Sciences for The 2.5-meter Wide Field Survey Telescope (WFST)

The Wide Field Survey Telescope (WFST) is a dedicated photometric survey facility under construction jointly by the University of Science and Technology of China and Purple Mountain Observatory. It is equipped with a primary mirror of 2.5m in diameter, an active optical system, and a mosaic CCD camera of 0.73 Gpix on the main focus plane to achieve high-quality imaging over a field of view of 6.5 square degrees. The installation of WFST in the Lenghu observing site is planned to happen in the summer of 2023, and the operation is scheduled to commence within three months afterward. WFST will scan the northern sky in four optical bands (u, g, r, and i) at cadences from hourly/daily to semi-weekly in the deep high-cadence survey (DHS) and the wide field survey (WFS) programs, respectively. WFS reaches a depth of 22.27, 23.32, 22.84, and 22.31 in AB magnitudes in a nominal 30-second exposure in the four bands during a photometric night, respectively, enabling us to search tremendous amount of transients in the low-z universe and systematically investigate the variability of Galactic and extragalactic objects. Intranight 90s exposures as deep as 23 and 24 mag in u and g bands via DHS provide a unique opportunity to facilitate explorations of energetic transients in demand for high sensitivity, including the electromagnetic counterparts of gravitational-wave events detected by the second/third-generation GW detectors, supernovae within a few hours of their explosions, tidal disruption events and luminous fast optical transients even beyond a redshift of 1. Meanwhile, the final 6-year co-added images, anticipated to reach g about 25.5 mag in WFS or even deeper by 1.5 mag in DHS, will be of significant value to general Galactic and extragalactic sciences. The highly uniform legacy surveys of WFST will also serve as an indispensable complement to those of LSST which monitors the southern sky.Comment: 46 pages, submitted to SCMP

Regional terrain-based V S30 prediction models for China

Abstract Time-averaged shear-wave velocity to 30 m (V S30) is commonly used in ground motion models as a parameter for evaluating site effects. This study used a collection of boreholes in Beijing, Tianjin, Guangxi, Guangdong, and three other municipalities and provinces, which were divided into three regions with reference to the seismic ground motion parameter zonation map of China, to establish V S30 prediction models based on terrain categories. Regional effects were verified by comparing morphometric parameter (topographic slope, surface texture, and local convexity) thresholds and terrain classification maps obtained from global digital elevation model (DEM) data and regional DEM data of the three regions. Additionally, V S30 prediction models for the three regions using both types of terrain classification maps were established and analyzed comparatively to provide credible regional V S30 models for China. Through analysis of the correlations between the measured V S30 values and the predicted V S30 values, calculation of the mean squared error and mean absolute percentage error in each region, and with consideration of the geological characteristics of the boreholes, the V S30 prediction models based on terrain classification maps from regional data were finally applied in developing regional V S30 models for China. Intercomparison of the V S30 prediction models for the three regions indicated that subregional consideration is necessary in terrain classification. Finally, a spatial analysis method adopting inverse distance weighting of the residuals was used to update the initial V S30 models. The developed V S30 models could be used both in developing regional ground motion models and in the construction of earthquake disaster scenarios. Graphical Abstrac

Straight-Line Path Tracking Control of Agricultural Tractor-Trailer Based on Fuzzy Sliding Mode Control

In order to solve the labor shortage problem, unmanned agricultural vehicles have been widely promoted in China. Among these vehicles, unmanned tractor-trailers have garnered the most interest thanks to their flexibility and efficiency. However, trapped by the factors of vehicle parameter uncertainties, unstructured farmland roads, etc., the current unmanned tractor-trailers have shown poor tracking accuracy and longer online times in straight-line path tracking. To overcome the aforementioned issues, a fuzzy sliding mode control (FSMC) approach is proposed in this paper. First, the tractor-trailer path tracking error model was established based on the kinematic model and a reference model. Then, according to the sliding mode control (SMC) theory, the FSMC was designed. Through a Lyapunov theory analysis, the proposed control method can ensure that the articulated angle, the position error, and the heading error all converge to zero. Finally, field tests and Simulink simulations demonstrated the effectiveness and robustness of the suggested control mechanism. According to field experiments, the proposed control method can increase the trailer steady-state tracking accuracy by between 10.5 and 36.8% and the tractor steady-state tracking accuracy by between 11.1 and 50%

