Papers

This paper presents a sequential analysis of the resolving power of a microparticle as a function of its refractive index and size parameter. We demonstrate that the resolution of a microparticle for incoherent point sources is fundamentally determined by the number of excited Mie modes. Based on this mechanism, we propose a novel quantitative assessment framework that defines the spatial resolution limit via the ratio of the squared field angular momentum of the source to that of the total particle field (comprising the superposition of the source and scattered fields). The analytical results demonstrate good agreement with both numerical modeling and classical limits.

Inferring directed effective interactions from neuronal spike trains is a central inverse problem in statistical physics and computational neuroscience. Kinetic Ising models provide a tractable framework for this task, but their application to neural data typically requires binning spike trains into binary activity variables, discarding within-bin timing and conflating collective network dynamics with single-neuron history effects. We introduce SpiKIsing, a latent-variable model that separates these two levels of description. A discrete-time asymmetric kinetic Ising model describes collective network activity, while continuous-time, history-dependent point processes generate the observed spikes conditional on the latent states, accounting explicitly for refractoriness and post-spike recovery. We derive a variational mean-field expectation-maximization scheme in which the point-process likelihood enters as an effective observation field, enabling joint inference of latent activity, network couplings, and emission parameters. The framework extends naturally to maximum-a-posteriori inference with structured priors, including sparsity and a hierarchical extension favouring Dale-consistent outgoing interactions. We validate parameter recovery on matched synthetic data and test the method on spike trains generated by a recurrent conductance-based leaky integrate-and-fire network. In this setting, SpiKIsing recovers sparse connection structure and correctly classifies all excitatory and inhibitory neurons despite the substantial mismatch between the data-generating dynamics and the SpiKIsing model.

Landau fluid closures are linear in the evolved moments. We examine the consequences of relaxing linearity. As an example, we introduce the nonlinear exact kinetic response closure, exact on an eigenmode but not on superpositions. A counting argument shows that a closure on $N$ instantaneous moments and the field can represent at most $(N+1)/2$ superposed eigenmodes. Guided by this, we train an equivariant neural-network and a learned Padé closure on analytic data. For bump-on-tail and slab ITG instabilities they reduce growth-rate errors by up to three orders of magnitude over Landau closures.

Beam-induced damage fundamentally limits the characterization of beam-sensitive materials by scanning transmission electron microscopy (STEM), since structural information must be recorded before the electron beam irreversibly modifies the specimen. Here, sodium chloride (NaCl) is employed as a model beam-sensitive ionic crystal to investigate beam-induced structural evolution and the low-dose regime in which useful structural information can be recovered prior to significant damage. Four-dimensional STEM (4D-STEM) datasets were acquired at 200 kV using a Timepix3 direct electron detector and reconstructed using real-time integrated centre of mass (riCoM) imaging. We first establish the characteristic damage behavior of NaCl under high electron doses. Across different dwell times and raster scan orientations, damage develops reproducibly into square-faceted voids whose boundaries align with the 100 crystallographic directions of the rock-salt lattice, indicating that the morphology is governed predominantly by the intrinsic crystallography rather than the scan geometry. We then investigate the low-dose imaging regime using dose-fractionated acquisitions. At 130 e-A-2 per frame, normalized cross-correlation with respect to the first frame quantitatively tracks progressive structural degradation and enables the onset of measurable damage to be identified as a function of accumulated dose. A subsequent acquisition at 32 e-A-2 per frame demonstrates that atomic-scale spatial information can still be recovered below this damage threshold. These results demonstrate how dose-efficient 4D-STEM acquisition combined with riCoM imaging can extend the accessible imaging regime of highly beam-sensitive ionic materials while preserving atomic-scale information.

Green economic complexity provides a generalizable framework for examining countries' productive capabilities in a defined product set. We apply this framework to AI-enabling goods within the full product space, linking current specialization with adjacent diversification opportunities. Using BACI exports for 2007-2023 and 103 AI-enabling goods, we measure complexity-weighted specialization (AECI), product-level adjacent opportunities (AIAP), and average complexity-weighted relatedness of remaining candidates (AECP). In 2023, Japan leads AECI, while China leads AECP; portfolio breadth accounts for much of the variation in raw AECI. Initial raw potential is positively associated with subsequent changes in the AI-enabling export share, but its associations with changes in AECI and specialization counts are not statistically significant at the 5% level. Our contribution is a trade-based assessment of AI-enabling productive capabilities and related opportunities. The results and public dashboard provide a preliminary complement to publication and patent indicators, not a comprehensive measure of national AI performance or a validated forecast of diversification.

