A Novel Local Community Detection Method Using Evolutionary Computation.

The local community detection is a significant branch of the community detection problems. It aims at finding the local community to which a given starting node belongs. The local community detection plays an important role in analyzing the complex networks and recently has drawn much attention from the researchers. In the past few years, several local community detection algorithms have been proposed. However, the previous methods only make use of the limited local information of networks but overlook the other valuable information. In this article, we propose an evolutionary computation-based algorithm called evolutionary-based local community detection (ELCD) algorithm to detect local communities in the complex networks by taking advantages of the entire obtained information. The performance of the proposed algorithm is evaluated on both synthetic and real-world benchmark networks. The experimental results show that the proposed algorithm has a superior performance compared with the state-of-the-art local community detection methods. Furthermore, we test the proposed algorithm on incomplete real-world networks to show its effectiveness on the networks whose global information cannot be obtained

Tunneling magnetoresistance in diluted magnetic semiconductor tunnel junctions

Using the spin-polarized tunneling model and taking into account the basic physics of ferromagnetic semiconductors, we study the temperature dependence of the tunneling magnetoresistance (TMR) in the diluted magnetic semiconductor (DMS) trilayer heterostructure system (Ga,Mn)As/AlAs/(Ga,Mn)As. The experimentally observed TMR ratio is in reasonable agreement with our result based on the typical material parameters. It is also shown that the TMR ratio has a strong dependence on both the itinerant-carrier density and the magnetic ion density in the DMS electrodes. This can provide a potential way to achieve larger TMR ratio by optimally adjusting the material parameters.Comment: 5 pages (RevTex), 3 figures (eps), submitted to PR

Intrinsic non-uniqueness of the acoustic full waveform inverse problem

SUMMARY In the context of seismic imaging, full waveform inversion (FWI) is increasingly popular. Because of its lower numerical cost, the acoustic approximation is often used, especially at the exploration geophysics scale, both for tests and for real data. Moreover, some research domains such as helioseismology face true acoustic media for which FWI can be useful. In this work, an argument that combines particle relabelling and homogenization is used to show that the general acoustic inverse problem based on band-limited data is intrinsically non-unique. It follows that the results of such inversions should be interpreted with caution. To illustrate these ideas, we consider 2-D numerical FWI examples based on a Gauss–Newton iterative inversion scheme and demonstrate effects of this non-uniqueness in the local optimization context.</jats:p

UCL OpenFOAM Course Notes 2019

The UCL OpenFOAM Course was initiated by the Department of Mechanical Engineering, totally free and registered under UCL doctoral school. It aims to popularise OpenFOAM among research students and help beginners to get through the initial painful stage dealing with the unfamiliar operation environment, also an excellent chance to exchange simulation skills and generate collaborations. In 2019, the course was held during 26-28 June, with our lecturers and 55 students attended. It was fantastic to see so many conversations getting started, and to feel that our UK/London community is getting stronger. We received very positive feedback, and more importantly, strong interests from worldwide users who wanted but could not join us in London. Thereby, this document is published online to demonstrate what we have taught. We hope this will be helpful for a wider audience. In Chapter 1-4, we present step-by-step guideline for installing/using/understanding OpenFOAM; subsequently, our Appendixes provides advanced tutorials for various purposes

Secretion dynamics of soyasaponins in soybean roots and effects to modify the bacterial composition

Soyasaponins are triterpenoid saponins widely found in legume plants. These compounds have drawn considerable attention because they have various activities beneficial for human health, and their biosynthesis has been actively studied. In our previous study, we found that legume plants including soybean secrete soyasaponins from the roots in hydroponic culture throughout the growth period, but the physiological roles of soyasaponins in the rhizosphere and their fate in soil after exudation have remained unknown. This study demonstrates that soyasaponins are secreted from the roots of field-grown soybean, and soyasaponin Bb is the major soyasaponin detected in the rhizosphere. In vitro analysis of the distribution coefficient suggested that soyasaponin Bb can diffuse over longer distances in the soil in comparison with daidzein, which is a typical isoflavone secreted from soybean roots. The degradation rate of soyasaponin Bb in soil was slightly faster than that of daidzein, whereas no soyasaponin Bb degradation was observed in autoclaved soil, suggesting that microbes utilize soyasaponins in the rhizosphere. Bacterial community composition was clearly influenced by soyasaponin Bb, and potential plant growth-promoting rhizobacteria such as Novosphingobium were significantly enriched in both soyasaponin Bb-treated soil and the soybean rhizosphere. These results strongly suggest that soyasaponin Bb plays an important role in the enrichment of certain microbes in the soybean rhizosphere

Traffic signal coordination for tramlines with passive priority strategy

Prioritizing traffic signals for trams crossing intersections without stops can increase the service punctuality and travel speed of trams, but it may also increase the delays of other vehicles at intersections. This paper presents a model on coordinated control of traffic signals among successive intersections along the tramline, taking into account driving characteristics of trams and vehicles. The objective is maximizing the valid bandwidth of vehicle green wave to reduce vehicle delays, while the trams cross intersections without stops. Linear Interactive and General Optimizer (LINGO) is applied to solve the proposed model and VISSIM simulation software is adopted to assess the solutions attained by the proposed model and the previous TRAMBAND model. Case studies show that the solutions given by the proposed model facilitate trams to go through all intersections along the tramline without stops. In comparison with the TRAMBAND model, the proposed model reduces tram delay by 13.14 s/pcu and increases the throughput of vehicles at intersections by 4.45% and reduces vehicle delays by 2.22%. Extensive simulations have verified that the performance of the proposed model is stable under different tram headways, dwell time, and traffic volumes. It is also found that the tram headway must be multiple of traffic signal cycle time to completely realize green wave control of all trams at all intersections along the tramline