• Restorative Dentistry and Endodontics
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• 제25권1호
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• pp.46-55
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• 2000

During bonding procedure of composite resin, the prepared cavity can be contaminated by saliva. In this study, marginal microleakage and shear bond strength of a composite resin to primed enamel and dentin treated with artificial saliva(Taliva$^{(R)}$) were evaluated. For the marginal microleakage test, Class V cavities were prepared in the buccal surfaces of fifty molars. The samples were randomly assigned into 5 groups with 10 samples in each group. Control group was applied with a bonding system (Scotchbond$^{TM}$ Multi-Purpose plus) according to manufacture's directions without saliva contamination. Experimental groups were divided into 4 groups and contaminated with artificial saliva for 30 seconds after priming: Experimental 1 group ; artificial saliva was dried with compressed air only, Experimental 2 group ; artificial saliva was rinsed and dried. Experimental 3 group ; cavities were etched with 35% phosphoric acid for 15 seconds after rinsing and drying artificial saliva. Experimental 4 group ; cavities were etched with 35% phosphoric acid for 15 seconds and primer was reapplied after rinsing and drying artificial saliva. All the cavities were applied a bonding agent and filled with a composite resin (Z-100$^{TM}$). Specimens were immersed in 0.5% basic fuschin dye for 24 hours and embedded in transparent acrylic resin and sectioned buccolingually with diamond wheel saw. Four sections were obtained from one specimen. Degree of marginal leakage was scored under stereomicroscope and their scores were averaged from four sections. The data were analyzed by Kruscal-Wallis test and Fisher's LSD. For the shear bond strength test, the buccal or occlusal surfaces of one hundred molar teeth were ground to expose enamel(n=50) or dentin(n=50) using diamond wheel saw and its surface was smoothed with Lapping and Polishing Machine(South Bay Technology Co., U.S.A.). Samples were divided into 5 groups. Treatment of saliva-contaminated enamel and dentin surfaces was same as the marginal microleakage test and composite resin was bonded via a gelatin capsule. All specimens were stored in distilled water for 48 hours. The shear bond strengths were measured by universal testing machine (AGS-1000 4D, Shimaduzu Co., Japan) with a crosshead speed of 5 mm/minute. Failure mode of fracture sites was examined under stereomicroscope. The data were analyzed by ANOVA and Tukey's studentized range test. The results of this study were as follows : 1. Enamel marginal microleakage showed no significant difference among groups. 2. Dentinal marginal microleakages of control, experimental 2 and 4 groups were lower than those of experimental 1 and 3 groups (p<0.05). 3. The shear bond strength to enamel was the highest value in control group (20.03${\pm}$4.47MPa) and the lowest value in experimental 1 group (13.28${\pm}$6.52MPa). There were significant differences between experimental 1 group and other groups (p<0.05). 4. The shear bond strength to dentin was higher in control group (17.87${\pm}$4.02MPa) and experimental 4 group (16.38${\pm}$3.23MPa) than in other groups, its value was low in experimental 1 group (3.95${\pm}$2.51 MPa) and experimental 2 group (6.72${\pm}$2.26MPa)(p<0.05). 5. Failure mode of fractured site on the enamel showed mostly adhesive failures in experimental 1 and 3 groups. 6. Failure mode of fractured site on the dentin did not show adhesive failures in control group, but showed mostly adhesive failure in experimental groups. As a summary of above results, if the primed tooth surface was contaminated with artificial saliva, primer should be reapplied after re-etching it.

• 한국작물학회지
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• 제22권1호
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• pp.135-166
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• 1977

