Papers

The assembly of multicomponent nanoparticles is often kinetically controlled and exhibits strong pathway dependence. Transport, solvent exchange, nucleation/growth, and collision-driven coalescence together determine not only ensemble-averaged properties but also particle-to-particle compositional heterogeneity. Here, we present a computational modeling framework for predicting nanoparticle property distributions by coupling processing conditions, early-stage self-assembly physics, and molecular chemical details with kinetic Monte Carlo (kMC) simulations. The framework combines (i) mixing conditions with solvent-exchange-mediated particle initialization and growth, and (ii) kMC simulations that resolve stochastic collision histories, electrostatics-controlled coalescence, and composition at the level of individual particles. Applied to mRNA lipid nanoparticles, the model predicts size-loading correlations and provides insight into how processing-dependent assembly pathways lead to heterogeneous payload distributions. The kMC simulations further provide merging lineage histories, which explain the emergence of log-normal volume and payload distributions through multiplicative particle-growth pathways. The same framework is also applied to PEI-DNA polyelectrolytic complexation, yielding single-particle-resolved DNA-PEI stoichiometry distributions. The framework and its open-source implementation, FormLNP, provide a process-aware route to predicting and controlling single-particle property distributions across a broad range of multicomponent nanoparticle systems.

For nearly four decades, cavity ring-down spectroscopy (CRDS) has been among the simplest, most widely used absorption techniques, requiring no calibration because it measures decay times, not intensities. Yet as accuracy requirements have tightened, CRDS has proven strikingly inconsistent: reported errors range from several percent to the permille level - the signature of an uncontrolled systematic effect so far worked around rather than resolved. This has confined applications demanding the highest accuracy - from testing ab initio theory to atmospheric sensing and quantum-based metrology - to a handful of laboratories relying on advanced calibration or architectures, at the cost of simplicity. Here we show the main source was hiding in plain sight: the detection chain's transient response. It distorts every ring-down, biasing the retrieved decay times differently from instrument to instrument - a bias that routine truncation of the early decay masks rather than removes. We measure this response with a calibration-free beat-note method and build it into the ring-down model. Validation uses a well-characterized carbon monoxide transition and an independent frequency-based reference. Across four detection configurations, line-intensity biases of up to 14 % shrink 41- to 87-fold, the results converge, reaching up to sub-permille agreement with the reference. This holds down to 4 us ring-down times, extending accurate CRDS to stronger absorption. Independent of the absorbing species and requiring no change to the CRDS setup, the approach turns a technique valued for its simplicity into one equally valued for its accuracy. Benchmark-quality measurement thus comes within reach of any CRDS laboratory.

Modeling complex physical phenomena directly from experimental data poses fundamental challenges: measurement devices introduce noise and biases into the data, the governing equations are often unknown or intractable, and the dynamics observed experimentally may exhibit stochastic behavior. In this paper, we consider capillary wave turbulence—an example of nonlinear wave interactions at a microfluidic interface—measured by ultra-high-speed digital holographic microscopy. With the goal to learn an efficient model directly from these data, we adapt a stochastic extension of Operator Inference to learn low-dimensional stochastic differential equation representations of the microscale wave dynamics from experimental measurements. Each experiment is repeated 16 times, and 10 different conditions are created by changing the nondimensional acoustic capillary number through an excitation device. In addition, we propose a new strategy to select the reduced-order model dimension, based on both mean and covariance errors. We further add Tikhonov regularization to the stochastic Operator Inference framework and show that, beyond its conventional role as a numerical stabilization technique in deterministic settings, it has a physically meaningful interpretation as controlling the spectral content of the learned stochastic dynamics. We demonstrate that the resulting stochastic reduced-order models faithfully capture salient physical features of capillary wave turbulence across a range of experimental conditions.

We present measurements of the hyperfine structure, transition frequencies and relative intensities in 14 lines of 169Tm+ ions starting from the $4f^{13}({}^2\!F^o_{7/2})6s_{1/2} (7/2, 1/2)_{3,4}^o$ levels. The magnetic dipole hyperfine ($A$) constant of all 15 involved ionic levels are determined, 14 of which have sub-MHz accuracy. For five levels no previous measurements were reported, while all others are in excellent agreement with the literature and improve on the previous accuracy by typically 1 - 2 orders of magnitude. We compare transition strengths and include improved transition frequencies. These results are useful for the quantum computing community, modelling of Tm$^+$ abundances in stellar spectra, as well as for an ongoing measurement program on short-lived Tm isotopes at ISOLDE/CERN.

