Papers

Standard benchmark tests for numerical magnetohydrodynamics codes focus on one- and two-dimensional problems that ignore the possibility of dynamos, i.e., an exponential instability converting kinetic energy into magnetic energy. They also tend to ignore magnetic helicity conservation that can have dramatic effects in periodic domains. The existence of magnetic diffusion is crucial for dynamos, as was exemplified by the impossibility of finding dynamo solutions when using Euler potentials, which is a perfectly valid approach in the absence of magnetic diffusion. The Roberts flows~I, II, III, and IV are two-dimensional flows that support three-dimensional dynamos, all of which are also large-scale dynamos in the sense that their planar averages are of significant strengths, even though flow~II is pointwise non-helical. Here we demonstrate the numerical convergence properties of solutions obtained using discretisation schemes of second, sixth, and tenth order in the mesh width in the Pencil Code, compare the results with the SPH-based SWIFT code, and present a list of averaged quantities that facilitate their use in characterising the solutions as benchmarks. In addition, we present a procedure to obtain two-dimensional time-independent magnetic field visualisation for all Roberts flows, and perform per-pixel comparison between the Pencil and SWIFT codes. The rich combination of features makes Roberts flows an excellent dynamo benchmark for the direct numerical simulations of magnetohydrodynamics equations.

We show that a single Lagrangian trajectory encodes not only temporal but also spatial cascade structure–recoverable without access to a spatially resolved Eulerian velocity field. We report a Markov-Switching Multifractal (MSM) framework that infers hidden multi-scale cascade states directly from single-particle trajectories. Kolmogorov scaling fixes the model's switching rates, leaving a single calibrated intermittency parameter; exact Bayesian filtering then returns the posterior of the layer-resolved cascade state along the trajectory. When we apply the MSM framework independently to each trajectory of a particle pair, we find that the inferred states carry genuine spatial information: consistent with Richardson's locality picture, the Kolmogorov $r^{1/3}$ scaling is preserved when fine-scale layers are randomized, but collapses once the randomization crosses the layer set by the pair separation. Single trajectories thus encode the spatial organization of the cascade, opening a route to cascade diagnostics for turbulent flows accessible primarily through sparse or individual Lagrangian trajectories–from particle-tracking velocimetry to atmospheric and oceanographic tracking–where the instantaneous Eulerian field is out of reach.

We study a semiflexible magnetic polymer in which each monomer carries a two-state spin, giving rise to coupled conformational, stiffness, and magnetic degrees of freedom. The model provides a minimal representation of nucleoprotein complexes in vivo, in which DNA bending rigidity and bridging proteins must be simultaneously accommodated. We employ Monte Carlo simulations on a face-centered-cubic (FCC) lattice, complemented by a variational free-energy upper-bound approach, to characterize the zero-field phase diagram as a function of bending stiffness and temperature. At low stiffness, we identify a first-order transition combining coil-to-globule collapse with a ferromagnetic-to-paramagnetic transition. At lower temperatures, an additional first-order transition separates the disordered globule from an ordered crystal characterized by nematic-like orientational order of the bond tangents. At high stiffness, the globule disappears, resulting in a direct first-order coil-to-crystal transition. These results demonstrate how chain stiffness and magnetic degrees of freedom couple to polymer conformation and reorganize the zero-field phase diagram. Our model connects the two limiting cases of flexible magnetic polymers and semiflexible homopolymers, the latter corresponding to the strong-field limit in which all spins are aligned.

