Papers

Background. Aircraft-wildlife collisions are a documented aviation hazard. The FAA National Wildlife Strike Database is the main public source of U.S. reports, but raw extracts are large and rarely released as versioned aggregates. Methods. We convert the public FAA Access export (STRIKE_REPORTS) to SQLite, define a report as damaging when DAMAGE_LEVEL is one of M, M?, S, D, and fold rows into open aggregates (year, airport, species, aircraft, animal family) via hierarchical SPECIES_ID prefixes. We analyze all civil reports and a commercial subset (AC_MASS 3-5). Rates are per reported strike, not per flight hour, so every rate is conditional on a report having been filed. Trend statistics use complete incident years 1990-2025; cross-sectional facets cover the full seed, incident years 1990-2026. Results. Numbers are locked to the 2026-07-04 freeze. Reported strikes in 2025 reach a series high (24,458 total; 901 damaging). Species that dominate strike counts are not those with the highest damage rates: perching songbirds account for 41.1% of identified strikes but damage aircraft in 1.5% of reports (95% CI 1.4-1.6), whereas deer account for 0.7% but damage aircraft in 81.8% of reports (95% CI 79.8-83.7). Airports ranked by total strikes (led by Denver Intl) diverge from rankings by damaging strikes (led by Sacramento Intl); across the 452 airports with at least 50 reports, volume and conditional damage rate are negatively rank-correlated (Spearman rho = -0.44, p < 0.001); large stations plausibly capture minor events more completely, so this is not a hazard ranking. The share of reports labeled damaging declines over decades, consistent with improved reporting of minor events, while absolute damaging counts peak in 2025. Conclusions. Versioned open aggregates and a documented damage-rate analysis enable independent reuse; released under CC BY 4.0.

The Aragats High-Altitude Research Station (3200 m a.s.l.) combines meteorological, atmospheric electric, and particle flux measurements, providing a unique setting for investigating thunderstorm ground enhancements (TGEs) and recent environmental variability. We analyze one-minute observations from 2012 to 2025, first focusing on the meteorological and electric-field conditions controlling TGEs and then on trends in air temperature and daytime solar radiation. From an archive of 610 TGEs, a quality-controlled subset of 284 events with particle flux enhancements larger than 10 percent was selected. Comparisons of May, August, and October show that strong near surface electric fields alone are insufficient for frequent TGEs. May combines strong-field conditions with low cloud bases and shows a pronounced inverse relation between TGE occurrence and cloud-base distance. Thus, TGE occurrence is jointly controlled by thundercloud electrification and cloud-to-detector distance, while the recent warming tendency cannot be attributed to increased measured short-wave radiation. The annual mean air temperature shows a statistically significant positive trend, whereas daytime solar radiation shows a significant negative trend. Thus, warming at Aragats cannot be explained solely by variations in solar radiation.

Kepler's first two laws state that a planet moves on an ellipse with the Sun at a focus and sweeps out equal areas in equal times (constant areal speed). In the Principia, Newton showed how these laws connect to universal gravitation. Since then, the equivalence between orbital laws and force laws has remained a central topic in celestial mechanics. We present fully geometric proofs, built from explicit Euclidean straightedge-and-compass constructions, of this equivalence in both directions. The proof system combines finite-step constructions, tangent and triangle geometry, affine transport, local displacement ratios, conic invariants, and several hodograph realizations. Within this broader framework, one contribution is to use the auxiliary circle as the primary hodograph proxy in configuration space rather than the directrix-circle normalization of radius 2a. Our emphasis is a Principia-style argument that avoids differential equations while remaining close to Euclidean methods.

