Papers

We present high-precision ab initio calculations of the four-point vertex function for the three-dimensional uniform electron gas using variational diagrammatic Monte Carlo. From these results, we extract Landau parameters that reveal a density-driven crossover from underscreening to overscreening, and obtain the full two-electron scattering amplitude on the Fermi surface with controlled accuracy. A residual analysis of the scattering amplitude against the charge-channel Kukkonen–Overhauser (KO$^+$) interaction shows that only a minimal s-wave correction in the antiparallel-spin channel is needed, defining the sKO$^+$ ansatz: KO$^+$ within the local-density approximation plus this short-range correction. Using both our direct VDMC amplitudes and the sKO$^+$ ansatz, we compute the electron-electron contribution to the thermal resistivity, obtaining quantitative agreement with experiments on simple metals (Al, Na, K, Rb). sKO$^+$ thus provides a controlled UEG-based effective interaction for simple-metal transport and future first-principles extensions.

Ghost imaging leverages a single-pixel detector with no spatial resolution to acquire object echo intensity signals, which are correlated with illumination patterns to reconstruct an image. This architecture inherently mitigates scattering interference between the object and the detector but sensitive to scattering between the light source and the object. To address this challenge, we propose an optical diffraction neural networks (ODNNs) assisted ghost imaging method for imaging through dynamic scattering media. In our scheme, a set of fixed ODNNs, trained on simulated datasets, is incorporated into the experimental optical path to actively correct random distortions induced by dynamic scattering media. Experimental validation using rotating single-layer and double-layer ground glass confirms the feasibility and effectiveness of our approach. Furthermore, our scheme can also be combined with physics-prior-based reconstruction algorithms, enabling high-quality imaging under undersampled conditions. This work demonstrates a novel strategy for imaging through dynamic scattering media, which can be extended to other imaging systems.

Understanding the structure and dynamics of Earth's inner core is essential for constraining its composition, thermal evolution, and seismic behavior. Silicon is widely considered a major light element in the core. Here, we use first-principles molecular dynamics and thermodynamic modeling to investigate the structural, elastic, and transport properties of iron-silicon (Fe-Si) alloys under inner-core conditions. By computing the Gibbs free energies of multiple solid phases, including ordered B2, hexagonal close-packed (hcp), face-centered cubic (fcc), and body-centered cubic (bcc) Fe-Si, we construct a phase diagram relevant to inner-core pressures and temperatures. We identify a pronounced miscibility gap between hcp and cubic Fe–Si phases, with phase coexistence over approximately 6–11 wt% Si at 6000 K. The cubic phase exhibits a strong order-dependent elastic response, with disordered bcc showing a large intrinsic shear anisotropy (22.9% at 6000 K), in contrast to the nearly isotropic hcp phase (0.6%). Meanwhile, disordered bcc Fe–Si produces shear wave velocities ($V_S=3.73$ km$\cdot$s$^{-1}$) and Poisson's ratios comparable with seismological constraints. In addition, disordered bcc Fe–Si exhibits a lower thermal conductivity than hcp Fe–Si, providing a quantitative constraint on phase-dependent electronic heat transport. These results demonstrate that chemical ordering strongly controls the stability and physical properties of cubic Fe–Si alloys, and suggest that metastable or localized cubic Fe–Si phases may contribute to the structural and transport complexity of Earth's inner core.

Magnetic resonance imaging-guided linear accelerators (MRI-Linacs) are an emerging treatment technology that enable online soft-tissue visualisation and adaptive radiotherapy. The Australian 1.0 T MRI-Linac employs a fixed, inline beamline, with treatment angles achieved via patient couch rotation, and currently relies on a radiolucent whole-body radiofrequency (RF) coil. However, the large coil-patient separation limits signal-to-noise ratio (SNR) and constrains the fast imaging required for real-time tumour tracking. Here, we show the design and validation of a dedicated, flexible 8-element receive-only RF torso array tailored to the geometry and beam access requirements of the Australian MRI-Linac. Electromagnetic simulations, bench measurements, and phantom imaging demonstrate approximately three-fold SNR improvement at the centre and eight-fold improvement at superficial regions compared with the whole-body coil. In vivo abdominal imaging confirms enhanced anatomical detail and sharper tissue boundaries, and the increased SNR enables parallel imaging with GRAPPA acceleration factors up to three without severe artefacts. Dosimetry measurements and dynamic beam-on noise analysis confirm that the coil array is effectively radiolucent, with dose differences below 1% and no measurable degradation in image noise. This work demonstrates a practical RF solution for accelerated, high-SNR imaging compatible with patient rotation and beam delivery on the Australian MRI-Linac.