The nature of the dissipation of turbulent kinetic energy (TKE) is investigated experimentally in the wake of a diameter $D=0.58$m wind turbine exposed to several “flavours” of high-Reynolds-number free-stream turbulence (FST). For low- and moderate-intensity FST, an annular region of elevated normalised dissipation, $C_{\varepsilon}$, develops in the outer wake, coinciding with a ring of enhanced turbulence intermittency at both small ($\ell \leq \lambda$) and large ($\ell \geq D$) scales, where $\lambda$ is the Taylor microscale. In the blade-tip region, $C_{\varepsilon}$ scales with $\sqrt{Re_D}/Re_{\lambda}$, where $Re_D$ is a global Reynolds number and $Re_{\lambda}$ a local turbulent Reynolds number based on $\lambda$-a scaling indicative of dissipation being out of equilibrium with the inter-scale flux of TKE in the inertial range of the energy cascade. This non-equilibrium regime is interpreted in light of the observed intermittency : large-scale intermittent events (i.e., low-wavenumber perturbations/“kicks”), driven by persistent tip-vortex and tip-shear-layer dynamics under low-intensity FST, require a finite time to cascade down to the dissipative scales, thereby introducing an imbalance between the inter-scale energy flux and dissipation. At the wake centreline, by contrast, $C_{\varepsilon}$ remains approximately constant with streamwise distance, reflecting either classical Kolmogorov-type equilibrium turbulence or balanced non-equilibrium turbulence, with intermittency confined to the small scales. Under high-intensity FST, large-scale intermittency is suppressed, consistent with the erosion of tip-vortex structures, and no comparable scaling for $C_{\varepsilon}$ could be identified using a single turbulent Reynolds number for these cases, where two similarly intense streams of turbulence, but of different origins, are adjacent to one another.

High-resolution cross-correlation spectroscopy (HRCCS) is a powerful technique for detecting molecules whose individual spectral lines are too weak to be identified directly, but its sensitivity is limited by the availability and quality of high-resolution opacity data. Many molecules of atmospheric and astrobiological interest lack complete line lists, restricting traditional template-based searches. In this work, we use Titan as a controlled testbed to develop and validate a new cross-section-based methodology for HRCCS template construction. We analyse K-band CRIRES+ observations of Titan (1.99 - 2.48 {\mu}m), and compute cross-correlation functions using both line-by-line and cross-section-based templates. Our analysis recovers known hydrocarbons such as methane (CH4) and acetylene (C2H2), and yields the first HRCCS detection of ethane (C2H6) with a significance of SNRpeak = 5.17 {\pm} 0.07. The ethane detection was made possible exclusively through cross-section-based templates, as no high-resolution line list currently exists for this molecule. These results demonstrate that cross-section-based template construction is a practical and powerful strategy for extending HRCCS to molecules that currently lack reliable line lists, and establish Titan as a benchmark for calibrating molecular detection techniques that can be applied to both solar system and exoplanet atmospheres. Future applications of this approach to other ground-based high-resolution spectrographs, as well as to JWST's highest-resolution modes and next-generation facilities such as the ELT, could significantly expand the inventory of molecules detectable in planetary atmospheres.

Three-dimensional linear stability of a weak, compressible shock layer is analyzed using exact Reynolds-number-independent forms of the governing base flow and linear stability equations, obtained by rescaling the problem onto the shock's own natural viscous length and time scales. For any base flow with zero transverse velocity components, depending on the shock-normal coordinate alone, the resulting eigenvalue problem is proved covariant under rotation of the transverse wavenumber vector: the eigenvalue depends on the transverse wavenumbers only through their magnitude, and every three-dimensional eigenmode, throughout the continuous spectrum, decouples exactly into two independent constituents. A two-dimensional, in-plane acoustic–entropy branch carries the disturbance's entire dilatation, pressure and thermodynamic coupling, and a one-dimensional, out-of-plane purely solenoidal vortical branch is governed by a single scalar shear-diffusion equation. Three-dimensional shock-layer stability is thus resolved exactly into its compressible and vortical constituents, confirmed to machine precision by the Grosch–Salwen far-field companion spectra. Both decoupled operators carry an explicit damping term growing with the total transverse wavenumber, so a three-dimensional, oblique disturbance is never less stable than the two-dimensional disturbance sharing its in-plane wavenumber alone; the vortical branch, whose spectrum is controlled directly by this term, is shown to retreat from the imaginary axis in close proportion to the wavenumber squared. This finding is an exact, Reynolds-free analogue of Squire's classical theorem, with the stabilizing role of Reynolds number played here by the transverse wavenumber magnitude.

