Papers

Data assimilation (DA) is the process of combining forecasts from a model with observations in order to optimally estimate the state of a system. This is critical for chaotic systems, such as the atmosphere, since if observations are not continually assimilated the model will quickly lose skill. DA is routinely performed (usually every 6 hours) at operational forecasting centres around the world. In this article we discuss the interface of machine learning (ML) and DA. This is still an emerging and quickly developing field, and this article tries to give an overview of some of the main topics and methods.

Which information tells a turbulent flow which way to cascade? We apply Haar-distributed Hermitian phases that retain prescribed same-mode quadratic information, including modal amplitudes, the energy spectrum, and same-mode two-time correlations. For the phase-Haar ensemble, the expectation of any integrable odd flux functional is zero. Finite ensembles strongly suppress mean transfer and forward-event bias in the primary block. An independent block retains sign separation but fails the scale-8 mean criterion. In matched evolution protocols, nonlinear Navier-Stokes dynamics restores the selected signed-flux scalar close to its natural reference, whereas linear evolution and an amplitude-matched frozen control do not. Flux and a local cubic geometry scalar recover together, yet high local correlation can survive erasure of the arrow. Signed transfer thus requires information beyond the retained quadratic record; the tested nonlinear dynamics rebuilds it.

We introduce a simple coupled cluster approach for multireference and open-shell systems. In the restricted active space coupled cluster framework, the cluster operator only includes excitations from doubly occupied to virtual orbitals, and this ensures the individual operators commute among each other. The ground and excited states are obtained by diagonalisation of the coupled cluster similarity transformed Hamiltonian in a restricted active space. We demonstrate the framework for the RAS-CCSD model, where single and double excitations are included in the cluster operator, and likewise for a complete active space. We consider systems with multireference character and open-shell systems in a spin-adapted manner. The approach captures significantly more dynamical correlation than without the coupled cluster similarity transformation.

We compare the predictions of the Euler-ensemble solution of freely decaying incompressible turbulence with simulations and experiments: the scaling function of the energy spectrum, the index of the second-order structure function and the decay laws. The predictions have a fixed shape; only their normalization and scale are fitted. In the final $4096^3$ direct numerical simulations of Rodhiya and Sreenivasan, the turbulent attractor is selected by a parabola law for the growth of the bulk length. Along it the collapsed spectrum depends only on the instantaneous Reynolds number, identically for Saffman and Loitsyansky initial spectra; extrapolated to infinite Reynolds number, it agrees with the theory to 1% in the bulk of the enstrophy range, below the dissipation range. The energy balance holds exactly. The energy integral of the theory diverges in the infrared, so energy and enstrophy depend on how a finite flow cuts off the spectrum: with a sharp cutoff $k_0$, the decay laws, energy $\propto t^{-5/4}$ and enstrophy $\propto t^{-9/4}$, are reached only when the cutoff enters the $k^{-7/2}$ tail of the spectrum. Such a cutoff is only an estimate; the reliable test is the bulk of the spectrum. Fitted to the simulations, the cutoff lies at the lower edge of the bulk, where the spectrum still carries the initial data, and the decay exponents depend on the initial infrared spectrum. In the Max Planck wind tunnel at $Re_\lambda$ up to 5779, the longitudinal index extrapolated to infinite Reynolds number matches the odd ensemble with one scale parameter at large separations, where standard crossover forms fail. The freely decaying turbulent blob of Matsuzawa et al. is too small, too inhomogeneous and too weak to display the attractor. The theory is derived in the first two papers of this series.