Serial crystallography experiments routinely produce thousands of diffraction patterns from crystals in random orientations. To turn this stream of images into a usable dataset, each pattern must be indexed before integration and merging can proceed. In practice, diffraction patterns may contain only a small number of reliable peaks, be contaminated by background or spuriously detected reflections, or arise from crystals with highly skewed unit cells. These factors make indexing unstable in the small-N regime. We introduce a robust indexing algorithm tailored to this setting. We formulate indexing as a symmetry-aware lattice decoding problem and design a loss that explicitly incorporates lattice symmetries while trimming outlier peaks that are inconsistent with any plausible orientation. We combine this objective with a reciprocal-space basis reparameterization that stabilizes decoding for skewed or poorly conditioned lattices, and we develop a dedicated small-N objective mode that couples refined peak scoring with a method to recover orientations from very few reflections. The resulting method is memory-efficient and suitable for robust indexing. We evaluate our approach on three protein datasets from the Coherent X-ray Imaging Data Bank collected at XFEL facilities, using identical preprocessing and unit-cell information across methods. Across all datasets, our algorithm matches or outperforms established indexers such as XGANDALF and TORO, with particularly large gains for patterns with few indexed peaks and for crystals with skewed unit cells. While slower, our method is extremely memory-efficient, and its structure allows high-parallelism on CPUs or larger batch sizes on GPUs. These results show that exploiting lattice structure, symmetry, and small-N-aware search yields substantial improvements in indexing robustness.

In many countries, declining demand in energy-intensive industries (EIIs) such as cement, steel, and aluminum is leading to industrial overcapacity. Although industrial overcapacity is traditionally envisioned as problematic and resource-wasteful, it could unlock EIIs' flexibility in electricity use. Here, using China's aluminum smelting industry as a case study, we evaluate the system-level cost-benefit of retaining EII overcapacity for flexible electricity use in decarbonized energy systems. We find that overcapacity can enable aluminum smelters to adopt a seasonal operation paradigm, ceasing production during winter load peaks that are exacerbated by heating electrification and renewable seasonality. This seasonal operation paradigm could reduce the investment and operational costs of China's decarbonized electricity system by 23-32 billion CNY/year (11-15% of the aluminum smelting industry's product value), sufficient to offset the increased smelter maintenance and product storage costs associated with overcapacity. It may also create labor complementarities between the aluminum and thermal power sectors.

Entangled photon-pair sources are indispensable building blocks of quantum information processing technologies. Among the available approaches, on-chip microresonators are particularly promising owing to their resonant enhancement, CMOS-compatible fabrication, and wafer-scale integration capabilities. In this study, we optimized the structure of silicon microring resonators to suppress sidewall scattering. As a result, we achieved an intrinsic Q-factor of 1.26$\times$10$^6$ using only standard fabrication processes. The high-Q resonator enabled a brightness coefficient of 3.9$\times$10$^9$ Hz/GHz/mW$^2$, with a maximum brightness of 22.0 MHz/GHz and a maximum photon-pair generation rate of 9.19 MHz. Furthermore, Franson-type two-photon interference exhibited a visibility of 98.0$\pm$0.2%, confirming time-energy entanglement. These results show that narrow bandwidth and high generation rate can be achieved simultaneously in CMOS-compatible silicon photonic sources, advancing their use in quantum repeaters.

