Papers

Thermal fluctuations play a central role in fluid dynamics at mesoscopic scales and must be incorporated into numerical schemes in a manner consistent with statistical mechanics. In this work, we develop a fluctuating lattice Boltzmann formulation based on an orthogonal central-moments-based representation. Stochastic forcing is introduced directly in the space of central moments (CMs) and consistently paired with mode-dependent relaxation, yielding a discrete kinetic model that satisfies the fluctuation-dissipation theorem exactly at the lattice level. Owing to the orthogonality of the basis, the equilibrium covariance matrix of the central moments is diagonal, and each non-conserved mode can be interpreted as an independent discrete Ornstein-Uhlenbeck process with variance fixed by equilibrium thermodynamics. The resulting formulation guarantees exact equipartition of kinetic energy at equilibrium, preserves Galilean invariance, and retains the enhanced numerical stability characteristic of CMs-based collision operators. Explicit fluctuating schemes are constructed for the D2Q9 and D3Q27 lattices. The extension to reduced-velocity discretisation is discussed too. A comprehensive set of numerical tests verifies correct thermalisation, isotropy of equilibrium statistics, and the expected scaling of velocity fluctuations with thermal energy, density, and relaxation time. In contrast to fluctuating BGK formulations, the present method remains stable and well posed in the over-relaxation regime, including in the immediate vicinity of the stability limit. These results demonstrate that CMs-based lattice Boltzmann methods provide a natural and robust framework for fluctuating hydrodynamics, in which dissipation, noise, and kinetic mode structure are consistently aligned at the discrete level.

Photonic integration offers the potential to bring complex high-performance optical systems to the form factor of a compact semiconductor chip. However, the range of system functions accessible critically depends on the extent to which free-space and fiber components can be made integrable. The ultralow-expansion cavity-stabilized laser$-$often used in precision metrology, high-resolution sensors, and advanced systems in atomic physics$-$is one component that currently has no direct parallel on chip. Lasers stabilized to photonically-integrated resonators exist, but exhibit considerably higher frequency noise and are accompanied by large levels of frequency drift. We demonstrate here a new architecture for an ultranarrow linewidth integrated laser based on stabilization to a sinusoidal fringe of an interferometer having a long 25-m unbalanced delay line. Our interferometric laser not only advances the state-of-the-art for on-chip lasers, but we in addition introduce an amplitude locking scheme that greatly suppresses the laser's long-term frequency wander. We achieve a record on-chip fractional frequency noise of $5.6 \times 10^{-14}$, corresponding to a linewidth of 12 Hz centered at 1348 nm. To showcase the utility of this laser, we divide the optical carrier to microwave frequencies, demonstrating the ability to outperform state-of-the-art quartz crystal oscillators by 15 dB or more.

Unsteady shock-boundary-layer interaction on the lower surface of a transport-aircraft wing can be caused or amplified by ultrahigh-bypass-ratio underwing nacelle installation. This work analyzes the resulting buffet dynamics on the Airbus XRF-1 configuration at a Mach number of $0.84$, a Reynolds number of $3.3\times 10^6$, and $-4^\circ$ angle of attack using scale-resolving delayed detached eddy simulations and unsteady pressure-sensitive paint measurements. Coherent structures are extracted employing a data-efficient multi-taper spectral POD. Dominant modes occur in the Strouhal number range $St \in [0.1,0.3]$. Surface modes reveal wave-like shock motions that originate near the pylon-wing intersection and propagate inboard towards the fuselage. These shock oscillations are linked to unsteady flow separation downstream of the shock. Additional dominant modes show spanwise oscillations of the separated flow region and shock oscillations phase-linked to shear layer instabilities. The modal analysis of volume data reveals pressure waves connected to these modes traveling upstream above and below the wing.

