Papers

In canonical turbulent wall-bounded flows, the inner-scaled wall-pressure variance is empirically well described by a constant offset plus a slope logarithmic in the friction Reynolds number ($\dep$). Because the fluctuating pressure is predominantly a Poisson response to only two source terms – a linear contribution from the mean shear coupled to a fluctuating velocity gradient, and a nonlinear contribution from the fluctuating velocity field – the origin of this growth can be pinned down by elimination: if the linear source saturates at a Reynolds-number-independent value, the nonlinear source must carry the logarithmic growth. Here we supply the complementary evidence for inner-scaled invariance of the linear source at $\dep$ up to $O(10^4)$, using the simultaneous velocity and velocity-gradient hot-wire measurements of Zimmermann et al. (2019 JFM, vol. 869, pp. 182–213) acquired with a single eight-sensor probe in both a zero-pressure-gradient turbulent boundary layer and a high-Reynolds-number pipe flow. The inner-scaled factors entering the linear source collapse across Reynolds number, and the inertial-layer variance of the relevant fluctuating velocity gradient decays inversely with wall distance. Together with the established inner scaling of the mean shear, this is consistent with a linear wall-pressure contribution that, under inner normalisation, remains $O(1)$ as $\dep\to\infty$. Both source terms then trace to one structural mechanism: the near-wall depletion of mean spanwise vorticity that caps the linear source also feeds, through vortex stretching, the inertial-layer fissures that carry the growing nonlinear contribution.

NASA and its data centers hold thousands of geoscience datasets and tools like Worldview, Giovanni, the Science Discovery Engine, and Harmony. Finding the right one is hard even for domain experts. We present an agentic search framework for geoscience data discovery that takes a natural-language research query and returns matching datasets and tools. We demonstrate that, in the era of large language models, the latent value of knowledge graphs (KGs) can be substantially amplified through agentic search. From the NASA Earth Observation Knowledge Graph (NASA EO-KG) we derive NASA-EO-Bench, an open benchmark of 47k query-dataset pairs (21k task-based queries). A neural scorer fine-tuned on NASA-EO-Bench beats cosine and BM25 baselines. Further combining it with BM25 via score fusion raises both Recall@10 (R@10) and MRR to over 5x the unadapted cosine baseline. On top of this supervised pipeline, a zero-shot reranking stage lifts MRR by 16%, significant under a paired bootstrap, with no additional training, and autonomous web and arXiv tool use adds a further gain, showing that LLM reasoning is complementary to supervised retrieval.

High-brilliance sources of polarized gamma rays are widely sought after to pump and probe matter at subatomic length scales. However, existing accelerator facilities and optical lasers cannot reach a sufficiently high center-of-mass energy to produce polarized, multi-GeV gamma rays from unpolarized electrons via inverse Compton scattering. Here we propose a scheme where the optical laser photons are first "accelerated" to the extreme ultraviolet in a beam-driven plasma wakefield, then reflected by a plasma mirror back onto a trailing electron beam, producing a flash of gamma rays. Numerical simulations demonstrate this light source can achieve a high peak-brilliance (10^25 photons/s mm^2 mrad^2 0.1% BW) and a high degree of circular (95 %) or linear (77 %) polarization at multi-GeV photon energies, paving the way for the production of spin-polarized positrons and tests of light-by-light scattering.

I revisit the Galam Majority Model (GMM) with contrarian agents from a statistical-mechanics perspective, revealing three fundamental features. First, in addition to the GMM simultaneous-update of small discussion groups, I construct a related single-agent stochastic dynamics, providing a Markovian microscopic representation, which is found to yield the same evolution equation. Second, I show that, contrary to what is often stated in the literature, the GMM closed evolution equation for the opinion density is not the result of a mean-field approximation. Indeed, I derive the conventional mean-field dynamics associated with majority-rule interactions and show that it yields a distinct, probabilistic evolution equation contrary the deterministic GMM equation. I therefore identify the GMM as an iterated mean-field dynamics. Third, I investigate the thermodynamic nature of the dynamics obtained from both single-agent and simultaneous updates. Both are shown to violate detailed balance. However, while Kolmogorov's cycle condition is satisfied for single-agent updates, it is violated for simultaneous updates, making the departure from equilibrium stronger in the latter case. I then compute the probability flux in the stationary state and show that it is non-vanishing, confirming the absence of an effective Hamiltonian and establishing that the stationary state is a genuine nonequilibrium steady state.These results clarify the statistical-mechanical foundations of the GMM and establish contrarian majority dynamics as an intrinsically non-equilibrium process with distinct regimes of irreversibility. Contrarians are not thermal noise.