On-chip erbium-doped optical amplifiers and lasers are essential for realizing fully integrated active silicon photonic circuits, but their performance has been limited by the low gain of amorphous host materials and the difficulty of direct integration on silicon. Here, we demonstrate optical amplification and lasing from erbium-doped single-crystal gadolinium oxide (Er:Gd$_2$O$_3$) thin films epitaxially grown on silicon. Optical gain measurements on waveguides fabricated on this platform exhibit a giant material gain of $78.3\pm2.1$ dB/cm and an on-chip net gain exceeding 13 dB in a 6-mm-long waveguide at 2.3 K, while a measurable gain is maintained up to room temperature. Continuous-wave lasing with low threshold, narrow linewidth, and large side-mode suppression ratio is also demonstrated in Er:Gd$_2$O$_3$ microring resonators. These results establish Er:Gd$_2$O$_3$ as the first monolithic crystalline gain medium directly integrated on silicon, providing a scalable route toward high-performance cryogenic and quantum photonic integrated circuits.

Recent work introduced a robust computational framework combining embedded mathematical structures, advanced optimization, and neural network architecture, leading to the discovery of multiple unstable self-similar solutions for key fluid dynamics equations, including the Incompressible Porous Media (IPM) and 2D Boussinesq systems. While this framework confirmed the existence of these singularities, an accuracy level approaching double-float machine precision was only achieved for stable and 1st unstable solutions of the 1D Córdoba-Córdoba-Fontelos model. For highly unstable solutions characterized by extreme gradients, the accuracy remained insufficient for validation. The primary obstacle is the presence of sharp solution gradients. Those gradients tend to induce large, localized PDE residuals during training, which not only hinder convergence, but also obscure the subtle signals near the origin required to identify the correct self-similar scaling parameter lambda of the solutions. In this work, we introduce a gradient-normalized PDE residual re-weighting scheme to resolve the high-gradient challenge while amplifying the critical residual signals at the origin for lambda identification. Coupled with the multi-stage neural network architecture, the PDE residuals are reduced to the level of round-off error across a wide spectrum of unstable self-similar singularities previously discovered. Furthermore, our method enables the discovery of new highly unstable singularities, i.e. the 4th unstable solution for IPM equations and a novel family of highly unstable solitons for the Nonlinear Schrödinger equations. This results in achieving high-gradient solutions with high precision, providing an important ingredient for bridging the gap between numerical discovery and computer-assisted proofs for unstable phenomena in nonlinear PDEs.

Fast and reliable surrogate models are critical for optimization, control and uncertainty analysis in geological carbon-storage projects, yet high-fidelity multiphase simulators remain too expensive. Dynamic Mode Decomposition (DMD) offers an attractive data-driven reduction framework, but its operators are trained for a single set of reservoir properties. When permeability or well location changes, conventional practice is to regenerate snapshots and retrain the surrogate, erasing most of the speed advantage. This work presents a lightweight alternative that updates an existing DMD or DMD-with-control model without incorporating new simulation data or retraining. Two complementary update strategies are introduced. For cases where permeability changes uniformly across the domain, the proposed updates adjust the models internal dynamics and control response to match the new flow timescale. When permeability varies in space, the approach modifies the spatial representation so that high-permeability zones are given greater influence on the models reduced basis. Numerical experiments demonstrate that the proposed updates recover plume migration and pressure build-up within three percent of a freshly trained surrogate yet execute hundreds of times faster than full retraining. These methods therefore enable real-time optimization and rapid what-if studies while preserving the physical fidelity demanded by carbon-storage workflows.

Background: Accurate proton resonance frequency (PRF) MR thermometry is essential for monitoring temperature rise during thermal ablation with high intensity focused ultrasound (FUS). Conventional referenceless methods such as complex field estimation (CFE) and phase finite difference (PFD) tend to exhibit errors when susceptibility-induced phase discontinuities occur at tissue interfaces.

This short note comments on \citet{Aerts2024Origin}, which proposes that ranked word frequencies in texts should be read through the lens of Bose–Einstein (BE) statistics and even used to illuminate the origin of quantum statistics in physics. The core message here is modest: the paper offers an interesting analogy and an eye-catching fit, but several key steps mix physical claims with definitions and curve-fitting choices. We highlight three such points: (i) a normalization issue that is presented as "bosonic enhancement", (ii) an identification of rank with energy that makes the BE fit only weakly diagnostic of an underlying mechanism, and (iii) a baseline comparison that is too weak to support an ontological conclusion. We also briefly flag a few additional concerns (interpretation drift, parameter semantics, and reproducibility).

