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동중국과 한반도 경상분지의 백악기초기 화성활동의 성인 고찰 (Petrogenesis of Early Cretaceous Magmatism in Eastern China and the Gyeongsang Basin, Korean Peninsula)

  • 최성희
    • 암석학회지
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    • 제25권1호
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    • pp.51-67
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    • 2016
  • 동중국과 우리나라 경상분지에 분포하는 백악기초기 화성활동의 지구화학적 특징을 고찰하였다. 동중국에 분포하는 암상은 피크라이트-현무암-안산암-조면암-유문암 및 황반암의 분출암과 반려암-섬록암-몬조니암-섬장암-화강암 및 휘록암의 관입암체로 다양하다. 이들은 고-칼륨 칼크-알칼리 내지는 쇼쇼나이트 계열에 속한다. 경상분지의 하양층군 속에 협재되어 있는 화산암은 같은 계열의 현무암질 조면안산암이다. 미량원소(Zr, Nb, Y)를 이용한 현무암류의 생성 지구조환경 분류도에서, 이들은 대개 판내부환경 현무암류의 범주에 도시된다. Sr-Nd 동위원소상관도에서 북중국지괴와 북 남중국지괴의 충돌대에 분포하는 현무암류는 대개 맨틀배열 보다 매우 부화된 동위원소비를 가진다. 남중국지괴 현무암류의 $^{87}Sr/^{86}Sr$ 비는 북중국지괴의 범위와 유사하나 ${\varepsilon}_{Nd}$ 값은 북중국지괴에 비하여 상대적으로 높은 편이다. 북중국지괴와 충돌대의 현무암류들은 대개 낮은 $^{206}Pb/^{204}Pb(t)$ 비를 가지는 것이 특징이며, $^{207}Pb/^{204}Pb(t)$-$^{206}Pb/^{204}Pb(t)$ 상관도에서 지오크론의 왼쪽에 도시된다. 남중국지괴 현무암류는 지오크론의 오른쪽에 도시되며, 상대적으로 높은 방사기원 Pb 동위원소비를 가진다. 하양층군 현무암류는 Sr-Nd과 Pb-Pb 동위원소 상관도에서 북중국지괴 현무암류의 범주 내에 도시된다. 오랜 기간 동안 변성교대작용에 의해 지구화학적으로 부화된 암석권맨틀이 위 현무암류를 생성한 근원물질로 추정된다. 백악기초기의 확장응력장에서 발생한 연약권의 용승이 열원이 되어 암석권맨틀의 부분용융이 가능하였을 것이다. 암석권맨틀을 부화시킨 매체로는 엽렬되어 침몰된 하부지각이 재활성화되어 생성된 액, 섭입된 양쯔지괴 대륙지각 기원 액 내지는 섭입된 고태평양판 기원의 유체/액 등을 들 수 있다.

