Fluid Dynamics
Turbulence enhancement of a fan array wind generator using geometric texturing and optimization-based control
Gengshou Cao, Tamir Shaqarin, Zhutao Jiang et al. (+5 more)
Fan array wind generators (FAWG) are designed to generate a rich set of turbulent flows reminiscent of those found in natural environments. In this study, we experimentally investigate a square FAWG consisting of 10x10 individually controllable fans with 4 cm width and a maximum velocity of 17 m/s. The goal is to maximize the
turbulence intensity in the test region. Two approaches for fan operation are investigated: first,
geometric texturing of the duty cycle distribution, and second, maximization of the
turbulence intensity at selected hot-wire sensors with particle-swarm optimization. We find that
geometric texturing (specifically a checkerboard pattern) yields a robust, uniform
turbulence field (Tu ~ 0.14) driven by jet interactions. Conversely, particle swarm optimization achieves higher local
turbulence (Tu ~ 0.28) but significantly sacrifices spatial uniformity. This study underscores the trade-off between local maximization and global uniformity in active
turbulence generation.
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All authors Gengshou Cao, Tamir Shaqarin, Zhutao Jiang, Yutong Liu, Yiqing Li, Nan Gao, Xiaozhou He, Bernd R. Noack
Biological Physics
Error Bound Analysis of Physics-Informed Neural Networks-Driven T2 Quantification in Cardiac Magnetic Resonance Imaging
Mengxue Zhang, Qingrui Cai, Yinyin Chen et al. (+13 more)
Physics-Informed Neural Networks (PINN) are emerging as a promising approach for quantitative parameter estimation of
Magnetic Resonance Imaging (MRI). While existing deep learning methods can provide an accurate quantitative estimation of the T2 parameter, they still require large amounts of training data and lack theoretical support and a recognized gold standard. Thus, given the absence of PINN-based approaches for T2 estimation, we propose
embedding the fundamental physics of MRI, the Bloch equation, in the loss of PINN, which is solely based on target scan data and does not require a pre-defined training database. Furthermore, by deriving rigorous upper bounds for both the T2 estimation error and the generalization error of the Bloch equation solution, we establish a theoretical foundation for evaluating the PINN's quantitative accuracy. Even without access to the ground truth or a gold standard, this theory enables us to estimate the error with respect to the real quantitative parameter T2. The accuracy of T2 mapping and the validity of the theoretical analysis are demonstrated on a numerical cardiac model and a water phantom, where our method exhibits excellent quantitative precision in the myocardial T2 range. Clinical applicability is confirmed in 94 acute myocardial infarction (AMI) patients, achieving low-error quantitative T2 estimation under the theoretical error bound, highlighting the robustness and potential of PINN.
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All authors Mengxue Zhang, Qingrui Cai, Yinyin Chen, Hang Jin, Jianjun Zhou, Qiu Guo, Peijun Zhao, Zhiping Mao, Xingxing Zhang, Yuyu Xia, Xianwang Jiang, Qin Xu, Chunyan Xiong, Yirong Zhou, Chengyan Wang, Xiaobo Qu
Classical Physics
The Thermal Unbalance Effect Induced by a Journal Bearing in Rigid and Flexible Rotors: Experimental Analysis
Thibaud Plantegenet, Mihai Arghir, Mohamed-Amine Hassini et al. (+1 more)
The present work presents the experimental analyses of a rigid (short) and a flexible (long) rotor subject to thermal unbalance effects. The rotors are supported by a ball bearing and by a cylindrical journal bearing. The differential heating generated in the journal bearing is responsible for the thermal unbalance. The results obtained with the short rotor at 7 krpm showed an increase in the synchronous amplitudes but slight phase changes before stabilization. The pronounced hysteresis of the synchronous amplitudes obtained during coast-down proved that the amplitude increase is due to the thermal unbalance. The results obtained for the long rotor at 6.6 krpm showed the same stabilized response when start-up was performed in 180___s. However, an instability leading to journal bearing contact was triggered when the start-up time was decreased to 80___s. The presented experimental results are the synchronous amplitudes and phases, the mean temperatures, and the maximum temperature differences of the journal and of the bearing and the phase lags between the high spot and the hot spot.