We present IRENE (Italian Radar Ensemble Nowcasting Experiment), a deep learning model for probabilistic short-range precipitation nowcasting over the Italian domain at \SI{1}{km} spatial and 5 min temporal resolution. IRENE adopts an encoder–forecaster architecture built on multi-scale Convolutional Gated Recurrent Units (ConvGRUs), trained on the national radar composite produced by the Italian Civil Protection Department (DPC). An importance-sampling scheme focuses training on precipitation-relevant events, while the almost-fair Continuous Ranked Probability Score (afCRPS) is adopted as the primary probabilistic loss function. Two additional training configurations are proposed: an adversarial (GAN) variant, IRENE-GAN, designed to improve the spatial sharpness of the generated forecasts, and a spectrally constrained variant, IRENE-GAN-RAPSD, in which the adversarial objective is complemented by an explicit penalty on the radially averaged power spectral density. The three configurations are evaluated against the stochastic extrapolation method STEPS and the pre-trained deep learning model DGMR. All IRENE configurations attain a lower Continuous Ranked Probability Score than both benchmarks at every lead time and rank histograms closer to uniformity, indicating better probabilistic skill and ensemble calibration. In terms of ensemble-mean mean absolute error the advantage is confined to the first 90 min, beyond which the strongly damped DGMR fields and, to a lesser extent, STEPS become competitive. Spectral analysis shows that the adversarial training removes the progressive loss of small-scale variance exhibited by IRENE, at the cost of an excess of fine-scale power at long lead times that the spectral penalty only partially controls.

Alfvén waves and their nonlinear interactions are ubiquitous in space and astrophysical plasmas, and are thought to play important roles in the dynamics of these systems, yet their nature remains to be fully understood. We describe experiments performed on the Large Plasma Device to study the nature of counter- and co-propagating wave interactions relevant to strong Alfvénic turbulence. Both interactions were found to produce a broad spectrum of nonlinear modes as a result of a dominant quadratic nonlinearity. The counter-propagating interaction can be explained through the standard reduced MHD nonlinearity, and the co-propagating interaction can be explained through a recently-proposed model that includes second-order nonlinear terms from Hall MHD that dominate at large imbalance and scale with the ion inertial length. The predictions of the latter model were tested in both the experiment and in 3D hybrid simulations, where the nonlinear mode growth rate and ion inertial scale dependence were found to be consistent. Finally, at the obtained interaction strengths, energy was seen to be transferred to progressively smaller perpendicular scales, consistent with a local cascade, although not a state of fully-developed turbulence. These results reveal and verify the mechanisms occurring in balanced and imbalanced turbulence (as well as other Alfvénic nonlinear processes), and represent an important step towards the generation of controlled Alfvénic turbulence in the laboratory.

Machine-learning surrogate models offer a promising alternative to high-fidelity Computational Fluid Dynamics (CFD) simulations for aerodynamic analysis and design. However, constructing accurate surrogates for realistic aircraft configurations remain challenging due to complex geometries, multiple flow regimes, and limited training data. This work presents the methodology that achieved first place in the ONERA CRM Wall Distribution Regression Challenge, which focuses on predicting pressure and skin-friction coefficient distributions over the NASA Common Research Model wing-body-pylon-nacelle configuration under different operating conditions. The proposed approach formulates the problem as a conditional neural field mapping spatial coordinates, surface normals, and operating conditions to aerodynamic wall quantities. Fourier feature encoding, a relative squared error objective aligned with the challenge metric, ensemble learning, and $k$-fold cross-validation are progressively introduced to improve prediction accuracy and exploit the limited training data. Beyond presenting the final methodology, the paper documents the successive model design choices that led to the winning solution through a comprehensive ablation study and discusses several alternative approaches that were investigated but ultimately discarded. On the hidden competition test set, the proposed methodology achieves an overall score of 8.81, outperforming the strongest organizer-provided baseline, which achieved a score of 8.64, while requiring approximately three orders of magnitude fewer trainable parameters. These results illustrate that carefully designed coordinate-based neural fields constitute an efficient and robust framework for aerodynamic surrogate modeling on complex geometries under limited-data conditions.

