다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Solid oxide fuel cell: Decade of progress, future perspectives and challenges

Life cycle assessment of H2O electrolysis technologies

A review on cathode processes and materials for electro-reduction of carbon dioxide in solid oxide electrolysis cells

A review on recent advances and trends in symmetrical electrodes for solid oxide cells

Understanding how much users disclose personal information in Online Social Networks (OSN) has served various scenarios such as maintaining social relationships and customer segmentation. Prior studies on self-disclosure have relied on surveys or users' direct social networks. These approaches, however, cannot represent the whole population nor consider user dynamics at the community level. In this paper, we conduct a quantitative study at different granularities of networks (ego networks and user communities) to understand users' self-disclosing behaviors better. As our first contribution, we characterize users into three types (open, closed, and moderate) based on the Communication Privacy Management theory and extend the analysis of the self-disclosure of users to a large-scale OSN dataset which could represent the entire network structure. As our second contribution, we show that our proposed features of ego networks and positional and structural properties of communities significantly affect self-disclosing behavior. Based on these insights, we present the possible relation between the propensity of the self-disclosure of users and the sociological theory of structural holes, i.e., users at a bridge position can leverage advantages among distinct groups. To the best of our knowledge, our study provides the first attempt to shed light on the self-disclosure of users using the whole network structure, which paves the way to a better understanding of users' self-disclosing behaviors and their relations with overall network structures.

/pdf/effects-of-ego-networks-and-communities-on-self-disclosure-1d4o7jmck2.pdf

Effects of ego networks and communities on self-disclosure in an online social network

Development of a PrBaMn2O5+δ-La0.8Sr0.2Ga0.85Mg0.15O3-δ composite electrode by scaffold infiltration for reversible solid oxide fuel cell applications

The high energy density of hydrogen, in addition to its convenience for transportation and infinite resource base, make it a promising energy carrier. Solid oxide fuel cells (SOFCs), in particular®which utilize the oxidation of hydrogen at high temperatures to generate electricity®have been studied widely because of their high efficiency and relatively low cost. However, the lack of a suitable mass production method currently precludes the commercialization of SOFCs. To address this, we herein evaluate tape-casting as a means to reduce the cost of SOFC mass production. A simple de-airing technique is used to simplify the production process and an electrolytesupported SOFC is produced without employing a buffer or functional layers. The rheological properties of green tape slurries are explored to improve tape completeness and electrolyte performance. Electrolyte conductivity is measured for a fabricated half-cell; the fine structural details are analyzed via scanning electron microscopy. As a result, a unit cell with an open-circuit voltage of 1.05V and an electric power density of 0.476Wcm12 at 800 °C was fabricated. ©2020 The Ceramic Society of Japan. All rights reserved.

Fabrication of electrolyte-supported solid oxide fuel cells using a tape casting process

Usability challenges and social acceptance of textual input in a context of extended realities (XR) motivate the research of novel input modalities. We investigate the fusion of inertial measurement unit (IMU) control and surface electromyography (sEMG) gesture recognition applied to text entry using a QWERTY-layout virtual keyboard. We design, implement, and evaluate the proposed multi-modal solution named MyoKey. The user can select characters with a combination of arm movements and hand gestures. MyoKey employs a lightweight convolutional neural network classifier that can be deployed on a mobile device with insignificant inference time. We demonstrate the practicality of interruption-free text entry with MyoKey, by recruiting 12 participants and by testing three sets of grasp micro-gestures in three scenarios: empty hand text input, tripod grasp (e.g., pen), and a cylindrical grasp (e.g., umbrella). With MyoKey, users achieve an average text entry rate of 9.33 words per minute (WPM), 8.76 WPM, and 8.35 WPM for the freehand, tripod grasp, and cylindrical grasp conditions, respectively.

MyoKey: Inertial Motion Sensing and Gesture-Based QWERTY Keyboard for Extended Realities

Study on Possibility of PrBaMn 2 O 5+ δ as Fuel Electrode Material of Solid Oxide Electrolysis Cell

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