The familiar variational principle provides an upper bound to the ground-state energy of a given Hamiltonian. This allows one to optimize a trial wave function by minimizing the expectation value of the energy. This approach is also trivially generalized to excited states, so that given a trial wave function of a certain symmetry, one can compute an upper bound to the lowest-energy level of that symmetry. In order to generalize further and build an upper bound of an arbitrary excited state of the desired symmetry, a linear combination of basis functions is generally used to generate an orthogonal set of trial functions, all bounding their respective states. However, sometimes a compact wave-function form is sought, and a basis-set expansion is not desirable or possible. Here we present an alternative generalization of the variational principle to excited states that does not require explicit orthogonalization to lower-energy states. It is valid for one-dimensional systems and, with additional information, to at least some $n$-dimensional systems. This generalized variational principle exploits information about the nodal structure of the trial wave function, giving an upper bound to the exact energy without the need to build a linear combination of basis functions. To illustrate the theorem we apply it to a nontrivial example: the $1s2s{}^{1}S$ excited state of the helium atom.

A discrete agent-based model on a periodic lattice of arbitrary dimension is considered. Agents move to nearest-neighbor sites by a motility mechanism accounting for general interactions, which may include volume exclusion. The partial differential equation describing the average occupancy of the agent population is derived systematically. A diffusion equation arises for all types of interactions and is nonlinear except for the simplest interactions. In addition, multiple species of interacting subpopulations give rise to an advection-diffusion equation for each subpopulation. This work extends and generalizes previous specific results, providing a construction method for determining the transport coefficients in terms of a single conditional transition probability, which depends on the occupancy of sites in an influence region. These coefficients characterize the diffusion of agents in a crowded environment in biological and physical processes.

We study the origin of phase transitions in several simplified models with long-range interactions. For the self-gravitating ring model, we are unable to observe a possible phase transition predicted by Nardini and Casetti [Phys. Rev. E 80, 060103R (2009).] from an energy landscape analysis. Instead we observe a sharp, although without any nonanalyticity, change from a core-halo to a core-only configuration in the spatial distribution functions for low energies. By introducing a different class of solvable simplified models without any critical points in the potential energy we show that a behavior similar to the thermodynamics of the ring model is obtained, with a first-order phase transition from an almost homogeneous high-energy phase to a clustered phase and the same core-halo to core configuration transition at lower energies. We discuss the origin of these features for the simplified models and show that the first-order phase transition comes from the maximization of the entropy of the system as a function of energy and an order parameter, as previously discussed by Hahn and Kastner [Phys. Rev. E 72, 056134 (2005); Eur. Phys. J. B 50, 311 (2006)], which seems to be the main mechanism causing phase transitions in long-range interacting systems.

We present the description of thermodynamic properties of a Bose gas by considering the hierarchy of equations satisfied by imaginary-time Green functions. In the case of an ideal gas the appearance of the off-diagonal long-range order (presence of condensate) is derived in a particularly simple way. The simplicity of the analysis persists at the level of the mean-field theory. Using Kac’s scaling of the repulsive binary potential we then determine asymptotic corrections to the mean-field theory for thermodynamic states far from the region of the mean-field condensation. Our predictions are in accordance with rigorous results obtained previously, whereas the Hartree-Fock approximation is shown to neglect a whole class of equally important contributions.

We experimentally investigate the flow field in plane geometry around a slowly moving rigid finger in a dry, randomly packed granular medium. The finger enters the medium vertically from its free surface, in analogy with indentation tests on ductile materials. By developing a particle imaging velocimetry technique, we identify a localized flow around the finger, limited by two nearly symmetric shear bands that nucleate near the fingertip and reach the free surface of the granular compact. Evolution of the shear bands is discontinuous, exhibiting nucleation-relaxation processes as the finger moves downward. We present a simple model accounting for the shape of the shear bands at early stages of nucleation. We measure the force applied by the finger and the sources of dilation as well. A mechanism that relates local dilations to the total volume increase of the medium is proposed.

We report on the dynamics in colloidal suspensions of stiff viral rods, called fd-Y21M. This mutant filamentous virus exhibits a persistence length 3.5 times larger than the wild-type fd-wt. Such a virus system can be used as a model system of rodlike particles for studying their self-diffusion. In this paper, the physical features, such as rod contour length and polydispersity have been determined for both viruses. The effect of viral rod flexibility on the location of the nematic-smectic phase transition has been investigated, with a focus on the underlying dynamics studied more specifically in the smectic phase. Direct visualization of the stiff fd-Y21M at the scale of a single particle has shown the mass transport between adjacent smectic layers, as found earlier for the more flexible rods. We could relate this hindered diffusion with the smectic ordering potentials for varying rod concentrations. The self-diffusion within the layers is far more pronounced for the stiff rods as compared to the more flexible fd-wt viral rod.