We present FESOM2-JAX, a Python re-implementation of the Finite-volumE Sea ice-Ocean Model (FESOM2) in JAX. The model retains the unstructured-mesh, cell-vertex finite-volume formulation of the original, runs unchanged from a laptop CPU to 256 GPUs, and is end-to-end differentiable. FESOM2-JAX is a code shadow of the Fortran model: a projection onto the Python ecosystem, translated with large language models and verified kernel by kernel against the original. It is built to lower the barrier to experimentation, from new numerics and parameterizations to gradient-based calibration and hybrid physics-machine-learning components, while remaining close enough to the original so that what is developed in the shadow can be transferred back. In a 1958-2019 hindcast at 1$^{\circ}$ equivalent resolution with identical physics and forcing, the mean states of the JAX and Fortran versions differ from each other by two orders of magnitude less than either differs from observations, and the two runs agree for six decades in global temperature, salinity, heat content, and sea ice. The complete 1$^{\circ}$ configuration fits on a single GPU, a node of four GH200 superchips integrates $\sim$113 simulated years per wall-clock day, and meshes of up to 7.4 million surface vertices ($\sim$5 km) scale to 128 GPUs. What limits the model is communication rather than arithmetic. What the shadow adds to the original is the gradient: a single reverse-mode pass through the full time loop returns the sensitivity of a model diagnostic to a parameter at every mesh vertex, verified against finite differences. To our knowledge, FESOM2-JAX is the first global ocean-sea-ice model of CMIP-class complexity written natively in a differentiable framework, and the first on an unstructured mesh.

The aeroacoustic resonance of round supersonic jets impinging on concave surfaces is investigated using compressible large-eddy simulations, vortex-sheet modelling, and Powell's feedback-loop analysis. The choked jets operate at an ideally expanded Mach number of $1.56$ and a Reynolds number of $6\times10^4$. Six geometries are considered: two flat plates at $L/D=2.08$ and $2.58$, where $L$ is the nozzle-to-wall distance and $D$ the nozzle exit diameter, and four Gaussian concave surfaces of fixed depth and indentation spread $\sigma\in\{0.4,0.8,1.6,4.0\}$. As the indentation narrows, the primary-tone amplitude increases by up to $23\,\mathrm{dB}$ relative to the flat-wall reference at $L/D=2.6$, together with larger wall-pressure fluctuations and moments. A Powell-Tam source-transfer budget attributes this amplification to increased Mach-disk source amplitude and more efficient return of the upstream feedback wave to the nozzle. The stronger upstream-propagating waves are consistent with acoustic focusing by the concave wall. For the helical cases, the measured frequencies and radial eigenfunctions agree closely with the guided jet mode predicted by the vortex-sheet model, supporting its role in closing the upstream feedback path. The same selection is recovered for concave and flat walls alike, so this tone is governed by the shear-layer profile of the equivalent ideally expanded jet rather than by the wall geometry. The axisymmetric frequencies, by contrast, coincide with no guided-mode branch and appear instead to follow Powell's classical loop-length criterion. The results identify distinct frequency-selection mechanisms for helical and axisymmetric screech and demonstrate that wall curvature provides effective control of screech amplitude and surface loading.

Policymaking relies on institutions that translate expertise into politically actionable knowledge. Yet little is known about how such knowledge is created, structured, and politically polarized, or how science shapes these processes. Using 2 million U.S. policy documents and nearly 1 million scientific citations from more than 200 think tanks and 100 government organizations, we analyze the supply side of science in policymaking: the production of policy knowledge. We find that think tanks are the dominant suppliers of science-based policy knowledge to government, but that this production has become increasingly politically polarized, driven primarily by growing insularity among left-leaning institutions. We further find that science is linked to lower polarization, as policy documents grounded in scientific evidence, especially those citing high-impact science, are less ideologically segregated and occupy more central positions in policy knowledge networks. These results reveal the potential of scientific expertise to shape and, at times, bridge the ideological landscape of policy knowledge production. Amid rising political polarization and the growing role of science in policymaking, understanding how policy knowledge is produced and how scientific expertise relates to its ideological structure is essential to strengthening the informational foundations of democratic governance.

We present a derivation at a level suitable for undergraduates of the Ostrogradsky formalism for Lagrangians in classical mechanics that depend upon an arbitrary number of time derivatives of the configuration. From the boundary term in the first variation of the Lagrangian we derive the Ostrogradsky formulas that define the Hamiltonian formulation of mechanical systems. Worked examples, exercises, and applications to the literature are also provided. An accompanying computer program that implements the formalism is discussed in the Supplementary Materials, and code for computing Hamiltonians via the Ostrogradsky formalism is provided in the Supplementary Materials and in a GitHub repository.

