Papers

Muscle contraction is a prototypical multiscale chemomechanical process in which ATP hydrolysis at the molecular level drives force generation and mechanical work at larger scales. A central challenge is to incorporate the free energy supplied by ATP hydrolysis into a mechanical cross-bridge model in a way that is thermodynamically consistent and connects microscopic motor cycling to macroscopic force generation. Here we use the Energetic Variational Approach (EnVarA) to combine Hill's cycle-affinity viewpoint with Huxley's sliding-filament mechanics in a single thermodynamically consistent framework. We formulate a three-state Fokker–Planck–jump description for cross-bridge densities evolving on state-dependent free-energy landscapes, and treat ATP, ADP, and Pi as reacting and diffusing chemical species that share the same free-energy functional as the cross-bridge densities. ATP hydrolysis enters the model through local detailed balance, which biases the transition rates. Filament sliding velocity is incorporated as a convective transport term in the Fokker–Planck dynamics, so mechanical power output is defined directly from the energy balance law. Under chemostatted conditions and a fast-equilibration assumption for the two attached states, the model reduces to a closed two-state molecular motor description; in a further small-diffusion limit, this reduction recovers a Huxley-type transport-reaction equation whose effective attachment and detachment rates are derived from the underlying three-state cycle rather than prescribed phenomenologically, with their strain, ATP, and phosphate dependence fixed by the energy landscapes and local detailed balance. We calibrate the reduced model against cross-bridge-level observables and show that it reproduces key velocity-dependent trends and yields a Hill-like force–velocity relation comparable to established cross-bridge models.

Neural operators have emerged as powerful deep learning frameworks for approximating solution operators of parameterized partial differential equations (PDE). However, current methods predominantly rely on multilayer perceptrons (MLPs) for mapping inputs to solutions, which impairs training robustness in physics-informed settings due to inherent spectral biases and fixed activation functions. To overcome the architectural limitations, we introduce the Physics-Informed Chebyshev Polynomial Neural Operator (CPNO), a novel mesh-free framework that leverages a basis transformation to replace unstable monomial expansions with the numerically stable Chebyshev spectral basis. By integrating parameter dependent modulation mechanism to main net, CPNO constructs PDE solutions in a near-optimal functional space, decoupling the model from MLP-specific constraints and enhancing multi-scale representation. Theoretical analysis demonstrates the Chebyshev basis's near-minimax uniform approximation properties and superior conditioning, with Lebesgue constants growing logarithmically with degree, thereby mitigating spectral bias and ensuring stable gradient flow during optimization. Numerical experiments on benchmark parameterized PDEs show that CPNO achieves superior accuracy, faster convergence, and enhanced robustness to hyperparameters. The experiment of transonic airfoil flow has demonstrated the capability of CPNO in characterizing complex geometric problems.

Phase transitions among Mg2SiO4 and its high-pressure polymorphs (wadsleyite and ringwoodite) are central to mantle dynamics and deep-mantle material cycling. However, the locations and Pressure-Temperature (P-T) dependences of these phase boundaries remain debated, largely due to experimental limitations at extreme conditions and the high computational cost of first-principles free-energy calculations. Here, a machine-learning-potential driven workflow combining non-equilibrium thermodynamic integration (NETI) and two-phase coexistence simulations is employed to enable large-scale, long-timescale molecular dynamics sampling. Within this workflow, the melting curve of forsterite is evaluated and a complete P-T phase diagram is constructed. Relative to conventional ab initio approaches, this strategy reduces computational expense while retaining thermodynamic consistency in phase-stability assessment. The workflow is applicable to efficient evaluation of phase stability and thermodynamic properties in deep-Earth silicate systems.

