Topological phase transitions, which do not adhere to Landau's phenomenological model (i.e., a spontaneous symmetry breaking process and vanishing local order parameters), have been actively researched in condensed matter physics. Machine learning of topological phase transitions has generally proved difficult due to the global nature of the topological indices. Only recently has the method of diffusion maps been shown to be effective at identifying changes in topological order. However, previous diffusion map results required adjustments of two hyperparameters: a data length scale and the number of phase boundaries. In this article we introduce a heuristic that requires no such tuning. This heuristic allows computer programs to locate appropriate hyperparameters without user input. We demonstrate this method's efficacy by drawing remarkably accurate phase diagrams in three physical models: the Haldane model of graphene, a generalization of the Su-Schreiffer-Haeger model, and a model for a quantum ring with tunnel junctions. These diagrams are drawn, without human intervention, from a supplied range of model parameters.

Drying of colloidal suspension towards the exploitation of the resultant nanoparticle deposition has been applied in different research and engineering fields. Recent experimental studies have shown that neck-based thermal structure (NTS) by colloidal nanoparticle deposition between microsize filler particle configuration (FPC) can significantly enhance vertical heat conduction in innovative three-dimensional chip stacks [Brunschwiler et al., J. Electron. Packag. 138, 041009 (2016)]. However, an in-depth understanding of the mechanisms of colloidal liquid drying, neck formation, and their influence on heat conduction is still lacking. In this paper, using the lattice Boltzmann method, we model neck formation in FPCs and evaluate the thermal performances of resultant NTSs. The colloidal liquid is found drying continuously from the periphery of the microstructure to its center with a decreasing drying rate. With drying, more necks of smaller size are formed between adjacent filler particles, while fewer necks of larger size are formed between filler particle and the top/bottom plate of the FPCs. The necks, forming critical throats between the filler particles, are found to improve the heat flux significantly, leading to an overall heat conduction enhancement of 2.4 times. In addition, the neck count, size, and distribution as well as the thermal performance of NTSs are found to be similar for three different FPCs at a constant filler particle volume fraction. Our simulation results on neck formation and thermal performances of NTSs are in good agreement with experimental results. This demonstrates that the current lattice Boltzmann models are accurate in modeling drying of colloidal suspension and heat conduction in microporous structures, and have high potentials to study other problems such as surface coating, salt transport, salt crystallization, and food preserving.

Using Levi-Cività's theory of ideal fluids, we derive the complex Korteweg–de Vries (KdV) equation, describing the complex velocity of a shallow fluid up to first order. We use perturbation theory, and the long wave, slowly varying velocity approximations for shallow water. The complex KdV equation describes the nontrivial dynamics of all water particles from the surface to the bottom of the water layer. A crucial step made in our work is the proof that a natural consequence of the complex KdV theory is that the wave elevation is described by the real KdV equation. The complex KdV approach in the theory of shallow fluids is thus more fundamental than the one based on the real KdV equation. We demonstrate how it allows direct calculation of the particle trajectories at any point of the fluid, and that these results agree well with numerical simulations of other authors.

In the recent past, the resonance energy transfer studies using metallic nanoparticles has become a matter of quintessence in modern technology, which considerably extends its applications in probing specific biological and chemical processes. In the present study, metallic-silver nanoparticles of 2–4 nm (diameter) capped with hexanethiol ligand are developed and dispersed in ferroelectric liquid crystal (FLC). The morphology of nanoparticles was characterized using HR-TEM and SEM techniques. Furthermore, a systematic study of energy transfer between the host FLC material (as donors) and metallic-silver nanoparticles (as acceptors) has been explored employing steady state and time resolved fluorescence spectroscopic techniques. The nanoparticle based surface energy transfer (NSET) parameters viz., transfer efficiency, transfer rate, and proximity distance between donor and acceptor, have been determined for NSET couples (FLC material–metallic-silver nanoparticle) composites. It is observed that various NSET parameters and quenching efficiency follow a linear dependence on the concentration of metallic-silver nanoparticles in host FLC material. The nonradiative energy transfer and superquenching effect were analyzed with the help of Stern-Volmer plots. The impact of present study about superquenching effect of the silver nanoparticles can be used for sensing applications that require high degree sensitivity.

