• Journal of Korean Society on Water Environment
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• v.39 no.2
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• pp.181-193
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• 2023

Distribution changes in microalgae communities were studied in the Nakdonggang River at two sampling stations (St.1 Gyeongcheongyo Bridge (GB) and St.2 Daedong Wharf (DW)) at monthly intervals from January 2021 to November 2021. A total of 83 taxa included 82 species, 1 forma, belonging to 49 genera, 32 families, 21 orders, and 8 classes. The most important groups were Bacillariophyta and Chlorophyta. The number of species ranged from 5 to 24 in GB, and from 9 to 21 taxa in DW. The contribution of Bacillariophyta to the total species richness was the highest during all survey periods, and Chlorophyta yielded the next highest value in the study area. The dominant taxa were Aulacoseira ambigua, A. ambigua f. japonica, and Ulnaria acus in this study. Cluster analysis and non-metric multidimensional scaling (nMDS) analysis based on Bray- Curtis similarity identified 4 major groups, which corresponded to microalgae assemblages and their characteristic species. Correlation was analyzed through the CCA analysis. It was found that there was a correlation between the microalgae and environmental factors. It was revealed that the divided groups were distinguished because of the differences by the survey period. Therefore, seasonal change was judged as a major factor affecting the distribution of microalgae communities.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.48-48
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• 2023

The intensified droughts under climate change are expected to threaten stable water resource availability. Droughts exceeding the magnitude of historical variability could occur increasingly frequently under future climate conditions. It is crucial to understand how drought will evolve over time because the assumption of hydrological stationarity of the past decades would be inappropriate for future water resources management. However, the timing of the emergence of unprecedented drought conditions under climate change has rarely been examined. Here, using multimodel hydrological simulations, we investigate the changes in the frequency of hydrological drought (defined as abnormally low river discharge) under high and low greenhouse gas concentration scenarios and with existing water resources management and estimate the timing of the first emergence of unprecedented regional drought conditions that persist for over several consecutive years. This new metric enables a new quantification of the urgency of adaptation and mitigation with regard to drought under climate change. The times are detected for several sub-continental-scale regions, and three regions, namely, southwestern South America, Mediterranean Europe, and northern Africa, exhibit particularly robust and earlier critical times under the high-emission scenario. These three regions are expected to confront unprecedented conditions within the next 30 years with a high likelihood, regardless of the emission scenarios. In addition, the results obtained herein demonstrate the benefits of the lower-emission pathway in reducing the likelihood of emergence. The Paris Agreement goals are shown to be effective in reducing the likelihood to the unlikely level in most regions. Nevertheless, appropriate and prior adaptation measures are considered indispensable to when facing unprecedented drought conditions. The results of this study underscore the importance of improving drought preparedness within the considered time horizons.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.314-314
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• 2023

Extreme climate events can have a large impact on human life by hampering social, environmental, and economic development. Global circulation models (GCMs) are the widely used numerical models to understand the anticipated future climate change. However, different GCMs can project different future climates due to structural differences, varying initial boundary conditions and assumptions about the physical phenomena. The multi-model ensemble (MME) approach can improve the uncertainties associated with the different GCM outcomes. In this study, a comprehensive rating metric was used to select the best-performing GCMs out of 11 CMIP5 and 13 CMIP6 GCMs, according to their skills in terms of four temporal and five spatial performance indices, in replicating the 21 extreme climate indices during the baseline (1975-2017) in South Korea. The MME data were derived by averaging the simulations from all selected GCMs and three top-ranked GCMs. The random forest (RF) algorithm was also used to derive the MME data from the three top-ranked GCMs. The RF-derived MME data of the three top-ranked GCMs showed the highest performance in simulating the baseline extreme climate which was subsequently used to project the future extreme climate indices under both the representative concentration pathway (RCP) and the socioeconomic concentration pathway scenarios (SSP). The extreme cold and warming indices had declining and increasing trends, respectively, and most extreme precipitation indices had increasing trends over the period 2031-2100. Compared to all scenarios, RCP8.5 showed drastic changes in future extreme climate indices. The coasts in the east, south and west had stronger warming than the rest of the country, while mountain areas in the north experienced more extreme cold. While extreme cold climatology gradually declined from north to south, extreme warming climatology continuously grew from coastal to inland and northern mountainous regions. The results showed that the socially, environmentally and agriculturally important regions of South Korea were at increased risk of facing the detrimental impacts of extreme climatology.