중국 단동 지역에서 국내 벼 품종의 출수 반응과 적응 출수생태 특성 (The Heading Response and Characterization of the Adaptable Heading Ecotypes of Korean Rice Varieties in Dandong, China)

  • 양운호;;김정주;한아름;양정욱;김은영;강신구;이대우;채미진;신명나
    • 한국작물학회지
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    • 제68권3호
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    • pp.106-113
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    • 2023
  • 북한 서북부 지역 적용을 위하여 인접한 중국 단동 지역시험포장에서 2021-2022 2년간 5월 6일과 5월 16일에 이앙하여 벼 품종의 출수기를 조사하고, 단동 지역의 벼 재배기간에 적응하는 출수생태 특성을 검정한 결과는 다음과 같다. 1. 벼 재배기간에 단동 지역의 평균기온은 북한의 신의주보다 약간 낮게, 수풍보다는 약간 높게 경과하였으며, 이들 세 지역의 일장 변화는 거의 동일하였다. 2. 시험 연도와 이앙시기에서 공통적으로 단동 지역의 안전출수한계기와 현지 품종 중 가장 늦은 출수기까지 출수한 국내 품종은 조생종 8개(진부올, 백일미, 조운, 진옥, 조평, 진부, 산호미, 오대)였으며, 북한 품종은 5개(올벼2, 선봉9, 온포1, 길주1, 평도15)였다. 3. 국내 13품종과 북한 7품종의 기본영양생장성은 12~43일, 감광성은 3~56일, 감온성은 15~33일 범위였는데, 국내 중생 및 중만생 3품종(선품, 신보, 소비)과 북한 평양 21은 감광성이 33~56일로 컸고, 다른 3품종(아세미, 진미, 평도5)은 기본영양생장성이 40~43일로 큰 특징을 보였다. 4. 단동 시험포장에서 나타난 벼 품종의 출수기는 감광성과 고도로 유의한 정의 상관을 나타내었으며, 감광성이 컸던 중생과 중만생 3품종을 제외하면 기본영양생장성과 유의한 정의 상관관계가 인정되었다. 5. 단동 지역의 벼 재배기간에 적응하는 품종은 기본영양생장성 35일 이하와 감광성 25일 이하를 모두 충족하는 출수생태 특성을 보였다.

${\beta}-Tyrosinase$에 관한 연구 -제2보 ${\beta}-Tyrosinase$에 의한 Halogen화(化) Tyrosine의 합성(合成)- (Studies on the ${\beta}-Tyrosinase$ -Part 2. On the Synthesis of Halo-tyrosine by ${\beta}-Tyrosinase$-)

  • 김찬조;장택투;곡길수;산전수명
    • Applied Biological Chemistry
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    • 제22권4호
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    • pp.198-209
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    • 1979
  • Esherichia intermedia A-21의 균체(菌體)에서 얻은 ${\beta}-tyrosinase$${\alpha},{\beta}$-탈리작용(脫離作用)의 역(逆)반응을 이용하여 L-tyrosine, 2-chloro-L-tyrosine, 2-bromo-L-tyrosine 및 2-iodo-L-tyrosine을 효소합성하고 그들의 원소분석(元素分析)과 NMR-spectrum, Mass-spectrum 및 IR-spectrum을 측정하여 그 구조해석(構造解析)을 하였다. 또한 ${\beta}-tyrosinase$에 의한 각(各) halogen화(化) tyrosine의 합성속도와 분해속도 그리고 halogen화(化) phenol의 ${\beta}-tyrosinase$에 대한 저해작용(阻害作用) 및 2-bromotyrosine의 합성에서 m-bromophenol의 경시적(經時的) 첨가효과 등을 검토하여 다음과 같은 결과를 얻었다. 1) ${\beta}-tyrosinase$를 이용하여 pyruvin산(酸), $NH_3$ 그리고 m-chlorophenol, m-bromophenol 및 m-iodophenol 등을 기질로 한 각(各) halogen화(化) tyrosine의 효소합성에서 m-chlorophenol에서 2-chloro-tyrosine은 약 15%, m-bromophenol에서 2-bromotyrosine은 약 13.8% 그리고 m-iodophenol에서 2-iodotyrosine은 약 9.8%의 회수율(回收率)로 각각 얻어졌었다. 2) ${\beta}-tyrosinase$에 의한 tyrosine 및 halogen화(化) tyrosine의 합성에서 tyrosine의 합성속도를 100으로 하였을 때 2-chlorotyrosine은 28.2, 2-bromotyrosine은 8.13 그리고 2-iodotyrosine은 0.98의 상대속도를 보여 halogen화(化) tyrosine의 합성속도가 느렸다. 특히 Cl, Br, I의 순(順)으로 원자반경(原子半經)이 증가(增加)함에 따라서 halogen화(化) tyrosine의 합성속도가 저하(低下)되는 것이 인정(認定)되었다. 한편 3-iodotyrosine은 합성이 되지 않았다. 3) ${\beta}-tyrosinase$에 의한 tyrosine의 분해속도를 100으로 하였을 때 2-chlorotyrosine은 70.7, 2-bromotyrosine은 39.0, 2-iodotyrosine은 12.6의 상대적인 분해속도를 보였다. 즉 Cl, Br, I의 순(順)으로 원자반경(原子半經)이 크고 전기음성도(電氣陰性度)가 적어짐에 따라서 분해속도가 저하(低下)되는 것이 분명(分明)하였다 그리고 역시 3-iodotyrosine은 분해를 받지 않았다. 4) ${\beta}-tyrosinase$의 활성(活性)에 대하여 phenol은 현저한 조해작용(阻害作用)을 보였으며 o- 및 m-chlorophenol와 o-bromophenol의 조해(阻害)도 현저하였다. 반면 iodophenol의 조해(阻害)는 근소(僅少)하였으며 이들의 조해작용(阻害作用)을 Lineweaver-Burk plot법에 따라 측정한 결과 m-chlorophenol은 혼합형(混合型)의 조해작용(阻害作用)을 보였으며 그 Ki값은 $5.46{\times}10^{-4}M$이였다. 5) ${\beta}-tyrosinase$에 의한 2-bromotyrosine의 합성에서 기질인 m-bromophenol은 경시적(經時的)으로 소량(少量)씩 첨가하는 것이 효과적이었다. 6) ${\beta}-tyrosinase$를 이용하여 pyruvin산(酸), $NH_3$ 및 각(各) halogen화(化) phenol에서 합성한 2-halogen화(化) tyrosine들을 각각(各各) 원소분석(元素分析)하고 또한 NMR-spectrum, Mass-spectrum 그리고 IR-spectrum 등으로 측정하여 그들의 구조(構造)를 해석(解析)한 결과 각각(各各) 2-chloro-L-tyrosine, 2-bromo-L-tyrosine 및 2-iodo-L-tyrosine 임을 인정(認定)할 수 있었다.

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한정된 O-D조사자료를 이용한 주 전체의 트럭교통예측방법 개발 (DEVELOPMENT OF STATEWIDE TRUCK TRAFFIC FORECASTING METHOD BY USING LIMITED O-D SURVEY DATA)

  • 박만배
    • 대한교통학회:학술대회논문집
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    • 대한교통학회 1995년도 제27회 학술발표회
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    • pp.101-113