Hot coronae are key regulators in Milky Way (MW)-type galaxy formation and are thought to emerge once a galaxy's dark matter halo crosses a critical mass threshold, $M_{\mathrm{vir}}\sim10^{12} {\mathrm{M}}_\odot$, so that accretion shocks can heat infalling gas to virial temperatures, $T_{\mathrm{vir}}$. Here we investigate the roles of stellar feedback and accretion shocks in establishing the circumgalactic medium (CGM) around MW-mass galaxies using the VINTERGATAN cosmological simulation suite. We find that stellar feedback drives the early formation of a hot CGM, with $T_{\mathrm{CGM}}\sim T_{\mathrm{vir}}$. By contrast, in feedback-free simulations, accretion shocks alone do not produce a CGM with comparably high average temperatures at early epochs ($z>2$), independent of gas metallicity. We show that CGM virialisation (defined when halo-gas cooling time exceeds the free-fall time) begins with the emergence of a hot corona ($T>10^5$ K) that can appear as early as $z\approx3$ at virial radius, $R_{\mathrm{vir}}$, when feedback is included. This is much earlier than predicted by accretion-shock theory or feedback-free models ($z\lesssim1$). While accretion shocks alone produce an inside-out virialisation pattern, feedback can alter this behaviour, driving a rapid transition ($\lesssim0.5$ Gyr) that is either outside-in or nearly simultaneous throughout the halo. Finally, these early-forming coronae are largely stable from formation through to the present day, but can appear to evolve simply because the definition of $R_{\mathrm{vir}}$ grows with time. This 'CGM pseudo-evolution' is analogous to the pseudo-evolution of dark matter haloes and should be taken into account when interpreting the evolution of hot coronae.

I propose a simple visualization for pure two-qubit states, constructed from three components: two Bloch balls for the marginal qubits (the mixed states obtained by ignoring the other qubit, respectively) and an entanglement component consisting of a rotoreflection and a contraction. I prove that this picture accurately captures the local and global structure of the relevant state space without gaps or degeneracies: the underlying parametrization is a global diffeomorphism onto the state space. Finally, I use this picture to visualize conditional states, the entanglement degree(s) of freedom, and single qubit rotations.

Radioactive nuclei with extreme proton-to-neutron ratios provide unique probes of nuclear structure, fundamental symmetries, astrophysical processes, and applications such as medicine. However, their short half-lives and low production rates mean that experiments must work with few desired ions, often amid contaminant backgrounds exceeding the signal by six orders of magnitude. Efficiently isolating, transporting, and delivering these ions through multi-stage apparatus is therefore a central challenge. We introduce a differentiable probabilistic surrogate for ion-beam transport in regimes dominated by particle loss. The model jointly captures the survival probability of ions along the beamline and the evolving phase-space distribution of the surviving particles by combining discrete-time survival analysis with conditional normalizing flows. Applied to a high-dimensional electrostatic beamline, the resulting likelihood enables gradient-based optimization of operating voltages and inference of beam properties from sparse detector observations. Once trained, the surrogate provides millisecond-scale differentiable predictions, transforming particle-tracking simulations with hard losses into an inference-ready model for experimental design, beamline optimization, and future digital twins of experiments with rare nuclei.

A gap between achievable rates and converse bounds persists for energy-constrained bosonic communication in the presence of excess noise. We derive a unified adaptive weak-converse bound for thermal attenuation, noisy amplification, and additive Gaussian noise under an unconditional mean transmitted-photon-number constraint. The bound applies to arbitrary adaptive protocols with quantum memories and constrains the two-way quantum, entanglement-distribution, private, and secret-key capacities. It vanishes throughout the entanglement-breaking region and approaches the corresponding PLOB bound at infinite energy. At finite energy, it improves the evaluated Gaussian squashed-entanglement and PLOB bounds in relevant parameter regimes, while comparison with hashing rates quantifies the remaining gap to achievability. The result provides a common energy-dependent benchmark for noisy bosonic Gaussian channels and extends finite-energy converse methods beyond the quantum-limited setting.

