Papers

Accurate modelling of electrostatic interactions and charge transfer is fundamental to computational chemistry, yet most machine learning interatomic potentials (MLIPs) rely on local atomic descriptors that cannot capture long-range electrostatic effects. We present a new electrostatic foundation model for molecular chemistry that extends the MACE architecture with explicit treatment of long-range interactions and electrostatic induction. Our approach combines local many-body geometric features with a non-self-consistent field formalism that updates learnable charge and spin densities through polarisable iterations to model induction, followed by global charge equilibration via learnable Fukui functions to control total charge and total spin. This design enables an accurate and physical description of systems with varying charge and spin states while maintaining computational efficiency. Trained on the OMol25 dataset of 100 million hybrid DFT calculations, our models achieve chemical accuracy across diverse benchmarks, with accuracy competitive with hybrid DFT on thermochemistry, reaction barriers, conformational energies, and transition metal complexes. Notably, we demonstrate that the inclusion of long-range electrostatics leads to a large improvement in the description of non-covalent interactions and supramolecular complexes over non-electrostatic models, including sub-kcal/mol prediction of molecular crystal formation energy in the X23-DMC dataset and a fourfold improvement over short-ranged models on protein-ligand interactions. The model's ability to handle variable charge and spin states, respond to external fields, provide interpretable spin-resolved charge densities, and maintain accuracy from small molecules to protein-ligand complexes positions it as a versatile tool for computational molecular chemistry and drug discovery.

Proximity effects at superconducting interfaces, between different superconductors (S-S') or between superconductors and normal metals (S-N), are fundamental to the performance of superconducting electronics, yet only few experiments have directly probed the spatial structure of proximity effects within a device. This is particularly relevant for transition edge sensors (TESs), where the interplay of direct and inverse proximity effects governs detector sensitivity. Here, we use scanning superconducting interference device (SQUID) susceptometry to directly image the local diamagnetic response in functional S-S'-S TES structures. We resolve long range proximity coupling extending over tens of micrometers, revealing that the local transition temperature is dramatically tuned by neighboring regions, being either enhanced by superconducting (S) leads or suppressed by normal metal (N) contacts. Our observations are quantitatively supported by Ginzburg Landau modeling of the device geometry and calculations of the temperature dependent diamagnetism based on self-consistent Usadel equations. By providing spatially resolved measurements of the interplay of proximity effects in TES devices, this work establishes a framework for understanding and controlling superconducting states in heterogeneous superconducting structures.

The following paper is a detailed review on the properties and nature in relation to the phenomena known as 'light'. The paper tries to chronologically detail the development of theories and related experiments from the philosophical Greco-Roman and Indian schools of thought to the modern quantum theories.

Predicting the occurrence of transitions in the qualitative dynamics of many natural systems is crucial, yet it remains a challenging task. Generic early warning signals like variance and lag-1 autocorrelation identify critical slowing down near tipping points but lack practical thresholds for predicting imminent transitions. More recent studies found that the dynamical eigenvalue is rooted in the framework of empirical dynamical modeling and then estimates the dominant eigenvalue of a system from time series, providing a threshold ($|$DEV$|$ = 1) to predict bifurcations and classify their types. However, its application requires careful calibration of the hyperparameters and focuses on reconstructing system dynamics directly from data. Here, we employ Ornstein-Uhlenbeck process to derive analytic approximations for the lag-$\tau$ autocorrelation function prior to period-doubling bifurcation thereby estimating the dominant eigenvalue of dynamical systems, named dominant eigenvalue extracted from autocorrelation (DE-AC), and revealing its dynamic behaviour when approaching a period-doubling bifurcation. Theoretically, dominant eigenvalue tends to $-1$ when the system approaches a period-doubling bifurcation. In particular, we evaluated DE-AC on simulation data from cardiac alternans model and on experimental data from chick heart aggregates undergoing a period-doubling bifurcation. DE-AC reliably detected the beginning of the cardiac arrhythmia (period-doubling bifurcation) in most cases. Moreover, it demonstrated superior sensitivity and specificity as an early warning signal compared to the three widely used indicators – variance, lag-1 autocorrelation, and dynamical eigenvalue. Our theoretical and empirical results suggest that DE-AC represents a quantitative measure for predicting the onset of potentially dangerous alternating rhythms in the heart.