One of the most promising strategies to treat cancer is attacking it with viruses designed to exploit specific altered pathways. Here, the effects of oncolytic virotherapy on tumors having compact, papillary, and disconnected morphologies are investigated through computer simulations of a multiscale model coupling macroscopic reaction-diffusion equations for the nutrients with microscopic stochastic rules for the actions of individual cells and viruses. The interaction among viruses and tumor cells involves cell infection, intracellular virus replication, and the release of new viruses in the tissue after cell lysis. The evolution over time of both the viral load and cancer cell population, as well as the probabilities for tumor eradication, were evaluated for a range of multiplicities of infection, viral entries, and burst sizes. It was found that in immunosuppressed hosts, the antitumor efficacy of a virus is primarily determined by its entry efficiency, its replicative capacity within the tumor, and its ability to spread over the tissue. However, the optimal traits for oncolytic viruses depend critically on the tumor growth dynamics and do not necessarily include rapid replication, cytolysis, or spreading, currently assumed as necessary conditions for a successful therapeutic outcome. Our findings have potential implications on the design of new vectors for the viral therapy of cancer.

We present a time-dependent level-crossing theory for linear dynamical systems perturbed by colored Gaussian noise. We apply these results to approximate the firing statistics of conductance-based integrate-and-fire neurons receiving excitatory and inhibitory Poissonian inputs. Analytical expressions are obtained for three key quantities characterizing the neuronal response to time-varying inputs: the mean firing rate, the linear response to sinusoidally modulated inputs, and the pairwise spike correlation for neurons receiving correlated inputs. The theory yields tractable results that are shown to accurately match numerical simulations and provides useful tools for the analysis of interconnected neuronal populations.

Large-scale dynamical properties of complete chromosome DNA sequences of eukaryotes are considered. Using the proposed deterministic models with intermittency and symbolic dynamics we describe a wide spectrum of large-scale patterns inherent in these sequences, such as segmental duplications, tandem repeats, and other complex sequence structures. It is shown that the recently discovered gene number balance on the strands is not of a random nature, and certain subsystems of a complete chromosome DNA sequence exhibit the properties of deterministic chaos.

We have studied the evolution of strategies in spatial public goods games where both individual (peer) and institutional (pool) punishments are present in addition to unconditional defector and cooperator strategies. The evolution of strategy distribution is governed by imitation based on the random sequential comparison of neighbors’ payoff for a fixed level of noise. Using numerical simulations, we evaluate the strategy frequencies and phase diagrams when varying the synergy factor, punishment cost, and fine. Our attention is focused on two extreme cases describing all the relevant behaviors in such a complex system. According to our numerical data peer punishers prevail and control the system behavior in a large segments of parameters while pool punishers can only survive in the limit of weak peer punishment when a rich variety of solutions is observed. Paradoxically, the two types of punishment may extinguish each other’s impact, resulting in the triumph of defectors. The technical difficulties and suggested methods are briefly discussed.

The investigation of striated microdischarges in barrier discharges in argon at atmospheric pressure is reported. Microdischarges were investigated by means of electrical measurements correlated with intensified CCD camera imaging. The scaling law theory known from low-pressure glow discharge diagnostics was applied in order to describe and explain this phenomenon. The investigated microdischarge is characterized as a transient atmospheric-pressure glow discharge with a stratified column. It can be described by similarity parameters $i/r\approx 0.13$ A/cm, $pr\approx 5$ Torr cm, and $3<\lambda /r<5$ with the current $i$, pressure $p$, interval of subsequent striations $\lambda$, and radius of the plasma channel $r$. An attempt to describe the mechanism of creation of a striated structure is given, based on an established model of the spatial electron relaxation.

We present a detailed description of the kinetic activation-relaxation technique (k-ART), an off-lattice, self-learning kinetic Monte Carlo (KMC) algorithm with on-the-fly event search. Combining a topological classification for local environments and event generation with ART nouveau, an efficient unbiased sampling method for finding transition states, k-ART can be applied to complex materials with atoms in off-lattice positions or with elastic deformations that cannot be handled with standard KMC approaches. In addition to presenting the various elements of the algorithm, we demonstrate the general character of k-ART by applying the algorithm to three challenging systems: self-defect annihilation in c-Si (crystalline silicon), self-interstitial diffusion in Fe, and structural relaxation in a-Si (amorphous silicon).

Why are some networks degree-degree correlated (assortative), while most of the real-world ones are anticorrelated (disassortative)? Here, we prove, by combinatorial methods, that the assortativity of a network depends only on three structural factors: transitivity (clustering coefficient), intermodular connectivity, and branching. Then, a network is assortative if the contributions of the first two factors are larger than that of the third. Highly branched networks are likely to be disassortative.

We derive a method to reconstruct Gaussian signals from linear measurements with Gaussian noise. This new algorithm is intended for applications in astrophysics and other sciences. The starting point of our considerations is the principle of minimum Gibbs free energy, which was previously used to derive a signal reconstruction algorithm handling uncertainties in the signal covariance. We extend this algorithm to simultaneously uncertain noise and signal covariances using the same principles in the derivation. The resulting equations are general enough to be applied in many different contexts. We demonstrate the performance of the algorithm by applying it to specific example situations and compare it to algorithms not allowing for uncertainties in the noise covariance. The results show that the method we suggest performs very well under a variety of circumstances and is indeed qualitatively superior to the other methods in cases where uncertainty in the noise covariance is present.