Structures crossing active faults are subjected to permanent ground deformation that can impose severe demands on infrastructure. Although probabilistic fault displacement hazard analysis (PFDHA) has advanced considerably, existing principal fault displacement models use aggregate displacement, implicitly assuming that overlapping rupture strands contribute simultaneously to site demand. This assumption is suitable for regional hazard characterization but does not represent finite structural dimensions, where only a subset of ruptures may intersect a structure. This study presents a probabilistic framework that reformulates aggregate principal displacement into a rupture-level representation for infrastructure-specific hazard assessment. Using the Fault Displacement Hazard Initiative (FDHI) database of 75 surface-rupturing earthquakes, principal rupture strands are reconstructed from mapped rupture geometries and displacement observations using event- and segment-based coordinate systems. The framework models three quantities: (1) the number of overlapping principal ruptures, (2) partitioning of aggregate displacement among individual rupture strands while preserving the aggregate displacement budget, and (3) the spatial distribution of rupture strands relative to the primary fault trace. These components are integrated into a generalized PFDHA formulation accounting for earthquake magnitude, style of faulting, rupture curvature, structural footprint, and fault-trace location uncertainty. Benchmark fault-crossing applications demonstrate the framework's ability to estimate the probability that individual rupture strands intersect a structure and the associated displacement demand. The framework provides a physically consistent extension of aggregate-based models and a basis for infrastructure-specific PFDHA that explicitly accounts for rupture geometry and finite crossing dimensions.

Petri nets have been used to describe chemical processes such as reactions.They map well to chemistry: Places are the bonds between atoms and the free valence of each atom, a token is a unit of bond order, a transition forms or breaks a bond, the conserved quantities are the valence budgets of the atoms, and the enabling rule is the valence rule. These semantics are not guaranteed by learned models of reactions or neural networks that are built on Petri nets that use the net as a scaffold for message passing. Here, we ask what architecture remains a Petri net for every value of its weights. We find the answer in the theory, where all semantics of a net share the firing form $m^\prime=m+C\sigma$, locality, as enabling reads only the inputs of a transition, and the enabling rule, and we prove that conservation forces the firing form and that non-negativity forces the enabling rule on local rate laws. This leaves free the rate law, which is the propensity of each transition to fire. We introduce Neural Petri Flow, which learns this rate law, or a readout for classification, and hard-wires the rest as parameter-free layers. On what we denote a valence net, atom mapping, reaction classification, and forward prediction become three tasks on one firing vector. Without training, the minimum firing vector maps 88.8% of the curated Golden set against 85.6% for RXNMapper, and 88.7 against 77.9% of the enzymatic reactions of EnzymeMap. On USPTO-480K, NPF trained on these firing vectors predicts 87.7% of the products and 67.4% when trained on a 1% subset of the training reactions. EC numbers of ECREACT are predicted at the third level for 90.2% of reactions, 5.6 points ahead of the best published method. With electrons as tokens, the same token game predicts 90.5% of the elementary steps of FlowER first, ahead of the published baseline, and every top-1 prediction is a valid molecule without a filter.

The moving-coil meter provides a simple context in which to model deterministic electromechanical dynamics, thermal fluctuations, and electronic readout. The movement is a linear electromechanical system in which electrical resistance and intrinsic mechanical damping act as two independent dissipative channels, each accompanied by thermal fluctuations. The stored energy provides a Lyapunov function for stability, and the algebraic Lyapunov equation yields the stationary covariance in closed form. The result exhibits equipartition among the energy terms; damping is shown to reshape the temporal memory of the fluctuations without changing their steady-state variance, giving a thermal floor of a few picoamperes for a representative movement. Exact stochastic integration reproduces the stationary covariance and autocorrelation structure; the covariance is also recovered by spectral integration. Embedding the movement in an active current-drive readout shifts attention to the noise of the complete readout, which a design-stage estimate expresses as a few parts per million of full scale, growing as the input sensitivity increases. The example illustrates that measurement sensitivity and resolution cannot be considered independently. The treatment is aimed at master's-level students in physics with some background in statistical physics and stochastic processes, and basic familiarity with analog electronics.