This paper documents the work of the Clean Air Task Force (CATF) International Working Group (IWG) on Fusion Cost Analysis in 2024-2025, and the methodological extensions implemented in the CATF-supported branch of the pyFECONs fusion power-plant costing framework. Using the standards-aligned chart-of-accounts and physics-to-economics workflow established by ARPA-E. The IWG development reorganizes and deepens the framework around three architecture-defining cost-driver tracks for Magnetic Fusion Energy (MFE), Inertial Fusion Energy (IFE), and Magneto-Inertial Fusion Energy (MIFE). In particular, the generic driver placeholder in Account 22.1.3 is treated as a controlled swap-point and replaced by a full cost-account development for the dominant driver in each class, enabling auditable traceability from requirements and geometry to rolled-up plant costs. On top of this driver-centric foundation, we introduce a probabilistic costing layer that compounds materials price uncertainty, TRL-based maturity uncertainty, and learning-curve uncertainty into cost distributions. We then describe safety-informed costing that enumerates fusion-relevant hazards and maps mitigating systems, structures, and provisions into standardized accounts, together with scenario-parameterized regulatory and financial adders. Finally, we document expanded macroeconomic and finance parameterization and a value-metrics module that complements LCOE with investment and planning measures (NPV, IRR MIRR, revenue requirements, WACC-based annualization, and residual and follow-on value), all computed from the same COA-mapped outputs. Collectively, these additions convert a deterministic, standards-aligned costing backbone into an extensible analysis environment suitable for transparent sensitivity studies, uncertainty propagation, and safety- and finance-coupled interpretation of fusion pilot-plant and NOAK scenarios.

In the nineteenth century, the Dutch established time signals in their Atlantic colonies to synchronise maritime navigation with European standards. In Paramaribo (Suriname), a sophisticated sequence of apparatus – including time balls, noon guns, discs and flags – operated from 1851 until World War I. Naval officers aboard guard ships used sextants equipped with artificial horizons to determine local noon, thus integrating the colony into the global Greenwich-based cartographic system. This infrastructure was not merely technical; it became a civic ritual, with the daily noon gun structuring urban life and becoming a point of political negotiation between naval commanders and the colonial governor. In contrast, the Dutch Caribbean islands employed simpler, pragmatic systems. Curaçao used a daily time flag, a cost-effective solution suited to its climate and harbour scale, while smaller islands like Aruba and St. Eustatius relied on occasional noon guns. This diversity reflected a decentralised colonial administration that adapted technologies to local conditions and budgets. The history of these time signals reveals a process of hybrid adaptation, not simply replication of European models. They were shaped by environmental challenges, fiscal constraints and local politics, functioning simultaneously as navigational aids and civic landmarks. Their eventual decline, owing to budgetary pressures and new technologies like wireless telegraphy, underscores the fragile and negotiated nature of colonial scientific infrastructures.

In the nineteenth century, the Netherlands quickly adopted the time ball – a British innovation for maritime chronometer calibration – in its main naval ports (Nieuwediep/Den Helder, Vlissingen, Hellevoetsluis) and commercial centres (Amsterdam, Rotterdam). A large sphere dropped from a mast at a fixed time, the device enabled ships to verify their chronometers against a standard, essential for accurate longitude determination and safe navigation. Its ready acceptance was eased by indigenous Dutch traditions. Rural communities had long used visual time signals like the sjouw on Terschelling island, a wicker ball raised on a mast to mark the lunch hour and milking time for farmers, and the lawei, a basket or sack used in the peat bogs of Friesland to regulate labourers' hours. The Dutch time-signal system was distinguished by its strong institutional backing from the country's Royal Navy, its Hydrographic Service and by professional astronomers. Among the latter, Frederik Kaiser was a pivotal figure, vehemently defending the system's accuracy and pioneering technical improvements. He successfully advocated for replacing the traditional falling ball with a system of rotating flaps, which provided a more instantaneous and reliable visual signal. Beyond their practical role, time signals became civic spectacles and symbols of Dutch scientific modernity and imperial reach. Their decline began with the electric telegraph and was finalised by wireless radio, which allowed ships to calibrate chronometers at sea.