본 실험은 대두 단백질의 생화학적 특성을 품종면에서 밝혀 고단백 대두 품종 육성을 위한 기초를 보강코저 실시하였다. 광범위한 재래종을 포함한 우리나라 품종 46, 일본 품종 9, 미국계 품종 31등 도합 86품종을 동일조건에서 재배하여 종실의 단백질, 지방, 탄수화물, 회분 함량 및 아미노산 조성의 품종간 변이, 그들 성분 함량간의 상호관계, acrylamide gel 전기영동법에 의한 종실 단백질 구성분 조합의 품종적 특성 그리고 그들과 단백질함량 및 기타 성분함량과의 상호관계를 분석 추구하였고, 고단백 품종 서해 20호, 저단백 품종 Shelby 및 중간형 광교의 종실성숙중 단백질 및 지방의 축적, 단백질구성분들의 소장을 종실 발달과정과 관계하여 그 품종간 차이를 밝히고저 하였다. 1. 공시 86품종의 종실 성분함량은 단백질 34.4(Capital)～50.6%(서해 20호), 지방 15.0(소립종)～27.9% (Capital), 탄수화물26.1(서해 20호)～35.7% (수계 6001), 회분 4.8(Comet)～6.6%(금강소립)이었다. 2. 단백질함량은 지방함량 및 탄수화물함량과 각각 -0.73$^{**}$ 및 -0.62$^{**}$ 고도의 부상관이 있었다. 공시 전품종중 단백질 및 지방함량이 모두 평균치이상인 품종은 13품종에 불과하였으며, 두 성분 모두 평균함량＋표준편차 이상의 고단백 고지방인 품종과 평균함량＋표준편차 이상의 고단백이면서 평균 지방함량을 갖고 있는 품종은 없었다. 3. 단백질함량 50.6～40.0% 범위에 있는 12품종에 대한 단백질의 필수아미노산 함유율은 32.82(Hill)～36.63%(부석)이었고 제한 아미노산인 함황 아미노산 함유율은 2.09(쥐눈콩)～2.73%(봉의)의 변이를 보였으며, 봉의와 서해 20호는 고단백이며 함황 아미노산 함량도 높았다. valine과 leucine 및 aspartic acid, glycine과 glutamic acid, leucine과 aspartic acid 간에는 고도의 정상관, glycine과 serine, valine과 phenylalanine, threonine과 proline, phenylalanine과 arginine, methionine과 glutamic acid, histidine과 lysine 간에는 유의 정상관, 그리고 isoleucine과 lysine 간에는 유의한 부상관이 있었다. 4. lysine 함량은 단백질 함량과 정산곤, isoleucine 함량은 단빅질 함량과 부상관을 보였으며, alanine, valine, leucine 함량은 지방함량과 각각 유의한 정산관을 보였다. 5. 대두 단백질은 7.5% acrylamide gel 전기영동에 의해 품종에 따라 12～16개의 구성분으로 분리되었으며, 이들중 주구성분들은 상대이동도가 0.06(a), 0.14(b). 0.24(d) 이었고, 구성분 b의 함량이 품종간에 가장 변이가 컸으며, 구성분 b는 그밖의 주요 구성분들의 함량과 부의 상관이 있었고, 구성분 a는 단백질 함량과 정상관이 있었다. 6. 종실단백질 구성분들의 조합 특성 면에서 공시 86품종은 11개 유형군으로 분류되었으며, 우리나라와 일본품종은 미국품종에 비해 단백질구성분 조성이 훨씬 다양하였다. 7. 이동도가 매우 빠른 단백질 구성분 o(Rm 0.77) p(Rm 0.81)를 모두 갖고 있는 품종은 3품종, 모두 갖고 있지 않은 품종은 1품종이었고, 나머지 82품종은 o나 p중 한 구성분을 갖고 있었으며 그 분포율은 30 : 65 이었는데 미국계 품종은 우리나라 품종에 비해 구성분 o를 간고 있는 비율이 현저히 적었다. 8. 대두 종실은 개화후 22일까지 완만히, 그 이후 20～30일간 급속히 직선적으로, 그후 5～15일간 서서히 건물중이 증가되어 최고에 달하고, 개화후 12일부터 27일까지 단백질함량은 급격히 감소되는 반면 지방함량은 급격히 증가되고 그 이후부터 개화후 40～47일까지는 단백질과 지방함량 모두 서서히 증가된후 성숙기까지 큰 변이가 없었다. 9. 고단백 품종 서해 20호는 광교 및 Shelly에 비해 전 성숙기간에 대한 비율로 보아 성숙 초기의 단백질함량 감소기간이 짧고 감소율도 적었으며, 그 후 단백질함량 증가기간이 길고 증가율도 큰 특성을 보였으나, 지방 축적은 일찍 정지되었다. 저단백 고지방 품종 Shelly는 서해 20호 및 광교에 비해 전 성숙기간에 대한 단백질함량 감소기간이 길고 증가기간은 짧았으며, 지방함량의 완만한 증가기간이 상대적으로 특히 길었다. 11. 대두 종실의 성숙중 단백질 구성분 변화 정도은 품종간차가 뚜렷하였고, 완숙 종실의 단백질 구성분들이 모두 출현하는 시기는 종실의 단백질함량의 감소가 끝난 시기와 대체로 일치되었으며, 완숙종실에서 나타나지 않는 상대이동도가 가장 작은 (Rm 0.03) 단백질이 개화후 17일까지 존재하였으나 그 이후에는 없었다. 12. 고단백 품종 서해 20호는 광교 및 Shelby에 비해 단백질 구성분 a의 조성비율이 성숙 초기에서부터 계속 높았고 개화후 47일까지 계속 증가되었으나, 광교의 Shelby에서는 구성분 a의 조성비율이 개화후 27일경 이후 거의 변화되지 않았다. 13. 단백질 구성분 b의 조성비율은 개화후 17일～42일 간에 계속적으로 증가되었는데, 고단백 품종서해 20호는 광교 및 Sheby에 비해 동기간에 그 증가율이 낮았다.

• 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.