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    • 1995
  • The objective of this research is to test the feasibility of developing a statewide truck traffic forecasting methodology for Wisconsin by using Origin-Destination surveys, traffic counts, classification counts, and other data that are routinely collected by the Wisconsin Department of Transportation (WisDOT). Development of a feasible model will permit estimation of future truck traffic for every major link in the network. This will provide the basis for improved estimation of future pavement deterioration. Pavement damage rises exponentially as axle weight increases, and trucks are responsible for most of the traffic-induced damage to pavement. Consequently, forecasts of truck traffic are critical to pavement management systems. The pavement Management Decision Supporting System (PMDSS) prepared by WisDOT in May 1990 combines pavement inventory and performance data with a knowledge base consisting of rules for evaluation, problem identification and rehabilitation recommendation. Without a r.easonable truck traffic forecasting methodology, PMDSS is not able to project pavement performance trends in order to make assessment and recommendations in the future years. However, none of WisDOT's existing forecasting methodologies has been designed specifically for predicting truck movements on a statewide highway network. For this research, the Origin-Destination survey data avaiiable from WisDOT, including two stateline areas, one county, and five cities, are analyzed and the zone-to'||'&'||'not;zone truck trip tables are developed. The resulting Origin-Destination Trip Length Frequency (00 TLF) distributions by trip type are applied to the Gravity Model (GM) for comparison with comparable TLFs from the GM. The gravity model is calibrated to obtain friction factor curves for the three trip types, Internal-Internal (I-I), Internal-External (I-E), and External-External (E-E). ~oth "macro-scale" calibration and "micro-scale" calibration are performed. The comparison of the statewide GM TLF with the 00 TLF for the macro-scale calibration does not provide suitable results because the available 00 survey data do not represent an unbiased sample of statewide truck trips. For the "micro-scale" calibration, "partial" GM trip tables that correspond to the 00 survey trip tables are extracted from the full statewide GM trip table. These "partial" GM trip tables are then merged and a partial GM TLF is created. The GM friction factor curves are adjusted until the partial GM TLF matches the 00 TLF. Three friction factor curves, one for each trip type, resulting from the micro-scale calibration produce a reasonable GM truck trip model. A key methodological issue for GM. calibration involves the use of multiple friction factor curves versus a single friction factor curve for each trip type in order to estimate truck trips with reasonable accuracy. A single friction factor curve for each of the three trip types was found to reproduce the 00 TLFs from the calibration data base. Given the very limited trip generation data available for this research, additional refinement of the gravity model using multiple mction factor curves for each trip type was not warranted. In the traditional urban transportation planning studies, the zonal trip productions and attractions and region-wide OD TLFs are available. However, for this research, the information available for the development .of the GM model is limited to Ground Counts (GC) and a limited set ofOD TLFs. The GM is calibrated using the limited OD data, but the OD data are not adequate to obtain good estimates of truck trip productions and attractions .. Consequently, zonal productions and attractions are estimated using zonal population as a first approximation. Then, Selected Link based (SELINK) analyses are used to adjust the productions and attractions and possibly recalibrate the GM. The SELINK adjustment process involves identifying the origins and destinations of