In a highly interdependent economic world, the nature of relationships between financial entities is becoming an increasingly important area of study. Recently, many studies have shown the usefulness of minimal spanning trees (MST) in extracting interactions between financial entities. Here, we propose a modified MST network whose metric distance is defined in terms of cross-correlation coefficient absolute values, enabling the connections between anticorrelated entities to manifest properly. We investigate 69 daily time series, comprising three types of financial assets: 28 stock market indicators, 21 currency futures, and 20 commodity futures. We show that though the resulting MST network evolves over time, the financial assets of similar type tend to have connections which are stable over time. In addition, we find a characteristic time lag between the volatility time series of the stock market indicators and those of the EU CO${}_2$ emission allowance (EUA) and crude oil futures (WTI). This time lag is given by the peak of the cross-correlation function of the volatility time series EUA (or WTI) with that of the stock market indicators, and is markedly different ($>$20 days) from 0, showing that the volatility of stock market indicators today can predict the volatility of EU emissions allowances and of crude oil in the near future.

When a barotropic shear layer becomes unstable, it produces the well-known Kelvin-Helmholtz instability (KHI). The nonlinear manifestation of the KHI is usually in the form of spiral billows. However, a piecewise linear shear layer produces a different type of KHI characterized by elliptical vortices of constant vorticity connected via thin braids. Using direct numerical simulation and contour dynamics, we show that the interaction between two counterpropagating vorticity waves is solely responsible for this KHI formation. We investigate the oscillation of the vorticity wave amplitude, the rotation and nutation of the elliptical vortex, and straining of the braids. Our analysis also provides a possible explanation for the formation and evolution of elliptical vortices appearing in geophysical and astrophysical flows, e.g., meddies, stratospheric polar vortices, Jovian vortices, Neptune's Great Dark Spot, and coherent vortices in the wind belts of Uranus.

We find that imposing crossing symmetry in the iteration process considerably extends the range of convergence for solutions of the parquet equations for the Hubbard model. When crossing symmetry is not imposed, the convergence of both simple iteration and more complicated continuous loading (homotopy) methods is limited to high temperatures and weak interactions. We modify the algorithm to impose the crossing symmetry without increasing the computational complexity. We also imposed time reversal and a subset of the point group symmetries, but they did not further improve the convergence. We elaborate the details of the latency hiding scheme which can significantly improve the performance in the computational implementation. With these modifications, stable solutions for the parquet equations can be obtained by iteration more quickly even for values of the interaction that are a significant fraction of the bandwidth and for temperatures that are much smaller than the bandwidth. This may represent a crucial step towards the solution of two-particle field theories for correlated electron models.

A univariate polynomial equation is presented. It provides on-lattice higher-order models of the thermal lattice Boltzmann equation. The models can be accurate up to any required level and can be applied to regular lattices, which allow efficient and accurate approximate solutions of the Boltzmann equation. We derive models approaching the complete Galilean invariant and providing accuracy of the fourth-order moment and beyond. We simulate one-dimensional thermal shock tube problems to illustrate the accuracy of our models. Moreover, we show the remarkably enhanced stability obtained by our models and our discretized equilibrium distributions.

For a liquid advancing or receding on a flat bare solid in a vapor atmosphere, we show that no singularities in fact arise at the contact (triple) line. Contrary to common expectations, this does not require any “regularizing” microscopic effect (such as slip at the substrate, disjoining pressure or precursor films). The key here is the Kelvin effect, i.e., a curvature-induced variation of saturation conditions. Importantly, no evaporation-related singularities appear either. We proceed within the lubrication approximation and a classical one-sided model for zero or finite contact angle.

Motivated by biological applications (e.g., bone tissue development and regeneration) we investigate coupling between mesoscopic mechanics and chemical kinetics. Governing equations of both dynamical systems are first written in a form expressing manifestly their compatibility with microscopic mechanics and thermodynamics. The same form is then required from governing equations of the coupled dynamics. The main result of the paper is an admissible form of the coupled dynamics.

