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COSINE software package provides various functions in thermal-hydraulic analysis of pressurized water reactor (PWR) after years of development. In order to meet the engineering design and safety analysis requirements of the advanced passive reactor CAP1400, the multiphase field sub-channel code with higher calculation accuracy was developed based on the homogeneous sub-channel program (cosSUBC) in COSINE software package. The multiphase sub-channel code contained 11 basic equations including the mass conservation equations of the vapor, continuous liquid, and droplet, the axial momentum conservation equations of the gas, continuous liquid phase, droplet phase, the transverse momentum conservation equation of the gas, continuous liquid, and droplet, and the energy conservation equations of the gas and total liquid phases. The continuous liquid and the droplet were in energy equilibrium states in one cell was assumed, therefore the continuous liquid and droplet were merged into total liquid phases in energy conservation equation. The constitutive relation models between different phases were introduced in the code to realize the closure of the basic equations, the staggered discrete format in space and a semi-implicit time discrete format were adopted in multiphase field sub-channel code. The typical test cases and representative experiments were modeled and calculated in this paper to verify the calculation capability of the code. First, the test of droplet behavior was modeled to verify the correctness of droplet model adopted in the code. Then the turbulent mixing experiment was modeled to verify the correctness of turbulent mixing model used in the code. In the end, the wall heat transfer experiment and the reflooding process experiment were modeled to verify the simulation ability in the heat transfer regime especially in the post critical heat flux (post-CHF) condition. The results show that the droplet behaviors including the generation and the flow distribution of droplet phase are in good agreement with the reference code. And the code can well simulate the flow distribution phenomenon according the comparison between the calculation results and experimental data in flow distribution experiment and the turbulent mixing experiment. Besides, the code can well simulate the distribution of the wall temperature and the reflooding process in the post-CHF and heat transfer condition. Therefore, the calculation of the thermal-hydraulic phenomena in the core of reactor by the multiphase field sub-channel code is reasonable and right, and the code has the ability to calculate and simulate the thermal-hydraulic conditions in the core of PWR.

We attempt to investigate how and when foreign direct investment (FDI) impacts different types of environmental pollution in host countries. Using provincial data from China between 1995 and 2015, we find that FDI mitigates air pollution, yet it has insignificant effect on water and solid pollution. We further reveal that it is the combination of the technology, scale and structure effects that jointly determines the impact of FDI on environmental pollution. Among them, the technology effect takes the most dominant role, followed by the scale effect and structure effect. In addition, by considering the time effect on environmental policy change, we suggest that the pollution halo effect mainly occurs after air pollution policy revision. Our findings provide insight on the complex mechanisms and theoretical boundary of FDI on different types of environmental pollutants. [ABSTRACT FROM AUTHOR]

Transfer learning technique is popularly employed for a lot of medical image classification tasks. Here based on convolutional neural network (CNN) and sparse coding process, we present a new deep transfer learning architecture for false positive reduction in lymph node detection task. We first convert the linear combination of the deep transferred features to the pre-trained filter banks. Next, a new point-wise filter based CNN branch is introduced to automatically integrate transfer features for the false and positive image classification purpose. To lower the scale of the proposed architecture, we bring sparse coding process to the fixed transferred convolution filter banks. On this basis, a two-stage training strategy with grouped sparse connection is presented to train the model efficiently. The model validity is tested on lymph node dataset for false positive reduction and our approach indicates encouraging performances compared to prior approaches. Our method reaches sensitivities of 71% /85% at 3 FP/vol. and 82% /91% at 6 FP/vol. in abdomen and mediastinum respectively, which compare competitively to previous approaches. [ABSTRACT FROM AUTHOR]

Surface flattening plays an important role in the whole process of garment design. We proposed a novel method by using three-dimensional triangle mesh flattening in this study. First, the three-dimensional triangle mesh is flattened to a two-dimensional plane to approximate the original surface. The initial flattening results are then used as preliminary guesses for subsequent optimizations. Considering that the deformation energy in the real woven fabric is related to tensile or shear deformation, a simplified fabric deformation model based on energy is proposed to update the energy distribution to determine the best two-dimensional pattern. An innovative unified axis system process is proposed to obtain the deformation energy, and energy relaxation in local flattening is proposed to release the distortion of flattening. Finally, the experimental results show that complex surfaces such as garments could achieve better flattening results. Compared with other energy-based methods in garment design, our proposed methods are more flexible and practical. [ABSTRACT FROM AUTHOR]