The mixing between flavor and mass eigenstates of active neutrinos is described by a $3\times3$ unitary matrix. However, the presence of additional heavy sterile neutrino states can lead to a non-unitary neutrino mixing scenario. Atmospheric neutrinos, with their wide range of baselines and energies, provide an excellent probe of such effects. In particular, Earth matter effects in neutrino oscillations play an important role, as the neutral-current potential contributes non-trivially in the presence of non-unitarity. In this work, we use 8 years of publicly available atmospheric neutrino data of IceCube DeepCore to probe this non-unitary neutrino mixing scenario. This high-purity $\nu_\mu$ CC sample provides strong sensitivity, especially to the non-unitary parameters appearing at leading order in the $\nu_\mu \rightarrow \nu_\mu$ channel. The data sample is found to be consistent with the standard unitary mixing framework with no significant deviation. Using this data sample, we place the most stringent bound to date of $\alpha_{33} > -0.027$ at 90% CL, while the other non-unitary parameters are constrained at competitive levels.

Smart metasurfaces capable of employing the momentum of light for manipulating nanoparticles hold the key to potential applications in science and nanotechnology. This article proposes a density-based topology optimization framework for optimizing plasmonic metasurfaces for nanoparticles optical trapping. The Maxwell stress tensor (MST) is employed to compute the optical force exerted on nanoparticles of different sizes and types. The metasurfaces' topologies are optimized to maximize the gradient (attractive) force on such nanoparticles subject to normally incident monochromatic excitation. Designs based on free-form optimization are investigated first, then manufacturing constraints are imposed to provide easy-to-manufacture planar designs. The results show that the topology of the optimized metasurfaces depends on the nanoparticle size and material, with a higher trapping stiffness associated with small nanoparticles. The optimized metasurfaces could offer selective mass trapping of nanoparticles for applications in biosensing, microfabrication, or assembly of quantum systems.

The steady final phonon occupation in waveguide optomechanical systems based on backward stimulated Brillouin-Mandelstam scattering has not been established in the strong-coupling regime. In this work, the displacement spectra of anti-Stokes optical modes and acoustic modes in tapered chalcogenide photonic crystal fiber are derived from the Lindblad (or Gorini-Kossakowski-Sudarshan-Lindblad) master equation. By analyzing the full spectral response, we indicate that the system can enter the strong-coupling regime through the emergence of normal-mode splitting and avoided crossings. Within a non-Hermitian framework, the threshold for strong coupling is identified, showing that it can be achieved at relatively low pump power even at room temperature. Furthermore, we derive a unified analytical expression for the final phonon occupation, revealing that quantum backaction and zero-point fluctuations impose additional fundamental limits that hinder the achievement of ground-state cooling. These results redefine the quantum limits of steady-state cooling in continuum optomechanics, motivating the search for new strategies to access the quantum ground-state of macroscopic phonons.

Variations in solar radius (hereafter R_Sun) is a key reference for solar magnetic activity in time. The sunlight amount may have varied with R_Sun and had an effect on the Earth's climate in the past. Eclipse observations offer a unique opportunity to measure the absolute R_Sun value before modern direct observations. The scientific community has discussed a possible long-term R_Sun variability from 1715 onward. Prior to their coverage, Clavius' eclipse reports had been subjected to qualitative debates regarding the local eclipse visibility and a possible secular R_Sun trend. This study leverages the recent dramatic developments of lunar topography data and ephemeris data to provide an effective resolution of this debate. Clavius' eclipse reports described an explicit totality in 1560 at Coimbra and a "slender circle" around the eclipsing Moon in 1567 at Rome. Our study revised the {\Delta}T constraints of -492 s =< {\Delta}T =< 200 s in 1560 and 140 s =< {\Delta}T =< 151 s in 1567 to satisfy Clavius' descriptions, considering the lunar limb profile and assuming Auwers' canonical R_Sun. This study constrains the R_Sun margin of 1567, utilising three scenarios to interpret Clavius' account. The local totality requires an upper R_Sun limit of 1567 as R_Sun =< 696200 km in absolute size (959.92" in angular size), indicating no linear secular R_Sun shrinkage but possible R_Sun oscillations on a centennial timescale. Conversely, the annularity scenario is considered unlikely because it requires an R_Sun decrease of 7.5" within 3 centuries, even beyond the capacity of extreme shrinking-Sun hypotheses.