We present a multitask surrogate for neutron-star equations of state (EoSs) that delivers distribution-free, certified uncertainty via split conformal prediction (CP) and its Mondrian variant. The surrogate ingests a six-parameter piecewise-polytropic representation $(\log_{10}p_1,\Gamma_1,\Gamma_2,\Gamma_3,\rho_1,\rho_2)$ – with fixed transition densities $\rho_1$ and $\rho_2$ – and jointly performs (i) validity classification under physical/observational constraints and (ii) regression of $M_{\max}$, $R(M_{\max})$, $R_{1.4}$, and $\Lambda_{1.4}$. Trained on a balanced set of $40{,}000$ EoSs, the model attains near-perfect discrimination (AUC $\approx 0.997$) and sub-percent relative errors for masses and radii, with few-percent error for tidal deformability. Across $\alpha\in[0.05,0.25]$, empirical coverages closely track $1-\alpha$ for both Standard and Mondrian CP; in conservative regimes, Mondrian yields narrower average physical widths at comparable coverage. To our knowledge, this is the first application of class-conditioned (Mondrian) conformal calibration to neutron-star EoS surrogates, enabling efficient, reproducible, and uncertainty-aware inference; the framework is readily extensible to functional targets (e.g., full $R(M)$ curves).

We report a combined experimental and computational study of the near-edge X-ray absorption fine structure (NEXAFS) spectrum of the propargyl radical, C$_3$H$_3^{\cdot}$. As a central intermediate in the formation of polycyclic aromatic hydrocarbons, the propargyl radical is a species of considerable relevance in combustion and astrochemistry and was here generated by pyrolysis from propargyl bromide. The NEXAFS spectrum shows a pronounced band at 282.2 eV corresponding to transitions from carbon 1s orbitals to singly occupied molecular orbitals. Ab initio calculations show that two transitions to the lowest lying states 1 $^2$A$_1$ and 2 $^2$A$_1$, which take place from the C1s orbital of the two terminal carbon atoms, contribute to this band. In addition, a 420 meV spacing of the first band is visible and is assigned to a vibrational progression in the symmetric CH$_2$ stretch. Transitions at higher energies are also described reasonably well by theory. The fragmentation pattern was investigated at the different resonant transitions and shows the cleavage of one as well as both C–C bonds.

We consider arbitrary-shaped microswimmers of spherical topology and propose a framework for expressing their slip velocity in terms of tangential basis functions defined on the boundary of the swimmer using the Helmholtz decomposition. Given a time-independent slip velocity profile, we show that the trajectory followed by the microswimmer is a circular helix. We derive analytical expressions for the translational and rotational velocities of a prolate spheroid swimmer in terms of its Helmholtz decomposition modes and explore the effect of aspect ratio on these rigid body velocities. Then, for a given arbitrary swimmer shape of spherical topology, we investigate which slip profile minimizes the total power loss. A partial minimization is performed in which the direction of net motion of the swimmer is prescribed, followed by a global optimization procedure in which the best net motion direction is determined. The optimization results suggest that the competition between linear and rotational optimal motion is linked to symmetries in the shape of the microswimmer.

