A very fast method to account for charged particle dynamics effects in calculations of spectral line shape emitted by plasmas is presented. This method is based on a formulation of the frequency fluctuation model (FFM), which provides an expression of the dynamic line shape as a functional of the static distribution of frequencies. Thus, the main numerical work rests on the calculation of the quasistatic Stark profile. This method for taking into account ion dynamics allows a very fast and accurate calculation of Stark broadening of atomic hydrogen high-$n$ series emission lines. It is not limited to hydrogen spectra. Results on helium-$\beta$ and Lyman-$\alpha$ lines emitted by argon in microballoon implosion experiment conditions compared with experimental data and simulation results are also presented. The present approach reduces the computer time by more than 2 orders of magnitude as compared with the original FFM with an improvement of the calculation precision, and it opens broad possibilities for its application in spectral line-shape codes.

We show that the recently introduced operator fidelity metric provides a natural tool to investigate the crossover to quantum chaotic behavior. This metric is an information-theoretic measure of the global stability of a unitary evolution against perturbations. We use random matrix theory arguments to conjecture that the operator fidelity metric can be used to discriminate phases with regular behavior from quantum chaotic ones. A numerical study of the onset of chaotic in the Dicke model is given in order to support the conjecture.

A recent paper claims that mean characteristics of chaotic orbits differ from the corresponding values averaged over the set of unstable periodic orbits, embedded in the chaotic attractor. We demonstrate that the alleged discrepancy is an artifact of the improper averaging. Since the natural measure is nonuniformly distributed over the attractor, different periodic orbits make different contributions into the time averages. As soon as the corresponding weights are accounted for, the discrepancy disappears.

We point out that the original work [Saiki and Yamada, Phys. Rev. E 79, 015201(R) (2009)] was aimed at addressing the question of why even a single periodic orbit of low period can capture averaged properties of physical quantities reasonably well in some dynamical systems including turbulent fluids. This point of view is quite different from that of the Comment [Zaks and Goldobin, Phys. Rev. E 81, 018201 (2010)].

Molecular dynamics (MD) and continuum simulations are carried out to investigate the influence of shear rate and surface roughness on slip flow of a Newtonian fluid. For weak wall-fluid interaction energy, the nonlinear shear-rate dependence of the intrinsic slip length in the flow over an atomically flat surface is computed by MD simulations. We describe laminar flow away from a curved boundary by means of the effective slip length defined with respect to the mean height of the surface roughness. Both the magnitude of the effective slip length and the slope of its rate dependence are significantly reduced in the presence of periodic surface roughness. We then numerically solve the Navier-Stokes equation for the flow over the rough surface using the rate-dependent intrinsic slip length as a local boundary condition. Continuum simulations reproduce the behavior of the effective slip length obtained from MD simulations at low shear rates. The slight discrepancy between MD and continuum results at high shear rates is explained by examination of the local velocity profiles and the pressure distribution along the wavy surface. We found that in the region where the curved boundary faces the mainstream flow, the local slip is suppressed due to the increase in pressure. The results of the comparative analysis can potentially lead to the development of an efficient algorithm for modeling rate-dependent slip flows over rough surfaces.

The Golgi apparatus plays a central role in processing and sorting proteins and lipids in eukaryotic cells. Golgi compartments constantly exchange material with each other and with other cellular components, allowing them to maintain and reform distinct identities despite dramatic changes in structure and size during cell division, development, and osmotic stress. We have developed three minimal models of membrane and protein exchange in the Golgi—a discrete, stochastic model, a continuous ordinary differential equation model, and a continuous stochastic differential equation model—each based on two fundamental mechanisms: vesicle-coat-mediated selective concentration of cargoes and soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) proteins during vesicle formation and SNARE-mediated selective fusion of vesicles. By exploring where the models differ, we hope to discover whether the discrete, stochastic nature of vesicle-mediated transport is likely to have appreciable functional consequences for the Golgi. All three models show similar ability to restore and maintain distinct identities over broad parameter ranges. They diverge, however, in conditions corresponding to collapse and reassembly of the Golgi. The results suggest that a continuum model provides a good description of Golgi maintenance but that considering the discrete nature of vesicle-based traffic is important to understanding assembly and disassembly of the Golgi. Experimental analysis validates a prediction of the models that altering guanine nucleotide exchange factor expression levels will modulate Golgi size.