High-subsonic turbulent jets trap acoustic modes in their potential core. The modes lie in narrow frequency bands that are $O(0.1)$-wide in Strouhal number, $St$. By studying a Mach 0.9, Reynolds number $10^6$ jet, this work confirms an earlier hypothesis: each band is made up of $O(10)$ discrete frequencies, $\Delta St \sim O(0.01)$ apart. To resolve them, we collect $O(100\,000)$ snapshots from dedicated schlieren experiments and large-eddy simulations (LES), and apply spectral proper orthogonal decomposition (SPOD). Our SPOD algorithm uses sine tapers and enforces the jet's rotational and reflectional symmetries. For both the schlieren and the LES, the trapped SPOD modes are bounded in the $St$-$x$ and $St$-$k$ planes, where $x$ and $k$ are respectively the axial coordinate and wavenumber. The bounds quantise the modes in $St$, $x$, and $k$, such that the $n_x$th mode in $St$ has $n_x$ antinodes along $x$ and $k$. We then predict trapped modes with local, global, modal, and non-modal stability theories. We find evidence of a competition between modal and non-modal stability. From only the theoretical bounds, we build a simple in-phase standing-wave resonance model that predicts the quantised global-mode frequencies.

As the field of nanoscale lightwave electronics matures from centering around fundamental science to focusing on device engineering, there is a growing need for compact models that can provide rapid and scalable quantitative analysis of the performance of petahertz-frequency optical-field-driven electronics. In this work, we developed a circuit model framework for describing nano-scale lightwave electronics. Our framework captures the physics governing nanoscale optical devices, such as electrically-connected nanoantennas, using compact circuit models that predict their electromagnetic response, their electron emission, and their charge transfer dynamics. This approach allows scalable simulation of large integrated systems containing networks of nanoscale lightwave electronic components, which is not possible using full-wave electromagnetic and particle-in-cell simulation methods. We implemented the compact model in an free commercial circuit solver (LTspice) and validated its electromagnetic response against a full-wave electromagnetic solver (MEEP). We propose an experiment to fully benchmark the model's ability to capture non-linear charge-transfer-based coupling between devices. The circuit model implementation speeds up the electromagnetic analysis from hours to seconds and accounts for charge-driven coupling, enabling rapid quantitative studies of device operation which provide new insights into how femtosecond signals propagate through nanoscale lightwave electronic structures. We anticipate that the methods we introduce here will become essential to the development and performance analysis of nanoscale lightwave electronics for communication, computation, and signal processing at optical frequencies.

We present a study of the shapes and mechanical properties of a knitted loop made of elastic and frictional yarns. First, for non-frictional yarns, we investigate how the elastic energy of a loop varies with its shape. This allows us to quantify the energetic contributions of stretching and bending, and to identify energetically favorable loop configurations. We then consider the case of thin frictional yarns. In this case, the stability of a loop can be reduced, even in the presence of friction, to a simple planar elastica problem. Friction gives rise to a multiplicity of stable loop shapes in a knitted fabric at rest, i.e., in the absence of externally applied forces. We show that, in the limit of thin yarns, a 2D elastica model can predict these rest shapes. Furthermore, the force-deformation curves under uniaxial stretching can be obtained straightforwardly within this framework. Measurements of loop dimensions and forces during uniaxial stretching of nylon knitted fabrics allow us to assess the qualitative and quantitative validity of the planar elastica model. Finally, we examine the limitations of this model for dense knitted fabrics.

Modern science has moved decisively toward larger and more collaborative research teams. Yet an influential 2019 study reported that small teams are more disruptive than large teams, a conclusion difficult to reconcile with both the well-known trend toward increasing collaboration and the disproportionately large teams behind widely recognized disruptive research, such as work that has been awarded Nobel Prizes. Here we show that the reported disruptive advantage of small teams is largely an artifact of measurement rather than a fact about teams. Analyzing about 60 million scientific publications, we find that larger teams cite more references and that the standard disruption index declines mechanically as reference lists lengthen. Equalizing reference-count distributions across team sizes reverses the negative gradient within the index's own framework. Evaluating each paper against one reference at a time yields a reference-robust measure that distinguishes recognized breakthroughs from ordinary papers more sharply and more consistently than the standard disruption index across six independently curated benchmarks, including the Nobel Prize. Under this validated measurement, scientific disruption increases with team size across four scientific domains and in every decade from the 1970s to the 2010s. For scientific publications, we conclude that larger teams are more, not less, disruptive.