Kolmogorov–Arnold Networks (KANs) offer a promising path toward interpretable machine learning: their learnable activations can be studied individually while collectively fitting complex data accurately. In practice, however, trained activations often lack *symbolic fidelity*, learning pathological decompositions with no meaningful correspondence to interpretable forms. We propose Softly Symbolified Kolmogorov–Arnold Networks (S2KAN), which integrate symbolic primitives directly into training. Each activation draws from a dictionary of symbolic and dense terms, with learnable gates that sparsify the representation. Crucially, this sparsification is differentiable, enabling end-to-end optimization, and uses an optimization objective inspired by minimum description length. S2KAN can discover interpretable activation functions while retaining dense splines for flexible approximation. We introduce a quantitative measure of symbolic fidelity, assessing how closely learned activations can be approximated by short expressions from a specified symbolic language. Across real-world prediction tasks, dynamical-system forecasting, and synthetic symbolic benchmarks, S2KAN generally achieves higher symbolic fidelity and often far smaller models, while maintaining competitive predictive accuracy on real-world data and low prediction errors on most symbolic benchmark configurations. We also examine the models' learned responses on scientific data, including the contributions of their symbolic components. Together, these results show that incorporating symbolic structure during training can yield compact, accurate KAN models whose learned responses can be examined through their symbolic components.

We develop a minimal nonlinear model to investigate the oscillatory dynamics of Saturn's F ring under dual-moon forcing from Prometheus and Pandora. The model extends classical predator–prey dynamics by incorporating both a nonlinear mass aggregation term $kM^n$ and explicit dual-frequency forcing, capturing how higher-order coagulation physics interacts with multi-moon perturbations. Through systematic numerical integration and dynamical-systems tools, including time-series, spectral, stroboscopic, and rotation number analysis, we identify distinct dynamical regimes controlled by the parameters $n$ and $k$. For moderate nonlinearity \((n=1.28,k=0.54)\), the numerical diagnostics are consistent with bounded quasiperiodic motion, characterized by smooth amplitude modulation, thin stroboscopic loops, and discrete spectral peaks. For stronger nonlinearity \((n=1.30,k=0.62)\), the same diagnostics indicate a transition toward strongly modulated oscillations, with broadened stroboscopic bands and sideband-rich spectra. A projected rotation-number diagnostic reveals organized regions in parameter space, including smooth quasiperiodic-like domains and near-locking bands analogous to Arnold tongues. Our results show that a reduced deterministic dual-forcing model can generate bounded quasiperiodic-like and strongly modulated aggregation-fragmentation cycles with timescales comparable to relevant moon-ring forcing periods, providing a possible low-dimensional mechanism contributing to F-ring variability.

Modeling complex human behavior, such as voter decisions in national elections, is a long-standing challenge for computational social science. Traditional agent-based models (ABMs) are limited by oversimplified rules, while large-scale statistical models often lack interpretability. We introduce FlockVote, a novel framework that uses Large Language Models (LLMs) to build a "computational laboratory" of LLM agents for political simulation. Each agent is instantiated with a high-fidelity demographic profile and dynamic contextual information (e.g. candidate policies), enabling it to perform nuanced, generative reasoning to simulate a voting decision. We deploy this framework as a testbed on the 2024 U.S. Presidential Election, focusing on seven key swing states. Our simulation's macro-level results successfully replicate the real-world outcome, demonstrating the high fidelity of our "virtual society". The primary contribution is not only the prediction, but also the framework's utility as an interpretable research tool. FlockVote moves beyond black-box outputs, allowing researchers to probe agent-level rationale and analyze the stability and sensitivity of LLM-driven social simulations.

The electron flux diverts electrons from optimal hydrogen production pathways to competitive pathways, which overall reduces the efficiency of the photo fermentation hydrogen production (PFHP) system. For tackling electron flux and metabolic pathway regulation, a hybrid material (nano-capattery (NC)) was developed based on cobalt-iron-nitrogen doped biochar (Co-Fe-NBC). The NC possessed both the capacitor property (287.91 F/g) and battery-like charge storage 38.3 mC/g with the highest energy density of 159.95 mWh/g. These properties existed due to its Fe2+/Fe3+ and Co2+/Co3+ redox cycle ability, a highly porous surface (291.81 m2/g BET surface area) caused by the defects (AD/AG 3.13) and abundant oxygen vacancies (OVs) observed through electro paramagnetic resonance. During PFHP, there is an 85% reduction in propionic acid, a 65.3% record electron management efficiency, an improved 1.34 NAD+/NADH ratio, along with a 87% increase in dehydrogenase activity, confirming the superior role of NC in efficient regulation of the metabolic pathway and electron flux management. These exceptional properties of biochar-based NC raised the cumulative hydrogen production from 151.03 mL (control) to 589.54 mL, which was an enormous increase of 367%.

