한국(韓國) 재래종(在來種) 옥수수의 계통분류(系統分類) 및 유전적(遺傳的) 특성(特性)에 관(關)한 연구(硏究) (Studies on Classification and Genetic Nature of Korean Local Corn Lines)
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- 농업과학연구
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- 제9권1호
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- pp.396-450
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- 1982
육종(育種) 자료(資料)를 얻기위해 수집(蒐集)한 한국(韓國) 재래종(在來種) 옥수수 57계통(系統)에 대(對)하여 주성분(主成分) 분석(分析)을 이용(利用)하여 재래종(在來種) 옥수수를 해석(解析)하고 계통분류(系統分類)를 하고 분류(分類)된 계통군별(系統群別)로 주요(主要) 특성(特性)에 대(對)한 유전적(遺傳的) 특성(特性)을 구명(究明)하고자 본(本) 연구(硏究)를 수행(修行)하였던 바 그 결과(結果)를 요약(要約)하면 다음과 같다. 1. 특성(特性)들의 평균치(平均値)는 모든 특성(特性)에서 계통간(系統間) 차이(差異)가 있었으며 비중(比重)을 제외(除外)한 모든 특성(特性)에서 교배유형별(交配類型別)로 차이(差異)가 있었는데 출수기(出穗期)까지의 일수(日數)를 제외(除外)한 모든 특성(特性)에서 자식(自殖)된 계통(系統)의 것은 세력(勢力)이 감소(減少)되었고 톱교배(交配)된 계통(系統)의 것은 세력(勢力)이 증대(增大) 되었다. 2. 특성간(特性間)의 상관계수(相關係數)는 0.99~-0.59 사이에 분포(分布)하였는데 특성간(特性間)의 상관(相關)은 대체(大體)로 낮은 것이 많았다. 그러나 수량구성요소(收量構成要素)와 관련(關聯)된 주요특성(主要特性)에서는 상관(相關)이 높았고 교배유형(交配類型)에 따른 특성간(特性間)의 상관계수(相關係數)의 크기는 별(別) 차이(差異)가 없었다. 3. 27개(個) 특성(特性) 가운데서 12개(個)의 주요특성(主要特性)을 이용(利用)한 주성분(主成分) 분석(分析)에서 제(第)4 주성분(主成分)까지를 가지고 전(全) 변동(變動)의 86.4%를 형매교배(兄妹交配)에서, 84.3%를 자식교배(自殖交配)에서 81.1%를 톱교배(交配)에서 각각(各各) 설명(說明)할 수 있었다. 4. 주성분(主成分)에 대(對)한 특성(特性)의 기여율(寄與率)은 특성(特性)에 따라 달랐고 상위(上位) 주성분(主成分)에서 컸으며 하위(下位) 주성분(主成分)에서 작았다. 5. 주성분(主成分)과 특성간(特性間)의 상관계수(相關係數)는 주성분(主成分)의 생물학적(生物學的) 의의(意義)와 주성분(主成分)에 대응(對應)한 식물체(植物體)의 형(型)을 명확(明確)히 하였는데 제(第)1 주성분(主成分)은 식물체(植物體)의 크기에 관련(關聯)된 주성분(主成分)이었고, 제(第)2 주성분(主成分)은 식물체(植物體)의 분화(分化) 및 생장기간(生長期間)에 관련(關聯)된 주성분(主成分)이었고, 제(第)3 주성분(主成分)과 제(第)4 주성분(主成分)은 형매교배(兄妹交配)된 계통(系統) 및 자식계통(自殖系統)에서는 뚜렷한 특징(特徵)이 없었으나 톱교배(交配)된 계통(系統)에서는 엽(葉)의 크기에 관련(關聯)된 주성분(主成分)이었다. 6. 계통간(系統間) 거리(距離)에 의(依)해 57계통(系統)은 4개(個)의 계통군(系統群)으로 분류(分類)되었으나 전계통(全系統)의 91.1%인 52계통(系統)이 계통군(系統群) I로 분류(分類)되어 수집(蒐集)된 재래종(在來種) 옥수수의 대부분(大部分)이 동일계통(同一系統)인 것으로 나타났고, 계통군(系統群) II에는 3계통(系統)이, 계통군(系統群) III과 계통군(系統群) IV에는 각각(各各) 1계통(系統)이 속(屬)하였다. 