Papers

Forecasting pandemic spread across heterogeneous regions demands models that are both accurate and interpretable for public health action. This paper proposes a hybrid forecasting framework that integrates an SEIR epidemiological model enhanced with a Temporal Fusion Transformer (TFT) to produce interpretable multi-horizon infection forecasts. Using region-wise COVID-19 data from the Our World in Data corpus, we present a unified pipeline that integrates epidemiological indicators (susceptible, exposed, infectious, recovered), intervention signals (masking, vaccination, policies), mobility covariates, and demographic context. The SEIR component estimates latent disease dynamics and injects mechanistic priors, while the TFT learns non-linear temporal dependencies and cross-feature interactions from historical observations and known future inputs. To ensure transparency, SHAP (SHapley Additive exPlanations) is applied at both global and local levels, quantifying feature contributions and revealing region-specific drivers of surges and slowdowns. Compared with a calibrated standalone SEIR baseline, the hybrid SEIR-TFT approach provides more stable and accurate forecasts across epidemic phases, particularly under shifting intervention regimes. The framework is further operationalized through structured prediction and explanation outputs for interactive dashboards, enabling time-series, policy-impact, and geospatial analysis. By unifying mechanistic modeling, deep temporal learning, and explainable AI, this study offers a practical decision-support system for policy-sensitive pandemic forecasting.

Commercial off-the-shelf~(COTS) Li ion cells offer an attractive and rechargeable power source for spacecraft applications. However, since these cells are typically designed for charging above 0\,\textdegree C, challenging sub-zero temperatures could inflict irreversible degradation and safety hazards during in-flight operation. To address these challenges, this work explores strategies affecting their low-temperature performance. Specifically, we investigate the Li plating behavior of the COTS~LG~Chem~18650~HG2 cell during galvanostatic cycling at $-20\,$\textdegree C and its dependence on prior high-temperature preconditioning protocols, involving either two-week galvanostatic cycling or storage at 30\,\textdegree C, as well as a reference case without preconditioning, i.e., direct cycling at $-20\,$\textdegree C. Experimental cycling tests reveal distinct differences in Li metal deposition between cells subjected to these conditions. To identify the origin of these discrepancies, we combine computed tomography of the electrode microstructure with three-dimensional, spatially resolved cell modeling and analyze the simulation results.

We study constitutive conditions of hyperelastic potentials for incompressible material behavior in three dimensions. By means of a counterexample, we show that polyconvexity does not imply true-stress-true-strain monotonicity. Thus, polyconvexity alone is not strong enough to guarantee a physically reasonable response for idealized elasticity.

Binary collision approximation (BCA) codes are potentially powerful tools to simulate ion irradiation ejecta properties, such as the composition and the angular and energy distributions of the sputter yield. However, recent advances in the sputtering of minerals have highlighted the low predictive fidelity of BCA codes such as SDTrimSP when compared to experimental measurements. We demonstrate how a sputtering model that underestimates the forward sputtering on a flat surface at large ion incidence angles from surface normal will lead to an erroneous result for rough and porous surfaces, where most ejected particles are directed along the surface normal. We demonstrate how this is the case for an existing model, which reliably predicts sputtering mass yields from a flat enstatite surface but fails to accurately reproduce the angular distribution of sputtered particles. We then compare this to a BCA model incorporating higher surface-binding energies$-$based on a molecular dynamics description of plagioclase$-$which underestimates mass yields but significantly reduces back-sputtering and better reproduces laboratory sputter angle distributions measured at large ion incidence angles. We conclude that the BCA model cannot simultaneously reproduce both the sputter yield and the sputter angle distribution arising from He irradiation of mineral targets, either due to the inherent geometric simplicity of the BCA or because the model neglects yield-enhancing processes such as molecule and cluster sputtering. This demonstrates a structural limitation of current BCA-based models when realistic surface morphologies are considered, rather than a problem that can be resolved by parameter tuning alone.