Peixoto et al. (arXiv:2602.16937v2) stress that graph-based formulations can express any interaction model, a point the literature on higher-order networks (HONs) has often overlooked. We agree with much of their critique. We argue that expressiveness, however, might not be the main reason hypergraphs are often preferred. A network is read from a model rather than assumed, and to properly study the role of structure-one of the most basic questions in complex systems research-one needs a representation that keeps that structure separate from the functional form of the interactions. We show that such representation is in general a directed hypergraph (equivalently, a directed factor graph), with one hyperarc per interaction term, recovering a (directed) graph for pairwise interactions, and an undirected hypergraph for symmetric, higher-order ones. Most of the HONs literature has focused so far on symmetric interactions, which, in light of what established here, might explain why there (undirected) hypergraphs are regularly presented as the natural representation for systems with higher-order interactions.

Accurate numerical simulation of nonlinear partial differential equation initial value problems typically requires high-dimensional full-order discretizations at substantial computational cost. While purely data-driven surrogates can speed up prediction, these "black box" approximations do not necessarily satisfy the governing equations during inference. In this paper we present a hybrid framework, which combines neural operators with projection-based reduced-order modeling for nonlinear PDE simulation. The key idea is to train a neural operator to map the initial condition to a time-dependent reduced subspace, and then evolve low-dimensional reduced coordinates by solving the governing dynamical equations projected on this learned moving trial subspace. In this way, the network predicts an adaptive reduced representation rather than the full trajectory directly, while the online solver preserves a physics-based reduced evolution. We test this method on two representative nonlinear PDEs: the viscous Burgers' equation and the Fisher–KPP reaction–diffusion equation. In both cases, the proposed approach achieves successful reduced-order simulation with substantial wall-clock speedup relative to the corresponding full-order solver while maintaining relative errors on the order of \(10^{-2}\) to \(10^{-1}\) across the tested resolutions. These results demonstrate that a low-dimensional learned time-dependent reduced subspace can be used to predict solutions to nonlinear PDEs while retaining the advantages of projection-based reduced dynamics. Overall, this method provides a promising framework for fast and physically grounded data-assisted simulation of nonlinear PDEs.

In September 2026 the U.S. Department of War released an infrared video recorded from a military aircraft over the Middle East, with a mission report describing six small spherical objects moving at an estimated 480 miles per hour. We measure the brightness of each object in every frame. All six brighten and dim steadily at 7.1-7.9 times per second, each at its own rate and out of step with the others. Tests with planted constant-brightness objects show that neither compression nor the sensor produces this flicker. The same analysis, applied to the Galileo Project's ground-based infrared cameras near Las Vegas, finds flying animals beating steadily at 3.4-10.0 times per second, within the range of bird wingbeats. A nearly stationary object at 60-80% of the aircraft's height would produce the reported ground-projected speed of 480 miles per hour. The six objects are best explained as a small flock of birds flying nearer to the aircraft than the crew assumed. The same analysis also finds seven objects with similar beats, at 3.0-11.0 times per second, in four other released military videos. More broadly, this work shows that even heavily redacted videos of unidentified anomalous phenomena can be analyzed using reproducible, quantitative methods to test specific hypotheses. Releasing the redacted sensor and platform data would provide information that could refute these hypotheses.