This work investigates the unsteady aerodynamic performance and vortex dynamics of avian-inspired flapping wings using our in-house sharp-interface immersed-boundary solver, VorteXdyn. A falcon-inspired body-wing model based on NACA 4312 profile is employed to examine aerodynamic force generation and vortex evolution during steady forward flapping flight at Reynolds numbers of 2,500, 5000, and 10000 and Strouhal numbers of 0.18, 0.225, and 0.27. The influence of physiologies is examined using three wing configurations: a simplified wing without distinct feather structures, a wing incorporating feather-like structures (serrations) along the trailing edge, and a geometrically detailed wing incorporating multiple feather layers consisting of primary, secondary, and median feathers over its span. The aerodynamic performance of these configurations is quantified using the temporal profiles and time-averaged characteristics of the lift and drag coefficients and the lift-to-drag ratio. The associated vortex dynamics are characterized through the formation and evolution of multiple leading-edge vortices (LEVs), their spanwise coherence, circulation, characteristic size, and persistence over the wings' surfaces. Particular emphasis is placed on the spanwise development of the LEVs from the root to the wingtip, their interactions with the tip vortices, and the resulting wake evolution during the downstroke. Our results demonstrate that increasing geometric fidelity modifies aerodynamic force production, the formation and evolution of multiple LEV structures, vortex-vortex and votex-wing interactions, and wake topology. These findings provide insight into the aerodynamic role of feather morphology in three-dimensional flapping flight for the aerodynamic design of efficient bio-inspired flapping-wing micro air vehicles.

We report the measurement of the critical velocity of a paraxial superfluid of light flowing past a mobile impurity, realized using a dual-wavelength optical scheme in a warm vapor of $^{87}\text{Rb}$. The superflow is two-dimensional by nature, while the impurity features a highly repulsive potential, a large tunable radius, and a finite mass. The critical velocity is evaluated by simultaneously monitoring three complementary observables: the number of vortices nucleated in the wake of the impurity, the hydrodynamic force exerted on it, and its displacement velocity relative to the flow. These independent methods yield consistent values for the critical velocity, which remain systematically below the Landau criterion and decrease with the impurity radius, in qualitative agreement with theoretical expectations. Our results demonstrate the reliability of these combined diagnostics, establishing paraxial superfluids of light as a versatile platform for exploring the impurity problem in quantum hydrodynamics.

Using a comprehensive set of three-dimensional, high-resolution direct numerical simulations, we investigate the existence of a dissipative anomaly in isothermal, homogeneous, isotropic compressible turbulence driven by solenoidal forcing. We find that the total kinetic-energy dissipation rate, as well as its solenoidal and dilatational components, approaches finite asymptotic values with increasing Reynolds number $Re$. The normalized mean dissipation rates collapse onto two distinct branches: one corresponding to the subsonic and transonic regimes, with root-mean-square Mach numbers ($M_{\rm rms}\lesssim 1$), and another to the highly supersonic regime, with ($M_{\rm rms}\ge 3$). This two-branch Mach-number dependence is most pronounced for the total kinetic-energy dissipation. For the solenoidal and dilatational dissipation rate components, the dependence on $Re$ depends in addition on the specific choice of the integral scale and root-mean-square velocity. Despite grid resolutions of up to $2048^3$ points the Reynolds numbers accessible are not sufficiently large to distinguish conclusively between a weak and a strong dissipative anomaly. We substantiate these findings using three complementary approaches: (i) a detailed analysis of the mechanisms responsible for dissipation generation, based on the corresponding dissipation-rate balance equations and their individual production terms; (ii) an investigation of precursors of anomalous dissipation using the Duchon–Robert framework extended to compressible flows; and (iii) a geometrical characterization of regions of intense dissipation. Taken together, these analyses provide consistent evidence for the existence of a dissipative anomaly in isothermal compressible turbulence, while leaving its precise weak or strong character unresolved.

