Clonal selection algorithms are considered. Two algorithms are designed and executed to obtain purely empirical analysis conclusions in order to turn to purely theoretical analysis results about the behavior of clonal selection algorithms as a nite dimensional Markov and lumped Markov chains, which conrm the conjectures from these experiments and in order to introduce a complete framework toward a new philosophy of machine intelligence. First, we model clonal selection algorithms using a nite dimensional Markov and lumped Markov chains. Second, we carry on a particle analysis (the basic component) and analyze the convergence properties of these algorithms. Third, we produce two unied Markov and lumped Markov approaches for analysis for a complete framework and propose unique chromosomes for a purely successful optimization of these algorithms. Furthermore, for the Markov approach, we obtain purely theoretical analysis for a classication and Stationary distributions of chains. For the lumped Markov approach, we obtain purely theoretical analysis for all possible conditional multivariate normal distributions of transition probability matrices and stationary multivariate normal distributions ofchains.

Genetic algorithms are considered. Three algorithms are designed and executed
to obtain purely empirical analysis conclusions in order to turn to purely theoretical analysis
results about the behavior of genetic algorithms as a finite dimensional Markov and....

Abstract Free convection over a vertical rectangular duct filled with porous matrix with variable viscosity and variable thermal conductivity is studied in this paper. We consider the two-dimensional steady laminar flow and Brinkman–Forchheimer extended Darcy model to define the porous medium. Using the appropriate variables the basic governing equations are transformed to non-dimensional governing equations. The fluid viscosity is assumed to vary exponentially with temperature whereas the thermal conductivity is assumed to vary linearly with temperature. One of the vertical walls of the duct is cooled with constant temperature while the other wall is heated to constant but different temperature. The governing coupled nonlinear momentum and energy equations are solved numerically using finite difference method. The effect of pertinent parameters such as variable viscosity, variable thermal conductivity, Darcy number, inertial parameter, Grashof number, Brinkman number and aspect ratio on the velocity, temperature, volumetric flow rate, shear stress and heat transfer are discussed.

Huntington's disease (HD) is one of the most common, dominantly inherited neurodegenerative disorders. It affects the striatum, cerebral cortex, and other subcortical structures leading to involuntary movement abnormalities, emotional disturbances, and cognitive impairments. HD is caused by a CAG•CTG trinucleotide-repeat expansion in exon 1 of the () gene leading to the formation of mutant HTT (mtHTT) protein aggregates. Besides the toxicity of the mutated protein, there is also evidence that mt transcripts contribute to the disease. Thus, the reduction of both mutated mRNA and protein would be most beneficial as a treatment. Previously, we designed a novel anti-gene oligonucleotide (AGO)-based strategy directly targeting the trinucleotide-repeats in DNA and reported downregulation of mRNA and protein in HD patient fibroblasts. In this study, we differentiate HD patient-derived induced pluripotent stem cells to investigate the efficacy of the AGO, a DNA/Locked Nucleic Acid mixmer with phosphorothioate backbone, to modulate transcription during neural development. For the first time, we demonstrate downregulation of mRNA following both naked and magnetofected delivery into neural stem cells (NSCs) and show that neither emergence of neural rosette structures nor self-renewal of NSCs is compromised. Furthermore, the inhibition potency of both mRNA and protein without off-target effects is confirmed in neurons. These results further validate an anti-gene approach for the treatment of HD.