The dynamic-shell concept for inertial confinement fusion (ICF) uses an initially homogeneous target and a carefully shaped laser pulse to form a shell and implode it. The laser pulse consists of a series of pickets that drive shocks into the target. The first few shocks converge inwards and rebound from the center of the target, creating an expanding, low-density plasma. Subsequent shocks are launched into the expanding plasma and eventually coalesce to form a shell, which is then imploded with a traditional ICF laser pulse. This study describes radiation-hydrodynamic simulations that investigate the sensitivity of dynamic-shell targets to imperfections in the laser drive. A one-dimensional (1D) study looks at mistiming and power variations in the pickets and a two-dimensional (2D) study examines irradiation perturbations imposed by the laser-beam geometry. Simulations show that less than $\sim 2\%$ power imbalance or 200 ps timing variation in the pickets is sufficient to keep the yield above 90% of the maximum. Additionally, the 2D simulations show that 72 or more beams are required to keep irradiation nonuniformities low enough to obtain fusion yields close to that of 1D simulations.

Coulomb collision is a fundamental diffusion process in plasmas that can be described by the Landau-Fokker-Planck (LFP) equation or the stochastic differential equation (SDE). While energy and momentum are conserved exactly in the LFP equation, they are conserved only on average by the conventional corresponding SDEs, suggesting that the underlying stochastic process may not be well defined by such SDEs. In this study, we derive new SDEs with exact energy-momentum conservation for the Coulomb collision by factorizing the collective effect of field particles into individual particles and enforcing Newton's third law. These SDEs, when interpreted in the Stratonovich sense, have a particularly simple form that represents pure diffusion between particles without drag. To demonstrate that the new SDEs correspond to the LFP equation, we develop numerical algorithms that converge to the SDEs and preserve discrete conservation laws. Simulation results are presented in a benchmark of various relaxation processes.

This Letter examines the full scope of long-standing conjectures identifying the invariant thermodynamic curvature $R$ as the correlation volume ${\xi }^d$ and also as a measure of underlying statistical interactions. To this end, we set up a two-parameter axial next nearest neighbor Ising (ANNNI) chain featuring two next nearest neighbor (nnn) and a nearest neighbor (nn) interaction. Competing interactions and resulting frustrations engender a rich phase behavior including a crossover between two ferrimagnetic subphases. We show that $R$ attests to all its conjectured attributes with valuable physical insights into the character of mesoscopic fluctuating substructures. In a remarkable demonstration of its relevance at a far-from-critical point, $R$ is shown to resolve a hitherto unnoticed tricky issue involving $\xi$. A physically transparent expression for the zero field $R$ helps bring into focus the pivotal role played by some third order fluctuation moments.

We study the angular diffusion in a classical $d-\mathrm{dimensional}$ inertial XY model with interactions decaying with the distance between spins as $r^{-\alpha }$, with $\alpha ⩾0$. After a very short-time ballistic regime, with ${\sigma }_{\theta }^2\sim t^2$, a superdiffusive regime, for which ${\sigma }_{\theta }^2\sim t^{{\alpha }_D}$, with ${\alpha }_D\simeq 1\text{.}45$ is observed, whose duration covers an initial quasistationary state and its transition to a second plateau characterized by the Boltzmann-Gibbs temperature $T_{\text{BG}}$. Long after $T_{\text{BG}}$ is reached, a crossover to normal diffusion, ${\sigma }_{\theta }^2\sim t$, is observed. We relate via the expression ${\alpha }_D=2/(3-q)$, the anomalous diffusion exponent ${\alpha }_D$ with the entropic index $q$ characterizing the time-averaged angles and momenta probability distribution functions (pdfs), which are given by the so called $q-\mathrm{Gaussian}$ distributions, $f_q\left(x\right)\propto e_q(-\beta x^2)$, where $e_q\left(u\right)\equiv {[1+(1-q)u]}^{\frac{1}{1-q}}$ ($e_1\left(u\right)=exp\left(u\right)$). For fixed size $N$ and large enough times, the index $q_{\theta }$ characterizing the angles pdf approaches unity, thus indicating a final relaxation to Boltzmann-Gibbs equilibrium. For fixed time and large enough $N$, the crossover occurs in the opposite sense.