Parameter degeneracy in blazar spectral energy distributions (SEDs) is known but rarely quantified. This paper introduces a Fisher Information approach to determine theoretical limits to information extraction in the context of one-zone models. By evaluating the total Fisher Information by varying $\delta$, $B$, $p$, $\gamma_{\rm min}$ and $\gamma_{\rm max}$, we find that EC models encode Fisher information $\gtrsim10^4$ times less than that in SSC models, establishing differences in limits of physical information extraction even in the case of perfect sampling. Moreover, the Fisher information in both SSC and EC models exhibit strong fluctuations across the parameter space, but since the magnitudes are orders of magnitude lower in EC, limits of parameter inference are expected to be worse in FSRQ SEDs than BL Lacs. We also find that the Doppler factor $\delta$ carries at least $10^{2-3}$ more Fisher information than that for $p$ and $B$ in both EC and SSC, making $\delta$ the most constrained SED parameter. Applying our Fisher Information motivated framework to real flaring SEDs of Flat Spectrum Radio Quasars (FSRQs) CTA 102 and 3C 279, we show that mild variations in $\delta$ and $p$ can appreciably produce the flaring SEDs starting from the quiescent model, while two other flares in 3C 279 simple geometric and spectral considerations cannot reproduce the flares, reducing the efficacy of one-zone models. We propose that time-resolved SED models are indispensable to constraining physical parameters in EC-dominated blazars.

Strong gradient regions in tokamaks, such as the pedestal or internal transport barriers, are regions of reduced turbulence where neoclassical transport can play a dominant role. However, standard neoclassical transport theory assumes that the gradient length scales of density, temperature, and potential are of the order of the system size. In the pedestal, gradient length scales are much shorter and are measured to be of the order of the ion poloidal gyroradius. We present an extension of neoclassical theory that is applicable in transport barriers of large aspect ratio tokamaks. We show that particle and momentum transport are connected in such a way that a source of parallel momentum can drive a significant neoclassical ion particle flux. In strong gradient regions, density, electric potential, mean parallel flow, and ion temperature are shown to no longer be flux functions. Instead, they have a small but important poloidally varying piece that modifies the transport equations to lowest order. This introduces a nonlinearity in the transport problem through the coupling with quasineutrality that yields multiple co-existing solutions when solving for the plasma profiles. The different solutions could be connected to low and high transport states and jumps between solutions could be an indication of H-L back-transitions.

Conventional ECG visualization and analysis methods typically emphasize either waveform morphology or rhythm variability. This work presents a visualization framework for quasiperiodic physiological signals that enables simultaneous assessment of beat-to-beat morphological changes and rhythm dynamics in a single representation. The proposed method converts quasiperiodic signals into two-dimensional carpet plots. Characteristic events (e.g., ECG R peaks) are used to align consecutive signal segments, which are transformed into color-coded rows and stacked in chronological order. The resulting image preserves both the temporal evolution of signal morphology and variations in cycle duration. The method was evaluated using ECG recordings from multiple publicly available databases containing healthy subjects and patients with diverse cardiac abnormalities, as well as synchronized multimodal physiological recordings. Carpet plots enabled rapid visualization of transient morphological changes alongside heart rate dynamics across recordings ranging from several minutes to hours. The representation highlighted clinically relevant phenomena, including ST segment alterations, QT interval variability, changes in T wave morphology, atrial fibrillation episodes, premature ventricular complexes, Wenckebach periodicity, and stress-test phase transitions. The image-based representation was also shown to be suitable for automated analysis using convolutional neural network feature extraction. Carpet plots provide a compact representation of quasiperiodic physiological signals, jointly visualizing rhythm and morphology across long-term recordings. The proposed framework facilitates both expert interpretation and image-based computational analysis, offering a general approach for investigating transient physiological phenomena in ECG and other synchronized quasiperiodic signals.

Schlieren imaging is a popular optical technique for visualizing flow in transparent media. In-water high-sensitivity flow visualization, using schlieren imaging, is usually performed with a large-footprint two-mirror z-configuration. Here, we present a small footprint, easy-to-implement, single-mirror schlieren imaging system for in-water flow visualization. The same system is capable of high-sensitivity flow visualization in air as well. At its core, our system uses a concave mirror with water immersion. We present theoretical analysis and experimental results to show that this water immersion helps reduce the system's footprint by 25%. Our water immersion-based single-mirror schlieren imaging method additionally reduces mirror surface artifacts, increasing the sensitivity of flow visualization. This technique enables a low-cost schlieren system, as demonstrated experimentally using an inexpensive concave mirror. We also provide the experimental validation of high sensitivity in-water flow visualization for some transparent chemicals or solutions.