Repolarization alternans, a beat-to-beat alternation in action potential duration, has been putatively linked to the onset of cardiac reentry. Anti-alternans control strategies can eliminate alternans in individual cells by exploiting the rate dependence of action potential duration. The same approach, when applied to a common measuring/stimulating site at one end of a cardiac fiber, has been shown to have limited spatial efficacy. As a first step toward spatially distributed electrode control systems, we investigated “off-site” control in canine Purkinje fibers, in which the recording and control sites are different. We found experimentally that alternans can be eliminated at, or very near, the recording site, and that varying the location of the recording site along the fiber causes the node (the location with no alternans) to move along the fiber in close proximity to the recording site. Theoretical predictions based on an amplitude equation [B. Echebarria and A. Karma, Chaos 12, 923 (2002)] show that those findings follow directly from the wave nature of alternans: the most unstable mode of alternans along the fiber is a wave solution of a one-dimensional Helmholtz equation with a node position that only deviates slightly from the recording site by an amount dependent on electrotonic coupling. Computer simulations using a Purkinje fiber model confirm these theoretical and experimental results. Although off-site alternans control does not suppress alternans along the entire fiber, our results indicate that placing the node away from the stimulus site reduces alternans amplitude along the fiber, and may therefore have implications for antiarrhythmic strategies based on alternans termination.

The collective dynamics of coupled oscillators has been well studied in fully occupied networks, but little attention has been paid to the case of partially occupied networks. We study this problem by a dynamic bipartite model and focus on the influence of population mobility. We find that when the density of occupied nodes is smaller than the percolation threshold ${\rho }_c$, the order parameter will show an effect of mobility with optimal value at a medium moving probability. Its mechanism can be revealed through three factors, i.e., the size of the largest component, the mixing degree in individual components, and the frequency of exchange information among components. When the density of occupied nodes is larger than ${\rho }_c$, the moving probability will act as a bifurcation parameter to synchronization. The effect of mobility also exists for other dynamics such as the epidemic spreading where the effect is shown through the number of infected agents.

Large eddy simulation of turbulent free round jet has been performed to study statistical properties of turbulence, including third-order structure functions. It has been shown the presence of a zone within the jet initial region where the spectral flux of turbulent energy is directed from smaller scales toward larger ones and the longitudinal third-order structure function becomes positive. Energy spectra calculated in this zone demonstrate the $E\left(k\right)\sim k^{-3}$ law specific for two-dimensional turbulence.

We analytically explore the fluctuation of the work-induced entropy production of an externally perturbed quantum harmonic oscillator interacting with several reservoirs. The quantum fluctuation of the work amounts to a non-Gaussian fluctuation of the entropy production, which interpolates the Gaussian and the Poissonian distributions at high- and low-temperature regimes. Also, it is shown that the corresponding fluctuation theorem symmetry is rigorously satisfied.

For understanding the behavior of a gas close to a channel wall it is important to model the gas-wall interactions as detailed as possible. When using molecular dynamics simulations these interactions can be modeled explicitly, but the computations are time consuming. Replacing the explicit wall with a wall model reduces the computational time but the same characteristics should still remain. Elaborate wall models, such as the Maxwell-Yamamoto model or the Cercignani-Lampis model need a phenomenological parameter (the accommodation coefficient) for the description of the gas-wall interaction as an input. Therefore, computing these accommodation coefficients in a reliable way is very important. In this paper, two systems (platinum walls with either argon or xenon gas confined between them) are investigated and are used for comparison of the accommodation coefficients for the wall models and the explicit molecular dynamics simulations. Velocity correlations between incoming and outgoing particles colliding with the wall have been used to compare explicit simulations and wall models even further. Furthermore, based on these velocity correlations, a method to compute the accommodation coefficients is presented, and these newly computed accommodation coefficients are used to show improved correlation behavior for the wall models.