Exact time-dependent density-functional theory (DFT) separates the exchange-correlation potential into interaction and kinetic-correlation components, but the structural relation between them remains unclear. We establish a representability constraint based on the off-diagonal expansion of the one-electron reduced density matrix (1RDM) relative to a chosen reference dynamics. A non-zero linear off-diagonal term of the 1RDM generates a current density beyond the representable space of the reference, while a nontrivial quadratic off-diagonal structure activates the kinetic-correlation component. The resulting hierarchy provides a criterion for assessing, from the spatial locality of electronic correlation, whether a given exchange-correlation functional or a more general electron-correlation approximation remains adequate. In a one-dimensional two-electron correlation quench, retaining the exact interaction component while setting the kinetic component to zero suppresses the density broadening of the exact evolution. These results establish a spatial-order hierarchy among the force components governing the density dynamics and provide an exact constraint for the construction and assessment of nonadiabatic approximations.

We investigate how sub-sonic compressible magnetohydrodynamic (MHD) turbulence properties that are relevant for cosmic-ray (CR) transport in the Galaxy are affected by the nature and amplitude of initial fluctuations, and by the plasma compressibility $\beta$. We perform 3D simulations of decaying compressible ideal-MHD turbulence at $1024^3$ resolution with the PLUTO code. The level of density fluctuations in fully developed turbulence is insensitive to whether this state is reached starting from Alfvénic or fast-magnetosonic perturbations. Fast-magnetosonic injection is characterized by an early phase of rapid shock dissipation, followed by a turbulence-dominated decay with a rate comparable to that of the Alfvénic case. The contribution of fast-magnetosonic fluctuations in fully developed turbulence remains relevant only when the initial injection consists exclusively of fast modes. Large-amplitude turbulence ($\delta B/B_0>1$) is characterized by a nearly isotropic Kolmogorov or Iroshnikov-Kraichnan spectrum for Alfvénic or fast-magnetosonic injection, respectively. At low amplitudes ($\delta B/B_0\ll1$), both initial Alfvénic and mixed-wave perturbations lead to strongly anisotropic turbulence with spectra $\propto k_\perp^{-5/3}$ and $\propto k_z^{-2}$ (becoming steeper at $\beta\gg1$), whereas fast-magnetosonic perturbations produce a turbulent state populated by shocks with a nearly isotropic $k^{-2}$ spectrum. Magnetic-field curvature and mirror structures are strongly sensitive to fluctuation amplitude and plasma $\beta$. The predicted -2.5 power-law scaling emerges only in the large-amplitude regime at high $\beta$. This work highlights that features of sub-sonic compressible MHD turbulence that may affect CR transport are sensitive to large-scale conditions and to the plasma $\beta$. Their effect on CR diffusion and field-line random walk is the object of Paper II.

Gravitational lensing is often introduced through the bending of light by mass, but its geometrical nature can be difficult to visualize without invoking the full machinery of general relativity. We present a geometrical-optics analogy in which selected lensing-like image morphologies are generated by ray tracing through axicon-type lenses. In contrast with analogies based on a spatially varying refractive index, the present model uses optical elements with a constant refractive index; the redistribution of rays is produced by the geometry of the refracting surfaces. After deriving the ray-tracing equations for a general two-surface axicon, we apply the model to conical and exponential profiles. Axially symmetric configurations generate ring-like images, misaligned incidence produces partial arcs, and broken axial symmetry leads to four-image patterns reminiscent of an Einstein cross. The model is not intended as a physical substitute for a relativistic lens equation, but as a computational and pedagogical tool for exploring how surface geometry and symmetry control ray deflection, redistribution, and image multiplicity.

In social learning, agents learn not only from direct evidence but also through interactions with their peers. We investigate the role of imprecision in such interactions and ask whether it can improve the effectiveness of the collective learning process. To that end we propose a model of social learning where beliefs are equivalent to formulas in a propositional language, and where agents learn from each other by combining their beliefs according to a fusion operator. The latter is parametrised so as to allow for different levels of imprecision, where a more imprecise fusion operator tends to generates a more imprecise fused belief when the two combined beliefs differ. In this context we describe both difference equation models and agent-based simulations of social learning under a variety of conditions and with different initial biases. The results presented suggest that for populations with a strong initial bias towards incorrect beliefs some level of imprecision in fusion can improve learning accuracy across a range of learning conditions. Furthermore, such benefits of imprecision are consistent with a stability analysis of the fixed points of the proposed difference equation models.