The formation of heterogeneous nematic and smectic liquid crystals in the general case of an arbitrary geometry is investigated in the framework of molecular-statistical approach [Emelyanenko and Khokhlov, J. Chem. Phys. 142, 204905 (2015)]. The molecular aspects of the orientational and translational orderings at the curved surfaces of small solid objects dispersed in liquid crystal are considered, and the differential equations for gradients of the order parameters in vicinities of the small objects are presented in the general form. The five elastic constants are obtained within the same approach, from which we were able to predict that a significant space variations of the order parameters can be observed within the $0.5–0.8\mu \text{m}$ area around any small object, almost independently of its own dimension. Therefore, the liquid crystals can be a simple tool for the optical visualization of nano-objects. It is also demonstrated that the kind of molecular self-organization (smectic, nematic or conventionally isotropic) at the surfaces of small solid objects can be different from that in the bulk of mesogenic material. Totally we predict eight various combinations of simple states at the surfaces and in the bulk depending on the solid objects' size and temperature. It is also shown that the surfaces of $10\mu \text{m}$-size solid objects and larger act almost as flat surfaces, while the surfaces of $1\mu \text{m}$-size solid objects and smaller act almost as point defects.

A liquid crystal (LC) cell was fabricated by putting together a monolayer graphene-coated glass substrate on one side, and a rubbed planar-aligning polyimide layer on an indium tin oxide (ITO) coated glass substrate on the other side. The monolayer graphene film served as the planar-alignment agent as well as the transparent electrode on one side of the cell. The cell was filled with an achiral LC alkoxyphenylbenzoate (9OO4). The presence of the graphene film on one substrate resulted in an induced chiral signature in the otherwise achiral LC 9OO4. The induced chirality was probed utilizing the electroclinic effect (a polar tilt of the LC director perpendicular to, and linear in, an applied electric field) in the smectic-A phase. The electroclinic effect showed significant pretransitional behavior on approaching the smectic-A to smectic-C transition temperature from above. The electroclinic effect revealed a low-frequency relaxation process indicating that the chirality was induced on the LC molecules at the graphene interface and did not propagate into the bulk. A soft shear mode can break the symmetry of the hexagonal lattice of graphene on a substrate and, consequently, graphene possesses strain chirality. The noncovalent π-π interaction between the LC and the strained graphene induces molecular conformational deracemization in the LC at the graphene interface, and the LC exhibits surface-induced chirality.

The excess work required to drive a stochastic system out of thermodynamic equilibrium through a time-dependent external perturbation is directly related to the amount of entropy produced during the driving process, allowing excess work and entropy production to be used interchangeably to quantify dissipation. Given the common intuition of biological molecular machines as internally communicating work between components, it is tempting to extend this correspondence to the driving of one component of an autonomous system by another; however, no such relation between the internal excess work and entropy production exists. Here we introduce the “transduced additional free-energy rate” between strongly coupled subsystems of an autonomous system, which is analogous to the excess power in systems driven by an external control parameter that receives no feedback from the system. We prove that this is a relevant measure of dissipation—in that it equals the steady-state entropy production rate due to the downstream subsystem—and demonstrate its advantages with a simple model system.

Here, we propose a simple algorithmic framework for creating power-law graphs with small diameters and then study structural properties, for instance, average degree, on graphs built. The results show that our graphs have not only some commonly seen properties including scale-free feature, small-world property, and disassortative structure, but also many rarely found characteristics, such as the density feature due to power-law exponents equal to 2 and the diameter equivalent to 2, compared to most previous scale-free models. In addition, we also consider the trapping problem on the proposed graphs and then find that they have more optimal trapping efficiency by means of their own average trapping times achieving the theoretical lower bound, a phenomenon that is seldom observed in existing scale-free models. We conduct extensive simulations, and the results show that empirical simulations are consistent with theoretical analysis.

Numerous problems of both theoretical and practical interest are related to finding shortest (or otherwise optimal) paths in networks, frequently in the presence of some obstacles or constraints. A somewhat related class of problems focuses on finding optimal distributions of weights which, for a given connection topology, maximize some kind of flow or minimize a given cost function. We show that both sets of problems can be approached through an analysis of the large-deviation functions of random walks. Specifically, a study of ensembles of trajectories allows us to find optimal paths, or design optimal weighted networks, by means of an auxiliary stochastic process (the generalized Doob transform). The paths are not limited to shortest paths, and the weights must not necessarily optimize a given function. Paths and weights can in fact be tailored to a given statistics of a time-integrated observable, which may be an activity or current, or local functions marking the passing of the random walker through a given node or link. We illustrate this idea with an exploration of optimal paths in the presence of obstacles, and networks that optimize flows under constraints on local observables.