all truck trips that are assigned to a specified "selected link" as the result of a standard traffic assignment. A link adjustment factor is computed as the ratio of the actual volume for the link (ground count) to the total assigned volume. This link adjustment factor is then applied to all of the origin and destination zones of the trips using that "selected link". Selected link based analyses are conducted by using both 16 selected links and 32 selected links. The result of SELINK analysis by u~ing 32 selected links provides the least %RMSE in the screenline volume analysis. In addition, the stability of the GM truck estimating model is preserved by using 32 selected links with three SELINK adjustments, that is, the GM remains calibrated despite substantial changes in the input productions and attractions. The coverage of zones provided by 32 selected links is satisfactory. Increasing the number of repetitions beyond four is not reasonable because the stability of GM model in reproducing the OD TLF reaches its limits. The total volume of truck traffic captured by 32 selected links is 107% of total trip productions. But more importantly, ~ELINK adjustment factors for all of the zones can be computed. Evaluation of the travel demand model resulting from the SELINK adjustments is conducted by using screenline volume analysis, functional class and route specific volume analysis, area specific volume analysis, production and attraction analysis, and Vehicle Miles of Travel (VMT) analysis. Screenline volume analysis by using four screenlines with 28 check points are used for evaluation of the adequacy of the overall model. The total trucks crossing the screenlines are compared to the ground count totals. L V/GC ratios of 0.958 by using 32 selected links and 1.001 by using 16 selected links are obtained. The %RM:SE for the four screenlines is inversely proportional to the average ground count totals by screenline .. The magnitude of %RM:SE for the four screenlines resulting from the fourth and last GM run by using 32 and 16 selected links is 22% and 31 % respectively. These results are similar to the overall %RMSE achieved for the 32 and 16 selected links themselves of 19% and 33% respectively. This implies that the SELINICanalysis results are reasonable for all sections of the state.Functional class and route specific volume analysis is possible by using the available 154 classification count check points. The truck traffic crossing the Interstate highways (ISH) with 37 check points, the US highways (USH) with 50 check points, and the State highways (STH) with 67 check points is compared to the actual ground count totals. The magnitude of the overall link volume to ground count ratio by route does not provide any specific pattern of over or underestimate. However, the %R11SE for the ISH shows the least value while that for the STH shows the largest value. This pattern is consistent with the screenline analysis and the overall relationship between %RMSE and ground count volume groups. Area specific volume analysis provides another broad statewide measure of the performance of the overall model. The truck traffic in the North area with 26 check points, the West area with 36 check points, the East area with 29 check points, and the South area with 64 check points are compared to the actual ground count totals. The four areas show similar results. No specific patterns in the L V/GC ratio by area are found. In addition, the %RMSE is computed for each of the four areas. The %RMSEs for the North, West, East, and South areas are 92%, 49%, 27%, and 35% respectively, whereas, the average ground counts are 481, 1383, 1532, and 3154 respectively. As for the screenline and volume range analyses, the %RMSE is inversely related to average link volume. 