Gleer: A Novel Gini-Based Energy Balancing Scheme for Mobile Botnet Retopology

Mobile botnet has recently evolved due to the rapid growth of smartphone technologies. Unlike legacy botnets, mobile devices are characterized by limited power capacity, calculation capabilities, and wide communication methods. As such, the logical topology structure and communication mode have to be redesigned for mobile botnets to narrow energy gap and lower the reduction speed of nodes. In this paper, we try to design a novel Gini-based energy balancing scheme (Gleer) for the atomic network, which is a fundamental component of the heterogeneous multilayer mobile botnet. Firstly, for each operation cycle, we utilize the dynamic energy threshold to categorize atomic network into two groups. Then, the Gini coefficient is introduced to estimate botnet energy gap and to regulate the probability for each node to be picked as a region C&C server. Experimental results indicate that our proposed method can effectively prolong the botnet lifetime and prevent the reduction of network size. Meanwhile, the stealthiness of botnet with Gleer scheme is analyzed from users’ perspective, and results show that the proposed scheme works well in the reduction of user’ detection awareness

Long Length Document Classification by Local Convolutional Feature Aggregation

The exponential increase in online reviews and recommendations makes document classification and sentiment analysis a hot topic in academic and industrial research. Traditional deep learning based document classification methods require the use of full textual information to extract features. In this paper, in order to tackle long document, we proposed three methods that use local convolutional feature aggregation to implement document classification. The first proposed method randomly draws blocks of continuous words in the full document. Each block is then fed into the convolution neural network to extract features and then are concatenated together to output the classification probability through a classifier. The second model improves the first by capturing the contextual order information of the sampled blocks with a recurrent neural network. The third model is inspired by the recurrent attention model (RAM), in which a reinforcement learning module is introduced to act as a controller for selecting the next block position based on the recurrent state. Experiments on our collected four-class arXiv paper dataset show that the three proposed models all perform well, and the RAM model achieves the best test accuracy with the least information

Genetic characteristics of the coxsackievirus A24 variant causing outbreaks of acute hemorrhagic conjunctivitis in Jiangsu, China, 2010.

During September 2010, an outbreak of acute hemorrhagic conjunctivitis reemerged in Jiangsu, three years after the nationwide epidemic in China in 2007. In total, 2409 cases were reported, 2118 of which were reported in September; 79.8% of those affected were students or teachers, with a median age of 16 years. To identify and demonstrate the genetic characteristics of the etiological agent, 52 conjunctival swabs were randomly collected from four different cities. After detection and isolation, 43 patients were positive for coxsackievirus A24 variant according to PCR and 20 according to culture isolation. Neither adenovirus nor EV70 was detected. A phylogenetic study of the complete 3Cpro and VP1 regions showed that the Jiangsu isolates clustered into a new lineage, GIV-C5, with two uniform amino-acid mutations that distinguished them from all previous strains. Another new cluster, GIV-C4, formed by Indian isolates from 2007 and Brazilian isolates from 2009, was also identified in this study. Interestingly, our isolates shared greatest homology with the GIV-C4 strains, not with the isolates that were responsible for the nationwide acute hemorrhagic conjunctivitis epidemic in China in 2007. Although all our isolates were closely related, they could be differentiated into two subclusters within GIV-C5. In conclusion, our study suggests that a new cluster of coxsackievirus A24 variant that had already evolved into diverse strains was associated with the acute hemorrhagic conjunctivitis outbreaks in Jiangsu in September 2010. These viruses might have originated from the virus isolated in India in 2007, rather than from the epidemic strains isolated in China in 2007