We study chaotic scattering phenomena in the paradigmatic Hénon-Heiles Hamiltonian when a rotating external force is applied. This analysis builds on and extends our previous work on the relativistic unperturbed case and the classical case with equal forcing terms affecting the equations of motion. In this more realistic scenario with a rotating external forcing, we compare the results obtained in the classical energy regime with those in the relativistic regime. Our comparison focuses on the analysis of the exit basins, as well as resonant and trapping phenomena, which correspond to fast and slow escape times, respectively. In addition to the dependence on the particle energy $E$, we also investigate the system's behavior at both low and high perturbing frequencies $\omega$. Notably, when the particles are in the relativistic regime, we observe a shift in the evolution of the resonant frequency as a function of both $E$ and $\omega$.

It is known that on directed graphs, the correlations between neighbors of a given site vanish and thus simple mean-field-like arguments can be used to describe exactly the behavior of Ising-like systems. We analyze heterogeneous modifications of such models where a fraction of agents is driven by either voter or antivoter dynamics, and align (voter) or antialign (antivoter) with a randomly chosen out-neighbor. It turns out that voter agents do not affect the dynamics of the model, and it behaves like a pure Ising model. Antivoter agents have a stronger impact since they act as a kind of noise, which weakens a ferromagnetic ordering. Only when Ising spins are driven by the heat-bath dynamics, the behavior of the model is correctly described by the mean-field approximation. The Metropolis dynamics generates some additional correlations that render the mean-field approach approximate. Simulations on annealed networks agree with the mean-field approximation but for the model with antivoters and with the Metropolis dynamics only its heterogeneous version provides such an agreement. Calculation of the Binder cumulant confirms that critical points in our models with the heat-bath dynamics belong to the Ising mean-field universality class. For the Metropolis dynamics, the phase transition is most likely discontinuous, at least for not too many antivoters.

As changing climates transform the landscape of wildfire management and suppression, agencies are faced with difficult resource allocation decisions. We analyze tradeoffs in temporal resource allocation using a simple but robust Markov model of a wildfire under suppression: the birth-death-suppression process. Though the model is not spatial, its stochastic nature and rich temporal structure make it broadly applicable in describing the dynamic evolution of a fire including ignition, the effect of adverse conditions, and the effect of external suppression. With strong analytical and numerical control of the probabilities of outcomes, we construct classes of processes which analogize common wildfire suppression scenarios and determine aspects of optimal suppression allocations. We model problems which include resource management in changing conditions, the effect of resource mobilization delay, and allocation under uncertainty about future events. Our results are consistent with modern resource management and suppression practices in wildland fire.

Studies of the static Stark effect are reported for the multiplets at 615.8, 645.6, 777.6, 822.4, 844.9, and 926.5 nm (vacuum wavelengths) of atomic oxygen, which are important for various applications. From the Stark patterns recorded at very high electric fields up to 780 kV/cm, we have determined the differences of the scalar polarizabilities $\mathrm{\Delta }{\alpha }_0$ and the tensor polarizabilities ${\alpha }_2$ for the investigated lines. For most of the lines we present the first-obtained experimental results. The Stark broadening of the studied lines by non-neutral plasmas in the ion density range of ${10}^{13}-{10}^{19}{\mathrm{cm}}^{-3}$ was analyzed based on these results. The quasistatic approximation and three distinct microfield distributions were employed, which corresponded to different types of coupling in the plasma, namely the Holtzmark, Mayer, and irregular cluster distributions. Ion broadened widths and shifts were obtained from the simulated line shapes for different microfield distributions and ion densities. The analysis of these outcomes enabled us to determine their correlations with the independently determined $\mathrm{\Delta }{\alpha }_0$ and microfield distribution, ultimately enabling us to ascertain the contour of the microfield distribution itself.