Beam-driven plasma wakefield acceleration (PWFA) is a promising candidate for realizing a future linear collider, owing to the extremely high field gradients that are produced in plasmas. Despite recent success in high-gradient and high-efficiency electron PWFA, efficient positron PWFA remains a challenge. The warm plasma column regime offers stable, high-quality acceleration of positron beams in a plasma electron filament, albeit at low energy-transfer efficiencies. We explore the possibility of employing an electron energy recovery beam to extract residual energy from the plasma wakefield and increase the efficiency of this scheme. Our particle-in-cell simulations reveal that certain mechanisms, including higher-order ionization and ion motion, limit our ability to further extract energy from the plasma wakefield.

The optical properties of monolayer transition metal dichalcogenides (TMDCs) are strongly impacted by exciton-phonon coupling, especially inter-valley scattering in the case of tungsten-based materials. Together with strain-tunability of the energetic K-Q valley separation this material class therefore provides a platform for semiconductor optomechanics. Here we investigate the influence of inter-valley scattering and strain on resonance Raman scattering in monolayer TMDCs using a microscopic model based on an effective deformation potential coupling for the exciton-phonon interaction. Considering exemplarily the resonance Raman profile of the A$_1$-mode in hBN-encapsulated monolayer WSe$_2$, we find that inter-valley scattering between the electronic K- and Q-valley leads to destructive interference between a first order and a third order Raman process. As a result we obtain a suppression of the incoming resonance in WSe$_2$, consistent with recently published experimental data.

Preface for the English Translation: While checking out early USSR journal articles for our literature search, we recently had tremendous difficulty acquiring this 1934 Klyachko original publication through our library systems. After obtaining and reading a scanned copy of it supplied by the Russian library in Moscow, we realized that relatively scarce citations of this work in the Western literature are often accompanied by inaccurate descriptions and comments, indicating that most Western authors might have gotten only second-hand knowledge about it and never seemed to have read the original article. Compared with various correlation formulas in the modern literature, the Klyachko 1934 formula for sphere drag in fluid flow appears to be the most elegant and cleanest one thus far. Therefore, we decided to make an effort to translate this historical work and publish it in the open literature to facilitate its accessibility to English readers worldwide.

The mixing of two aqueous streams in small channels is usually modeled with constant density when the difference in density between the streams is only a few percent or less. In this paper, we investigate whether this holds in a confluence cell where calcium chloride and sodium sulfate meet and form gypsum. A constant density simulation of the reacting flow results in deposition on the floor along the channel centreline. However, in the experiment, gypsum instead forms bands on both side walls, and the deposition bands start further upstream as the flow rate decreases. Dye experiments with density differences between the streams of 0.14 to 2.1\,%, three channel heights, and 32 flow rates show that the distance at which the dyed stream reaches the opposite side wall is governed by the bulk Richardson number, $Ri=g'H/U^2$, and follows $L/H = 3.31\,Ri^{-0.66}$ over four decades of $Ri$ ($R^2=0.89$). When the two streams' densities match, the dyed stream no longer reaches the opposite wall under most conditions, and in a density-matched reacting experiment, gypsum deposits on the floor, as the constant-density simulation predicts. A small relative density difference is thus not a sufficient condition for neglecting buoyancy: the Richardson number must also be small. For the flow rates typical of centifluidic devices, it often is not.

Analytical solutions to the three-dimensional ideal magnetohydrodynamic equilibrium equations $(\nabla \times \vec{B}) \times \vec{B} = \nabla p$ and $\nabla \cdot \vec{B} = 0$ provide benchmarks for stellarator equilibrium codes. Two families of non-axisymmetric solutions were recently discovered by Landreman [arXiv:2609.26742] as counterexamples to Grad's conjecture, but both satisfy stellarator symmetry. Here, we generalize both families by introducing a symmetry-breaking parameter into each of the solutions, resulting in new non-axisymmetric solutions that break stellarator symmetry and reduce to the stellarator symmetric solutions when the newly introduced parameter is set to zero.

