목 적 : 고에너지 광자선을 이용한 방사선치료 시 발생되는 광중성자는 치료대상인 종양 주위의 인접한 정상조직에서 2차적인 암의 발생에 기인 될 수 있다고 알려져 있다. 본 연구에서는 방사선치료계획 시에 고려할 수 있는 변수 중 치료방법, 선속평탄여과판, 선량률, 갠트리 각도들을 변환시켰을 때 발생된 광중성자의 선량 변화를 비교 분석하였다. 대상 및 방법 : 실험에는 TrueBeam
논벼농사의 노동력을 덜기 위하여 이중 가장 중요한 호미질에 의한 중경제초의 노력을 살초제에 의한 것으로 전환시키고자 기초적인 면과 실제적인 면으로 시험조사를 하였다. 건국대학교에서 1m평방 안높이 21cm 및 36cm의 두 형의 concrete tank를 각 4개씩 만들고 그 안에 채수관장치를 한 뒤 양토작토로 논벼를 심어 수도물로 관수하고 투수속도와 산소침투 정도와 수량과의 관계를 보았다. 농림부 자재 검사소 내에서 투수가 심한 사질답전포장에 전면적으로 살초제 Stam F-34를 살포하여 제초하고 제 1구는 중경을 하지 않고, 제 2구는 중경 1회, 그리고 제 3구는 중경 2회하여 중경의 효과를 비교하였다. 이리 호남시험장내 건답보통논과 습답에 무중경, 중경 1회 중경 2회의 구와 살초제 Pamcon을 살포한 것들 중, 무중경, 중경 1회 제초 2회, 중경 2회 제초 1회, 중경 3회 제초 2회의 구와 그리고 관행구를 설치하여 중경제초의 비용과 살초제에 의한 비용을 비교하였던바 그 결과를 요약하면 다음과 같다. (1) 투수속도와 산소침투와 수량과의 관계는 일당(24시간당) 투수속도 4cm이상 될 때 지표 2cm하에 산소가 조금 들어가며 3cm보다 적을 때는 산소가 지표하의 매우 옅은 층에만 내려가는 것으로 나타나며 그리고 경심 15cm구에는 일당 투수속도의 0cm(무투수) 1.5cm에서 수량이 많고 2.5cm와 3.4cm에는 감수되며, 경심 30cm구에는 투수속도 1.5cm 및 30cm구에서 수량이 많고 무투수와 투수속도 4cm구에는 수량이 떨어졌다. (2) 투수가 심한 농림부 자재검사소 사질토양에서는 중경의 효과가 없었다. (3) 호남작물시험장내 건답에서는 살초제 Pamcon 처리 무중경구 수량에 대해서 유의성이 보이는 증수구는 없었다. 그리고 습답에서는 무중경구에 있어서 중경제초 및 관행구가 유의성 있게 증수되었다. (4) 논매기 호미질에 의한 10a당 노동력은 정미 37.1시간 식사와 휴식시간 포함하여 53.5시간이고, 노임은 2,346원인데 대하여 살초제 사용에의 한제 초노력은 수동식으로 0.5일 즉 약 5시간 제비용과 감수추정량을 추정할 때 1,130원이고 습답을 제외하면 750원이 되는 것으로 추정 되었다.
한국(韓國)에 있어서 벼 잎집무늬마름병(病)의 발생변동(發生變動), 병원균(病原菌)의 배양적(培養的) 특성(特性)과 병원성(病原性), 발병환경(發病環境), 재배법(栽培法)과 발병관계(發病關係), 품종(品種)의 저항성(抵抗性) 등(等)에 관(關)하여 연구(硏究)한 결과(結果)를 요약(要約)하면 다음과 같다. 1. 한국(韓國)에 있어서 벼 잎집무늬마름병(病)의 발생면적(發生面積)은 다비재배(多肥栽培), 조기조식재배(早期早植栽培) 및 통일계품종(統一系品種)의 확대보급등(擴大普及等)으로 인(因)하여 매년(每年) 증가(增加)되었고 특(特)히 1971년(年)부터 전국적(全國的)으로 급격(急激)히 발생면적(發生面積)이 증가(增加)되어서 1976년(年)에는 전체수도재배면적(全體水稻栽培面積)의 약 65.2%에서 본병(本病)이 발생(發生)하였다. 2. 우리나라에 분포(分布)하는 벼 잎집무늬마름병균(病菌)의 배양적(培養的) 성질(性質) 및 병원성(病原性)에는 많은 변이(變異)가 있었으며 병원균(病原菌)의 생육최적(生育最適) 온도(溫度)도
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.