• Title/Summary/Keyword: 수집율

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The Survey on Actual Condition Depending on Type of Degraded area and Suggestion for Restoration Species Based on Vegetation Information in the Mt. Jirisan Section of Baekdudaegan (식생정보에 기초한 백두대간 지리산권역 내 훼손지 유형별 실태조사)

  • Lee, Hye-Jeong;Kim, Ju-Young;Nam, Kyeong-Bae;An, Ji-Hong
    • Korean Journal of Environment and Ecology
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    • v.34 no.6
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    • pp.558-572
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    • 2020
  • The purpose of this study was to classify the types of degraded areas of Mt. Jirisan section in Baekdudaegan and survey the actual condition of each damage type to use it as basic data for the direction of the restoration of damaged areas according to damage type based on the vegetation information of reference ecosystem. The analysis of the Mt. Jirisan section's actual degraded conditions showed that the total number of patches of degraded areas was 57, and the number of patches and size of degraded areas was higher at the low average altitude and gentle slope. Grasslands (deserted lands) and cultivated areas accounted for a high portion of the damage types, indicating that agricultural land use was a major damage factor. The survey on the conditions of 14 degraded areas showed that the types of damage were classified into the grassland, cultivated area, restoration area, logged-off land, and bare ground. The analysis of the degree of disturbance (the ratio of annual and biennial herb, urbanized index, and disturbance index) by each type showed that the simple single-layer vegetation structure mostly composed of the herbaceous and the degree of disturbance were high in the grassland and cultivated land. The double-layer vegetation structure appeared in the restoration area where the pine seedlings were planted, and the inflow of naturalized plants was especially high compared to other degraded areas due to disturbances caused by the restoration project and the nearby hiking trails. Although the inflow of naturalized plants was low because of high altitude in bare ground, the proportion of annual and biennial herb was high, indicating that all surveyed degraded areas were in early succession stages. The stand ordination by type of damage showed the restoration area on the I-axis, cultivated area, grassland, logged-off land, and bare ground in that order, indicating the arrangement by the damage type. Moreover, the stand ordination of the degraded areas and reference ecosystem based on floristic variation showed a clear difference in species composition. This study diagnosed the status of each damage type based on the reference ecosystem information according to the ecological restoration procedure and confirmed the difference in species composition between the diagnosis result and the reference ecosystem. These findings can be useful basic data for establishing the restoration goal and direction in the future.

Clinical Characteristics in Panic Disorder Patients in Emergency Department (공황발작으로 응급실에 내원한 공황장애 환자들의 임상 특징)

  • Lee, Chang-Ju;Nam, Beom-Woo;Sohn, In-Ki
    • Korean Journal of Psychosomatic Medicine
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    • v.29 no.1
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    • pp.26-33
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    • 2021
  • Objectives : This study was designed to investigate datas related to panic attack and treatment in emergency room of panic disorder patients who visited emergency room for panic attack. Methods : A retrospective analysis of medical records was conducted on 92 patients with panic disorder who visited Chungju Konkuk university hospital emergency department due to panic attack and had bodily symptoms from 1st January 2010 to 31th December 2019. In addition to demographic characteristics and comorbid disorders, triggering stressors and alcohol consumption were corrected as pre-panic attack datas, bodily symptoms at the time of panic attack were corrected as datas during attack, electrocardiogram trial, consultation with psychiatrist, admission and information of used psychotropic drugs were corrected as post-attack data. Depending on size of data, Chi-square test or Fisher's exact test was used. Collected data was analyzed using R 4.03. Results : Cardiovascular disease was accompanied by 5.4% and depressive disorder was the most common coexisting mental disorder. Among triggering stressors, economic problem/work-related stress was significantly higher in men than women (𝛘2=4.322, p<0.005). The most common physical symptom during attack was circulatory (65.2%), followed by respiratory (57.6%), numbness-paralysis (33.7%), dizziness (19.6%), gastro-intestinal (14.1%) and autonomic symptom (12.0%). Electrocardiogram was taken at higher rate when patients complained circulatory symptom (𝛘2=8.46, p<0.005). The psychotropic drug most commonly used in emergency room was lorazepam, used in 92.1%. Conclusions : The most common bodily symptom during panic attack was circulatory symptom and the most common triggering stressor in men was economic problem/work-related stress. The most commonly used psychotropic for panic attack was lorazepam.