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All authors Thibaud Plantegenet, Mihai Arghir, Mohamed-Amine Hassini, Pascal Jolly
Atmospheric and Oceanic Physics · Cross-listed from quant-ph
Quantum Machine Learning for Climate Modelling
Mierk Schwabe, Lorenzo Pastori, Valentina Sarandrea et al. (+1 more)
Quantum machine learning (QML) is making rapid progress, and QML-based models hold the promise of
quantum advantages such as potentially higher expressivity and generalizability than their classical counterparts. Here, we present work on using a
quantum neural net (QNN) to develop a parameterization of cloud cover for an Earth system model (ESM). ESMs are needed for predicting and projecting
climate change, and can be improved in hybrid models incorporating both traditional physics-based components as well as machine learning (ML) models. We show that a QNN can predict cloud cover with a performance similar to a classical NN with the same number of free parameters and significantly better than the traditional scheme. We also analyse the learning capability of the QNN in comparison to the classical NN and show that, at least for our example, QNNs learn more consistent relationships than classical NNs.
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All authors Mierk Schwabe, Lorenzo Pastori, Valentina Sarandrea, Veronika Eyring
Optics
An Algebraic Approach to Bifurcations in Kerr Ring and Fabry-Perot Resonators
Juan Diego Mazo-Vasquez, Julius T. Gohsrich, Flore K. Kunst et al. (+1 more)
Nonlinear phenomena such as optical bistability and spontaneous
symmetry breaking play a central role in Kerr resonators, and are increasingly exploited in photonic integrated
circuits for all-optical information processing. In this work, we present an analytical framework allowing to find the stationary states and their bifurcations for the propagating fields in Kerr ring and Fabry-Perot resonators, which can be generalized to other nonlinear systems. Using tools from nonlinear
algebra, namely,
polynomial resultants and Groebner bases, we derive compact
polynomial expressions describing the system's full solution in both intensity and amplitude representations. The bifurcations follow directly from these expressions, and are additionally characterized as exceptional points of an auxiliary linear non-Hermitian system. Together, these results unify optical bistability and spontaneous
symmetry breaking within a single analytical framework, and offer a route toward improved control of nonlinear optical systems, and the design of photonic devices.
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All authors Juan Diego Mazo-Vasquez, Julius T. Gohsrich, Flore K. Kunst, Lewis Hill
History and Philosophy of Physics · Cross-listed from quant-ph
QBism, Polishing Some Points
Christopher A. Fuchs and Blake C. Stacey
QBism pursues the real by first eliminating the elements of
quantum theory too fragile to be ontologies on their own. Thereafter, it seeks an "ontological lesson" from whatever remains. Here, we explore this program by highlighting three tenets of QBism. First, the Born Rule is a normative statement. It is about the decision-making behavior any individual
agent should strive for, not a descriptive "law of nature." Second, all probabilities, including all
quantum probabilities, are so subjective they never tell nature what to do. This includes probability-1 assignments.
Quantum states thus have no "ontic hold" on the world, which implies a more radical kind of indeterminism in
quantum theory than other interpretations understand. Third,
quantum measurement outcomes just are personal experiences for the
agent gambling upon them. Thus all
quantum measurement outcomes are local in the sense of the
agent enacting them. Through these tenets, we explain four points better than previously: 1) how QBism contrasts with Bohr's concern over unambiguous
language, 2) how QBism contrasts with the Everett interpretation, 3) how QBism understands the meaning of Bell inequality violations, and 4) how QBism responds to Wigner's "suspended animation" argument. Finally, we consider the ontological lesson of the tenets and ask what it might mean for the next one hundred years of
quantum theory and humankind more generally.