Proton dissociation constants (pKa) are critical for functional molecule discovery and molecular modeling. Building on iBonD, the largest experimental pKa database established, we and other researchers have developed several methods including machine-learning-based empirical prediction and high-accuracy energy calculations. Despite this foundation, the rapid augmentation of high-quality pKa data remains fundamentally constrained. As part of this work, we performed large-scale regression-based pKa prediction on unlabeled molecular datasets using a collection of extensively optimized machine-learning models. The results indicate that, since the feature distributions of unlabeled molecular datasets, the pKa data distribution approximates normality, with extreme scarcity of tail-region samples. Although such augmentation is highly valuable for improving overall data availability and predictive modeling, it remains insufficient for efficiently discovering molecules with broad-spectrum pKa properties. To address this, we explore the targeted generation of molecules with sparse pKa properties from the vast chemical space. Given that traditional continuous latent space VAE-RNN methods for molecular generation suffer from insufficient stability and fail to demonstrate clear advantages in complementing sparse data, we design and implement a quantum-assisted sparse-pKa molecular generation. Feasibility is validated on a simulated quantum annealer, and superior extreme-value sampling is further achieved on physical coherent Ising machines (CIMs). (to be continued)

The Coupled Model Intercomparison Project Phase 6 (CMIP6) has generated thousands of peer-reviewed publications documenting model configurations, evaluation procedures, emergent constraints, and projection uncertainties. As the community transitions toward CMIP7, efficiently extracting and operationalizing this unstructured knowledge alongside live data analysis represents a critical bottleneck. Here we present CMIP-Forge, a hybrid retrieval-augmented generation (RAG) and autonomous analysis system that bridges the gap between scientific literature and Earth System Grid Federation (ESGF) data archives. The system pairs a curated corpus of 6,581 CMIP6-related open-access publications (101,828 indexed chunks) with an agentic pipeline in which a tool-augmented worker plans and executes Python workflows over live climate data, while a panel of independent reviewer models audits its methodology end to end. CMIP-Forge introduces a multi-layered Defense-in-Depth architecture that enforces physical and methodological invariants through executable mechanisms: Abstract Syntax Tree (AST) static analysis, audited scientific primitives, and an autonomous adversarial peer-review protocol. We demonstrate the system's capabilities through end-to-end autonomous research pipelines spanning atmospheric teleconnections, ocean dynamics, regional extremes, and global warming projections. An agentic analysis system grounded in peer-reviewed literature, constrained by automated code guardrails, and audited by an independent adversarial review loop can complete complex climate-research workflows autonomously. The same experiments expose concrete failure modes of the review loop (sycophantic regression, REVISE verdicts that are never resolved, and the submission of stub code for review), each diagnosable from the immutable telemetry and provenance record released with the article.

We investigate the effective four-dimensional cosmology induced by unimodular gravity in $D=4+d$ dimensions, where the internal extra-dimensional volume is encoded in a scalar degree of freedom. After dimensional reduction, we show that the resulting FLRW equations admit a natural autonomous formulation whose phase-space structure differs qualitatively from that of general relativity. In the vacuum sector, the reduced system exhibits a continuous family of finite equilibrium points, $\lambda=dH$, together with well-defined asymptotic Poincaré directions. In the matter sector, we focus on the five-dimensional case $d=1$ and use the reduced Bianchi relation as the consistency condition that links the ordinary matter component to the internal-volume degree of freedom. The system is then closed by adopting the minimal higher-dimensional conservation prescription, according to which matter is diluted by both the external volume and the internal-volume modulus. This leads to a reduced matter–geometry dynamics with isolated critical points and a globally organized compactified flow. Numerical examples illustrate how the internal-volume degree of freedom affects the background evolution and the global phase-space structure. The comparison with $\Lambda$CDM is used only as a benchmark, while a full observational analysis and more general matter–geometry exchange prescriptions are left for future work.

Metastable reaction rates are commonly inferred from transition-state fluxes, mean first-passage times, or fitted kinetic models. We show that they are directly encoded in the plateau gap of an occupation-time Poisson corrector. For a centered basin-occupation observable, the Poisson corrector develops metastable plateaus in the reactant and product basins, and their separation determines the forward and backward transition rates. This construction requires only the generator, stationary measure, and metastable partition, and therefore does not rely on a predefined transition-state surface. In overdamped and underdamped double-well dynamics, the plateau-gap rate recovers the Kramers, Grote-Hynes, and Pollak-Grabert-Hänggi hierarchy. The same corrector-martingale decomposition yields a reactive-noise density, revealing where stochastic forcing contributes to transitions in configuration or phase space. Thus, reaction rates and their fluctuation sources emerge from a single corrector field.