Recently years, convolutional neural networks (CNNs) have proven to be powerful tools for a broad range of computer vision tasks. However, training a CNN from scratch is difficult because it requires a large amount of labeled training data, which remains a challenge in medical imaging domain. To this end, deep transfer learning (TL) technique is widely used for many medical image tasks. In this paper, we propose a novel multisource transfer learning CNN model for lymph node detection. The mechanism behind it is straightforward. Point‐wise (1 × 1) convolution is used to fuse multisource transfer learning knowledge. Concretely, we view the transferred features as priori domain knowledge and 1 × 1 convolutional operation is implemented after pre‐trained convolution layers to adaptively combine the transfer information for target task. In order to learn non‐linear transferred features and prevent over‐fitting, we present an encode process for the pre‐trained convolution kernels. At last, based on convolutional factorization technique, we train the proposed CNN model and the encoder process jointly, which improves the feasibility of our approach. The effectiveness of the proposed method is verified on lymph node (LN) dataset: 388 mediastinal LNs labeled by radiologists in 90 patient CT scans, and 595 abdominal LNs in 86 patient CT scans for LN detection. Our method demonstrates sensitivities of about 85%/71% at 3 FP/vol. and 92%/85% at 6 FP/vol. for mediastinum and abdomen respectively, which compares favorably to previous methods. [ABSTRACT FROM AUTHOR]

Many medical and real images are suffered from intensity inhomogeneity and weak edges. For higher image segmentation quality, lots of level set-based methods have been proposed. Some of them however cannot take advantage of image gradient information. And severe intensity inhomogeneity and weak edges are not disposed properly. To address these problems, a new level set method integrated with local direction gradient information is presented in this paper. Firstly, according to the two assumptions on image intensity inhomogeneity adopted by many existing methods, a new pixel classification model based on image gradient is introduced. Secondly, we employ variational level set method combined with image spatial information, which improves the anti-noise capability of the proposed method. Finally, considering the gray gradients in homogeneous regions are close to constants, an improved diffusion process is incorporated into the level set function to make the evolving curves stay around true image edges. To verify our method, different testing images including synthetic images, magnetic resonance imaging (MRI) and real-world images are introduced. The image segmentation results demonstrate that our method can deal with the relatively severe intensity inhomogeneity and obtain the comparatively ideal segmentation results efficiently. [ABSTRACT FROM AUTHOR]

In this paper, an interactive dynamic simulation method is proposed to solve computational models of soft tissue undergoing large deformation, collision detection, and volume conservation in medical surgical simulation visualization. During the process of implementation of the interactive dynamic simulation method, the point-based method is used to simulate the elastic solids undergoing large deformations and the position-based method is used to simulate the objects collision, friction and volume conservation. Numerical results demonstrate that the proposed method improves the efficiency and stability of the response of heterogeneous soft tissue undergoing contact or even the multi-organs interactions, and it can be extended to interactive biopsy and cutting simulation. [ABSTRACT FROM AUTHOR]

We attempt to investigate how and when foreign direct investment (FDI) impacts different types of environmental pollution in host countries. Using provincial data from China between 1995 and 2015, we find that FDI mitigates air pollution, yet it has insignificant effect on water and solid pollution. We further reveal that it is the combination of the technology, scale and structure effects that jointly determines the impact of FDI on environmental pollution. Among them, the technology effect takes the most dominant role, followed by the scale effect and structure effect. In addition, by considering the time effect on environmental policy change, we suggest that the pollution halo effect mainly occurs after air pollution policy revision. Our findings provide insight on the complex mechanisms and theoretical boundary of FDI on different types of environmental pollutants. Keywords: Foreign Direct Investment (FDI), Environmental Pollution, Pollution Haven argument, Pollution Halo argument, China, 1. INTRODUCTION Over the last two decades, emerging economies have been among the largest recipients of foreign direct investment (FDI) in the world (Kudina and Pitelis, 2014; Majumdar and Kar, [...]