Boykincrowell1764

We mimic random nanowire networks by the homogeneous, isotropic, and random deposition of conductive zero-width sticks onto an insulating substrate. The number density (the number of objects per unit area of the surface) of these sticks is supposed to exceed the percolation threshold, i.e., the system under consideration is a conductor. To identify any current-carrying part (the backbone) of the percolation cluster, we have proposed and implemented a modification of the well-known wall follower algorithm-one type of maze solving algorithm. The advantage of the modified algorithm is its identification of the whole backbone without visiting all the edges. The complexity of the algorithm depends significantly on the structure of the graph and varies from O(sqrt[N_V]) to Θ(N_V). selleckchem The algorithm has been applied to backbone identification in networks with different number densities of conducting sticks. We have found that (i) for number densities of sticks above the percolation threshold, the strength of the percolation cluster quickly approaches unity as the number density of the sticks increases; (ii) simultaneously, the percolation cluster becomes identical to its backbone plus simplest dead ends, i.e., edges that are incident to vertices of degree 1. This behavior is consistent with the presented analytical evaluations.An accurate understanding of ion-beam transport in plasmas is crucial for applications in inertial fusion energy and high-energy-density physics. We present an experimental measurement on the energy spectrum of a proton beam at 270 keV propagating through a gas-discharge hydrogen plasma. We observe the energies of the beam protons changing as a function of the plasma density and spectrum broadening due to a collective beam-plasma interaction. Supported by linear theory and three-dimensional particle-in-cell simulations, we attribute this energy modulation to a two-stream instability excitation and further saturation by beam ion trapping in the wave. The widths of the energy spectrum from both experiment and simulation agree with the theory.We investigate the possibility of extending the notion of temperature in a stochastic model for the RNA or protein folding driven out of equilibrium. We simulate the dynamics of a small RNA hairpin subject to an external pulling force, which is time-dependent. First, we consider a fluctuation-dissipation relation (FDR) whereby we verify that various effective temperatures can be obtained for different observables, only when the slowest intrinsic relaxation timescale of the system regulates the dynamics of the system. Then, we introduce a different nonequilibrium temperature, which is defined from the rate of heat exchanged with a weakly interacting thermal bath. Notably, this "kinetic" temperature can be defined for any frequency of the external switching force. We also discuss and compare the behavior of these two emerging parameters, by discriminating the time-delayed nature of the FDR temperature from the instantaneous character of the kinetic temperature. The validity of our numerics are corroborated by a simple four-state Markov model which describes the long-time behavior of the RNA molecule.Dynamics of dislocations and defects are investigated in 2D dusty plasma experiments with two counterpropagating flows. It is experimentally demonstrated that the Orowan equation is able to accurately determine the plastic strain rate from the motion of dislocations, well agreeing with the shear rate defined from the drift velocity gradient. For a higher shear rate, the studied system is in the liquidlike flow state, as a result, the determined shear rate from the Orowan equation deviates from its definition. The obtained probability distribution function of dislocations from the experiments clearly shows that the dislocation motion can be divided into the local and gliding ones. All findings above are further verified by the corresponding Langevin dynamical simulations with various levels of shear rates. The dislocation and defect analysis results from these simulations clearly indicate that the defect and dislocation dynamics in the sheared dusty plasmas clearly exhibit two stages as the shear rate increases.We study the position distribution P(R[over ⃗],N) of a run-and-tumble particle (RTP) in arbitrary dimension d, after N runs. We assume that the constant speed v>0 of the particle during each running phase is independently drawn from a probability distribution W(v) and that the direction of the particle is chosen isotropically after each tumbling. The position distribution is clearly isotropic, P(R[over ⃗],N)→P(R,N) where R=|R[over ⃗]|. We show that, under certain conditions on d and W(v) and for large N, a condensation transition occurs at some critical value of R=R_c∼O(N) located in the large-deviation regime of P(R,N). For RR_c. Finally, we study the model when the total duration T of the RTP, instead of the total number of runs, is fixed. Our analytical predictions are confirmed by numerical simulations, performed using a constrained Markov chain Monte Carlo technique, with precision ∼10^-100.The thermodynamic and structural properties of two-dimensional dense Yukawa liquids are studied with molecular dynamics simulations. The "exact" thermodynamic properties are simultaneously employed in an advanced scheme for the determination of an equation of state that shows an unprecedented level of accuracy for the internal energy, pressure, and isothermal compressibility. The "exact" structural properties are utilized to formulate a novel empirical correction to the hypernetted-chain approach that leads to a very high accuracy level in terms of static correlations and thermodynamics.We investigate majority rule dynamics in a population with two classes of people, each with two opinion states ±1, and with tunable interactions between people in different classes. In an update, a randomly selected group adopts the majority opinion if all group members belong to the same class; if not, majority rule is applied with rate ε. Consensus is achieved in a time that scales logarithmically with population size if ε≥ε_c=1/9. For ε less then ε_c, the population can get trapped in a polarized state, with one class preferring the +1 state and the other preferring -1. The time to escape this polarized state and reach consensus scales exponentially with population size.