The Proton Improvement Plan-II (PIP-II) project is under construction to provide the Fermilab Accelerator Complex with a new H$^-$ superconducting radiofrequency (SRF) linac and beam transfer line delivering 800 MeV protons at Booster injection at double energy and double intensity (6.7x10$^{12}$ protons per 20 Hz pulse) in comparison with the present Linac. The design goals of PIP-II Linac will be described, including delivering 1.2 kW beam power on Long Baseline Neutrino Facility (LBNF) target, continuous wave (CW) operation compatibility and future upgrades. The progress of the Linac construction and the development of the Commissioning Plan will be presented. Finally, the challenges of operating the SRF Linac to drive the Fermilab Booster will be addressed.

Over the last twenty years, particle-in-cell simulations with Monte Carlo collisions (PIC-MCC) have given the low-temperature plasma community much of what it knows about electron heating in RF discharges, sheaths and secondary emission, striations, and the instabilities of magnetised plasmas. But these results remain in papers, and in codes that only their authors can run. JC-PIC is a free Windows program: a one-dimensional, three-velocity (1D3V) electrostatic PIC-MCC code with a parallel Fortran engine (OpenMP) and a complete graphical interface. It is built around a library of about one hundred ready-to-run cases, each documented, referenced and explained. About half of them reproduce a published result under the original conditions; the others compare the code with a classical analytical result. This overview presents the program, its companion book, and six of the cases from the physics point of view: Langmuir waves from Landau damping to electron trapping, the heating-mode transition in argon RF discharges, the electrical asymmetry effect, the Franck-Hertz experiment treated as a swarm, the striations of a positive column, and the ExB drift instability of Hall thrusters.

Whereas the effect of monotonically decaying memory kernels on reaction dynamics has been studied extensively, the effect of oscillatory memory, observed for widely different systems, is much less explored. We analyze non-Markovian barrier-crossing dynamics in the presence of a single exponentially decaying oscillatory friction memory kernel by simulations and analytical theory. We identify three scaling regimes: two in which the mean first-passage time $\tau_{\mathrm{MFP}}$ scales quartically and linearly with the memory kernel decay time $\tau_{\mathrm{o}}$, respectively, and a third in which $\tau_{\mathrm{MFP}}$ scales quartically with the oscillation period $\tau_\varphi$ of the memory kernel. Our results are relevant to a broad class of many-body dynamical systems such as dihedral isomerization processes and chemical reactions in solvents.

The Long Baseline Neutrino Facility (LBNF) will produce the world's most intense neutrino beam. While the magnetic horns are expected to be replaced, the LBNF horn power supply is expected to last the lifetime of the project, 30 years. Three series connected magnetic horns will require a 300kA, 800$\mu$s pulses at a rate of 1.4 pps to focus the beam. Prototyping proved the component choices and designs. Test results for a single cell and the full 32 cell system will be presented.

An accurate theoretical treatment of periodically driven quantum systems is crucial for various fields in the exact sciences, for instance Nuclear Magnetic Resonance (NMR) spectroscopy. Conventionally, either average Hamiltonian theory or Floquet theory is used to predict or describe experimental outcomes, such as the time evolution or the spectra yielding the information of the sample under study. A detailed analysis of the equivalence of these two approaches with an emphasis on applications in NMR will help to improve the theoretical understanding of NMR experiments. In this work, we identify the Floquet-Magnus expansion as essential to prove the mathematical equivalence of Floquet theory and average Hamiltonian theory. We advocate a calculation scheme which is less prone to algebraic mistakes because explicit integration is avoided. On this basis, we provide the first four orders of both theories. We further examine their applicability to some experiments in NMR. As examples, we investigate the Bloch-Siegert shift and dipolar coupled spin systems under magic-angle spinning. Based on our analysis, we recommend the use of the Floquet-Van Vleck approach including both the effective Hamiltonian and the kick operator. The consistent separation of secular and non-secular contributions appears to be especially advantageous for numerical robustness. Its accuracy is about three times better than the one provided by average Hamiltonian theory despite their formal equivalence. Our findings provide important insights into the theoretical background of Floquet theory and average Hamiltonian theory. This includes the extent of their algebraic and perturbative equivalence, with an emphasis on how these findings are of relevance in the analysis of solid-state NMR experiments.

