• Analytical Science and Technology
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• v.20 no.5
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• pp.434-441
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• 2007

The aim of this paper is to investigate composition of fatty acids in sweat on purpose of latent fingerprint detectant developing and crime evidence searching. Fingerprint from 5 male donors (aged 29-50 years) were collected. We identified fatty acid components on sweat using methylester mixture (37species) as standard fatty acid and analyzed them by GC-FID. As donor was aged, the level of total fat was found to decrease markedly (aged 20-30 years: 56.4-72.0 %, aged 50 years : 32.4-45.4 %). We identifided 28 species fatty acid, primarilly C16:0(palmitic acid), C16:1 (palmitoleic acid), C18:1n9c(oleic acid), C18:0 (stearic acid), C14:0 (tetradecanoic acid) and all sweats were found to contain C12:0 (lauric acid), C15:0 (pentadecanoic acid), C18:2n6c (linoleic acid), C18:2n6t (linolelaidic acid), C20:0 (arachidic acid), C24:0/C20:5n3 (lignoceric acid/eicosapentaenoic acid), but with differing frequencies and at varying levels. C14:1 (myristoleic acid), C15:1 (pentadecenoic acid), C21:0 (heneicosanoic acid), C22:1n9 (erucic acid) were often observed in sample. Ratio of saturated and unsaturated fatty acid was from 0.94:1 to 2.6:1. And decrease of total fatty acids components caused by loss of saturated fatty acid and monounsaturated fatty acid. In case of sweat amino acids, we detected serine ($0-31.9{\mu}L/mL$), threonine ($0-26.2{\mu}L/mL$), glycine ($0-18.9{\mu}L/mL$) and 20-30 years old, highly protein intake ratio individuals increased (10 times) than 50 years old. We observed greatly individual characterization of amino acid compounds in sweat.

• Journal of Korea Water Resources Association
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• v.56 no.10
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• pp.631-639
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• 2023

Flash drought (FD), characterized by the rapid onset and intensification, can significantly impact ecosystems and induce immediate water stress. A more comprehensive understanding of the causes and characteristics of FD events is required to enhance drought monitoring. Therefore, we investigated the FD events took place over the Korean peninsula using Global Land Data Assimilation System (GLDAS) data from 2012 to 2022. We first detected FD events using the stress-based method (Standardized Evaporative Stress Ratio, SESR), and analyzed the frequency and duration of FDs. The FD events were classified into three cases based on the variations in Actual Evapotranspiration (AET) and potential Evapotranspiration (PET), and spatially analyzed. Results revealed that there are regional disparities in frequency and duration of FDs, with a mean frequency of 6.4 and duration of 31 days. When classified into Case 1 (normal condition), Case 2 (AET-driven), and Case 3 (PET-driven), we found that Case 2 FDs emerged approximately 1.5 times more frequently than those driven by PET (Case 3) across the Korean peninsula. Case 2 FDs were found to be induced under water-limited conditions, and led both AET and PET to be decreased. Conversely, Case 3 FDs occurred under energy-limited conditions, with increase in both. Case 2 FDs predominantly affected the northwestern and central-southern agricultural regions, while Case 3 occurred in the eastern region, characterized by forested land cover. These findings offers insights into our understanding of FDs over the Korean peninsula, considering climate factors, land cover, and water availability.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1283-1297
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• 2023

Mid-wave infrared (MWIR) imagery, due to its ability to capture the temperature of land cover and objects, serves as a crucial data source in various fields including environmental monitoring and defense. The KOMPSAT-3A satellite acquires MWIR imagery with high spatial resolution compared to other satellites. However, the limited spatial resolution of MWIR imagery, in comparison to electro-optical (EO) imagery, constrains the optimal utilization of the KOMPSAT-3A data. This study aims to create a highly visible MWIR fusion image by leveraging the edge information from the KOMPSAT-3A panchromatic (PAN) image. Preprocessing is implemented to mitigate the relative geometric errors between the PAN and MWIR images. Subsequently, we employ a pre-trained pixel difference network (PiDiNet), a deep learning-based edge information extraction technique, to extract the boundaries of objects from the preprocessed PAN images. The MWIR fusion imagery is then generated by emphasizing the brightness value corresponding to the edge information of the PAN image. To evaluate the proposed method, the MWIR fusion images were generated in three different sites. As a result, the boundaries of terrain and objects in the MWIR fusion images were emphasized to provide detailed thermal information of the interest area. Especially, the MWIR fusion image provided the thermal information of objects such as airplanes and ships which are hard to detect in the original MWIR images. This study demonstrated that the proposed method could generate a single image that combines visible details from an EO image and thermal information from an MWIR image, which contributes to increasing the usage of MWIR imagery.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.933-948
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• 2023