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Fluid Dynamics
Efficient Time-Resolved Pressure Estimation by Sparse Sensor Optimization and Non-Time-Resolved PIV
Neetu Tiwari and Ajit Kumar Dubey
Pressure field estimation from PIV data has been a well-established technique. However, time-resolved pressure estimation strongly depends on the temporal resolution of the PIV measurements. Generally, PIV data has limited time resolution creating challenges to understand high Reynolds number flows. To overcome this challenge, sensor data measured at few optimized locations with higher time resolution is combined with PIV data using data driven methods to reconstruct time resolved velocity fields. In this context, if we wish to estimate time resolved pressure fields from non-time resolved PIV data, there are two possible approaches. Approach 1: reconstruct time-resolved velocity field first from non-time resolved PIV data using sensor data, and then time-resolved pressure fields are estimated from time-resolved pressure fields by applying pressure Poisson equation. Approach 2: first estimate non-time resolved pressure fields from non-time resolved velocity field measurements using pressure Poisson equation and then reconstruct time resolved pressure fields directly from non-time resolved pressure fields. These two approaches are compared in this study. These approaches are demonstrated for actual PIV data of flow over a cylinder. Time-resolved PIV measurements are down-sampled to mimic non-time-resolved velocity data. It was found that the approach two is approximately thirty times faster than approach one when time resolution is improved from 1 Hz to 50 Hz. This is expected because in the second approach the pressure Poisson equation needs to be solved only with non-time resolved velocity fields which reduces the computational load.
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Geophysics
Seesaw of Saltwater and Inundation Drives Methane Emissions in Coastal Tidal Wetlands
Xun Cai, Xiucheng Yang and Peter A. Raymond
Wetlands are significant carbon sinks, yet methane emissions partially offset this function due to its high global warming potential. Coastal
tidal wetlands, unlike non-
tidal wetlands, are regulated by oceanic drivers like salinity gradients and
tidal inundation, which strongly influence methane production and release but remain poorly represented in regional assessments. Here, we estimate methane emissions from U.S. East Coast
tidal marshes, by integrating ocean model, remote sensing
datasets, empirical relationships from metadata. Spatially, emissions reflect the combined effects of marsh extent and per-unit-area flux rates, with hotspots occurring under lower salinity, higher inundation, and lower latitudes. Temporally, temperature and salinity dominate decadal-scale interannual variability. Between 2001 to 2020, total methane emissions are estimated at 0.019 - 0.038 Tg yr-1, with local fluxes rate ranging from 0 to 20 g m-2 day-1. Following pronounced hydrological variability in the early 2000s, emissions have increased steadily since 2007 at approximately 802 t yr-1, driven by warming, freshening, and enhanced inundation. Projections under IPCC
climate scenarios indicate that increasing inundation will amplify methane emissions with sea-level rise, until a threshold near 0.75 m SLR, beyond which saltwater intrusion increasingly suppresses further growth, highlighting the critical role of salinity-inundation interactions in coastal methane dynamics.
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Biological Physics
When many noisy genes optimize information flow
Nicholas Lawson and William Bialek
It often is emphasized that gene expression is noisy. A seemingly contradictory view is that control mechanisms have been optimized to squeeze as much information as possible out of a limited number of molecules. Here we revisit these issues in a simple model where a single transcription factor (TF) controls a large number of target genes. We include only the physically required noise sources: random arrival of TFs at their targets and counting noise in the synthesis and degradation of mRNA. If the
cell has a limited total number of mRNA molecules, then the capacity to transmit information about TF concentration is maximized when these resources are distributed across the largest possible number of target genes. To realize this capacity the distribution of TF concentrations must be biased toward smaller values. Thus, in some limits, information transmission is optimized when individual expression levels are noisy. In addition, the dependence of information transmission on the parameters of this multi-gene system has a "sloppy" spectrum, so that optimal performance can co-exist with substantial variability.