Photon orbital angular momentum (OAM) generated in nonlinear Compton scattering has attracted considerable interest as a route toward vortex $\gamma$-ray sources. Existing theories describe photon OAM primarily through angular-momentum transfer involving structured incident particles and laser fields, while the role of electron-wavepacket has remained unexplored. Here, we develop a general analytical theory of nonlinear Compton scattering for arbitrarily shaped electron wave packets and demonstrate that the Fourier spectrum of the transverse electron wave packet directly determines the OAM spectrum of the emitted photons through a generalized angular-momentum selection rule. Our theory unifies Gaussian wave packets, vortex electrons, and arbitrarily shaped electron states within a single framework, revealing the transverse Fourier structure of electron wave packets as a fundamental degree of freedom governing photon orbital angular momentum and enabling deterministic engineering of photon OAM distributions.

Moire superlattices formed by twisting periodic systems provide a powerful platform for emergent topological phenomena, but their use for programming real-space topology in acoustic wave fields remains largely unexplored. Here we report a phase-controlled spoof surface acoustic wave platform for constructing moire topological textures in the acoustic particle-velocity field. On a perforated acoustic metasurface, two twisted skyrmion lattices are synthesized and superposed, yielding acoustic skyrmion bags with controllable topological numbers and geometries. The twist angle and rotation center control the scale and configuration of the bags, enabling deterministic reshaping of the composite texture. We further introduce controlled defects to assess the defect tolerance of the moire skyrmion bags. The skyrmion bags retain their composite topology over a finite range of defect densities, and comparison with an untwisted single-layer skyrmion lattice suggests enhanced stability of the enclosed skyrmion cluster relative to the single-layer reference at higher defect densities. The same programmable platform also supports transitions from skyrmion lattices to meron lattices and enables twist-induced meron clusters. These findings establish acoustic moire superlattices as a reconfigurable platform for engineering robust real-space topological textures, with potential applications in topology-guided acoustic manipulation and information encoding.

Efficient thermal dissipation has become critical in emerging electronic devices. However, most existing studies have primarily focused on engineering heat dissipation pathways, largely overlooking the intrinsic behavior of the heat source itself. We demonstrate an active passivation technology that proactively tunes hotspot locations in GaN transistors. By adjusting the active passivation layer length, the hotspot is shifted from the gate edge to the drain-side AP edge, establishing a clear one-to-one spatial correlation. In-situ thermal-mechanical visualization via micro-Raman thermography, combined with multi-physics electro-thermal-mechanical simulations, directly captures the spatial redistribution of both temperature and thermal stress profiles. Electrical analysis confirms that this hotspot migration is driven by the spatial shift of the peak electric field and localized Joule heating. This proactive heat-source tuning strategy provides critical design guidelines for power electronics.

Turbulent transport in magnetic confinement fusion devices governs the overall plasma confinement properties and regulates the plasma-material interaction at the first wall. In the plasma boundary, turbulence is typically investigated through three-dimensional two-fluid flux-driven turbulence simulations. In this work, the GBS boundary turbulence code is extended to enable turbulence simulations in reversed field pinch configurations, encompassing the reversal surface and an arbitrary level of magnetic chaos. The differential operators implemented in the code are modified to avoid the approximations of large-aspect ratio and weak poloidal magnetic field. Three-dimensional Poisson and Ampere solvers are implemented to allow for turbulence simulations in conditions of partially or fully disrupted magnetic flux surfaces. This modified version of the GBS code is then applied to simulate turbulence in the boundary of RFX-mod reversed field pinch plasmas. Turbulent eddies across the reversal surface show properties similar to those typically found in tokamak boundary turbulence simulations. Despite the good agreement found with experimental measurements, these simulations reveal a significant limitation of the fluid-based turbulence modeling of the edge region in reversed field pinch plasmas, which arises from the intrinsically short parallel connection length. This conclusion is also supported by a linear gyrokinetic analysis that identifies trapped electron modes as the dominant microinstability in this region.

