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Keywords: Gaussian process model Dynamic systems modeling Spatial statistics Reduced-order model Nanoparticle synthesis a b s t r a c t Gaussian process modeling (also known as kriging) is an empirical modeling approach that has been widely applied in engineering for the approximation of deterministic functions, due to its flexibility and ability to(More)
We present a de novo re-determination of the secondary (2°) structure and domain architecture of the 23S and 5S rRNAs, using 3D structures, determined by X-ray diffraction, as input. In the traditional 2° structure, the center of the 23S rRNA is an extended single strand, which in 3D is seen to be compact and double helical. Accurately assigning nucleotides(More)
Many models for the origin of life have focused on understanding how evolution can drive the refinement of a preexisting enzyme, such as the evolution of efficient replicase activity. Here we present a model for what was, arguably, an even earlier stage of chemical evolution, when polymer sequence diversity was generated and sustained before, and during,(More)
— We propose a Model Predictive Control (MPC) method to facilitate self-assembly of a quadrupole colloidal system for defect-free two-dimensional crystals. A Langevin equation model is developed to model the thermodynamics of the colloidal system and provides predictions for optimization. A finite prediction horizon is used to optimize the input trajectory(More)
— We propose a Markov decision based dynamic programming method to manipulate the self-assembly of a quadrupole colloidal system for grain-boundary-free two-dimensional crystals. To construct the optimal control policy, we developed a Markov chain model, based on information extracted from a Langevin dynamics simulation model, which originated from a more(More)