Preparation And Optical Properties Of Cdte/Cdo Center Dot Nh(2)O Core/Shell Nano-Composites In Aqueous Solution

The deposition of CdO center dot nH(2)O On CdTe nanoparticles was studied in an aqueous phase. The CdTe nanocrystals (NCs) were prepared in aqueous solution through the reaction between Cd2+ and NaHTe in the presence of thioglycolic acid as a stabilizer. The molar ratio of the Cd2+ to Te2- in the precursory solution played an important role in the photoluminescence of the ultimate CdTe NCs. The strongest photoluminescence was obtained under 4.0 of [Cd2+]/[Te2-] at pH similar to 8.2. With the optimum dosage of Cd(II) hydrous oxide deposited on the CdTe NCs, the photoluminescence was enhanced greatly. The photoluminescence of these nanocomposites was kept constant in the pH range of 8.0-10.0, but dramatically decreased with an obvious blue-shifted peak while the pH was below 8.0. In addition, the photochemical oxidation of CdTe NCs with cadmium hydrous oxide deposition was markedly inhibited.

Fast Computing For Lurr Of Earthquake Prediction

The LURR theory is a new approach for earthquake prediction, which achieves good results in earthquake prediction within the China mainland and regions in America, Japan and Australia. However, the expansion of the prediction region leads to the refinement of its longitude and latitude, and the increase of the time period. This requires increasingly more computations, and the volume of data reaches the order of GB, which will be very difficult for a single CPU. In this paper, a new method was introduced to solve this problem. Adopting the technology of domain decomposition and parallelizing using MPI, we developed a new parallel tempo-spatial scanning program.

Probabilistic inference for fast learning in control

A novel framework is provided for very fast model-based reinforcement learning in continuous state and action spaces. It requires probabilistic models that explicitly characterize their levels of condence. Within the framework, exible, non-parametric models are used to describe the world based on previously collected experience. It demonstrates learning on the cart-pole problem in a setting where very limited prior knowledge about the task has been provided. Learning progressed rapidly, and a good policy found after only a small number of iterations.