The weak wall-normal pressure variation in pressure-driven rarefied Poiseuille flow is a stringent test of higher-order constitutive models: it is almost invisible in the total-pressure norm, yet it is generated by anisotropic molecular stress. A tangent-form nonlinear coupled constitutive relation (NCCR) reproduces its convex topology, but the physical reason for its quantitative success remains unresolved. We ask whether the constitutive stress relation and reduced streamwise forcing are independently accurate or whether their effects compensate. A direct simulation Monte Carlo (DSMC) campaign is analysed using two-dimensional momentum budgets, a pressure-error norm based on the transverse signal, a matched-outlet-Knudsen comparison and componentwise tests of the pre-elimination NCCR balance. The non-equilibrium wall-normal stress over-supports the measured pressure defect, while streamwise transport of shear stress supplies an opposing correction. The state map, momentum budgets and forcing diagnostics show that neither outlet Knudsen nor outlet Mach number alone organises the pressure amplitude; along the fixed-ratio sequence, rarefaction is accompanied by larger changes in the constitutive diagnostics than in pressure amplitude. The DSMC-inferred stress relation departs from the fixed reduced coefficient, and even the best common scalar closes the dominant shear component far more accurately than the normal components that carry the pressure field. Correcting the coefficient alone can therefore worsen the reconstruction, whereas restoring omitted streamwise-momentum terms reduces the amplitude bias in strongly accelerated cases. The reduced law can remain accurate through stress–momentum compensation, showing that agreement of a weak non-equilibrium observable need not imply correct internal closure mechanics.

Time-periodic modulation introduces a synthetic degree of freedom to manipulate topological phases. This synthetic dimension can trigger a phase transition that localizes a boundary state at the temporal interface. Noise is widely deemed a fundamental threat to topological protection, universally anticipated to weaken or even destroy topological states. Here, we introduce periodically repeated temporal noise and fully random temporal noise into a 2D periodically driven non-Hermitian system. Paradoxically, under ensemble averaging, such temporal noise drives an exponential enhancement of the response intensity at the temporal interface. An averaged superoperator analysis shows that the noiseless band structure is preserved under both types of noise. Yet the noise increases the growth rate of growing modes while suppressing the decay rate of decaying ones. Finally, we experimentally realize the noise-enhanced temporal boundary state in a robotic metamaterial network. This finding establishes temporal noise as a constructive ingredient, enabling the unambiguous emergence of topological states and conferring exceptional robustness upon topological devices.

One-way spatial marching methods separate upstream- from downstream-propagating disturbances using a rational approximation of a spectral projector. Among existing one-way Navier–Stokes (OWNS) formulations, the recursive variant, OWNS-R, is the most economical, evaluating the approximation as a product of $N$ resolvent factors. This product amplifies rounding errors multiplicatively, imposing a flow-dependent upper limit on the approximation order that cannot be known in advance. We reformulate the same approximation as an additive partial-fraction sum (OWNS-Summation, OWNS-S). In exact arithmetic, the recursive and summation evaluations are equivalent when supplied with identical weights and poles; in floating-point arithmetic, they are not. Each of the $N$ resolvent solves acts on the same input state and contributes independently to a weighted sum, preventing multiplicative error amplification. The approximation order $N$ therefore becomes a pure convergence parameter, and the solves can run in parallel. Using the same auxiliary poles as OWNS-R, OWNS-S remains accurate in every configuration tested, showing that the recursive evaluation, rather than the poles, is the dominant source of instability. A paired greedy parameter-selection procedure is also introduced, with candidates drawn from analytic estimates of the upstream and downstream spectral regions, avoiding eigen-decomposition during the numerical march. OWNS-S is validated on incompressible, hypersonic and transonic boundary layers. In the transonic case, the disturbance spectrum reorganises from a subsonic to a supersonic topology during the march. The one-way computation proceeds continuously through this transition at a streamwise resolution unattainable by the parabolised stability equations.