We perform molecular dynamics simulation on a glass-forming liquid binary mixture with the soft-core potential in three dimensions. We investigate crossover of the configuration changes caused by stringlike jump motions. With lowering the temperature $T$, the motions of the particles composing strings become larger in sizes and displacements, while those of the particles surrounding strings become smaller. Then the contribution of the latter to time-correlation functions tends to be long-lived as $T$ is lowered. As a result, the relaxation time ${\tau }_{\alpha }$ and the viscosity $\eta$ grow more steeply than the inverse diffusion constant $D^{-1}$ at low $T$, leading to breakdown of the Stokes-Einstein relation. At low $T$, the diffusion occurs as activation processes and may well be described by short-time analysis of rare jump motions with broken bonds and large displacements. Some characteristic features of the Van Hove self-correlation function arise from escape jumps over high potential barriers. We also visualize the particle motions at string formation taking place in a very short time.

Many types of membrane receptors are found to be organized as clusters on the cell surface. We investigate the potential effect of such receptor clustering on the intracellular signal transduction stage. We consider a canonical pathway with a membrane receptor ($R$) activating a membrane-bound intracellular relay protein ($G$). We use Monte Carlo simulations to recreate biochemical reactions using different receptor spatial distributions and explore the dynamics of the signal transduction. Results show that activation of $G$ by $R$ is severely impaired by $R$ clustering, leading to an apparent blunted biological effect compared to control. Paradoxically, this clustering decreases the half maximal effective dose (ED50) of the transduction stage, increasing the apparent affinity. We study an example of inter-receptor interaction in order to account for possible compensatory effects of clustering and observe the parameter range in which such interactions slightly counterbalance the loss of activation of $G$. The membrane receptors’ spatial distribution affects the internal stages of signal amplification, suggesting a functional role for membrane domains and receptor clustering independently of proximity-induced receptor-receptor interactions.

When subject to constant tension, a DNA or RNA hairpin will typically make abrupt transitions between the open and closed state. Although the transition kinetics are an intrinsic property of the molecule, the transition rates measured in single-molecule experiments can be influenced by the configuration of the measurement system. We investigate the transition kinetics for a DNA hairpin held under constant force by an optical trap as a function of microsphere size and double-stranded DNA handle length. We find the apparent transition lifetime cannot be expressed as a function of the drag coefficient of the microsphere alone or as a function of time scales relevant to the optical trap. The apparent transition lifetime is found to be a linear function of the factor ${\beta }_{\mathrm{eff}}·{\alpha }_{\mathrm{handle}}$, where ${\beta }_{\mathrm{eff}}$ is the effective drag coefficient of the microsphere near the surface and ${\alpha }_{\mathrm{handle}}$ is the stiffness of the DNA tether. The results provide insight into the perturbation to the hairpin transition kinetics due to experimental configuration and guidance for designing single-molecule experiments which determine the intrinsic molecular kinetics.

This paper connects the study of thermoplasmonic properties in nanoscale particles with areas of biophysics involving a cell membrane with or without conductive pores. Using a quasistatic finite element modeling of the heat transfer equation in three dimensions we simulate the stationary heat generation and temperature field around several types of gold-based nanostructures. Models were constructed that emphasized the importance of obtaining precise temperature fields that might subsequently be used for biosensing and selective targeting applications. By analyzing the observed temperature increase, effective complex permittivity, and electric field enhancement that result from plasmonic resonance, this theoretical framework provides insight into the role of the nanoparticle shape in heat generation. To illustrate the usefulness of this approach for biosensing applications, we consider how the positioning of the nanoantenna affects heating efficiency. Linear response calculations of the temperature increase reveal that symmetric gold nanosphere dimers are not only suitable for sensing applications, but can also play the role of heat sources which are more efficient than the case of a single nanosphere. We also predict that this specific type of nanoantenna allows us to detect the presence and size of a hole in the cell membrane. These results provide insight into the physics of the cell membrane and provide guidance for more detailed studies of the nanoscale control of temperature in biological materials.

