국내 미기록종인 배나무를 가해하는 배잎녹응애(Phyllocoptes pyrivagrans Kadono, 1985)를 발견하였으므로 이에 기재 보고한다.

We examine the corecollapse times of isolated, two-mass-component star clusters using Fokker-Planck models. With initial condition of Plummer models, we find that the corecollapse times of clusters with M1/M2 >> 1 are well correlated with (N1/N2)^0.5 (m1/m2)^2 Trh, where (M1/M2) and (m1/m2) are the light to heavy component total and individual mass ratios, respectively, N1/N2 is the number ratio, and Trh is the initial half-mass relaxation time scale. We also find two-component cluster parameters that best match multi-component (thus more realistic) clusters with power-law mass functions.

본 시험은 우리나라에서 발생하는 푸른곰팡이병균의 종의 빈도와 발병환경 및 방제법을 구명코져 실시하였다. 시험결과 Trichoderma koningi, T. lignorum, T. glaucum과 미동정의 1종등 4종의 병원균이 분리되었고 이들의 빈도는 각각 와 이었다. 푸른곰팡이병균은 감자배양액, 왁스만배양액과 리차드배양액에서 생육이 잘 되었으며 중성-염기성배지에서는 생육이 불량한 반면 산성에서 생육이 왕성하였으며 최적산도는 pH4였다. 양송이 수확기간 중 재배사내의 온도는 내외 일 때 본명의 발생이 적었고 수량이 많으며 이상에서는 본병의 발생이 격심하였다. 푸른곰팡이병균은 복토흙 소독시 에서 60분, 혹은 에서 30분간 열처리하므로서 완전히 사멸하였고 퇴비 후발효 과정에서도 사멸되었다.

The purpose of this study was to determine the optimal sound absorption conditions by comparing the sound absorption characteristics of fresh and air-dried leaves of Quercus glauca, the main species of evergreen broadleaf trees (EBLT) in southern Korea. The sound absorption coefficients (SACs) obtained under 18 conditions were comparatively analyzed. The SAC of air-dried leaves improved significantly with increasing leaf layer thickness. The highest average SAC in the fresh leaf group was 0.617, which was observed under the condition of a leaf specimen size of 0.5 × 0.5 cm2 and a leaf layer thickness of 1.75 cm. In a group of air-dried leaves, this was 0.615 under the condition of a leaf specimen size of 0.5 × 0.5 cm2 and a leaf layer thickness of 2.50 cm. The maximum value of SAC for each wavelength was observed under the condition of a leaf layer thickness of 2.50 cm consisting of 0.5 × 0.5 cm2 leaf specimens, ranging from 1,400 Hz to 1,500 Hz.

Purpose: In modern society, many urban problems are occurring, such as aging, hollowing out old city centers and polarization within cities. In this study, we intend to apply big data and machine learning methodologies to predict depression symptoms in the elderly population early on, thus contributing to solving the problem of elderly depression. Research design, data and methodology: Machine learning techniques used random forest and analyzed the correlation between CES-D10 and other variables, which are widely used worldwide, to estimate important variables. Dependent variables were set up as two variables that distinguish normal/depression from moderate/severe depression, and a total of 106 independent variables were included, including subjective health conditions, cognitive abilities, and daily life quality surveys, as well as the objective characteristics of the elderly as well as the subjective health, health, employment, household background, income, consumption, assets, subjective expectations, and quality of life surveys. Results: Studies have shown that satisfaction with residential areas and quality of life and cognitive ability scores have important effects in classifying elderly depression, satisfaction with living quality and economic conditions, and number of outpatient care in living areas and clinics have been important variables. In addition, the results of a random forest performance evaluation, the accuracy of classification model that classify whether elderly depression or not was 86.3%, the sensitivity 79.5%, and the specificity 93.3%. And the accuracy of classification model the degree of elderly depression was 86.1%, sensitivity 93.9% and specificity 74.7%. Conclusions: In this study, the important variables of the estimated predictive model were identified using the random forest technique and the study was conducted with a focus on the predictive performance itself. Although there are limitations in research, such as the lack of clear criteria for the classification of depression levels and the failure to reflect variables other than KLoSA data, it is expected that if additional variables are secured in the future and high-performance predictive models are estimated and utilized through various machine learning techniques, it will be able to consider ways to improve the quality of life of senior citizens through early detection of depression and thus help them make public policy decisions.

