An Inter-molecular Adaptive Collision Scheme for Chemical Reaction Optimization

Optimization techniques are frequently applied in science and engineering research and development. Evolutionary algorithms, as a kind of general-purpose metaheuristic, have been shown to be very effective in solving a wide range of optimization problems. A recently proposed chemical-reaction-inspired metaheuristic, Chemical Reaction Optimization (CRO), has been applied to solve many global optimization problems. However, the functionality of the inter-molecular ineffective collision operator in the canonical CRO design overlaps that of the on-wall ineffective collision operator, which can potential impair the overall performance. In this paper we propose a new inter-molecular ineffective collision operator for CRO for global optimization. To fully utilize our newly proposed operator, we also design a scheme to adapt the algorithm to optimization problems with different search space characteristics. We analyze the performance of our proposed algorithm with a number of widely used benchmark functions. The simulation results indicate that the new algorithm has superior performance over the canonical CRO

Power-controlled cognitive radio spectrum allocation with chemical reaction optimization

Cognitive radio is a promising technology for increasing the system capacity by using the radio spectrum more effectively. It has been widely studied recently and one important problem in this new paradigm is the allocation of radio spectrum to secondary users effectively in the presence of primary users. We call it the cognitive radio spectrum allocation problem (CRSAP) in this paper. In the conventional problem formulation, a secondary user can be either on or off and its interference range becomes maximum or zero, respectively. We first develop a solution to CRSAP based on the newly proposed chemical reaction-inspired metaheuristic called Chemical Reaction Optimization (CRO). We study different utility functions, accounting for utilization and fairness, with the consideration of the hardware constraint, and compare the performance of our proposed CRO-based algorithm with existing ones. Simulation results show that the CRO-based algorithm always outperforms the others dramatically. Next, by allowing adjustable transmission power, we propose power-controlled CRSAP (PC-CRSAP), a new formulation to the problem with the consideration of spatial diversity. We design a two-phase algorithm to solve PC-CRSAP, and again simulation results show excellent performance. © 2002-2012 IEEE.published_or_final_versio

Nephron organoids derived from human pluripotent stem cells model kidney development and injury

Kidney cells and tissues derived from human pluripotent stem cells (hPSCs) would enable organ regeneration, disease modeling, and drug screening in vitro. We established an efficient, chemically defined protocol for differentiating hPSCs into multipotent nephron progenitor cells (NPCs) that can form nephron-like structures. By recapitulating metanephric kidney development in vitro, we generate SIX2+SALL1+WT1+PAX2+ NPCs with 90% efficiency within 9 days of differentiation. The NPCs possess the developmental potential of their in vivo counterparts and form PAX8+LHX1+ renal vesicles that self-pattern into nephron structures. In both 2D and 3D culture, NPCs form kidney organoids containing epithelial nephron-like structures expressing markers of podocytes, proximal tubules, loops of Henle, and distal tubules in an organized, continuous arrangement that resembles the nephron in vivo. We also show that this organoid culture system can be used to study mechanisms of human kidney development and toxicity

KL Estimation of the Power Spectrum Parameters from the Angular Distribution of Galaxies in Early SDSS Data

We present measurements of parameters of the 3-dimensional power spectrum of galaxy clustering from 222 square degrees of early imaging data in the Sloan Digital Sky Survey. The projected galaxy distribution on the sky is expanded over a set of Karhunen-Loeve eigenfunctions, which optimize the signal-to-noise ratio in our analysis. A maximum likelihood analysis is used to estimate parameters that set the shape and amplitude of the 3-dimensional power spectrum. Our best estimates are Gamma=0.188 +/- 0.04 and sigma_8L = 0.915 +/- 0.06 (statistical errors only), for a flat Universe with a cosmological constant. We demonstrate that our measurements contain signal from scales at or beyond the peak of the 3D power spectrum. We discuss how the results scale with systematic uncertainties, like the radial selection function. We find that the central values satisfy the analytically estimated scaling relation. We have also explored the effects of evolutionary corrections, various truncations of the KL basis, seeing, sample size and limiting magnitude. We find that the impact of most of these uncertainties stay within the 2-sigma uncertainties of our fiducial result.Comment: Fig 1 postscript problem correcte