• Journal of the Korea Society for Simulation
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• v.18 no.4
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• pp.185-195
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• 2009

As most systems get more complicated, system analysis using simulation has been taken notice of. One of the core parts of simulation analysis is validation of a simulation model, and we can identify how well the simulation model represents the real system with this validation process. The difference between input data of two systems has an effect on the comparison between a simulation model and a real system at validation stage, and the result with such difference is not enough to ensure high credibility of the model. Accordingly, in this paper, we construct a model based on Trace-driven simulation which uses identical input data with the real system. On the other hand, to validate a model by each class, not by an unique statistic, we validate the model using a metric transformed from F-measure which estimates performance of a classifier in data mining field. Finally, this procedure enables precise validation process of a model, and it helps modification by offering feedback at the validation phase.

• Smart Structures and Systems
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• v.30 no.4
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• pp.339-351
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• 2022

Evaluating the current condition of existing structures is of primary importance for economic and safety reasons. This can be addressed by Structural System Identification (SSI). A reliable static SSI depends on well-designed sensor configuration and loading cases, as well as efficient parameter estimation algorithms. Static SSI by the Measurement Error-Minimizing Observability Method (MEMOM) is a model-based deterministic static SSI method that could estimate structural parameters from static responses. In the current state of the art, this method is only applicable when structures are subjected to one loading case. This might lead to lack of information in some local regions of the structure (such as the null curvatures zones). To address this issue, the SSI by MEMOM using multiple loading cases is proposed in this work. Observability equations obtained from different loading cases are concatenated simultaneously and an optimization procedure is introduced to obtain the estimations by minimizing the discrepancy between the predicted response and the measured one. In addition, a Genetic-Algorithm (GA)-based Optimal Sensor Placement (OSP) method is proposed to tackle the OSP problem under multiple static loading cases for the very first time. In this approach, the Fisher Information Matrix (FIM)'s determinant is used as the metric of the goodness of sensor configurations. The numerical examples of a 3-span continuous bridge and a 13-story frame, are analyzed to validate the applicability of the extended SSI by MEMOM and the GA-based OSP method.

• Journal of Dairy Science and Biotechnology
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• v.16 no.2
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• pp.106-118
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• 1998

Whey is a natural product obtained during cheese production. With the advent of new technology, whey protein concentrates and whey fractions have become readily available and versatile food ingredients. Whey protein concentrates are highly functional ingredients that have gelling, emulsifying, whipping, water-binding and fat-replacement properties. New fractions derived from whey (such as alpha-lactalbumin, lactoferrin, lactoperoxidase and peptides) attract considerable interest worldwide because of their bioactive or health-enhancing properties. Some of these fractions also find new uses as natural antibiotic, natural preservative and immunity-enhancing agents. With the growth of the functional foods industry sector, an increasing number of manufacturers take advantage of whey's nutritional and functional benefits to develop successful new products. The United States is the world's largest single producer and exporter of whey products. In 1997, more than 1 million metric tons of whey products were manufacturers in the U.S.

• Journal of the Korean institute of surface engineering
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• v.34 no.5
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• pp.403-408
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• 2001

In this work, the cyclic perfluorinated ether (c-C$_4$F$_{8}$O) with very high destructive removal efficiency (DRE) than other alternative gases, such as $C_3$F$_{8}$, c-C$_4$F$_{8}$ and NF$_3$ was used as an alternative process chemical. The plasma cleaning of silicon nitride using gas mixtures of c-C$_4$F$_{8}$O/O$_2$ and c-C$_4$F$_{8}$O/O$_2$+ $N_2$ was investigated in order to evaluate the effects of adding $N_2$ to c-C$_4$F$_{8}$O/O$_2$ on the global warming effects. Under optimum condition, the emitted net perfluorocompounds (PFCs) during cleaning of silicon nitride were quantified and then the effects of additive $N_2$ by obtaining the destructive removal efficiency (DRE) and the million metric tons of carbon equivalent (MMT-CE) were calculated. DRE and MMTCE were obtained by evaluating the volumetric emission using. Fourier transform-infrared spectroscopy (FT-IR). During the cleaning using c-C$_4$F$_{8}$O/O$_2$+$N_2$, DRE values as high as (equation omitted) 98% were obtained and MMTCE values were reduced by as high as 70% compared to the case of $C_2$F$_{6}$O$_2$. Recombination characteristics were indirectly investigated by combining the measurements of species in the chamber using optical emission spectroscopy (OES), before and after the cleaning, in order to understand any correlation between plasma and emission characteristics as well as cleaning rate of silicon nitride.silicon nitride.

