Work Function of Single-wall Silicon Carbide Nanotube

Using first-principles calculations, we study the work function of single wall silicon carbide nanotube (SiCNT). The work function is found to be highly dependent on the tube chirality and diameter. It increases with decreasing the tube diameter. The work function of zigzag SiCNT is always larger than that of armchair SiCNT. We reveal that the difference between the work function of zigzag and armchair SiCNT comes from their different intrinsic electronic structures, for which the singly degenerate energy band above the Fermi level of zigzag SiCNT is specifically responsible. Our finding offers potential usages of SiCNT in field-emission devices.Comment: 3 pages, 3 figure

Water use patterns of forage cultivars in the North China Plain

Water shortage is the primary limiting factor for crop production and long-term agricultural sustainability of the North China Plain. Forage cultivation emerged recently in this region. A fiveryear field experiment studies were conducted at Yucheng Integrated Experiment Station to quantify the water requirement and water use efficiency of seven forage varieties under climate variability, that is five annuals, i.e., ryegrass (Secale cereale L.), triticale (×Triticosecale Wittmack), sorghum hybrid sudangrass (Sorghum biolor × Sorghum Sudanense c.v.), ensilage corn (Zea mays L.), prince's feather (Amaranthus paniculatus L.) and two perennials alfalfa (Medicago sativa L.) and cup plant (Silphium perfoliatum L.). Average ET for five annual varieties ranged from 333 to 371 mm, significantly lower than that of the perennial varieties. ET of alfalfa is 789 mm, which is higher than that of cup plant. Ryegrass and triticale need 1.5 to 2.0 mm water per day, while others 2.9-4.4 mm. Ensilage corn and Sorghum hybrid sudangrass performed better as their irrigation demand is smaller in the dry seasons than others. Ryegrass needs 281 mm irrigation requirement, which is higher than triticale in dry years. Prince's feather is sensitive to climate change and it can be selected when rainfall is greater than 592.9 mm in the growing season. Mean WUE for prince's feather is 20 Kg ha -1 mm -1, for ensilage corn is 41 Kg ha -1 mm -1 and others is close to 26 Kg ha -1 mm -1. Our experiments indicate that excessive rain will reduce the production of alfalfae. The results of this experiment have implications for researchers and policy makers with water management strategy of forage cultivars and it also very useful in addressing climate change impact and adaptation issues

Ferromagnetism in 2p Light Element-Doped II-oxide and III-nitride Semiconductors

II-oxide and III-nitride semiconductors doped by nonmagnetic 2p light elements are investigated as potential dilute magnetic semiconductors (DMS). Based on our first-principle calculations, nitrogen doped ZnO, carbon doped ZnO, and carbon doped AlN are predicted to be ferromagnetic. The ferromagnetism of such DMS materials can be attributed to a p-d exchange-like p-p coupling interaction which is derived from the similar symmetry and wave function between the impurity (p-like t_2) and valence (p) states. We also propose a co-doping mechanism, using beryllium and nitrogen as dopants in ZnO, to enhance the ferromagnetic coupling and to increase the solubility and activity

LSTM-based Flight Trajectory Prediction

© 2018 IEEE. Safety ranks the first in Air Traffic Management (ATM). Accurate trajectory prediction can help ATM to forecast potential dangers and effectively provide instructions for safely traveling. Most trajectory prediction algorithms work for land traffic, which rely on points of interest (POIs) and are only suitable for stationary road condition. Compared with land traffic prediction, flight trajectory prediction is very difficult because way-points are sparse and the flight envelopes are heavily affected by external factors. In this paper, we propose a flight trajectory prediction model based on a Long Short-Term Memory (LSTM) network. The four interacting layers of a repeating module in an LSTM enables it to connect the long-term dependencies to present predicting task. Applying sliding windows in LSTM maintains the continuity and avoids compromising the dynamic dependencies of adjacent states in the long-term sequences, which helps to improve accuracy of trajectory prediction. Taking time dimension into consideration, both 3-D (time stamp, latitude and longitude) and 4-D (time stamp, latitude, longitude and altitude) trajectories are predicted to prove the efficiency of our approach. The dataset we use was collected by ADS-B ground stations. We evaluate our model by widely used measurements, such as the mean absolute error (MAE), the mean relative error (MRE), the root mean square error (RMSE) and the dynamic warping time (DWT) methods. As Markov Model is the most popular in time series processing, comparisons among Markov Model (MM), weighted Markov Model (wMM) and our model are presented. Our model outperforms the existing models (MM and wMM) and provides a strong basis for abnormal detection and decision-making