Many modern ultrasound beamformers report improved image quality when evaluated using classical criteria like the contrast ratio and contrast-to-noise ratio, which are based on summary statistics of regions of interest (ROIs). However, nonlinear beamformers and post-processing methods can substantially alter these statistics, raising concerns that the reported improvements may reflect changes in dynamic range or remapping rather than a reflection of true information gain, such as clutter suppression. New criteria like the generalized contrast-to-noise ratio (gCNR) address these concerns, but rely on noisy estimates of the underlying distribution. To address this, we introduce a new image quality criterion, called the contrast order (CO), defined as the expected value of the sign of the difference in brightness between two ROIs. The CO is invariant under all strictly monotonic transformations of the image values, as it depends only on their relative ordering, and is interpretable as the probability that one ROI is brighter than the other minus the probability that it is darker. Unlike the gCNR, the CO has a simple unbiased estimator whose variance decreases with the number of samples in each ROI. We further propose the effective contrast ratio (ECR), which calibrates the contrast order to the familiar contrast ratio such that the two coincide under ideal Rayleigh-speckle statistics. Together, the CO and ECR provide order- and sign-preserving, dynamic-range-invariant criteria for evaluating lesion contrast, offering a principled alternative to classical and newer image quality criteria when assessing modern beamformers.

Cellular metabolism is globally regulated by various currency metabolites such as ATP, GTP, and NAD(P)H. These metabolites cycle between charged (high-energy) and uncharged (low-energy) states to mediate energy transfer. While distinct currency metabolites are associated with different metabolic functions, their charged and uncharged forms are generally interchangeable via biochemical reactions such as ${\rm ATP{\,+\,}GDP{\,\rightleftharpoons\,}ADP{\,+\,}GTP}$ and $\rm NADP^+{\,+\,}NADH{\,\rightleftharpoons\,}NADPH{\,+\,}NAD^+ $. Thus, their energetic states are generally coupled and influence each other, which would hinder the independent regulation of different currency metabolites. Despite the extensive knowledge of the molecular biology of individual currency metabolites, it remains poorly understood how the coordination of various coupled currency metabolites shapes metabolic regulation, efficiency, and ultimately the evolution of organisms. Here, we present a minimal theoretical model of metabolic currency coupling and reveal a fundamental tradeoff relationship between metabolic controllability and thermodynamic cost: increasing the capacity to independently regulate multiple currency metabolites generally requires comparable abundances of those metabolites, which in turn incurs a higher entropy production rate. The tradeoff suggests that in complex environments, organisms evolutionarily favor an equal abundance of currency metabolites to enhance metabolic controllability at the expense of a higher thermodynamic cost; conversely, in simple environments, organisms evolve to have imbalanced amounts of them to reduce heat dissipation. These considerations also offer a hypothesis regarding evolutionary trends in nucleotide-pool balance and genomic GC content.

The water-repellence properties of superhydrophobic surfaces make them promising for many applications. However, in some extreme environments, such as high humidities and low temperatures, condensation on the surface is inevitable, which induces the loss of surface superhydrophobicity. In this study, we propose a freezing-melting strategy to achieve the dewetting transition from the Wenzel state to the Cassie-Baxter state. It requires freezing the droplet by reducing the substrate temperature and then melting the droplet by heating the substrate. The condensation-induced wetting transition from the Cassie-Baxter state to the Wenzel state is analyzed first. Two kinds of superhydrophobic surfaces, i.e., single-scale nano-structured superhydrophobic surface and hierarchical-scale micro-nano-structured superhydrophobic surface, are compared and their effects on the static contact states and impact processes of droplets are analyzed. The mechanism for the dewetting transition is analyzed by exploring the differences in the micro/nano-structures of the surfaces and it is attributed to the unique structure and strength of the superhydrophobic surface. These findings will enrich our understanding of the droplet-surface interaction involving phase changes and have great application prospects for the design of superhydrophobic surfaces.

