We report results of evaluation of several measures of chemical disequilibrium in living and abiotic systems. The previously defined measures include $R_T$ and $R_L$, which are Euclidean distances of a coarse grained polymer length distribution from two different chemical equilibrium states associated with equilibration to an external temperature bath and with isolated equilibration to a distribution determined by the bond energy of the system, respectively. The determination uses a simplified description of the energetics of the constituent molecules. We evaluated the measures for data from the ribosome of E. coli, a variety of yeast, and the proteomes (with certain assumptions) of a large family of prokaryotes, and for mass spectrometric data from the atmosphere of the Saturn satellite Titan and for nonliving commercial copolymers. We find with surprising consistency that $R_L$ is much smaller than $R_T$ for all these systems. The living (protein) systems have a well defined value of $R_T$ that is sharply defined and distinct from that obtained from the nonliving Titan and copolymer systems. The living systems are also distinguishably characterized by larger values of $R_L$ than most of the nonliving systems, but $R_L$ values vary more from one living system to another than the $R_T$ values do. These data suggest that the measures $R_L$ and $R_T$ can distinguish living from nonliving systems.

Using video microscopy, we measure local spatial constraints in disordered binary colloidal samples, ranging from dilute fluids to jammed glasses, and probe their spatial and temporal correlations to local dynamics during the glass transition. We observe the emergence of significant correlations between constraints and local dynamics within the Lindemann criterion, which coincides with the onset of glassy dynamics in supercooled liquids. Rigid domains in fluids are identified based on local constraints and demonstrate a percolation transition near the glass transition, accompanied by the emergence of dynamical heterogeneities. Our results show that spatial constraint instead of the geometry of amorphous structures is the key that connects the complex spatial-temporal correlations in disordered materials.

We present a generalized analytical model to investigate acoustic scattering by a lattice of voids of arbitrary shape in a viscoelastic matrix. To this end, we represent the lattice of voids using effective boundary conditions that incorporate multiple scattering effects. Applying analogies between acoustics and electrostatics, the model is derived for voids of nonspherical shape and different lattice arrangements. Our analytical results are compared with those from numerical simulations as well as experimental results from the literature.

Recent path-integral Monte Carlo and quantum molecular dynamics simulations have shown that computationally efficient average-atom models can predict thermodynamic states in warm dense matter to within a few percent. One such atom-in-jellium model has typically been used to predict the electron-thermal behavior only, although it was previously developed to predict the entire equation of state (EOS). We report completely atom-in-jellium EOS calculations for Be, Al, Si, Fe, and Mo, as elements representative of a range of atomic number and low-pressure electronic structure. Comparing the more recent method of pseudoatom molecular dynamics, atom-in-jellium results were similar: sometimes less accurate, sometimes more. All these techniques exhibited pronounced effects of electronic shell structure in the shock Hugoniot which are not captured by Thomas-Fermi based EOS. These results demonstrate the value of a hierarchical approach to EOS construction, using average-atom techniques with shell structure to populate a wide-range EOS surface efficiently, complemented by more rigorous three-dimensional multiatom calculations to validate and adjust the EOS.

In the present study rate coefficients for vibrational excitation of ${\mathrm{CO}}_2$ gas molecules by electrons in the presence of uniform electric and magnetic fields are investigated. Calculations are performed for transition from the ground state to each of the symmetric, asymmetric, and bending vibrational states. A Monte Carlo simulation is used to produce nonequilibrium electron energy distribution functions. Results are obtained for the electric field over gas number density ratio, E/N, ranging from 20 to 1000 Td, and for the magnetic field over gas number density ratio, B/N, with values of 0, 1000, 2000, and 3000 Hx.

Resonant three-wave coupling is an important mechanism via which waves interact in a nonlinear medium. When the medium is a magnetized warm-fluid plasma, a previously unknown formula for the coupling coefficients is derived by solving the fluid-Maxwell's equations to second order using multiscale perturbative expansions. The formula is not only general but also evaluable, whereby numerical values of the coupling coefficient can be determined for any three resonantly interacting waves propagating at arbitrary angles. To illustrate how the general formula can be applied, coupling coefficient governing laser scattering is evaluated as one example. In conditions relevant to magnetized inertial confinement fusion, Raman and Brillouin instabilities are replaced by scattering from magnetized plasma waves when lasers propagate at oblique angles. As another example, coupling coefficient between two Alfvén waves via a sound wave is evaluated. In conditions relevant to solar corona, the decay of a parallel Alfvén wave only slightly prefers exact backward geometry.

