• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.294-294
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• 2022

In 2013, the Asian Development Bank classified the Philippines among the countries facing high food security risks. Evidence has suggested that climate change has affected agricultural productivity, and the effect of extreme climatic events notably drought has worsened each year. This had resulted in serious hydrological repercussions by limiting the timely water availability for the agriculture sector. Laguna is the 3^rd most populated province in the country, and it serves as one of the food baskets that feed the region and nearby provinces. In addition to climate change, population growth, rapid industrialization, and urban encroachment are also straining the delicate balance between water demand and supply. Studies have projected that the province will experience less rainfall and an increase in temperature, which could simultaneously affect water availability and crop yield. Hence, understanding the composite threat of climate change for crop yield and water consumption is imperative to devise mitigation plans and judicious use of water resources. The water footprint concept elaborates the water used per unit of crop yield production and it can approximate the dual impacts of climate change on water and agricultural production. In this study, the water footprint (WF) of six main crops produced in Laguna were estimated during 2010-2020 by following the methodology proposed by the Water Footprint Network. The result of this work gives importance to WF studies in a local setting which can be used as a comparison between different provinces as well as a piece of vital information to guide policy makers to adopt plans for crop-related use of water and food security in the Philippines.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.140-148
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• 2023

With the OTT media market growing rapidly, the significance of trustworthy data verification and certification cannot be emphasized enough. This study delves into the crucial need for such measures in South Korea, exploring the steps involved, the technological and policy-related considerations, and the challenges that may arise once these measures are put into place. Drawing on in-depth interviews and the analytical hierarchy process (AHP), this study surveyed various stakeholder groups, both directly and indirectly related to OTT data authentication and certification. By assessing the severity of OTT data-related issues and identifying the requirements for reliability-improvement policies, participants shared their valuable insights and opinions on this pressing matter. The survey results clearly indicate a divided opinion among stakeholders and industry experts on the reliability of OTT data, with some expressing trust while others remain skeptical. However, there was a consensus that advertising-based AVOD is more reliable than SVOD. By analyzing the priorities of authentication and verification, this study paves the way for the establishment and operation of a Korean MRC (KMRC), centered on the OTT media industry. The KMRC will serve as a vital platform for ensuring the authenticity and accuracy of OTT data in South Korea, providing businesses and industry players with a reliable source of information for informed decision-making. This study highlights the pressing need for reliable data authentication and certification in the rapidly growing OTT media market, and provides a persuasive case for the establishment of a KMRC in South Korea to meet this critical need.

• Journal of the Korean earth science society
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• v.43 no.6
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• pp.680-690
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• 2022

Tephrochronology uses regional tephra for age dating and stratigraphic correlations. Regional tephras are important in Quaternary geology and archaeology because they can be used as stratigraphic time-markers. In this review, identification and dating methods of tephra are summarized. In addition, the characteristics of regional tephras in terrestrial deposits of the Korean Peninsula are elaborated, and geological applications and limitations of the regional tephra layers are also discussed. So far, AT, Ata, and Kb-Ks tephra layers from Kyushu, Japan have been found in Pleistocene paleosol, marine terrace deposits, and lacustrine deposits in Korea. Also, although not officially confirmed, Aso-4 tephra is likely to occur in terrestrial deposits. The regional tephra layers are vital for dating, especially with regard to sediments over 50 ka beyond the range of radiocarbon dating, and for dating of active faults. Furthermore, it can provide important information for preparing countermeasures against volcanic disasters. However, in order to use the tephra layer geologically, it must be confirmed whether it is a primary deposit based on sedimentological study.

• Journal of Korea Trade
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• v.27 no.2
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• pp.96-114
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• 2023