Physics-informed neural networks (PINNs) offer a promising framework by embedding partial differential equations (PDEs) into the loss function together with measurement data, making them well-suited for inverse problems. However, standard PINNs face challenges with time-dependent PDEs due to the high computational cost of space-time training and the risk of convergence to local minima. These limitations are particularly pronounced in hemodynamic analysis, where 4D-flow magnetic resonance imaging (4D-flow MRI) yields temporally sparse velocity snapshots over the cardiac cycle. To address this challenge, we propose a PINN framework that reconstructs instantaneous flow fields from transient velocity snapshots by inferring the acceleration term in the incompressible Navier-Stokes equations. By designing the network without explicit time as an input, the proposed approach enables physics enforcement using spatial evaluations alone, improving training efficiency while maintaining physical consistency with transient flow characteristics. In addition, we introduce an acceleration-mismatch loss that penalizes discrepancies between predicted and measured accelerations, which improves prediction accuracy through regularization. Numerical examples on pulsatile flow behind a stenosis using temporally and spatially downsampled synthetic data generated from time-resolved CFD demonstrate that the proposed framework reliably reconstructs velocity fields even under sparse temporal sampling, and appropriate regularization for acceleration improves predictions of pressure-gradient and acceleration fields.

Low-dimensional ice trapped in nanocapillaries is a fascinating phenomenon and is ubiquitous in our daily lives. As a decisive factor of the confinement effect, the size of nanocapillary significantly affects the critical crystallization pressure and crystalline structure, especially for multi-layered ices. By choosing square ice as a typical two-dimensional (2D) multi-layered ice pattern and using all-atom molecular dynamics simulations, we further unveil the variation mechanism of critical crystallization pressure with the nanocapillary size. The results show a strong dependence of the critical crystallization pressure on the size of the graphene sheet for monolayer, bilayer, and trilayer square ice. The quasi-macroscopic crystallization pressure, the actual pressure of water molecules, and the freezable region between them are all strongly dependent on the nanocapillary width. As the size of the capillary becomes larger in all three directions, the critical crystallization pressure converges to the true macroscopic crystallization pressure, which is very close to the value of the crystallization pressure for bulk ice. A direct correlation is established between 2D square ice and three-dimensional (3D) bulk ice by the critical crystallization pressure. There is an unfreezable threshold for crystallizing spontaneously in practice when the quasi-macroscopic crystallization pressure is equal to the actual pressure, which can explain the limit of nanocapillary width for multi-layered ice.

Three-dimensional spiral injection enables beam storage in ultra-compact rings with nanosecond revolution periods. We report first storage of a $297 \, \mathrm{keV/}c$ electron beam in a $22 \,\mathrm{cm}$ weak-focusing ring with a $4.7\,\mathrm{ns}$ revolution period using a $140\,\mathrm{ns}$ kicker pulse. A scintillating-fiber detector observes signals $>5\sigma$ above noise for $\geq 1\, \mathrm{\mu s}$, and varying the weak-focusing field potential shifts the stored-beam region, consistent with Monte Carlo predictions, validating beam storage. This proof-of-principle opens a path to ultra-compact storage rings for next-generation precision measurements.

Degenerate Cahn-Hilliard phase-field models provide a robust approximation of surface-diffusion-driven interface motion without explicit front tracking. In computations, however, the geometric volume enclosed by the interface – the region where the order parameter $\phi$ is positive – may drift at finite interface thickness, producing artificial shrinkage or growth even when the sharp-interface limit conserves volume. We revisit and extend the improved-conservation framework of Zhou et al., where one replaces classical mass conservation by the exact conservation of a designed monotone mapping $Q(\phi)$ that more accurately approximates a step function. Building on this framework, we (i) carry out the matched-asymptotic analysis in the unscaled physical time formulation, (ii) derive a simplified representation of the first-order inner correction to the interface profile, and (iii) identify an integral-moment cancellation condition that controls the leading geometric-volume defect. This mechanism becomes a practical design rule: we select regularization kernels within parameterized families – including exponential and Pade-type – to reach effective higher-order behavior and satisfy the cancellation condition at moderate parameter values. As a result, the proposed kernels achieve formal third-order accuracy in geometric-volume conservation with respect to interface thickness. Finally, we describe an unconditional energy-dissipative numerical discretization that exactly preserves the discrete conserved quantity. Numerical benchmarks on multi-scale droplet coarsening and shape relaxation demonstrate that the moment-balanced kernels virtually eliminate artificial drift and prevent premature extinction of small droplets.