Recurrent neural networks excel at temporal tasks and video processing but require energy-intensive sequential memory operations. We demonstrate that multimode optical fibers naturally implement spatiotemporal recurrent computation through passive light propagation. Video frames are encoded onto separate optical beams with controlled time delays; these beams combine and recirculate through a fiber loop where interference and nonlinear propagation generate high-dimensional states encoding both current inputs and fading memory. Remarkably, the entire optical system remains fixed with no trainable parameters or electronic feedback, yet this single physical configuration achieves competitive performance across diverse temporal and spatiotemporal learning tasks: chaotic time-series forecasting, human action recognition, steering angle prediction, and surgical skill assessment. Our results show that recurrent temporal processing can emerge directly from spatiotemporal wave dynamics. This paradigm shift from algorithmic to physical recurrence offers an energy-efficient pathway to temporal artificial intelligence by leveraging intrinsic spatiotemporal optical nonlinearities within multimode fibers.

Most methods for estimating probable maximum precipitation (PMP) – the greatest depth of precipitation that is physically possible over a given area and duration – rely on storm transposition (ST), the process of transporting a storm, either historically observed or simulated, from its original location to a target basin. Existing ST approaches, whether classical or physically based, involve assumptions and manipulations that can introduce inconsistencies, leaving the physical validity of the transposed storm uncertain. In this study, the internal variability leveraging (IVL) approach is used to transpose an atmospheric river cluster that affected the U.S. West Coast during 20-29 October 2021. Steering the storm toward the target basin and determining its transposition region are achieved by considering an ensemble of plausible storm evolutions and trajectories obtained from archived ECMWF medium-range forecasts. The Willamette River and Nass River watersheds, located approximately 6 deg N, 2 deg W and 16 deg N, 8 deg W, respectively, from the area most affected by the observed precipitation, were selected as target basins. For each basin, the IVL realization yielding the largest 24-h basin-average precipitation depth was identified, and the initial and boundary condition shifting method was subsequently applied to further enhance its impact, producing 24-h precipitation depths of 119 mm for the Willamette and 98 mm for the Nass.

We develop a rigorous Temporal Coupled-Mode Theory (TCMT) specifically tailored for a single Floquet-sheet resonator governed by time-modulated conductivities. By invoking photon-number conservation during frequency conversion, we derive characteristic radiative decay rates and coupling coefficients that account for the frequency ratio between channels. We establish a systematic bridge to the Floquet Transfer Matrix Method (TMM), providing closed-form analytical expressions that map scattering parameters to the Drude-type physics of the sheet. Our model explicitly captures the resonant coupling between the 0th-order propagating channel and the -1st-order surface-mode channel. Validated by COMSOL numerical simulations, the theory remains robust even when intrinsic material loss is incorporated. This framework offers an intuitive pole-expansion representation for designing time-varying photonic interfaces.

A coupled spintronic oscillator array has been considered attractive for neuromorphic computing applications. Experimental reports have shown the nano-constriction geometry to be a relatively easier-to-fabricate platform for implementing such spin oscillators, but most prior reports on training and inference algorithms for neuromorphic computing using spin oscillators have been mostly restricted to the nano-pillar geometry. Also, those prior reports involve updating the natural frequency values of the oscillators and moving the synchronization regions on to the data clusters during the offline learning phase, which has associated challenges. In this context, we design and simulate a novel artificial neural network (ANN) - oscillator neural network (ONN) algorithm where in the offline learning phase, the weight parameters of the ANN are updated such that the data clusters are instead moved to the synchronization regions of spin Hall nano oscillators (SHNOs) in the nano-constriction geometry, as obtained through micromagnetic simulations. We further simulate the on-chip inference part of the ANN-ONN algorithm where the ANN is implemented on a crossbar array of domain-wall synapse devices, as simulated here through micromagnetics, and the ONN is implemented on nano-constriction SHNOs. We show successful data classification for both binary and multi-class classification tasks to demonstrate the generalizability of our proposed scheme.