Imploding solutions to the radially symmetric, isentropic, compressible Euler equations have been well-studied, inspired by the work of Guderley. However, these smooth imploding solutions are shown to be numerically unstable and difficult to compute in practice. On the other hand, the imploding solution of Kidder has a closed form solution and is numerically computable. But, it is unbounded in the far field. We consider Kidder's formulation in one dimension in which the unbounded far field condition is replaced with a constant density cutoff of the initial data. Strikingly, a non-centered rarefaction emerges from the cutoff and suppresses the implosion. We present an exact analytic solution to the problem with the cutoff and support our theoretical predictions with numerical simulations.

The resilience literature measures urban performance as recovery: the degree to which a city returns to its pre-shock baseline. This paper develops a stronger concept – civic ascent – as part of a broader research program on the ethology of coupled agent-environment systems, of which the city is the deepest available empirical instance. Civic ascent is defined as the condition in which a city emerges from shock with higher functional capacity than before. We develop a conceptual framework in the ethological tradition, treating the city as a coupled system of three slow state variables – topos (physical structure), nomos (institutional structure), and hexis (civic judgment) – together with a fast affective channel (delta) through which shocks to topos and nomos reach hexis. The framework distinguishes three structurally distinct pressures on civic systems: shocks (discontinuities in T or M), decay (continuous entropy), and leakage (active extraction of civic surplus into non-civic pools). The ascent condition is that reinforcement from cross-coupling of T, M, and H exceeds the combined loss from decay and leakage. Post-shock ascent is measured by a normalised improvement index A(T) applied to a composite civic performance signal P(t) constructed from scale-adjusted key performance indicators, distinguishing intrinsic civic ascent from demographically driven growth. New York City after September 11, 2001, is proposed as the primary empirical case; the operational measurement program is specified in the companion NYC Civic Data Map (Washburn 2026c, 133 KPIs) and executed in Paper 2. The reader for whom only the urban contribution is of interest will find it complete in itself; the reader interested in the larger program will find this paper its formal core.

Bichromatically pumped Kerr microresonators exhibit phase bistability and multistability arising from four-wave mixing between pump and generated modes. Here we analyze the symmetry structure of stationary solutions in the coupled-mode description of such systems. We show that the two-pump coupled-mode equations are a particular case of a more general model with pumps placed at equally spaced modes. For this model, any stationary solution generates a finite family of stationary solutions through a discrete phase transformation. This transformation leaves the equations invariant and preserves the stability type of the corresponding stationary states. As a consequence, the possible stationary values of each individual mode form a regular polygon in the complex plane. The number of vertices is determined by the order of the mode index $\mu$ in the group $\mathbb{Z}_n$, where $n$ is the separation between the pumped modes. Thus, the phase multistability structure depends not only on nonlinear dynamics but also on the arithmetic relation between the mode index and the pump separation. These results provide a simple symmetry-based explanation for the discrete phase structure observed in multimode Kerr resonators under bichromatic pumping.