Information sharing, threat perception, and collective evasion highlight the advantages of swarm intelligence. Here, we study the behavior of flocks of cognitive agents, based on numerical simulations of the augmented inertial spin model, with an explicit predator. Each agent on the predator-facing side of the flock perceives the threat, triggering collective turns through local interactions. We show that the augmented inertial spin model sustains nearly linear information propagation in the under-damped regime, even with realistic flocking interactions such as local collision avoidance and swarm cohesion in addition to alignment. The turning distance of the flock to a stationary predator scales with the ratio $\Delta_p/(v_0\sqrt{\eta})$, identifying an optimal balance between response strength $\Delta_p$, flock speed $v_0$, and damping $\eta$ of information propagation. Fragmentation arises when different parts of the flock turn in different directions, however, fast information propagation can synchronize these responses and maintain flock cohesion while under pursuit. For two predators, a combination of stability analysis and simulations reveals a stark contrast between flock and single-particle reactions, with scattering angle shaped by flock geometry, predator position, and the heterogeneous responses of the detecting front. Together, these results show how information propagation, flock geometry, and dynamics together shapes collective escape, providing general principles relevant to both biological flocks and robotic swarms.

We introduce a stationary, field-free billiard that reproduces the adiabatic confinement mechanism of a magnetic mirror through boundary geometry alone. Rapid transverse reflections generate an approximately conserved action that plays the role of the magnetic moment, producing an effective longitudinal barrier and a nominal loss cone for escape. For the half-width profile considered here, confinement is controlled by a competition between mirror strength and adiabaticity: shortening the profile increases the mirror ratio but also reduces the accuracy of the adiabatic approximation. As a result, the escaping fraction is nonmonotonic and confinement is strongest at an intermediate profile length. We characterize the associated phase-space structure, escape-time statistics, and escape basins, revealing long-lived transient motion and a strong sensitivity of the final escape direction to the initial condition that persists as the uncertainty in the initial condition is reduced.

Learning constitutive laws of complex fluids from velocity data (i.e. indirect observations) is a PDE-constrained inverse problem in which expressive neural parameterizations risk breaking the well-posedness of the forward physics model. We address this tension by learning, rather than the constitutive law itself, the dissipation potential: a scalar function whose convexity, frame-indifference, and dissipativity propagate into the underlying continuum mechanics and guarantee the structural properties needed for a well-posed and generalizable forward PDE. We introduce ICNNE, an input-convex neural architecture that exactly enforces convexity, frame-indifference, and evenness in the second strain-rate invariant via symmetrization, alleviating the numerical instabilities that occur at small strain rates in standard formulations. By weakly enforcing zero gradients in the origin of the potential via penalization, ICNNE also satisfies the dissipativity property. The loss objective and its gradient are computed using a combination of finite element and neural network methods, coupling the Firedrake and PyTorch libraries. We evaluate on four problems spanning compressible and incompressible regimes: compressible Navier-Stokes, Herschel-Bulkley yield-stress flow, Hibler's viscous-plastic sea-ice model, and discrete-element-method data with no known constitutive law. We show that the learned potentials (i) recover ground-truth physics where it is known, (ii) transfer accurately to geometries unseen during training, and (iii) succeed in regimes where unstructured methods diverge. These results suggest that embedding mathematical structure into the parameterization, rather than into the loss, is a robust path to learning physics from indirect data.

