Weak topological insulators induced by the inter-layer coupling: A first-principles study of stacked Bi2TeI

Based on first-principles calculations, we predict Bi2TeI, a stoichiometric compound synthesized, to be a weak topological insulator (TI) in layered subvalent bismuth telluroiodides. Within a bulk energy gap of 80 meV, two Dirac-cone-like topological surface states exist on the side surface perpendicular to BiTeI layer plane. These Dirac cones are relatively isotropic due to the strong inter-layer coupling, distinguished from those of previously reported weak TI candidates. Moreover, with chemically stable cladding layers, the BiTeI-Bi2-BiTeI sandwiched structure is a robust quantum spin Hall system, which can be obtained by simply cleaving the bulk Bi2TeI.Comment: 4.5 pages, 4 figure

Polymorphic genetic characterization of the ORF7 gene of porcine reproductive and respiratory syndrome virus (PRRSV) in China

<p>Abstract</p> <p>Background</p> <p>Porcine reproductive and respiratory syndrome virus (PRRSV) exhibits extensive genetic variation. The outbreak of a highly pathogenic PRRS in 2006 led us to investigate the extent of PRRSV genetic diversity in China. To this end, we analyzed the Nsp2 and ORF7 gene sequences of 98 Chinese PRRSV isolates.</p> <p>Results</p> <p>Preliminary analysis indicated that highly pathogenic PRRSV strains with a 30-amino acid deletion in the Nsp2 protein are the dominant viruses circulating in China. Further analysis based on ORF7 sequences revealed that all Chinese isolates were divided into 5 subgroups, and that the highly pathogenic PRRSVs were distantly related to the MLV or CH-1R vaccine, raising doubts about the efficacy of these vaccines. The ORF7 sequence data also showed no apparent associations between geographic or temporal origin and heterogeneity of PRRSV in China.</p> <p>Conclusion</p> <p>These findings enhance our knowledge of the genetic characteristics of Chinese PRRSV isolates, and may facilitate the development of effective strategies for monitoring and controlling PRRSV in China.</p

Joint Trajectory and Communication Design for Buffer-Aided Multi-UAV Relaying Networks

With the rapid development and evolvement of unmanned aerial vehicle (UAV) technology, UAV aided wireless communication technology has been widely studied recently. In this paper, a buffer aided multi-UAV relaying network is investigated to assist blocked ground communication. According to the mobility and implementation flexibility of UAV relays, it is assumed that the communication link between air-to-ground is the Rician fading channel. On the basis of information causality, we derive the state change of the information in the buffer of UAV relays and maximize the end-to-end average throughput by join the relay selection, UAV transmit power, and UAV trajectory optimization. However, the considered problem is a mixed integer non-convex optimization problem, and therefore, it is difficult to solve directly with general optimization methods. In order to make the problem tractable, an efficient iterative algorithm based on the block coordinate descent and the successive convex optimization techniques is proposed. The convergence of the proposed algorithm will be verified analytically at the end of this paper. The simulation results show that by alternately optimizing the relay selection, UAV transmit power, and UAV trajectory, the proposed algorithm is able to achieve convergence quickly and significantly improve the average throughput, as compared to other benchmark schemes

Numerical Simulation of Fluid Flow through Fractal-Based Discrete Fractured Network

Abstract: In recent years, multi-stage hydraulic fracturing technologies have greatly facilitated the development of unconventional oil and gas resources. However, a quantitative description of the “complexity” of the fracture network created by the hydraulic fracturing is confronted with many unsolved challenges. Given the multiple scales and heterogeneity of the fracture system, this study proposes a “bifurcated fractal” model to quantitatively describe the distribution of induced hydraulic fracture networks. The construction theory is employed to generate hierarchical fracture patterns as a scaled numerical model. With the implementation of discrete fractal-fracture network modeling (DFFN), fluid flow characteristics in bifurcated fractal fracture networks are characterized. The effects of bifurcated fracture length, bifurcated tendency, and number of bifurcation stages are examined. A field example of the fractured horizontal well is introduced to calibrate the accuracy of the flow model. The proposed model can provide a more realistic representation of complex fracture networks around a fractured horizontal well, and offer the way to quantify the “complexity” of the fracture network in shale reservoirs. The simulation results indicate that the geometry of the bifurcated fractal fracture network model has a significant impact on production performance in the tight reservoir, and enhancing connectivity of each bifurcate fracture is the key to improve the stimulation performance. In practice, this work provides a novel and efficient workflow for complex fracture characterization and production prediction in naturally-fractured reservoirs of multi-stage fractured horizontal wells