We studied mixtures of the achiral bent-core mesogen NOBOW 1,3-phenylene bis[4-(4-9-alkoxyphenyliminonetyl)benzoates] (P-9-O-PIMB) and the small, rodlike liquid crystal 8CB using high-resolution synchrotron x-ray diffraction, freeze fracture transmission electron microscopy, and differential scanning calorimetry. NOBOW and 8CB mix in an isotropic state at high temperatures but phase separate at lower temperatures when NOBOW transforms into the B4 phase and forms chiral helical nanofilaments. In pure NOBOW, the nanofilaments are close packed but at moderate 8CB concentrations, they are separated by nanosized gaps filled by 8CB. At higher concentrations of 8CB, macroscopic phase separation occurs.

Little is known about the biological mechanisms that shape the distribution of intervals between the completion of RNA molecules $\left(T_p^{\text{RNA}}\right)$, and thus transcriptional noise. We characterize numerically and analytically how the promoter open complex delay $\left({\tau }_P\right)$ and the transcription initiation rate $\left(k_t\right)$ shape $T_p^{\text{RNA}}$. From this, we assess the noise and mean of transcript levels and show that these can be tuned both independently and simultaneously by ${\tau }_P$ and $k_t$. Finally, we characterize how ${\tau }_P$ affects bursting in RNA production and show that the ${\tau }_P$ measured for a lac promoter best fits independent measurements of the burst distribution of the same promoter. Since ${\tau }_P$ affects noise in gene expression, and given that it is sequence dependent, it is likely to be evolvable.

Intermingled neural connections apparent in the brain make us wonder what controls the traffic of propagating activity in the brain to secure signal transmission without harmful crosstalk. Here, we reveal that inhibitory input but not excitatory input works as a particularly useful traffic controller because it controls the degree of synchrony of population firing of neurons as well as controlling the size of the population firing bidirectionally. Our dynamical system analysis reveals that the synchrony enhancement depends crucially on the nonlinear membrane potential dynamics and a hidden slow dynamical variable. Our electrophysiological study with rodent slice preparations show that the phenomenon happens in real neurons. Furthermore, our analysis with the Fokker-Planck equations demonstrates the phenomenon in a semianalytical manner.

Empirical analyses show that after the update of a browser, or the publication of the vulnerability of a software, or the discovery of a cyber worm, the fraction of computers still using the older browser or software version, or not yet patched, or exhibiting worm activity decays as a power law $\sim 1/t^{\alpha }$ with $0<\alpha \le 1$ over a time scale of years. We present a simple model for this persistence phenomenon, framed within the standard priority queuing theory, of a target task which has the lowest priority compared to all other tasks that flow on the computer of an individual. We identify a “time deficit” control parameter $\beta$ and a bifurcation to a regime where there is a nonzero probability for the target task to never be completed. The distribution of waiting time $\mathcal{T}$ until the completion of the target task has the power law tail $\sim 1/t^{1/2}$, resulting from a first-passage solution of an equivalent Wiener process. Taking into account a diversity of time deficit parameters in a population of individuals, the power law tail is changed into $1/t^{\alpha }$, with $\alpha ∊\left(0.5,\infty \right)$, including the well-known case $1/t$. We also study the effect of “procrastination,” defined as the situation in which the target task may be postponed or delayed even after the individual has solved all other pending tasks. This regime provides an explanation for even slower apparent decay and longer persistence.