Autonomous-research agents have demonstrated end-to-end LLM automation in machine-learning sandboxes where execution provides calibration. Frontier physical science differs categorically: physical reasoning underlies every methodology choice, toolchains are often underdocumented, and calibration must come from external literature anchors - which unscaffolded agents cite but do not confront, hallucinating plausible, unverifiable results from internal priors. We present a pipeline that runs end-to-end from a corpus of 11,083 recent condensed-matter physics arXiv papers to a publication-grade manuscript with three substantive physics findings (here on altermagnetic piezomagnetism): the agent autonomously conceives a research direction by mapping the corpus, calibrates methodology by reproducing published references, conducts novel first-principles computations, and writes the manuscript - grounded in literature throughout, across 47 fresh-context sessions in six phases sharing only on-disk state, with 2,162 literature-consultation events. Fault tolerance emerges from redundancy: fresh-context isolation, distributed grounding, and adversarial review catch what any single session misses; pre- and post-pilot stages are fully autonomous, and pilot requires bounded human intervention only at reproduction failures - operational knowledge curation, not scientific direction. Two paired failure modes - a pre-architecture baseline and a no-pilot ablation - isolate structurally enforced numerical confrontation at calibration checkpoints as the operative grounding mechanism. The primitives, characterized failure modes, and quantified intervention pattern lay a foundation for autonomous research in high-stakes scientific domains beyond computational physics.

Microwave plasma sources play a critical role in scientific research and a wide range of industrial, biomedical, and space applications. Resonant microwave structures have recently enabled highly energy-efficient plasma generation by concentrating electromagnetic energy within compact volumes. However, once plasma is ignited, the formation of a conductive region at the resonator's electric-field hotspot significantly perturbs the resonant impedance, resulting in severe impedance mismatch, increased reflection, and reduced power-transfer efficiency. This limitation arises because conventional resonant operation relies on critical coupling, in which the input coupling simultaneously provides impedance matching and perturbs the resonator. This paper overcomes this fundamental limitation by operating the resonator in an over-coupled regime and achieving dynamic impedance matching through temporally modulated excitation. Specifically, an exponentially growing incident waveform is used to emulate the critical coupling condition without physically modifying the resonator, a concept known as virtual critical coupling. The proposed approach enables the resonator to store up to four times as much electromagnetic energy as a conventionally critically coupled resonator. Experimental results demonstrate ultra-efficient resonant microwave plasma generation with multi-fold reductions in ignition energy consumption and enhanced dynamic control over plasma dynamics.

Quantum-enhanced metrology relies on entanglement to achieve sensitivities beyond the standard quantum limit. While remarkable progress has been made in generating highly entangled many-body states, extracting their metrological advantage remains a central challenge because the encoded information is often inaccessible to realistic measurements. A key development of the past decade has been the realization that many-body interactions can play a dual role: they can be used not only to generate entanglement, but also to decode it. This idea underlies interaction-based readout and time-reversal protocols, in which controlled non-linear dynamics transform weakly encoded signals into experimentally accessible observables. Cavity quantum electrodynamics (QED) provides a particularly powerful setting for these approaches because it combines collective enhancement, tunable interactions, and controllable reversibility within a single platform. In this review, we discuss the emergence of time-reversal protocols in cavity QED, from their conceptual roots in Loschmidt echoes to modern implementations of signal amplification through a time-reversed interaction (SATIN), scrambling-enhanced metrology, and more general interaction-based readout schemes. We examine the physical mechanisms that enable reversible many-body dynamics, review key experimental demonstrations, and discuss future directions involving complex entangled states, nonlinear decoding, and emerging quantum platforms. Together, these developments suggest that the ability to decode quantum information may become as important as the ability to generate it, establishing reversible many-body dynamics as a central resource for quantum-enhanced sensing.