Despite significant analysis of bird flight, generative physics models for flight dynamics do not currently exist. Yet the underlying mechanisms responsible for various flight manoeuvres are important for understanding how agile flight can be accomplished. Even in a simple flight, multiple objectives are at play, complicating analysis of the overall flight mechanism. Using the data-driven method of dynamic mode decomposition (DMD) on motion capture recordings of hawks, we show that multiple behavioral states such as flapping, turning, landing, and gliding, can be modeled by simple and interpretable modal structures (i.e. the underlying wing-tail shape) which can be linearly combined to reproduce the experimental flight observations. Moreover, the DMD model can be used to extrapolate naturalistic flapping. Flight is highly individual, with differences in style across the hawks, but we find they share a common set of dynamic modes. The DMD model is a direct fit to data, unlike traditional models constructed from physics principles which can rarely be tested on real data and whose assumptions are typically invalid in real flight. The DMD approach gives a highly accurate reconstruction of the flight dynamics with only three parameters needed to characterize flapping, and a fourth to integrate turning manoeuvres. The DMD analysis further shows that the underlying mechanism of flight, much like simplest walking models, displays a parametric coupling between dominant modes suggesting efficiency for locomotion.

Following the introduction of the coincident formulation of General Relativity and the rapid development of $f(Q)$ gravity after 2018, symmetric teleparallel gravity has become one of the most active research areas in modified gravity. As the field has expanded rapidly, some of the conceptual and geometric developments established during its formative years have become less visible in the contemporary literature. This article revisits the early development of symmetric teleparallel gravity between 2004 and 2013 and places these contributions within the broader historical evolution of metric-affine gravity. Particular attention is devoted to Einstein-equivalent field equations, five-parameter quadratic non-metricity Lagrangians and their field equations, the metric formulation in the natural (pure) gauge, exact solutions, and spinor couplings. Several of these developments were originally published in the Turkish Journal of Physics, including the use of the terminology “symmetric teleparallel gravity” and its abbreviation “STPG”. We distinguish these contributions from the earlier symmetric teleparallel formulation of Nester and Yo, who had already used a vanishing connection in a coordinate frame. A short comparison with representative recent work on $f(Q)$ gravity places the historical account in its present context. This letter documents the continuity between the early geometric foundations and subsequent developments of the field.

Accurately predicting plasma behavior based on discharge configurations is essential for the safe and efficient operation of tokamak experiments. While physics-based integrated modeling codes provide valuable insights, their high computational cost limits their applicability for fast scenario design and control optimization. In this study, we propose a transformer-based machine learning model to predict key global plasma parameters on the WEST tokamak, including the normalized beta ($\beta_{n}$), toroidal beta ($\beta_{t}$), poloidal beta ($\beta_{p}$), plasma stored energy ($W_{\mathrm{mhd}}$), safety factor at the magnetic axis ($q_{0}$), and safety factor at the 95% flux surface ($q_{95}$). The model uses only signals that can be defined before the discharge, such as magnetic coil currents, auxiliary heating power, plasma current reference, and line-averaged plasma density. Trained on 550 discharges from the WEST campaigns, the model demonstrates an average mean square error (MSE) loss of 0.026, an average coefficient of determination $R^{2}$ of 0.94, and achieves inference times on the order of 0.1 seconds. These results highlight the potential of data-driven surrogate models for assisting in discharge planning, scenario evaluation, and real-time control of tokamak plasmas.

Observations of supermassive black holes by the Event Horizon Telescope reveal significant inhomogeneities, most likely related to density and magnetic field perturbations. To model these features, we conduct high-resolution 2D general-relativistic magnetohydrodynamics (GRMHD) simulations of a Fishbone-Moncrief torus around a Kerr black hole using the Black Hole Accretion Code $\texttt{BHAC}$. We compare unperturbed accretion with a case featuring plasma density bubbles with pressure balanced magnetic islands of different amplitudes. Power spectrum analysis of accretion time series, performed via the Blackman-Tukey method, shows that the perturbed case exhibits (1) steeper spectral indices compared to the unperturbed case, deviating from the characteristic $1/\omega$ noise spectrum, and (2) increased correlation times, providing evidence for absorption of macro-structures at the event horizon. Spatial auto-correlation analysis of near-horizon turbulence confirms larger energy-containing coherent structures in the perturbed case altering the accretion rate. These results provide new insights for interpreting observations of supermassive black hole environments, where near-horizon turbulence may play a key role in the accretion process.