Optimal healthcare allocation requires accounting for spatial heterogeneity in disease burden, while demographic heterogeneity may further alter local vulnerability when outcomes vary strongly with age. Tuberculosis (TB) provides a useful case because treatment requires sustained access to care and fatality risk rises markedly with age. In South Korea, TB incidence remains relatively high, while regional incidence, mortality, and hospital data are systematically recorded, allowing these effects to be examined empirically. Finer spatial resolution provides more local observations, helping reveal regional heterogeneity and spatial patterns. However, due to privacy concerns, official district-level TB statistics are provided only in age-aggregated form, whereas age-specific statistics are available at province level. We therefore propose a method to reconstruct age-resolved TB cases and fatalities across 228 districts for 2014–2022 by combining province-level age distributions with district-level totals, using the finest available age and spatial information. Building on an existing hospital-allocation framework, we incorporate age-dependent vulnerability into the fatality-minimization objective. We find that age-aware and age-agnostic optimizations yield similar total minimized fatalities but distinct district-level allocations. The oldest age group's rescaled patient density is strongly associated with this difference and the direction of hospital redistribution. Age-weighting schemes clarify how countervailing contributions across age groups affect optimization.

Many numerical studies validate droplet wall impact using only maximum spreading diameter, yet this metric alone cannot ensure correct droplet dynamics. We present a combined dynamic contact angle (DCA) model that merges the geometric accuracy of the generalized Hoffman-Voinov-Tanner law with the kinematic consistency of a Hoffman function-based approach, improving predictions of droplet spreading and receding. We simulate water-glycerol droplet impact on sapphire glass at Weber numbers 20 – 250 and assess both contact angle formulations. Simulated radial velocity fields are processed in Python using SciPy and compared with Particle Image Velocimetry measurements in the longitudinal section of the spreading droplet. The Hoffman function-based model captures the main droplet kinematic trends and provides more consistent receding dynamics. The generalized Hoffman-Voinov-Tanner law matches the maximum spreading diameter within 7%. However, during receding, it shows a median absolute error in radial velocity up to three times higher than that of the Hoffman function-based solution. Average radial velocity and spreading velocity can differ from experimental trends even when maximum spreading is reproduced. These findings support validation combining geometric and kinematic metrics and motivate the combined model for predicting spreading and receding. Using the maximum spreading factor $\beta_{max}$ as the ratio of the maximum spreading diameter over the initial droplet diameter and the characteristic capillary number $Ca_{char}$ defined from the mean internal horizontal velocity at 300 micrometer above the substrate, we introduce a $(\beta_{max},\,Ca_{char})$ diagram to relate spreading characteristics to internal flow dynamics. We hypothesize that, given sufficient data, the contact-line geometry may be used to estimate internal kinematics.

The recent reconstruction of the Drosophila brain provides a neural network of unprecedented size and level of details. In this work, we study the geometrical properties of this system by applying network embedding techniques to the graph of synaptic connections. Since previous analysis have revealed an inhomogeneous degree distribution, we first employ a hyperbolic embedding approach that maps the neural network onto a point cloud in the two-dimensional hyperbolic space. In general, hyperbolic embedding methods exploit the exponentially growing volume of hyperbolic space with increasing distance from the origin, allowing for an approximately uniform spatial distribution of nodes even in scale-free, small-world networks. By evaluating multiple embedding quality metrics, we find that the network structure is well captured by the resulting two-dimensional hyperbolic embedding, and in fact is more congruent with this representation than with the original neuron coordinates in three-dimensional Euclidean space. In order to examine the network geometry in a broader context, we also apply the well-known Euclidean network embedding approach Node2vec, where the dimension of the embedding space, $d$ can be set arbitrarily. In 3 dimensions, the Euclidean embedding of the network yields lower quality scores compared to the original neuron coordinates. However, as a function of the embedding dimension the scores show an improving tendency, surpassing the level of the 2d hyperbolic embedding roughly at $d=16$, and reaching a maximum around $d=64$. Since network embeddings can serve as valuable inputs for a variety of downstream machine learning tasks, our results offer new perspectives on the structure and representation of this recently revealed and biologically significant neural network.

We numerically study the dynamics of a three-dimensional contractile fluid droplet in the bulk and under confinement. We show that varying activity leads to a variety of shapes and motile regimes whose motion is driven by an interplay between spontaneous flows and elasticity. In the bulk the droplet self-propels unidirectionally, acquiring either an almost spherical shape at intermediate activity or a peanut-like geometry for larger values. Under confinement, the droplet exhibits a previously unreported oscillating dynamics characterized by periodic hits against opposite walls of a microchannel while moving forward. These results could be of interest for the study of artificial microswimmers and their biological analogs, such as living cells.