In this work we address the application of pseudopotentials directly on high-order lattice Boltzmann models. We derive a general expression for the pressure tensor on high-order lattices considering all nonideal interactions, including intra- and intermolecular interactions, following the discrete lattice theory introduced by X. Shan [Phys. Rev. E 77, 066702 (2008)]. From the derived expression, a generalized continuum approximation, truncated at fourth-order isotropy, is obtained that is readily applicable to high-order lattices. With this, we demonstrate that high-order lattice models with pseudopotentials can satisfy thermodynamic consistency. The derived generalized expression and continuum approximation are validated for the case of a flat interface and compared against the standard definition available from the literature. The generalized expression is also shown to accurately reproduce the Laplace experiment for a variety of high-order lattice structures. This work sets the preliminary steps towards the application of high-order lattice models for simulating nonideal fluid mixtures.

This work concerns noise reduction for one-dimensional spectra in the case that the signal is corrupted by an additive white noise. The proposed method starts with mapping the noisy spectrum to a partial circulant matrix. In virtue of singular-value decomposition of the matrix, components belonging to the signal are determined by inspecting the total variations of left singular vectors. Afterwards, a smoothed spectrum is reconstructed from the low-rank approximation of the matrix consisting of the signal components only. The denoising effect of the proposed method is shown to be highly competitive among other existing nonparametric methods, including moving average, wavelet shrinkage, and total variation. Furthermore, its applicable scenarios in precision storage-ring mass spectrometry are demonstrated to be rather diverse and appealing.

The three-dimensional (3D) structure of a digital core can be reconstructed from a single two-dimensional (2D) image via mathematical modeling. In classical mathematical modeling algorithms, such as multipoint geostatistics algorithms, the optimization of pattern sets (dictionaries) and the mapping problems are important issues. However, they have rarely been discussed thus far. Pattern set (dictionary)-related problems include the pattern set (dictionary) size problem and the one-to-many mapping problem in a pattern set (dictionary). The former directly affects the completeness of the dictionary, while the latter is manifested such that a single to-be-matched 2D patch has multiple matching patterns in the library and it is hence necessary to select these modes to establish an optimal mapping relationship. Whether the two above-mentioned problems can be properly resolved is directly related to the accuracy of the reconstruction results. Super-dimension reconstruction is a new 3D reconstruction method proposed by introducing the concepts of training dictionary, prior model, and mapping into the reconstruction of the digital core from the field of super-resolution reconstruction. In addition, mapping relationship extraction and dictionary building are also key issues in super-dimension reconstruction. Therefore, this paper discusses these common dictionary-related problems from the perspective of super-dimension dictionaries. We propose dictionary optimization using augmentation dictionaries and clustering based on the boundary features of the dictionary elements to improve the completeness and expand the expression ability of the dictionary. Furthermore, we propose constraint neighbor embedding-based dictionary mapping to establish a more reasonable dictionary mapping relationship for super-dimension reconstruction, and we solve the one-to-many mapping problem in the dictionary. Our experimental results show that the performance of the super-dimension dictionary can be improved by the above-mentioned algorithm. Thus, through the optimized dictionary structure and mapping relationship determined by the above-mentioned methods, the 2D patch to be reconstructed can match a more accurate 3D block in the dictionary. Consequently, the reconstruction precision is improved.

We investigate the emergence of temperature $T$ in the system-plus-reservoir paradigm starting from the fundamental microcanonical scenario at total fixed energy $E$ where, contrary to the canonical approach, $T=T\left(E\right)$ is not a control parameter but a derived auxiliary concept. As shown by Schwinger for the regime of weak coupling $\gamma$ between subsystems, $T\left(E\right)$ emerges from the saddle-point analysis leading to the ensemble equivalence up to corrections $O(1/\sqrt{N})$ in the number of particles $N$ that defines the thermodynamic limit. By extending these ideas for finite $\gamma$, while keeping $N\to \infty$, we provide a consistent generalization of temperature $T(E,\gamma )$ in strongly coupled systems, and we illustrate its main features for the specific model of quantum Brownian motion where it leads to consistent microcanonical thermodynamics. Interestingly, while this $T(E,\gamma )$ is a monotonically increasing function of the total energy $E$, its dependence with $\gamma$ is a purely quantum effect notably visible near the ground-state energy and for large energies differs for Markovian and non-Markovian regimes.