Purpose - This paper elucidates a nexus between the occurrence of rare disaster events and the volatility of economic growth by distinguishing the likelihood of rare events from stochastic volatility. We provide new empirical facts based on a quarterly time series. In particular, we focus on the role of financial liberalization in spreading the economic crisis in developing countries. Design/methodology - We use quarterly data on consumption expenditure (real per capita consumption) from 44 countries, including advanced and developing countries, ending in the fourth quarter of 2020. We estimate the likelihood of rare event occurrences and stochastic volatility for countries using the Bayesian Markov chain Monte Carlo (MCMC) method developed by Barro and Jin (2021). We present our estimation results for the relationship between rare disaster events, stochastic volatility, and growth volatility. Findings - We find the global common disaster event, the COVID-19 pandemic, and thirteen country-specific disaster events. Consumption falls by about 7% on average in the first quarter of a disaster and by 4% in the long run. The occurrence of rare disaster events and the volatility of gross domestic product (GDP) growth are positively correlated (4.8%), whereas the rare events and GDP growth rate are negatively correlated (-12.1%). In particular, financial liberalization has played an important role in exacerbating the adverse impact of both rare disasters and financial market instability on growth volatility. Several case studies, including the case of South Korea, provide insights into the cause of major financial crises in small open developing countries, including the Asian currency crisis of 1998. Originality/value - This paper presents new empirical facts on the relationship between the occurrence of rare disaster events (or stochastic volatility) and growth volatility. Increasing data frequency allows for greater accuracy in assessing a country's specific risk. Our findings suggest that financial market and institutional stability can be vital for buffering against rare disaster shocks. It is necessary to preemptively strengthen the foundation for financial stability in developing countries and increase the quality of the information provided to markets.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.146-146
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• 2022

A flash flood is one of the most hazardous natural events caused by heavy rainfall in a short period of time in mountainous areas with steep slopes. Early warning of flash flood is vital to minimize damage, but challenges remain in the enhancing accuracy and reliability of flash flood forecasts. The forecasters can easily determine whether flash flood is occurred using the flash flood guidance (FFG) comparing to rainfall volume of the same duration. In terms of this, the hydrological model that can consider the basin characteristics in real time can increase the accuracy of flash flood forecasting. Also, the predicted radar rainfall has a strength for short-lead time can be useful for flash flood forecasting. Therefore, using both hydrological models and radar rainfall forecasts can improve the accuracy of flash flood forecasts. In this study, FFG was applied to simulate some flash flood events in the Taehwa river basin by using of SURR model to consider soil moisture, and applied to the flash flood forecasting using predicted radar rainfall. The hydrometeorological data are gathered from 2011 to 2021. Furthermore, radar rainfall is forecasted up to 6-hours has been used to forecast flash flood during heavy rain in August 2021, Wulsan area. The accuracy of the predicted rainfall is evaluated and the correlation between observed and predicted rainfall is analyzed for quantitative evaluation. The results show that with a short lead time (1-3hr) the result of forecast flash flood events was very close to collected information, but with a larger lead time big difference was observed. The results obtained from this study are expected to use for set up the emergency planning to prevent the damage of flash flood.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.313-322
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• 2020

The recognition of the risk hazards is a vital step to effectively prevent accidents on a construction site. The advanced development in computer vision systems and the availability of the large visual database related to construction site made it possible to take quick action in the event of human error and disaster situations that may occur during management supervision. Therefore, it is necessary to analyze the risk factors that need to be managed at the construction site and review appropriate and effective technical methods for each risk factor. This research focuses on analyzing Occupational Safety and Health Agency (OSHA) related to risk zone identification rules that can be adopted by the image recognition technology and classify their risk factors depending on the effective technical method. Therefore, this research developed a pattern-oriented classification of OSHA rules that can employ a large scale of safety hazard recognition. This research uses joint reasoning of risk zone Identification and numeric input by utilizing a stereo camera integrated with an image detection algorithm such as (YOLOv3) and Pyramid Stereo Matching Network (PSMNet). The research result identifies risk zones and raises alarm if a target object enters this zone. It also determines numerical information of a target, which recognizes the length, spacing, and angle of the target. Applying image detection joint logic algorithms might leverage the speed and accuracy of hazard detection due to merging more than one factor to prevent accidents in the job site.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.68 no.3
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• pp.175-187
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• 2023

Peanuts, also known as groundnuts (Arachis hypogaea L.), are globally recognized as a vital oilseed crop. Peanuts are rich in proteins (e.g., arginine), oils (e.g., oleic acid and linoleic acid), fiber, vitamins (e.g., niacin and tocopherol), and carbohydrates and are consumed worldwide. However, the presence of aflatoxin (AF) has garnered substantial attention since its initial discovery as the causative agent of Tukey's X disease in the United Kingdom in 1960. Among the 18 aflatoxins identified, aflatoxin B₁ (AFB₁) has the highest toxic activity and causes hepatocellular carcinoma. It is classified as Group I by the International Agency for Research on Cancer (IARC) of the World Health Organization (WHO). The present study was conducted to evaluate aflatoxin B₁ resistance of 102 peanut accessions and select putative aflatoxin B₁-resistant peanut accessions to aflatoxin B₁. One hundred and one Korean germplasms harvested in 2020 were inoculated with A. flavus to identify aflatoxin-resistant cultivars, and the aflatoxin B₁ concentration was measured using an ultra-performance liquid chromatography-photodiode array detector. Twenty-six accessions with aflatoxin B₁ concentrations lower than those of the check plant 55-437 were chosen for the development of aflatoxin-resistant varieties in Korea. As Korean aflatoxin-resistant varieties have not yet been developed, the findings of the present study are expected to provide useful information for the development of aflatoxin-resistant cultivars.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.909-917
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• 2023