Traditional models of slow slip events (SSEs) oversimplify fault geometry, although imaging shows subduction faults are segmented and complex. We examine how fault interactions control slip behavior using 3-D quasi-dynamic earthquake sequence simulations of two parallel faults with uniform rate-weakening friction accelerated by hierarchical matrices. Four regimes emerge-periodic earthquakes, coexisting SSEs and earthquakes, only SSEs, and complex sequences-whereas with the same friction condition a single planar fault produces only earthquakes. We quantify interaction using the maximum Coulomb stress induced on a target fault by a spatially uniform unit stress drop on a neighboring fault. Because the stress drop is normalized, the metric depends only on geometry and is independent of friction, allowing extension to arbitrary fault systems. SSEs occur only at intermediate fault interaction strengths. At low interaction strengths, the system produces regular, periodic earthquakes. At high interaction strengths, fault interactions generate complex earthquake sequences with irregular recurrence and variable magnitudes. Simulations reproduce observed moment-duration scaling and show sensitivity to detection thresholds. These results demonstrate geometric complexity alone generates both slow and fast earthquakes through evolving traction heterogeneity.

Understanding why and how crystalline solids melt remains a central problem in condensed-matter physics. Dislocation loops are fundamental topological excitations that control the thermodynamic stability of crystals, yet their role in setting universal aspects of melting has remained unclear. Here we show, within dislocation-mediated melting theory, that the free-energy condition for loop proliferation leads to a universal ratio between the energy of a minimal dislocation loop and the thermal energy at melting. For minimal dislocation loops that begin to proliferate at the onset of melting, this ratio takes the purely geometric value $\mathcal{E}_* = E_{\rm loop}/(k_B T_m) \approx 25.1$, independent of elastic moduli and chemistry-dependent details. This result provides a microscopic explanation for recent empirical findings by Lunkenheimer et al., who identified a closely related universal energy scale $\approx 24.6$ from viscosity data. The same framework also rationalizes the empirical $2/3$ rule relating the glass-transition and melting temperatures.

We report a direct demonstration of quantum-enhanced sensing in the Fourier domain by comparing single- and two-photon interference in a fiber-based interferometer under strictly identical noise conditions. The simultaneous acquisition of both signals provides a common-mode reference that enables a fair and unambiguous benchmark of quantum advantage. Spectral analysis of the interferometric outputs reveals that quantum correlations do not increase the amplitude of the modulation peak, but instead lower the associated noise floor, resulting in the expected 3 dB improvement in signal-to-noise ratio. This enhancement persists in the sub-shot-noise regime, where the classical signal becomes buried in the spectral background while the two-photon contribution remains resolvable. These observations establish Fourier-domain quantum super-sensitivity as an operational and broadly applicable resource for precision interferometric sensing.

Transportable all-optical atomic clocks represent the next-generation devices for precision time keeping, ushering a new era in encompassing a wide range of PNT (Positioning, Navigation and Timing) applications in the civil and strategic sectors. Their performance relies on ultra-stable, narrow-linewidth lasers, frequency stabilized to a compact portable optical cavity. Among various designs, the cubic spacer-based ultra-stable cavity is particularly well-suited for transportable applications due to its low sensitivity to vibrations, owing to its symmetric geometry and robust mounting structure. While longer cavities offer a lower fundamental thermal noise floor, one needs to strike a balance between transportability and size. In this aspect, the 7.5 cm dual-axis cubic cavity offers a lower fundamental thermal noise floor in comparison to smaller counterparts, while still retaining a reasonable SWaP (Size, Weight and Power) for terrestrial and aerial PNT applications. Its dual-axis design also enables multi-wavelength laser stabilization, making it a promising candidate for future transportable clock applications. This work presents a detailed study of the 7.5 cm dual-axis cubic cavity using FEM (Finite Element Method) to evaluate its mechanical and thermal stability. We analyze the impact of various geometric factors, mounting forces, and machining imperfections, while also modelling thermal effects such as conduction, radiation, and mirror heating within a vacuum chamber and thermally shielded environment. Our findings provide design insights for developing robust dual-axis optical reference cavities, advancing the deployment of portable atomic clocks for next-generation applications in PNT, geodesy, VLBI (Very Long Baseline Interferometry) and deep space missions.