We develop a statistical framework for wealth allocation in which equilibrium-like statistics follow from unbiased counting of admissible configurations rather than postulated exchange rules. Each agent is described by a value–wealth map $V_i(w)$, whose local resolution fixes the microscopic weight through a Jacobian relation. In a closed system, the microcanonical marginal and a reservoir expansion yield an emergent canonical distribution for the regular sector. Its partition sum gives a general condensation criterion: if this sector has finite wealth capacity, excess wealth concentrates on a small subset of agents. We extend the construction to open systems with variable wealth and agent number and to weak quasistatic driving. The global constraint determines an evolution equation for the common parameter $\lambda(t)$, while simultaneous changes in total wealth and value–wealth geometry produce a unified first-order response. The susceptibility $\chi_W=\sum_i\mathrm{Var}_i(w)$ equals the Fisher information of the joint canonical family, and Legendre duality gives $\mathrm{d}s^2=\chi_W\mathrm{d}\lambda^2=\chi_W^{-1}\mathrm{d}W^2=-\mathcal{S}''(W)\mathrm{d}W^2$. For power-law critical tails, finite capacity requires $p>2$; within this regime, $\chi_W$ diverges for $2<p\leq3$ and remains finite for $p>3$, while the critical boundary lies at finite Fisher–Rao distance. We also derive a qualified Cram'er–Rao duality and an open-system mixed-response relation. Contact-geometric, Airy-scaling, and stochastic-dynamical interpretations are identified only as conjectures or future work. The time-dependent results are quasistatic and do not determine microscopic relaxation times, while the information geometry describes the canonical family.

The rapid evolution of Large Language Models (LLMs) has led to the emergence of Multi-Agent Systems where collective cooperation is often threatened by the "Tragedy of the Commons." This study investigates the effectiveness of Anchoring Agents–pre-programmed altruistic entities–in fostering cooperation within a Public Goods Game (PGG). Using a full factorial design across three state-of-the-art LLMs, we analyzed both behavioral outcomes and internal reasoning chains. While Anchoring Agents successfully boosted local cooperation rates, cognitive decomposition and transfer tests revealed that this effect was driven by strategic compliance and cognitive offloading rather than genuine norm internalization. Notably, most agents reverted to self-interest in new environments, and advanced models like GPT-4.1 exhibited a "Chameleon Effect," masking strategic defection under public scrutiny. These findings highlight a critical gap between behavioral modification and authentic value alignment in artificial societies.

Photonic computing offers a low-power, high-bandwidth paradigm for information processing; however, the analogue nature of conventional architectures means that intrinsic noise and fabrication imperfections greatly impact performance, thereby severely limiting scalability. Recent work on optical skyrmions offers a route to overcoming these limitations by exploiting perturbation-resilient topological invariants assigned to the optical field for computation. Crucially, owing to its relative novelty, an architectural perspective on integrating individual components that manipulate topological charge into a functional system remains an important open goal. In this paper, we take concrete steps toward system-level design by introducing a platform-independent architecture for skyrmion-based logic, built around a modular library of topologically robust optical primitives, including generators, converters, registers, and adders. This framework enables the synthesis and arithmetic manipulation of topological numbers within a unified programmable architecture. We then experimentally validate this approach using multichannel arrays, demonstrating accurate charge readout and high robustness against alignment errors and environmental noise. These results provide a scalable foundation for topologically robust programmable logic arrays, paving the way for compact and integrated photonic processing circuits.