We first prove a rigidity result for uniformly rotating vortex patch solutions to Euler equations in bounded simply-connected planar domains. Namely, if a bounded simply-connected domain with real-analytic boundary contains a nontrivial vortex patch that remains strictly inside the domain while undergoing a uniform rigid rotation, then the ambient domain must be a disc centered at the rotation center. The proof combines the rotating-frame formulation with harmonic unique continuation and the generalized Schwarz's reflection principle across analytic boundary arcs. These tools imply that, unless the outer domain is rotationally invariant, the velocity must vanish in an open region surrounding the patch, contradicting the nonzero circulation generated by the patch. In the same spirit, we show that only circular domains possess a circular point vortex orbit. As a second result, we construct quasi-periodic vortex patch solutions to the Euler equations in domains that are small perturbations of the unit disc $\mathbb{D}$. The amplitudes of the solutions are linked to the size of the domain deformation, allowing the classical Rankine vortices $b\cdot\mathbb{D}$, $b\in(0,1)$, to be regarded as equilibrium states. Our analysis is perturbative with respect to the construction developed in the undeformed case, and the resulting quasi-periodic solutions exist for most values of the inner radius $b$. This provides the first construction of quasi-periodic vortex patch solutions to the Euler equations in non-radial domains. Moreover, combined with the rigidity result established above, this shows that, in the periodic case, these solutions cannot arise from a purely rigid rotation.

We investigate how spatially uniform, time-modulated heating can be used to stabilize a thin liquid mirror in microgravity. Periodic heating generates oscillatory thermocapillary flows that suppress the instability associated with sustained heating, allowing the free surface to relax through the combined effects of thermal radiation, thermocapillary transport, and capillarity. Using multiple-scale analysis of a long-wave model, we derive the coupled surface-temperature evolution over many heating cycles. A subsequent linear stability analysis yields the stability criteria and decay times of individual surface modes. Numerical simulations validate the analytical predictions and demonstrate stabilization of meter-scale surface deformations over timescales of days.

The under-the-barrier recollision pathway during the tunneling ionization of an atom, despite its minuscule amplitude, leaves its signature on the observable photoelectron momentum distribution due to a phase shift relative to the direct ionization path, that can be interpreted as a tunneling time delay. We investigate how this time delay is modified in the nonadiabatic regime, where the tunneling barrier evolves dynamically during the ionization process. Our analysis is based on a simple model of a one-dimensional short-range potential driven by a strong laser pulse which remains amenable to a systematic analytical treatment with the strong field approximation (SFA). We show a significant contribution of the high-order SFA corrections, which describes the recollision channel with recombination and further ionization, and is additional to the rescattering. Whereas the recombination channel dominates over the rescattering for the tunneling time delay in the quasistatic regime at relatively large laser fields, their contributions are reversed in weak fields.

Orbitronics has emerged as a promising approach for generating orbital currents using low cost, abundant materials through the orbital Hall effect. Since orbital current cannot directly interact with the local magnetization of a conventional ferromagnet, a non-magnetic layer with strong spin orbit coupling is required for converting orbital current into spin current. However, the propagation of orbital current across the interface between an orbital source and an orbital to spin converter remains poorly understood. Here, we tuned the Ru/Pt orbital interface from an abrupt interface to a graded interface by introducing a controlled RuPt intermixed layer and quantified the resulting damping like torque efficiencies. A small, intermixed thickness of 0.26 nm enhances the damping like torque efficiency by 15% compared with the abrupt Ru/Pt interface and by 70% compared with the Pt reference. Further increasing the intermixing thickness progressively suppresses the torque efficiency. We attribute this nonmonotonic behavior to the competition between enhanced orbital transmission through a gradual crystal field transition and orbital current dephasing due to alloy disorder and scattering. Our results demonstrate that the orbital-source/converter interface is an active element for controlling orbital transport and provides an experimentally accessible approach for optimizing orbital current transmission and torque generation in orbitronic heterostructures.

The development of national policy and legal outputs toward space resource utilization across several countries accelerated after the introduction of the Artemis Accords in 2020. The announcement also further intensified the debate about the necessity of specifying a regime for using space resources on the international level. In 2021 the Legal Subcommittee of the UN Committee on the Peaceful Uses of Outer Space finally approved the establishment of its Working Group on Legal Aspects of Space Resource Activities. Therefore, this chapter builds on diplomacy under conditions of polycentric governance and aims to map the diversity of actors' ideas and interests about governance for space resource utilization on the level of the Working Group. The research process follows qualitative analysis methodology and assigns the results into the matrix of prospective alternatives for governing space resources. The available materials of the Working Group including reports and recordings, constitute the core empirical data used in the analysis.

