The need for ready-to-wear clothing increases as the problem comes to arise from the fit of custommade clothing due to the characteristics of middle-aged women's somatotype. At this point of time, a study on the realities of production of custom-made clothing in middle-aged women's clothing business firms is of very greatly significance. Therefore, this study was intended to identify the problem and improvements through the survey research of production of custom-made clothing in middle-aged women's clothing business firms and further present the plan for development of custom-made clothing business. The questionnaire was framed based on the contents extracted from the preliminary questionnaire research for the pattern section chief of each business firm. Collected data were statistically processed using the SPSS 10.0 Windows program. As a result, the following findings were obtained: 1. The target age of the middle-aged women's clothing business firms ranged from more than 45 years to less than 50 years of age. Clothing business firms much made inroads into the ready-to-wear clothing market largely in the 1980s and the 1990s. Their active entry into the custom-made clothing market occurred in the 1970s and the 1980s. 2. In terms of the clothing production method of middle-aged women's clothing firms, some private boutique and designer brand clothing firms entered the clothing market with a focus on custom-made clothing in the beginning of its organization and introduced the production method of ready-to-wear clothing in accordance with changes in production methods and consumers' needs and wants. National brand clothing firms manufactured clothing with a focus on ready-to-wear clothing from the beginning of its organization, but at last they manufactured both partial custom-made and whole custom-made as the problem arose from ready-to-wear clothing. Seeing that their clothing production showed the ratio readyto-wear to custom-made clothing of 2.58:1. And it was found that the manufacture of ready-to-wear and custom-made clothing took into consideration the great difference in the pattern, size and design plan. The research of the clothing production process showed that whole custom-made and partial custommade were distinguished according to whether or not the sample was presented. 3. The ready-to-wear pattern of middle-aged women's clothing firms were used with a focus on the 'patternmaker-developed pattern' and company-developed pattern'. Most clothing businesses produced clothing in 4 to 5 basic sizes, which is found to be insufficient to complement the physical characteristics of middle-aged women with many specific somatotypes. In the pattern of custom-made clothing, the 'pattern of ready-to-wear were applied' or the 'customized pattern was developed'. Actual measurements were most used as the size of custom-made, and accordingly it is predicted that the level of satisfaction is higher with the fit of custom-made clothing than that of ready-to-wear. The selling place and the head office showed the similar percent as the place for measuring the size of custom-made clothing. Size measurers were mostly the shop master. And it was found that most clothing business firms had a problem when the measured size was applied to the pattern. Accordingly, it is necessary to provide education on size measurement for shop masters. 4. It was found that in the middle-aged women's clothing firms, the pattern correction of the length of sleeve, jacket and slacks occupied the highest percent. Accordingly, it is necessary to provide for the size system to complement the accurate somatotype characteristics of middle-aged women. 5. In custom-made clothing customer management, most firms engaged in customer somatotype management through size management. They provided customers with commodity information by informing them of the sales and event period and practiced human management for customers by maintaining the get-together and friendly relationship. 6. Middle-aged women's clothing businesses responded that it would be necessary to improve the fit of custom-made clothing and complement their pursuit for individuality as the plan to improve its quality. In consequence, it suggests that middle-aged women's clothing businesses should provide middle-aged women with the clothing of better-suited size and refined design. Middle-aged women's clothing businesses responded that it was the most urgent task to form the custom-made clothing manufacturing team as the plan to expand the custom-made clothing market, which is identified as their emphasis on the systematized production of custom-made clothing.
Microbial quality of baked egg products was evaluated by counting the levels of sanitary indicative bacteria (aerobic plate counts, coliforms, and E. coli), L. monocytogenes and Salmonella spp. at the critical control points (CCPs) of manufacturing process. In addition, the survival and growth of foodborne pathogens in various egg products (cheese, tuna, tteokgalbi, pizza omelets, baked egg, and steamed egg) were investigated at 4, 10, and
The Purpose of this study is to find the effect factors of R&D investment in SMEs which plays an important role in the national economy, and the differences of the effect factors by the type of SMEs. The subject of this study is about 3,400 SMEs mentioned in "The survey of technical statistics on SMEs in 2007" by Korea Federation of Small and Medium Business. The effect factors are related with the size of business, the infrastructure of R&D and the activities of R&D which have been studied by many researchers. The methods of analysis are regression analysis, moderating effect analysis and the software package used is SPSS 12.0. The results of the study are as fallow. First, it was found that unlike in previous studies which show the effect of the elements of business's size, research infrastructure, research activities on R&D investment, one element alone can't be considered for meaningful result but the various elements have effect on R&D investment at the same time. In other words, the number of employees and the sales as the elements of business's size, the ratio of researchers, the technical ability, the ratio of equipment possession and the intellectual properties as the elements of R&D infrastructure, the activity of ideas and joint research as the elements of R&D activities have positive(+) effect, whereas the participation of CEO in the activity of R&D as the elements of R&D activities activity has negative(-) one. The number of employees, the ratio of researchers, and the sales had relatively high influence whereas equipment possession, technical ability, intellectual properties, the participation of CEO in the research, the activity of idea, joint research had relatively low influence. Next, it was also found that there are differences of the effect factors over the types of SMEs. SMEs were classified into 19 types by eight criteria such as start-ups and existing business by business age; small business and medium business by size; manufacturing business and service business by product type;independent business and subcontractor business by dealing type; businesses in the entering, growing, maturing and restructuring stage by growth stage; businesses with low, medium and high technology by technological level; pioneering business and non-pioneering business by industrial type; and businesses with state-of-the-art technology and non-advanced business by the level of business activities. The meaning of this study lies in the fact that it found the various effect factors should be considered at the same time when conducting study on SMEs' R&D investment, and the differences by the type should be acknowledged. This study surpassed the limitations of the previous studies which focused on a couple of factors and types. This study result can also be considered for other studies on achievement, organization, marketing and others. Moreover, it shows that a differential policy by business type is needed when formulating SME policy.
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.