We introduce Version 3 (V3) of the gridded near real-time Multi-Source Weighted-Ensemble Precipitation (MSWEP) product – the first fully global, historical machine learning powered precipitation (P) dataset, developed to meet the growing demand for timely and accurate P estimates amid escalating climate challenges. MSWEP V3 provides hourly data at 0.1$^\circ$ resolution from 1979 to the present, continuously updated with a latency of approximately two hours. Development follows a two-stage process. First, baseline P fields are generated using machine learning model stacks that integrate satellite- and (re)analysis-based P and air-temperature products, along with static variables. The models are trained using hourly and daily observations from 15,959 P gauges worldwide. Second, these baseline P fields are corrected using daily and monthly gauge observations from 57,666 and 86,000 stations globally. To assess MSWEP V3's baseline performance, we evaluated 19 (quasi-) global gridded P products – including both uncorrected and gauge-based products – using observations from an independent set of 15,958 gauges excluded from the first training stage. The MSWEP V3 baseline achieved a median daily Kling-Gupta Efficiency (KGE) of 0.69, outperforming all evaluated products. Other uncorrected products achieved median daily KGE values of 0.61 (ERA5), 0.46 (IMERG-L V7), 0.38 (GSMaP V8), and 0.31 (CHIRP). Using leave-one-out cross-validation, the daily gauge correction was found to improve the median daily correlation by 0.09, constrained by the already strong baseline performance. We anticipate that MSWEP V3 – accessible at www.gloh2o.org/mswep – will enable more reliable monitoring, forecasting, and management of water-related risks in a variable and changing climate.

Satellite-based quantum communications represent a critical advancement in the pursuit of secure, global-scale quantum networks. Leveraging the principles of quantum mechanics, these systems offer unparalleled security through Quantum Key Distribution (QKD) and other quantum communication protocols. This review provides a comprehensive overview of the current state of satellite-based quantum communications, focusing on the evolution from terrestrial to space-based systems. We explore the distinct advantages and challenges of discrete-variable (DV) and continuous-variable (CV) quantum communication technologies in the context of satellite deployments. The paper also discusses key milestones such as the successful implementation of quantum communication via the Micius satellite and outlines the primary challenges, including atmospheric turbulence and the development of quantum repeaters, that must be addressed to achieve a global quantum internet. This review aims to consolidate recent advancements in the field, providing insights and perspectives on the future directions and potential innovations that will drive the continued evolution of satellite-based quantum communications.

The PCIe400 is a generic readout board designed for high-throughput data acquisition in future high-energy physics experiments. It interfaces up to 48 bidirectional links supporting custom protocols from 1 to 26 Gbps to modern back-end systems providing up to 400 Gbps bandwidth.The board is developed as a technological demonstrator for the LHCb Upgrade II, which foresees an aggregated throughput of approximately 200 Tbps. In addition to increased bandwidth, the PCIe400 targets deterministic clock distribution to front-end electronics. At a maximum instantaneous luminosity of $1.5~\times~10^{34}~cm^{-2}s^{-1}$, up to 40 proton-proton interactions per bunch crossing are expected in LHCb Upgrade II. The adoption of 4D tracking detectors with time resolutions down to 20 ps motivates clock distribution with phase determinism below 10 ps peak-to-peak across large-scale systems exceeding 2000 nodes. This paper presents the qualification of the PCIe400 prototype board, focusing on high-bandwidth interfaces, including next-generation QSFP112 links, and phase-deterministic clock distribution.

Self Attraction and Loading (SAL), which includes the deformation of the solid Earth under the load of the ocean tide and the self-gravitation of the so-deformed Earth as well as of the ocean tides themselves, is an important term to include in numerical models of the ocean tides. Computing SAL is a challenging problem that is usually tackled using spherical harmonics. The spherical harmonic approach has several drawbacks which limit its accuracy. In this work, we propose an alternative technique based on a spherical convolution. We implement the convolution technique in the Modular Ocean Model, version 6, and demonstrate that it allows for more accurate tides when measured against tidal datasets based upon satellite altimetry. The convolution based SAL reduces the error by reducing spurious oscillations associated with the Gibbs phenomenon. These oscillations are large in coastal regions under the traditional spherical harmonic approach.

Light propagating in an optical waveguide can gain or lose power through interaction with a travelling acoustic wave or radio-frequency modulation of permittivity. Here, we model this propagation by considering an optical wave interacting with a weak travelling-wave permittivity perturbation whose frequency is much smaller than the optical frequency and whose amplitude decays exponentially along the propagation direction. Four modulation cases are analyzed: instantaneous modulation, synchronous modulation, and the Stokes and anti-Stokes resonances. For these cases, simple expressions are obtained for the carrier and sideband transmission and reflection powers as well as for the total gain and loss powers. Although the achievable total gain remains small for realistic modulation and waveguide parameters, the anti-Stokes resonance is identified as the most promising condition for observing modulation-induced light amplification.