Hydrogen tunneling is central to many chemical and biological processes. Herein, we introduce the vibronic-SHAKE (V-SHAKE) approach to comprehensively sample energy-conserving molecular configurations enabling hydrogen tunneling. A constrained form of nuclear-electronic orbital multistate density functional theory (NEO-MSDFT) dynamics, where the tunneling hydrogen nucleus is quantized, is used to sample geometries in the diabatic seam space corresponding to the intersection of the reactant and product NEO-DFT diabatic vibronic surfaces. V-SHAKE is applied to hydrogen and deuterium tunneling in 4-cyanobutanolate and Z-4-hydroxybut-3-en-2-one. The vibronic coupling is found to vary significantly in the diabatic seam space, mainly due to changes in the donor-acceptor distance. The reaction coordinate and gradient of the vibronic coupling at the minimum energy crossing point are nearly orthogonal and are dominated by motions stabilizing the product relative to the reactant or decreasing the donor-acceptor distance, respectively. V-SHAKE provides fundamental insights and validation for assumptions underlying rate theories.

Metallic nanoring networks can serve as promising flexible transparent electrodes. These materials are crucial components in a wide range of applications, including solar cells, touchscreens and displays. In this work, a computational investigation considers in detail (i) the electrical conductance and optical performance of nanoring networks and (ii) the breakdown of these networks due to electrical damage. The electrical resistance of both the nanorings and the contacts between the rings (junctions) is taken into account. In part (i), the effects of 5 parameters on the electrical sheet resistance and optical transparency are presented. It is shown that several parameter combinations achieve better performance in comparison to indium tin oxide, currently the most widely used transparent electrode. In part (ii), due to electrical damage, the nanoring systems display the formation of a crack, running parallel to the vertical terminals, where a voltage difference is applied. The network degradation is measured by its sheet resistance, and a universal effect is observed: networks with varying filling factors exhibit the same degradation profile.

Deng et al. discovered that Drt3b synthesizes protein-primed poly(AC) DNA without a nucleic-acid template. The structures reveal a finite protein architecture and product-associated contacts, but not the renewable dynamical rule that converts a bounded pocket into a long period-2 sequence. We propose that Drt3b is a regenerating protein-substrate free-energy register. The framework specifies a sampled quantum mechanics/molecular mechanics (QM/MM) potential of mean force and a preregistered, within-domain first-order electronic-descriptor map for edge-specific activation barriers; physical curvature is admitted only through a training-frozen Taylor-remainder bound that widens uncertainty but never corrects a held-out mean. Conditional hazards derived from those barriers are averaged over unresolved conformational, hydration, protonation, and metal-coordination trajectories to yield a semi-Markov event kernel. Pointwise ratios of competing hazards are therefore fixed by the same barrier differences and preregistered prefactor rules that govern exit timing, so nucleotide choice and dwell statistics are co-generated rather than separately fitted. A regime gate separates stationary or slowly driven experiments, analyzed by a joint sequence-time cycle determinant, from genuinely nonstationary protocols, analyzed by the full age-structured tilted propagator. The decisive test is one locked parameterization: without condition-specific refitting, it must jointly predict nucleotide choice, waiting-time laws, product-length tails, and the rank, minors, null spaces, and rescue structure of perturbation responses. An independent 2026 DRT3 study supplies a stringent cross-construct transport test after sequence and structural alignment are frozen. The proposal requires neither coherent quantum computation nor coupling to an external field.

From protein complexes to electronic circuits, many natural and engineered systems function only if assembled in an exact, reproducible fashion. The structure of each of these systems can be understood as a network, yet network science lacks the mechanisms to consistently reproduce exact topologies, focusing instead on generating network ensembles. We introduce the framework of network design where we encode the local constraints obeyed by a system's building blocks in a design set, and derive the Unigraphical Design Theorem, which determines when these constraints guarantee reproducible assembly into a unique structure, a process we call unigraphical assembly. For systems whose design sets do not specify a unique outcome, we identify guided assembly as a second route to reproducibility, in which temporal ordering decomposes construction into unigraphical steps. Applying these results to 3,618 reproducible systems, including protein complexes, molecules, and robots, we classify those that undergo unigraphical assembly and those that require guided assembly. We further identify a diversity-redundancy boundary that explains how systems trade component variety for structurally interchangeable parts while retaining unique assembly. Finally, we experimentally test the theory using 3D-printed components to re-engineer generative construction sets into systems that assemble unigraphically into prescribed topologies. Network design thus reframes reproducibility as a mathematically testable property of real networks, opening a route to the rational engineering of complex systems.