Fermilab has embarked on its next evolution for future neutrino experiments through the Proton Improvement Plan II (PIP-II). The injection system now requires an increase from a 15 Hz to a 20 Hz injection rate with approximately a 30% increase in the field strength from the original Orbital Bump (ORBUMP) dipole magnets in the Booster ring [1]. The four series connected dipoles will require 3 kV, 20 kA, 660 $\mu$s pulses at the aforementioned injection rate with a flatness of $\pm$0.12%. This is achieved through a dual-capacitor pulsed power supply (PPS) comprised of ten parallel cells and must last for 25 years. The supply utilizes high-voltage film caps for the rise and fall portion of the waveform; however, it switches over to a 6 F electrolytic capacitor bank for the flattop portion to meet flatness. The supply utilizes energy recovery to reduce high voltage charging supplies and required power to operate.

Simulating nuclear magnetic resonance (NMR) spectra is a promising application of quantum simulation. Using Quantinuum's System Model H2, a trapped-ion quantum computer, we demonstrate an end-to-end, large-scale digital NMR simulation of a classically challenging benchmark molecule, 1,2-di-tert-butyl-diphosphane. We implement a hardware-efficient reduction of the nuclear-spin Hamiltonian, enabling Trotterized real-time evolution of an effective 21-spin model with tailored error suppression to reduce the effects of device noise. The reconstructed liquid-state proton NMR spectrum agrees with classical reference calculations and reproduces key spectroscopic features that previous quantum hardware demonstrations did not capture. Given the widespread use of NMR in chemical analysis and industrial research, these results advance digital quantum simulation of NMR spectra towards practical quantum utility.

This course on two-dimensional quantum gases, prepared for the São Paulo ICTP-SAIFR IVth School on Light and Cold Atoms, is organized into three lectures. The first introductory lecture addresses Bose-Einstein condensation in an ideal gas, the effect of interactions in the weakly interacting limit, Gross-Pitaevskii equation and its hydrodynamic formulation, as well as superfluidity. The second lecture is devoted to the main features of two-dimensional quantum gases: importance of the interactions, enhanced phase fluctuations and the emergence of a quasi long-range order, superfluidity through the Berezinskii-Kosterlitz-Thouless mechanism, scaling symmetry. The third lecture has taken the form of a seminar on fast rotating quasi two-dimensional Bose gases and thermal melting of the vortex lattice. The slides accompanying these notes can be found on the school website: https://www.ictp-saifr.org/slca2025/.

Muon-spin rotation/relaxation is one of the most powerful microscopic probes for understanding magnetic order, spin dynamics, superconductivity, and complex magnetic phases in emerging quantum materials. Quantitative interpretation depends critically on the accurate identification of the muon stopping site, a problem traditionally addressed using density functional theory-based structural relaxation. However, conventional DFT approaches are computationally demanding, time-consuming, and require extensive calculations. Here, we develop MEOWN (Muon Engine for Optimized Weighted Networks), a machine-learning-based, physics-informed computational framework that combines a Polarizable Unperturbed Electrostatic Potential (P-UEP) model with machine-learning-guided optimization and symmetry-driven relaxation to predict energetically favorable muon stopping sites in crystalline solids. MEOWN explicitly incorporates electrostatic interactions, electronic screening, polarization effects, and zero-point motion into the learning workflow, providing physically interpretable predictions with substantially reduced computational cost while maintaining physical consistency and scientific rigor. To validate the framework, we investigated several well-established benchmark materials, including MnSi, CoF2, CaF2, LiF, and NaF. The predicted muon stopping sites and dipolar fields are in close agreement with previously reported DFT+mu results, demonstrating the reliability and transferability of the approach across chemically diverse systems. Notably, MEOWN predicts the equilibrium muon stopping site within a few minutes, offering a significant speedup over conventional DFT-based calculations. This manuscript presents the theoretical foundations, computational methodology, and validation of MEOWN as a general-purpose software for rapid and reliable muon stopping-site prediction in crystalline materials.