'The SELINK adjustments of productions and attractions resulted in a very substantial reduction in the total in-state zonal productions and attractions. The initial in-state zonal trip generation model can now be revised with a new trip production's trip rate (total adjusted productions/total population) and a new trip attraction's trip rate. Revised zonal production and attraction adjustment factors can then be developed that only reflect the impact of the SELINK adjustments that cause mcreases or , decreases from the revised zonal estimate of productions and attractions. Analysis of the revised production adjustment factors is conducted by plotting the factors on the state map. The east area of the state including the counties of Brown, Outagamie, Shawano, Wmnebago, Fond du Lac, Marathon shows comparatively large values of the revised adjustment factors. Overall, both small and large values of the revised adjustment factors are scattered around Wisconsin. This suggests that more independent variables beyond just 226; population are needed for the development of the heavy truck trip generation model. More independent variables including zonal employment data (office employees and manufacturing employees) by industry type, zonal private trucks 226; owned and zonal income data which are not available currently should be considered. A plot of frequency distribution of the in-state zones as a function of the revised production and attraction adjustment factors shows the overall " adjustment resulting from the SELINK analysis process. Overall, the revised SELINK adjustments show that the productions for many zones are reduced by, a factor of 0.5 to 0.8 while the productions for ~ relatively few zones are increased by factors from 1.1 to 4 with most of the factors in the 3.0 range. No obvious explanation for the frequency distribution could be found. The revised SELINK adjustments overall appear to be reasonable. The heavy truck VMT analysis is conducted by comparing the 1990 heavy truck VMT that is forecasted by the GM truck forecasting model, 2.975 billions, with the WisDOT computed data. This gives an estimate that is 18.3% less than the WisDOT computation of 3.642 billions of VMT. The WisDOT estimates are based on the sampling the link volumes for USH, 8TH, and CTH. This implies potential error in sampling the average link volume. The WisDOT estimate of heavy truck VMT cannot be tabulated by the three trip types, I-I, I-E ('||'&'||'pound;-I), and E-E. In contrast, the GM forecasting model shows that the proportion ofE-E VMT out of total VMT is 21.24%. In addition, tabulation of heavy truck VMT by route functional class shows that the proportion of truck traffic traversing the freeways and expressways is 76.5%. Only 14.1% of total freeway truck traffic is I-I trips, while 80% of total collector truck traffic is I-I trips. This implies that freeways are traversed mainly by I-E and E-E truck traffic while collectors are used mainly by I-I truck traffic. Other tabulations such as average heavy truck speed by trip type, average travel distance by trip type and the VMT distribution by trip type, route functional class and travel speed are useful information for highway planners to understand the characteristics of statewide heavy truck trip patternS. Heavy truck volumes for the target year 2010 are forecasted by using the GM truck forecasting model. Four scenarios are used. Fo~ better forecasting, ground count- based segment adjustment factors are developed and applied. ISH 90 '||'&'||' 94 and USH 41 are used as example routes. The forecasting results by using the ground count-based segment adjustment factors are satisfactory for long range planning purposes, but additional ground counts would be useful for USH 41. Sensitivity analysis provides estimates of the impacts of the alternative growth rates including information about changes in the trip types using key routes. The network'||'&'||'not;based GMcan easily model scenarios with different rates of growth in rural versus . . urban areas, small versus large cities, and in-state zones versus external stations. cities, and in-state zones versus external stations.

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