The DNA prism is a modification of the standard pulsed-field electrophoresis protocol to provide a continuous separation, where the DNA are deflected at an angle that depends on their molecular weight. The standard switchback model for the DNA prism predicts a monotonic increase in the deflection angle as a function of the frequency for switching the field until a plateau regime is reached. However, experiments indicate that the deflection angle achieves a maximum value before decaying to a size-independent value at high frequencies. Using Brownian dynamics simulations, we show that the maximum in the deflection angle is related to the reorientation time for the DNA and the decay in deflection angle at high frequencies is due to inadequate stretching. The generic features of the dependence of the deflection angle on molecular weight, switching frequency, and electric field strength explain a number of experimental phenomena.

We consider the kick- (tilt-) induced mobility of two-dimensional (2D) fundamental dissipative solitons in models of bulk lasing media based on the 2D complex Ginzburg-Landau equation including a spatially periodic potential (transverse grating). The depinning threshold, which depends on the orientation of the kick, is identified by means of systematic simulations and estimated by means of an analytical approximation. Various pattern-formation scenarios are found above the threshold. Most typically, the soliton, hopping between potential cells, leaves arrayed patterns of different sizes in its wake. In the single-pass-amplifier setup, this effect may be used as a mechanism for the selective pattern formation controlled by the tilt of the input beam. Freely moving solitons feature two distinct values of the established velocity. Elastic and inelastic collisions between free solitons and pinned arrayed patterns are studied too.

Compressible isothermal magnetohydrodynamic turbulence is analyzed under the assumption of statistical homogeneity and in the asymptotic limit of large kinetic and magnetic Reynolds numbers. Following Kolmogorov we derive an exact relation for some two-point correlation functions which generalizes the expression recently found for hydrodynamics. We show that the magnetic field brings new source and flux terms into the dynamics which may act on the inertial range similarly as a source or a sink for the mean energy transfer rate. The introduction of a uniform magnetic field simplifies significantly the exact relation for which a simple phenomenology may be given. A prediction for axisymmetric energy spectra is eventually proposed.

An alternative artificial compressibility (AC) scheme is proposed to allow the explicit simulation of the incompressible Navier-Stokes (INS) equations. Traditional AC schemes rely on an artificial equation of state that gives the pressure as a function of the density, which is known to enforce isentropic behavior. This behavior is nonideal, especially in viscously dominated flows. An alternative, the entropically damped artificial compressibility (EDAC) method, is proposed that employs a thermodynamic constraint to damp the pressure oscillations inherent to AC methods. The EDAC method converges to the INS in the low-Mach limit, and is consistent in both the low- and high-Reynolds-number limits, unlike standard AC schemes. The proposed EDAC method is discretized using a simple finite-difference scheme and is compared with traditional AC schemes as well as the lattice-Boltzmann method for steady lid-driven cavity flow and a transient traveling-wave problem. The EDAC method is shown to be beneficial in damping pressure and velocity-divergence oscillations when performing transient simulations. The EDAC method follows a similar derivation to the kinetically reduced local Navier-Stokes (KRLNS) method [Borok et al., Phys. Rev. E 76, 066704 (2007)]; however, the EDAC method does not rely on the grand potential as the thermodynamic variable, but instead uses the more common pressure-velocity system. Additionally, a term neglected in the KRLNS is identified that is important for accurately approximating the INS equations.

We introduce a multidiscontinuity algorithm for the efficient global update of world-line configurations in Monte Carlo simulations of interacting quantum systems. This algorithm is a generalization of the two-discontinuity algorithms introduced in Refs. [N. Prokof'ev, B. Svistunov, and I. Tupitsyn, Phys. Lett. A 238, 253 (1998)] and [O. F. Syljuåsen and A. W. Sandvik, Phys. Rev. E 66, 046701 (2002)]. This generalization is particularly effective for studying Bose-Einstein condensates (BECs) of composite particles. In particular, we demonstrate the utility of the generalized algorithm by simulating a Hamiltonian for an $S=1$ antiferromagnet with strong uniaxial single-ion anisotropy. The multidiscontinuity algorithm not only solves the freezing problem that arises in this limit, but also allows the efficient computing of the off-diagonal correlator that characterizes a BEC of composite particles.