This study was conducted to determine the number of days required to break a plant’s dormancy and promote subsequent crop growth in new varieties of Gomchwi through the 4℃ treatment. Three new varieties of Gomchwi namely, ‘Sammany’, ‘Gommany’, and ‘Damogy’ were observed in this study. The rate of leaf emergence of ‘Sammany’ after 15-day of 4℃ treatment was 100%, while ‘Gommany’, and ‘Damogy’ took 20-days and 10-days, respectively to reach to 97.9% rate of leaf emergence. After 10-days of 4℃ treatment, ‘Damogy’ grew faster than the other varieties. and Harvest time for ‘Damogy’ was on January 18th, after 5-days of 4℃ treatment and yield was observed to be the highest at 15-days of 4℃ treatment. ‘Sammany’ was next with a minimum of 10-days of 4℃ treatment, although 15-days is more preferred for better harvest. ‘Gommany’ on the other hand, did not grow enough for harvest by January 18th, and its harvest time was delayed to January 31st. It needed a minimum of 15-days and preferentially 20-days of 4℃ treatment to grow normally and be ready for harvest. The plant height, leaf length and leaf petiole length appeared to grow better by extending duration of the 4℃ treatment. The number of leaves of ‘Sammany’ and ‘Gommany’ varieties was three leaves for the 5-days treatment which may be due to the incomplete breaking of dormancy. Regarding the yield per plant, ‘Sammany’ yielded 112.3 grams (g) in 15-days treatment, and ‘Gommany’ yielded 106.5 g in 25-days treatment. In the case of ‘Damogy’, it yielded 123.5 g and 183 g in the 10-days and 25-days treatment respectively. It is concluded that ‘Damogy’, ‘Sammany’ and ‘Gommany requires 10, 15, and 20 days of 4℃ treatment to break the plant’s dormancy and promote better plant growth.

A solitary fibrous tumor (SFT) is a rare mesenchymal neoplasm that was first documented in pleura, but ubiquitous in nature, involving extrapleural sites such as the upper respiratory tract, mediastinum, head and neck. To date, about 21 cases of SFT of the breast have been reported. Herein, we presented a 60-year-old Korean female with a palpable mass in the left breast. Ultrasonography with near-oval shape, and well-circumscribed margins suggested a benign lesion, so complete resection was performed. With immunohistochemical staining, we could obtain a definitive diagnosis. Since some SFTs have unpredictable clinical course, long-term follow-up is needed for all patients.

최근 기후변화와 도시화로 인한 수재해 문제가 증가하고 있으며, 이에 대응방안인 저영향개발(Low-Impact Development, LID) 기법에 관한 연구가 확대되고 있다. LID 기법은 도시 내의 우수유출수를 저감시켜 다양한 수재해 문제를 친환경적으로 제어하고, 도시 개발 이전의 물순환 체계로 회복시키는 기술이다. 하지만 LID 기법에 관한 정량적 데이터가 부족한 실정이다. 따라서 본 연구에서는 저류형 LID 기술인 식생화분(Planter Box)의 Curve Number (CN)값을 산정하여, 물순환(침투, 유출, 월류수) 분석을 실시하였다. Planter Box의 물순환 분석에 관한 강우강도 시나리오(60.4 mm/hr, 83.1 mm/hr, 97.4 mm/hr, 108.2 mm/hr)는 부산시 확률강우강도표(2010)를 이용하여 선정하였다. 실험 결과는 건물화분3 (BPB-3)과 거리화분3(SPB-3)에서 우수저류율이 각각 43.5%∼52.9%, 33.4%∼39.0%로 나타났다. 또한 BPB-3에서 CN값은 평균 83이 산출되었고, Horton 침투능 곡선식 적용에 따른 우수유출효과는 17%∼96%로 나타났다.