Extreme Learning Machine for Heartbeat Classification with Hybrid Time-Domain and Wavelet Time-Frequency Features

Automatic heartbeat classification via electrocardiogram (ECG) can help diagnose and prevent cardiovascular diseases in time. Many classification approaches have been proposed for heartbeat classification, based on feature extraction. However, the existing approaches face the challenges of high feature dimensions and slow recognition speeds. In this paper, we propose an efficient extreme learning machine (ELM) approach for heartbeat classification with multiple classes, based on the hybrid time-domain and wavelet time-frequency features. The proposed approach contains two sequential modules: (1) feature extraction of heartbeat signals, including RR interval features in the time-domain and wavelet time-frequency features, and (2) heartbeat classification using ELM based on the extracted features. RR interval features are calculated to reflect the dynamic characteristics of heartbeat signals. Discrete wavelet transform (DWT) is used to decompose the heartbeat signals and extract the time-frequency features of the heartbeat signals along the timeline. ELM is a single-hidden layer feedforward neural network with the hidden layer parameters randomly generated in advance and the output layer parameters calculated optimally using the least-square algorithm directly using the training samples. ELM is used as the heartbeat classification algorithm due to its high accuracy training accuracy, fast training speed, and good generalization ability. Experimental testing is carried out using the public MIT-BIH arrhythmia dataset to perform a 16-class classification. Experimental results show that the proposed approach achieves a superior classification accuracy with fast training and recognition speeds, compared with existing classification algorithms

Measuring accessibility to health care services for older bus passengers: a finer spatial resolution

Health care accessibility is a vital indicator for evaluating areas where there are medical shortages. However, due to the lack of population data with a satisfactory spatial resolution, efforts to accurately measure health care accessibility among older individuals have been hampered to some extent. To address this issue, we attempt to measure accessibility to health care services for older bus passengers in Nanjing, China, using a finer spatial resolution. More specifically, based on one month's worth of bus smart card data, a framework for identifying the home stations (i.e., a passenger's preferred station near their residence) of older passengers is developed to measure the aggregate demand at the bus stop scale. On this basis, a measurement that integrates the Gaussian two-step floating catchment area (2SFCA) and the adjusted 2SFCA methods (referred to as the adjusted Gaussian 2SFCA method) is proposed to measure accessibility to health care services for older people. The results show that: (1) almost all home stations experience inflated demand, especially those located in the suburbs; (2) despite abundant health care resources, home stations in urban districts are rarely identified as high accessibility stations, due to high demand densities among the older population; and (3) more attention should be paid to two types of home stations – those with a medical institution and those with bed shortages, respectively. The first type is predominantly distributed in the periphery of the city, in the suburbs where the travel time required to access the nearest health care service by bus is longer. The second type is mostly located in the outskirts of urban districts and in the central area of one suburb. These findings could help policy makers to implement more appropriate measures to design and reallocate health care resources

Stable Dirac semimetal in the allotropes of group-IV elements

Three-dimensional topological Dirac semimetals represent a novel state of quantum matter with exotic electronic properties, in which a pair of Dirac points with a linear dispersion along all momentum directions exists in the bulk. Herein, by using first-principles calculations, we discover the metastable allotropes of Ge and Sn in the staggered layered dumbbell structure, named germancite and stancite, to be Dirac semimetals with a pair of Dirac points on their rotation axis. On the surface parallel to the rotation axis, a pair of topologically nontrivial Fermi arcs are observed and a Lifshitz transition is found by tuning the Fermi level. Furthermore, the quantum thin film of germancite is found to be an intrinsic quantum spin Hall insulator. These discoveries suggest novel physical properties and future applications of the metastable allotrope of Ge and Sn.W.C. and W.D. acknowledge support from the Ministry of Science and Technology of China and theNational Natural Science Foundation of China (Grant No. 11334006). A.R. acknowledges financial support from the European Research Council Grant DYNamo (ERC-2010-AdG No. 267374) Spanish Grants (FIS2010-21282-C02-01), Grupos Consolidados UPV/EHU del Gobierno Vasco (IT578-13), and EC project CRONOS (280879-2 CRONOS CPFP7). P.T. and S.-C.Z. acknowledge NSF under Grant No. DMR-1305677 and FAME, one of six centers of STARnet, a Semiconductor Research Corporation program sponsored by MARCO and DARPA.Peer reviewe