Optical levitation relies on light forces to trap mesoscopic objects in a vacuum and assemble pristine oscillators, whose mechanical properties are commonly tamed through laser-intensity modulation–e.g., to provide leverage over the stiffness or engineer quantum states. When approaching the quantum regime at low pressure, the oscillator's dynamics is governed by recoil heating, which stems from the backaction of scattered photons and whose control is highly desirable. Here, we demonstrate that spatial degrees of freedom accessible within the trapping-light field can be harnessed to modulate recoil heating. Specifically, we introduce an optimization routine that spatially shapes the optical field's wavefront to either maximize or minimize the recoil-heating rate independently of other parameters like the mechanical stiffness. Importantly, this approach can serve to devise sequences of patterns providing smooth and deep modulations. Implementable into common experimental setups, this work leads the way to the dynamic modulation of optomechanical coupling in optical levitation.

Quasi-neutral hybrid models for simulating plasmas, with kinetic ions and fluid electrons, have shown great promise for approaching the multi-scale nature of plasmas. In this paper we present a novel hybrid scheme for electromagnetic settings that conserves mass, momentum, and energy, employing a low-rank tensor method utilizing the Tensor Train decomposition to avoid the \enquote{curse of dimensionality.} Electrons are simulated as a continuous fluid, while the kinetic ions are modeled as a distribution function governed by the Vlasov equation. The Vlasov equation is updated with the implicit midpoint method. We use a modified version of a conservative truncation method to guarantee conservation of mass, momentum, and energy throughout the method, which corrects the truncated distribution function without needing to advance a separate set of fluid-moment equations. A series of electrostatic and electromagnetic test problems is used to demonstrate the correctness of the method and its conservation properties.

Nonlinear spectroscopy is widely used to probe ultrafast dynamics in matter. Signals of higher nonlinear orders encode valuable many-body dynamics but only recently have become individually retrievable. However, their physical interpretation in terms of double-sided Feynman diagrams is cumbersome and non-intuitive. Here, we establish an alternative framework that connects them to the underlying excitation conditions and many-body interactions. This approach predicts two classes of dynamic nonlinearities, due either to direct many-body interactions or to saturable kinetic channels. The method provides a direct physical interpretation of the retrieved signals, explaining their sign, magnitude, and dynamics. It also enables model-free reconstruction of the dynamics of a sub-ensemble of particles starting with a given number of excitons, which we demonstrate to be especially insightful. We validate the framework using colloidal CdSe/CdS core/shell quantum dots, which enable us to model nonlinearities of both classes in isolation and also combined.

Strain engineering is a leading route to control valley-selective transport in monolayer transition metal dichalcogenides. Whether this quantum effect survives inside a fully contacted device remains an open question. Quantum transport models capture the underlying valley deflection but are limited to isolated nanoscale fragments, leaving it unclear if a full device registers strain-induced valley sorting in the bulk, at the contacts, or at all. Here, the quantum valley-deflection tensor of strained monolayer MoS$_2$ is embedded directly into a macroscopic Poisson-drift-diffusion model, extending this physics to a complete, contact-inclusive device. Applying this framework to a 300~nm monolayer MoS$_2$ transistor with a 3~nm HfO$_2$ gate dielectric at 77~K, under a localized 6.14~T pseudo-magnetic strain field, shows the resulting 63.6% suppression of the ON-state current is not a bulk property. It is set almost entirely by the source and drain metal-semiconductor interfaces. The full suppression is reproduced when only the 15~nm contact regions are strained and nearly vanishes when the channel interior alone is strained, identifying the metal contact as the dominant site of strain-induced valley physics. Observing it requires tunneling-transparent, degenerately-doped contacts, since classical Ohmic barriers overwhelm the topological signal. A transmission-line analysis reduces this behavior to a single valley-topological contact resistance ($R_{c,V}$) and a predictive inverse-length scaling law. This framework applies to any strain-engineered 2D-material heterostructure and can be tested with standard multi-length contact structures. Ultimately, these results identify the metal-semiconductor interface as the decisive location for engineering and detecting valley-selective transport in real 2D-material devices.

