배경: 심폐바이패스 없이 시행하는 관상동맥우회술("Off-pump" Coronary Artery Bypass grafting, OPCAB)은 체외순환의 부작용을 피할 수 있는 이점이 있으며 합병증 발생을 감소시킴으로써 특히 고 위험군 환자에서 유리한 것으로 보고되고 있다. 저자들은 심한 허혈성 좌심실 기능부전 환자들에서 관상동맥우회슬시 체외순환 유무에 따른 수술 성적을 비교하였으며 좌심실 기능의 회복정도를 비교분석 하였다. 대상 및 방법 1997년 3월부터 2004년 2월까지 본원에서 관상동맥우회술을 시행 받은 환자들 중 술 전 좌심실 박출계수(Left Ventricular Ejection Fraction, LVEF)가
대학생 시기는 실질적으로 직업선택을 해야 하는 시기이다. 우리 사회가 빠르게 고도로 발달하는 만큼, 직업은 다양화, 세분화, 전문화되어 대학생들의 취업 준비기간은 또한 갈수록 길어지고 있다. 본 연구는 대학생들이 학교 내외에서 하는 경험하는 다양한 활동들이 취업에 어떤 영향이 있을지 대학생들의 로그데이터를 중심으로 분석해 보았다. 실험을 위하여 학생들의 다양한 활동을 체계적으로 분류하고 활동 데이터를 6개의 핵심역량(직무전문성강화 역량, 리더십 및 팀웍 역량, 세계화 역량, 직무몰입 역량, 직업탐색 역량, 자율이행역량)으로 구분하였고, 여기서 구분된 6개의 역량 값이 취업여부(취업그룹, 미취업그룹)에 미치는 영향을 분석하였다. 분석 결과 6개의 역량 모두 취업집단과 미취업집단의 수준차이가 유의한 것을 확인할 수 있어 학교에서의 활동은 취업에 유의미함을 유추할 수 있었다. 다음으로 6개의 역량이 취업의 질적성과에 미치는 영향을 분석하기 위하여 6개의 역량수준을 상·하로 나누고, 첫연봉액을 기준으로 6개의 그룹을 만든 후 관계를 확인해 보았는데, 그 결과 6개의 역량 중 세계화역량, 직업탐색역량, 자율이행역량 수준이 높은 학생이 연봉을 기준으로 한 취업성과 또한 높은 것으로 확인되었다. 본 연구의 이론적 공헌은 다음과 같다. 첫 번째, 학창경험으로부터 추출할 수 있는 역량을 인사조직관리분야의 역량과 연결하며, 개인의 경력성공을 위해 대학생으로서 필요한 역량을 직업탐색역량과 자율이행역량을 추가하였다는 점이다. 두 번째, 활동로그의 실데이터 기반으로 각각의 역량을 측정하고 결과변수와 검증을 한 점이다. 세 번째, 양적성과(취업률)뿐만 아니라 질적성과(연봉수준)를 분석한 점이다. 본 연구의 실무적 활용은 다음과 같다. 첫 번째, 대학생들의 경력개발계획 수립 시 가이드가 될 수 있다. 전략이 없거나 균형을 갖추지 못한 또는 과도한 스펙을 쌓기는 지양하고 직업세계와 직무에 대한 분석을 바탕으로 자신의 강점을 표현할 수 있는 취업준비가 필요하다. 두 번째, 학교와 기업, 지자체, 정부 등 대학생들을 위한 행사를 기획하는 담당자는 대학생들이 필요로 하는 경험을 설계할 본 연구에서 제시한 6대 역량을 참고할 수 있다. 이벤트의 수요자인 대학생이 필요한 역량을 키우면서 하면서 각 기관의 목적을 더할 때 수요자와 공급자 모두 만족스러운 결과를 만들 수 있다. 세 번째, 디지털 대전환 시대, 국가의 균형발전을 구상하는 정부의 정책담당자는 대학생들의 호기심과 에너지를 대학생들의 역량개발과 국가의 균형발전을 함께 성취하는 방향으로 정책을 만들 수 있다. 기존에 없던 플랫폼서비스를 시도하고, 기존의 아날로그 상품이나 서비스와 기업문화를 디지털화 하는 데에는 많은 인력이 필요하며 디지털세대인 현 대학생들의 활약은 전 산업에서 촉매가 될 뿐 아니라 성공적인 경력개발을 위한 대학생들에게도 필요한 경험이라 사료된다.
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70
본 연구는 소비자가 지각하는 유통업체의 위치기반 모바일 쇼핑정보 서비스에 대한 정보의 상황관련성과 정보자극에 대한 PAD 감정변수들(환기, 지배력, 즐거움) 간의 상호 인과관계와 이용의도에 대한 이들의 효과를 실증 연구 하였다. 미국 내 모바일 이용자를 대상으로 무작위 표본추출법에 근거하여 추출되었고, 총 335명의 사용가능한 응답이 수거되었다. 분석결과, 환기와 상황관련성은 즐거움에 정(+)의 영향을 주었으나 지배력은 즐거움에 유의한 영향력을 나타내지 않았다. 즐거움은 이용의도에 정(+)의 영향을 주었다. 본 연구를 통해 위치기반 모바일 서비스에 대한 소비자의 인지적 반응과 감정적 반응을 통합적으로 살펴보았으며, PAD 감정차원간의 체계적인 관계를 규명하였다. 연구결과를 바탕으로 모바일 쇼핑서비스 개발자, 유통업체, 그리고 마케팅 실무자를 위한 시사점을 논의하였으며, 연구의 한계점과 더불어 향후 연구 방향을 제시하였다.
목적: 아교모세포종의 방사선치료에서 국소제어율과 생존율을 향상시켜 보고자 3차원 입체조형치료기법을 이용한 방사선선량 증가 연구를 전향적으로 시행하였다. 대상 및 방법: 1997년 1월부터 2002년 7월까지 아교모세포종으로 조직학적 진단이 되고 전신수행도(KPS)가 60 이상으로 수술 후 방사선치료를 받은 환자를 대상으로 하였다. 프로토콜에 따라 전향적으로 연구에 참여한 42예의 고선량군과 후향적 대조군인 33예의 저선량군을 비교 분석하였다 고선량군은 3차원 입체조형치료법에 의해
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.