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Fluid Dynamics
The influence of surface tension in thin-film hydrodynamics: gravity free planar hydraulic jumps
Rajesh Kumar Bhagat
Hydraulic jumps in thin films are traditionally explained through
gravity-driven shallow-water theory, with surface tension assumed to play only a secondary role via Laplace pressure. Recent experiments, however, suggest that surface tension can be the primary mechanism. In this work we develop a theoretical framework for surface tension driven hydraulic jumps in planar thin-film flows. Starting from the full interfacial stress conditions, we show that the deviatoric component of the normal stress enters at leading order and fundamentally alters the balance. A dominant-balance analysis in the zero-
gravity limit yields parameter-free governing equations, which admit a similarity solution for the velocity profile. Depth-averaged momentum conservation then reveals a singularity at unit Weber number, interpreted as the criterion for hydraulic control. This singularity is regularised by a non-trivial pressure gradient at the jump. This work establishes the theoretical basis for surface-tension-driven hydraulic jumps, providing analytical predictions for the jump location and structure.
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Optics
Confinement-Induced Nonlocality and Optical Nonlinearity of Transdimensional Titanium Nitride in the Epsilon-Near-Zero Region
Fan-Ting Tseng, I-Hung Ho, Ting-Jui Kuo et al. (+3 more)
Ultrathin plasmonic films that approach the trans-dimensional (TD) thickness limit provide a promising route for light_matter interaction control and manipulation, yet their nonlinear optical response near the epsilon_near_zero (ENZ) condition remains poorly understood. Here, we report the strongly enhanced optical nonlinearity for their typical representative high quality TiN epitaxial films with thicknesses down to a few nanometers. Systematic Z_scan measurements reveal a pronounced increase in nonlinear absorption with decreasing thickness. Especially in the ENZ spectral region, the TD TiN films exhibit nearly two orders of magnitude stronger nonlinear absorption over a broad range of incidence angles as compared to conventional thin films. The enhanced nonlinear absorption observed is well described by a nonlinear nonlocal electromagnetic response model that accounts for electron confinement effects unique to the TD plasmonic systems. Comparison with Ti1_xAlxN highlights the necessity of low-loss ENZ response for nonlinear enhancement. These findings identify TiN and similar TD plasmonic systems as a robust refractory platform for exploiting ENZ mediated nonlinear processes in ultrathin photonic material structures.
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All authors Fan-Ting Tseng, I-Hung Ho, Ting-Jui Kuo, Shangjr Gwo, Igor V. Bondarev, Hyeyoung Ahn
Instrumentation and Detectors
Liquid Handling of the JUNO Experiment
Jiajun Li, Yuekun Heng, Jiajie Ling et al. (+18 more)
The Filling, Overflow, and Circulation (FOC) system is a critical subsystem of the Jiangmen Underground
Neutrino Observatory (JUNO), responsible for the safe handling of the
Liquid Scintillator (LS) and water throughout the detector's commissioning and operational lifetime. This paper details the design and operation of the FOC system, which accomplished the filling of the world's largest LS detector–taking 45 days for water (6.4*10^4 m^3) and 200 days for LS (2.3*10^4 m^3). Throughout water filling, the
liquid level difference between the Central Detector and Water Pool was rigorously maintained within safety limits. During LS filling, level control achieved +/-2 cm precision with flow regulation within +/-0.5% of setpoints. An automated control system based on Programmable Logic Controllers and the Experimental Physics and Industrial Control System framework ensured reliable operation. The system preserved LS radiopurity, maintaining 222Rn below 1 mBq/m^3 during filling and achieving 238U/232Th concentrations below 10^-16 g/g. The successful commissioning and operation of the FOC system have established it as an indispensable foundation for the stable long-term operation of the JUNO detector.