Self-accelerating Airy matter waves offer a clean setting to access the intrinsic cubic-in-time phase. Here we reconstruct the relative phase of simulated Airy-shaped Bose-Einstein condensates from interference fringes in free space, a regime approached in microgravity. The cubic phase dynamics are quantified via approximately debiased, windowed polynomial fits with systematics-aware uncertainty estimates that account for window-induced correlations. We compare two physically feasible phase-extraction methods, heterodyne-based and density-based, and show that an Airy-Gaussian geometry yields substantially improved robustness to fit-window selection relative to an Airy-Airy collision. In the weakly interacting regime, the extracted cubic coefficient responds linearly to leading order to the effective interaction strength, its shift from the noninteracting value providing a calibrated probe of weak mean-field nonlinearities in self-accelerating condensates.

Machine learning (ML) offers transformative potential for computational fluid dynamics (CFD), promising to accelerate simulations, improve turbulence modelling, and enable real-time flow prediction and control-capabilities that could fundamentally change how engineers approach fluid dynamics problems. However, the exploration of ML in fluid dynamics is critically hampered by the scarcity of large, diverse, and high-fidelity datasets suitable for training robust models. This limitation is particularly acute for highly turbulent flows, which dominate practical engineering applications yet remain computationally prohibitive to simulate at scale. High-Reynolds number turbulent datasets are essential for ML models to learn the complex, multi-scale physics characteristic of real-world flows, enabling generalisation beyond the simplified, low-Reynolds number regimes often represented in existing datasets. This paper introduces WAKESET, a novel, large-scale CFD dataset of highly turbulent flows, designed to address this critical gap. The dataset captures the complex hydrodynamic interactions during the underwater recovery of an autonomous underwater vehicle by a larger extra-large uncrewed underwater vehicle. It comprises 1,091 high-fidelity Reynolds-Averaged Navier-Stokes simulations, augmented to 4,364 instances, covering a wide operational envelope of speeds (up to Reynolds numbers of 1.09 x 10^8) and turning angles. This work details the motivation for this new dataset by reviewing existing resources, outlines the hydrodynamic modelling and validation underpinning its creation, and describes its structure. The dataset's focus on a practical engineering problem, its scale, and its high turbulence characteristics make it a valuable resource for developing and benchmarking ML models for flow field prediction, surrogate modelling, and autonomous navigation in complex underwater environments.

Optimisation plays a central role in a wide range of scientific and industrial applications, and quantum computing has been widely proposed as a means to achieve computational advantages in this domain. To date, research into the design of noise-resilient quantum algorithms has been dominated by variational approaches, while alternatives remain relatively unexplored. In this work, we introduce a novel class of quantum optimisation heuristics that forgo this variational framework in favour of a hybrid quantum-classical approach built upon Markov Chain Monte Carlo (MCMC) techniques. We introduce Quantum-enhanced Simulated Annealing (QeSA) and Quantum-enhanced Parallel Tempering (QePT), before validating these heuristics on hard Sherrington-Kirkpatrick instances and demonstrate their superior scaling over classical benchmarks. These algorithms are expected to exhibit inherent robustness to noise and support parallel execution across both quantum and classical resources with only classical communication required. As such, they offer a scalable and potentially competitive route toward solving large-scale optimisation problems with near-term quantum devices.

We perform a comparative study of the breakup of positronium and ionization of atomic hydrogen by projectile-nuclei in the weak perturbation collision regime, $Z_p e_0^2/\hbar \ll v < c $ ($v$ is the collision velocity, $Z_p$ the projectile atomic number, $e_0$ the elementary charge and $c$ the speed of light). In this regime the only principal difference between the collisions with these atomic systems lies in the masses of their positively charged constituents. We have shown that the corresponding mass effects strongly influence the spectra of the target fragments and the total cross sections. This influence manifests itself via i) the significantly smaller binding energy in positronium resulting in smaller momentum transfers necessary to break the system, ii) a strong constructive interference between the inelastic scattering of the projectile on the electron and the positron in collisions with positronium that also increases the chances for the breakup and iii) the "passive" role of the hydrogen nucleus caused by its heavy mass that prohibits hydrogen ionization to proceed via the interaction between the projectile and the nucleus.

