• 치위생과학회지
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• 제13권4호
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• pp.471-479
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• 2013

본 연구는 남자 고등학생들의 스트레스와 구강악습관, 학업 수준과 경제수준에 따른 스트레스와 구강악습관에 대한 연관성을 파악하여 구강악습관에 대한 교육 필요성을 강조하며 구강악안면 부위의 구강건강까지 올바르게 이루어지도록 기초자료를 제공하고자 한다. 2013년 5월부터 7월까지 충남에 위치한 2개 고등학교 1, 2학년을 대상으로 편의표본추출 법에 의한 자기기입식으로 설문조사를 실시하여 다음과 같은 결과를 얻었다. 1. 5개 영역 스트레스 중 학교생활 스트레스가 2.11점으로 가장 높았고, 가정문제 스트레스가 1.51점으로 가장 낮게 나타났다. 2. 학년별 스트레스는 2학년이 1학년보다 모든 영역에서 높았으며, 학교생활 스트레스(p<0.01), 대인관계 스트레스(p<0.01), 자신 문제 스트레스(p<0.05)에서 유의한 차이를 보였다. 3. 잠재변수 스트레스에 따른 5가지 영역의 스트레스와 구강악습관과의 유의성 분석 결과 모두 유의한 것으로(p<0.001)로 나타났으며, 스트레스와 구강악습관의 상관관계는 -0.30의 약한 음의 상관관계를 보였고 학교생활 스트레스와 자신 문제 스트레스, 환경문제 스트레스와 대인관계 스트레스와는 0.7 이상의 매우 높은 양의 상관관계를 보였다. 4. 스트레스, 학업 수준, 가정경제수준 모델의 적합도 검정 결과는 GFI, AGFI, NFI에서 모두 0.9 이상을 보였으며, RMR과 RMSEA 값이 모두 0.1보다 작은 값이 추정되어 양호한 모형이다. 이상의 연구결과를 보면 스트레스와 구강악습관과의 연관성이 있다고 볼 수 있다. 또한 학생의 성적수준, 경제수준과도 구강악습관 발생과 연관성이 있음을 확인되었으며, 본인의 구강건강을 스스로 책임져야 할 시기인 고등학생들이 구강건강을 위하여 바람직한 습관 형성을 위한 구강보건교육이 시행돼야 할 것이다.

• Parasites, Hosts and Diseases
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• 제29권3호
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• pp.245-258
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• 1991

요꼬가와흠흡에 감염된 숙주에서 항체생성을 유도하는 물질의 충체 래에 분포하고 있는 부위를 확인하기 위하여 대조군의 실험동물 IgG와 요꼬가와흡충에 감염된 실험고양이의 특이항체 IgG를 요꼬가와흡충 성충조직에 반응시키고 면역황금표식법을 이용하여 전자현미경으로 관찰하여 다음과 같은 결과를 얻었다. 대조군의 IgG를 반응시킨 조직항원에는 환금입자의 표지가 관찰되지 않았으나 표피층은 표피세포들의 원형질돌기들이 표피합포체를 구성하고 있었으며 기저층은 분명하게 구별되고 경육층도 잘 발달되어 있었다. 난황세포는 크기가 다양한 분필과립들을 포함하고 있었으며 조면소포체가 특히 발달되어 있었다. 맹관의 막구조물은 잘 발달되었으며 장상피 합포체도 잘 발달되어 있었다. 저정낭에는 정자가 전자밀도가 릎은 두부와 미세소관이 잘 발달한 미부로 구성되어 있었으며, 배설낭은 배설낭 상피에 막구조물이 관찰되었고 충란은 전자밀도가 높은 난각이 관찰되었다. 요꼬가와흡충에 감염된 실험동물의 IgG를 반응시킨 조직항원의 표피충에는 표피합포체와 표피세포의 세포질에 황금입자가 높은 밀도로 표지되어 강한 항원성을 나타내었다. 그리고 난황세포의 분필과립과 맹관 막구조물과 맹관 내강에도 환금입자가 높은 밀도로 관찰되어 항원성이 강한 것으로 생각되었다. 저정낭의 정자와 배설낭의 막구조물에는 적은 수의 황금입자가 표지되어 항원성이 미약한 것으로 생각되었으나, 자궁의 충란 난각에는 황금입자가 다소 표지되어 항원성물질이 존재하는 것으로 생각되었다. 이상의 결과로 요꼬가와홉충에 감염된 실험 고양이는 요꼬가와흡충의 표피합포체와 표피세포에서 생성된 물질과 난황세포에서 생성된 물질에 의하여 면역항체가 형성되며 일부는 맹관 내용물에 의하여 면역항체가 유도되는 것으로 생각되었다.는 69.7%의 같은 비율로 높았다. 이상으로 요충 감염은 아직까지 높은 상태임을 알았고 예방을 위한 집단 구충에는 전 집단에 대한 3회 이상의 반복구충이 효과적이었다. gas layers is also made. Low resolution spectral intensities at the boundary are obtained for uniform, parabolic and boundary layer type temeprature profiles using the obtained for uniform, parabolic and boundary layer type temperature profiles using the obtained WSGGM's with 9 gray gases. The results are compared with the narrow band spectral intensities as obtained by a narrow band model-based code with the Curtis-Godson approximation. Good agreement is found between them. Local heat source strength and total wall heat flux are also compared for the cases of Kim et al, which again gives promising agreement.면적 306~453$\textrm{cm}^2$, 유색계의 경우 수당면적 340~453$\textrm{cm}^2$ 일 때 경제능력을 제대로 발휘할 수 있고 경제성이 있는 것으로 나타났다. 첨가구가 높은 경향이 있었다8.4%. 79.7% 그리고