Atmospheric aerosols not only have adverse effects on human health but also exert direct and indirect impacts on the climate system. Consequently, it is imperative to comprehend the characteristics and spatiotemporal distribution of aerosols. Numerous research endeavors have been undertaken to monitor aerosols, predominantly through the retrieval of aerosol optical depth (AOD) via satellite-based observations. Nonetheless, this approach primarily relies on a look-up table-based inversion algorithm, characterized by computationally intensive operations and associated uncertainties. In this study, a novel high-resolution AOD direct retrieval algorithm, leveraging machine learning, was developed using top-of-atmosphere reflectance data derived from the Geostationary Ocean Color Imager-II (GOCI-II), in conjunction with their differences from the past 30-day minimum reflectance, and meteorological variables from numerical models. The Light Gradient Boosting Machine (LGBM) technique was harnessed, and the resultant estimates underwent rigorous validation encompassing random, temporal, and spatial N-fold cross-validation (CV) using ground-based observation data from Aerosol Robotic Network (AERONET) AOD. The three CV results consistently demonstrated robust performance, yielding R²=0.70-0.80, RMSE=0.08-0.09, and within the expected error (EE) of 75.2-85.1%. The Shapley Additive exPlanations(SHAP) analysis confirmed the substantial influence of reflectance-related variables on AOD estimation. A comprehensive examination of the spatiotemporal distribution of AOD in Seoul and Ulsan revealed that the developed LGBM model yielded results that are in close concordance with AERONET AOD over time, thereby confirming its suitability for AOD retrieval at high spatiotemporal resolution (i.e., hourly, 250 m). Furthermore, upon comparing data coverage, it was ascertained that the LGBM model enhanced data retrieval frequency by approximately 8.8% in comparison to the GOCI-II L2 AOD products, ameliorating issues associated with excessive masking over very illuminated surfaces that are often encountered in physics-based AOD retrieval processes.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.5
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• pp.1-16
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• 2022

Although start-up is a key national strategy to increase national competitiveness and create employment, the survival rate of start-ups has not improved significantly. This is an important reason for the inability to provide timely and appropriate support to startups, which are in the early stages of start-up, due to the unique limitations of existing start-up support institutions and investors. The relatively recent accelerator is attracting attention as a subject of solving the above problems through professional incubation and investment. However, there are only a few empirical studies on investment determinants that affect the survival and success of accelerators, and there is a lack of theoretical evidence. Accordingly, in previous studies, 12 investment determinants were derived from a static, strategic, and dynamic perspective as accelerator investment determinants based on a business model innovation framework. This study subdivided the accelerator investment determinants derived through previous studies into 21 and analyzed the importance and priority of each factor using AHP (Analytic Hierarchy Process) analysis technique for domestic accelerator investment experts. As a result of the analysis, the top factors of importance of accelerator investment determinants were in the order of 'human resources', 'customer and market', 'intellectual resources', and 'entrepreneur's ability to realize opportunities'. It can be seen that the accelerator considers the core competencies of startups to implement solutions as the most important factor when making startup investment decisions. It was also confirmed that accelerators are strategic to create a clear value proposition and differentiated market position based on the core competitiveness of startups, and that the core value delivery method prefers a market-oriented business model and recognizes entrepreneurs's innovation capability is an important factor to realize a business model with limited resources in a rapidly changing market. This study is of academic significance in that it analyzes the importance and priority of accelerator investment determinants through demonstration as a follow-up study on accelerator investment determinants derived based on business model innovation theory that reflects the nature, goals, and major activities of accelerator investment. In addition, it is of practical value as it contributes to revitalizing the domestic startup investment ecosystem by providing accelerators with theoretical grounds for investment decisions and specific information on detailed investment determinants.