Electrochemical impedance spectroscopy (EIS) is a widely used technique to understand time-dependent response and relaxation under applied voltage. While these spectra contain a wealth of information, major gaps in our understanding can hinder our ability to interpret EIS spectra in terms of microscopic chemical mechanisms. We propose an alternative approach to common empirical fitting procedures for describing the contribution of the bulk electrolyte to the EIS spectrum. This new approach is rooted in determining the moments of the frequency-dependent conductivity, with molecular interpretability provided by a generalized Langevin equation description of an effective single particle dynamics; the `itinerant oscillator' (IO) model. In contrast to a Debye–Falkenhagen description, the IO model makes no assumptions regarding the concentration of the electrolyte, a fact we demonstrate by analysing molecular dynamics simulations of a room-temperature ionic liquid. By analysing the memory function from simulation within the framework provided by the IO model, we reveal the importance of capturing the separation of timescales within the memory function for describing the temperature dependent $\beta$-relaxation process. We go on to show how our impedance model directly reports on this distribution of timescales while retaining the simplicity of commonly employed workflows.

We study Poiseuille flow of a one-component fluid between adiabatic plates in liquid-gas coexistence with a stationary interface. Using only bulk transport laws and particle and energy conservation, we identify small dimensionless parameters $A^\mathrm{L}$ and $A^\mathrm{G}$ governed by squared microscopic-to-macroscopic length ratios. In weak driving with macroscopic bulk regions and a regular interfacial temperature response, the pressure drop is concentrated across the interface, strongly suppressing the particle current. For equal-temperature reservoirs, the residual current produces interfacial cooling. Experimental feasibility is also discussed.

We experimentally analyze the effect of a surfactant on wetting following drop impact on rough surfaces, paying special attention to the role of dynamic surface tension. To this end, we compare the results obtained with Triton X-100, SDS, and Surfynol 465. For concentrations below the critical micelle concentration $c_{\textin{cmc}}$, the evolution of the coverage area is nearly identical for all three surfactants, suggesting that the surfactant concentration is too low to significantly influence droplet spreading. In contrast, pronounced differences emerge due to the distinct dynamic surface tensions of the surfactants at $c/c_{\textin{cmc}}=2$. The evolution of the coverage area during spreading is nearly the same for pure water droplets and those containing Surfynol 465, indicating that surfactant depletion is negligible during the rapid spreading stage. As the Weber number increases, droplet spreading becomes progressively less sensitive to surface tension, thereby reducing the influence of surfactant adsorption kinetics. Nevertheless, Surfynol 465 produces larger coverage areas than Triton X-100 and SDS. The final coverage area is governed by the quasi-static recession of the triple contact line, which is controlled by the receding contact angle. Surfynol 465 consistently yields substantially larger final coverage areas across the range of surface roughness considered in this study.

Quantum networks require qubits that combine efficient optical access, coherent control, and long-lived quantum memory, but realizing all three in one scalable platform remains a central bottleneck. Diamond color centers are leading candidates, yet widely studied defects retain tradeoffs among these capabilities. Here, we show that transition-metal defects in diamond provide a distinct route beyond these platforms by combining spin-orbit protected ground-state coherence, all-optical control, and near-infrared emission. Using a single nickel-vacancy (NiV$^-$), we demonstrate an all-optically controlled diamond spin qubit with coherence exceeding one millisecond at 1.65 K, compatible with compact closed-cycle cryogenics. We implement Raman Rabi oscillations and Ramsey interferometry and use all-optical dynamical decoupling to extend coherence from $T_2^*$ = 371 ns to $T_2^{CPMG-4}$ = 1.27 ms, establishing NiV$^-$ as a deployable diamond spin-photon interface.