Long-lived dark states can dominate the lowest-energy manifold of optically driven quantum materials, yet their occupation remains difficult to quantify, leaving it unclear whether it reflects thermal redistribution or kinetic trapping. We combine microsphere-enabled far-field access with quantitative optical-response calibration to retrieve dark-to-bright population ratios in monolayer WSe2. Temperature-dependent measurements and controlled defect enhancement separate mobile and localized contributions. Near room temperature, the mobile dark-to-bright ratio reaches approximately 65% of its Boltzmann limit, indicating substantial but incomplete quasi-thermalization, whereas the low-temperature excess is dominated by defect-assisted localization. Dark-state dominance alone therefore does not establish equilibration, a distinction essential for interpreting transport and collective phases in optically hidden quasiparticle reservoirs.

We examine entropy production in reduced descriptions of collisionless plasmas. Introducing a closure dependent entropy hierarchy, we show that non-conservative moment closures generate a residual entropy associated with irreversible information loss, whereas invariant closures remain reversible. The monotonic growth of this residual entropy imposes a statistical realizability constraint on macroscopic plasma states, excluding regions of phase space independent of dynamical stability. For anisotropic plasmas, we evaluate entropy production within a second-order moment closure, identifying contributions from transport and magnetic field inhomogeneity. The resulting entropy exclusion boundary is broadly consistent with observed anisotropy distributions in space plasmas. Statistical realizability thus emerges as an organizing principle for reduced collisionless plasma descriptions.

The study of the spatial layout of Christian churches has been of great interest since late Antiquity and the beginning of the Early Middle Ages, and has received a new impetus in recent literature when it was recognized that the orientation of their main axes represents a key feature of their architecture. From the earliest Christian communities, the orientation of the church allowed the faithful to pray facing the east, towards the rising Sun. Several authors were careful to point this out in their writings; in particular, prior to the Council of Nicaea (325 AD), the Apostolic Constitutions indicated: "And let it be, first, the elongated building, with its head facing east". We present a detailed analysis of the spatial orientation of the historic churches located in the Jesuit towns of Chiquitania (Bolivia). We have measured in situ the main characteristics of eight churches currently standing and the ruins of a ninth construction. In all cases, we carry out a thorough survey of the landscape surrounding each church, trying to find some common pattern, possibly astronomical, that explains its orientations. To our list we also add the orientation measures of a tenth church based on work with plans and satellite maps. We complement our data with a detailed cultural and historical study of the characteristics of missionary towns. Unlike the churches of the Guarani towns of the historic province of Paraquaria where the meridian orientations stand out, in the case of the Jesuit churches of Chiquitos, half of the measured constructions show orientations that fall within the solar range, with three churches oriented equinoctially with high precision. We analyze the reasons for these orientations and delve further on the possible relevance that lighting effects could have had for the architects of these churches, which represent true hidden cathedrals in the virgin tropical forest.

In quantum teleportation, a pair of entangled photons are prerequisite to serve as the quantum channel for quantum state transfer distantly. Here, we report a new strategy of quantum-teleportation-inspired classical optical imaging, which also works non-locally at a distance; however, only a classically correlated light source is used instead of entanglement. In our experiment, we explore the pseudo-thermal light source to offer the teleportation-like channel and employ the sum-frequency generation to perform the Bell-like state measurement. We successfully demonstrate the teleportation-inspired optical imaging of simple characters, Taiji diagram, and the superposition of orbital angular momentum modes. Moreover, we experimentally observe that a better coherence of pseudo-thermal light will result in a lower contrast of the formed images, and thus revealing that non-zero quantum discord offered by pseudo-thermal light, regardless of zero entanglement, plays the pivotal role in sustaining the teleportation-like channel for imaging at a distance.

