Purpose : Photoneutron dose in high-energy photon radiotherapy at linear accelerator increase the risk for secondary cancer. The purpose of this investigation is to evaluate the dose variation of photoneutron with different treatment method, flattening filter, dose rate and gantry angle in radiation therapy with high-energy photon beam (
Experiments and investigations were done basically and practically for the purpose of labor saving in paddy rice cultivation especially on Homizil i.e. hoeing and herbicide, 1969. 8 concrete tanks were established on the open base of Keon Kuk University for comparison of percolation, dissolved oxygen and yield test of rice in the paddy plot of tank. The dimension of the bottom of each tank is square meter. Each of the 4 of the 8 tanks is 21cm in height and each of the remaining 4 tanks is 36cm. Each tank has a system that comprises 2 sets of tubes, each of which has 20 holes of 5mm in diameter scattered every side and is covered with nylon cloth taking water in the tank. One set consists of 4 P.V.C tubes. The first set is situated 8cm below the top of the tank and the second set is located at bottom layer inside the tank. The 4 tubes of each set are combined together and led to the glass tube which protects from inside to outside. And this inside-outside glass tube is connected to the small rubber tube. Also a glass tube is set 4cm below the top of the tank. Paddy loam was filled on sand in each of the tanks in the soil depth of either 15cm or 30cm. The depth of sand was 5cm in the soil depth of 15cm and 10cm in the soil depth of 30cm. (Fig. 1, 2 and 3). The paddy rice was grown in the tank. The percolation of water, the dissolved oxygen and the yield of rice were observed in the tank. And the dissolved oxygen was detected by Winkler method. A sandy paddy field of heavy percolation was selected at the field of the National Agricultural Material Inspection Center in Seoul. It was divided into 9 plots. These plots were given 3 treatments: (A) not hoeing, (B) hoeing one time and (C) hoeing two times. These treatments were replicated 3 times along the latin square design. The paddy rice was grown and sprayed with Stam F-34 in the all plots for the purpose of killing weeds before hoeing. The two types of paddy of field i.e. one for normal percolation and the other for ill drainage were selected at Iri Crop Experiment Station, Jeonla-Bukdo. Each field was divided into 24 plots for 8 treatments. They are: (A) not hoeing; (B) hoeing one time; (C) hoeing two times; (D) not hoeing but treating with herbicide, Pamcon; (E) hoeing one time and weeding two times also treating with herbicide, Pamcon; (F) hoeing two times and weeding one time a], o treating with herbicide, Pamcon; (G) hoeing two times and weeding two times also treating with herbicide, Pamcon, ; (H) usual manner. The labor hours and expenses needed for weeding in the paddy by hoeing were investigated in a farmer at Suwon and the price of herbicide and the yield of rice were taken out at Iri, Jeonla-Bukdo. The results obtained from the above experiments and investigations are as follows: 1. The relationship between percolation and dissolved oxygen shows that a very small amount of oxygen is detected in the soil water under 2cm below surface of earth in the paddy even when percolation is over 4.0cm per 24 hours (Tab. 1). 2. The relationship between percolation and yield of rice shows that the yield of rice increases in the percolation of 0cm and 1.5cm per 24 hours and decreases in the percolation of 2.5cm and 3.4cm in the plot of the 15cm ploughing depth and increases in the percolation of 1.4cm and 3.0cm and decreases in the percolation of 0cm and 4.0cm in the plot of 30cm ploughing depth (Tab. 1 and Fig. 5). 3. The yield of paddy weeded with Stam F-34 in the sandy field of heavy percolation in Seoul was 3.02 tons in the plot of not hoeing, 2.99 tons in hoeing one time and 3.05 tons in hoeing two times per hectare (Tab. 5). 4.1). 4. 1) The yield of rice per 10 ares in the field of normal percolation at Iri was 338kg in not hoeing, 379kg in hoeing one time, 383kg in hoeing two times, 413kg in spraying herbicide, Pamcon, and not hoeing, 433kg in spraying herbicide, Pamcon, and hoeing one time and weeding two times, 399kg in spraying herbicide, Pamcon, and hoeing two times and weeding one time, 420kg in spraying herbicide, Pamcon, and hoeing two times and weeding two times and 418kg in usual manner (Tab. 6-1). 2) The yield of rice per 10 ares in the field of ill drainage at Iri was 323kg in not hoeing, 363kg in hoeing one time, 342kg in hoeing two times, 388kg in spraying herbicide, Pamcon, and not hoeing, 425kg in spraying herbicide, Pamcon, and hoeing one time and weeding two times, 427kg in spraying herbicide, Pamcon, and hoeing two times and weeding one time, 449kg in spraying herbicide, Pamcon, and hoeing two times and weeding two times and 412kg in usual manner (Tab. 6-2). 5. 1) The labor hours for weeding by hoeing was 37.1 hours but 53.5 hours if hours for meal, smoking and so on are included, and the expenses including labor cost needed for weeding by hoeing in the paddy rice was 2, 346 Won per 10 ares at Suwon (Tab. 7). 2) The labor hours for weeding by spraying herbicide with hand sprayer in the paddy rice was about 5 hours per 10 ares at Suwon and the expenses for weeding by spraying herbicide in the paddy rice was 750 Won but 1130 Won if the loss by decrement of rice in the paddy field of ill drainage per 10 ares is calculated in estimation at Iri (Tab. 8). From these observations and investigations it is known that using of some kinds of herbicides Saves labor and expenses of weeding, almost without giving damages to the rice itself, in the field of normal or heavy percolation comparing usual manner of hoeing.
In an attempt to obtain a basic information to develop an effective integrated system of controlling sheath blight of rice in Korea, the transition of this disease, the variation of cultural characters and pathogenicity of the pathogen, environmental conditions affecting the disease outbreak and varietal resistance have been investigated. 1. Rice sheath blight which has been minor disease in the past was widely spread, especially since 1971. This disease has promptly spread all over the country and infected 65.2% of total rice growing area in 1976. Various factors are considered to be related to such transition of this disease. Above all, increace of application of nitrogenous fertilizer, early season and earlier cultivation of rice, introduction of more susceptible "Tongil" varieties etc. must be important factors influencing the outbreak of this disease. 2. Great variations in cultural characteristics-such as mycelial growth rate, color of the medium, amount of the aerial mycelium, shape and color of the sclerotia- and in the pathogenicity of isolates of the pathogen, Thanatephorus cucumeris
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.