Gliding mammals exhibit diverse patagium and tail/uropatagium morphologies that may influence aerodynamic performance and maneuverability. Here, we use computational fluid dynamics to isolate the aerodynamic effects of representative gliding-mammal-inspired morphologies under controlled flow conditions. Three patagium configurations were compared to evaluate the effects of membrane outline on lift generation, drag, stall behavior and pitching moment. Three tail/uropatagium configurations were further tested under baseline, symmetric-deflection and asymmetric-deflection conditions to assess their longitudinal and lateral control authority. The results show that a broader patagium configuration generated the highest lift and lift coefficient, whereas an intermediate patagium morphology showed a smoother post-stall response with lower drag. For the tail configurations, the colugo-like integrated uropatagium enhanced lift and pitch-control authority under symmetric deflection, while the flat-tail configuration produced stronger rolling and yawing responses under asymmetric deflection. These findings indicate that gliding-mammal-inspired morphologies produce distinct aerodynamic trade-offs rather than a single optimal design. The results provide insight into the functional diversity of gliding mammal morphology and offer design guidance for bioinspired morphing aerial robots.

The time auto-correlation of auxiliary wave functions (TACAW) method enables efficient simulations of ultra-low-loss electron energy loss spectra (EELS) arising from vibrational and magnon excitations. In practical applications to realistic materials systems, however, TACAW calculations become challenging due to the large system sizes required for models containing defects, interfaces, impurities, or grain boundaries, as well as the substantial computational cost and data throughput associated with molecular dynamics and multislice calculations. Here we discuss a practical methodology for large-scale TACAW simulations and present torched-TACAW, a freely available implementation of the TACAW part of the described workflow for efficient STEM-EELS simulations. The overall approach combines molecular dynamics based on foundational machine-learned interatomic potentials, partitioning of elongated supercells, and on-the-fly processing of multislice outputs in order to enable near ab initio quality simulations with tractable memory use and data flow. Using rutile TiO2 as a model system, we analyze important numerical aspects of the method, including windowing and supercell partitioning, and demonstrate atomic-resolution STEM-EELS simulations for thick samples.

Midlatitude storm tracks play a central role in Earth's climate by transporting heat from low to high latitudes. Their strength is therefore set by the meridional temperature gradients, traditionally thought to be maintained solely by differential solar heating. This framework predicts a substantial seasonal reduction in storm activity, as meridional insolation gradients vanish during summer. Yet in the Southern Hemisphere, storm activity decreases by only 30% from its seasonal maximum. Here, we show that cloud radiative effects are essential for the seasonal maintenance of storm activity by reinforcing the temperature gradients that sustain it. Satellite observations reveal that sunlight reflected by midlatitude clouds in early summer creates a substantial meridional gradient in surface heating, despite the nearly uniform summer insolation. Idealized aquaplanet simulations then show that shortwave cloud radiative effects reinforce meridional sea-surface temperature gradients, thereby strengthening storm activity primarily during late summer and autumn, while longwave cloud effects partly offset this response. To interpret these results, we develop a simple theoretical model linking storms, clouds, and sea-surface temperature gradients. The model reproduces the simulated seasonal response and identifies two emergent cloud properties that control the feedback strength: the maximum attainable cloud albedo and the sensitivity of cloud cover to storm activity. Together, these findings indicate that cloud radiative feedbacks are key to maintaining the thermal gradients that sustain storm activity. More broadly, they reveal a strong coupling among storms, clouds, and the ocean spanning distinct spatial and temporal scales.

Broadening of spectral and spatial responses due to intrinsic loss in real materials often hides sharp features. One recently recognized route to recover those features is to probe the system with complex-frequency (CF) signals that decay exponentially in time: a suitably tailored temporal decay can compensate for loss and reveal an intrinsic, narrow response. However, generating rapidly decaying optical waveforms in real time is often challenging (the required decay times may be in the range of tens of femtoseconds). A recently proposed alternative synthesizes the CF response numerically after detection of conventional, real-frequency signals using Fourier post-processing. Here we explore advantages and challenges of these approaches: we show that a physical CF excitation robustly sharpens spectral features in the presence of noise, while a post-detection synthesized CF response shows only limited improvement once realistic detection and readout noise is considered. At the same time, in low-noise conditions a much simpler post-detection filtering procedure attains equal or better recovery than the synthesized CF reconstruction, making the synthesis unnecessary in practice.