In a recent paper [Phys. Rev. E 86, 040102(R) (2012)], Santos presented a self-consistency condition that can be used to limit the possible forms of fundamental measure theory. Here, the direct correlation function, resulting from the Santos functional, is derived, and it is found to be very close to the result of the White Bear density functional, except near the origin where it diverges.

The Lévy walk model is studied in the context of the anomalous heat conduction of one-dimensional systems. In this model, the heat carriers execute Lévy walks instead of normal diffusion as expected in systems where Fourier's law holds. Here we calculate exactly the average heat current, the large deviation function of its fluctuations, and the temperature profile of the Lévy walk model maintained in a steady state by contact with two heat baths (the open geometry). We find that the current is nonlocally connected to the temperature gradient. As observed in recent simulations of mechanical models, all the cumulants of the current fluctuations have the same system-size dependence in the open geometry. For the ring geometry, we argue that a size-dependent cutoff time is necessary for the Lévy walk model to behave like mechanical models. This modification does not affect the results on transport in the open geometry for large enough system sizes.

In traditional thermodynamics the Carnot cycle yields the ideal performance bound of heat engines and refrigerators. We propose and analyze a minimal model of a heat machine that can play a similar role in quantum regimes. The minimal model consists of a single two-level system with periodically modulated energy splitting that is permanently, weakly, coupled to two spectrally separated heat baths at different temperatures. The equation of motion allows us to compute the stationary power and heat currents in the machine consistent with the second law of thermodynamics. This dual-purpose machine can act as either an engine or a refrigerator (heat pump) depending on the modulation rate. In both modes of operation, the maximal Carnot efficiency is reached at zero power. We study the conditions for finite-time optimal performance for several variants of the model. Possible realizations of the model are discussed.

Results of a recent comparative experimental assessment of methods for network community detection applied to benchmark graphs indicate that the two best methods use different objective functions but a similar local search-based optimization (LSO) procedure. This observation motivates the following research question: Given the LSO optimization procedure, how much does the choice of the objective function influence the results and in what way? We address this question empirically in a broad graph clustering context, that is, when graphs are either given as such or are $k$-nearest-neighbor graphs generated from a given data set. We consider normalized cut, modularity, and infomap, as well as two new objective functions. We show that all these objectives have a resolution bias, that is, they tend to prefer either small or large clusters. When removing this bias, by forcing the objective to generate a given number of clusters, LSO achieves similar performance across the considered objective functions on benchmark networks with built-in community structure. These results indicate that the resolution bias is the most important difference between objective functions in graph clustering with LSO. Spectral clustering is an alternative to LSO, which has been used to optimize the popular normalized cut and modularity objectives. We show experimentally that LSO often achieves superior performance than spectral clustering on various benchmark, real-life, and $k$-nearest-neighbor graphs. These results, the flexibility of LSO  and its efficiency, provide arguments in favor of this optimization method.

We investigate epidemic spreading in annealed directed scale-free networks with the in-degree ($k$) distribution $P_{\text{in}}\left(k\right)\sim k^{-{\gamma }_{\text{in}}}$ and the out-degree ($\ell$) distribution, $P_{\text{out}}\left(\ell \right)\sim {\ell }^{-{\gamma }_{\text{out}}}$. The correlation $\langle kl\rangle$ of each node on the networks is controlled by the probability $r(0\le r\le 1)$ in two different algorithms, the so-called $k$ and $\ell$ algorithms. For $r=1$, the $k$ algorithm gives $\langle kl\rangle =\langle k^2\rangle$, whereas the $\ell$ algorithm gives $\langle kl\rangle =\langle {\ell }^2\rangle$. For $r=0$, $\langle kl\rangle =\langle k\rangle \langle \ell \rangle$ for both algorithms. As the prototype of epidemic spreading, the susceptible-infected-susceptible model and contact process on the networks are analyzed using the heterogeneous mean-field theory and Monte Carlo simulations. The directedness of links and the correlation of the network are found to play important roles in the spreading, so that critical behaviors of both models are distinct from those on undirected scale-free networks.