Predicting the Pre-Harvest Sprouting Rate in Rice Using Machine Learning (기계학습을 이용한 벼 수발아율 예측)

  • Ban, Ho-Young;Jeong, Jae-Hyeok;Hwang, Woon-Ha;Lee, Hyeon-Seok;Yang, Seo-Yeong;Choi, Myong-Goo;Lee, Chung-Keun;Lee, Ji-U;Lee, Chae Young;Yun, Yeo-Tae;Han, Chae Min;Shin, Seo Ho;Lee, Seong-Tae
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.22 no.4
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    • pp.239-249
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    • 2020
  • Rice flour varieties have been developed to replace wheat, and consumption of rice flour has been encouraged. damage related to pre-harvest sprouting was occurring due to a weather disaster during the ripening period. Thus, it is necessary to develop pre-harvest sprouting rate prediction system to minimize damage for pre-harvest sprouting. Rice cultivation experiments from 20 17 to 20 19 were conducted with three rice flour varieties at six regions in Gangwon-do, Chungcheongbuk-do, and Gyeongsangbuk-do. Survey components were the heading date and pre-harvest sprouting at the harvest date. The weather data were collected daily mean temperature, relative humidity, and rainfall using Automated Synoptic Observing System (ASOS) with the same region name. Gradient Boosting Machine (GBM) which is a machine learning model, was used to predict the pre-harvest sprouting rate, and the training input variables were mean temperature, relative humidity, and total rainfall. Also, the experiment for the period from days after the heading date (DAH) to the subsequent period (DA2H) was conducted to establish the period related to pre-harvest sprouting. The data were divided into training-set and vali-set for calibration of period related to pre-harvest sprouting, and test-set for validation. The result for training-set and vali-set showed the highest score for a period of 22 DAH and 24 DA2H. The result for test-set tended to overpredict pre-harvest sprouting rate on a section smaller than 3.0 %. However, the result showed a high prediction performance (R2=0.76). Therefore, it is expected that the pre-harvest sprouting rate could be able to easily predict with weather components for a specific period using machine learning.

Topic Modeling Insomnia Social Media Corpus using BERTopic and Building Automatic Deep Learning Classification Model (BERTopic을 활용한 불면증 소셜 데이터 토픽 모델링 및 불면증 경향 문헌 딥러닝 자동분류 모델 구축)

  • Ko, Young Soo;Lee, Soobin;Cha, Minjung;Kim, Seongdeok;Lee, Juhee;Han, Ji Yeong;Song, Min
    • Journal of the Korean Society for information Management
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    • v.39 no.2
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    • pp.111-129
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    • 2022
  • Insomnia is a chronic disease in modern society, with the number of new patients increasing by more than 20% in the last 5 years. Insomnia is a serious disease that requires diagnosis and treatment because the individual and social problems that occur when there is a lack of sleep are serious and the triggers of insomnia are complex. This study collected 5,699 data from 'insomnia', a community on 'Reddit', a social media that freely expresses opinions. Based on the International Classification of Sleep Disorders ICSD-3 standard and the guidelines with the help of experts, the insomnia corpus was constructed by tagging them as insomnia tendency documents and non-insomnia tendency documents. Five deep learning language models (BERT, RoBERTa, ALBERT, ELECTRA, XLNet) were trained using the constructed insomnia corpus as training data. As a result of performance evaluation, RoBERTa showed the highest performance with an accuracy of 81.33%. In order to in-depth analysis of insomnia social data, topic modeling was performed using the newly emerged BERTopic method by supplementing the weaknesses of LDA, which is widely used in the past. As a result of the analysis, 8 subject groups ('Negative emotions', 'Advice and help and gratitude', 'Insomnia-related diseases', 'Sleeping pills', 'Exercise and eating habits', 'Physical characteristics', 'Activity characteristics', 'Environmental characteristics') could be confirmed. Users expressed negative emotions and sought help and advice from the Reddit insomnia community. In addition, they mentioned diseases related to insomnia, shared discourse on the use of sleeping pills, and expressed interest in exercise and eating habits. As insomnia-related characteristics, we found physical characteristics such as breathing, pregnancy, and heart, active characteristics such as zombies, hypnic jerk, and groggy, and environmental characteristics such as sunlight, blankets, temperature, and naps.