Operator learning has emerged as a powerful tool for modeling complex physical systems in functional spaces. However, their neural network-based architectures make them opaque models, obscuring the reasoning behind their predictions. In this work, we introduce a self-explainable operator learning framework that overcomes this challenge by reformulating operator learning as a linear combination of generalized functional linear models expressed through integral equations. Exploiting the additive decomposability of these integral equations, we divide the input domain into subdomains and compute localized integrals to evaluate the contribution of each region to the final prediction. This decomposition enables direct interpretability where the model explains both inputs and outputs by linking specific input regions to corresponding output patterns, thereby revealing which spatial features drive predictions. We demonstrate the framework on function-to-scalar and function-to-function mappings in fluid flow problems involving blood flow and unsteady aerodynamics. The results show that the operator most often prioritizes regions with strong feature gradients, providing physically meaningful insight into the model's decision-making process. Comparisons with established post-hoc explainability methods demonstrate qualitative agreement while highlighting the key advantage of the proposed approach: explainability is embedded directly within the operator structure itself and does not require an external tool. Therefore, our framework provides a mathematically transparent and physically interpretable approach to uncover relationships within data, fostering trust in machine learning for scientific applications by enabling more informed data-driven analysis of physical systems.

Purpose: To evaluate the feasibility of an integrated, free-breathing workflow for automated 2D pulmonary relaxometry (T1, T2) at 0.55T. Methods: A 2D inversion recovery ultra-fast balanced steady-state free precession (IR-uf-bSSFP) sequence was adapted to achieve high-temporal sampling of the transient phase at 0.55T. The technique was validated in a phantom and tested in eight healthy volunteers as well as one patient. A fully automated pipeline was developed, featuring multi-contrast registration for motion correction and deep learning based lung segmentation to enable voxel-wise nonlinear fitting for T1 and T2 map generation. Results: Phantom results were in close agreement with reference scans. In-vivo, the proposed free-breathing framework effectively mitigated respiratory motion, yielding quantitative maps in close agreement with breath-hold references. Healthy lung parenchyma relaxation times were T1 = (930+-40)ms and T2 = (90+-8)ms. In a patient case, the method successfully distinguished a solid lung mass from healthy parenchyma, with the lesion showing elevated T1 (960ms vs 810ms in the surrounding parenchyma). Conclusions: Simultaneous free-breathing T1 and T2 mapping of the lung is feasible at 0.55T using a fully automated pipeline. By eliminating breath-holds and external gating, this approach improves patient compliance and potentially facilitates the use of quantitative lung MRI in routine clinical practice.

Physics-informed neural networks (PINNs) have emerged as a promising route to solve partial differential equations, yet they have struggled to reach the precision of classical solvers. The obstacle is increasingly understood to be one of optimisation, owing to the severely ill-conditioned loss landscape. We present $\textbf{DSGNAR}$: Doubly-Sketched Gauss-Newton with Adaptive Ratio, a scalable second-order optimisation framework that confronts this ill-conditioning and, in doing so, obtains unprecedented accuracy and speed. $\textbf{DSGNAR}$ couples a doubly-sketched Gauss-Newton model with a novel strategy that carefully controls both regularisation and step length. Across a suite of problems spanning nonlinear, chaotic, multi-scale, high-dimensional, and Navier-Stokes, the framework greatly improves on the state of the art: able to attain relative $\ell_2$ errors as low as $3\times10^{-16}$ in double precision, improve contemporary results by five orders of magnitude on the canonical Burgers' equation, and as much as eight orders on a high-dimensional Poisson problem, while remaining markedly faster. We further show that, in single precision, solutions at the limit of round-off error can be obtained very quickly: Burgers' equation to $\ell_2^{\text{rel}} = 4.75 \times 10^{-7}$ in under ten seconds. The framework is also robust to the choice of architecture, arithmetic precision, and initial hyperparameters. The code is available at https://www.github.com/wephy/physics-informed-neural-networks

We introduce a setup for coherent two-dimensional electronic spectroscopy in the pump-probe reflection geometry that is integrated with a confocal back focal plane imaging microscope. The angle-resolved capability is utilized to control pump and probe wavevectors, while real space imaging enables co-localization of the collection spots for linear and ultrafast experiments. Compression of pulses down to 20 fs is achieved. We demonstrate the capabilities of this approach on an exfoliated WSe$_2$ monolayer on Si/SiO$_2$. The setup is suited to investigate excitons and exciton-polaritons in 2D Materials and their heterostructures.