Two-component linear-response time-dependent density functional theory (TDDFT) provides a unified framework that encompasses noncollinear excitations in noncollinear reference states, as well as both spin-conserving and spin-flip excitations in collinear reference states. In this work, we present a general formalism for evaluating the expectation value $\langle \hat{S}^2 \rangle$ of electronically excited states obtained within two-component TDDFT. We then derive and analyze specialized forms of the resulting equations for collinear reference determinants, for which the two-component formalism decomposes into conventional spin-conserving and spin-flip TDDFT. The resulting working equations are systematically compared with previously proposed theoretical approaches. On the basis of our analysis, $\langle \hat{S}^2 \rangle$ in the excited states is shown to arise from two distinct sources: (i) $\langle \hat{S}^2 \rangle_0$ in the reference state and (ii) additional $\Delta\langle \hat{S}^2 \rangle$ introduced by the excitation process itself. Finally, we evaluate the expectation value $\langle \hat{S}^2 \rangle$ by performing two-component TDDFT calculations based on two-component DFT, unrestricted Kohn-Sham (UKS), and restricted open-shell Kohn-Sham (ROKS) reference states, respectively.

This report presents the design, characterization, and application of a high-sensitivity optical detection system based on plastic scintillators coupled to Multi-Pixel Photon Counters (MPPCs). The primary objective was to evaluate the performance of MPPCs (Silicon Photomultipliers) as robust, low-voltage alternatives to traditional photomultiplier tubes for detecting faint scintillation light. The optoelectronic properties of the sensors were analyzed, including single-photoelectron gain calibration and dark count rate measurements, to optimize the signal-to-noise ratio. By embedding wavelength-shifting fibers to enhance light collection efficiency, the system was configured into a three-fold coincidence telescope. The angular distribution of the cosmic ray muon flux was measured to validate the detector's stability and geometric acceptance. Fitting the experimental data to a $\bm{\cos^n(\theta)}$ distribution yielded an angular exponent of $\bm{n = 1.44 \pm 0.06}$, consistent with literature values. These results demonstrate the efficacy of the MPPC-scintillator coupling for precise photon counting and timing applications in high-energy physics instrumentation.

We report on singular jets in a free-falling liquid tin droplet following nanosecond laser-pulse impact. Following impact, the droplet (with diameter $D_0=50$ or 70\,$\mu$m) undergoes rapid radial expansion and subsequent retraction, resulting in the formation of an axisymmetric jet. Using numerical simulations in tandem with our experiments, we reveal that a delicate interplay between radial flow and the curvature of the retracting droplet governs jet formation. The resulting dynamics is characterized using the impact Weber number, $\We$ (in the experiments $2 \lesssim \We \lesssim 16$), and a pressure width, W (typically $1 \lesssim \W \lesssim 2$), which describes the angular distribution over the droplet surface of the instantaneous pressure impulse exerted by the transient laser-produced plasma. %, within the range $0-20$. For values $\We<10$, the droplet presents a pronounced forward curvature during the retraction, leading to the formation of a cavity. The collapse of such a cavity leads to a singular jet that greatly enhances the jetting velocity up to ten times the impact propulsion velocity, an effect that narrowly peaks around $\We\sim6-8$, reminiscent of singular jets in droplet-solid impact. We identify a further sensitivity of the jet velocity enhancement on the pressure width W and capture the dynamics in a phase diagram connecting the various deformation morphologies with jet velocity.

We present numerical results on dynamo action in a flow driven by an azimuthal body force localized near the end of an elongated cylindrical container. The analysis focuses on the central region of the cylinder, where axial variations in the flow are relatively weak, allowing the magnetic field to be represented as a helically traveling wave. Four magnetic impeller configurations and multiple forcing intensities are examined. In all cases, the velocity profiles in the central region display a similar \propto r^{-2} dependence across a wide range of Reynolds numbers and forcing region widths. The magnetic field is found to start growing under conditions similar to those of the Riga dynamo. However, the growing modes exhibit a substantial nonzero group velocity, indicating that the associated instability is convective: the flow can amplify an externally applied magnetic field but cannot sustain it autonomously. We outline several approaches for overcoming this limitation in order to realize a working laboratory dynamo based on an internally unconstrained swirling jet-type flow.