Integrated photonic biosensors provide compact, highly sensitive, and label-free platforms for biochemical detection, making them attractive for on-chip and real-time sensing applications. However, their design remains challenging due to complex resonance behaviour, strong coupling effects, and the computational cost associated with repeated full-wave electromagnetic simulations. In particular, inverse design of microring resonator-based sensors requires accurate modelling of geometry-spectrum relationships while satisfying physical constraints such as resonance conditions and spectral sensitivity requirements. In this work, we propose a physics-informed graph neural network (PI-GNN) for the inverse design of a microring resonator biosensor operating in the 1550 nm band. By representing the photonic structure as a graph and embedding resonance-based physical constraints directly into the learning objective, the model captures both structural connectivity and underlying electromagnetic principles. The proposed approach enables efficient prediction of device geometries that achieve target spectral characteristics, reducing reliance on costly simulations while maintaining physical consistency and competitive design accuracy.

High-quality and prebunched electron beams can produce coherent x-rays with high intensity and narrow bandwidth, which is essential for modern light sources. An all-optical scheme based on plasma-based acceleration to produce bright electron beams that are pre-bunched on nanometer-scales is proposed. By using a density modulation created by two low intensity counter-propagating lasers, the phase velocity of the plasma wake excited by an intense driver laser in a uniform plasma can be modulated at a frequency twice that of the colliding lasers and thus turn the injection on and off. The injected electrons are micro-bunched at the Doppler shifted wavelength of the modulated wavelength using the corresponding phase velocity of the gradual expansion of the wakefield. It is demonstrated that by controlling the properties of the drive and colliding lasers, that beams with exotic pre-bunched structures can be produced, which may have critical applications in ultrafast high power x-rays. This extremely compact, all-optical scheme to produce ultra-bright pre-bunched electron beams may therefore enable novel applications for ultrafast x-ray users and arouse general interest in various fields.

Amphibious robots, operating seamlessly across land and water, are advancing applications in conservation, disaster response, and defense. Their performance depends on locomotion mechanisms, actuation technologies, and sensor-control integration. This review highlights recent progress in these areas, examining movement strategies, material-based actuators, and control systems for autonomy and adaptability. Challenges and opportunities are outlined to guide future research toward more efficient, resilient, and multifunctional amphibious robots.

Moiré patterns, stacking and twisting multilayer periodic lattices into superlattices, have become cornerstones of many physical systems from condensed matter to wave phenomena, but have never been properly studied in water waves. Here, we demonstrate twisted multilayer moiré water surface waves carrying robust skyrmionic topologies. Using a custom water tank of circular multi-channel phased array, we precisely generate water-wave skyrmion lattices and superimpose them into moiré superlattices with higher-order topological textures, e.g., skyrmion bags and clusters, programmed via the twist angle. We also quantitatively compare the topological robustness of bilayer and trilayer configurations under spatiotemporal perturbations. The trilayer moiré superlattices exhibit more enhanced stability, stronger field localization and energy concentration than the bilayer. Our work establishes water waves as a macroscopic, tunable, and visually accessible platform for moiré physics, towards robust particle manipulation and classical analogues of topological quantum phenomena.