At Earth's quasi-perpendicular bow shock, electrons can be reflected and accelerated to high velocities, forming beams. These beams excite Langmuir and beam-mode waves, which can then be converted to radio waves. We aim to understand the properties and evolution of Langmuir waves excited in the electron foreshock region using the Magnetospheric Multiscale (MMS) mission. We use fields and particle data from the four MMS spacecraft to investigate the properties of Langmuir/Z-mode waves in Earth's electron foreshock. MMS provides extended high-resolution snapshots of the three-dimensional electric field, enabling detailed analysis of wave properties. Probability distributions of the electric field are used to investigate the evolution of the waves and the role of density fluctuations. Distinct spectral peaks near the electron plasma frequency are often observed, suggestive of simultaneous observations of beam-mode and Langmuir waves, as well as nonlinear electrostatic decay of Langmuir waves or reflection off density gradients. In addition, the electric fields often have large perpendicular components, consistent with Z-mode waves. The statistical results show that the electric fields are largest near the electron foreshock boundary with the solar wind. Both the parallel and perpendicular components of the electric field exhibit close to log-normal probability distribution functions, consistent with predictions from Stochastic Growth Theory. These results suggest that small-scale density perturbations in the ambient plasma, in addition to nonlinear three-wave decay, are crucial to the evolution of Langmuir waves and the generation of radio waves. These results apply to Langmuir waves in the solar wind, such as in Type II and Type III solar radio burst source regions, where the same density fluctuations are expected and large-amplitude Langmuir waves with similar properties are observed.

Thermal transport in polymer chains is commonly attributed to ballistic propagation of long-wavelength acoustic phonons, which act as Goldstone modes protected by translational symmetry, whereas transport by higher-frequency phonons is suppressed by Anderson localization. Consistent with this picture, highly aligned polymers exhibit exceptionally high thermal conductivity, while poorly aligned polymers are orders of magnitude less conductive and serve as efficient thermal insulators. Here we show that this striking sensitivity to molecular alignment originates from acoustic-phonon scattering by molecular kinks. In the long-wavelength limit, longitudinal acoustic (LA) phonons are completely reflected by a single kink, whereas transverse acoustic (TA) phonons exhibit a universal transmission coefficient of one half. We show that the strong reflection results from the breaking of translational symmetry caused by the change in molecular-axis direction at the kink, while the universal TA transmission originates from virtual scattering through an evanescent transverse Bloch mode. The resulting strong suppression of long-wavelength phonon transport dramatically reduces the thermal conductivity of poorly aligned chains. These findings identify kink engineering as a promising strategy for controlling thermal transport in polymeric materials.

Efficient and stable microwave-optical transduction is a key enabling technology for distributed superconducting quantum computing and heterogeneous quantum networks. Electro-optic transducers based on thin-film lithium niobate (TFLN) have shown strong promise, but demonstrations to date have been limited by various factors such as low frequency bias drift, low efficiency, fabrication complexity, and scalability. Here we demonstrate the first integrated electro-optic microwave-optical transducers realized in thin-film lithium tantalate (TFLT), a material platform offering Pockels nonlinearity comparable to TFLN together with improved bias stability and high-power handling. We fabricate superconducting microwave resonators coupled to tunable photonic-molecule optical resonators using wafer-scale deep ultraviolet lithography, offering high-throughput production of hundreds of devices per wafer. Across six devices we observe coherent bidirectional conversion between C-band optical photons and 4.9-5.5 GHz microwave photons, with measured on-chip efficiencies and inferred single-photon coupling rates g_0/2{\pi} ~ 1 kHz consistent with theory. Continuous operation over multiple days is achieved using a static bias field with minimal feedback, demonstrating a major operational advantage. We further characterize optical loss statistics, microwave resonator performance, and optically induced added noise under pulsed pumping, finding less than one added photon for 100 microsecond pulses at the highest measured efficiencies. These results establish TFLT as a scalable and robust electro-optic platform for future quantum interconnects and modular quantum processors.

We present an asymptotic analysis and high-order boundary integral method for the three-dimensional Neumann Green's function in general closed and smooth geometries. The Neumann Green's function is a fundamental quantity which arises in numerous fields of science and engineering. In the application of singular perturbation methods to strongly localized reactions and diffusive transport, the Green's function plays the key role in mediating global dynamics. However, this essential quantity can only be determined in closed-form for a limited set of geometries. The Green's function for the Laplacian is an elliptic problem with a Dirac forcing term. Accurate resolution of the solution requires a careful decomposition into a singular and a regular part. The bulk scenario is where the source is placed off surface and the singularity is given by the free-space function. In the surface case, where the source is placed at a curved point on the boundary, we use asymptotic analysis to determine a three-term singularity structure. With explicit knowledge of these singularities, we develop a high-order boundary integral method for the determination of the remaining regular part. To resolve the singular boundary data, our integral method uses a custom discretization with Duffy patches near the source. We validate our method using several test cases in which closed-form solutions can be developed, including spheres, prolate spheroids and constructed domains. We demonstrate the applicability of our method to address some open problems in narrow capture theory.