The Earth's gravitational field exerts a significant influence on atmospheric dynamics, including the behavior of seasonal wind flux, defined by periodic variations in wind speed and direction. While temperature gradients and Earth's rotation are established drivers of wind patterns, the role of gravitational forces in modulating these processes remains poorly understood. This study investigates the mathematical relationship between gravitational variations and seasonal wind flux in Nigeria, a region of pronounced climatic variability and varied wind patterns. Utilizing Navier-Stokes equations for atmospheric dynamics, Fourier decomposition for seasonal wind flux analysis, and Pearson correlation coefficients for gravitational-wind interactions, we analyze 2010-2020 meteorological data, alongside gravitational field measurements from the GRACE (Gravity Recovery and Climate Experiment) satellite. Results show significant annual fluctuations in average wind speed (5.1-5.6 m/s) and gravitational variations (9.60-9.95 mGal), with an inverse relationship observed in certain years, suggesting a coupling between atmospheric dynamics and gravitational forces. Seasonal wind flux exhibits a distinct sinusoidal pattern, peaking mid-year and declining toward year-end, consistent with Nigeria's monsoon climate. Correlation coefficients between gravitational variations and wind flux range from 0.79 to 0.87, indicating a strong positive relationship. These findings underscore the importance of gravitational forces in modulating wind patterns and highlight the potential for integrating gravitational data into climate models, thereby enhancing accuracy of weather forecasting and renewable energy planning. This study provides a foundational framework for further exploration of gravitational influences on atmospheric processes, with implications for global climate science and sustainable energy strategies.

Suspensions of motile microswimmers such as bacteria and other active colloids frequently encounter porous environments where obstacles and complex shear flows strongly influence their dynamics. Here, we study the distribution and transport of a dilute suspension of active particles in a square lattice of pillars, which serves as a model porous medium. The microswimmers are modeled as slender point particles, and Brownian Dynamics simulations are performed to determine how their number density and polarization fields change with systematic variations in the medium porosity, polydispersity, flow strength, and self-propulsion strength. We find that in the absence of flow, self-propulsion drives particle accumulation and radial polarization at the pillar surfaces. In the presence of a background flow, particles preferentially accumulate in the wake of pillars and exhibit upstream polarization near their surface, consistent with experimental observations. At moderate flow strengths, topological defects nucleate in the polarization field. These defects are of purely kinematic origin and mark the transition from global upstream swimming at low flow strengths to the coexistence of upstream and downstream swimming regions in the lattice at high flow strengths. The structured lattice studied here provides a controlled framework for isolating the physical mechanisms governing active transport in complex geometries, with direct relevance to transport in structured microfluidic settings.

This paper presents a closed-form test that decides, before the maneuver begins, whether a chaser can reach and hold station at the hold point of a tumbling, uncooperative target inside a line-of-sight (LOS) corridor that turns with the target. A closed-loop controller cannot answer this question: a receding-horizon controller checks the corridor only over its prediction horizon, and near a tumbling target the corridor sweeps past faster than bounded thrust can follow, so a start that is feasible at the first step can become unrecoverable later. Today the only ways to know are to fly the controller in simulation or to compute a Hamilton–Jacobi reachable set, both too slow onboard. The test combines two criteria derived from bounded-thrust relative orbital dynamics: a directional erosion margin, the corridor margin that rotation-induced drift consumes before the thruster arrests it, and a synchronization radius, beyond which the apparent rotational velocity cannot be cancelled. Guidance pairs a three-regime tracking law with a receding-horizon quadratic program. Benchmarked against polytopic backward and forward reachable sets, Hamilton–Jacobi level sets and closed-loop Monte Carlo simulation, the test runs over two orders of magnitude faster than Hamilton–Jacobi and, over 500 closed-loop cases, predicts feasibility with 91% recall and 80% precision. The gap to Hamilton–Jacobi is structural, not a method error: reaching the hold point and co-rotating with it is a stronger requirement than arriving with arbitrary velocity, and the gap widens with tumble rate. The test therefore gives an onboard go/no-go answer where Hamilton–Jacobi reachability is too expensive.

