrigeration systems, and its characteristics could strongly affect the stability and performance of such systems. To enhance heat transfer, air cooling of forced convection remains one of the main solutions. For example, conventional rotary-fan air cooling is still dominant in many areas. However, with the increasing of heat generation in these systems, the limitation of the conventional rotary-fan air cooling is become more obvious. So, demands in novel air cooling technology become necessary, e.g., silent and high efficient air cooling. Recently, ionic wind, which has no moving part and is easily miniaturized, shows great potential in heat dissipation and attracts widespread attentions. In this work, ionic wind, which is produced by wire to plate configuration for forced convection enhancement of horizontal flow along the plate, is numerically investigated. Firstly, a multi-physic model, which accounts for electric field, charge distribution, fluid dynamics, and heat transfer phenomenon, is presented. Comparisons between the simulation and literature data are conducted. Results show that better agreements are achieved by the developed model. Secondly, influences of the emitting electrodes numbers are analyzed. Results show that multiple electrodes configuration has higher performance in terms of heat transfer coefficient than that of the single electrode. Investigations are also carried out on the influences of the distances between the emitting electrodes. Thirdly, effects of the main parameters of ionic wind, such as the inlet velocity, and voltage applied on the electrodes etc., are investigated. Finally, by using the multi-physic model of ionic wind, characteristics of the heat transfer are predicted. It is found that the maximum enhancement of average heat transfer coefficient could reach around 150 %

PRSim: Sublinear Time SimRank Computation on Large Power-Law Graphs

{\it SimRank} is a classic measure of the similarities of nodes in a graph. Given a node $u$ in graph $G =(V, E)$, a {\em single-source SimRank query} returns the SimRank similarities $s(u, v)$ between node $u$ and each node $v \in V$. This type of queries has numerous applications in web search and social networks analysis, such as link prediction, web mining, and spam detection. Existing methods for single-source SimRank queries, however, incur query cost at least linear to the number of nodes $n$, which renders them inapplicable for real-time and interactive analysis. { This paper proposes \prsim, an algorithm that exploits the structure of graphs to efficiently answer single-source SimRank queries. \prsim uses an index of size $O(m)$, where $m$ is the number of edges in the graph, and guarantees a query time that depends on the {\em reverse PageRank} distribution of the input graph. In particular, we prove that \prsim runs in sub-linear time if the degree distribution of the input graph follows the power-law distribution, a property possessed by many real-world graphs. Based on the theoretical analysis, we show that the empirical query time of all existing SimRank algorithms also depends on the reverse PageRank distribution of the graph.} Finally, we present the first experimental study that evaluates the absolute errors of various SimRank algorithms on large graphs, and we show that \prsim outperforms the state of the art in terms of query time, accuracy, index size, and scalability.Comment: ACM SIGMOD 201

Types of the geodesic motions in Kerr-Sen-AdS4_{4} spacetime

We consider the geodesic motions in the Kerr-Sen-AdS$_4$ spacetime. We obtain the equations of motion for light rays and test particles. Using the parametric diagrams, we shown some regions where the radial and latitudinal geodesic motions are allowed. We analyse the impact of parameter related to dilatonic scalar on the orbit and find that it will result in more rich and complex orbital types.Comment: 12 pages, 14 figure

Automatically learning topics and difficulty levels of problems in online judge systems

Online Judge (OJ) systems have been widely used in many areas, including programming, mathematical problems solving, and job interviews. Unlike other online learning systems, such as Massive Open Online Course, most OJ systems are designed for self-directed learning without the intervention of teachers. Also, in most OJ systems, problems are simply listed in volumes and there is no clear organization of them by topics or difficulty levels. As such, problems in the same volume are mixed in terms of topics or difficulty levels. By analyzing large-scale users’ learning traces, we observe that there are two major learning modes (or patterns). Users either practice problems in a sequential manner from the same volume regardless of their topics or they attempt problems about the same topic, which may spread across multiple volumes. Our observation is consistent with the findings in classic educational psychology. Based on our observation, we propose a novel two-mode Markov topic model to automatically detect the topics of online problems by jointly characterizing the two learning modes. For further predicting the difficulty level of online problems, we propose a competition-based expertise model using the learned topic information. Extensive experiments on three large OJ datasets have demonstrated the effectiveness of our approach in three different tasks, including skill topic extraction, expertise competition prediction and problem recommendation

Improving multi-hop knowledge base question answering by learning intermediate supervision signals

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding InitiativeThe code is available at https://github.com/RichardHGL/WSDM2021_NSM</p

Characterizing and Predicting Early Reviewers for Effective Product Marketing on E-Commerce Websites

Online reviews have become an important source of information for users before making an informed purchase decision. Early reviews of a product tend to have a high impact on the subsequent product sales. In this paper, we take the initiative to study the behavior characteristics of early reviewers through their posted reviews on two real-world large e-commerce platforms, i.e., Amazon and Yelp. In specific, we divide product lifetime into three consecutive stages, namely early, majority and laggards. A user who has posted a review in the early stage is considered as an early reviewer. We quantitatively characterize early reviewers based on their rating behaviors, the helpfulness scores received from others and the correlation of their reviews with product popularity. We have found that (1) an early reviewer tends to assign a higher average rating score; and (2) an early reviewer tends to post more helpful reviews. Our analysis of product reviews also indicates that early reviewers' ratings and their received helpfulness scores are likely to influence product popularity. By viewing review posting process as a multiplayer competition game, we propose a novel margin-based embedding model for early reviewer prediction. Extensive experiments on two different e-commerce datasets have shown that our proposed approach outperforms a number of competitive baselines

A generalized public goods game with coupling of individual ability and project benefit

Facing a heavy task, any single person can only make a limited contribution and team cooperation is needed. As one enjoys the benefit of the public goods, the potential benefits of the project are not always maximized and may be partly wasted. By incorporating individual ability and project benefit into the original public goods game, we study the coupling effect of the four parameters, the upper limit of individual contribution, the upper limit of individual benefit, the needed project cost and the upper limit of project benefit on the evolution of cooperation. Coevolving with the individual-level group size preferences, an increase in the upper limit of individual benefit promotes cooperation while an increase in the upper limit of individual contribution inhibits cooperation. The coupling of the upper limit of individual contribution and the needed project cost determines the critical point of the upper limit of project benefit, where the equilibrium frequency of cooperators reaches its highest level. Above the critical point, an increase in the upper limit of project benefit inhibits cooperation. The evolution of cooperation is closely related to the preferred group-size distribution. A functional relation between the frequency of cooperators and the dominant group size is found