미국 9.11 사고 이후 테러는 과거에 비하여 다중이용시설 공격을 통한 불특정 다수의 공격이 증가하고 있다. 연이은 런던 폭탄테러, 파키스탄의 자폭 등은 사람들의 공포심 및 사회적 불안감을 증가시켰다. 최근 국내에서 다양한 국제행사가 개최되고 있어, 방사능테러 위협에 대비한 방사성물질의 국가 안보 의식이 증대되고 있다. 본 논문에서는 HotSpot Code를 사용하여 서로 상이한 기상조건에 따른 결과를 비교하였다. 국내에서 발생 가능한 테러 시나리오 작성 후, RDD(Radiological Dispersal Device) 및 더티밤에 사용될 가능성이 높은 선원을 조사하였다. 기상조건은 Pasquill-Gifford 안정도 등급에 따라 가장 안정된 조건의 F, 가장 불안정한 조건의 A를 선택하여 비교하였다. 시뮬레이션을 통한 A, F 등급 결괏값은 방사선학적 영향에 의해 시민들이 급성 영향으로 사망하는 경우는 없다고 판단하였다. 또한, 풍속 및 기상 안정도에 따라 방사능의 도달 정도가 서로 다르며, 기상 조건에 따라 방사능 희석정도가 서로 다름을 확인할 수 있다. 분석결과는 방사능테러 발생 시 초동 대응에 활용할 수 있을 것으로 예상된다.

This study was conducted to compare the volatile flavor compounds of Artemisia annua L. after extraction by simultaneous steam distillation extraction (SDE) and solid-phase micro extraction (SPME) followed by gas chromatography-mass spectrometry (GC-MS) analysis. Via SDE and SPME processes, 79 (1,254.00 mg/kg) and 39 (488.74 mg/kg) compounds were identified respectively. The compounds extracted by SDE included 27 alcohols, 13 aldehydes, 22 hydrocarbons, 3 esters, 12 ketones, 1 oxide and 1 N-containing compound, on the other hand, using the SPME method, 7 alcohols, 5 aldehydes, 1 ester, 18 hydrocarbons, 7 ketones, and 1 oxide were extracted. The major volatile flavor compounds of Artemisia annua L. isolated by the two methods were caryophyllene oxide, -caryophyllene, camphor, -selinene, -muurolene, 1,8-cineol, (E)-pinocarveol and pinocarvone. β β γ The sesquiterpene named caryophyllene oxide was the most abundant volatile flavor compound with relative contents of 234.16 mg/kg and 195.44 mg/kg obtained by the SDE and SPME methods, respectively. Among the identified volatiles, sabinene, β-pinene, α-terpinene, γ-terpinene, yomogi alcohol, myrtenol, (Z)-nerolidol, p-cymen-8-ol and eugenol were detected by the SDE method only while (E)-anethole and α-cubebene were detected by the SPME method only. This study confirmed that the composition and contents of the volatile flavor compounds vary between different extraction methods. More volatile flavor compounds were identified using the SDE method than the SPME method.

흡연 유무의 남성을 대상으로 뇌 회백질의 손상 유무를 파악 할 수 있는 확산텐서영상을 검사하여 영상을 획득 한 후 Tract-Based Spatial Statics(TBSS)방법으로 뇌 회백질 부위의 기저핵 신경섬유로의 비등방도 FA(fractional anisotropy)값을 측정 분석한 결과 모든 영역에서 흡연자가 비흡연자보다 비등방성 측정값이 낮게 관찰되었으며 FA값은 통계적으로 유의하였다. 본 연구의 측정한 FA결과 값으로 추측하자면 즉, 흡연이 뇌 회백질 기저핵의 모든 해부학적 미세 구조성 변화에 크게 영향을 미치며 신경 섬유로를 손상시키고 이와 관련된 기능적 이상에 영향을 준다고 할 수 있다.