An old but recurring question in ground-state density functional theory, relevant to the construction of density functional approximations, is the existence of a second-order gradient expansion for the exchange energy that is exact for densities that vary slowly over space. Here we will argue that there is such an expansion, with a gradient coefficient 10/7 of Sham's, as suggested by theoretical/mathematical derivations and by numerical calculations on model slowly-varying metallic densities, although some standard derivations do not quite capture the correct gradient coefficient. We also discuss long-range anomalies in the gradient expansions for the exchange and correlation holes around an electron. It is plausible that, as suggested earlier and recently, standard derivations are still correct for the coefficient of the second-order gradient expansion for exchange and correlation together, in which case the correct gradient coefficient for correlation is $C_{\mathrm{c}}$ = $C_{\mathrm{xc}}$ - $C_{\mathrm{x}}$. We propose possible numerical evaluations of $C_{\mathrm{xc}}$ and $C_{\mathrm{x}}$ that are free from long-range anomalies, and discuss implications for density functional approximations. Axel Becke's 1988 generalized gradient approximation (the B88 GGA) for the exchange energy brought density functional theory into chemistry, with a gradient coefficient for exchange that is optimal at the GGA level for atoms and molecules but too large for solids. Meta-GGAs seem to work as well for molecules as for solids, but may not be perfected yet. The article concludes with a few memories that the first author has of discussions with Axel Becke.

The CosmicWatch Desktop Muon Detector (v3X) is a compact and low-cost scintillator-based instrument designed to detect ionizing radiation, including cosmic-ray muons. Built around a plastic scintillator, a silicon photomultiplier (SiPM), and a dual-core Raspberry Pi Pico microcontroller, the v3X achieves a 400 microsecond dead time per event, sustained event logging at 700 Hz, and two-detector coincidence triggering for more accurate counts. This paper first summarizes the design, firmware, and demonstrated performance of the v3X prototype. It then outlines the design changes needed to prepare the detector for operation aboard a satellite, including a passive copper thermal strap that conducts heat from the microcontroller to the spacecraft chassis, a dedicated flight enclosure, material changes to meet outgassing standards, and a Controller Area Network (CAN) bus interface to connect the detector to the host spacecraft. The flight unit is targeted for launch via Oligo Space.

Nanophotonic sensors amplify analyte-induced optical signals through resonantly enhanced light-matter interactions. While effective for refractometric sensing, conventional sensing platforms typically collapse spatial variations in analyte distributions into ensemble-averaged readouts, thereby obscuring local heterogeneity. Here we present a dual-channel metasurface sensor capable of detecting both net analyte loading and nanoscale spatial inhomogeneity at deep-subwavelength scales. The device supports two distinct high-Q modes with deeply structured near-field profiles of opposite parity. Their complementary modal responses decouple analyte perturbations into bulk and differential components, which are encoded into two spectrally resolvable channels. We demonstrate these sensing modalities across analyte configurations exhibiting diverse forms of nanoscale heterogeneity, achieving a refractometric sensitivity of 357 nm/RIU and inhomogeneity detection below $\lambda/10$. This dual-channel capability, which extends nanophotonic sensing from analyte quantity to its spatial variation, opens a new avenue for multidimensional analysis of biological and material systems.

Controlling valley degrees of freedom with mechanical strain is a promising approach for solid-state information processing. Current theoretical literature routinely predicts strain-tuned valley splitting at the microscopic level but rarely evaluates whether these quantum predictions remain statistically recoverable in realistic macroscopic devices. This manuscript establishes a fully classical, partial differential equation-constrained multiscale inverse framework for quantifying the device-level identifiability of predicted strain-tunable valley effects in two-dimensional magnetic heterostructures, demonstrated here for a molybdenum disulfide and chromium tribromide heterostructure. First-principles structural relaxations confirm a chiral $C_3$ point-group symmetry which mathematically reduces the relevant exchange-strain coupling tensor to a single scalar. A partial differential equation-constrained adjoint-state architecture successfully bridges continuum elastodynamics to valley-resolved anomalous Hall transport. Density functional theory yields a coupling estimate of $\eta \approx -0.07$ meV whose 95% confidence interval is consistent with zero. Evaluating this specific coupling magnitude against established thermal noise and velocity saturation limits defines a safe operating window bounded between 262.0 and 22,337.6 V/cm. Rather than asserting a confirmed nonzero material property this bounded operational window functions as a precise diagnostic threshold. Deploying this rigorous statistical identifiability framework provides a necessary mathematical filter to determine the true experimental viability of theoretically predicted two-dimensional materials before complex physical fabrication.