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All authors Jiajun Li, Yuekun Heng, Jiajie Ling, Zhi Wu, Xiao Tang, Cong Guo, Jinchang Liu, Xiaolan Luo, Xiao Cai, Chengfeng Yang, Xiaoyan Ma, Xiaohui Qian, Tao Huang, Bi Wu, Pengfei Yang, Shiqi Zhang, Baobiao Yue, Shuaijie Li, Lei Yang, Mei Ye, Shenghui Liu
Computational Physics
Physics-Informed Machine Learning for Two-Phase Moving-Interface and Stefan Problems
Che-Chia Chang, Te-Sheng Lin and Ming-Chih Lai
The Stefan problem is a classical free-boundary problem that models phase-change processes and poses computational challenges due to its moving interface and nonlinear temperature-phase coupling. In this work, we develop a physics-informed neural network framework for solving two-phase Stefan problems. The proposed method explicitly tracks the interface motion and enforces the discontinuity in the temperature gradient across the interface while maintaining global consistency of the temperature field. Our approach employs two neural networks: one representing the moving interface and the other for the temperature field. The interface network allows rapid categorization of thermal diffusivity in the spatial domain, which is a crucial step for selecting training points for the temperature network. The temperature network's input is augmented with a modified zero-level set function to accurately capture the jump in its normal derivative across the interface. Numerical experiments on two-phase dynamical Stefan problems demonstrate the superior accuracy and effectiveness of our proposed method compared with the ones obtained by other neural network methodology in literature. The results indicate that the proposed framework offers a robust and flexible alternative to traditional numerical methods for solving phase-change problems governed by moving boundaries. In addition, the proposed method can capture an unstable interface
evolution associated with the Mullins-Sekerka instability.
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Optics
Self-sustained microcomb lasing in an integrated hybrid oscillator
Bitao Shen, Huajin Chang, Junhao Han et al. (+8 more)
Microcavity optical frequency combs (microcombs) are compact, coherent light sources whose chip-scale integrability is poised to drive advances in metrology, communications, and sensing. Among available microcomb generation methods, hybrid cavities uniquely co-locate gain and Kerr dynamics, where the lasing mode directly resonates in the nonlinear microcavity, simultaneously enabling self-sustained and highly efficient microcomb generation. However, their implementation is often limited by partial integration or the need for external injection, which complicates operation architecture, raises power and hampers system miniaturization. In this work, we present a fully integrated hybrid cavity for self-sustained microcomb generation, relying solely on the co-oscillation of lasing and Kerr nonlinearity without external driving. The system collapses the pump
laser, nonlinear resonator and feedback loops into a minimalist on-chip two-element cavity, consisting of a high-Q microresonator with engineered intracavity reflection and a reflective
semiconductor optical amplifier (RSOA). The scheme delivers self-starting operation and stable performance without active feedback. The generated coherent microcomb achieves intrinsic linewidths below 1 kHz and integrated linewidths around 100 kHz, with self-sustained operation exceeding 24 hours. This ultra-compact architecture provides a practical path toward scalable, coherent multi-wavelength sources for integrated photonic systems.
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All authors Bitao Shen, Huajin Chang, Junhao Han, Yimeng Wang, Xuguang Zhang, Haoyu Wang, Zihan Tao, Ruixuan Chen, Yandong He, Haowen Shu, Xingjun Wang
Applied Physics
Electrified EHL line contact with dielectric breakdown of lubricant – a numerical model
Yang Xu, Nick Morris and Yue Wu
With the rapid growth of the electric vehicles with drive systems with higher voltages, power outputs, frequencies, and speeds, mitigating electrically induced bearing damage (EIBD) in electric motors has become critical. In this study, a novel numerical model characterizing discharge-induced current density and voltage drop at the elastohydrodynamic lubrication line contact interface is presented. The current density and voltage drop constitute a linear complimentarily problem, which is efficiently solved using the conjugate gradient method. This paper sheds light on electrical characteristics at the inaccessible lubrication interface during discharge, highlighting the significance of roughness radius of
curvature on current density. This numerical model lays the groundwork for future research on mitigating or even permanently solving EIBD problems in electric motor bearings.