I analyzed ice water content (IWC) data from the Aura Microwave Limb Sounder (MLS) and sea surface temperature (SST) data from NOAA's Optimum Interpolation SST (OISST) product from 2004 to 2024. Using these data, I derived monthly infrared (IR) leakage over the tropics and computed derivatives of both the IR leakage and tropical SST time series from 2005 to 2023. These two derivatives produced a Pearson correlation of -0.49, indicating that IR leakage decreases when SST increases. This behavior contradicts the trend predicted by the Iris hypothesis, suggesting that tropical cirrus clouds strengthen, rather than weaken, as the ocean warms.

Motivated by ideas from effective field theories, we conduct a twofold investigation in CP-violating non-linear electrodynamics models. On the one hand, features related to field equations and wave propagation in the presence of a magnetic background field are evaluated. Interestingly, we show that the CP-violating term in our framework induces a bi-anisotropic behavior of the vacuum. On the other hand, blackbody radiation thermal laws in this situation are studied. Here, we provide a general formalism for photons at low temperature, namely, with $k_{B}T\ll{m_{e}c^{2}}$. The deviations from standard values of the thermodynamic quantities, such as energy, pressure, entropy, and specific heat, are discussed. In addition, we investigate the modifications in the Planck distribution and the Stefan-Boltzmann law induced by a non-trivial CP-violating parameter.

This work discusses the concept of roulette, the generated curves that occur when one curve rolls without slipping along another, tracing the path of a fixed point. The coin paradox and Aristotle's wheel paradox are used as pedagogical motivations to discuss the parametric equations of epicycloids and hypocycloids, providing a geometrical intuition for the mathematical derivations and computational implementation of those curves. Python code is provided to motivate the application of the derived parametric equations, resulting in concrete visualizations and animations.

Large atomic-orbital (AO) basis sets of at least triple and preferably quadruple-zeta (QZ) size are required to adequately converge Kohn-Sham density functional theory (DFT) calculations towards the complete basis set limit. However, incrementing the cardinal number by one nearly doubles the AO basis dimension, and the computational cost scales as the cube of the AO dimension, so this is very computationally demanding. In this work, we develop and test a natural atomic orbital (NAO) scheme in which the NAOs are obtained as eigenfunctions of atomic blocks of the density matrix in a one-center orthogonalized representation. The NAO representation enables one-center compression of the AO basis in a manner that is optimal for a given threshold, by discarding NAOs with occupation numbers below that threshold. Extensive tests using the Hartree-Fock functional suggest that a threshold of $10^{-5}$ can yield a compression factor (ratio of AO to compressed NAO dimension) between 2.5 and 4.5 for the QZ pc-3 basis. The errors in relative energies are typically less than 0.1 kcal/mol when the compressed basis is used instead of the uncompressed basis. Between 10 and 100 times smaller errors (i.e., usually less than 0.01 kcal/mol) can be obtained with a threshold $10^{-7}$, while the compression factor is typically between 2 and 2.5.

Identifying central entities and interactions is a fundamental problem in network science. While well-studied for graphs (pairwise relations), many biological and social systems exhibit higher-order interactions best modeled by hypergraphs. This has led to a proliferation of specialized hypergraph centrality measures, but the field remains fragmented and lacks a unifying framework. This paper addresses this gap by providing the first systematic survey of 39 distinct measures. We introduce a novel taxonomy classifying them as: (1) structural (topology-based), (2) functional (impact on system dynamics), or (3) contextual (incorporating external features). We also present an experimental assessment comparing their empirical similarity and computation time. Finally, we discuss applications, establishing a coherent roadmap for future research in this area.