We investigate whether a perfectly ordered periodic nanolaminate is intrinsically less mechanically dissipative than an isotropic co-sputtered mixture designed to provide the exact same optical function. Focusing on a room-temperature SiO2/TiO2 system, we compare coating Brownian dissipation under strict parity of optical thickness and effective refractive index. Amorphous phases are modeled using complex Young's moduli and real Poisson's ratios. The comparison is highly non-trivial: while the nanolaminate is optically optimal, its structural anisotropy forces in-plane shear to operate at the highly dissipative Voigt upper bound, mechanically penalizing it against an isotropic medium. The nanolaminate is treated via exact optical (Wiener) and mechanical (Backus) relations. The isotropic mixture is described by optical closures (Lorentz–Lorenz, Bruggeman) coupled to a complex mechanical self-consistent scheme. To assess uncertainty from the unknown co-sputtered morphology, we explore rigorous optical and mechanical admissible regions in the complex plane. Owing to the small intrinsic losses of SiO2/TiO2, these regions collapse into extremely narrow lenses, strictly limiting microstructural uncertainty. Across all realistic isotropic closures, the nanolaminate systematically exhibits lower mechanical dissipation than the co-sputtered mixture. Even against the thermodynamic lower bound of the isotropic state, this topological advantage remains valid for high effective refractive indices. We prove the ordered architecture exploits the optical upper bound to minimize the required volume fraction of the highly dissipative high-index phase, overwhelmingly overcompensating for its intrinsic mechanical anisotropy penalty.

Rare-event search experiments demand ever-lower backgrounds, and the resulting radio-pure materials and suppressive shielding make related background simulations computationally expensive without event biasing. We present a method for optimising the widely used importance splitting and Russian roulette technique for rare-event searches, in which a whole number of equal-width importance layers fill the shielding geometry. Balancing relative uncertainty against execution time, we optimise the layer thickness for both $\gamma$-rays and neutrons, and across different shielding materials and thicknesses. Expressed as a multiple of the primary particle's mean free path, the optimal thickness generalises across energies for a given particle type and yields a physics-based prediction applicable to any shielding design. The same trend reveals that over-biasing is possible, with relative uncertainty worsening at high layer counts for fixed execution time. The optimal configuration gives an $\mathcal{O}$(20) gain in computational efficiency over the unbiased case.

Fluid shear can induce detachment of a thin elastic sheet adhered to a flat substrate. This peeling process is important in a variety of environmental and technological systems. The condition for peeling depends on: the shear rate $\dot{\gamma}$, the fluid viscosity $\eta$, the length of the detached portion of the sheet $L$, the bending rigidity $B$ and the adhesion energy $\Gamma$. What are the laws governing the detachment? We address this question experimentally in the regime of intermediate adhesion, using macroscopic sheets bonded to a substrate and immersed in a shear cell containing a viscous fluid. The experiments indicate a critical shear rate for peeling of the order of $\dot{\gamma} \sim B/(\eta L^3)$. This threshold is, unexpectedly, independent of adhesion. We rationalise this result by applying Griffith's fracture theory to optical measurement data of the shape of the sheet, under conditions of freely moving peeling front or clamped boundary. The results indicate that the large curvature of the sheet for $\dot{\gamma} \sim B/(\eta L^3)$ yields a nearly diverging strain energy release rate at this threshold. This approximate divergence in turn yields a peeling threshold that depends at most weakly on $\Gamma$, confirming a theory that was proposed recently (Salussolia et al., J. Mech. Phys. Solids, 2020, 134). Among other applications, our work provides a quantitative formula that can aid the production at scale of 2D materials such as graphene.

Enzymatic degradation of biopolymers underpins critical processes in biotechnological applications and natural processes, such as food digestion and cancer development, yet the interplay between enzyme propagation in a dense substrate and substrate degradation remains unresolved. It remains especially unclear whether degradation of a dense matrix facilitates or impedes enzyme propagation. We simultaneously track with spatiotemporal resolution unidirectional enzyme diffusion and biopolymer degradation in a model system. We demonstrate that enzyme diffusion is decoupled from catalytic activity, while the degradation front progression is dictated by enzyme diffusion, reaction kinetics and slow enzyme deactivation. These findings establish a quantitative framework to optimize enzymatic processes for applications in biomedicine, biomass valorization, and nanotechnology.