Multi-state HLL-type approximate Riemann solutions of ideal magnetohydrodynamics (MHD) typically assume that the medium within the Riemann fan is incompressible, and thus the slow magnetoacoustic mode cannot be included in their space-time wave configurations for the approximated states. We propose a new strategy to design multi-state HLL-type approximate solutions, allowing for the medium within the Riemann fan to be compressible. In particular, for the complete seven-wave configuration of the MHD Riemann problem, we first estimate the slow magnetoacoustic speeds and a longitudinal flow speed between the slow modes, and then follow the conservation laws and Rankine-Hugoniot jump relations across the fast, Alfvén, and slow waves, to calculate all the intermediate states within the Riemann fan. Moreover, we discuss the solutions when certain wave modes degenerate, ensuring well-posedness and smooth transitions between complete and degenerate wave configurations. Numerical simulations using a Finite Volume (FV) solver show that the proposed approximate Riemann solution is less diffusive than the classic HLLD scheme, particularly for slow mode waves. For example, in a 1D test case with a strong longitudinal magnetic field, the new scheme needs one order of magnitude fewer grid cells compared to the classic HLLD scheme to resolve all wave modes.

Even exact knowledge of the Markov generator at one environmental condition does not determine how that generator changes with the environment unless additional reaction-structure information is supplied. We establish this non-identifiability for mass-action reaction networks driven by a chemostatted species. Distinct chemical embeddings can share the same generator and path law at a reference concentration u0 while differing in how the chemostat enters individual reaction channels. Changing integer chemostat reactant molecularities and rescaling the corresponding kinetic constants preserves all propensities at u0 . This ambiguity already occurs for low-molecularity elementary reactions: a one-species birth–death example has identical path measures at the reference condition but dimensionless stationary sensitivities one and zero. Single-condition data can therefore determine the observed Markov dynamics at u0 without determining their parametric response. As an illustration of the complementary role of prior chemical structure, we also show that a known stoichiometric integral coordinate imposes a conditional zero-frequency identity within the linear-noise approximation.

Structural nonlinearity is the emergence of nonlinear input-output transformations from purely linear processes. In optics, this arises from the multiple interactions of light with input data. Current implementations achieve either a finite number of interactions, or partial modulation of the optical field. In this study, we present an architecture that combines both arbitrarily many interactions and full modulation. Our design consists of a Fabry-Pérot cavity, where one mirror is replaced by a spatial light modulator on which data is displayed. We develop an analytical model from which we derive closed-form expressions for the main metrics of nonlinearity, and find good agreement with experiment. Our analysis extends beyond the present implementation and reveals relationships that are independent of a specific physical system, providing a basis for a unified description of structural nonlinearity, and a first-principles design guide for machine learning applications.

Adaptive-Optics assisted astronomical imaging and satellite imaging from the ground share many commonalities but have also specificities. In particular satellites are non-stationary objects in terms of both shape and flux, encouraging relatively short exposure times. Besides, very short exposures freeze the turbulence and preserve high spatial frequency information in speckles, but at the cost of a low image SNR, which complicates the post-processing. Conversely, long exposures lead to speckle-less images where some high frequency information is lost. In this communication, we propose a method for determining the exposure time that optimizes the image quality after post-processing. In particular, we focus on the reduction of the jitter effects – induced by atmospheric tip-tilt residues, vibrations and object non stationarity – which may dramatically reduce the image quality when imaging satellites. Our model takes into account the object type and flux, the residual aberrations power spectral density and the noise level.

Ionization profile monitors (IPM) are used in many accelerator laboratories around the world for non-destructive profile measurements of high-energy particle beams. They have been used in nearly all the synchrotrons built at Fermilab and are presently used in the Main Injector (MI), Recycler (RR), and Booster. This paper will present the current status of the design of a new pair of IPMs to be installed in the Integrable Optics Test Accelerator (IOTA), an experimental proton storage ring with a beam energy of 2.5 MeV. The design incorporates aspects of several IPMs previously or currently used at FNAL. Similar to the Booster IPM, ions are collected rather than electrons, thus requiring no external magnetic field. To increase the signal, each IPM uses two microchannel plates in the chevron configuration as is done in the MI/RR IPMs, and due to the ring's ultrahigh vacuum conditions, the local vacuum pressure is increased with a calibrated leak, akin to the former Tevatron IPMs, to increase the ionization yield per bunch. Data acquisition includes upgraded preamplifiers and leveraging of the digitizer design of the beam position monitor system already in use at IOTA. In addition to the mechanical and electronic designs, simulation results of the effects of the electric fields and injected gas on the circulating beam will also be presented.

