• 국제지역연구
• /
• 제15권1호
• /
• pp.109-134
• /
• 2011

미국의 지원 하에 추진된 우리베 정권(Alvaro Uribe:2002-2010)의 마약퇴치 및 불법무장조직에 대한 강경책은 가시적 성과에도 불구하고 마약과 관련된 범죄는 증가했으며, 마약재배와 거래량의 변화를 동반하지 않았다. 오히려 불법작물 재배권을 둘러싼 좌.우익 불법무장단체들의 무력분쟁은 심화되었고, 강제실향민으로 인한 난민은 급증했다. 2005년 실향민등록위원회 RUPD(el Registro Único de Población Desplazada)는 콜롬비아 전체인구의 7.3%에 해당하는 3,316,862명이 난민상태라고 보고했다. 특히 2002년 강제실향민으로 인한 난민 수는 전년대비 624%의 대폭적인 증가율을 나타냈다. 강제실향민 주요 배출지역은 마약범죄와 불법무장조직의 활동과 관련을 맺고 있는 곳으로 불법무장단체와 민병대의 점령지 확장과정에서 무력분쟁이 지속적으로 증가했다. 불법무장조직과 민병대가 불법작물경작지를 차지하는 과정에서 농민에 대한 무차별적인 공격을 감행하여 강제실향민이 급증하였고, 이들은 상주지를 떠나 국경을 넘어 접경지역으로 이동했다. 2002년 우리베 정권이 등장하기 이전 강제실향민문제는 일부 특정지역에 국한되었지만, 국가안보정책 추진이후 강제실향민은 양적인 팽창뿐만 아니라, 전국적으로 확산되는 경향을 나타냈다. 마약거래의 주요 루트였던 아마존 지역이 폐쇄되자 불법무장조직의 활동거점이 태평양 역으로 이동되었고, 특히 파나마 접경지역은 이전에 존재하지 않았던 강제실향민으로 인한 콜롬비아 난민이 증가했다. 본 연구는 2002년 우리베 정권등장 이후 증가한 국경지역 강제실향민을 대상으로 콜롬비아 난민증가 원인에 대해 고찰해 보고자 한다. 역사적으로 콜롬비아의 강제실향민이 가장 많이 분포되어 있는 베네수엘라와 1990년대 말 이후 강제실향민의 새로운 정착지로 변모한 파나마 그리고 2000년 들어 다른 인접국보다도 급격하게 증가한 에콰도르 접경지역의 강제실향민을 중심으로 강제실향민 증가원인을 고찰하고 이를 바탕으로 난민문제 해결을 위한 콜롬비아 정부의 노력을 살펴볼 것이다.

• 대한교통학회:학술대회논문집
• /
• 대한교통학회 1995년도 제27회 학술발표회
• /
• pp.101-113
• /
• 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.