Foldable proteins exhibit funnel-like energy landscapes, but their evolution under selection for function remains unclear. We study this in a lattice protein model, defining fitness as the equilibrium probability of a fixed active-site motif and using multicanonical sequence sampling. At intermediate temperature, rare high-fitness sequences show funnel-like energy and double-well free-energy landscapes. At very low temperature, abundant high-fitness sequences retain glass-like landscapes. Thus, thermal fluctuations turn a local functional requirement into a global structural constraint.

When does a noisy quantum sampler yield an end-to-end polynomial-time optimization algorithm with performance guarantees? Building on finite-depth and finite-shot guarantees for Constraint-Enhanced QAOA, we show that inverse-polynomial ideal probability on the optimal set, together with independent sampling, polynomial-time feasibility repair, and scoring, produces an exact-hit fully polynomial randomized approximation scheme, which we call an FPRASq. This guarantee survives device noise within an instance-dependent window. For effective circuit depth linear in the product of layer count and problem size, preserving an inverse-depth fraction of the ideal optimal mass increases the required shot complexity by one power of the problem size. Beyond this window, deterministic repair guarantees feasibility and provides an instance-dependent approximation guarantee whenever the induced objective inflation is controlled. The resulting NP-HQ algorithm fits the Chen-Cotler-Huang-Li oracle model. On any NP-hard kernel-admissible promise family, reproducing its inverse-polynomial optimal overlap with a polynomial-time classical sampler would imply that NP is contained in BPP, even with identical repair and perfect access to the constraint structure. Thus, the separation lies in generating the sampling distribution. We further introduce Heavy-Hitter QAOA, which preserves these conditional guarantees while reducing the retained candidate set and classical post-processing cost by one power of the problem size. Hardware experiments on IBM Eagle r3 processors cover instances with up to one hundred logical variables and match or improve every tested QOptlib reference tour.

Efficient separation and localization of multifrequency acoustic waves are essential for underwater target recognition and acoustic energy harvesting. The underwater implementation of topological rainbow trapping remains challenging because of complex fluid-solid interactions and the difficulty of integrating long-range transport with frequency-selective localization in an open system. Here, we theoretically develop and experimentally demonstrate two underwater spatial-frequency demultiplexing mechanisms based on the acoustic analogues of the QVHE and QSHE. Both mechanisms employ SSAWs, whose fields are confined near a structured surface and decay evanescently into the surrounding water, enabling experiments without an enclosed waveguide. In the QVHE mechanism, a spatial gradient along a valley-Hall edge channel shifts the local edge-state dispersion, causing different frequency components to become localized at distinct positions and thereby realizing spectral and spatial demultiplexing. In the QSHE mechanism, one-dimensional topological edge states are coupled to frequency-selective zero-dimensional higher-order corner states. Multifrequency signals first propagate robustly along a common boundary and are then transferred to prescribed remote corners according to frequency, producing a transport-then-confinement process. This mechanism combines defect-tolerant edge transport, frequency-selective corner localization, and remote rainbow trapping. Numerical simulations and experiments verify the frequency-dependent localization and the persistence of the designed transport pathways in the presence of structural defects. The proposed open SSAW platform performs robust frequency demultiplexing at the physical layer, reducing reliance on digital signal processing and offering potential for underwater target recognition and frequency-selective acoustic energy harvesting.

An electron crosses the cold Fermi sea. The sea recoils, screens, remembers. It returns the electron dressed: a quasiparticle with a mass, a lifetime, and a Green function. This was Nozières' lesson. We carry it to water. Electrons become dipoles; the Fermi sea becomes a hydrogen-bonded polar liquid. We ask the same question: can the collective modes of water live long enough and reach far enough for molecular machines to interact and synchronize?

Compact and efficient frequency conversion within optical fibers is highly desirable for nonlinear and quantum photonic technologies, yet it remains challenging due to weak nonlinear interactions and limited coupling efficiencies onto optical fibers. Here, we demonstrate resonantly enhanced second-harmonic generation (SHG) through the all-fiber integration of a gallium nitride (GaN) hole-type circular Bragg grating (h-CBG) cavity, directly transferred onto a standard optical fiber. Together with the large second-order nonlinear susceptibility and wide optical transparency window of GaN, the fabricated h-CBG membrane cavity on GaN enables strong field confinement and vertically directional out-coupling of the generated SHG signal. As a result, we observe drastically enhanced SHG signals from the h-CBG device compared with the bulk GaN and the unpatterned freestanding GaN membrane. Using a deterministic pick-and-place transfer technique, we demonstrate robust and precise fiber integration of the GaN cavity device, enabling in-line SHG generation from a conventional fiber platform. This work establishes a compact and scalable approach for incorporating optical nonlinearity into fiber-based photonic systems.

