• Journal of the Korean Chemical Society
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• v.66 no.2
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• pp.92-95
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• 2022

Failure Renal is the function of the kidneys to remove waste products and keep them on the periphery. and minerals for the body. Chronic renal failure is a syndrome characterized by a slow, irreversible deterioration of renal function due to the slow destruction of renal parenchyma. Calcium is one of the important minerals that the body contains in the blood and important tissues, and it has an important role in vital processes such as muscle contraction, nerve impulse conduction, the efficiency of heart muscle work, and blood clotting processes. The aim of the study is to study and compare calcium levels in men and women. It includes studying abnormal levels of calcium that cause many diseases, including chronic renal failure, and studying changes associated with renal failure. The method of this study was conducted on patients with chronic renal failure at Murjan Teaching Hospital in Babylon city during the period. The study included a sample of 70 patients (40 males, 30 females) with chronic renal failure, their ages ranged from 30-65, and 60 (30 males, 30 females) healthy without the disease of the same age. The result was a significant decrease in the number of red and white blood cells, hemoglobin concentration, hematocrit and platelets in patients with chronic renal failure, The result has been showed significant level in enzymes activity for transfer of amine group (alanine amino transferase, aspartate amino transferas) and phosphatase alkaline and also concentration of total bilirubin in patient with compare with healthy, Significantly increases, were found in the concentration of urea, uric acid and creatinine, as well as the concentration of calcium and phosphorous ions in the blood serum of patients compared to healthy controls.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1608-1614
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• 2022

As competition between universities intensifies due to the recent decrease in the number of students, it is recognized as an essential task of universities to predict students who are underperforming at an early stage and to make various efforts to prevent dropouts. For this, a high-performance model that accurately predicts student performance is essential. This paper proposes a method to improve prediction performance by removing or amplifying abnormal data in a classification prediction model for identifying underperforming students. Existing anomaly data processing methods have mainly focused on deleting or ignoring data, but this paper presents a criterion to distinguish noise from change indicators, and contributes to improving the performance of predictive models by deleting or amplifying data. In an experiment using open learning performance data for verification of the proposed method, we found a number of cases in which the proposed method can improve classification performance compared to the existing method.

• Journal of the Semiconductor & Display Technology
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• v.20 no.4
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• pp.161-166
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• 2021

Errors in the semiconductor process are generated by a change in the state of the equipment, and errors usually arise when the state of the equipment changes or when parts that make up the equipment have flaws. In this investigation, we anticipated that aging of the mass flow controller in the plasma enhanced chemical vapor deposition SiO2 thin film deposition method caused a minute flow rate shift. In seven cases, fourier transformation infrared film quality analysis of the deposited thin film was used to characterize normal and pathological processes. The plasma condition was monitored using optical emission spectrometry data as the flow rate changed during the procedure. Preprocessing was used to apply the collected OES data to the artificial immune system algorithm, which was then used to process diagnosis. Through comparisons between datasets, the learning algorithm compared classification accuracy and improved the method. It has been confirmed that data characterized as a normal process and abnormal processes with differing flow rates may be discriminated by themselves using the artificial immune system data mining method.

• The Korea Journal of Herbology
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• v.28 no.2
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• pp.61-65
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• 2013

Objectives : Samchulgeonbi-tang (shenzhujianpi-tang) has been prescribed as one of traditional herbal medicine for treatment of stomach diseases since ancient time in Korea. Samchulgeonbi-tang extract was fermented by Lactobacillus spp. for improving the effect. However, the toxicity and safety of fermented Samchulgeonbi-tang (FS) extract were not confirmed. Therefore, this study was performed to evaluate the acute toxicity and safety of FS extract. Methods : To evaluate the acute toxicity and safety of FS extract, several doses of FS extract, 0, 500, 1000 and 2000 mg/kg, were orally administered to 20 male and 20 female ICR mice, respectively. After treatment with FS extract, we observed mortality, general toxicity, behavior and change of body weight for the 14 days. After 14 days of oral administration, all mice were sacrificed and hematological parameters were analyzed from blood serum. Results : In present study, the toxic signs such as mortality or abnormal behaviors by FS extract were not observed. There are no significant differences between FS-treated group and control group in body weight, organ weights, and hematological parameters. Conclusions : The remarkable adverse effects by FS extract were not observed in ICR mice. Also, any death was not occurred at all treated FS doses, 500, 1000 and 2000 mg/kg. Therefore, the approximate lethal dose (ALD) of FS extract may be more than 2000 mg/kg.

• Korean Journal of Veterinary Service
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• v.46 no.3
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• pp.227-234
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• 2023

It has been addressed that heat stress due to high atmospheric temperature during summer in Korea induces impaired release of reproductive hormones, followed by occurring abnormal ovarian cyclicity, lower pregnancy ratio, and reduced litter size. Therefore, the present study attempted to compare seasonal change (spring versus summer) of the ovarian aromatase expression, an enzyme for converting testosterone into estrogen. While serum estrogen level in summer group was significantly lower than that of spring group, testosterone was not different between groups. Consistent with estrogen level, the ovarian aromatase expression in summer at follicular phase was significantly lower than the counterpart of spring. The ovarian aromatase expression was positively related with serum estrogen level significantly (r=0.689; P=0.008) and strongly negative correlation was identified (r=-0.533; P=0.078) with atmospheric temperature. The ovarian aromatase expression was not detected in immature ovarian follicles but specifically localized in the granulosa cell layers in both seasons. However, the aromatase intensity in the granulosa cell layers was stronger in spring than summer. Because testosterone level was not different between groups, it could be concluded that the lower level of estrogen during summer might be derived by not lack of substrate but lower expression of ovarian aromatase by heat stress.