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Atmospheric and Oceanic Physics
An intercomparison of generative machine learning methods for downscaling precipitation at fine spatial scales
Neelesh Rampal, Bryn Ward-Leikis, Yun Sing Koh et al. (+7 more)
Machine learning (ML) offers a computationally efficient approach for generating large ensembles of high-resolution
climate projections, but deterministic ML methods often smooth fine-scale structures and underestimate extremes. While
stochastic generative models show promise, few studies have compared their skill under both present-day and future climates. This study compares Generative
Adversarial Networks (GANs), flow matching and
diffusion models across multiple configurations for downscaling daily precipitation from a regional
climate model (RCM) over New Zealand. Model skill is assessed across spatial structure, distributional metrics, climatological means, extremes, ensemble calibration, and
climate change signals. Unlike GANs,
diffusion and flow matching models generate predictions through many sequential steps. Here we show that using higher-order differential equation solvers, the number of steps required can be reduced with only a minor reduction in skill, heavily reducing the computational burden for downscaling large ensembles, which may have otherwise prevented their use in operational settings. Overall, GANs, flow matching and
diffusion perform competitively across most metrics, except that
diffusion and flow matching produce higher-fidelity predictions and better-calibrated ensembles compared to GANs -which are under-dispersive. Most approaches capture mean precipitation signals reasonably well, but underestimate end-of-century
climate change signals of extreme precipitation, despite being trained on RCM simulations spanning the future period. Only one GAN and one flow matching configuration can reproduce this change signal reliably. These results highlight the importance of evaluating model performance across a comprehensive set of metrics, and that neither visual realism nor good skill on standard metrics guarantee skill in predicting
climate change signals.
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All authors Neelesh Rampal, Bryn Ward-Leikis, Yun Sing Koh, Peter B. Gibson, Hong-Yang Liu, Vassili Kitsios, Tristan Meyers, Jeff Adie, Yang Juntao, Steven C. Sherwood
Optics
First-return statistics in bounded radiative transport: A Motzkin polynomial framework
Claude Zeller and Robert Cordery
A
photon entering a scattering medium executes a three-dimensional random walk determined by the Henyey-Greenstein phase function. The
photon either reaches the boundary for a first passage or is absorbed. Projecting the walk onto the axial direction produces a one-dimensional alternating process whose peaks and valleys correspond to changes in the sign of the projected step. This reduction preserves first-return and first-passage events and leads to a representation in terms of Motzkin-type
polynomials. The analytical formulation is complete except for boundary-constrained return terms, which appear as high-order integrals. We treat these contributions with a single truncation factor determined from Monte Carlo simulations of first-return distributions over a wide range of anisotropy g and scattering steps ms. The resulting factor follows a Cauchy distribution. Incorporating it yields first-return probabilities in agreement with full three-dimensional Monte Carlo to within 2% for g<=0.7. The approach gives backscattering coefficients from phase-function integrals and provides an efficient alternative to full three-dimensional simulations for problems of radiative transport in semi-infinite media.
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Geophysics
A Generalized Formulation for Accurate and Robust Determination of Soil Shear Strength from Triaxial Tests
Altamirano-Muñiz Emilio Fernando
This work presents an extended formulation of the Least Squares with Virtual Displacements (LSVD) method for estimating shear strength parameters from multiple soil samples under varying resistance conditions including cohesionless, frictional, and mixed types. LSVD is designed to identify a common tangent across n Mohr circles, even in the presence of measurement errors that render an exact solution infeasible. Beyond its original linear formulation, we introduce generalized LSVD variants like logarithmic,
parabolic,
polynomial, power law and generalized forms allowing the method to adapt to diverse failure envelope shapes observed in geotechnical materials. We
benchmark these variants against established approaches such as the p-q method and CTPAC, analyzing performance under synthetic noise to simulate measurement uncertainty. This provides a comparative framework to assess each method's robustness, especially considering their differing selections of representative points on the Mohr circles. The results highlight LSVD's flexibility and reliability in modeling complex soil behavior and suggest its potential as a versatile tool for geomechanical analysis.