Electronic neurons are a keystone for construction of the spiking neural networks which have numerous applications in neuroprosthetics, artificial memory, intensive calculations etc. A number of concepts of electronic neurons has been already proposedm with some of them implemented in hardware. However, new schemes are of significant interest since the existing ones do not fit all requirements: either they are too complex and expensive in realization, or they are not able to demonstrate all demanded regimes, or their do not have a appropriate mathematical description and therefore may be investigated only experimentally etc. In this study we propose a new design of bursting electronic neuron constructed as a circuit implementation of the equations of a phase-locked loop system. To succeed, we use a novel hybrid approach: we start from the phenomenological equations providing the demanded, then we adjust and modify these equations to simplify the implementation rather than implementing the biophysical equations into thee hardware directly or writing equations for the already constructed circuit. The resulting circuit is simple in implementation and well matches the underlying equations. It can be used for description of not only a single neuron, but small neural circuits too.

Ultra-thin films are fundamental components of modern nanoelectronics, where reducing thickness to the few-nanometer scale leads to a dramatic increase in electrical resistivity. For decades, this behavior has been interpreted in terms of classical size effects, primarily surface scattering within the Fuchs–Sondheimer theory and grain-boundary scattering in the Mayadas–Shatzkes model. While these approaches successfully describe transport when the film thickness is comparable to the electronic mean free path, growing experimental evidence indicates that they become insufficient under extreme confinement. This review discusses the crossover from classical scattering to a quantum-confinement regime in which the electronic states available for transport are fundamentally restructured by finite size. We review the recently proposed reciprocal-space confinement theory, which predicts an exponential increase of resistivity with decreasing thickness at the nanoscale, and discuss how it can be combined with classical surface-scattering models to provide a unified description of ultra-thin metallic and semiconducting films. Finally, we summarize recent experimental evidence supporting this picture and discuss its implications for future nanoelectronic devices, nanoscale interconnects, and quantum transport under extreme spatial confinement.

Optical coherent Ising machines based on time-multiplexing have demonstrated significant progress in terms of connectivity and spin scalability. However, they are constrained by large physical footprints, high power consumption, poor thermal stability, and high cost. Here, we present a time-multiplexed Ising machine leveraging propagating wave packets in solid-state delay lines at microwave frequencies, enabling thermally stable, robust, low-power, tabletop, and affordable design. We use two serially connected 20.5 MHz, 707 microseconds bulk acoustic wave delay lines supporting 2,048 spins. Our design provides all-to-all connectivity with 15-bit coupling resolution and finds approximate MAX-CUT solutions in 341 ms, potentially scalable to sub-ms by using higher frequency delay lines. Additionally, we demonstrate solutions to number partitioning and Sudoku problems. Compared with state-of-the-art Coherent Ising machines, our machine exhibits four orders of magnitude higher thermal stability. Against the simulated bifurcation algorithm, our design achieves comparable results on the MAX-CUT problem, while outperforming it on the more complex number-partitioning and Sudoku problems.

We investigate the epistemic opacity of computer simulations and machine learning methods in the context of black hole imaging. We argue that there are forms of opacity-including opacity resulting from the use of machine learning-which do not need to affect the reliability of an inference when it is seen as a part of a broader inferential framework. We propose conditions under which that can plausibly be the case, and discuss how opaque methods can be useful in the context of the (next generation) Event Horizon Telescope. However, we also argue that at least one problematic form of opacity is currently present in black hole imaging: GRMHD models of Sagittarius A* are opaque. This form of opacity signals the limitations of current understanding of the models of this source, and constrains the potential uses of ML models in future observations.