A Comparison of Body Shape Changes Between Deep Tissue Massage and Illite-Combined Deep Tissue Massage - Focusing on women in their 30s - (딥티슈마사지와 일라이트병행 딥티슈마사지의 체형변화 비교 -30대 여성을 대상으로-)

  • Jeong, In-Sun;Park, Jeong-Yeon
    • Journal of Korea Entertainment Industry Association
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    • v.14 no.6
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    • pp.279-287
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    • 2020
  • This study aims to put forth an efficient way of improving body shapes by examining the effects of deep tissue massage and illite-combined deep tissue massage on body shape changes, and identifying body shape changes when applying each method. This study targeted twenty women in their thirties, and ten separate subjects were placed in different groups. Then deep tissue massage and illite-combined deep tissue massage were performed once a week, for a total of eight weeks. Moire Topography was applied before the experiments, four weeks later and eight weeks later to compare changes in spinous process inclination, shoulders and hips. The data collected were analyzed using SPSS v. 21.0, and the study results are as follows. In relation to general characteristics of the subjects, professionals occupied the highest proportion of them, and 90% of them were married. Here, 77.8% of them had experience in giving birth, and 78.6% of them chose natural birth. In addition, 57.1% of the subjects holding a majority had two children. When measuring spinous process inclination, shoulders and hips in the illite-combined deep tissue massage group and in the deep tissue massage group before the experiments, the illite-combined deep tissue massage group showed somewhat higher values in every area than the deep tissue massage group, but no statistically significant differences were not found, which means the homogeneity existed between them. When comparing body shape changes between the two massage methods, there were significant differences(p<.05, p<.01), because the illite-combined deep tissue massage group showed a much higher decline in spinous process inclination, shoulders and hips than the deep tissue massage group. This implies illite-combined deep tissue massage was more effective in improving body shapes than deep tissue massage. Therefore, illite-combined deep tissue massage is considered to be helpful in improving body shapes, and it is anticipated that this massage method can be used in relevant fields, including the skin care industry.

Possibility of Combined Meningitis in Under 90-Day-Old Infants With Urinary Tract Infection (생후 90일 미만 영아의 요로 감염에서 세균성 수막염의 동반 가능성)

  • Hwang, Jun Ho;Kim, Su Yeong;Lee, Na Mi;Yi, Dae Yong;Yun, Sin Weon;Chae, Soo Ahn;Lim, In Seok;Park, Ji Young
    • Pediatric Infection and Vaccine
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    • v.29 no.2
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    • pp.84-95
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    • 2022
  • Purpose: Urinary tract infections (UTIs) are the most common serious bacterial infections in young infants. Lumbar puncture (LP) has been used to diagnose coexisting meningitis in infants under 90 days of age with suspected UTI in many hospitals. However, the incidence of bacterial meningitis associated with UTIs is low. We aimed to describe the prevalence of concomitant bacterial meningitis in young infants with UTIs. Methods: The medical records of infants with the first episode of UTI admitted to the Chung-Ang University Hospital from January 2010 to December 2019 were retrospectively reviewed. Infants aged < 90 days who underwent LP with initial evaluation were included. Demographic and clinical features, laboratory findings, and imaging findings were collected and analyzed. Results: Eighty-six infants with UTIs were enrolled in the study. The median age was 61.5 days (interquartile range, 42.3-73.8 days) and boys (90.7%) were predominant. Escherichia coli was the most common pathogen (n=80, 93.0%) and followed by Klebsiella species (n=5, 5.8%). Fifteen (18.1%) specimens produced extended spectrum β-lactamase (ESBL). Five (5.8%) infants had positive blood culture results. Seven (8.1%) infants showed pleocytosis in the cerebrospinal fluid, but none had coexisting bacterial meningitis. Twenty-four (30.8%) infants showed renal dilatation or hydronephrosis on ultrasonography. Dimercaptosuccinic acid (DMSA) scans revealed cortical defects in 17 (21.3%) infants while voiding cystourethrography revealed vesicoureteral reflux in 6 (46.2%) infants. Conclusion: Co-existing bacterial meningitis was not observed in young infants with UTIs. LP could not be routinely performed considering the clinical condition of <90 days old UTI patients.