Semiotic theory plays a vital role in the cognition of realistic films. Realistic films aim to reflect events and situations in real life, and arouse the audience's thinking about real problems through realistic depiction. In this process, the theory of semiotics helps to reveal the symbolic elements in the film and the profound meaning behind them. This study focused on the realism movie 도 가 니 "(Silenced), on the basis of the theory of saussure's semiotics, combined with its proposed the concept of "signifier" and "mean", through the study of arbitrariness and secondary film symbol sign system, designed to dig deeper into the symbolic meanings in the movie, and their cultural significance and social evaluation. By analyzing the implied narrative structure, meaning structure and ideology in the film, this paper probes into the influence and effect of mass culture on society and reveals the potential information conveyed by symbols in the film. This study aims to provide creative insights and explanations to provide useful references for research in related fields.

• Archives of Craniofacial Surgery
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• v.25 no.2
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• pp.71-76
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• 2024

Background: The rising incidence of dialysis-dependent end-stage renal disease (ESRD) has underscored the need for collaboration between plastic surgeons and nephrologists, particularly concerning preoperative and postoperative management for facial reconstruction. This collaboration is essential due to a scarcity of comprehensive information in this domain. Methods: A study initiated in January 2015 involved 10 ESRD cases on dialysis undergoing Mohs micrographic surgery for facial skin cancer, followed by reconstructive surgery under general anesthesia. To ensure surgical safety, rigorous measures were enacted, encompassing laboratory testing, nephrology consultations, and preoperative dialysis admission. Throughout surgery, meticulous control was exercised over vital signs, electrolytes, bleeding risk, and pain management (excluding nonsteroidal anti-inflammatory drugs). Postoperative assessments included monitoring flap integrity, hematoma formation, infection, and cardiovascular risk through plasma creatinine levels. Results: Adherence to the proposed guidelines yielded a notable absence of postoperative wound complications. Postoperative plasma creatinine levels exhibited an average decrease of 1.10 mg/dL compared to preoperative levels, indicating improved renal function. Importantly, no cardiopulmonary complications or 30-day mortality were observed. In ESRD patients, creatinine levels decreased significantly postoperatively compared to the preoperative levels (p< 0.05), indicating favorable outcomes. Conclusion: The consistent application of guidelines for admission, anesthesia, and surgery yielded robust and stable outcomes across all patients. In particular, the findings support the importance of adjusting dialysis schedules. Despite the limited sample size in this study, these findings underscore the effectiveness of a collaborative and meticulous approach for plastic surgeons performing surgery on dialysis-dependent patients, ensuring successful outcomes.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.31-42
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• 2024

Different industries mostly rely on quality certification for promoting their products or brands. Although getting quality certification, specifically by human experts is a tough job to do. But the field of machine learning play a vital role in every aspect of life, if we talk about quality certification, machine learning is having a lot of applications concerning, assigning and assessing quality certifications to different products on a macro level. Like other brands, wine is also having different brands. In order to ensure the quality of wine, machine learning plays an important role. In this research, we use two datasets that are publicly available on the "UC Irvine machine learning repository", for predicting the wine quality. Datasets that we have opted for our experimental research study were comprised of white wine and red wine datasets, there are 1599 records for red wine and 4898 records for white wine datasets. The research study was twofold. First, we have used a technique called backward elimination in order to find out the dependency of the dependent variable on the independent variable and predict the dependent variable, the technique is useful for predicting which independent variable has maximum probability for improving the wine quality. Second, we used a robust machine learning algorithm known as "XGBoost" for efficient prediction of wine quality. We evaluate our model on the basis of error measures, root mean square error, mean absolute error, R2 error and mean square error. We have compared the results generated by "XGBoost" with the other state-of-the-art machine learning techniques, experimental results have showed, "XGBoost" outperform as compared to other state of the art machine learning techniques.