• Journal of Ecology and Environment
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• v.47 no.1
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• pp.1-13
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• 2023

Background: The positive effects of Arctic plants on the soil environment and plant-species co-occurrence patterns are known to be particularly important in physically harsh environments. Although three dominant plants (Cassiope tetragona, Dryas octopetala, and Silene acaulis) are abundant in the Arctic ecosystem at Ny-Ålesund, Svalbard, few studies have examined their occurrence patterns with other species and their buffering effect on soil-temperature and soil-moisture fluctuation. To quantify the plant-species co-occurrence patterns and their positive effects on soil environments, I surveyed the vegetation cover, analyzed the soil-chemical properties (total carbon, total nitrogen, pH, and soil organic matter) from 101 open plots, and measured the daily soil-temperature and soil-moisture content under three dominant plant patches and bare soil. Results: The Cassiope tetragona and Dryas octopetala communities increased the soil-temperature stability; however, the three dominant plant communities did not significantly affect the soil-moisture stability. Non-metric multidimensional scaling separated the sampling sites into three groups based on the different vegetation compositions. The three dominant plants occurred randomly with other species; however, the vegetation composition of two positive co-occurring species pairs (Oxyria digyna-Cerastium acrticum and Luzula confusa-Salix polaris) was examined. The plant species richness did not significantly differ in the three plant communities. Conclusions: The three plant communities showed distinctive vegetation compositions; however, the three dominant plants were randomly and widely distributed throughout the study sites. Although the facilitative effects of the three Arctic plants on increases in the soil-moisture fluctuation and richness were not quantified, this research enables a deeper understanding of plant co-occurrence patterns in Arctic ecosystems and thereby contributes to predicting the shift in vegetation composition and coexistence in response to climate warming. This research highlights the need to better understand plant-plant interactions within tundra communities.

• Atmosphere
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• v.33 no.3
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• pp.307-317
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• 2023

This study investigates the physical mechanisms that contributed to the 2022 European record-breaking heatwave throughout May-August (MJJA). The European climate has experienced surface warming and drying in the recent decade (1979~2022) which influences the development of the 2022 European heatwave. Since its spatial pattern resembles the 2003 European heatwave which is a well-known case developed by the strong coupling of near-surface conditions to land surface processes, the 2022 heatwave is compared with the 2003 case. Understanding heatwave development is carried out by the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5) and daily maximum surface temperature released by NCEP (National Centers for Environmental Prediction) CPC (Climate Prediction Center). The results suggest that the persistent high pressure along with clear sky tends to increase the downward shortwave radiation which leads to enhanced sensible heat flux with the land surface dryness. Terrestrial Coupling Index (TCI), a process-based multivariate metric, is employed to quantitatively measure segmented feedback processes, separately for the land, atmosphere, and two-legged couplings, which appears to the development of the 2022 heatwave, can be viewed as an expression of the recent trends, amplified by internal land-atmosphere interactions.

• Journal of the Korea Society of Computer and Information
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• v.14 no.4
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• pp.87-99
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• 2009

The top-down method for SOA delivery is recommended as a best way to take advantage of SOA. The core step of SOA delivery is the step of service modeling including service analysis and design based on ontology. Most enterprises know that the top-down approach is the best but they are hesitant to employ it because it requires them to invest a great deal of time and money without it showing any immediate results, particularly because they use well-defined component based systems. In this paper, we propose a service identification method to use a well-defined components maximally as a bottom-up approach. We assume that user's inputs generates events on a GUI and the approximate business process can be obtained from concatenating the event paths. We first find the core GUIs which have many outgoing event calls and form event paths by concatenating the event calls between the GUIs. Next, we adapt sequential pattern mining algorithms to find the maximal frequent event paths. As an experiment, we obtained business services with various granularity by applying a cohesion metric to extracted frequent event paths.