Testing the effect of varying environments on the speed of evolution

One of the most important tasks in computer science and artificial intelligence is optimization. Computer scientists use simulation of natural evolution to create algorithms and data structures to solve complex optimization problems. This field of study is called evolutionary computation. In evolutionary computation, the speed of evolution is defined as the number of generations needed for an initially random population to achieve a given goal. Recent studies have shown that varying environments might significantly speed up evolution, and suggested modularly varying goals can accelerate optimization algorithms. In this thesis, we study the effect of varying goals on the speed of evolution. Two test models, the NK model and the midunitation model, are used for this study. Three different evolutionary algorithms are used to test the hypothesis. Statistical analyses of the results showed that under NK model, evolution with fixed goal is faster than evolution with switching goals. Under midunitation model, different algorithms lead to different results. With some string lengths using hill climbing, switching goals sped up evolution. With other string lengths using hill climbing, and using the other evolutionary algorithms, either evolution with a fixed goal was faster or results were inconclusive

Regional Ground Surface Mass Variations Inversed by Radial Point-mass Model Method with Spatial Constraints

Radial point-mass model method is the disturbance gravity downward continuation in essence, which is an ill-posed problem. In general, the regularization method is an efficient way to get the reliable solution. To solve this problem, the radial point-mass model method is improved by using Helmert variance component estimation with adding spatial constraints from a practical point of view. Taking South America continent as study area, radial point-mass model method with spatial constraints is verified by experimental results. The experiments results show that the condition number of normal equations is decreasing obviously after adding spatial constraints. The inversion results of radial point-mass model method with spatial constraints are consistent with results of other methods. Furthermore, the radial point-mass model method with spatial constraints provides an alternative way to monitor regional surface mass variations by satellite gravimetry

Silica accelerates the selective hydrogenation of CO2 to methanol on cobalt catalysts.

The reaction pathways on supported catalysts can be tuned by optimizing the catalyst structures, which helps the development of efficient catalysts. Such design is particularly desired for CO2 hydrogenation, which is characterized by complex pathways and multiple products. Here, we report an investigation of supported cobalt, which is known for its hydrocarbon production and ability to turn into a selective catalyst for methanol synthesis in CO2 hydrogenation which exhibits good activity and stability. The crucial technique is to use the silica, acting as a support and ligand, to modify the cobalt species via Co‒O‒SiOn linkages, which favor the reactivity of spectroscopically identified *CH3O intermediates, that more readily undergo hydrogenation to methanol than the C‒O dissociation associated with hydrocarbon formation. Cobalt catalysts in this class offer appealing opportunities for optimizing selectivity in CO2 hydrogenation and producing high-grade methanol. By identifying this function of silica, we provide support for rationally controlling these reaction pathways

Author Correction: Silica accelerates the selective hydrogenation of CO2 to methanol on cobalt catalysts

An amendment to this paper has been published and can be accessed via a link at the top of the paper