In this study, we evaluated the performance of SAIPy, an open-source Python package for deep learning-based seismic data analysis, by applying its single-station monitoring tools and extending its use to a seismic network based approach, using data from a local seismic network deployed in a Caldera. Although the integrated models into SAIPy for earthquake detection,magnitude estimation, seismic phase picking, and P-wave polarity classification, were originally trained on tectonic signals, we assess their performance in a more complex seismic environment that includes volcano-tectonic events, along with signal interference from distant earthquakes.We also demonstrate the advantages of integrating outputs using multiple stations to improve event detection. SAIPy was able to identify a significantly larger number of local events than those included in previously published catalogs. SAIPy demonstrated reliable phase picking and P-wave polarity estimation, particularly for local volcano-tectonic events, with some limitations observed in the magnitude estimation for complex volcanic signals. These results support the utility of SAIPy for processing continuous seismic data and suggest that future retraining using data with physically standardized units, removing instrumental response, and including data from more diverse seismic sources, could improve its generalization for magnitude estimation to complex scenarios and different seismic networks and sensor types.

As Generative AI becomes a key component in physics education, a significant ethical challenge has emerged: the tendency of students to anthropomorphize Large Language Models (LLMs), treating them as authoritative "oracles" that retrieve fixed facts from an internal database. However, LLMs operate fundamentally as probabilistic engines. This paper describes the design and implementation of a didactic activity, a reduced version of the "20 Questions" game, aimed at making this stochastic nature directly observable. Unlike a human player who fixes a target object at the start of the game, students discover that the model generates answers based solely on local coherence with the interaction history. By utilizing functionalities such as re-sampling and history rewinding, students act as experimenters, observing how identical interaction histories can yield diverging narrative paths. We discuss how mapping these behaviors to familiar physics concepts provides the epistemic scaffolding necessary to promote informed skepticism, framing the verification of AI outputs not merely as a compliance rule, but as a technical necessity derived from the system's probabilistic nature.

Reliable estimation of the raindrop size distribution (RSD) is important for applications including quantitative precipitation estimation, soil erosion modelling, and wind turbine blade erosion. While in situ instruments such as disdrometers provide detailed RSD measurements, they are spatially limited, motivating the use of polarimetric radar for remote retrieval of rain microphysical properties. This study presents a comparative evaluation of analytical and machine-learning approaches for retrieving RSD parameters from polarimetric radar observables. One-minute OTT Parsivel2 disdrometer measurements collected between September 2020 and May 2022 at Sheepdrove Farm, UK, were quality-controlled using collocated weighing and tipping-bucket rain gauges. Measured RSDs were fitted to a normalised three-parameter gamma distribution, from which a range of polarimetric radar variables were analytically simulated. Analytical retrievals, neural networks, and decision tree models were then trained to estimate the gamma distribution parameters across multiple radar feature sets and model architectures. To assess robustness and equifinality, each model configuration was trained 100 times using random 70/30 train-test splits, yielding approximately 17,000 trained models in total. Machine-learning approaches generally outperform analytical methods; however, no single model class or architecture is uniformly optimal. Model performance depends strongly on both the target RSD parameter and the available radar observables, with decision trees showing particular robustness in reduced-feature regimes. These results highlight the importance of aligning retrieval model structure with operational data constraints rather than adopting a single universal approach.

A semi-implicit Lax-Wendroff scheme is developed for electron-phonon coupling process in metals based on the two-temperature kinetic equations. The core of this method is to integrate the evolution information of physical equations into the numerical modeling process, which leads to that the time step or cell size is not limited by the relaxation time and mean free path. Specifically, the finite difference method is used to solve the kinetic model again when reconstructing the interfacial distribution function, through which the particle migration, scattering and electron-phonon coupling processes are coupled together within a single time step. Numerical tests demonstrate that this method could efficiently capture electron-phonon coupling or heat conduction processes from the ballistic to diffusive regimes. It provides a new tool for describing electron-phonon coupling or thermal management in microelectronic devices.