This work presents ESRIcascade.jl, a Julia package for computing the Economic Systemic Risk Index (ESRI). The package combines computational efficiency with a documented interface designed for research workflows. Our benchmarks show that ESRIcascade.jl is from 140 to 300 times faster than existing implementations. For a realistically sized synthetic economy, this reduces computational time from 16 hours to 7 minutes. By reducing computation times, ESRIcascade.jl makes repeated analyses of larger production networks practical and feasible and may enable broader use of ESRI in empirical and methodological research. The package and its documentation are freely available at https://github.com/Devetak/ESRIcascade.jl under the MIT License.

The ultralow transverse emittance of diffraction-limited electron storage rings leads to high beam densities, making direct space-charge effects increasingly relevant despite their relativistic suppression at high energy. In this work, we investigate the impact of direct space charge on transverse coupled-bunch instabilities, using PETRA-IV as a representative example. A Vlasov solver, multi-bunch particle tracking, and analytical calculations in normalized phase space are combined to study the coupled-bunch mode spectrum, the nonlinear incoherent tune spread, and the associated Landau damping. Particle-tracking simulations show that the space-charge-induced tune spread can provide sufficient Landau damping to stabilize higher-order head-tail modes. Landau stability diagrams constructed from the analytical tune spread of a frozen bi-Gaussian beam predict the stabilization of these higher-order modes, in agreement with the tracking simulations. These results demonstrate the important role of direct space charge in transverse coupled-bunch dynamics. The analysis framework developed here can also be extended to collective-instability studies in both electron and hadron accelerators.

Physics-informed neural networks for high-order partial differential equations require mixed input derivatives and their gradients with respect to the network parameters. Standard implementations obtain an order-$K$ derivative by repeated automatic differentiation. We instead organize the forward recursion of the multivariate Faà di Bruno formula and its explicit backpropagation over a prescribed downward-closed set of multi-indices, with the Bell-polynomial convolutions tabulated once. The forward pass carries only the derivatives the differential operator requires. The backward pass propagates gradients from losses formed from any subset of them, including nonlinear products and coupled fields. Both recursions are exact up to roundoff and avoid nested computational graphs. An independent Taylor-jet implementation, symbolic checks of the test problems, and finite differences verify derivatives and loss gradients through order seven. On one CPU core, the method evaluates 330 mixed derivatives with respect to four inputs through order seven and the corresponding loss gradient without the memory failures observed for several nested implementations. Numerical tests include third-, fifth-, and seventh-order dispersive equations, incompressible flow, and a manufactured five-field electrohydrodynamic system. The seventh-order Zakharov-Kuznetsov test in $3+1$ dimensions has a relative solution error of $6\times10^{-4}$.

Understanding the 3D fiber architecture of the brain at the microscopic scale is essential for revealing its structural connectivity and function. Polarization-based optical imaging (3D-PLI) techniques enable high-fidelity reconstruction of single nerve fiber orientations but struggle to resolve fiber crossings, which are critical for recovering the full connectome. Scattering-based imaging provides access to the structure factor of three-dimensionally oriented fibers. By probing a fixed scattering angle under multiple azimuthal illumination angles, in-plane fiber orientations and crossings can be recovered using computational scattered light imaging (SLI). However, despite containing 3D information, a theoretical framework to extract full 3D orientations has been lacking. In this talk, a simple analogical approximation of Rayleigh-Gans scattering theory is introduced to extract the 3D orientation of nerve fibers from one-dimensional SLI measurements. The theory is validated using tilted microscopic glass phantoms consisting of 2 um-thick rod lattices fabricated by two-photon lithography. Finally, the method is applied to brain tissue samples and compared with 3D-PLI.

Accurate cloud-cover forecasts are important for temperature prediction, radiation forecasting, and solar-power operations. Short-range forecasting methods can preserve observed cloud placement during the first forecast hours, but their skill decreases when cloud fields evolve through formation, dissipation and deformation. Longer lead times require accounting for atmospheric evolution, but operational numerical weather prediction (NWP) forecasts may not accurately represent the satellite-observed cloud state at initialization. We develop CloudCast v2, a machine-learning model for 12-hour cloud-cover forecasting from observation-based initial conditions. The model is first trained on the Copernicus European Regional Reanalysis (Ridal2024) to learn cloud-evolution dynamics, and is then adapted to satellite-derived cloud fields using conditional flow matching (Lipman2023), a generative method that transforms noise into cloud-cover forecasts conditioned on the observed initial cloud fields and NWP inputs. CloudCast v2 reduces mean absolute error by 10% relative to its predecessor, CloudCast v1 (Partio2025), over the 1-12 h range. It also overtakes CloudCast v1 in fractions skill score, a neighborhood-based measure of spatial agreement, after approximately 3-6 h, depending on the cloudiness category. These results show that observation-initialized machine-learning forecasts can extend beyond the usual 1-3-hour nowcasting range while retaining spatial detail from satellite cloud fields.