Natural birth and death are fundamental mechanisms of population dynamics in ecosystems and have played pivotal roles in shaping population dynamics. Nevertheless, in studies of cyclic competition systems governed by the rock-paper-scissors (RPS) game, these mechanisms have often been ignored in analyses of biodiversity. On the other hand, given the prevalence and profound impact on biodiversity, understanding how higher-order interactions (HOIs) can affect biodiversity is one of the most challenging issues, and thus HOIs have been continuously studied for their effects on biodiversity in systems of cyclic competing populations, with a focus on neutral species. However, in real ecosystems, species can evolve and die naturally or be preyed upon by predators, whereas previous studies have considered only classic reaction rules among three species with a neutral, nonparticipant species. To identify how neutral species can affect the biodiversity of the RPS system when species' natural birth and death are assumed, we consider a model of neutral species in higher-order interactions within the spatial RPS system, assuming birth-and-death processes. Extensive simulations show that when neutral species interfere positively, they dominate the available space, thereby reducing the proportion of other species. Conversely, when the interference is harmful, the density of competing species increases. In addition, unlike traditional RPS dynamics, biodiversity can be effectively maintained even in high-mobility regimes. Our study reaffirms the critical role of neutral species in preserving biodiversity.

Many anisotropic layered materials, despite their strong in-plane birefringence, exhibit substantial visible absorption, which severely restricts cavity lengths and hinders the observation of purely birefringence-governed optical phenomena. Here, we realize a birefringence-driven anisotropic optical cavity using $\alpha$-MoO3 flakes, capitalizing on their ultralow optical loss and pronounced in-plane birefringence. Using angle-resolved polarized Raman (ARPR) spectroscopy, we observe a mode-sensitive enhancement of anisotropy, dependent on both flake thickness and Raman shift. A unified model that incorporates the intrinsic Raman tensor, birefringence, and chromatic dispersion accurately reproduces the experimental data, elucidating how cavity resonances at both excitation and scattered wavelengths interact. Within this framework, the intrinsic phonon anisotropy is quantified, providing invaluable insights for accurately predicting ARPR responses and identifying crystallographic orientation. This work provides fundamental insights into birefringence-governed cavities and opens avenues for high-performance birefringent optics and cavity-enhanced anisotropic phenomena.

Photonic devices depend critically on the dielectric materials from which they are made, with higher refractive indices and lower absorption losses enabling new functionalities and higher performance. However, these two material properties are intrinsically linked through the empirical Moss rule, which states that the refractive index of a dielectric decreases as its band gap energy increases. Materials that surpass this rule, termed super-Mossian dielectrics, combine large refractive indices with wide optical transparency and are therefore ideal candidates for advanced photonic applications. This Review surveys the expanding landscape of high-index dielectric and semiconductor materials, with a particular focus on those that surpass the Moss rule. We discuss how electronic band structures with a large joint density of states near the band edge give rise to super-Mossian behavior and how first-principles computational screening can accelerate their discovery. Finally, we establish how the refractive index sets the performance limits of nanoresonators, waveguides, and metasurfaces, highlighting super-Mossian dielectrics as a promising route toward the next performance leap in photonic technologies.