Many biological processes involve macromolecules searching for their specific targets that are surrounded by other objects, and binding to these objects affects the target search. Acceleration of the target search by nonspecific binders was observed experimentally and analyzed theoretically, for example, for DNA-binding proteins. According to existing theories this acceleration requires continuous transfer between the nonspecific binders and the specific target. In contrast, our analysis predicts that (i) nonspecific binders could accelerate the search without continuous transfer to the specific target provided that the searching particle is capable of sliding along the binder; (ii) in some cases such binders could decelerate the target search, but provide an advantage in competition with the “binder-free” target; (iii) nonbinding objects decelerate the target search. We also show that although the target search in the presence of binders could be considered as diffusion in inhomogeneous media, in the general case it cannot be described by the effective diffusion coefficient.

Mobile charge in an electrolytic solution can in principle be represented as the divergence of ionic polarization. After adding explicit solvent polarization a finite volume of an electrolyte can then be treated as a composite nonuniform dielectric body. Writing the electrostatic interactions as an integral over electric-field energy density we show that the Poisson-Boltzmann functional in this formulation is convex and can be used to derive the equilibrium equations for electric potential and ion concentration by a variational procedure developed by Ericksen for dielectric continua [J. L. Ericksen, Arch. Rational Mech. Anal. 183, 299 (2007)]. The Maxwell field equations are enforced by extending the set of variational parameters by a vector potential representing the dielectric displacement which is fully transverse in a dielectric system without embedded external charge. The electric-field energy density in this representation is a function of the vector potential and the sum of ionic and solvent polarization making the mutual screening explicit. Transverse polarization is accounted for by construction, lifting the restriction to longitudinal polarization inherent in the electrostatic potential based formulation of Poisson-Boltzmann mean field theory.

We study nanoscale pattern formation on the surface of a solid that is bombarded with two diametrically opposed, broad ion beams for ion energies low enough that sputtering can be neglected. We focus on the case in which the angle of ion incidence is just above the threshold angle for pattern formation. The equation of motion at sufficiently long times is derived using a generalized crater function formalism. This formalism also yields expressions for the coefficients in the equation of motion in terms of crater function moments. We find that virtually defect-free ripples with a sawtooth profile can emerge at sufficiently long times. The ripples also coarsen as time passes, in contrast to the near-threshold behavior of ripples in the higher energy regime in which sputtering is significant.

The formation and structure of a collisional shock wave in a fully ionized plasma is studied via full particle-in-cell simulations, which allows the complex momentum and energy transfer processes between different charged particles to be treated self-consistently. The kinetic energy of the plasma flow drifting towards a reflecting piston is found to be rapidly converted into thermal motion under the cooperative effects of ion-ion collisions, ion-electron collisions, and electric field charged-particle interactions. The subsequent shock evolution is influenced by the “precursor” ion beam before a quasisteady state is reached. The shock wave structure is then analyzed from a two-fluid transport viewpoint, which is found to be affected by “flux-limiting” electron transport, the nonthermal ions, and the charge separation electric field.

We investigate how the properties of inhomogeneous patterns of activity, appearing in many natural and social phenomena, depend on the temporal resolution used to define individual bursts of activity. To this end, we consider time series of microscopic events produced by a self-exciting Hawkes process, and leverage a percolation framework to study the formation of macroscopic bursts of activity as a function of the resolution parameter. We find that the very same process may result in different distributions of avalanche size and duration, which are understood in terms of the competition between the 1D percolation and the branching process universality class. Pure regimes for the individual classes are observed at specific values of the resolution parameter corresponding to the critical points of the percolation diagram. A regime of crossover characterized by a mixture of the two universal behaviors is observed in a wide region of the diagram. The hybrid scaling appears to be a likely outcome for an analysis of the time series based on a reasonably chosen, but not precisely adjusted, value of the resolution parameter.

The inference of Shannon entropy out of sample histograms is known to be affected by systematic and random errors that depend on the finite size of the available data set. This dependence was mostly investigated in the multinomial case, in which states are visited in an independent fashion. In this paper the asymptotic behavior of the distribution of the sample Shannon entropy, also referred to as plug-in estimator, is investigated in the case of an underlying finite Markov process characterized by a regular stochastic matrix. As the size of the data set tends to infinity, the plug-in estimator is shown to become asymptotically normal, though in a way that substantially deviates from the known multinomial case. The asymptotic behavior of bias and variance of the plug-in estimator are expressed in terms of the spectrum of the stochastic matrix and of the related covariance matrix. Effects of initial conditions are also considered. By virtue of the formal similarity with Shannon entropy, the results are directly applicable to the evaluation of permutation entropy.