Odd slip at solid-liquid interfaces, characterized by a velocity component perpendicular to the applied shear stress, introduces a novel class of odd transport phenomena with transverse momentum transfer at boundaries, distinct from odd viscosity, odd elasticity, or odd diffusivity. This effect arises on chiral active surfaces with persistent energy input and defined handedness, such as carpets of rotating biological or synthetic cilia with flexible anchoring. Although odd slip itself is dissipationless, its maintenance comes at an energetic cost, which we estimate from the microscopic model. With respect to its orientation dependence, odd slip can be classified as pseudo-scalar or pseudo-vectorial. We use a perturbative expansion to determine the resistance tensors of problems with different levels of broken symmetries. They reveal how the surface chirality and body asymmetry together violate the Onsager symmetry, but uphold the Onsager-Casimir reciprocity. Strikingly, suspensions of particles with odd slip embedded in a conventional Newtonian bulk fluid exhibit odd viscosity, providing a new minimal mechanism for its emergence beyond current microscopic models.

The Zero-Degree Calorimeter (ZDC) at the proposed Electron-Ion Collider in China (EicC) is essential for detecting forward-going neutral particles and supporting the core nucleon spin and 3D imaging physics programs. In this work, a highly compact Spaghetti Calorimeter (SPACAL) architecture is proposed and optimized as the baseline design for the ZDC hadronic section. To systematically and quantitatively evaluate its physics capabilities, a comprehensive end-to-end Geant4 simulation framework was developed, integrating energy deposition, optical photon transport, photo-detection, and modeled front-end signal digitization. The optimized detector demonstrates excellent performance for neutron detection, achieving an energy resolution of $33.42\%/\sqrt{E/\mathrm{GeV}} + 1.47\%$ and a timing resolution of approximately 500 ps, outperforming the targeted specification. Furthermore, full-system simulations incorporating the upstream electromagnetic calorimeter yield a sub-centimeter transverse position resolution. Moreover, topological shower shape analysis across the full detector system provides robust particle identification (PID), with photon-neutron separation accuracy reaching over $99\%$ for high-energy incident particles. These quantitative results confirm that the SPACAL design fully satisfies the stringent operational and physics requirements of the EicC forward kinematic region.

Numerical simulations of ocean flows typically require fine computational meshes to resolve the Munk scale, leading to high computational costs. Filtering-based large eddy simulation (LES) provides a way to relax the mesh size requirement by modeling the effects of the unresolved scales. For the implementation of this strategy, we propose a three-step algorithm called Evolve-Filter-Relax (EFR) that requires (i) the solution of a QGE problem, (ii) a nonlinear Helmholtz filter for the potential vorticity field leveraging an indicator function, and (iii) a final relaxation step. We show that the EFR algorithm can be interpreted as a splitting scheme for a perturbed QGE problem with additional dissipation and provide a practical choice for the relaxation parameter. For comparison, we also investigate a nonlinear Bardina regularization of the QGE. Numerical results on a classical benchmark show that both the EFR approach and the nonlinear Bardina regularization significantly improve the accuracy and stability of coarse mesh simulations with no LES model. Additionally, the EFR method with a deconvolution-based indicator function delivered the best balance between accuracy, stability, and computational efficiency in a test case involving a more realistic geometry (Mediterranean Sea).

The European XFEL generates bursts of up to 4096 ultra-short X-ray flashes with a spacing of only 222 ns every 100 ms. These flashes are produced in the linear electron acceler- ator by undulators and guided 1 kilometer in vacuum pipes until they reach the scientific experiments. Accurate synchronization and the availability of information about all configurable pulses within a single burst, which can vary every 10 Hz cycle, enable the full potential of control, monitoring, and measurement in terms of accuracy and utilization. The MicroTCA-based timing system provides all the necessary capabilities. However, the industrial programmable logic controllers (PLCs), which manage most of the control electronics of the beamlines and experiments, are only synchronized on a millisecond-level through UART and NTP interfaces. This paper presents a new development based on an FPGA SoC EtherCAT solution in MicroTCA that implements an interface to the PLC using distributed clocks to achieve synchronization on nanosecond-level and provide beam-related information in a deterministic manner.

