By considering heterogeneity in abilities and self-selection in educational choice, this paper adopts the heterogeneous human capital model to estimate rate of return to university education using data from the 1990 and 2000 Taiwan's Manpower Utilization Surveys. The Taiwan empirical study shows that significant heterogeneous return to education does exist, and that the educational choice was made according to the principle of comparative advantage. The estimated rates of return for attaining university were 19% and 15%, much higher than the average rate of return of 11.55 and 6.6%, for 1990 and 2000, respectively. The declining trend of return to university education may have been caused by the rapid expansion of the number of colleges and universities and the increasing supply of college graduates in the 1990s.
This study investigated the relationship between a subject's evaluation of injection molding machines (IMMs) and formal design features using Kansei engineering. This investigation used 12 word pairs to evaluate the IMM configurations and employed the semantic differential method to explore the perception of 60 interviewees of 12 examples. The relationship between product feature design and corresponding words was derived by multiple regression analysis. Factor analysis reveals that the 12 examples can be categorized as two styles-advanced style and succinct style. For the advanced style, an IMM should use a rectangular form for the clamping-unit cover and a full-cover for the injection-unit. For the succinct style, the IMM configuration should use a beveled form for the safety cover and a vertical rectangular form for the clamping-unit cover. Quantitative data and suggested guidelines for the relationship between design features and interviewee evaluations are useful to product designers when formulating design strategies.
As a kind of wood-based panel, particleboard is widely used in production and daily life. The physical and mechanical properties (PMPs) of particleboard play a decisive role in its practical application. At present, destructive methods are primarily used to measure the actual properties
of particleboard on the production line, which is a waste of resources and time-consuming method. In order to solve these problems, this paper uses several data-driven methods to predict the PMPs of particleboard. Firstly, the data set is constructed based on the parameters of particleboard
production process. Secondly, seven commonly used data-driven methods are used to build models to predict the PMPs. Finally, three different assessment indexes are used to determine the most suitable method for property prediction. The results showed that the random forest method is better
for predicting the PMPs of particleboard.
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