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本研究的焦点是品牌的拟人化. 品牌拟人化被定义为将品牌看作是人类. 具体来说, 本研究的目标是理解如何将品牌拟人化的方法. 通过分析消费者对食品包装上的故事的阅读, 我们试图展示行销者和消费者如何将一系列品牌拟人化并创造意义. 我们的研究问题是一个品牌对不同的消费者具有多个或单一意义, 联想, 个性的可能性. 我们首先强调了本研究在理论和实践方面的重要性, 解释了为什么我们关注作为品牌意义传递工具的包装. 然后我们阐述了我们量性研究方法, 讨论了结果. 最后总结了管理方面的启示和对未来研究的建议. 本研究先让消费者直接阅读品牌意义传递的工具然后让这些消费者口头自由表达他们所感受到的意义. 具体来说, 为了获得有关感知意义的数据, 我们要求参与者去阅读选择的品牌食品包装上的非营养的故事. 包装在消费者研究方面还没有得到足够的关注(Hine, 1995). 直到现在, 研究还是仅关注包装的实用功能并形成了探索营养信息的影响的研究主体. (例如Lourei ro, McCluskey and Mittelhammer, 2002; Mazis and Raymond, 1997; Nayga, Lipinski and Savur, 1998; Wansik, 2003). 一个例外是最近的研究, 将注意力转向非营养信息的包装说明, 并视其为文化产品和将品牌神话的工具(Kniazeva and Belk, 2007). 下一步就是探索这些神话活动如何影响品牌个性感知以及这些感知如何与消费者相关. 这些都是本研究所要强调的. 我们用深度访谈来帮助消除量性研究的局限性. 我们的便利样本的构成具有人口统计和消费心态学的多样化以达到获得消费者对包装故事的不同的感知. 我们的参与者是美国的中产居民, 并没有表现出Thompson(2004)所描述的 "文化创造者" 的极端生活方式. 九名参与者被采访关于他们食品消费偏好和行为的问题. 他们被要求看看12个展示的食品产品包装并阅读包装上的文字信息. 之后, 我们继续进行关注消费者对阅读材料的解释的问题. (Scott and Batra, 2003). 平均来看, 每个参与者感知4-5个包装. 我们的深度访谈是一对一的并长达半个小时. 采访内容被录音下来并转录, 最后有140页的文字. 产品赖在位于美国西海岸的当地食品杂货店, 这些产品代表了食品产品类别的基本范围, 包括零食, 罐装食品, 麦片, 婴儿食品和茶. 我们使用Strauss和Corbin (1998)提出的发展扎根理论的步骤来分析数据. 结果表明, 我们的研究不支持先前的研究所假设的一个品牌/一个个性的概念. 因此我们展示了在消费者看来多个品牌个性可以在同一品牌身上很好的共存, 尽管行销者试图创造更多单一的品牌个性. 我们延伸了Fournier's (1998) 的假设, 某人的人生计划可以形成与品牌关系的强度和本质. 我们发现这些人生计划也影响感知的品牌拟人化和意义. Fournier提出了把消费者人生主题(Mick和Buhl, 1992)和拟人化产品的相关作用联系在一起的概念框架. 我们发现消费者人生计划形成了把品牌拟人化和品牌与消费者现有的关注相关联的方式. 我们通过参与者发现了两种品牌拟人化的方法. 第一种, 品牌个性通过感知的人口统计, 消费心态学和社会个性所创造. 第二, 第二, 在我们的研究还涉及到品牌的消费者所存在的问题与消费者的个性被混合, 以连接到他们(品牌为朋友, 家庭成员, 隔壁邻居)或远离自己的品牌个性和疏远他们(品牌作为二手车推销员, "一群高管".) 通过关注食品产品包装, 我们阐明了非常具体的, 被广泛使用, 但很少深入研究的营销传播工具: 品牌故事. 近期的研究已经视包装为神话制造者. 对行销者来说要创作出和产品及消费它们的消费者相连的文字故事的挑战越来越大, 并建议 "为创造需求的消费者神话的构成材料的多样化是后现代消费者可论证的需求"(Kniazeva和Belk, 2007). 作为叙述故事的的工具, 食品包装可以食品包装可以用理性和感性的方式, 为消费者提供无论是 "讲座" 或 "戏剧"(Randazzo, 2006), 神话(Kniazeva and Belk, 2007; Holt, 2004; Thompson, 2004), 或意义(McCracken, 2005) 作为他们拟人化产品的构成材料. 孕育工艺品牌个性掌握在作家/营销人员手中, 在读者/消费者心目中. 这些消费者会赋予品牌有意义的脸谱.
