New Era of Air Quality Monitoring from Space: Geostationary Environment Monitoring Spectrometer (GEMS)

GEMS will monitor air quality over Asia at unprecedented spatial and temporal resolution from GEO for the first time, providing column measurements of aerosol, ozone and their precursors (nitrogen dioxide, sulfur dioxide and formaldehyde). Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in late 2019 - early 2020 to monitor Air Quality (AQ) at an unprecedented spatial and temporal resolution from a Geostationary Earth Orbit (GEO) for the first time. With the development of UV-visible spectrometers at sub-nm spectral resolution and sophisticated retrieval algorithms, estimates of the column amounts of atmospheric pollutants (O3, NO2, SO2, HCHO, CHOCHO and aerosols) can be obtained. To date, all the UV-visible satellite missions monitoring air quality have been in Low Earth orbit (LEO), allowing one to two observations per day. With UV-visible instruments on GEO platforms, the diurnal variations of these pollutants can now be determined. Details of the GEMS mission are presented, including instrumentation, scientific algorithms, predicted performance, and applications for air quality forecasts through data assimilation. GEMS will be onboard the GEO-KOMPSAT-2 satellite series, which also hosts the Advanced Meteorological Imager (AMI) and Geostationary Ocean Color Imager (GOCI)-2. These three instruments will provide synergistic science products to better understand air quality, meteorology, the long-range transport of air pollutants, emission source distributions, and chemical processes. Faster sampling rates at higher spatial resolution will increase the probability of finding cloud-free pixels, leading to more observations of aerosols and trace gases than is possible from LEO. GEMS will be joined by NASA's TEMPO and ESA's Sentinel-4 to form a GEO AQ satellite constellation in early 2020s, coordinated by the Committee on Earth Observation Satellites (CEOS)

Inference of Drawing Elements and Space Usage on Architectural Drawings Using Semantic Segmentation

Artificial intelligence presents an optimized alternative by performing problem-solving knowledge and problem-solving processes under specific conditions. This makes it possible to creatively examine various design alternatives under conditions that satisfy the functional requirements of the building. In this study, in order to develop architectural design automation technology using artificial intelligence, the characteristics of an architectural drawings, that is, the architectural elements and the composition of spaces expressed in the drawings, were learned, recognized, and inferred through deep learning. The biggest problem in applying deep learning in the field of architectural design is that the amount of publicly disclosed data is absolutely insufficient and that the publicly disclosed data also haves a wide variety of forms. Using the technology proposed in this study, it is possible to quickly and easily create labeling images of drawings, so it is expected that a large amount of data sets that can be used for deep learning for the automatic recommendation of architectural design or automatic 3D modeling can be obtained. This will be the basis for architectural design technology using artificial intelligence in the future, as it can propose an architectural plan that meets specific circumstances or requirements

Analysis of Trends in Korean BIM Research and Technologies Using Text Mining

Building information modeling (BIM) has emerged as one of the key trends in the architecture, engineering, and construction (AEC) industry. As interest in BIM rapidly increased, the quantity of related literature also increased, thus, it has become important to analyze and identify the key topics and trends in BIM research over this period. Therefore, in order to analyze the research and technology trends related to BIM in Korea, we used the text-mining technique. In addition, by analyzing BIM-related papers using text-mining technology, we can analyze the patterns, main research trends, and trends in specific fields through data preprocessing, which formalizes the unstructured data of sentences, thus presenting us with a specific strategic plan for the future direction of research and technology related to BIM in Korea. In order to propose a strategic direction for future BIM-related research and technology in Korea, in this study, many researches related to BIM in Korea were collected and changes in the patterns and research trends of the BIM research periods were analyzed by dividing them it into the periods of introduction, development, and adaptation using text-mining methods and techniques, frequency analysis, and topic modeling methods. Therefore, in the future, BIM will be developed not only in the field of architecture but in all fields related to the construction industries, such as civil engineering, aviation, and shipping, and more active research will be conducted in various fields

Simple and Efficient Strategy for Site-Specific Dual Labeling of Proteins for Single-Molecule Fluorescence Resonance Energy Transfer Analysis

Analysis of protein dynamics using single-molecule fluorescence resonance energy transfer (smFRET) is widely used to understand the structure and function of proteins. Nonetheless, site-specific labeling of proteins with a pair of donor and acceptor dyes still remains a challenge. Here we present a general and facile method for site-specific dual labeling of proteins by incorporating two different, readily available, unnatural amino acids (<i>p</i>-acetylphenylalanine and alkynyllysine) for smFRET. We used newly evolved alkynyllysine-specific aminoacyl-tRNA synthetase/tRNA_UCA and <i>p</i>-acetylphenylalanyl-tRNA synthetase/tRNA_CUA. The utility of our approach was demonstrated by analyzing the conformational change of dual-labeled calmodulin using smFRET measurements. The present labeling approach is devoid of major limitations in conventional cysteine-based labeling. Therefore, our method will significantly increase the repertoire of proteins available for FRET study and expand our ability to explore more complicated molecular dynamics

Simultaneous Measurements of Chemical Compositions of Fine Particles during Winter Haze Period in Urban Sites in China and Korea

We performed simultaneous measurements of chemical compositions of fine particles in Beijing, China and Gwangju, Korea to better understand their sources during winter haze period. We identified PM2.5 events in Beijing, possibly caused by a combination of multiple primary combustion sources (biomass burning, coal burning, and vehicle emissions) and secondary aerosol formation under stagnant conditions and/or dust sources under high wind speeds. During the PM2.5 events in Gwangju, the contribution of biomass burning and secondary formation of nitrate and organics to the fine particles content significantly increased under stagnant conditions. We commonly observed the increases of nitrogen-containing organic compounds and biomass burning inorganic (K+) and organic (levoglucosan) markers, suggesting the importance of biomass burning sources during the winter haze events (except dust event cases) at both sites. Pb isotope ratios indicated that the fraction of Pb originated from possibly industry and coal combustion sources increased during the PM2.5 events in Gwangju, relative to nonevent days