Coulomb friction is a non-associated law: it contains a threshold that depends on the contact pressure, i.e. a stress that is not conjugate to any internal variable, hence it admits no dissipation potential in the sense of generalized standard materials (GSM), and the incremental frictional contact problem is not a minimization problem. In this paper, we propose an alternative friction model that retains Coulomb-like behavior while being associated and fully GSM-compatible. Key to our formulation is the introduction of a new internal variable motivated by the microscopic contact state of rough surfaces, whose evolution is driven by the plastic deformation of the asperities. Under a non-decreasing contact pressure and below a threshold pressure, the model recovers Coulomb friction up to an asperity-scale penetration depth. This depth is physically meaningful and can be calibrated to reproduce known tribology results. Under decreasing contact pressures, the model exhibits a memory effect due to irreversible plastic deformation of the asperities, whereas above the pressure threshold it yields a saturation of the frictional traction. We present both penalty and Lagrange multiplier formulations of the model. A 2D finite element implementation of the penalty approach confirms excellent agreement with the classical Coulomb predictions across two benchmark tests.

Classical theories of heat conduction in magnetized plasma predict that thermal transport, mediated by Coulomb collisions of electrons, occurs predominantly along magnetic field lines. Recent theoretical and computational studies challenge this description for weakly collisional, magnetized plasmas in which thermal pressure dominates magnetic pressure (so-called high-e̱ta} plasmas). Such plasmas, which include the intracluster medium of galaxy clusters and inertial-confinement-fusion hot spots, are thought to be susceptible to kinetic-scale microinstabilities that can suppress parallel heat conduction. Revised theories of heat conduction accounting for these instabilities have been proposed, but experimental data with which to benchmark them are lacking. Here, we report experiments at the Orion laser facility in which the temporal evolution of temperature of a high-e̱ta}, weakly collisional plasma is sensitive primarily to the thermal conductivity along its initially quasi-laminar magnetic field. We characterize the temperature, density and magnetic field using x-ray spectroscopy and imaging, and proton imaging. Once stochastic magnetic fluctuations develop, the measured temperature evolution requires suppression of thermal conduction by at least an order of magnitude relative to classical predictions, providing the first laboratory evidence linking magnetic microturbulence with suppression of heat conduction in a high-e̱ta} plasma.

We propose G-EXO, an inverse-designed intrastromal corneal exoskeleton for keratoconus. Unlike conventional intracorneal ring approaches, G-EXO is conceived as a patient-customizable, sectorial and anisotropic scaffold whose geometry and stiffness are optimized to redistribute corneal deformation and reduce asymmetric optical aberrations. We develop a three-dimensional finite-element screening model with localized ectatic weakening, physiological intraocular pressure, and an intrastromal reinforcement domain. A representative design produced a modest but consistent reduction in pressure-induced coma, with a mesh-refined estimate of about 5%, while displacement and stress metrics were more numerically stable. The study is presented as a computational feasibility framework rather than a clinical validation. Its main contribution is to formulate intrastromal reinforcement as a free-form optical-biomechanical inverse-design problem. Patient-specific tomography, nonlinear anisotropic corneal mechanics, full optical modeling, and ex-vivo validation are identified as the next steps.

Dissolved oxygen is important for the ocean's ecosystems and biogeochemical cycles. Yet, Earth System Models (ESMs) vary in their simulations of the present-day and future ocean oxygen inventory. To narrow down the uncertainty in estimates of ocean oxygen content, we train a conditional generative diffusion model on outputs of a multi-model ESM ensemble to learn the conditional distribution of upper-ocean oxygen given physical input variables such as temperature and salinity. This generative model has considerable skill in the Atlantic and Southern Oceans, and can generate realistic oxygen samples under conditions not seen in the training data. We validate this model using observational datasets, and use it to generate oxygen fields for models without oxygen data using only temperature and salinity as conditioning inputs. This physics-conditioned extrapolation suggests that model biases in the tropical Pacific Oxygen Minimum Zone may be smaller than currently assumed when considering a larger set of physical ocean states. Our approach provides a complementary way to represent probabilistic multi-model climate distributions.