Adapting machine-learned interatomic potential (MLIP) foundation models to specialised tasks through fine-tuning is an increasingly important practice, yet systematic guidance on when and how to fine-tune is currently limited. We evaluate seven fine-tuning strategies – naive full-parameter updates, two layer-freezing variants, Low-Rank Adaptation (LoRA), multihead replay, pseudolabelled replay, and replay combined with LoRA – across five chemically diverse benchmarks (aqueous NaCl, ice polymorphs, S$_\mathrm{N}$2 reactions, SPICE biomolecules, and lithium electrolytes), three generations of foundation models, and training sets spanning five orders of magnitude. To support this evaluation we implement three capabilities in the MACE codebase: LoRA adapted for equivariant message-passing architectures, including both scalar and equivariant linear layers; pseudolabelled replay, which decouples the replay data source from the original pretraining corpus; and model-aware atomic reference energy (E0) reestimation for fine-tuning workflows. We find that foundation model quality, correct E0 initialisation, and well-chosen hyperparameters are prerequisites whose impact routinely exceeds that of the fine-tuning strategy itself. Once these prerequisites are met, most strategies achieve strong target-task accuracy, consistently surpassing models trained from scratch. The practical distinction depends on deployment scope: naive fine-tuning offers the best convergence for single-system applications, while multihead replay – with either original or pseudolabelled data – is the only approach tested that consistently preserves out-of-distribution robustness, maintaining both pretraining-distribution accuracy for broader deployment and many-body short-range repulsion.

Protected reconstruction is formulated for a codimension-three timelike Codazzi defect in a four-dimensional Lorentzian branch. The oriented rank-three normal geometry yields a primitive projective link and, after scalar traces are removed, a graded source $V_1[1]\oplus V_2[2]$. Its principal $SU(2)$ content identifies compact $A_2$, while Borel-Weil visibility and faithful central marking reconstruct the minimal $3+2$ carrier. Determinant and Clifford closure give $S(U(3)\times U(2))$, its global $\mathbb Z_6$ form, and $k_Y=5/3$. The equianharmonic Coxeter lattice supplies the three-point family torsor and the first primitive norm-seven commensurate class. In an Alena-Codazzi collar, structural reconstruction is separated from realization by projector, Riesz-response, Rees-Picard, Hodge, and neutral-relay residuals. The selected projector has an intrinsic polynomial certificate and reconstructs the packet $(1,4,5;7)$; the finite first two responses determine the primitive Rees geometry, while a global $2{:}3{:}5$ principal lift links family determinant holonomy with the projected weak-leg rate. After the marked operator gates are passed, one universal dimensionless scalar $u$ is fixed once and the remaining CKM, neutral, and PMNS quantities are evaluated without retuning. On the frozen reference branch, the downstream readings include $\delta_{\rm CKM}=65.36^\circ$ and $\Delta m_{21}^2/\Delta m_{31}^2=0.029655$. These values are retained as falsifiers of the completed branch rather than structural inputs. Absolute normalization and the global Riesz-Callias attachment remain separate.

Solar wind stream interaction regions (SIRs) drive recurrent geomagnetic storms, yet most existing catalogs rely on expert inspection and simple thresholds that are subjective and can miss events with complex morphologies. We present SIREN (SIR Encoder Network), a lightweight Transformer based model for per timestep SIR detection from in situ solar wind observations. The model ingests sequences of 11 solar wind parameters, spanning magnetic field, velocity, and thermodynamic properties. With approximately 100,000 trainable parameters in a two layer encoder architecture, SIREN is trained using weighted binary cross entropy loss and a cosine annealing learning rate. Platt scaling is applied to produce well-calibrated detection probabilities. On a held-out test set of 102 events, the calibrated model achieves a ROC-AUC of 0.93, F1 score of 0.78, and true skill statistic of 0.67. Analysis of the self-attention weights confirms that the model concentrates on the SIR, grounding its decisions in the physically relevant portion of each sequence. Integrated Gradients attribution reveals a quantifiable feature hierarchy: proton density (24.3%) and magnetic field magnitude (21.6%) dominate, followed by temperature (13.9%) and bulk speed (12.1%). Notably, the transverse velocity component Vy and east-west flow angle together contribute 13-17%, identifying flow deflection as a consistent but previously under-quantified SIR signature. By producing continuous probabilities rather than binary labels, SIREN enables flexible threshold tuning for operational use and provides a template for compact, interpretable deep-learning systems in space weather.