Critical hysteresis in the zero-temperature random-field Ising model on a two-dimensional triangular lattice was studied earlier with site dilution on one sublattice. It was reported that criticality vanishes if less than one-third of the sublattice is occupied. This appears at variance with recently obtained exact solutions of the model on dilute Bethe lattices and prompts us to revisit the problem using an alternate numerical method. Contrary to our speculation that criticality may not be exactly zero below one-third dilution, the present study indicates it is nearly zero if approximately less than two-thirds of the sublattice is occupied. This suggests that hysteresis on dilute periodic lattices is qualitatively different from that on dilute Bethe lattices. Possible reasons are discussed briefly.

Driven dissipative many-body systems are described by differential equations for macroscopic variables which include fluctuations that account for ignored microscopic variables. Here, we investigate the effect of deterministic fluctuations, drawn from a system in a state of phase turbulence, on front dynamics. We show that despite these fluctuations a front may remain pinned, in contrast to fronts in systems with Gaussian white noise fluctuations, and explore the pinning-depinning transition. In the deterministic case, this transition is found to be robust but its location in parameter space is complex, generating a fractal-like structure. We describe this transition by deriving an equation for the front position, which takes the form of an overdamped system with a ratchet potential and chaotic forcing; this equation can, in turn, be transformed into a linear parametrically driven oscillator with a chaotically oscillating frequency. The resulting description provides an unambiguous characterization of the pinning-depinning transition in parameter space. A similar calculation for noise-driven front propagation shows that the pinning-depinning transition is washed out.

Collagen fibers, an important component of the extracellular matrix (ECM), can both inhibit and promote cellular migration. In vitro studies have revealed that the fibers' orientations are crucial to cellular invasion, while in vivo investigations have led to the development of tumor-associated collagen signatures (TACS) as an important prognostic factor. Studying biophysical regulation of cell invasion and the effect of the fibers' orientation not only deepens our understanding of the phenomenon, but also helps classify the TACSs precisely, which is currently lacking. We present a stochastic model for random or chemotactic migration of cells in fibrous ECM, and study the role of the various factors in it. The model provides a framework for quantitative classification of the TACSs, and reproduces quantitatively recent experimental data for cell motility. It also indicates that the spatial distribution of the fibers' orientations and extended correlations between them, hitherto ignored, as well as dynamics of cellular motion all contribute to regulation of the cells' invasion length, which represents a measure of metastatic risk. Although the fibers' orientations trivially affect randomly moving cells, their effect on chemotactic cells is completely nontrivial and unexplored, which we study in this paper.

We use perturbative renormalization group theory to study the kinetics of protein aggregation phenomena in a unified manner across multiple timescales. Using this approach, we find that, irrespective of the specific molecular details or experimental conditions, filamentous assembly systems display universal behavior in time. Moreover, we show that the universality classes for protein aggregation correspond to simple autocatalytic processes and that the diversity of behavior in these systems is determined solely by the reaction order for secondary nucleation with respect to the protein concentration, which labels all possible universality classes. We validate these predictions on experimental data for the aggregation of several different proteins at several different initial concentrations, which by appropriate coordinate transformations we are able to collapse onto universal kinetic growth curves. These results establish the power of the perturbative renormalization group in distilling the ultimately simple temporal behavior of complex protein aggregation systems, creating the possibility to study the kinetics of general self-assembly phenomena in a unified fashion.