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Biological Physics
Traveling chimeras and collective coordination in beta-cell networks
Carine Simo, Venceslas Nguefoue Meli, Patrick Louodop et al. (+2 more)
Pancreatic $\beta$-
cells play a central role in maintaining glucose homeostasis through the pulsatile secretion of insulin. This essential function relies not only on intracellular regulatory mechanisms but also on coordinated interactions among $\beta$-
cells within the islets of Langerhans. Disruptions in this intercellular coordination are increasingly implicated in metabolic disorders such as type~I and type~II diabetes. In this work, we employ a computational framework to investigate the collective dynamics of a network of coupled $\beta$-
cells interacting through a nonlocally coupled ring
topology that incorporates both electrical and metabolic coupling pathways. This
topology captures short- and long-range interactions known to shape islet communication. Numerical simulations reveal a variety of emergent behaviors, including synchronization, traveling waves, and traveling chimera states, in which coherent and incoherent domains coexist and propagate across the network. These findings provide new insight into the mechanisms governing coordinated $\beta$-
cell activity and the regulation of pulsatile insulin secretion. By clarifying how coupling structure and intercellular communication shape islet-wide dynamics, this work contributes to a deeper understanding of the dysfunctions underlying diabetes.
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All authors Carine Simo, Venceslas Nguefoue Meli, Patrick Louodop, Samuel Bowong, Thierry Njougouo
Applied Physics
Record Responsivity-conductance Performance in Sub-bandgap-triggered Ga2O3 PCSS
Vikash Jangir, Sourojit K. Mazumder and Sudip K. Mazumder
We present an investigation into the role of anode grid pitch and excitation spectrum on the performance of high-power optoelectronic switches utilizing Fe-doped $\beta$-Ga$_2$O$3$. By systematically varying the anode grid pitch ($20-80\ \mu\text{m}$) and the excitation spectrum ($235-500\ \text{nm}$), we identify a crucial sub-bandgap regime, centered at $272\ \text{nm}$, that effectively activates deep-level defect states. This activation is shown to enable highly efficient bulk carrier transport, a significant contrast to conventional above-bandgap excitation which is hampered by shallow surface absorption. The sub-bandgap illumination promotes strong photocurrent generation and substantially improved carrier collection efficiency. Under optimized conditions, specifically utilizing a $40\ \mu\text{m}$ anode pitch, the fabricated device achieves a high peak photocurrent of $4.14\ \text{A}$ and a record-low on-resistance of $10.4\ \Omega$. To quantify this simultaneous high-performance achievement, we introduce a responsivity-conductance figure of merit ($\text{FoM}{_{RC}}$), which attains a record value of $4.7 \times 10^{-6}\ \text{S/W}$. These findings robustly demonstrate the superior suitability of Fe-doped $\beta$-Ga$_2$O$_3$ for next-generation high-power optoelectronic switching applications, enabling reliable ampere-level photocurrents coupled with minimized on-resistance through strategic device geometry optimization and sub-bandgap excitation.
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Biological Physics
Encounter Times of Intermittently Running Particles
Lizzy Teryoshin, Mario Hidalgo-Soria and Elena F. Koslover
Intracellular processes often rely on the timely encounter of mobile reaction partners, including intermittently motor-driven organelles. The underlying cytoskeletal network presents a complex landscape that both directs particle movement and introduces quenched disorder through filament organization. We investigate the mean first encounter times for pairs of intermittently processive and diffusive particles, moving in two dimensions with and without a fixed filament network. In unstructured domains, increasing particle run-length enhances exploration of the domain, but tends to slow down the encounter times compared to equivalent diffusing particles. Encounters for long-running particles occur preferentially near the periphery, contrasting with bulk encounters for the purely diffusive case. When particles are unbiased in their runs along dense filament networks, encounters are shown to be well approximated by a continuum run-and-tumble model. For biased particles, regions of convergent filament orientation can serve as traps that slow the overall spatial exploration but can allow for faster encounter rates by funneling particles into regions of reduced dimensionality. These findings provide a framework for estimating intracellular encounter kinetics, highlighting the role of key physical features such as the effective diffusivity, run times, and network architecture.