계통군(系統群) I은 조생(早生), 단간(短稈), 중형자수(中型雌穗), 중립(中粒) 및 중수(中收) 계통(系統)들이었고, 계통군(系統群) II는 만생(晩生), 중간(中稈), 소형자수(小型雌穗), 소립(小粒), 다자수(多雌穗) 및 다수(多收) 계통(系統)들이었다. 계통군(系統群) III은 중생(中生), 장간(長稈), 소형자수(小型雌穗) 및 소립(小粒), 소수(少收) 계통(系統)들이었고, 계통군(系統群) IV는 중생(中生), 장간(長稈), 대형자수(大型雌穗), 소자수(少雌穗) 및 중수계통(中收系統)이었다. 7. 특성(特性)들의 자식열세도(自殖劣勢度)는 계통(系統)에 따라 차이(差異)가 있었으며 수량(收量), 이삭중(重), 초장(草長) 등(等)에서 비교적(比較的) 크게 나타났고, 분류(分類)된 군별(群別) 자식열세도(自殖劣勢度)는 100 입중(粒重), 엽수(葉數), 엽장(葉長) 및 출수기(出穗期)까지의 일수(日數) 등(等)의 특성(特性)이 계통군(系統群) I에서 컸고, 기타의 특성(特性)은 계통군(系統群) II에서 컸다. 8. 특성(特性)들의 잡종강세도(雜種强勢度)는 계통간(系統間) 차이(差異)가 있었으며 이삭중(重), 이삭당(當) 입중(粒重), 100입중(粒重) 및 엽장(葉長) 등(等)에서 높았으며 분류(分類)된 군별(群別)로 보면 이삭길이, 이삭직경(直徑), 이삭중(重), 이삭당(當) 입중(粒重), 100 입중(粒重) 및 엽장(葉長) 등(等)의 특성(特性)은 계통군(系統群) II에서 높았고 기타의 특성(特性)은 계통군(系統群) I에서 높았다. 9. 특성(特性)들의 동질접합체(同質接合體) 정도(程度)는 이삭중(重)(79.1%)에서 가장 높았으며 이삭 수(數)(-2.1%)에서 가장 낮았는데 특성별(特性別)로 큰 차이(差異)가 있었다. 분류(分類)된 군별(群別)에 있어서도 동질접합체(同質接合體) 정도(程度)는 특성(特性)에 따라 차이(差異)가 있었는데 계통군(系統群) II에서 높은 것이 많았고 계통군(系統群) I에서 낮은 것이 많았다. 10. 형매교배(兄妹交配)된 계통(系統)의 특성(特性)과 톱 교배(交配)된 계통(系統)의 특성(特性)과의 상관관계(相關關係)는 모든 특성(特性)에서 정(正)의 상관(相關)을 나타내었으며 이삭수(數), 초장(草長), 엽장(葉長), 수량(收量) 및 단백질(蛋白質) 함량(含量) 등(等)에서 높은 상관(相關)을 나타내었으며 이삭 직경(直徑), 100입중(粒重) 및 엽수(葉數) 등(等)에서는 유의성(有意性)이 인정(認定)되지 않았다.
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.
우리 나라에 있어서 수도작의 안전다수를 위한 재배법, 특히 시료의 합리화를 기하기 위한 기초적 자료를 얻기 위하여 수도 독자의 영양생리적 반응, 형태형성 내지 수량구성에 대한 특징을 살펴보았으며, 우리 나라의 수도 재배환경조건(온도ㆍ일조ㆍ강수 및 토양조건)을 대국적 견지에서 인접국인 일본과 지역별로 비교 검토하였고, 그 특징으로 본 시료에 관한 개선조건을 위해 비료의 3요소와 규산 및 그 밖에 수종의 미량요소에 대하여 검토하였다. 1. 우리 나라의 최근 14개년간의 10a당 현미평균수량은 204kg인데 이에 비하여 일본은 77%, 대만은 13% 높으며, 년간평균증가량은 우리나라가 4.2kg이고, 이에 비해 일본은 81%, 대만은 62% 더 증가되고 있다. 그리고 수량의 년간변이계수는 우리 나라가 7.7%이며 일본은 6.7%, 대만이 2.5%로서 우리 나라는 년간변이가 매우 커서 생산의 안전도가 가장 낮다. 2. 풍흉고조시험성적으로 본 우리 나라 수도와 일본의 수도를 형태형성면에서 비교하여 본즉 다음과 같았다. (1) 3.3
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70