Analysis of the Content Components of 'Consumer Life' Area of Middle School Home Economics Curriculum of the U.S.: Focusing on the States of Ohio, Minnesota, and Wisconsin (미국 중학교 가정과 교육과정의 '소비생활' 영역 내용요소 분석: 오하이오, 미네소타, 위스콘신 주를 중심으로)

  • Kim, Seat Byeol
    • Journal of Korean Home Economics Education Association
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    • v.33 no.4
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    • pp.139-157
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    • 2021
  • The purpose of this study is to derive implications for Korean home economics curriculum to emphasize consumer competency of adolescents by analyzing the content components of consumer competency presented in 'consumer life' area of middle school home economics curriculum of 3 states in the U.S. The analysis results and implications are summarized as follows: First, the U.S. home economics curriculum is composed of various contents, including credit management, savings/investment/ insurance, taxes, and financial situation, and financial decision-making, to improve adolescent's understanding of finance. In the next revision of Korean curriculum, for financial stability in prolonged life after retirement, it is would be necessary to include contents on basic financial knowledge and technology for financial information utilization so that students can establish financial plans for different life stages in consideration of various variables such as changes in economic environment, etc. Second, the U.S. home economics curriculum was developed to help students make better purchase decisions by applying economic concepts such as prices and interest rates, economic trends and the impact of demand and supply, purchase methods and contract conditions, etc. However, Korean home economics curriculum only focus on purchase plan and purchase decision-making process. It would be necessary to foster consumer transaction competency by introducing economic concepts suitable middle school level. Third, to emphasize "consumer civic competency", Ohio was focusing on "claim of consumer rights" and Wisconsin was focusing on the "acceptance of consumer responsibility." In order to enhance adolescent's consumer civic competency, it would be necessary for Korean curriculum to balance the claim of right and the acceptance of consumer responsibility in the following term, and to emphasize the contents on consumer policies, laws and consumer advocacy to create a consumer environment where consumer sovereignty is realized.

Deep Learning Approaches for Accurate Weed Area Assessment in Maize Fields (딥러닝 기반 옥수수 포장의 잡초 면적 평가)

  • Hyeok-jin Bak;Dongwon Kwon;Wan-Gyu Sang;Ho-young Ban;Sungyul Chang;Jae-Kyeong Baek;Yun-Ho Lee;Woo-jin Im;Myung-chul Seo;Jung-Il Cho
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.25 no.1
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    • pp.17-27
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    • 2023
  • Weeds are one of the factors that reduce crop yield through nutrient and photosynthetic competition. Quantification of weed density are an important part of making accurate decisions for precision weeding. In this study, we tried to quantify the density of weeds in images of maize fields taken by unmanned aerial vehicle (UAV). UAV image data collection took place in maize fields from May 17 to June 4, 2021, when maize was in its early growth stage. UAV images were labeled with pixels from maize and those without and the cropped to be used as the input data of the semantic segmentation network for the maize detection model. We trained a model to separate maize from background using the deep learning segmentation networks DeepLabV3+, U-Net, Linknet, and FPN. All four models showed pixel accuracy of 0.97, and the mIOU score was 0.76 and 0.74 in DeepLabV3+ and U-Net, higher than 0.69 for Linknet and FPN. Weed density was calculated as the difference between the green area classified as ExGR (Excess green-Excess red) and the maize area predicted by the model. Each image evaluated for weed density was recombined to quantify and visualize the distribution and density of weeds in a wide range of maize fields. We propose a method to quantify weed density for accurate weeding by effectively separating weeds, maize, and background from UAV images of maize fields.