• Journal of Service Research and Studies
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• v.14 no.2
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• pp.1-17
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• 2024

One of the most important decisions for managers in the online banner advertising industry, is to choose the best banner alternative for exposure to customers. Since it is difficult to know the click probability of each banner alternative in advance, managers must experiment with multiple alternatives, estimate the click probability of each alternative based on customer clicks, and find the optimal alternative. In this reinforcement learning process, the main decision problem is to find the optimal balance between the level of exploitation strategy that utilizes the accumulated estimated click probability information and exploration strategy that tries new alternatives to find potentially better options. In this study we analyze the impact of word-of-mouth effects and the number of alternatives on the optimal exploration-exploitation strategies. More specifically, we focus on the word-of-mouth effect, where the click-through rate of the banner increases as customers promote the related product to those around them after clicking the exposed banner, and add it to the overall reinforcement learning process. We analyze our problem by employing the Multi-Armed Bandit model, and the analysis results show that the larger the word-of-mouth effect and the fewer the number of banner alternatives, the higher the optimal exploration level of advertising reinforcement learning. We find that as the probability of customers clicking on the banner increases due to the word-of-mouth effect, the value of the previously accumulated estimated click-through rate knowledge decreases, and therefore the value of exploring new alternatives increases. Additionally, when the number of advertising alternatives is small, a larger increase in the optimal exploration level was observed as the magnitude of the word-of-mouth effect increased. This study provides meaningful academic and managerial implications at a time when online word-of-mouth and its impact on society and business is becoming more important.

• Journal of Life Science
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• v.34 no.4
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• pp.271-278
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• 2024

Redox factor (Ref)-1, a ubiquitously expressed protein, acts as a modulator of redox-sensitive tran- scription factors and as an endonuclease in the repair pathway of damaged DNA. However, the function of Ref-1 in the differentiation of monocytes into macrophages has not been defined. In this study, we investigated the effects of Ref-1 on the monocyte differentiation process using the human monocytic cell line THP-1. The differentiation agent PMA increased cell adhesion over time and showed a sig- nificant increase in phagocytic function but decreased the intracellular amount of Ref-1. Ref-1 inhibitor E3330 and Ref-1 knockdown using the siRNA technique reduced cell adhesion and the expression of differentiation markers, such as CD14, ICAM-1, and CD11b, by PMA stimulation. This means that the role of Ref-1 is absolutely necessary in the initial process of differentiating THP-1 cells stimulated by PMA. Next, the distribution of Ref-1 was examined in the cytoplasm and nucleus of THP-1 cells stimulated with PMA. Surprisingly, PMA stimulation resulted in the rapid translocation of Ref-1 to the nucleus. To prove that movement of Ref-1 to the nucleus is required for monocyte differentiation, a Ref-1 vector with the nuclear localization sequence (NLS) deleted was used. As a result, overexpression of ∆NLS Ref-1, which restricted movement to the nucleus, suppressed the expression of differentiation markers and notably reduced phagocytic function in PMA-stimulated THP-1 cells. In conclusion, these data suggest that the differentiation of monocytic THP-1 cells requires Ref-1 nuclear translocation during the initial process of biochemical events following stimulation from PMA.