• Smart Media Journal
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• v.11 no.9
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• pp.56-63
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• 2022

Recently, the frequency and range of algae occurrence in major rivers and lakes are increasing due to the increase in water temperature due to climate change, the inflow of excessive nutrients, and changes in the river environment. Abnormal algae include green algae and red algae. Green algae is a phenomenon in which blue-green algae such as chlorophyll (Chl-a) in the water grow excessively and the color of the water changes to dark green. In this study, a 3D virtual world of digital twin was built to monitor and control water quality information measured in ecological rivers and lakes in the living environment in real time from a remote location, and a sensor measuring device for water quality information based on the Internet of Things (IOT) sensor. We propose to build a smart water environment service platform that can provide algae warning and water quality forecasting by predicting the causes and spread patterns of water pollution such as algae based on AI machine learning-based collected data analysis.

• Journal of Korean Society of Disaster and Security
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• v.15 no.4
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• pp.79-86
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• 2022

The National Park Service is making efforts to create a safe environment by installing rockfall prevention facilities (rockfall prevention nets, fences, and Piam tunnels) in areas at risk of falling rocks. However, the occurrence of falling rocks is increasing every year due to torrential rains caused by climate change, abnormal temperatures in winter, and aging of the ground, and the need to improve the existing rock risk area management plan has emerged. In this study, a pilot area at risk of falling rocks was selected for the Hwanggol district of Chiaksan National Park among Korean national parks, and rockfall analysis was performed using the Rockfall program, and monitoring was conducted by applying a countermeasure method combined with the measurement system to the pilot area. Through this, a rockfall management plan was proposed for continuous management and monitoring of rockfall.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.32-41
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• 2023

Recently, many studies are being conducted to extract emotion from text and verify its information power in the field of finance, along with the recent development of big data analysis technology. A number of prior studies use pre-defined sentiment dictionaries or machine learning methods to extract sentiment from the financial documents. However, both methods have the disadvantage of being labor-intensive and subjective because it requires a manual sentiment learning process. In this study, we developed a financial sentiment dictionary that automatically extracts sentiment from the body text of analyst reports by using modified Bayes rule and verified the performance of the model through a binary classification model which predicts actual stock price movements. As a result of the prediction, it was found that the proposed financial dictionary from this research has about 4% better predictive power for actual stock price movements than the representative Loughran and McDonald's (2011) financial dictionary. The sentiment extraction method proposed in this study enables efficient and objective judgment because it automatically learns the sentiment of words using both the change in target price and the cumulative abnormal returns. In addition, the dictionary can be easily updated by re-calculating conditional probabilities. The results of this study are expected to be readily expandable and applicable not only to analyst reports, but also to financial field texts such as performance reports, IR reports, press articles, and social media.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.2
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• pp.99-107
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• 2023

Recently, the abnormal weather events and crop damages occurred frequently likely due to climate change. The importance of meteorological data in agricultural research is increasing. Researchers can download weather observation data by accessing the websites provided by the KMA (Korea Meteorological Administration) and the RDA (Rural Development Administration). However, there is a disadvantage that multiple inquiry work is required when a large amount of meteorological data needs to be received. It is inefficient for each researcher to store and manage the data needed for research on an independent local computer in order to avoid this work. In addition, even if all the data were downloaded, additional work is required to find and open several files for research. In this study, data collected by the KMA and RDA were uploaded to GitHub, a remote storage service, and a package was created that allows easy access to weather data using Python. Through this, we propose a method to increase the accessibility and usability of meteorological data for agricultural personnel by adopting a method that allows anyone to take data without an additional authentication process.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.208-208
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• 2022

Recently, the frequency of abnormal weather due to complex factors such as global warming is increasing frequently. From the past rainfall patterns, it is evident that climate change is causing irregular rainfall patterns. This phenomenon causes difficulty in predicting rainfall and makes it difficult to prevent and cope with natural disasters, casuing human and property damages. Therefore, accurate rainfall estimation and rainfall occurrence time prediction could be one of the ways to prevent and mitigate damage caused by flood and drought disasters. However, rainfall prediction has a lot of uncertainty, so it is necessary to understand and reduce this uncertainty. In addition, when accurate rainfall prediction is applied to the rainfall-runoff model, the accuracy of the runoff prediction can be improved. In this regard, this study aims to increase the reliability of rainfall prediction by analyzing the uncertainty of the Korean rainfall ensemble prediction data and the outflow analysis model using the Limited Area ENsemble (LENS) and the Grid based Rainfall-runoff Model (GRM) models. First, the possibility of improving rainfall prediction ability is reviewed using the QM (Quantile Mapping) technique among the bias correction techniques. Then, the GRM parameter calibration was performed twice, and the likelihood-parameter applicability evaluation and uncertainty analysis were performed using R², NSE, PBIAS, and Log-normal. The rainfall prediction data were applied to the rainfall-runoff model and evaluated before and after calibration. It is expected that more reliable flood prediction will be possible by reducing uncertainty in rainfall ensemble data when applying to the runoff model in selecting behavioral models for user uncertainty analysis. Also, it can be used as a basis of flood prediction research by integrating other parameters such as geological characteristics and rainfall events.