• 한국작물학회지
• /
• 제5권1호
• /
• pp.69-86
• /
• 1969

인삼식물의 종간교잡에 있어서 일대잡종식물은 양친에 대하여 약 1.6~3.0배의 강제를 나타내지만 심한 불임현상으로 거의 잡종 제삼세대를 얻을수 없었다는 점에서 그 원인을 밝히고저 고려인삼${\times}$인 미국인삼의 잡종에 대한 발생학적조사관찰을 하였던 바 다음과 같은 결과를 얻었다. 1. 잡종인삼의 영양생장은 양친과 같이 정상적이며 강세를 나타내나 생식생장에서는 심한 조해를 받는다. 2. 생식기관형성에 있어서도 감수분열기 이전까지는 제조직의 발생은 거의 정상적으로 진행된다. 3. 대포자모세포나 소포자모세포의 감수분 장과정은 심한 불규칙성을 나타내며 어떠한 것은 분열직전부터 퇴화되기 시작한다. 4. 소포자모세포의 핵분열에 있어서 제1분열 중기 또는 후기에 일가염색체 또는 염색체교 등이 출현하는 이상분열상을 관찰할 수 있었으나 감수분열이 끝난 것은 역시 사분자가 대부분이고 이분자나 사분자 이상의 소포자형성은 볼 수 없었다. 5. 소포자형성 또는 화분형성과정에 있어서 한 약내에서 여러 단계의 발육상을 볼 수 있었다. 6. 거대, 미소, 공허화분은 극히 적었다.(Fig. 23). 7. 대포자모세포기 이후 배주의 발육속도는 전반적으로 지연된다. 8. 감수분열을 마친 후 대포자는 오분자를 형성하는 것도 있다.(Fig. 5). 9. 대개는 합점측의 대포자가 활성화하는데 중간에 위치하는 것이 활성대포자인 것도 불 수 있다.(Fig. 6). 10. 배주의 퇴화는 대포자모세포기부터 팔핵배낭기까지 사이에 일어나는데 그 시작 시기는 개체마다 조만이 있으며 각양각색이다. 11.0 대포자의 배열은 양친에서는 선장, 중간형인데 F1에서는 선장, 중간형, T형, ㅗ형 등 여러 가지 형을 볼 수 있다.(Fig. 5, 7). 12. 배주에 있어서 감수분열이나 배낭핵분열 또는 배낭형성에 불규칙성에 심할수록 합점기부에 잔재하는 배심조직이 크다(Fig. 8, 10). 13. 배낭형성기까지 도달한 것이라 하더라도 배낭핵은 항시 불안정하여 정해진 장소에 배치되지 못한다.(Fig. 10, 11, 12). 14. 배유조직을 결한 배낭내에 선장의 4세포원배를 형성한 것을 볼 수 있었다.(Fig. 20) 15. 인삼의 잡종에 있어서의 불임원인을 다음과 같이 추정하였다. a) 잡종의 불임현상은 교잡에 의한 Gene-action system의 재조합으로 생체대사계에 혼란을 일으켜 배우자형성세포와 위요세포간의 우열관계가 전도되여 성적결함을 가져오는데 있다고 보았다. 즉 정상배낭에서는 배우자형성세포는 그것을 둘러싸고 있는 위요세포보다 크고 농염되며 활성적이어서 위요세포를 소화흡수하여 발육케 된다. 그러나 퇴화배낭에서는 재조합으로 인한 세포질의 변화는 극성 (Polarity) 또는 내생리듬 (Endogneousrhythm)의 변화 혹은 교란을 가져와 발육과정에서 성적결함을 일으켜 불임으로 된다고 추정하였다.

• 한국육종학회지
• /
• 제51권3호
• /
• pp.214-221
• /
• 2019

'아람'은 보석(IT213209)과 Camp (IT267356)를 모본으로 2007년 인공교배 하여 F₁, F₂ 분리집단을 전개하고 F₃-F₄ 세대는 세계 채소 센터(AVRDC, Asian Vegetable Research and Development Center)에서 세대촉진을 수행하고 F₅ 세대에서 계통을 선발하였다. 2012년~2013년 생산력검정시험을 통해 수량과 농업적 형질을 평가하고, 2014~2016년 수원, 나주, 달성, 제주에서 지역적응시험을 수행하여 지역별 적응성과 재배 안정성을 평가하였다. 아람은 유한신육형이며 엽형이 삼각형, 화색이 백색, 모용색이 회색, 협색은 황색이며 종실은 소립 구형으로 백립중이 9.9 g으로 풍산나물콩보다 1.0 g 가볍고 종피색은 황색, 배꼽색은 담갈색의 질적 특성을 가지고 있다. 개화기는 8월 5일, 성숙기는 10월 15일로 만숙종이며 풍산나물콩 보다 7일 느리다. 경장은 65 cm, 착협고는 13 cm, 마디수는 16개, 분지수는 4.5개이며 분지가 길고 각도가 작은 도원추형 초형이다. 내병성 검정결과 불마름병, 종자 이병립율, 검은뿌리 썩음병, 콩나방 저항성 등이 모두 강하였으며, 콩모자이크바이러스는 G5, G6H, G7H 접종 시 모자이크 증상이 나타났으나 3년간의 자연 이병 검정결과에서는 병징이 조사되지 않았다. 발아특성은 발아세와 발아율 모두 풍산나물콩보다 우수하였으며 콩나물 특성은 풍산나물콩과 비슷한 수준이었다. 수량성은 지역적응시험 결과 남부지역 평균 3.59 ton/ha로 풍산나물콩 대비 12% 증수되었다.