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Medical Physics
Super-Resolution Posterior Ocular Microvascular Imaging Using 3-D Ultrasound Localization Microscopy With a 32X32 Matrix Array
Junhang Zhang, U-Wai Lok, Jingke Zhang et al. (+16 more)
The purpose of this study is to enable in-vivo three-dimensional (3-D) ultrasound localization microscopy (ULM) of posterior ocular microvasculature using a 256-channel system and a 1024-element
matrix array, and to overcome limitations of restricted transmit angles, sound speed mismatch caused by the crystalline lens and surrounding tissues, and the low signal-to-noise ratio (SNR) of microbubble signals. To address phase distortions from the crystalline lens, which has a higher speed of sound (SOS) than surrounding tissues, a region-dependent SOS beamforming approach was implemented to improve microbubble resolution. A 4-D non-local means filter was subsequently applied to suppress background noise and enhance microbubble contrast. The proposed method improved localization accuracy and image quality, achieving a spatial resolution of 63 um, while
Fourier shell correlation (1/2-bit threshold) confirmed a global resolution of approximately 59 um. Higher mean normalized cross-correlation coefficients between the microbubbles and the system point-spread function, obtained with the proposed method (approximately 0.67), compared with those without the proposed method (approximately 0.60), indicate enhanced microbubble signal quality. Furthermore, the 3-D bi-directional vessel density and flow-velocity maps were reconstructed, capturing detailed choroidal vascular and hemodynamic patterns. These results demonstrate that region-dependent SOS beamforming combined with spatiotemporal denoising enables high-resolution posterior ocular ULM and provides a practical pathway toward quantitative 3-D assessment of retinal and choroidal microvasculature for potential clinical use.
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All authors Junhang Zhang, U-Wai Lok, Jingke Zhang, Chengwu Huang, Xin Sun, Chi-Feng Chang, Baoqiang Liu, Chen Gong, Yushun Zeng, Kaipeng Ji, Ryan M. DeRuiter, Jingyi Yin, Lijie Huang, Yanzhe Zhao, Ying Liu, Brian Song, Mark Humanyun, Shigao Chen, Qifa Zhou
Computational Physics
Generative Monte Carlo Sampling for Constant-Cost Particle Transport
Joseph A. Farmer, Aidan Murray, Johannes Krotz et al. (+1 more)
We present Generative Monte Carlo (GMC), a novel paradigm for particle transport simulation that integrates generative artificial intelligence directly into the
stochastic solution of the linear Boltzmann equation. By reformulating the
cell-transmission problem as a conditional generation task, we train neural networks using conditional flow matching to sample particle exit states, including position, direction, and path length, without simulating scattering histories. The method employs optical coordinate scaling, enabling a single trained model to generalize across any material. We validate GMC on two canonical
benchmarks, namely a heterogeneous
lattice problem characteristic of
nuclear reactor cores and a linearized hohlraum geometry representative of high-energy density radiative transfer. Results demonstrate that GMC preserves the statistical fidelity of standard Monte Carlo, exhibiting the expected $1/\sqrt{N}$ convergence rate while maintaining accurate scalar flux profiles. While standard Monte Carlo computational cost scales linearly with optical thickness in the diffusive limit, GMC achieves constant $O(1)$ cost per
cell transmission, yielding order-of-magnitude speedups in optically thick regimes. This framework strategically aligns particle transport with modern computing architectures optimized for neural network inference, positioning transport codes to leverage ongoing advances in AI hardware and
algorithms.
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All authors Joseph A. Farmer, Aidan Murray, Johannes Krotz, Ryan G. McClarren