Molecular hydrogen is predominantly formed on dust-grain surfaces in the interstellar medium, where relative gas-dust motion can arise in dynamically active environments. While the dependence of H$_2$ formation on grain temperature and surface properties is well studied, the impact of gas-dust drift has received little attention. We investigate how gas-dust drift modifies H$_2$ formation, focusing on the competition between the drift-enhanced H-atom collision rate and reduced sticking at higher impact energies. We use an event-driven kinetic Monte Carlo model that follows individual H atoms on spherical silicate and carbonaceous grains, including adsorption, surface migration, thermal desorption, and Langmuir-Hinshelwood (LH) and Eley-Rideal (ER) reactions. Drift is described by a shifted Maxwellian velocity distribution, and we compare constant and impact-energy-dependent sticking probabilities. Drift produces increasingly anisotropic distributions of adsorbed H and H$_2$ formation across the grain surface. Assuming constant sticking, increasing drift enhances H$_2$ formation through the higher collision rate, with efficiencies up to $\epsilon=0.3-0.4$. With energy-dependent sticking, strong drift instead suppresses formation on both materials, reducing efficiencies to $\epsilon=0.01-0.03$. Carbonaceous grains remain efficient to higher dust temperatures than silicate grains. ER reactions dominate over most of the investigated parameter space and become increasingly important at strong drift as the reduced surface population suppresses LH reactions. Thus, enhanced collision rates under gas-dust drift do not necessarily increase H$_2$ formation. Models of dynamically active environments should account for both relative gas-dust velocities and their effects on sticking.

A collisionless, low-$\beta$ plasma undergoing strong sub-Alfvénic turbulence is expected to form a broad hierarchy of reconnecting current sheets immersed in a strong guide magnetic field. If the reconnecting field strength of a sheet scales with its length as a power law of index $\kappa$, and the number of sheets per unit interval of sheet length decreases as a power law of index $x$, the current-sheet ensemble injects nonthermal particles with a power-law energy spectrum $\propto \gamma^{-s}$, where $s = 1 + (x-2)/(1 + \kappa)$. The spectrum is hard ($s < 2$) when the longest sheets deliver most of the injected energy, which requires $x < 3 + \kappa$. Past numerical studies have measured $2 \lesssim x \lesssim 3.3$, and the scalings of intense current sheets support $\kappa \gtrsim 0$, resulting in $1 \lesssim s \lesssim 2.3$. This injection may yield flat or mildly steep radio spectra in optically thin sources and hard ion spectra in the coronae of active galactic nuclei.

A structural simulation needs a constitutive law, and experiments rarely determine one uniquely: several laws may fit the same data and satisfy the same physical constraints, yet differ where the structure is loaded in ways the tests never were. Constitutive-Set Mechanics (CSM) keeps all of those laws. It replaces the single law in the variational formulation by the admissible set, and lets the mechanics itself decide which members of the set affect the structural prediction. The framework rests on one observation: a finite element assembly consults the law only at the strains the structure reaches, so the incremental energy depends on the law through a weighted record of those strains, the occupation measure of the state. The energy of a state under the set is a support function on that measure, and the equilibrium solve one performs anyway supplies the information needed to bound the worst case. Equilibrium states become certificates on the robust response, rigorous on the upper side for any mechanics solver and two-sided under global minimisation; the certificate has at most one more state than the number of constitutive directions the structure interrogates; a coherence theorem identifies when the pointwise-worst material is one no single material can be; and constitutive inference acts on the same support function. The method is demonstrated on linear elasticity, a data-constrained constitutive function, phase-field fracture, an assembled finite-strain composite shell, and a membrane characterised by one-mode tests alone. On the shell the structure interrogates four of 226 constitutive directions; on the membrane the certified worst-case energy exceeds the reference more than fourfold, and the experiment that most contracts the certified prediction is not the one where the constitutive uncertainty is largest.