• Journal of the Korean Society of Radiology
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• v.18 no.1
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• pp.45-52
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• 2024

In this study, an AI-based algorithm was developed to prevent image quality deterioration and reading errors due to patient movement in PET/CT examinations that use radioisotopes in medical institutions to test cancer and other diseases. Using the Mothion Free software developed using, we checked the degree of correction of movement due to breathing, evaluated its usefulness, and conducted a study for clinical application. The experimental method was to use an RPM Phantom to inject the radioisotope 18F-FDG into a vacuum vial and a sphere of a NEMA IEC body Phantom of different sizes, and to produce images by directing the movement of the radioisotope into a moving lesion during respiration. The vacuum vial had different degrees of movement at different positions, and the spheres of the NEMA IEC body Phantom of different sizes produced different sizes of lesions. Through the acquired images, the lesion volume, maximum SUV, and average SUV were each measured to quantitatively evaluate the degree of motion correction by Motion Free. The average SUV of vacuum vial A, with a large degree of movement, was reduced by 23.36 %, and the error rate of vacuum vial B, with a small degree of movement, was reduced by 29.3 %. The average SUV error rate at the sphere 37mm and 22mm of the NEMA IEC body Phantom was reduced by 29.3 % and 26.51 %, respectively. The average error rate of the four measurements from which the error rate was calculated decreased by 30.03 %, indicating a more accurate average SUV value. In this study, only two-dimensional movements could be produced, so in order to obtain more accurate data, a Phantom that can embody the actual breathing movement of the human body was used, and if the diversity of the range of movement was configured, a more accurate evaluation of usability could be made.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.1
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• pp.67-76
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• 2024

In this paper, we present a DIP-MLS testing method that combines digital image processing with a rigid body-based MLS differencing approach to measure mechanical variables and analyze the impact of target location and image resolution. This method assesses the displacement of the target attached to the sample through digital image processing and allocates this displacement to the node displacement of the MLS differencing method, which solely employs nodes to calculate mechanical variables such as stress and strain of the studied object. We propose an effective method to measure the displacement of the target's center of gravity using digital image processing. The calculation of mechanical variables through the MLS differencing method, incorporating image-based target displacement, facilitates easy computation of mechanical variables at arbitrary positions without constraints from meshes or grids. This is achieved by acquiring the accurate displacement history of the test specimen and utilizing the displacement of tracking points with low rigidity. The developed testing method was validated by comparing the measurement results of the sensor with those of the DIP-MLS testing method in a three-point bending test of a rubber beam. Additionally, numerical analysis results simulated only by the MLS differencing method were compared, confirming that the developed method accurately reproduces the actual test and shows good agreement with numerical analysis results before significant deformation. Furthermore, we analyzed the effects of boundary points by applying 46 tracking points, including corner points, to the DIP-MLS testing method. This was compared with using only the internal points of the target, determining the optimal image resolution for this testing method. Through this, we demonstrated that the developed method efficiently addresses the limitations of direct experiments or existing mesh-based simulations. It also suggests that digitalization of the experimental-simulation process is achievable to a considerable extent.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.2
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• pp.109-124
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• 2024

The value of platform companies is rapidly increasing, exerting significant influence across industries. Identifying and fostering promising platform companies is crucial for enhancing national competitiveness. Consequently, tailored evaluation standards are necessary for such companies. This study derived investment decision factors specific to platform companies and compared the importance of each factor using Analytic Hierarchy Process (AHP) analysis. Key factors included platform characteristics, finance, entrepreneur (team), market, and product/service attributes. The findings revealed that platform characteristics were deemed the most crucial factor for investors. Specifically, factors such as platform size, ease of value fixation, core participant group, and data value were identified as pertinent for evaluating platform companies. Moreover, analysis distinguished between investors with prior platform investment experience and those without. Significantly, investors with platform investment experience placed greater emphasis on the value of data secured by platform Furthermore, it was observed that investors prioritized future value and growth potential over current value when investing in platform. Notably, founder/team characteristics, typically highly regarded in previous studies, ranked lower in importance in this study, highlighting a shift in focus. The discrepancy between this study's results and prior research on investment decision factors is attributed to the specificity of the questions posed. By focusing on investment decision factors for platform startups rather than generic startup inquiries, investor responses aligned more closely with platform-focused considerations. Given the burgeoning venture investment landscape, there's a growing need for detailed research on startups within specific sectors like IT, travel, and biotech. This approach can replace extensive research covering all startup types to identify investment decision factors suited to the characteristics of each individual industry.