Electron-doped phosphorene: A potential monolayer superconductor

We predict by first-principles calculations that the electron-doped phosphorene is a potential BCS-like superconductor. The stretching modes at the Brillouin-zone center are remarkably softened by the electron-doping, which results in the strong electron-phonon coupling. The superconductivity can be introduced by a doped electron density ($n_{2D}$) above $1.3 \times10^{14}$ cm$^{-2}$, and may exist over the liquid helium temperature when $n_{2D}>2.6 \times10^{14}$ cm$^{-2}$. The maximum critical temperature is predicted to be higher than 10 K. The superconductivity of phosphorene will significantly broaden the applications of this novel material

Diversity of eukaryotic plankton of aquaculture ponds with Carassius auratus gibelio, using denaturing gradient gel electrophoresis

PCR-denaturing gradient gel electrophoresis (DGGE) and canonical correspondence analysis (CCA) were used to explore the relationship between eukaryotic plankton community succession and environmental factors in two aquaculture pond models with gibel carp Carassius auratus gibelio. The main culture species of pond 1 were gibel carp and grass carp, and the combined density was 46224 fingerling/ha (gibel carp/grass carp/silver carp/bighead carp, 17:4:6:1). The main culture species of pond 2 was gibel carp, and the combined density was 37551 fingerling/ha (gibel carp/silver carp/bighead carp, 52:1:1). Water samples were collected monthly. The results showed that the annual average concentrations of TP and PO_4-P in pond 1 were significantly higher than pond 2 (p>0.05). The concentration of chlorophyll a (chl a) has no significantly difference between pond 1 and pond 2. DGGE profiles of 18S rRNA gene fragments from the two ponds revealed that the diversity of eukaryotic plankton assemblages was highly variable. 91 bands and 71 bands were detected in pond 1 and pond 2, respectively. The average Shannon–Wiener index of pond 1 was significantly higher than pond 2. Canonical correspondence analysis (CCA) revealed that temperature played a key role in the structure of the eukaryotic plankton community in both ponds, but the nutrient concentration did not affect it. Our results suggest that DGGE method is a cost-effective way to gain insight into seasonal dynamics of eukaryotic plankton communities in culture ponds, and the increase in the number of filter-feeding silver carp and bighead carp could increase the diversity of the eukaryotic plankton community

Effect of Relief-hole Diameter on Die Elastic Deformation during Cold Precision Forging of Helical Gears

During cold precision forging of helical gears, the die experiences high forming pressure resulting in elastic deformation of the die, a main factor affecting dimensional accuracy of a formed gear. The divided flow method in material plastic deformation is an effective way to reduce the forming force and the die pressure during cold precision forging of helical gears. In this study, by utilizing the flow-relief-hole method, a billet design with different initial diameters of the relief-hole is developed to improve the dimensional accuracy of cold forging gears. Three-dimensional Finite Element (FE) models are established to simulate the plastic deformation process of billet during cold precision forging of a helical gear and to determine the forming force acting on the die. Further models of die stress analysis are developed to examine the die elastic deformation and distribution of the displacement. Effects of the relief-hole diameters on die elastic deformation are studied. The results show that the elastic deformation of the die is different in the addendum, dedendum, and involute parts of forging gear using different relief-hole diameters. The die elastic deformation increases firstly and then decreases when the relief-hole diameter increases. The tooth portions are of larger elastic deformation and the peak value locates in the addendum. It shows the importance of optimizing the relief-hole diameter to minimize the dimensional inaccuracy of forging gears caused by the die elastic deformation

Recognizing human actions from low-resolution videos by region-based mixture models

© 2016 IEEE. Recognizing human action from low-resolution (LR) videos is essential for many applications including large-scale video surveillance, sports video analysis and intelligent aerial vehicles. Currently, state-of-the-art performance in action recognition is achieved by the use of dense trajectories which are extracted by optical flow algorithms. However, the optical flow algorithms are far from perfect in LR videos. In addition, the spatial and temporal layout of features is a powerful cue for action discrimination. While, most existing methods encode the layout by previously segmenting body parts which is not feasible in LR videos. Addressing the problems, we adopt the Layered Elastic Motion Tracking (LEMT) method to extract a set of long-term motion trajectories and a long-term common shape from each video sequence, where the extracted trajectories are much denser than those of sparse interest points(SIPs); then we present a hybrid feature representation to integrate both of the shape and motion features; and finally we propose a Region-based Mixture Model (RMM) to be utilized for action classification. The RMM models the spatial layout of features without any needs of body parts segmentation. Experiments are conducted on two publicly available LR human action datasets. Among which, the UT-Tower dataset is very challenging because the average height of human figures is only about 20 pixels. The proposed approach attains near-perfect accuracy on both of the datasets

A Destination Prediction Network Based on Spatiotemporal Data for Bike-Sharing

© 2019 Jian Jiang et al. Bike-sharing is a new low-carbon and environment-friendly mode of public transport based on the "sharing economy". Since 2017, the bike-sharing market has boomed in China's major cities. Bikes equipped with GPS transmitters are docked along sidewalks that can be easily accessed through smartphone apps. However, this new form of transport has also led to problems, such as illegal parking, vandalism, and theft, each of which presents a major administrative challenge. Further, imbalances in user demand and bike availability need to be overcome to ensure a convenient, flexible service for customers. Hence, predicting a cyclist's destination could be of great importance to shared-bike operators. In this paper, we propose an innovative deep learning model to predict the most probable destination for each user. The model, called destination prediction network based on spatiotemporal data (DPNst), comprises three steps. First, the data is preprocessed and a pool of likely candidate destinations is generated based on frequent item mining. This candidate set is then used to build the DPNst model: a long short-term memory network learns the user's behavior; a convolutional neural network learns the spatial relationships between the origin and the candidate destinations; and a fully connected neural network learns the external features. In the final step, DPNst dynamically aggregates the output of the three neural networks based on the given data and generates the predictions. In a series of experiments on real-world stationless bike-sharing data, DPNst returned an F1 score of 42.71% and demonstrated better performance overall than the compared baselines