This study applies $\textit{4D topology optimization}$, a framework for simultaneously optimizing the morphology and motion of a system, to a rigid body that induces fluid flow. The rigid body shape is represented on a design grid that is independent of the analysis grid, and at each time step, it undergoes rigid-body motion before being mapped onto the analysis grid. The shape is represented using a pseudo-density method, while the motion is directly parametrized by the positions at discrete time steps and smoothed using a temporal filtering technique. The fluid dynamics are evaluated through the lattice kinetic scheme, an extended version of the lattice Boltzmann method. Design sensitivities with respect to both shape and motion are derived via the adjoint variable method, and the shape and motion are intentionally updated simultaneously during the optimization process. Finally, two- and three-dimensional numerical examples are presented and discussed from a physical perspective. Furthermore, the effectiveness of the proposed method is demonstrated by comparison with cases in which only the shape or motion is optimized, as well as through several parameter studies.

This paper presents a simple and compact microwave sensing approach using conformal frequency-diverse metasurfaces to jointly localize and identify materials within a pipe. The frequency-diverse metasurfaces consist of an electrically large SIW patterned with metamaterial radiators with diverse resonant frequencies. These antennas can generate spatially distinct patterns that can encode information into simple frequency sweeps. Two such antennas are wrapped around a pipe to localize and identify objects inside, thereby replacing large, complex tomographic antenna arrays with simple frequency sweeps. To do that, a sensing matrix is experimentally populated using targets made of different materials placed in a grid of possible locations. Using computational processing, we show that the system successfully distinguishes among metal, wood, and nylon rods and accurately localizes their positions using unique frequency signatures. The extension of this method to two-object localization is examined. The results highlight a simple, low-cost, and reliable framework for microwave sensing that enables material characterization and localization without large arrays, scanning, or complex hardware.

A 13.56 MHz, capacitively coupled plasma is investigated experimentally to determine the power absorption mechanism across a wide pressure range ({\approx} 5 - 600 mTorr) at {\approx} 10 W. Axial profiles of plasma parameters are measured along with V_{DC}, the DC self-bias voltage on the powered electrode (PE), from which the DC sheath voltage drop, V_{s} is determined. Axial profiles of electron ohmic power absorption reveal that power deposition is highest in low-density regions and lowest in high-density regions, indicating that plasma formation is not driven by Ohmic heating. Probability arguments were correlated with locations of the density peaks to determine the ionization mean free paths ({\lambda}_{iz}) at each pressure. Electron acceleration and average electron velocity acquired in the DC sheath voltage drop at PE were also calculated to determine {\lambda}_{iz} independently for comparing with those calculated from the density profiles. The agreement is good for all pressures, barring the lowest pressure ({\approx} 5 mTorr) for which there is significant deviation. The electron sheath transit times are a small fraction of the RF period, implying that the accelerated electrons experience the RF field as instantaneous "spot values" superimposed on V_{s}, the DC sheath drop. The negative self-bias on PE renders the RF swing asymmetric making it negative for most of the cycle. Since the RF accelerates electrons when it is negative and takes energy from them when it is positive, net power is transferred to the electrons during the course of steady-state measurements, averaged over many RF cycles. Except at {\approx} 5 mTorr where stochastic heating dominates, the RF field at higher pressures exhibits a novel role, not hitherto reported.