Magnetic nanostructures find application in diverse technological domains, and their behavior is significantly influenced by long-range dipolar interactions. However, simulating these systems using the traditional Metropolis Monte Carlo method poses high computational demand. Several methods, including the clock, stochastic cutoff, and Tomita approaches, can reduce the computational burden of simulating 2D systems with dipolar interactions. Although these three methods rely on distinct theoretical concepts, they all achieve complexity reduction by a common strategy. Instead of calculating the energy difference between a spin and all its neighbors, they evaluate the energy difference with only a limited number of randomly chosen neighbors. This is achieved through methods like the dynamic thinning and Fukui-Todo techniques. In this article, we compared the performance of the clock, SCO, Tomita, and Metropolis methods near the critical point of the dipolar triangular lattice to identify the most suitable algorithm for this type of simulation. Our findings show that while these methods are less suitable for simulating this system in their untuned implementation, incorporating the boxing near-neighbors method and overrelaxation moves makes them significantly more efficient and better suited than the Metropolis method with overrelaxation.

Active particles are self-propelled microswimmers with potential applications in biomedicine and microfluidics. With recent advances in manufacturing technology, it is now possible to experimentally realize soft and flexible artificial microswimmers, which afford more complex dynamics and offer exciting new routes for trajectory control versus traditional rigid designs. Inspired by landmark work simulating the dynamics of flexible active filaments, we present a simple method to study the dynamics of active poroelastic microbots of general shape. Focusing on filament designs, we predict that sufficiently flexible “Janus pusher” filaments may exhibit oscillating dynamics as they swim forward and that under certain conditions, “Saturn” filaments may perform locomotion on spiral trajectories and exhibit “run-and-tumble” dynamics reminiscent of bacterial motion. Finally, we consider a simple extension of our framework that qualitatively incorporates stimuli-responsive hydrogel behavior, which we demonstrate may be used to steer Janus rods. Our results suggest that flexibility and responsivity may play a key role in expanding the functionality of active particles in intelligent artificial microswimming studies.

Growing experimental evidence shows that cell monolayers can induce long-lived perturbations to their environment, akin to footprints, which in turn influence the global dynamics of the system. Inspired by these observations, we propose a comprehensive theoretical framework to describe systems where an active field dynamically interacts with a non-advected footprint field, deposited by the active field. We derive the corresponding general hydrodynamics for both polar and nematic fields. Our findings reveal that the dynamic coupling to a footprint field induces remarkable effects absent in classical active hydrodynamics, such as symmetry-dependent modifications to the isotropic-ordered transition, alterations in spontaneous flow transitions, and initial condition-dependent aging dynamics characterized by long-lived transient states. Our results suggest that footprint deposition could be a key mechanism determining the dynamical phases of cellular systems, or more generally active systems inducing long-lived perturbations to their environment.

We consider the hydrodynamic origin of anomalous current fluctuations in a family of stochastic charged cellular automata. Using ballistic macroscopic fluctuation theory, we study both large and typical fluctuations of the charge current and reproduce microscopic results available for the deterministic single-file limit of the models. Our results indicate that, in general, initial fluctuations propagated by Euler equations fully characterize anomalous fluctuations on both scales. In the stochastic case, we find an additional contribution to typical fluctuations and conjecture the functional form of the typical probability distribution, which agrees with numerical simulations.

Will the strategy of resetting help a stochastic process to reach its target efficiently, with its environment continually toggling between a strongly favorable and an unfavorable (or weakly favorable) state? A diffusive run-and-tumble motion, transport of molecular motors on or off a filament, and motion under flashing optical traps are special examples of such state toggling. For any general process with toggling under Poisson reset, we derive a mathematical condition for continuous transitions where the advantage rendered by resetting vanishes. For the case of diffusive motion with linear potentials of unequal strength, we present exact solutions, which reveal that there is quite generically a re-entrance of the advantage of resetting as a function of the strength of the strongly favorable potential. This result is shown to be valid for quadratic potential traps by using the general condition of transition.

Ocean currents exhibit strong time dependence at all scales that influences physical and biochemical dynamics. Network approaches to fluid transport permit to address explicitly how connectivity across the seascape is affected by the spatiotemporal variability of currents. However, such temporal aspect is mostly neglected, relying on a static representation of the flow. We here investigate the role of current variability on networks describing physical transport across the Mediterranean basin. We first focus on degree distributions and community structure comparing ensembles of temporal networks that explicitly resolve time dependence and their aggregated, i.e., time-averaged, counterparts. Furthermore, we explore the implications of the two approaches in a simple reaction dispersal model for a generic tracer. Our analysis evidences that aggregation induces structural network changes that cannot be easily avoided, not even introducing a pruning of the aggregated adjacency matrix. We also highlight that, depending on the time scales considered, the importance of the temporal features of the networks can vary significantly. Finally, we find that the tracer evolution obtained from a temporal dispersal kernel cannot be always approximated by aggregated adjacency matrices, in particular during transients of the dynamics.