시카고협약 일부 체약국은 자국 항공사에게 AOC(Air Operator Certificate)를 승인하여 발행하는 것 이외에 외국 항공사에게도 FAOC(Foreign AOC)를 발행하고 있으며 다양한 항공안전평가도 실시하고 있다. 외국 항공사에게 FAOC 승인 발행 및 항공안전 평가 실시는 점차 확대되고 있는 추세로 전 세계적으로 항공안전증진 및 항공기 사고율 감소에 기여한 공로가 크다고 볼 수 있으나, 항공사 입장에서는 추가적인 허가제도 및 운항제한으로 인하여 항공기 운항 상 불편이 초래되고 있다. 유럽항공안전청(EASA)은 European public law 인 Basic Regulation에 의해 2003년에 설립되어 운영되고 있는 유럽의 단일 항공안전전문기관이다. EASA의 주요 임무는 민간항공분야의 안전기준 및 환경보호기준을 최상의 기준으로 증진하는 것이며, 감항, 승무원, 항공기 운항, 공항 및 ATM 등에 대한 입법업무 및 표준설정 업무를 관장하고 있으며 업무 범위가 점점 확대되고 있다. 유럽에서 TCO(Third country operator) Implementing Rule이 발효(2014.5.26.)됨에 따라, EASA는 32개 EASA 회원국으로 운항하고자 하는 모든 항공운송사업용 TCO에 대하여 안전에 대한 승인을 행할 권한을 가진다. 이에 따라, TCO에 대한 평가 및 승인을 할 때, 안전관련 부문에 대한 평가 및 승인은 EASA가 담당하고 운영허가(Operating permit) 부문은 종전과 같이 각 국가의 항공당국이 수행하게 된다. EU/EASA를 운항하는 TCO가 불편 없이 항공운송사업을 행할 수 있도록 신규제도 도입을 위한 전환기간으로 30개월이 적용 된다. 현재 EASA 회원국을 운항하는 항공사는 TCO 규정 발효 후 6개월 이내인 2014.11.26.까지 EASA에 TCO 허가 신청서를 제출해야 하며, EASA는 TCO 규정 발효 후 30개월 이내에 평가를 완료해야 한다. 유효한 TCO 허가는 운영허가 전에 취득해야 할 사전 요건으로, TCO 허가를 받지 못한 TCO는 EASA 회원국이 발행하는 운영허가를 발급받을 수 없다. TCO 허가 필요 여부는 항공운송사업에 해당하는지에 따라 결정되며 항공운송사업을 행할 경우 TCO 허가를 받아야 한다. 부정기편을 운항하는 항공사의 경우 일정기준을 충족한다면 TCO 허가 없이 운항이 가능하기는 하나 잠재적인 미래 수요가 예상되는 경우 원활한 서비스 제공을 위해 사전에 TCO 허가를 취득하는 것이 바람직하다고 본다. 본 논문에서는 EU의 TCO 규정 도입과 관련하여, EASA의 기능 및 TCO 규정을 포함한 EU의 항공법규체계에 대한 법적 근거와 내용을 고찰하고, 우리나라가 착안하고 개선해야 할 몇 가지 제언과 개선방안을 제시하였다. 본 논문이 1) 항공사가 TCO 허가를 준비하는데 도움이 되고, 2) 정부, 학계 및 항공사 등 유관부문에서 항공안전증진을 위한 국제 동향을 이해하는데 도움이 되고, 3) 국내 항공법규 개선 및 정부조직의 기능을 개선하는데 도움을 주고, 4)아울러, 국제표준 준수 및 항공안전증진에 기여하길 기대한다.
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
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.