This paper re-evaluates the relationship between the observed sunward electron cutoff energy and the depth of the Sun's global electrostatic potential. It investigates whether taking into account the effects of local traps formed by magnetic fluctuations provides an alternative explanation for the observed electron deficit. The fluctuations of the highly variable interplanetary magnetic field form a series of shallow magnetic mirror traps that move at approximately the speed of the solar wind. The study investigates the dynamics of electrons as they move outward against an attractive solar electrostatic potential and interact with these traps. By following the motion of the electrons using first-principles calculations, we assess the effect of the traps on the velocity distribution of the particles. Electrons that escape the local trap continue to lose energy as they move outward until they are eventually captured by subsequent traps, preventing them from returning to the observer as sunward-moving particles. We derive a mathematical expression for the cutoff velocity, defined as the limit beyond which particles can no longer overtake the outer endpoint of a local trap. It turns out that the observed cutoff energy characterizes only the local potential drop within a trap, rather than the total depth of the Sun's potential well. The true potential well can be significantly deeper, scaled by the ratio of the radial distance from the Sun to the trap size. Furthermore, electrons captured by these moving traps contribute to the formation of the solar wind core population. The Sun's electrostatic potential is a more significant factor in solar wind acceleration than previously interpreted from cutoff data. The interaction between electrostatic deceleration and moving magnetic traps provides a new framework for understanding the origin and behavior of the solar wind core electrons.

This paper introduces a novel approach to sea ice modeling using Graph Neural Networks (GNNs), utilizing the natural graph structure of sea ice, where nodes represent individual ice pieces, and edges model the physical interactions, including collisions. This concept is developed within a one-dimensional framework as a foundational step. Traditional numerical methods, while effective, are computationally intensive and less scalable. By utilizing GNNs, the proposed model, termed the Collision-captured Network (CN), integrates data assimilation (DA) techniques to effectively learn and predict sea ice dynamics under various conditions. The approach was validated using synthetic data, both with and without observed data points, and it was found that the model accelerates the simulation of trajectories without compromising accuracy. This advancement offers a more efficient tool for forecasting in marginal ice zones (MIZ) and highlights the potential of combining machine learning with data assimilation for more effective and efficient modeling.

Photochromic materials enable dynamic optical modulation through reversible transitions between distinct absorption states, with broad potential for smart windows, adaptive optics, and reconfigurable photonic devices. Micron-scale photochromic hybrid films present a particularly attractive platform for these applications, combining straightforward preparation with substantial optical modulation and scalability for high-volume fabrication. However, rational design of such films remains fundamentally constrained by the absence of well-defined optical constants. Unlike homogeneous thin films, micron-scale hybrid photochromic materials comprise active particles dispersed non-uniformly within polymer matrices. Conventional first-principles electromagnetic simulations face substantial computational costs and discrepancies between simulated and experimental particle distributions. Here, we introduce a data-driven framework that extracts effective optical constants directly from minimal experimental transmittance measurements. Our dual-state effective model approximates the complex inhomogeneous photochromic layer as a compressed homogeneous medium characterized by pseudo-refractive indices and pseudo-extinction coefficients for both pristine and UV-irradiated states. Through systematic optimization against experimental data from tungsten oxide-polyvinylpyrrolidone hybrid films, we determine wavelength-dependent pseudo-optical constants and compression ratios that enable accurate prediction of optical modulation within the tested thickness range. Our methodology establishes a framework for engineering hybrid photochromic systems and demonstrates how data-driven modeling can overcome limitations in characterizing complex nanostructured materials.

Directional ion transport across membranes maintains living systems in nonequilibrium, which underlies chemiosmotic energy conversion. However, the physical origin of collectively organized ion transport in primitive cellular systems remains unclear. Here, we propose a minimal model in which ion pumps collectively align through feedback between ion transport and electrostatic interactions. In the model, directional ion transport generates a membrane potential, while the resulting electrochemical potential biases pump orientation, leading to self-organized collective alignment. Numerical simulations and mean-field analysis reveal a nonequilibrium transition from a disordered state without net transport to a pump-alignment state with sustained membrane potentials. The critical behavior is consistent with the mean-field Ising universality class; however, the effective field is generated self-consistently by nonequilibrium ion transport. We further show that protocell asymmetry can bias the polarity of the membrane potential. These results provide a generic self-organizing mechanism for the emergence of bioelectricity and a physical route toward chemiosmotic coupling in protocells.