We study phonon relaxation in chains of particles coupled through polynomial-type pair-interaction potentials and obeying quantum dynamics. We present detailed calculations for the sixth-order potential and find that the wave-vector-dependent relaxation rate follows a power-law behavior, $\Gamma \left(q\right)\sim q^{\delta }$, with $\delta =5/3$, which is identical to that of the fourth-order potential. We argue through diagrammatic analysis that this is a generic feature of even-power potentials. Our earlier analysis has shown that $\delta =3/2$ when the leading-order term in the nonlinear potential is odd, suggesting that there are two universality classes for the phonon relaxation rates dependent on a simple property of the potential. This implies that the thermal conductivity $\kappa$ which diverges as a function of chain size $N$ as $\kappa \propto N^{\alpha }$ also has two universal behaviors, in that $\alpha =1-1/\delta$ as follows from a finite-size argument. We support these arguments by numerical calculations of conductivity for chains obeying classical dynamics for polynomial potentials of some even and odd powers.

We present a physically intuitive model of ice-lens formation and growth during the freezing of soils and other dense, particulate suspensions. Motivated by experimental evidence, we consider the growth of an ice-filled crack in a freezing soil. At low temperatures, ice in the crack exerts large pressures on the crack walls that will eventually cause the crack to split open. We show that the crack will then propagate across the soil to form a new lens. The process is controlled by two factors: the cohesion of the soil and the geometrical supercooling of the water in the soil, a new concept introduced to measure the energy available to form a new ice lens. When the supercooling exceeds a critical amount (proportional to the cohesive strength of the soil) a new ice lens forms. This condition for ice-lens formation and growth does not appeal to any ad hoc, empirical assumptions, and explains how periodic ice lenses can form with or without the presence of a frozen fringe. The proposed mechanism is in good agreement with experiments, in particular explaining ice-lens pattern formation and surges in heave rate associated with the growth of new lenses. Importantly for systems with no frozen fringe, ice-lens formation and frost heave can be predicted given only the unfrozen properties of the soil. We use our theory to estimate ice-lens growth temperatures obtaining quantitative agreement with the limited experimental data that are currently available. Finally we suggest experiments that might be performed in order to verify this theory in more detail. The theory is generalizable to complex natural-soil scenarios and should therefore be useful in the prediction of macroscopic frost-heave rates.

We present a method to measure the viscosity of polymer thin films. The material is spin coated onto a silicon substrate and specially designed nanopatterns are imprinted on the film using thermal nanoimprint. A brief reflow is performed during which patterns flow under surface tension. Spectral densities of the topology before and after annealing are compared and the rheologic properties, such as viscosity, are extracted as fitting parameters of an evolution model. Contrary to previous similar approaches, emphasis was put on the spatial description rather than the temporal decay of the patterns. We used this method to measure the viscosity of polystyrene for two molecular weights at various temperatures and successfully recovered results of previous authors.

Electrospun polymer nanofibers demonstrate outstanding mechanical and thermodynamic properties as compared to macroscopic-scale structures. Our previous work has demonstrated that these features are attributed to nanofiber microstructure [Nat. Nanotechnol. 2, 59 (2007)]. It is clear that this microstructure is formed during the electrospinning process, characterized by a high stretching rate and rapid evaporation. Thus, when studying microstructure formation, its fast evolution must be taken into account. This study focuses on the dynamics of a highly entangled semidilute polymer solution under extreme longitudinal acceleration. The theoretical modeling predicts substantial longitudinal stretching and transversal contraction of the polymer network caused by the jet hydrodynamic forces, transforming the network to an almost fully stretched state. This prediction was verified by x-ray phase-contrast imaging of electrospinning jets of poly(ethylene oxide) and poly(methyl methacrylate) semidilute solutions, which revealed a noticeable increase in polymer concentration at the jet center, within less than 1 mm from the jet start. Thus, the proposed mechanism is applicable to the initial stage of the microstructure formation.

We present a theoretical study of a rotatory molecular motor under a conservative torque regime. We show that conservative and dissipative regimes present a different observable phenomenology. Our approach starts with a preliminary deterministic calculation of the motor cycle, which is complemented with stochastic simulations of a Langevin equation under a flashing ratchet potential. Finally, by using parameter values obtained from independent experimental information, our theoretical predictions are compared with experimental data of the $F_1$-ATPase motor of the Bacillus PS3.

Morphogen gradients are concentration fields of molecules acting as spatial regulators of cell differentiation in developing tissues and play a fundamental role in various aspects of embryonic development. We discovered a family of self-similar solutions in a canonical class of nonlinear reaction-diffusion models describing the formation of morphogen gradients. These solutions are realized in the limit of infinitely high production rate at the tissue boundary and are given by the product of the steady state concentration profile and a function of the diffusion similarity variable. We solved the boundary value problem for the similarity profile numerically and analyzed the implications of the discovered self-similarity on the dynamics of morphogenetic patterning.