Photoelectrochemical (PEC) cells are a compelling route to solar-driven chemical energy storage and feedstock synthesis, yet their deployment is hindered by coupled losses spanning light absorption, carrier transport, interfacial charge transfer, and semiconductor-electrolyte matching. Existing models address these losses in an architecture-specific manner and fall short of quantitative experimental diagnosis or actionable design guidance. Here, we introduce a unified loss-analysis framework applicable to both built-in junction (BIJ) and semiconductor-electrolyte junction (SEJ) photoelectrodes within a consistent set of physically meaningful parameters. The framework delivers current-voltage curves and efficiency metrics under ideal and real conditions, constructing efficiency maps to delineate theoretical limits and material-selection windows. Critically, by fitting experimental current-voltage data, it enables quantitative energy-loss decomposition into thermodynamic, optical, recombination, and interfacial contributions, pinpointing performance bottlenecks in real devices and mapping them directly onto optimization strategies such as co-catalyst integration or nanostructuring. Energy flows are visualized through Sankey diagrams, providing an intuitive picture of how incident solar energy is absorbed, dissipated, or converted into chemical output. Validated against state-of-the-art literature results spanning solar water splitting, CO2 reduction, NH3 synthesis, and solar redox flow batteries, the framework further enables systematic comparison of photovoltaic-grade absorbers (e.g., Si, perovskites) with intermediate-bandgap semiconductors (e.g., hematite, BiVO4), identifying key factors limiting each material class. Together, these capabilities support a paradigm shift from empirical optimization to mechanism-informed rational design of high-efficiency PEC energy-conversion systems.

While vertically-sheared stratified flows have been studied extensively, their horizontally-sheared counterparts have received considerably less attention. Yet, horizontal shear instabilities remain active even when the mean Richardson number is large or even formally infinite, and can drive turbulence in strongly stratified (low Froude number) flows at sufficiently high Reynolds number. In this work, we combine linear theory with direct numerical simulations to investigate two pathways to turbulence in low Froude / high Reynolds number horizontally-sheared flow with no initial vertical shear. In the first pathway, vertical shear emerges directly from vertically-modulated eigenmodes of the primary horizontal shear instability, and becomes unstable to secondary small-scale Kelvin-Helmholtz (KH) instabilities on the buoyancy scale at sufficiently large buoyancy Reynolds number $Re_b$. In the second pathway, a vertically-invariant eigenmode of the primary horizontal shear instability initially dominates, causing the background flow to evolve nonlinearly into a long-lived time-dependent two-dimensional (columnar) vortical flow. The vortices are subsequently unstable to secondary three-dimensional hyperbolic instabilities from which vertical shear emerges, which is finally unstable to tertiary small-scale KH instabilities on the buoyancy scale at sufficiently large $Re_b$. This shows that the emergence of vertical shear driving small-scale KH instabilities is an inevitable by-product of horizontal shear instabilities in strongly stratified flows at sufficiently large $Re_b$. However, we also find that the two pathways excite different ranges of vertical scales, which results in different peak mixing efficiencies.

Electromagnetism is one of the few core physics topics without simple two-dimensional examples to start from: the cross product and curl require three dimensions. Previous work described magnetism as a bivector field, visualized with oriented (clockwise/counterclockwise) "tiles" rather than the traditional (pseudo)vector "arrows." Here, we express Maxwell's equations in this bivector language: magnetic flux is understood as a sum along a surface rather than through it, and the magnetic field tiles encircle the boundary of an Amperian loop or ribbon in a natural way. This allows a gentle two-dimensional starting point and makes symmetry arguments natural for magnetism.