Flat-top (FT) solitons are optical pulses that arise from the balance of dispersion and self-phase modulation in media with the competing cubic-quintic nonlinearity. Previously, FT solitons were studied only in the case of the second-order dispersion ($m=2$). Following the recent observation of pure-quartic solitons (corresponding to $m=4$), we here construct families of FT solitons in the setting with pure-high-even-order dispersion (PHEOD), including $m=4,6,8$, and $10$, and address interactions between them. The PHEOD solitons are completely stable, and, unlike the conventional solitons, they feature oscillatory tails. Interactions between the PHEOD solitons are anomalous, featuring repulsion and attraction between in- and out-of-phase solitons, respectively. These results expand the variety of optical solitons maintained by diverse dispersive nonlinear media.

Cycling is a cornerstone of a sustainable mobility transition in cities. Cycling research depends on the data available, but it has been difficult to produce or access these data in comparable ways. Sports tracking platforms like Strava have been transformative in mass-tracking cycling patterns and data sharing through applications and data competitions. Nevertheless, access to data has remained limited. Here, we present a framework that draws on the openly accessible Strava Global Heatmap to estimate spatial patterns of relative cycling intensity on an urban scale. To refine the raw heatmap outputs, we weighted them with population and point of interest (POI) counts within varying buffers. The cycling patterns were validated in a global context, comparing the heatmap values with cycle count data from 29 cities. Both population and POI weighting delivered high correlations in most cases between the heatmap and the cycle counts, POI weighting performing better overall. The strongest associations between Strava heatmap and cycle counts were observed in European cities and along the North American east coast, with p>0.7 for all, and p>0.8 for most cities. Additionally, the performance of our approach improved with higher cycling modal share at the city level. We demonstrate that a POI-weighted Strava heatmap can accurately represent urban cycling patterns and provide estimates of categorical cycling volumes. Our approach can be applied with relatively low effort to support the planning for urban cycling if official counts are sparse. Furthermore, it can enable the use of consistent cycling data for large-scale urban cycling analyses.

June 22nd, 2020 was an important day for nuclear test monitoring. The US undersecretary, Thomas DiNanno, made an official claim that a secret nuclear test was conducted at the Lop Nor testing site on that day. However, the Provisional Technical Secretariat (PTS) of the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) reported that there were no seismic events consistent with a nuclear explosion on that date. This means that the event was either hidden under the threshold of the international monitoring system (IMS), or it was missed by the international data center (IDC). Alternatively, no event occurred. All scenarios are of interest to the scientific community. The routine processing of seismic data by the IDC is well documented in open sources. The threshold for detection is not uniform across the globe, especially in continental areas. Additionally, it is not low enough to detect weapon-sized explosions using the cavity decoupling method. There are methods to lower this threshold using waveform cross-correlation (WCC) techniques. On June 22th, 2020, the IDC ran a preliminary pipeline for processing seismic data from the IMS. This was done in a testing mode. Since 2020, the development of WCC methods has allowed for revisiting cases and obtaining high-quality reports on events that were not reported five years ago.

The high computational cost of phase field simulations remains a major limitation for predicting dendritic solidification in metals, particularly in additive manufacturing, where microstructural control is critical. This work presents a surrogate model for dendritic solidification that employs uncertainty-driven adaptive sampling with XGBoost and CNNs, including a self-supervised strategy, to efficiently approximate the spatio-temporal evolution while reducing costly phase field simulations. The proposed adaptive strategy leverages model uncertainty, approximated via Monte Carlo dropout for CNNs and bagging for XGBoost, to identify high-uncertainty regions where new samples are generated locally within hyperspheres, progressively refining the spatio-temporal design space and achieving accurate predictions with significantly fewer phase field simulations than an Optimal Latin Hypercube Sampling optimized via discrete Particle Swarm Optimization (OLHS-PSO). The framework systematically investigates how temporal instance selection, adaptive sampling, and the choice between domain-informed and data-driven surrogates affect spatio-temporal model performance. Evaluation considers not only computational cost but also the number of expensive phase field simulations, surrogate accuracy, and associated $CO_2$ emissions, providing a comprehensive assessment of model performance as well as their related environmental impact.