There are many methods to estimate the quasistationary infected fraction of the SIS process on (random) graphs. A challenge is to adequately incorporate correlations, which is especially important in sparse graphs. Methods typically are either significantly biased in sparse graphs, or computationally very demanding already for small network sizes. The former applies to heterogeneous mean field and to the $N$-intertwined mean field approximation, the latter to most higher order approximations. In this paper we introduce a method to determine the infected fraction in sparse graphs, which we test on Erdős-Rényi graphs. Our method is based on degree pairs, does take into account correlations, and gives accurate estimates. At the same time, computations are very feasible and can easily be done even for large networks.

We study the allocation of synthetic portfolios under hierarchical nested, one-factor, and diagonal structures of the population covariance matrix in a high-dimensional scenario. The noise reduction approaches for the sample realizations are based on random matrices, free probability, deterministic equivalents, and their combination with a data science hierarchical method known as two-step covariance estimators. The financial performance metrics from the simulations are compared with empirical data from companies comprising the S&P 500 index using a moving window and walk-forward analysis. The portfolio allocation strategies analyzed include the minimum variance portfolio (both with and without short-selling constraints) and the hierarchical risk parity approach. Our proposed hierarchical nested covariance model shows signatures of complex system interactions. The empirical financial data reproduce stylized portfolio facts observed in the complex and one-factor covariance models. The two-step estimators proposed here improve several financial metrics under the analyzed investment strategies. The results pave the way for new risk management and diversification approaches when the number of assets is of the same order as the number of transaction days in the investment portfolio.

Phytophthora species are plant pathogens that cause considerable damage to agrosystems and ecosystems, and have a major impact on the economy. Infection occurs when their biflagellate zoospores move and reach a root on which they aggregate. However, the communication between the plant and the zoospores and how this communication modifies the behavior of the swimming zoospores is not fully understood. Here we use a microfluidic device incorporating a growing Arabidopsis thaliana root to study the real-time kinetics of Phytophthora parasitica zoospores approaching the root and accumulating (or aggregating) around a specific area called the elongation zone. We show that zoospore kinetics are modified only below a distance of a few hundred microns from the aggregation center, with a decrease in velocity coupled to an increase in the number of turns taken. Furthermore, we show that the rate of aggregation is constant throughout a one-hour experiment, and is dependent on zoospore density. This rate is consistent with the fact that zoospores randomly encounter the region close to the elongation zone, a result compatible with an absence of attraction beyond a few hundred microns. Finally, we show that in our configuration, this absence of attraction can be explained by a residual flow responsible for limiting the diffusion of the signal supposedly emitted by the root to a boundary layer of a few hundred microns.

Harmonizing serology measurements (i.e., rendering them interchangeable) is critical for comparing results across different diagnostics platforms, developing associated reference materials, and thereby informing medical decisions. However, the theoretical foundations of such tasks have yet to be fully explored in terms of antibody thermodynamics and uncertainty quantification (UQ). In the context of SARS-CoV-2, for example, this has restricted the usefulness of standards currently deployed, limited the scope of materials considered as viable standards, and ultimately decreased confidence in serology. To address these problems, we develop rigorous theories of antibody normalization and harmonization. We begin by proposing a mathematical definition of harmonization equipped with structure needed to quantify uncertainty associated with the choice of standard, assay, etc. We then show how a thermodynamic description of serology measurements (i) relates this structure to the Gibbs free energy of antibody binding, and thereby (ii) induces a regression analysis that directly harmonizes measurements. We supplement this with an optimization-based normalization (not harmonization!) method that validates consistency between the behavior of a reference material and biological samples. A key result of these analyses is that under physically reasonable conditions, the choice of reference material does not increase uncertainty associated with harmonization. We validate main ideas via an interlab study that considers monoclonal antibodies as a reference for SARS-CoV-2 serology measurements and discuss connections to correlates of protection.