No abstract

We have fabricated 6.5 in. flexible full-color top-emission active matrix organic light-emitting diode display on a polyimide (PI) substrate driven amorphous indium gallium zinc oxide thin-film transistors (a-IGZO TFTs). The a-IGZO TFTs exhibited field-effect mobility (μFE) of 15.1 cm2/V s, subthreshold slope of 0.25 V/dec, threshold voltage (VTH) of 0.9 V. The electrical characteristics of TFTs on PI substrate, including a bias-stress instability after 1 h long gate bias at 15 V, were indistinguishable from those on glass substrate and showed high degree of spatial uniformity. TFT samples on 10 μm thick PI substrate withstood bending down to R=3 mm under tension and compression without any performance degradation.

No abstract

Droughts are destructive climatic extreme events, which may cause significant damages both in natural environments and human lives. Drought forecasting plays an important role in the control and management of water resources systems. In this study, a conjunction model is presented to forecast droughts. The proposed conjunction model is based on dyadic wavelet transforms and neural networks. Neural networks have shown great ability in modeling and forecasting nonlinear and nonstationary time series in a water resources engineering, and wavelet transforms provide useful decompositions of an original time series. The wavelettransformed data aids in improving the model performance by capturing helpful information on various resolution levels. Neural networks were used to forecast decomposed sub-signals in various resolution levels and reconstruct forecasted sub-signals. The performance of the conjunction model was measured using various forecast skill criteria. The model was applied to forecast droughts in the Conchos River Basin in Mexico, which is the most important tributary of the Lower Rio Grande/Bravo. The results indicate that the conjunction model significantly improves the ability of neural networks for forecasting the indexed regional drought.

No abstract