We define an activity-dependent branching ratio that allows comparison of different time series $X_t$. The branching ratio $b_x$ is defined as $b_x=E\left[{\xi }_x/x\right]$. The random variable ${\xi }_x$ is the value of the next signal given that the previous one is equal to $x$, so ${\xi }_x=\left\{X_{t+1}\mid X_t=x\right\}$. If $b_x>1$, the process is on average supercritical when the signal is equal to $x$, while if $b_x<1$, it is subcritical. For stock prices we find $b_x=1$ within statistical uncertainty, for all $x$, consistent with an “efficient market hypothesis.” For stock volumes, solar x-ray flux intensities, and the Bak-Tang-Wiesenfeld (BTW) sandpile model, $b_x$ is supercritical for small values of activity and subcritical for the largest ones, indicating a tendency to return to a typical value. For stock volumes this tendency has an approximate power-law behavior. For solar x-ray flux and the BTW model, there is a broad regime of activity where $b_x\simeq 1$, which we interpret as an indicator of critical behavior. This is true despite different underlying probability distributions for $X_t$ and for ${\xi }_x$. For the BTW model the distribution of ${\xi }_x$ is Gaussian, for $x$ sufficiently larger than 1, and its variance grows linearly with $x$. Hence, the activity in the BTW model obeys a central limit theorem when sampling over past histories. The broad region of activity where $b_x$ is close to one disappears once bulk dissipation is introduced in the BTW model—supporting our hypothesis that it is an indicator of criticality.

We investigate the one- to two-dimensional zigzag transition in clusters consisting of a small number of particles interacting through a Yukawa (Debye) potential and confined in a two-dimensional biharmonic potential well. Dusty (complex) plasma clusters with $n\le 19$ monodisperse particles are characterized experimentally for two different confining wells. The well anisotropy is accurately measured, and the Debye shielding parameter is determined from the longitudinal breathing frequency. Debye shielding is shown to be important. A model for this system is used to predict equilibrium particle configurations. The experiment and model exhibit excellent agreement. The critical value of $n$ for the zigzag transition is found to be less than that predicted for an unshielded Coulomb interaction. The zigzag transition is shown to behave as a continuous phase transition from a one-dimensional to a two-dimensional state, where the state variables are the number of particles, the well anisotropy and the Debye shielding parameter. A universal critical exponent for the zigzag transition is identified for transitions caused by varying the Debye shielding parameter.

The behavior of nanoparticles in dual-frequency capacitively coupled silane discharges is investigated by employing a one-dimensional self-consistent fluid model. The numerical simulation tries to trace the formation, charging, growth, and transport of dust particles during the discharge, under the influences of the high- and low-frequency electric sources, as well as the gas pressure. The effects of the presence of the nanoparticles and larger anions on the plasma properties are also discussed, especially, for the bulk potential, electron temperature, and densities of various particles. The calculation results show that the nanoparticle density and charge distribution are mainly influenced by the voltage and frequency of the high-frequency source, while the voltage of the low-frequency source can also exert an effect on the nanoparticle formation, compared with the frequency. As the discharge lasts, the electric potential and electron density keep decreasing, while the electron temperature gets increasing after a sudden drop.

The feasibility of path integral Monte Carlo ground state calculations with very few beads using a high-order short-time Green’s function expansion is discussed. An explicit expression of the evolution operator which provides dramatic enhancements in the quality of ground-state wave functions is examined. The efficiency of the method makes possible to remove the trial wave function and thus obtain completely model-independent results still with a very small number of beads. If a single iteration of the method is used to improve a given model wave function, the result is invariably a shadow-type wave function, whose precise content is provided by the high-order algorithm employed.

In this paper, we present an integrodifferential diffusion equation for continuous-time random walk that is valid for a generic waiting time probability density function. Using this equation, we also study diffusion behaviors for a couple of specific waiting time probability density functions such as exponential and a combination of power law and generalized Mittag-Leffler function. We show that for the case of the exponential waiting time probability density function, a normal diffusion is generated and the probability density function is Gaussian distribution. In the case of the combination of a power law and generalized Mittag-Leffler waiting probability density function, we obtain the subdiffusive behavior for all the time regions from small to large times and probability density function is non-Gaussian distribution.