We report on a phase-based lock-in thermography approach, combined with a multilayered thermal model (often employed in thermoreflectance analysis), to measure the thermal conductivity of bulk materials and layered structures. The spatial distribution of the material's thermal phase is monitored with an infrared camera, which is locked into the frequency of a modulated laser used to heat the material. This phase distribution is then fit with a thermal model, in which properties such as thermal conductivity are extracted as fit parameters. This approach enables non-contact, front-side measurements, which are insensitive to surface roughness. The technique does not strictly require the application of a transducer layer, but we highlight the practical benefits of applying a removable adhesive layer to serve as a near-surface absorber. We demonstrate the efficacy of the method by measuring materials with thermal conductivities that span over three orders of magnitude (approximately 1 W/m/K to > 2000 W/m/K).

Wave turbulence describes the statistical dynamics of dispersive waves with weakly nonlinear interactions. While the classical kinetic equation captures the mean evolution of the wave spectrum, the study of its fluctuations due to finite-size effects and intermittency requires a probabilistic framework for space-time trajectories of the spectrum dynamics. Following the previous large deviation theories for wave turbulence, we develop a simplification meant for qualitative and numerical predictions of measurable quantities. We derive a new large deviation principle in the case of local wave interactions. It fully characterizes typical and rare fluctuations of the spectrum. In a joint article, we obtain a theory which is a generalised form of Macroscopic Fluctuation Theory, but with 2 conserved quantities (mass and energy). In this paper, we use it to analyse the structure of the equation for Gaussian fluctuations around out-of-equilibrium spectra. In addition to the usual equilibrium contribution, we obtain long-range correlations, which can be decomposed into 3 contributions: one is driven by the flux in the bulk and another is driven by the forcing and its possible fluctuations. In addition, these contributions are computed for the first time with a method adapted to boundary conditions where only the fluxes are fixed. The results provide a general, replicable method for analyzing wave turbulence in more complex settings. Finally, the generalization of this theory to the inhomogeneous wave turbulence provides a possible explanation to the instability of the Kolmogorov-Zakharov spectra in some 1D inhomogeneous models with 4-wave interactions such as the Majda-McLaughlin-Tabak. This work opens the discussion regarding universal and non universal properties in two-point correlation functions. This opens new range of study on the phenomena of intermittency which is partially developed here.

Periodic equation-of-motion coupled-cluster theory with single and double excitations (EOM-CCSD) has shown promise for quantitative calculations of band structures in solids. However, its steep computational scaling has limited calculations to relatively coarse $k$-point meshes, leading to sizable finite-size errors and discrepant estimates of thermodynamic-limit band gaps in recent benchmarks. In this work, we revisit EOM-CCSD band gaps for ten semiconductors and insulators using interacting-bath dynamical embedding theory (ibDET), a systematically improvable Green's function embedding framework that enables dense Brillouin-zone sampling at modest computational cost. By pushing the $k$-point sampling up to $10\times10\times10$, well beyond the system sizes accessible in canonical periodic EOM-CCSD calculations, we significantly reduce finite-size errors and obtain stable thermodynamic-limit extrapolations. We further compare $G_0W_0$@PBE, $G_0W_0$@HF, and EOM-CCSD on an equal footing using the same numerical settings in PySCF. We find that EOM-CCSD yields a mean absolute error of 0.32 eV relative to experimental band gaps for a test set of ten semiconductors and insulators, lower than that of $G_0W_0$@PBE. For ZnO, EOM-CCSD also accurately describes the Zn $3d$-band binding energy, despite overestimating the band gap. These results demonstrate that ibDET offers a practical route to high-accuracy many-body electronic structure calculations in periodic systems.

Background: An in-house dose engine independent of clinic TPS is not only a reliable tool for patient QA verification. More importantly, it plays vital role in cutting-edge research due to its flexibility in implementing new functions. In this study, we upgraded our existing beam model with using double-Gaussian distributions for both spatial and opening angle distributions of particles to obtain more accurate phase space files. It is expected to potentially improve the performance of this independent dose engine in both clinic and research at the expanded MD Anderson proton center.

