Suchergebnisse

This work studies the factors influencing the labor productivity of small and medium‐sized enterprises (SMEs) and large firms using Taiwan as a case study. A special emphasis is placed on two possible international channels: exports and foreign direct investment (FDI). Different from conventional studies, we employ the two‐stage switching regressions to correct the firm‐size effect on labor productivity and estimate labor productivity for SMEs and large firms. The main findings are as follows. First, the estimates of the selectivity variable are statistically significant for both SMEs and large firms, supporting the hypothesis of correcting the effect of firm‐size truncation. Second, while a larger trade intensity significantly increases the labor productivity of SMEs, it deteriorates significantly that of large firms. Third, FDI enhances the labor productivity of SMEs internally, whereas it has a negative spillover on that of other small and large firms in the industry. While the first outcome lends supports to the role of self‐selection, the remaining stands in sharp contrast to conventional wisdom.

Proposes and empirically tests a model of the relationships between knowledge of HIV transmission, specifically the misinformation that AIDS can be transmitted easily through casual contact with HIV-infected persons; attitudes toward homosexuals, the most prominent of the social groups presently affected by the AIDS crisis; and support for restrictive public policies aimed at HIV-infected persons. (Abstract amended)

Abstract. Precise predictions of storm surges during typhoon events have the necessity for disaster prevention in coastal seas. This paper explores an artificial neural network (ANN) model, including the back propagation neural network (BPNN) and adaptive neuro-fuzzy inference system (ANFIS) algorithms used to correct poor calculations with a two-dimensional hydrodynamic model in predicting storm surge height during typhoon events. The two-dimensional model has a fine horizontal resolution and considers the interaction between storm surges and astronomical tides, which can be applied for describing the complicated physical properties of storm surges along the east coast of Taiwan. The model is driven by the tidal elevation at the open boundaries using a global ocean tidal model and is forced by the meteorological conditions using a cyclone model. The simulated results of the hydrodynamic model indicate that this model fails to predict storm surge height during the model calibration and verification phases as typhoons approached the east coast of Taiwan. The BPNN model can reproduce the astronomical tide level but fails to modify the prediction of the storm surge tide level. The ANFIS model satisfactorily predicts both the astronomical tide level and the storm surge height during the training and verification phases and exhibits the lowest values of mean absolute error and root-mean-square error compared to the simulated results at the different stations using the hydrodynamic model and the BPNN model. Comparison results showed that the ANFIS techniques could be successfully applied in predicting water levels along the east coastal of Taiwan during typhoon events.

Abstract. This study anatomized algorithm effects of specific contributing area (SCA) on soil wetness estimation, consequently landslide prediction, in SHALSTAB. A subtropical mountainous catchment during three typhoon invasions is targeted. The peak 2-day rainfall intensity of the three typhoons: Haitang, Mindulle and Herb are 144, 248 and 327 mm/day, respectively. We use modified success rate (MSR) to retrieve the most satisfying mean condition for model parameters in SHALSTAB at three rainfall intensities and respective pre-typhoon NDVI themes. Simulation indicates that algorithm affects the prediction of landslide susceptibility (i.e. FS, Factor of Safety) significantly. Based on fixed NDVI and the mean condition, we simulate by using full scale rainfall intensity from 0 to 1200 mm/day. Simulations show that predicted unstable area coverage increases non-linearly as rainfall intensity increases for all algorithms yet with different increasing trends. Compared to Dinf, D8 always gives lower coverage of predicted unstable area during three typhoons. By contrast, FD8 gives higher coverage areas. The absolute difference (compared to Dinf) in predicted unstable area ranges from ~−3% to +4% (percent watershed area). The relative difference (compared to Dinf) ranges from −15% to as high as +40%. The maximum absolute and relative differences in unstable area prediction occur around the condition of 100–300 mm/day, which is common in subtropical mountainous region. Theoretical relationship among slope, rainfall intensity, SCA and FS value was derived in which FS values are very sensitive to algorithms in the field of slope from 37 to 52degree. Results imply any comparison among SCA-related landslide models or engineering application of rainfall return period analysis must base on the same algorithm to obtain comparable results. This study clarifies the SCA algorithm effect on FS prediction and deepens our understanding on landslide modeling.