Accurate modeling of Short Fiber Reinforced Composites (SFRCs) remains computationally expensive for full-field simulations. Data-driven surrogate models using Artificial Neural Networks (ANNs) have been proposed as an efficient alternative to numerical modeling, where Recurrent Neural Networks (RNNs) are increasingly being used for path-dependent multiscale modeling by predicting the homogenized response of a Representative Volume Element (RVE). However, recently, transformer models have been developed and they offer scalability and efficient parallelization, yet have not been systematically compared with RNNs in this field. In this study, we perform a systematic comparison between RNNs and transformer models trained on sequences of homogenized response of SFRC RVEs. We study the effect on two types of hyperparameters, namely architectural hyperparameters (such as the number of GRU layers, hidden size, number of attention heads, and encoder blocks) and training hyperparameters (such as learning rate and batch size). Both sets of hyperparameters are tuned using Bayesian optimization. We then analyze scaling laws with respect to dataset size and inference accuracy in interpolation and extrapolation regimes. The results show that while transformer models remain competitive in terms of accuracy on large datasets, the RNNs demonstrate better accuracy on small datasets and show better extrapolation performance. Furthermore, under extrapolation, there is a clear difference, where the RNN remains accurate, while the transformer model performs poorly. On the other hand, the transformer model is 7 times faster at inference, requiring 0.5 ms per prediction compared to the 3.5 ms per prediction for the RNN model.

The superposition principle is fundamental to linear wave systems, ensuring that their physical behaviour is independent of the chosen basis representation. While this principle underpins many analytical techniques, including modal decompositions and scattering formulations, we show that superposition expansion can fail in multilayered media when fields are expressed as infinite series of evanescent and inhomogeneous waves. Using the Airy formula and the scattering-matrix formalism, we identify conditions under which the superposition of partial waves diverges, particularly in systems with three or more interfaces. This divergence occurs because evanescent wave components cannot be normalised within the conventional basis and is not a numerical artefact. To address this, we introduce power flux modes corresponding to orthonormal basis wave solutions that preserve energy conservation in scattering events and consequently restore convergence. We prove that in the flux-orthonormal basis, interface scattering is unitary and propagation eigenvalues are bounded guaranteeing convergence. Our approach generalises to scalar, electromagnetic, and elastic wave systems, providing a robust framework for eliminating evanescent mode divergence without regularisation or renormalisation.

The formation and stability of the solid electrolyte interphase (SEI) play a central role in determining the long-term performance and safety of modern electrochemical energy storage systems. Despite decades of research, the SEI's heterogeneous, dynamic, and multi-phase nature has defied comprehensive molecular-level characterization, creating a critical knowledge gap that limits rational battery design. In this work, we introduce a computational-experimental framework that integrates high-throughput quantum chemistry calculations, data-driven electro-chemical reaction networks (eCRNs), stochastic algorithms, and Laser Desorption/Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (LDI-FTICR-MS) to unravel SEI formation in carbonate-based electrolytes without imposing predefined mechanisms. We constructed the most comprehensive eCRN to date, spanning over 10,000 species and 209 million reactions. Through stochastic network analysis, we successfully recovered 27 species that were previously reported in literature and predicted 28 novel SEI species, nearly doubling our scientific knowledge in this area. Each new species was rigorously confirmed through advanced mass spectra analysis of its distinct molecular and isotopic signatures. We kinetically refined the formation pathways for a select set of both previously reported and novel SEI products, revealing kinetically feasible elementary reaction mechanisms with activation barriers below 1 eV. This computational-experimental approach deepens our molecular-level understanding of SEI chemistry and supports the rational design of advanced electrolytes and engineered interphases for next-generation lithium-based batteries.

Recent experimental investigations have demonstrated that doping a semiconductor is a route to increase the thermal boundary conductance at metal/semiconductor interfaces. In this work, the influence of the electrical properties on heat transfer across metal/doped semiconductor junctions is investigated. Specifically, thermal boundary conductance at the interfaces between p- and n-doped silicon and titanium is measured by employing frequency-domain photothermal radiometry under varying external conditions. The influence of the doping level of the semiconductor, the barrier height, and the space charge area is analyzed. In particular, a 40% increase in the interface thermal conductance with the application of a current at n-doped silicon/titanium interfaces is reported. The enhancement of the thermal boundary conductance is explained by the shrinking of the surface charga area induced by the electric current. This study opens the way to modulating interfacial heat transfer at metal/semiconductor interfaces through fine tuning of electrical effects.