• 지능정보연구
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• 제18권2호
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• pp.29-45
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• 2012

회사채 신용등급은 투자자의 입장에서는 수익률 결정의 중요한 요소이며 기업의 입장에서는 자본비용 및 기업 가치와 관련된 중요한 재무의사결정사항으로 정교한 신용등급 예측 모형의 개발은 재무 및 회계 분야에서 오랫동안 전통적인 연구 주제가 되어왔다. 그러나, 회사채 신용등급 예측 모형의 성과와 관련된 가장 중요한 문제는 등급별 데이터의 불균형 문제이다. 예측 문제에 있어서 데이터 불균형(Data imbalance) 은 사용되는 표본이 특정 범주에 편중되었을 때 나타난다. 데이터 불균형이 심화됨에 따라 범주 사이의 분류경계영역이 왜곡되므로 분류자의 학습성과가 저하되게 된다. 본 연구에서는 데이터 불균형 문제가 존재하는 다분류 문제를 효과적으로 해결하기 위한 다분류 기하평균 부스팅 기법 (Multiclass Geometric Mean-based Boosting MGM-Boost)을 제안하고자 한다. MGM-Boost 알고리즘은 부스팅 알고리즘에 기하평균 개념을 도입한 것으로 오분류된 표본에 대한 학습을 강화할 수 있으며 불균형 분포를 보이는 각 범주의 예측정확도를 동시에 고려한 학습이 가능하다는 장점이 있다. 회사채 신용등급 예측문제를 활용하여 MGM-Boost의 성과를 검증한 결과 SVM 및 AdaBoost 기법과 비교하여 통계적으로 유의적인 성과개선 효과를 보여주었으며 데이터 불균형 하에서도 벤치마킹 모형과 비교하여 견고한 학습성과를 나타냈다.

• 마케팅과학연구
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• 제18권4호
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• pp.1-32
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• 2008

기업연상(corporate association)이 제품 평가(product responses)에 어떻게 영향을 미치는 가에 대한 연구가 부진하다는 Brown and Dacin(1997)의 문제 제기가 있은 후, 기업연상이 제품 판단에 미치는 영향과 과정에 대한 조절변수와 매개변수들을 파악하려는 연구가 진행되어 왔다. 본 연구에서는 기업연상의 두가지 유형인 CA(corporate ability) 연상과 CSR(corporate social responsibility) 연상이 성능과 재무위험에 미치는 영향력과 그 영향력을 조절하는 변수들을 조사하였다. 분석 결과에 의하면, 주효과(main effects)에 있어서는, 가설에서 기대한 바와 같이 CA 연상이 성능위험과 재무위험에 유의한 영향력을 갖는 것으로 나타난 반면, CSR 연상은 성능위험과 재무위험에 대해 유의한 영향력을 갖지 않는 것으로 나타났다. 조절변수로 인한 상호작용효과와 관련해서는, CA 연상이 성능위험과 재무위험에 미치는 주효과에 대해 제품범주 지식과 관여는 각각 유의한 조절효과를 나타내었다. 하지만, CSR 연상이 성능위험과 재무위험에 미치는 주효과에 대해서는 제품범주 지식과 관여의 조절효과는 나타나지 않았다. 이러한 연구 결과를 통하여 제품의 기능적인 속성에 대한 정보가 부족한 제품에 대해 소비자가 지각하는 위험을 감소시키기 위하여, 기업은 CSR 연상보다는 CA 연상에 대해 강조할 필요가 있다는 결론을 내리게 되었다.

• 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.