We derive predictive formulas for the scattering mean free path $\ell_s$ of statistically homogeneous two-phase dielectric media in dimensions $d=1,2,3$. Unlike Mie-based estimates limited to identical circular or spherical scatterers, the formulas apply to arbitrarily shaped and polydisperse particulate media as well as nonparticulate media, with microstructure entering through the spectral density. The formulas are based on the exact strong-contrast expansion for the effective dynamic dielectric constant. We apply them to five nonhyperuniform and hyperuniform models and validate selected cases using finite-difference time-domain simulations. For $k_1/s \lesssim 1$, where $k_1$ is the incident wavenumber and $s$ is the specific surface, the predictions agree well with simulations and are consistent with Mie theory where applicable, while improving accuracy for two-dimensional transverse-magnetic polarization. Mie estimates become more accurate for $k_1/s \gtrsim 1$. For hyperuniform media with $\widetilde{\chi}_V(k)\sim k^\alpha$ at small $k$, the theory predicts $\ell_s\sim k_1^{-(d+1+\alpha)}$; stealthy hyperuniform media are transparent over a finite wavenumber interval. These results provide a microstructure-based route to predict and design wave transport in general disordered dielectric materials.

A two-level analytical framework is presented for modeling random walk transport of messenger ribonucleic acid (mRNA) molecules along neuronal microtubules from soma to synapses. Motivated by empirical observations of mRNA cargo motion, the transport within a dendrite is modeled by a persistent telegraph process with pauses. Theoretical expressions for the probability of traversing the dendrite and the mean time for such travel are derived for different and equal probabilities of persistence. These results are used for the construction of a semi-Markov model of motion of mRNA cargo within the whole neuron. The semi-Markov model provides the probabilities of absorption at a given synapse and corresponding mean first-passage times (MFPTs) from the soma, where mRNA is transcribed. The theoretical expressions, together with experimentally obtained parameter values, are used to calculate MFPTs for neurons with empirically reconstructed morphology. The model predicts that when retrograde persistence is stronger, the MFPT to each synapse is effectively the same. Otherwise, when the persistence is more pronounced in the anterograde direction, the transport in the neuron resembles the motion along a single dendrite – nearly linear dependence of MFPT on the distance between soma and synapse. These findings are theoretically justified when the lengths of dendrites are considerably longer than the distance traversed during a typical run.

The enthalpy method was introduced in the late 1970s to simulate phase-change problems on fixed grids without explicitly tracking the moving phase-change front. It remains one of the most widely used approaches in academic and commercial software for the simulation of industrial melting and solidification processes. For pure phase-change materials (PCMs) that melt or solidify at a single temperature, the enthalpy method introduces an artificial mushy region bounded by the solidus temperature, $T^{\rm sol}$, and the liquidus temperature, $T^{\rm liq}$. As the numerical parameter $\Delta T=T^{\rm liq}- T^{\rm sol}$ approaches zero, the solution obtained with the enthalpy method is generally assumed to converge to that of the classical Stefan problem, in which the phase-change front is infinitesimally thin. This assumption is largely based on benchmark studies performed under the simplifying assumption of equal solid and liquid densities. In this work, we systematically investigate the accuracy and spatio-temporal convergence properties of the enthalpy method for both low- and high-density-ratio phase-change problems. Because the limiting behavior $\Delta T\rightarrow0$ is difficult to realize numerically, owing to the diminishing thickness of the mushy region, we formulate and analyze the finite-$\Delta T$ mushy Stefan problem solved by the enthalpy method. We show that this problem possesses a self-similar structure that reduces the governing equations to a boundary-value problem involving two unknown parameters. The theoretical analysis also enables improvements to our previously developed low-Mach enthalpy method, enhancing its stability and accuracy as the density ratio between the two phases increases from $\mathcal{O}(1)$ to $\mathcal{O}(3)$.

Increasing the temperature of an ordinary many-state system increases access to a wider range of states and hence increases its entropy. We find the opposite in ChatGPT-like AIs, even though raising the decoder temperature likewise increases access to a wider range of states (next-token choices). Across 12,000 continuations from 11 AIs, autoregressive feedback drives the long-time output population through an entropy maximum and into population inversion. The transition features frozen states, cycles, intermittency and noise-induced ordering. We present evidence of a hidden coordinate that acts as the state variable of an effective nonlinear map. Its trajectory average strongly predicts output repetition in separate test trajectories. ChatGPT-like AIs therefore behave not as `stochastic parrots', but as a new class of controllable nonlinear physical systems whose internal dynamics can be measured and perturbed.