Many practical systems can be described by complex networks. These networks produce, day and night, rich data which can be used to extract information from the systems. Often, output data of some nodes in the networks can be successfully measured and collected while the structures of networks producing these data are unknown. Thus, revealing network structures by analyzing available data, referred to as network reconstruction, turns to be an important task in many realistic problems. Limitation of measurable data is a very common challenge in network reconstruction. Here we consider an extreme case, i.e., we can only measure and process the data of a pair of nodes in a large network, and the task is to explore the relationship between these two nodes while all other nodes in the network are hidden. A driving-response approach is proposed to do so. By loading a high-frequency signal to a node (defined as node $A$), we can measure data of the partner node (node $B$), and work out the connection structure, such as the distance from node $A$ to node $B$ and the effective intensity of interaction from $A$ to $B$, with the data of node B only. A systematical smoothing technique is suggested for treating noise problem. The approach has practical significance.

Several studies have investigated the dynamics of a single spherical bubble at rest under a nonstationary pressure forcing. However, attention has almost always been focused on periodic pressure oscillations, neglecting the case of stochastic forcing. This fact is quite surprising, as random pressure fluctuations are widespread in many applications involving bubbles (e.g., hydrodynamic cavitation in turbulent flows or bubble dynamics in acoustic cavitation), and noise, in general, is known to induce a variety of counterintuitive phenomena in nonlinear dynamical systems such as bubble oscillators. To shed light on this unexplored topic, here we study bubble dynamics as described by the Keller-Miksis equation, under a pressure forcing described by a Gaussian colored noise modeled as an Ornstein-Uhlenbeck process. Results indicate that, depending on noise intensity, bubbles display two peculiar behaviors: when intensity is low, the fluctuating pressure forcing mainly excites the free oscillations of the bubble, and the bubble's radius undergoes small amplitude oscillations with a rather regular periodicity. Differently, high noise intensity induces chaotic bubble dynamics, whereby nonlinear effects are exacerbated and the bubble behaves as an amplifier of the external random forcing.

This article proposes a phase-field–simplified lattice Boltzmann method (PF-SLBM) for modeling solid-liquid phase change problems within a pure material. The PF-SLBM consolidates the simplified lattice Boltzmann method (SLBM) as the flow solver and the phase-field method as the interface tracking algorithm. Compared with conventional lattice Boltzmann modelings, the SLBM shows advantages in memory cost, boundary treatment, and numerical stability, and thus is more suitable for the present topic which includes complex flow patterns and fluid-solid boundaries. In contrast to the sharp interface approach, the phase-field method utilized in this work represents a diffuse interface strategy and is more flexible in describing complicated fluid-solid interfaces. Through abundant benchmark tests, comprehensive validations of the accuracy, stability, and boundary treatment of the proposed PF-SLBM are carried out. The method is then applied to the simulations of partially melted or frozen cavities, which sheds light on the potential of the PF-SLBM in resolving practical problems.

We compute exactly the mean perimeter and the mean area of the convex hull of a two-dimensional isotropic Brownian motion of duration $t$ and diffusion constant $D$, in the presence of resetting to the origin at a constant rate $r$. We show that for any $t$, the mean perimeter is given by $\langle L\left(t\right)\rangle =2\pi \sqrt{\frac{D}{r}}{f}_1\left(rt\right)$ and the mean area is given by $\langle A\left(t\right)\rangle =2\pi \frac{D}{r}{f}_2\left(rt\right)$ where the scaling functions $f_1\left(z\right)$ and $f_2\left(z\right)$ are computed explicitly. For large $t\gg 1/r$, the mean perimeter grows extremely slowly as $\langle L\left(t\right)\rangle \propto ln\left(rt\right)$ with time. Likewise, the mean area also grows slowly as $\langle A\left(t\right)\rangle \propto {ln}^2\left(rt\right)$ for $t\gg 1/r$. Our exact results indicate that the convex hull, in the presence of resetting, approaches a circular shape at late times due to the isotropy of the Brownian motion. Numerical simulations are in perfect agreement with our analytical predictions.