The recent discovery of excitons coupled to the magnetic order, and the consequent strong magneto-optic responses, in some van der Waals magnetic semiconductors has triggered intense activity at the interface of magnetism and semiconductor optics. Here, we present an analytically tractable minimal model that describes magnetic order, electrons, holes, and excitons within a unified framework, thereby capturing a wide range of phenomena. It treats the magnetic order and itinerant carriers to be comprised by distinct electronic orbitals that are mutually coupled via orbital-dependent onsite exchange, similar to the treatment of metallic magnets using an s-d model. Investigating CrSBr bilayer as a case study, we benchmark our model and its predictions against recent experimental and ab-initio results finding good agreement as well as new insights enabled by the model's simplicity. Examining the optical selection rules, we find the conservation of a quantum number formed from a combination of spin and layer pseudospin to be a useful guiding principle, even in noncollinear magnetic configurations. Our analysis finds a series of bright and dark excitonic states in such layered A-type antiferromagnets. The presented framework should be valuable in achieving intuitive understanding of recently discovered excitonic phenomena and guiding the discovery of other excitonic states in layered magnetic semiconductors.

Computer-aided engineering (CAE) simulation is among the largest and most demanding areas of engineering, where setting up a solver such as OpenFOAM, FEniCS, or COMSOL takes real expertise. Large language model (LLM) agents promise to turn a natural-language request into a working simulation, and recent CAE agents add simulation-specific machinery: multi-agent decomposition, domain retrieval, and scripted reflection. That machinery suited weak base models; modern harnesses already supply multi-turn reasoning, tool use, and execution feedback. We ask what a CAE simulation agent still needs beyond a generic harness. With information access and repair budget held fixed, a single-agent harness matches or beats multi-agent specialized systems (FoamBench 96.4% vs.\ 88.2%). Ablations trace this to capabilities the harness already provides: execution-feedback repair lifts FoamBench from 71.8% with no repair round to 96.4%, while scripted reflection adds nothing. The one input that still helps is domain knowledge supplied as solver tutorials, our largest measured gain (80.9% to 96.4%).

The role of large-scale pre-existing interplanetary structures, including preceding coronal mass ejections (CMEs) and stream interaction regions (SIRs), in shaping the shock acceleration environment for energetic electrons remains not fully understood. In this study, we investigate nine interplanetary shocks observed by the Solar Terrestrial Relations Observatory (STEREO) that are associated with significant MeV electron enhancements, as such enhancements are rarely observed at interplanetary shocks. We combine remote-sensing observations, drag-based modeling, and in-situ measurements to analyze the shock propagation through pre-existing interplanetary structures. Eight of the nine events are associated with a preceding slow or intermediate-speed CME, while six shocks are in-situ observed propagating within preceding ICMEs, indicating that large-scale upstream trapping structures are a common feature of these events. Further analysis identifies three distinct scenarios associated with enhanced electron acceleration: shocks propagating through preceding ICMEs, shock-SIR interactions, and direct injection of flare-accelerated electrons into SIRs. As a representative shock-in-ICME event, the 2012 January 29 low-$\beta$, quasi-perpendicular shock ($\theta_{Bn}\sim87^\circ$) is further investigated using observations together with one-dimensional Monte Carlo test-particle simulations of a shock propagating into a large-scale upstream magnetic loop. The simulation suggests that the upstream loop prolongs electron residence near the shock and substantially enhances acceleration efficiency. These results demonstrate that large-scale interplanetary structures can precondition the upstream magnetic environment, providing favorable conditions for prolonged electron residence and efficient shock acceleration.

The vertex model is widely used to describe the mechanics of epithelial tissues, but its conventional formulation assumes that all cells remain tightly packed and always share edges with their neighbors, making it difficult to represent local detachment or gap formation. Here, we propose a minimal extension of the vertex model that enables cell detachment by introducing a new topological transformation, T2-inverse, which acts as the inverse of the classical T2 transition. When the imbalance of forces acting on a vertex, quantified by a tension metric $T$, exceeds a threshold, the T2-inverse splits the vertex into multiple vertices and creates a closed polygon that is incorporated as a pseudo-cell. This operation allows the model to represent the emergence and propagation of local detachment events. Using this framework, we simulate the stretching of a cell sheet and show that force-induced local detachments can accumulate to produce macroscopic tissue rupture. These results demonstrate that the proposed model extends the capability of the vertex model to describe tissue-level breakdown processes, including detachment and tearing, and provides a foundation for studying a broader class of epithelial mechanical phenomena.