We present peapods (github.com/PeaBrane/peapods), an open-source Python package for Monte Carlo simulation of Ising spin systems with arbitrary coupling constants on periodic Bravais lattices with user-specified neighbor offsets. The computational core is written in Rust and exposed to Python via PyO3, combining the ergonomic interface of Python with the performance of compiled, memory-safe code. The package implements Metropolis and Gibbs single-spin-flip algorithms, Swendsen-Wang and Wolff cluster updates, parallel tempering, and three replica cluster moves for spin glasses: the Houdayer isoenergetic cluster move, the Jorg stochastic variant, and the Chayes-Machta-Redner (CMR) blue-bond algorithm. Overlap statistics between replica pairs enable computation of the spin glass order parameter and Binder ratio. Replica-level parallelism is achieved through the Rayon work-stealing scheduler. We validate the implementation against the exact critical temperatures of the two-dimensional Ising model on the square and triangular lattices via finite-size scaling of the Binder cumulant.

We formulate a phenomenological effective field theory in which biological intelligence emerges as a macroscopic order parameter sustained by continuous metabolic flux. By modeling cognition as a coarse-grained neural activity field governed by a variational free energy, we derive closed-form expressions for information capacity and structural susceptibility using a Gaussian maximum entropy approximation. The theory predicts a universal algebraic divergence of the susceptibility, $\chi \sim K^{-3/2}$, as the structural stiffness $K$ approaches the instability threshold. The exponent $\gamma = 3/2$ is consistent with the mean-field branching process universality class, thereby providing a theoretical rationale for the observed avalanche size exponent $\tau \approx 3/2$ in cortical dynamics without invoking microscopic equivalence. We identify adult cognition as a metabolically pinned non-equilibrium steady state maintained near the critical regime $\Gamma \equiv K/\alpha \approx 1$ by continuous metabolic regulation, while pathological decline corresponds to a delocalization transition triggered by the violation of structural stability conditions. The framework generates concrete, falsifiable predictions for attention scaling, altered states of consciousness, and transcranial magnetic stimulation responses, each of which can be tested against existing neuroimaging and electrophysiological datasets.

The effective second-harmonic generation (SHG) coefficient is a crucial data that quantifies the efficiency of transforming fundamental frequency light into its second harmonic. With the help of the symmetry of nonlinear optical susceptibility tensors, we mainly discuss the computability of such a effective SHG coefficient in uniaxial crystals. For one thing, the calculation of effective SHG coefficient is converted into the optimization models with some geometric constraints by means of the peculiarity of fundamental frequency light. Secondly, the number of variables of such maximum models are cutted in half to $2$ to calculate it easier, and a comparison between the effective SHG coefficient and C-eigenvalue of susceptibility tensor is given also. Finally, some examples of typical crystal classes are presented to verify the correctness and broader applicabilities of the theoretical results.

The distance between the last closed flux surface (LCFS) and the nearest electromagnetic coils is a dominating factor in the cost, size, and engineering difficulty of stellarators. The smallest magnetic gradient scale length on the LCFS - denoted L_gradB - has been shown to be a good proxy for minimum coil-surface distance in optimizations of a current potential on a winding surface, such as through the REGCOIL method. However, it has not been shown the same is true for filament coils, or that the magnetic gradient scale length is an effective objective function in optimization. In this paper, we explore examples in which min(L_gradB) is correlated with the minimum coil-surface distance for filament coils. First, we analyze a subset of the single-stage-optimized equilibria from the QUASR dataset [Giuliani et al. JPP (2024)]. We find that the majority of configurations have min(L_gradB) located nearby the point of closest coil-surface distance. Second, we optimize quasihelically symmetric equilibria to have improved min(L_gradB), and optimize coils via a continuation method. We then traced alpha particles to test confinement. Finally, we compare min(L_gradB) to the minimum coil-surface distance with filament coils optimized for a set of finite beta equilibria with random boundary shapes. For all datasets, we find that min(L_gradB) is correlated with both the minimum coil-surface and coil-coil distances if sufficient coil length is allowed. Even when there is a trade-off with proxies for confinement, optimizing for improved min(L_gradB) can result in better confinement in the presence of coils, up to a point. This is because - when holding coil-coil distance constant - equilibria with lower min(L_gradB) have a larger normal field error dominated by coil ripple causing particle loss. Both can be reduced by increasing coil-surface distance for equilibria with a high min(L_gradB).