We construct magnetohydrostatic equilibria in rotationally symmetric toroidal domains whose magnetic lines define an $(m,n)$ Seifert fibration for arbitrary coprime integers $m,n$.

Machine learning can accelerate molecular discovery by designing molecules and planning experiments. However, many scientific challenges demand molecules with very rare properties, and in this sparse setting, existing algorithms offer little gain over random guessing. We propose a method to efficiently search large regions of molecular space using algorithmically controlled stochastic synthesis. Rather than design, make and test individual molecules, we design and make complex mixtures, test them as a pool, then deconvolute the molecule-activity map. We optimize synthesis to encode maximal information. Theoretically, this approach can reduce the number of experiments required to find the optimal molecule among $d$ candidates from $\mathcal{O}(d)$ to $\mathcal{O}(\log d)$ or $\mathcal{O}(1)$. In simulation, on estimated protein fitness landscapes, it finds active molecules with an order of magnitude fewer experiments than existing Bayesian optimization methods.

Scientific productivity and collaboration are closely related, yet the causal direction between these two dimensions of academic careers remains unclear. Here we analyze the careers of 26,876 Brazilian researchers holding a nationally prestigious fellowship across multiple disciplines to test whether productivity shapes collaboration or the reverse. Focusing on total publications and distinct coauthors, we assess their directional relationship using a causal discovery framework based on asymmetries in conditional and distributional structure to determine the more plausible generative direction. Collaboration conditioned on productivity exhibits a highly regular mean-variance relation consistent with a geometric model, whereas the reverse conditioning yields a much less coherent pattern. Consistently, publication counts follow a lognormal distribution, whereas the coauthor distribution is reproduced by a geometric-lognormal mixture where productivity determines the expected number of collaborators. Surrogate tests indicate that the data are considerably more compatible with productivity shaping collaboration than with the reverse. The two quantities also accumulate asymmetrically: publication inequality rises steadily with career age, whereas coauthor inequality does not, indicating that advantage compounds more in output than in network breadth. Area-resolved analyses reveal a more nuanced picture, and although the direction where collaboration shapes productivity is never favored, the two directions are most clearly distinguished in areas more organized around laboratory and research-group models, where productivity inequality also grows most clearly. Among this cohort of prominent scientists, larger collaboration networks are thus more likely to reflect cumulative advantage in productivity than to independently generate greater output.

The conservative species weighting scheme enables the direct simulation Monte Carlo (DSMC) method to resolve trace species such as electrons and ions in weakly ionized flows. The present study extends this scheme to reacting flows, utilizing a split-merge concept for particles with species-dependent weights. Reactive collisions between differently weighted species are processed according to their respective weights, introducing the production of an integer number of product particles and a probabilistic approach applied to both reactant and product particles to satisfy mass conservation. The extended scheme was validated in single-cell dissociation and exchange reaction test cases, verifying mass and total energy conservation and the mole-fraction histories against conventional DSMC. This extension provides the electron number density required for predicting the onset of communication blackout at high altitudes during hypersonic reentry. The method was applied to the Orbital Re-entry Experiment (OREX) flight conditions from 105.0 km to 92.8 km altitude. Translational and internal temperatures showed good agreement with conventional DSMC results, validating the macroscopic flow-field computation. The scheme enabled continuous spatial distributions of trace electron number density, consistent with the communication blackout onset observed in the flight data. The computed ion current reproduced the overall trend of the in-flight electrostatic probe data. The influences of the ion recombination at the vehicle surface and of the uncertainty of the N + O associative-ionization rate were quantified, and the range of the computed currents encompassed the available measurements. The 105.0 km case extended the prediction beyond available measurements, consistent with the overall trend.