The amplification cycle of many replicators (natural or artificial) involves the usage of a host compartment, inside of which the replicator expresses phenotypic compounds necessary to carry out its genetic replication. For example, viruses infect cells, where they express their own proteins and replicate. In this process, the host cell boundary limits the diffusion of the viral protein products, thereby ensuring that phenotypic compounds, such as proteins, promote the replication of the genes that encoded them. This role of maintaining spatial colocalization, also called genotype-phenotype linkage, is a critical function of compartments in natural selection. In most cases, however, individual replicating elements do not distribute systematically among the hosts, but are randomly partitioned. Depending on the replicator-to-host ratio, more than one variant may thus occupy some compartments, blurring the genotype-phenotype linkage and affecting the effectiveness of natural selection. We derive selection equations for a variety of such random multiple occupancy situations, in particular considering the effect of replicator population polymorphism and internal replication dynamics. We conclude that the deleterious effect of random multiple occupancy on selection is relatively benign, and may even completely vanish is some specific cases. In addition, given that higher mean occupancy allows larger populations to be channeled through the selection process, and thus provide a better exploration of phenotypic diversity, we show that it may represent a valid strategy in both natural and technological cases.

Population dynamics reflects an underlying birth-death process, where the rates associated with different events may depend on external environmental conditions and on the population density. A whole family of simple and popular deterministic models (such as logistic growth) supports a transcritical bifurcation point between an extinction phase and an active phase. Here we provide a comprehensive analysis of the phases of that system, taking into account both the endogenous demographic noise (random birth and death events) and the effect of environmental stochasticity that causes variations in birth and death rates. Three phases are identified: in the inactive phase the mean time to extinction $T$ is independent of the carrying capacity $N$ and scales logarithmically with the initial population size. In the power-law phase $T\sim N^q$, and in the exponential phase $T\sim exp\left(\alpha N\right)$. All three phases and the transitions between them are studied in detail. The breakdown of the continuum approximation is identified inside the power-law phase, and the accompanying changes in decline modes are analyzed. The applicability of the emerging picture to the analysis of ecological time series and to the management of conservation efforts is briefly discussed.

We investigate the different boiling regimes around a single continuously laser-heated 80 nm gold nanoparticle and draw parallels to the classical picture of boiling. Initially, nanoscale boiling takes the form of transient, inertia-driven, unsustainable boiling events characteristic of a nanoscale boiling crisis. At higher heating power, nanoscale boiling is continuous, with a vapor film being sustained during heating for at least up to $20\mu \mathrm{s}$. Only at high heating powers does a substantial stable vapor nanobubble form. At intermediate heating powers, unstable boiling sometimes takes the form of remarkably stable nanobubble oscillations with frequencies between 40 MHz and 60 MHz, frequencies that are consistent with the relevant size scales according to the Rayleigh-Plesset model of bubble oscillation, though how applicable that model is to plasmonic vapor nanobubbles is not clear.

The vibration-induced droplet shedding mechanism on microstructured superhydrophobic surfaces was simulated using the lattice Boltzmann method. The numerical simulations of natural droplet oscillations for various surface structures show that the natural frequency of the droplet is strongly dependent on surface morphology. The results show good agreement with basic theoretical values. Furthermore, simulations of the motion of the droplet subjected to vertical surface vibration demonstrate that droplets in the Cassie wetting state are easily removed from the surface, whereas for Wenzel state droplets, pinch-off occurs and only partial removal is possible. Microstructure spacing was found to be a key factor in the shedding process. On a surface with small microstructure spacing, the increased surface adhesion leads to a decrease of droplet departure velocity. In contrast, for large roughness spacing, the droplet is impaled on the microstructures, which causes the departure velocity to decrease. Reperforming the simulations under different vibration intensities reveals that as the vibration amplitude is increased, the optimum frequency for droplet removal decreases. The findings of this study shed light on the underlying mechanisms involved in forced vibrations of droplets and can be helpful in engineering applications in which droplet shedding processes are critical.

We set up a proposal to extend significantly recent works on neutrino-plasma interaction, allowing the possibility of deep degenerate and relativistic electrons, which are often present in compact stars such as high-density white dwarfs. The methodology involves the covariant hydrodynamic formulation of ultradense plasmas. We propose the generalization of previous studies, on the interaction between ion-acoustic waves and resonant neutrino flavor oscillations in a mixed neutrino beam, admitting degenerate and relativistic electron populations. Destabilization of the ion acoustic wave has higher growth rates, thanks to the very high densities present in plasmas under these extreme conditions. We take into account applications to white dwarf stars in the process of collapsing, producing type II supernovas.