This chapter provides three tutorial exercises on physics-constrained regression. These are implemented as toy problems that seek to mimic grand challenges in (1) the super-resolution and data assimilation of the velocity field in image velocimetry, (2) data-driven turbulence modeling, and (3) system identification and digital twinning for forecasting and control. The Python codes for all exercises are provided in the course repository.

Significance: Monte Carlo (MC) methods are the gold-standard for modeling light-tissue interactions due to their accuracy. Mesh-based MC (MMC) offers enhanced precision for complex tissue structures using tetrahedral mesh models. Despite significant speedups achieved on graphics processing units (GPUs), MMC performance remains hindered by the computational cost of frequent ray-boundary intersection tests. Aim: We propose a highly accelerated MMC algorithm, RT-MMC, that leverages the hardware-accelerated ray traversal and intersection capabilities of ray-tracing cores (RT-cores) on modern GPUs. Approach: Implemented using NVIDIA's OptiX platform, RT-MMC extends graphics ray-tracing pipelines towards volumetric ray-tracing in turbid media, eliminating the need for challenging tetrahedral mesh generation while delivering significant speed improvements through hardware acceleration. It also intrinsically supports wide-field sources without complex mesh retesselation. Results: RT-MMC demonstrates excellent agreement with traditional software-ray-tracing MMC algorithms while achieving 1.5x to 4.5x speedups across multiple GPU architectures. These performance gains significantly enhance the practicality of MMC for routine simulations. Conclusion: Migration from software- to hardware-based ray-tracing not only greatly simplifies MMC simulation workflows, but also results in significant speedups that are expected to increase further as ray-tracing hardware rapidly gains adoption. Adoption of graphics ray-tracing pipelines in quantitative MMC simulations enables leveraging of emerging hardware resources and benefits a wide range of biophotonics applications.

This study investigates the effect of duct aspect ratio ($AR$), defined as the ratio of major to minor axis in an elliptical duct, on shock train dynamics for a freestream Mach number of 2.1. The aspect ratio $AR$ is varied from 1.0 to 3.0 while maintaining a constant cross-sectional area and identical upstream conditions, thereby ensuring the same inlet mass flow and nearly constant boundary-layer-induced blockage across all $AR$. This isolates shape-induced confinement effects. Simulations are performed using an embedded-boundary method with adaptive mesh refinement which enabled a finest resolution of 48$\mu$m resolving the shocks in the shock train. The results show that increasing $AR$ significantly modifies the shock train morphology. The number of discrete shock cells decreases, and the leading shock front elongates along the major axis while contracting along the minor axis. The normal shock stem prominent in the circular duct (AR=1.0) nearly disappears at AR=3.0. Despite these morphological changes, the wall-pressure trace and stagnation-pressure loss remain largely insensitive to $AR$. These results indicate that while duct cross-section governs the detailed shock train structure, the overall efficiency of flow deceleration and pressure recovery is dictated primarily by the effective blockage imposed by the turbulent boundary layer, rather than the aspect ratio itself for a given mass flow rate and pressure ratio across the pseudo shock.

We first briefly describe the history and motivation behind Cherenkov and scintillation light detection. We then discuss the instrumentation needed to detect these photons as it applies to both photodetectors and readout electronics. One of the motivations is future large neutrino detectors that could in principle differentiate between Cherenkov and scintillation light if using novel water-based scintillators. In this paper, we present the first measurements utilizing the second generation of Large Area Picosecond Photodetectors (LAPPDs) in conjunction with commercial system-on-a-chip readouts from Nalu Scientific – specifically, the High Density System on Chip (HDSoC) and Advanced ASoC Rapid Digitizer, Variable Adaptive Readout Chip (AARDVARC) platforms. These state-of-the-art full-waveform digitizers feature sampling rates on the order of 1 and 10 samples per nanosecond, respectively. Using a picosecond laser, we measured the timing jitter between a pair of LAPPD channels, demonstrating the potential of this setup for precise timing applications.