Starting from our previously proposed line element and considering more “surface color” datasets, we derive a simplified version which matches experimental datasets equally well and resulted into a conformally-Euclidean line element, which is conceptually much simpler than any existing color difference metrics. The color difference is written as an Euclidean difference multiplied with a simple factor which depends on the luminance only. In a subspace with constant luminance, as considered by MacAdam, this factor becomes constant and the subspace is flat. The same holds for sufficiently large luminances. Based on this LE we derive perceptual coordinates $\left(A,l_{c},s_{c}\right)$ very similar to the CIELab $\left(L^{*},a^{*},b^{*}\right)$.

A compact liquid hydrogen target system has been developed for the Silicon Tracker for RAdioactive nuclei Studies at SAMURAI Experiments (STRASSE) at the RIKEN Nishina Center. This target, designed for proton-induced quasi-free scattering measurements in inverse kinematics, features a customizable cylindrical cell with a volume up to 125~mL which increases the reaction rate/luminosity, and thin Mylar walls to minimize the protons' angular straggling. The cryogenic system, operated at 20~K, is optimized for rapid cool-down ($\leq$ 5~h) and empty-target measurements, avoiding long experimental dead time. This new setup will allow for high-precision studies of nuclear structure using both missing-mass and in-flight prompt $\gamma$-ray spectroscopy techniques at the RIBF facility.

We study the unsteady dynamics of floating planar bodies undergoing lateral oscillations along an air–water interface. Scaling arguments indicate that when the viscous penetration depth and oscillation amplitude are both small compared to the body size, the flow beneath the body can be approximated by an oscillatory Stokes boundary layer, yielding a leading-order description of the hydrodynamic resistance. Using magnetic actuation, we drive the interfacial bodies harmonically and measure the amplitude response and phase lag in steady state over a range of frequencies, masses, sizes, and shapes. This frequency-response framework enables direct extraction of effective added mass and damping coefficients, which we find to be consistent with oscillatory boundary-layer theory in the limit of small interfacial deformation. The transient behavior during startup is also shown to be accurately predicted by a history integral that captures the development of the oscillatory boundary layer beneath the body. This work also establishes a simple experimental platform for quantifying unsteady hydrodynamic forces at fluid interfaces.

We present an adjoint deep learning framework describing the evolution of fluid moments and the energy distribution of the runaway electron (RE) population. We demonstrate that a careful formulation of the adjoint problem allows for the temporal evolution of these quantities for arbitrary initial electron distributions, and in combination with a physics-informed neural network (PINN), we show that the resulting surrogates can resolve a broad range of plasma parameters. This combination of the adjoint formulation and rapid inference of neural networks enables orders of magnitude faster predictions of RE kinetics than traditional methods. Here, we detail the mathematical formulation and the design of three PINNs which recover the temporal evolution of the RE current, average energy and energy distribution. Predictions are validated against a traditional RE solver, with good agreement across a broad range of scenarios.

We develop a four-compartment susceptible–casual–addicted–resistant (SCAR) model for adolescent substance use in a high-school setting. The model divides students into susceptible non-users, casual or experimental users, students with sustained or substance-use-disorder (SUD)-level involvement, and resistant students in protective anti-use environments. It includes peer-driven initiation, escalation from casual to problematic use, protective peer influence, school disengagement, and partial re-entry after rehabilitation. Qualitative analysis and bifurcation diagrams show three main results. First, the return parameter \(\phi\) separates two regimes: when \(\phi=1\), the total population is conserved and interior equilibria may exist; when \(\phi<1\), problematic use causes net school-population loss, so positive scaled equilibria may not represent true endemic equilibria. Second, initiation and escalation are governed by distinct thresholds, meaning first use and progression to problematic use are dynamically different. Third, the model can exhibit multistability, including bistability between a substance-free state and a stable high-use state, so long-term outcomes may depend on initial conditions. These findings suggest that effective school policy should combine universal prevention, early intervention for casual users, targeted support for students at risk of problematic use, recovery-supportive environments, and strong school re-engagement pathways.