The Effects of Family Friendship on the Elderly's Consciousness: A Study on the Effects of Mediation on the Recognition of the Elderly and the Attitude to Dementia (청소년이 지각한 가족친밀감이 노인부양의식에 미치는 영향: 노인인식과 치매에 대한 태도의 매개효과 검증)

  • Choi, Yun Ji;Oh, Kwang Soo
    • 한국노년학
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    • v.39 no.4
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    • pp.723-739
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    • 2019
  • This study is to verify the mediated effects of attitudes toward old people and dementia in the influence of elderly couples in the aging society amid the rapidly changing family structure and functions due to the combination of individualization, marital status and divorce rate. In order to carry out such research purposes, data were collected from students of elementary, middle and high schools in Gwangju, through self-subscribed questionnaire. For statistical analysis, the SPSS 20.0 and AMOS 18.0 programs were used and frequency, percentages, technical statistics, correlation, factor analysis, structural model validation, and the Sobel-Test were performed. The results of this study are as follows. First, family intimacy, elderly awareness, and elderly care were the highest among elementary school students, followed by middle school and high school students (P.<.001). Also, in religion, the family intimacy of teenagers with religion was higher than those without religion (p.001). Second, family intimacy directly affected elderly people's attitudes toward dementia and elderly care, old people's attitudes toward dementia and attitudes toward dementia directly affected elderly care. Third, family intimacy (parent-child) was found to be 7.8% for older adults, 20.2% for family intimacy and attitudes toward dementia, and 34.1% for elderly care (p.<.001). Fourth, it has been verified that the absolute value of attitudes toward dementia and elderly people's awareness of elderly people and attitudes between family intimacy and elderly care has been higher than 1.96 and thus acts as a mediating role. These findings are intended to contribute to the welfare of senior citizens' education to improve the quality of life for senior citizens through the resolution of conflicts between generations, as well as the resolution of positive stimulus, by developing various programs such as family friendship, elderly awareness, culture with parents, and various experiences to improve attitudes toward dementia in early adolescence.

Identifying sources of heavy metal contamination in stream sediments using machine learning classifiers (기계학습 분류모델을 이용한 하천퇴적물의 중금속 오염원 식별)

  • Min Jeong Ban;Sangwook Shin;Dong Hoon Lee;Jeong-Gyu Kim;Hosik Lee;Young Kim;Jeong-Hun Park;ShunHwa Lee;Seon-Young Kim;Joo-Hyon Kang
    • Journal of Wetlands Research
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    • v.25 no.4
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    • pp.306-314
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    • 2023
  • Stream sediments are an important component of water quality management because they are receptors of various pollutants such as heavy metals and organic matters emitted from upland sources and can be secondary pollution sources, adversely affecting water environment. To effectively manage the stream sediments, identification of primary sources of sediment contamination and source-associated control strategies will be required. We evaluated the performance of machine learning models in identifying primary sources of sediment contamination based on the physico-chemical properties of stream sediments. A total of 356 stream sediment data sets of 18 quality parameters including 10 heavy metal species(Cd, Cu, Pb, Ni, As, Zn, Cr, Hg, Li, and Al), 3 soil parameters(clay, silt, and sand fractions), and 5 water quality parameters(water content, loss on ignition, total organic carbon, total nitrogen, and total phosphorous) were collected near abandoned metal mines and industrial complexes across the four major river basins in Korea. Two machine learning algorithms, linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the sediments into four cases of different combinations of the sampling period and locations (i.e., mine in dry season, mine in wet season, industrial complex in dry season, and industrial complex in wet season). Both models showed good performance in the classification, with SVM outperformed LDA; the accuracy values of LDA and SVM were 79.5% and 88.1%, respectively. An SVM ensemble model was used for multi-label classification of the multiple contamination sources inlcuding landuses in the upland areas within 1 km radius from the sampling sites. The results showed that the multi-label classifier was comparable performance with sinlgle-label SVM in classifying mines and industrial complexes, but was less accurate in classifying dominant land uses (50~60%). The poor performance of the multi-label SVM is likely due to the overfitting caused by small data sets compared to the complexity of the model. A larger data set might increase the performance of the machine learning models in identifying contamination sources.