Scent detection work is a form of interspecies cooperation. Dogs sample odors that handlers cannot perceive, and handlers decide where the team searches and which of the dogs' responses counts as a find. However, detection performance is predominantly measured by outcomes and dog characteristics. To better observe the search process itself, we collected data at two judged detection events (109 searches of 37 professional narcotics teams). All searches were video-recorded and included an outdoor area, a vehicle, and a building search, each with two targets and food and currency distractors. We synchronized computer-vision tracking of dogs and handlers with continuous behavioral coding. We observed variation across teams and in total 42% of targets were found. Finds were rarely missed because dogs never came close to a target or because handlers did not call an indication. Rather, most were missed at the encounter itself, as dogs came within 1~m of nearly every target but indicated on only about one in six of these passes. Handlers did not behave differently near targets than near distractors before their dogs responded. Finding targets was moderately consistent within teams, whereas false calls depended on the individual search and on specific items. Observing the search process suggests that success is a result of the dog's response, the handler's decision, and their coordination.

Direct-write pattering, using chemical vapor deposition through a nozzle, enables localized film growth using high-purity precursors standard to the electronics industry. Yet, achieving sub 100 $\mu\mathrm{m}$ feature control has been limited by nozzle design and unquantified precursor surface diffusion. Here, we report a direct-write chemical vapor deposition platform that employs a 10 $\mu\mathrm{m}$ tip diameter glass micro-nozzle mounted on a scanning stage above a heated substrate in ultra-high vacuum. Using this approach, we demonstrate the continuous single-pass writing of 500 $\mathrm{nm}$ thick metallic aluminum lines with a 30 $\mu\mathrm{m}$ linewidth on titanium nitride-coated silicon. This capability enables maskless, localized growth under systematically varied deposition conditions on a single substrate. To analyze the resulting deposit profiles, we introduce a transport framework coupling Knudsen gas emission from the micro-nozzle with surface diffusion to extract the effective precursor diffusion length, $L_d$. $L_d$ captures the combined impact of precursor surface mobility, residence time, substrate temperature ($165^{\circ}\mathrm{C}$ to $205^{\circ}\mathrm{C}$), growth duration, and nozzle height, contracting from 17.4 $\mu\mathrm{m}$ down to 3.2 $\mu\mathrm{m}$ at elevated temperatures. These results establish $L_d$ as a quantitative parameter for evaluating laterally confined chemical vapor growth and predicting feature resolution limits. This approach provides a practical methodology to measure and quantify precursor surface transport in localized film synthesis.

Resonant nanostructures are powerful for chiral control of light, but widely assumed to require achiral geometry. Here we demonstrate that optical chirality can emerge in geometrically achiral nanostructures, through the misalignment of independent linear responses. By rotating the conductivity tensor of an anisotropic plasmonic medium relative to a geometrically symmetric nanostructure, we generate a spin-selective optical response that can be continuously tuned both in magnitude and handedness, including reversible switching between left- and right-handed behaviour. We experimentally realize this concept using an aligned plasmonic conducting polymer and by controlling the orientation of its polymer backbone relative to otherwise identical rectangular nanoantennas. This leads to strong planar chirality that can also be reversibly modulated by varying the carrier density of the polymer. Moreover, the rotation angle of rectangular meta-atoms made through this principle encodes no longer only the local geometric phase but simultaneously the local optical chirality. This enables a new design paradigm for phase-gradient metasurfaces , enabling simultaneous control of optical chirality and wavefronts through a single rotational degree of freedom.