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It is required to calculate the stored reactive energy of an antenna in order to evaluate its Q factor. Although it has been investigated for a long time, some issues still need further clarification. The main difficulty involved is that the reactive energy of an antenna tends to become infinitely large when integrating the conventionally defined energy density in the whole space outside a small sphere containing the antenna. The reactive energy is usually made to be bounded by subtracting an additional term associated with the radiation fields. However, there exists no well-accepted accurate definition for this additional term that is valid for all cases. By re-checking the definition of reactive energies, an alternative formulation is proposed in this paper which can separate the reactive energy and the radiation energy explicitly based on source-potentials. The clearly defined reactive energy is bounded without necessity to subtract that additional term, and the resultant formulae are easy to implement.

There are several formulations for calculating the Q factors of antennas. The evaluated Q factors are not always in agreement because of the differences in the definition of Q factor and the method in addressing the stored reactive energies. This paper gives a short comparison of five of them with numerical examples.

In this paper, we solve volume integral equations using discontinuous Galerkin method and half Schaubert-Wilton-Glisson (HSWG) basis functions. It is not required to enforce geometric continuity at the interface between two elements. Thus, this method can be applied for scattering problems for complex structures using domain decomposition method, even with slit or overlap, compared with traditional method. The good performance of this method has been verified with numerical examples.

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.

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