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Sliding electrical contact between rail and armature in rail launchers is characterized by high speed and large current. Melt erosion is caused by the current concentration on the contact surface of armature. Because the current melt erosion (CME) was considered to be a main mechanism of armature-rail contact failure, it was experimentally and theoretically studied in the early years using current density of 30–40 kA/mm, which is the exact driving current of rail launches. However, the critical behavior at the onset of CME cannot be observed due to the serious melt erosion at such high current densities. In this paper, the CME of armatures has been experimentally studied in the current range of 10–20 kA/mm with a lab-scale rail launcher. A payload separated method was used to keep the recovered armatures intact. The critical process for the onset of melting was observed and the erosion spreading patterns on the contact surface was analyzed. It is found that the current melt-wave model postulated in early years cannot describe the development of CME in our experiments. The result shows that current erosion mostly occurs at the static and the low-velocity stage of armature. The CME begins at the point of maximum contact pressure provided by armature-rail interference fit, and then, the melt erosion spreads longitudinally and transversely. The current erosion is affected by both current distribution and the movement of liquid aluminum. In longitudinal direction, the flow of liquid aluminum results in erosion propagation to leading edges. The transverse width of erosion zone expands with increasing current magnitude along the edge of interference fit.

With the development and progress of astronomical observation technology and equipment, astronomy research has entered the era of big data. It's a huge challenge how massive astronomical data is stored, processed, accessed, and shared. It becomes more and more difficult how to access and utilize all astronomical data because of astronomical data with unique format, variety of structures, wide variety of species and global astronomical research trend. To solve this series of problems, this paper proposes to design a distributed prototype system-astronomical virtual telescope for astronomical information management and retrieval to achieve the storage, processing, and access of astronomical data and provide support for the research and application of astronomy.

Fine-grained classification of cervical cells into different abnormality levels is of great clinical importance but remains very challenging. Contrary to traditional classification methods that rely on hand-crafted or engineered features, convolution neural network (CNN) can classify cervical cells based on automatically learned deep features. However, CNN in previous studies do not involve cell morphological information, and it is unknown whether morphological features can be directly modeled by CNN to classify cervical cells. This paper presents a CNN-based method that combines cell image appearance with cell morphology for classification of cervical cells in Pap smear. The training cervical cell dataset consists of adaptively re-sampled image patches coarsely centered on the nuclei. Several CNN models (AlexNet, GoogleNet, ResNet and DenseNet) pre-trained on ImageNet dataset were fine-tuned on the cervical dataset for comparison. The proposed method is evaluated on the Herlev cervical dataset by five-fold cross-validation at patient level splitting. Results show that by adding cytoplasm and nucleus masks as raw morphological information into appearance-based CNN learning, higher classification accuracies can be achieved in general. Among the four CNN models, GoogleNet fed with both morphological and appearance information obtains the highest classification accuracies of 94.5% for 2-class classification task and 64.5% for 7-class classification task. Our method demonstrates that combining cervical cell morphology with appearance information can provide improved classification performance, which is clinically important for early diagnosis of cervical dysplastic changes., 7 pages, 4 figures

The engineered cementitious composite (ECC) cracking process involves discontinuities. To solve this difficulty, a semi-discrete model for ECC based on peridynamics (PD) is established. In this paper, the pairwise force function is modified by using an improved damage coefficient. This model is established by modeling the matrix and the interactions between the fiber and matrix. The interactions between the fiber and matrix are applied to the material point in the form of forces. A reasonable horizon size and grid spacing ratio are obtained by analyzing the convergence of the ECC plate’s crack propagation. The validity of the PD model in this paper is demonstrated by comparing the uniaxial tensile simulation results of ECC plates with the relevant experimental results. Finally, the effects of pre-crack position and fiber volume fraction on dynamic fracture under an impact load are studied. This work helps to predict the crack propagation path of ECC structures which is important to guide engineering practice.

The different semantics of Chinese words will make a variety of relations between Chinese words. In the known relationship, synonymy, antisense, hypernym, etc. are the most common relationship. Because of the intricate relationship between Chinese words, so that words and other words form a series of knowledge network, which contains a variety of useful information. Based on these knowledge networks, this paper establishes the knowledge graph of Chinese words and analyzes the inherent characteristics of Chinese words. Establish the semantic database of words, and improve the semantic library structure, including the relationship between the number of semantic library words and words (synonymy, hypernym, etc). Based on the semantic database, the semantic network is formed, and community discovery is carried out for the collection of synonyms in the semantic network, and the community properties are extracted to form a complete semantic relation network.