Flow Characteristics Around Step-Up Street Canyons with Various Building Aspect Ratios

We investigate the flow characteristics around step-up street canyons with various building aspect ratios (ratio of along-canyon building length to street-canyon width, and upwind building height to downwind building height) using a computational fluid dynamics (CFD) model. Simulated results are validated against experimental wind-tunnel results, with the CFD simulations conducted under the same building configurations as those in the wind-tunnel experiments. The CFD model reproduces the measured in-canyon vortex, rooftop recirculation zone above the downwind building, and stagnation point position reasonably well. We analyze the flow characteristics, focusing on the structural change of the in-canyon flows and the interaction between the in- and around-canyon flows with the increase of building-length ratio. The in-canyon flows undergo development and mature stages as the building-length ratio increases. In the development stage (i.e., small building-length ratios), the position of the primary vortex wanders, and the incoming flow closely follows both the upstream and downstream building sidewalls. As a result, increasing momentum transfer from the upper layer contributes to a momentum increase in the in-canyon region, and the vorticity in the in-canyon region also increases. In the mature stage (i.e., large building-length ratios), the primary vortex stabilizes in position, and the incoming flow no longer follows the building sidewalls. This causes momentum loss through the street-canyon lateral boundaries. As the building-length ratio increases, momentum transfer from the upper layer slightly decreases, and the reverse flow, updraft, and streamwise flow in the in-canyon region also slightly decrease, resulting in vorticity reduction

Unsupervised Hyperbolic Representation Learning via Message Passing Auto-Encoders

Most of the existing literature regarding hyperbolic embedding concentrate upon supervised learning, whereas the use of unsupervised hyperbolic embedding is less well explored. In this paper, we analyze how unsupervised tasks can benefit from learned representations in hyperbolic space. To explore how well the hierarchical structure of unlabeled data can be represented in hyperbolic spaces, we design a novel hyperbolic message passing auto-encoder whose overall auto-encoding is performed in hyperbolic space. The proposed model conducts auto-encoding the networks via fully utilizing hyperbolic geometry in message passing. Through extensive quantitative and qualitative analyses, we validate the properties and benefits of the unsupervised hyperbolic representations. Codes are available at https://github.com/junhocho/HGCAE

Labisia pumila extract protects skin cells from photoaging caused by UVB irradiation

Labisia pumila (Myrsinaceae), known as "Kacip Fatimah," has been used by many generations of Malay women to induce and facilitate child birth as well as a post partum medicine. However, its topical application on skin has not been reported yet. In this study, we have focused on the anti-photoaging effects of L. pumila. Extract of L. pumila was first analyzed for their antioxidant activities using DPPH (2,2-diphenyl-1-picrylhydrazyl) since UV irradiation is a primary cause of reactive oxygen species (ROS) generation in the skin. The 50% free radical scavenging activity (FSC(50)) of L. pumila extract was determined to be 0.006%, which was equal to that produced by 156 microM ascorbic acid. TNF-alpha and cyclooxygenase (COX-2) play a primary role in the inflammation process upon UV irradiation and are known to be stimulated by UVB. Treatment with L. pumila extract markedly inhibited the TNF-alpha production and the expression of COX-2. Decreased collagen synthesis of human fibroblasts by UVB was restored back to normal level after treatment with L. pumila extract. On the other hand, the enhanced MMP-1 expression upon UVB irradiation was down regulated by L. pumila extract in a dose-dependent manner. Furthermore, treatment of normal keratinocytes with L. pumila extract attenuated UVB-induced MMP-9 expression. These results collectively suggest L. pumila extract has tremendous potential as an anti-photoaging cosmetic ingredient

A Study on Thermal Modeling and Heat Load Mitigation for Satellite Electronic Components

Since most of the satellite components are using various EEE (Electrical, Electronic and Electromechanical) parts, the reliability of EEE parts acts very important in the satellite system. There are many factors that influence the reliability of EEE parts in the satellite system. Excessively dissipated heat can cause the failure of EEE parts and consequently, leading to a failure of total satellite system. In this paper, the thermal modeling using nodal network was compared with that using plate modeling to find out which one is the most suitable methodology. For a comparison, KOMPSAT- 1 SAR was modeled by two different modeling and the result was discussed. There was almost no difference in the numerical results between the two modeling methods. However, while it took much more time to perform thermal analysis using the nodal network modeling method, and the debugging was more difficult in the plate modeling method when the error is occurred. The computation time was considerably reduced by developing and implementing the input file format transfer code when using nodal network modeling method. It was found that the nodal network modeling method is suitable for the complicated components, such as SAR or transponder, because of its simple debugging ability. Excessive heat load was expected on some EEE parts of SAR such as high heat-dissipated diodes, transistors, and inductors due to increased power requirements of KOMPSAT-2 satellite system. The methods for the mitigation of heat load were studied through the design change of housing or the layout change of high power parts

Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning

We propose a symmetric graph convolutional autoencoder which produces a low-dimensional latent representation from a graph. In contrast to the existing graph autoencoders with asymmetric decoder parts, the proposed autoencoder has a newly designed decoder which builds a completely symmetric autoencoder form. For the reconstruction of node features, the decoder is designed based on Laplacian sharpening as the counterpart of Laplacian smoothing of the encoder, which allows utilizing the graph structure in the whole processes of the proposed autoencoder architecture. In order to prevent the numerical instability of the network caused by the Laplacian sharpening introduction, we further propose a new numerically stable form of the Laplacian sharpening by incorporating the signed graphs. In addition, a new cost function which finds a latent representation and a latent affinity matrix simultaneously is devised to boost the performance of image clustering tasks. The experimental results on clustering, link prediction and visualization tasks strongly support that the proposed model is stable and outperforms various state-of-the-art algorithms.Comment: 10 pages, 3 figures, ICCV 2019 accepte

Optimal design of quadratic electromagnetic exciter

The vibration acceleration of collecting plates, which is the core indicator of rapping performance in an electrostatic precipitator’s vibration rapping process, is determined by magnetic force of a quadratic electromagnetic exciter. The larger exciter provides the larger magnetic force, but the installation space for the exciter is limited. Accordingly, this paper presents the optimal design of quadratic electromagnetic exciter to maximize the magnetic force with constraint that the size of exciter is constant. A design optimization problem was formulated in order to find the quadratic electromagnetic exciter shape parameters that maximized the magnetic force. The magnetic force of the quadratic electromagnetic exciter was evaluated using the commercial electromagnetic analysis software “MAXWELL”. For efficient design, we employed metamodel-based design optimization using design of experiments (DOE), metamodels, and an optimization algorithm equipped in PIAnO (Process Integration, Automation and Optimization), a commercial PIDO (Process Integration and Design Optimization) tool. Using the proposed design approach, the optimal magnetic force was increased by 1.68 % compared to the initial one. This result demonstrates the effectiveness of the established analysis and design procedure for the quadratic electromagnetic exciter