Driven Tunneling: Chaos and Decoherence

Chaotic tunneling in a driven double-well system is investigated in absence as well as in the presence of dissipation. As the constitutive mechanism of chaos-assisted tunneling, we focus on the dynamics in the vicinity of three-level crossings in the quasienergy spectrum. The coherent quantum dynamics near the crossing is described satisfactorily by a three-state model. It fails, however, for the corresponding dissipative dynamics, because incoherent transitions due to the interaction with the environment indirectly couple the three states in the crossing to the remaining quasienergy states. The asymptotic state of the driven dissipative quantum dynamics partially resembles the, possibly strange, attractor of the corresponding damped driven classical dynamics, but also exhibits characteristic quantum effects.Comment: 32 pages, 35 figures, lamuphys.st

Self-adaptive Scouting---Autonomous Experimentation for Systems Biology

We introduce a new algorithm for autonomous experimentation. This algorithm uses evolution to drive exploration during scientific discovery. Population size and mutation strength are self-adaptive. The only variables remaining to be set are the limits and maximum resolution of the parameters in the experiment. In practice, these are determined by instrumentation. Aside from conducting physical experiments, the algorithm is a valuable tool for investigating simulation models of biological systems. We illustrate the operation of the algorithm on a model of HIV-immune system interaction. Finally, the difference between scouting and optimization is discussed

A stochastic particle system: Fluctuations around a nonlinear reaction-diffusion equation

AbstractA locally interacting particle system is studied which can be interpreted as a stochastic model of a chemical reaction with diffusion. We establish a central limit theorem for the empirical distribution, using an asymptotic expansion of correlation functions

Measuring and comparing the (in)efficiency of German and Swiss hospitals

A nonparametric Data Envelopment Analysis (DEA) is performed on hospitals in the federal state of Saxony (Germany) and in Switzerland. This study is of interest from three points of view. First, contrary to most existing work, patient days are not treated as an output but as an input. Second, the usual DEA assumption of a homogeneous sample is tested and rejected for a large part of the observations. The proposed solution is to restrict DEA to comparable observations in the two countries. Finally, hospital beds are treated as a discretionary input in one DEA and as a fixed input in the other, and the effect on efficiency is related to differences in hospital planning in Germany and Switzerland. Based on the comparable observations, hospitals of Saxony have higher efficiency scores than their Swiss counterparts. --International efficiency comparison,Hospitals,Data Envelopment Analysis

Electron density of a benzoylated tetrafructopyranose

The electron density distribution (EDD) of a tetrasaccharide composed of four benzoylated fructopyranosyl units was obtained by refinement with scattering factors from the invariom library. X-ray diffraction data was downloaded from the Cambridge Structural Database (CSD). Bond topological and atomic properties were obtained by application of Bader’s QTAIM formalism. From a large number of 105 C–C bonds in the molecule average bond orders for 33 single and 72 aromatic bonds were calculated yielding values of 1.33 and 1.61. Molecular Hirshfeld and electrostatic potential (ESP) surfaces show that only weak non-covalent interactions exist. The phenyl rings of the benzoyl fragments in the outer regions of the molecule generate a positive ESP shell with repulsive properties between adjacent molecules. Weak surface interactions result in a rather unusual low density around 1.3 g cm−3, which is understandable when compared to other carbohydrates where strong O–H⋯O hydrogen bonds allow a 20% more dense packing with densities >1.5 g cm−3 as determined by single crystal X-ray diffraction

A multidisciplinary survey of modeling techniques for biochemical networks

All processes of life are dominated by networks of interacting biochemical components. The purpose of modeling these networks is manifold. From a theoretical point of view it allows the exploration of network structures and dynamics, to find emergent properties or to explain the organization and evolution of networks. From a practical point of view, in silico experiments can be performed that would be very expensive or impossible to achieve in the laboratory, such as hypothesis-testing with regard to knockout experiments or overexpression, or checking the validity of a proposed molecular mechanism. The literature on modeling biochemical networks is growing rapidly and the motivations behind different modeling techniques are sometimes quite distant from each other. To clarify the current context, we present a systematic overview of the different philosophies to model biochemical networks. We put particular emphasis on three main domains which have been playing a major role in the past, namely: mathematics with ordinary and partial differential equations, statistics with stochastic simulation algorithms, Bayesian networks and Markov chains, and the field of computer science with process calculi, term rewriting systems and state based systems. For each school, we evaluate advantages and disadvantages such as the granularity of representation, scalability, accessibility or availability of analysis tools. Following this, we describe how one can combine some of those techniques and thus take advantages of several techniques through the use of bridging tools. Finally, we propose a next step for modeling biochemical networks by using artificial chemistries and evolutionary computation. This work was funded by ESIGNET (Evolving Cell Signaling Networks in Silico), an European Integrated Project in the EU FP6 NEST Initiative (contract no. 12789)

Hierarchical Genetic Programming using Local Modules

Banzhaf, Wolfang; Banscherus, Dirk; Dittrich, Peter: Hierarchical Genetic Programming using Local Module