Road passenger transport faces acute disruption risks from flash flooding from heavy rainfall. To capture system complexity and dynamics for assessing its flood resilience, this study (a) proposes performance metrics to quantify disruption impacts from physical, operational, environmental, social, and economic dimensions, and (b) develops an integrated modelling framework to assess selected metrics by combining rainfall modelling, surface water flood modelling, and agent-based transport modelling to simulate asset-level disruptions and user behavioural responses. This methodology is demonstrated through a 1-in-30-year rainfall event in Tyne and Wear, North East England. Three behavioural response scenarios to rainfall are simulated, including a worst-case scenario, an ideal-response scenario, and an early-warning scenario. Results highlight the potential of behavioural adaptation in enhancing road flood resilience by reducing journey delays, cancellations, and economic losses. The framework and multidimensional performance metrics offer a transferable approach for stress-testing rainfall scenarios and evaluating interventions, supporting evidence-based climate-resilient transport planning.

The mechanical performance of structural materials is governed by their microstructure, which is itself set by the thermal history imposed during processing. Predicting how that microstructure evolves along an arbitrary thermal trajectory, however, remains a challenging problem. Conventional continuous-cooling-transformation diagrams, for instance, provide only a static description, and they do not exist for all materials, especially emerging and novel ones. Deep-learning approaches address this issue, but they treat image-based characterization, temporal prediction, and microstructure forecasting as separate tasks. Here, we introduce a probabilistic deep-learning framework that unifies these tasks for in situ high-temperature confocal laser scanning microscopy data. The initial surface image is compressed with a beta-variational autoencoder into a low-dimensional latent representation, which, together with the cooling rate and the full temperature history, is passed to a temporal fusion transformer that produces a quantile forecast of the Widmanstatten ferrite ratio. In parallel, a Gaussian process predicts the end-state Widmanstatten ferrite ratio and corrects the forecast equilibrium level, yielding probabilistic prediction intervals. We demonstrate the efficacy of our method on Widmanstatten ferrite evolution in S235 steel across five cooling regimes and showcase significant accuracy. This verifies the relevance of our framework and paves the way for a streamlined and highly accelerated investigation of material evolution under thermal treatment.

Ensemble nitrogen-vacancy (NV) diamond magnetometers combine high sensitivity with vector-field reconstruction, but practical deployment is limited by errors arising from high-frequency laser noise during short-duration operations and slow-varying gain fluctuations and offset drift during long-term operation. Here, we present an integrated digital architecture for achieving NV magnetometry stability across distinct timescales. A dynamic differential readout continuously balances fluorescence and reference channels to suppress correlated laser noise. Second-derivative Lorentzian lineshape tracking enables in-situ correction of slope variations arising from slow changes in gain and optical excitation. We further identify temperature-induced bias-magnet fluctuations as a dominant source of long-term drift and introduce a magnetic eigenvector transformation that uses the intrinsic response of NV resonances to eliminate these variations. We show, under unshielded ambient conditions, that this architecture achieves an off-resonance noise factor of 1.0 $\pm$ 0.1, matching the fundamental limit with ten-fold suppression of long-term drift, enabling stable, field-ready quantum magnetometry.

A physics-inspired dynamical model of interpersonal communication is proposed to describe how information interpretation can influence the evolution of a relationship between two interacting systems. The model distinguishes between intended information and observed information, with the former represented as an asymptotic limit in an abstract information space. An information potential landscape with local minima and increasing potential barriers is introduced to represent stable information states and limited information accessibility. Information transmission and processing are described through a general thought-processing function, while the separation between information states determines the coupling strength between the interacting systems. The relationship dynamics is then defined from the coupling strength and the independent sentiments of the two systems. Numerical simulations demonstrate the potential landscape, information dynamics, and several relationship regimes under deterministic and fluctuating information processing. An illustrative dynamical scenario further shows that an emerging information discrepancy can lead to either relationship recovery or deterioration depending on the subsequent evolution of information separation, coupling, and individual sentiment. The model provides a simple physics-inspired framework for describing interpersonal communication as a coupled dynamical process.