This paper studies the statistics of extreme cold air surface temperatures in a climate that experiences abrupt changes. We employ a CCSM4 simulation of Last Glacial Maximum conditions that exhibits rapid switching between stadial and interstadial states as data. Non-stationary linear Generalized Extreme Value (GEV) distributions are fitted to find the regions that show the most prominent changes and associate them with physical processes. The results are then compared with a non-linear model, which gives a more detailed picture of how the parameters change as a function of the AMOC strength. While different regions of the world show different extremal behaviours, many regions show an approximately linear relationship between the parameters of the GEV distributions and the strength of the AMOC within the stadial and interstadial states, with some non-linear oscillation or jump where the transition between them takes place. Physical processes such as the expansion and retreat of the sea ice and the relative changes in the strength of the currents are shown to impact the magnitude and variability of the extremes, with significant changes observed in the three parameters of the GEV. They also create teleconnections that are compared whenever possible with various proxies for the temperature of the air and the water. We show how mapping the parameters of the GEV distributions into the AMOC strength gives a way to compare results between different climate models and different climate states. Comparisons, however, need to pay heed to the dynamical characteristics of the state and the location to be meaningful.

Surface air over land warms substantially more than over the ocean under greenhouse forcing, a phenomenon known as the land-ocean warming contrast. Current explanations for this contrast are commonly expressed either in terms of energetic constraints, from top-of-atmosphere and surface energy balance, or dynamical constraints, from large-scale atmospheric dynamics. We show that these perspectives are complementary when viewed through the lens of atmospheric moist static energy (MSE) transport, and that connecting them yields new insight into the controls of the warming contrast and the spread in climate models. We use this framework to construct an interpretable emulator that reproduces the land-ocean warming response across 22 models from the latest Coupled Model Intercomparison Project (CMIP6). We find that the strength of the land-ocean warming contrast emerges from the interplay between a model-dependent radiative baseline and a robust dynamical restoring mechanism that favors greater warming over land. This interplay leads to two broad model regimes that align with climate sensitivity. In low-climate-sensitivity models, more stabilizing radiative feedbacks over the ocean directly favor greater land warming. In high-climate-sensitivity models, radiative feedbacks alone would instead favor greater ocean warming, but a strong MSE-transport feedback (approximately 0.2 PW/K) more than compensates for this tendency. The intermodel spread in the land-ocean warming contrast is closely related to the ratio of radiative feedbacks over land and ocean, highlighting a broader connection between climate sensitivity and the land-ocean warming contrast.

Accurate numerical models of sound propagation through biological media are an important tool for many applications, from medical physics to studying the auditory system of humans or other animals. We model high-frequency elastic and acoustic wave propagation through the head anatomy of a common bottlenose dolphin (Tursiops truncatus), by means of the open-source software packages specfem3d, based on the spectral-element method, and \textsc{k-wave}, based on the pseudospectral method. To achieve sufficiently high performance, we ported the latter solver to C++, for Multi-GPU CUDA support via the slab-decomposition of three-dimensional Fast Fourier Transform approach. Because the two schemes differ fundamentally in how the propagation medium is discretized and internal (in particular fluid-solid) interfaces are treated, similarity between modeled signals across methods is a legitimate measure of model accuracy. Plane waves, depending on time like four-cycle sinusoidal bursts of varying central frequency (20–100 kHz), are numerically propagated through a computed-tomography-based anatomy model using both solvers. The sound is “recorded” in front of the rostrum and at the right inner-ear locations for comparison. We successfully cross-validate both solvers on high-fidelity High-Performance Computing (HPC) clusters. We find that the stability of results from both solvers grows as the corresponding spatial resolution is refined. At their highest resolutions, the two methods show excellent agreement, with normalized correlation exceeding 0.99 across the entire 20–100 kHz frequency range. Together, our results provide a validated HPC-simulation framework for wave propagation in biological media, with broader implications, e.g., for biosonar research, auditory biomechanics, and medical ultrasound.