This series of papers studies the stability of accretion discs to the reflex motion of the central object under the gravitational pull of the disc itself. In this second paper, we use linear analysis to reproduce or discover seven distinct instabilities. We outline their defining characteristics and propose a classification. Of the four instabilities that had already been discovered, three are artificial: one does not survive the transition to modern surface density profiles such as power-laws, one is due to the location of the domain's boundaries, and one is due to the use of damping layers near the domain's boundaries. Only the SLING instability of Adams et al. (1989) and the three new 'cavity' instabilities seem capable of affecting accretion discs. All four can be described to a high degree of precision by WKB methods. They may drive eccentricity growth, affect planet migration, create spiral and crescent substructures in the absence of any planet, and boost accretion onto the central object. The issue is that the three artificial instabilities are extremely hard to avoid in simulations, and would bias the same observables. This suggests that our community may not have the tools to model the reflex motion of the central object yet.

If the mass distribution within an accretion disc is asymmetric, it will exert a gravitational pull on its central object. The central object must then move away from the origin of the reference frame, resulting in atypical gravitational pulls on the disc. The goal of the present series of papers is to investigate the stability of this feedback loop. In this first paper, we report a surprising finding: the choice of reference frame affects the number and nature of the waves and instabilities contained in the analytical equations. To show this, we use a simple example: the modes discovered by Sanchez et al. (2025, Appendix A.3). We provide the first analytical description of those modes, we explain their physical mechanism, and propose to call them 'bracket modes' because they arise from the bracket side of an integration by parts. We show that the same disc can be stable in the stellocentric reference frame but unstable in the barycentric reference frame. This is because the boundaries of the simulation domain are frame-dependent. To avoid this, one must model the entire disc all the way from the central cavity to the outer edge. But this is prohibitively expensive, and some discs do not have a central cavity anyway. Our results suggest that extreme caution is required when interpreting simulations of asymmetric discs, and that our community may not have the tools to model the reflex motion of the central object yet.

Phonon lasers amplify mechanical vibrations, serving as key tools for coherent acoustics research and applications such as ultrasensitive sensing and information processing, which has been extensively studied in Markovian systems but remains unexplored in non-Markovian ones. In this paper, we propose the nonreciprocal phonon laser in a coupled cavity system composed of nonlinear optomechanical resonator and degenerate optical parametric amplifier (DOPA) under the Markovian approximation. The mechanical gain and threshold power of the phonon laser are influenced by Sagnac-Fizeau effects, which leads to the unidirectional phonon laser and enhances the phonon laser performance via DOPA. Moreover, we generalize the nonreciprocal phonon laser to non-Markovian regimes. We find that non-Markovian effects can lead to the further enhancement of the phonon laser at a lower phonon laser threshold power with DOPA fixed. Our work reveals connections between low-power mechanical isolation and non-Markovian effects, offering a pathway to manipulate cavity optomechanical devices across a broader range of applications.

Using aerodynamic databases of progressively increasing complexity - from linear analytical theory to nonlinear separated RANS flows - we demonstrate that an autoencoder, trained solely by reconstruction, can spontaneously organize the latent representation according to physically meaningful variables and aerodynamic laws. Although the latent coordinates themselves vary under random initialization, the underlying physical organization is reproducible, even quantitatively. In addition, we present a three-dimensional latent space representation of the airfoil flow obtained by an unsupervised autoencoder with sequential training in which the inviscid field is decoupled from the boundary layer effects with the third latent variable strongly correlated with the Reynolds number and aerodynamic drag. Cross-validated regression and affine-alignment measures show that physical parameters and responses are strongly and linearly encoded in the learned coordinates, consistently across independent trainings.