We propose geometrically nonlinear (finite) continuum models of flexomagnetism based on the Cosserat micropolar and its descendent couple-stress theory. These models introduce the magneto-mechanical interaction by coupling the micro-dislocation tensor of the micropolar model with the magnetisation vector using a Lifshitz invariant. In contrast to conventional formulations that couple strain-gradients to the magnetisation using fourth-order tensors, our approach relies on third-order tensor couplings by virtue of the micro-dislocation being a second-order tensor. Consequently, the models permit centrosymmetric materials with a single new flexomagnetic constant, and more generally allow cubic-symmetric materials with two such constants. We postulate the flexomagnetic action-functionals and derive the corresponding governing equations using both scalar and vectorial magnetic potential formulations, and present numerical results for a nano-beam geometry, confirming the physical plausibility and computational feasibility of the models.

The article discusses the possibility of a Type-III extraterrestrial civilization constructing megastructures around a galaxy in regions where the galactic radiation becomes indistinguishable from the CMB radiation. For a Milky Way-like galaxy, we estimated the corresponding distance from its center at which a solar mass megastructure would need to be placed. We also showed that, from an energetic standpoint, placing such a massive object into orbit poses no fundamental difficulties. The detectability of the megastructure was also addressed.

Transient computational fluid dynamics (CFD) remains expensive when long horizons and multi-scale turbulence are involved. Data-driven surrogates promise relief, yet many degrade over multiple steps or drift from physical behavior. This work advances a hybrid path: an incremental time-stepping U Net LSTM model that forecasts unsteady dynamics by predicting field updates rather than absolute states. A U-Net encoder decoder extracts multi-scale spatial structures, LSTM layers carry temporal dependencies, and the network is trained on per-step increments of the physical fields, aligning learning with classical time marching and reducing compounding errors. The model is designed to slot into solvers based on projection methods (such as SIMPLE, PISO, etc), either as an initializer that delivers a sharper first guess for pressure-velocity coupling or as a corrective module that refines provisional fields. Across representative test cases, the approach improves long-term stability (54.53 to 84.21 % reduction of cumulative errors) and preserves engineering metrics, integral and averaged quantities, more reliably than standard learning baselines. These properties make it a plausible component of hybrid CFD-ML pipelines designed to accelerate unsteady simulations without compromising quantitative fidelity.

Reactive dopant atoms embedded in inert host metal surfaces define the active sites in single-atom alloys (SAAs), yet SAA synthesis remains challenging. To address this, we elucidate how dopant adatoms deposited on Cu and Ag surfaces become incorporated into the metal and identify periodic trends from early to late transition metals (TMs) using density functional theory. Adatoms diffuse nearly freely across terraces, as diffusion barriers are small, whereas direct incorporation into terraces is unfavourable. In line with conventional wisdom, step edges and kink sites strongly facilitate dopant incorporation, confirming their critical role in alloy formation. Attachment of adatoms to steps and kinks from the lower terrace is favoured. Incorporation then proceeds either from this attached state or when adatoms approach a step edge from above, where reactions often proceed without barrier. Incorporation barriers are generally lower for early and central TMs, increase towards late TMs, and are slightly higher on Cu than on Ag surfaces. Repulsive interactions between Pd adatoms and dopants explain the experimental observation that a dopant-rich brim on the upper terrace of Cu surfaces inhibits incorporation from above. In contrast, attractive interactions, as found for Ru, anchor diffusing adatoms (even on terraces) and promote the formation of adatom islands, yet hinder incorporation next to the dopant and may impede the growth of embedded dopant clusters. By rationalising periodic trends and experimental observations, we show how specific surface sites and adatom–dopant interactions shape dopant incorporation, offering guidance on the surface environments most conducive to SAA synthesis for different dopant elements.

The directed motion of active colloids is governed by spatial variations in surface chemistry and interfacial stress, yet these properties remain extremely difficult to measure directly. We introduce a physics-informed neural network framework that infers the slip distribution driving propulsion from partial observations of the surrounding flow. By combining sparse fluid velocity measurements with the Stokes equations and boundary constraints, the method reconstructs both the near-surface slip and the full velocity and pressure fields. Validation against analytical solutions and Boundary Element Method calculations for canonical active colloid models shows quantitative agreement in both unbounded and confined geometries. Crucially, the framework recovers the surface slip even when no flow data are available near the particle, demonstrating that accessible bulk measurements encode the interfacial stresses responsible for active motion. These results establish physics-informed inference as a powerful tool for characterizing and ultimately controlling interfacially driven transport in colloidal active matter.