Fluid Dynamics · Cross-listed from eess.SY
Dynamic Modeling and Control of Phosphate-Pebble Drying Systems – A Comprehensive Approach
Jose M. Campos-Salazar, Felipe Santander and Eduardo Keim
Dryers play a central role in the processing of phosphate rock, where moisture removal is essential for downstream handling and energy efficiency. Due to the inherently nonlinear and multivariable nature of these systems, accurate modeling and control remain industrial challenges. This article presents a comprehensive nonlinear dynamic model of a phosphate-pebble rotary drying process, built from first principles to capture coupled heat and mass transfer, evaporation kinetics, and subsystem interactions.
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History and Philosophy of Physics
A Four-Level Ontological Framework for Quantum Field Theory: From Quantum Vacuum to Phenomenal Reality
Ali Reza Mirzaee
One of the foundational challenges in both
Quantum Field Theory and the philosophy of physics lies in the traditional binary classification of entities as either real or unreal. The classical view posits that material entities are real, while mental constructs or theoretical entities are unreal. However,
quantum phenomena such as virtual particles challenge this simplistic dichotomy. These unobservable entities, by influencing measurable phenomena, raise profound questions about the nature and modes of existence. To address this, we propose a four-level ontological hierarchy that spans from the
quantum vacuum and the virtual level to the
quantum level and phenomenal reality, providing a coherent continuum from potentiality to actualization. This framework not only clarifies the interpretation of
quantum theory but also offers a unified perspective linking the unobservable with the observable. In QFT, virtual particles play a crucial role in mediating forces and in correcting the measurable properties of real particles. They arise within the mathematical structure of Feynman diagrams and, despite being unobservable, produce real, measurable effects such as the Lamb shift and the Casimir effect. Philosophically, this raises deep ontological questions: if unobservable entities affect measurable phenomena, can they truly be dismissed as unreal? Conversely, if they are real, what mode of existence do they possess? The traditional dichotomy of real versus unreal is thus insufficient for interpreting the ontological implications of
quantum processes. To overcome these limitations, we propose a four-level ontological hierarchy.
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Physics and Society
Spatial Selection and the Multiscale Dynamics of Urban Change
Jordan T Kemp, Laura Fürsich, Elias Tannira et al. (+1 more)
Growth is a multi-layered phenomenon in human societies, composed of socioeconomic and demographic change at many different scales. Yet, standard macroeconomic indicators average over most of these processes, blurring the spatial and hierarchical heterogeneity driving people's choices and experiences. To address this gap, we introduce here a framework based on the Price equation to decompose aggregate growth exactly into endogenous and selection effects across nested spatial scales. We illustrate this approach with population and income data from the Chicago metropolitan area (2014-2019) and show that both growth rates and spatial selection effects are most intense at local levels, fat-tailed and spatially correlated. We also find that selection, defined as the covariance between prevailing income and relative population change, is concentrated in few spatial units and exhibits scaling behavior when grouped by county. Despite the intensity of local sorting, selection effects largely cancel in the aggregate, implying that fast heterogeneous micro-dynamics can yield deceptively stable macro-trends. By treating local spatial units (neighborhoods) as evolving subpopulations under selection, we demonstrate how methods from complex systems provide new tools to classify residential selection processes, such as abandonment and gentrification, in an urban sociological framework. This approach is general and applies to any other nested economic systems such as networks of production, occupations, or innovation enabling a new mechanistic understanding of compositional change and growth across scales of organization.
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All authors Jordan T Kemp, Laura Fürsich, Elias Tannira, Luís M A Bettencourt
Medical Physics · Cross-listed from eess.IV
Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation
Jyun-Ping Kao, Shinyeong Rho, Shahar Lazarev et al. (+5 more)
Early diagnosis of
attention-deficit/hyperactivity disorder (ADHD) in children plays a crucial role in improving outcomes in education and mental health. Diagnosing ADHD using neuroimaging data, however, remains challenging due to heterogeneous presentations and overlapping symptoms with other conditions. To address this, we propose a novel parameter-efficient transfer learning approach that adapts a large-scale 3D convolutional foundation model, pre-trained on CT images, to an MRI-based ADHD classification task. Our method introduces Low-Rank Adaptation (LoRA) in 3D by factorizing 3D convolutional kernels into 2D low-rank updates, dramatically reducing trainable parameters while achieving superior performance. In a five-fold cross-validated evaluation on a public
diffusion MRI database, our 3D LoRA fine-tuning strategy achieved state-of-the-art results, with one model variant reaching 71.9% accuracy and another attaining an AUC of 0.716. Both variants use only 1.64 million trainable parameters (over 113x fewer than a fully fine-tuned foundation model). Our results represent one of the first successful cross-modal (CT-to-MRI) adaptations of a foundation model in neuroimaging, establishing a new
benchmark for ADHD classification while greatly improving efficiency.
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All authors Jyun-Ping Kao, Shinyeong Rho, Shahar Lazarev, Hyun-Hae Cho, Fangxu Xing, Taehoon Shin, C.-C. Jay Kuo, Jonghye Woo
Plasma Physics
Fusion alpha particle momentum deposition in thermonuclear burn dynamics
A. J. Crilly, B. D. Appelbe, E. A. Ferdinandi et al. (+6 more)
In inertial confinement fusion, the DT fusion alpha particles carry not only energy but also appreciable momentum that is typically neglected in models of thermonuclear burn. In the central hotspot ignition scheme, the hotspot must self-heat and propagate thermonuclear burn before disassembly. Using radiation hydrodynamics simulations with a Monte Carlo alpha particle transport model, we investigate the effect of alpha momentum deposition across sub-ignition to robustly igniting regimes by hydrodynamic scaling of current central hotspot ignition designs from the National Ignition Facility (NIF). We find that the effective alpha particle ram pressure accelerates the shell at burn, reducing hotspot compression, increasing the rate of disassembly and decreasing yield. This causes a notable (~ 30%) reduction in yield at current NIF scale, with a persistent (~ 10%) penalty at larger hydrodynamic scales. These results demonstrate that alpha momentum deposition is a significant effect for present ignition-scale implosions, necessitating its inclusion in ignition criteria, burn models, and designs for high-gain inertial confinement fusion.
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All authors A. J. Crilly, B. D. Appelbe, E. A. Ferdinandi, S. T. O'Neill, H. Biragnet, N. Chaturvedi, J. P. Chittenden, B. Duhig, P. W. Moloney
Geophysics
Axial Seamount Eruption Forecasting Experiment
Qinghua Lei, Didier Sornette, William W. Chadwick Jr. et al. (+3 more)
We introduce the Axial Seamount Eruption
Forecasting Experiment (EFE), a real-time initiative designed to test the predictability of volcanic eruptions through a transparent, physics-based framework. The experiment is inspired by the Financial Bubble Experiment, adapting its principles of digital authentication, timestamped archiving, and delayed disclosure to the field of volcanology. The EFE implements a reproducible protocol in which each forecast is securely timestamped and cryptographically hashed (SHA-256) before being made public. The corresponding forecast documents, containing detailed diagnostics and probabilistic analyses, will be released after the next eruption or, if the forecasts are proven incorrect, at a later date. This procedure ensures full transparency while preventing premature interpretation or controversy surrounding public predictions. Forecasts will be issued monthly, or more frequently if required, using real-time monitoring data from the Ocean Observatories Initiative's Regional Cabled Array at Axial Seamount. By committing to publish all forecasts, successful or not, the EFE establishes a scientifically rigorous, falsifiable protocol to evaluate the limits of eruption
forecasting. The ultimate goal is to transform eruption prediction into a cumulative and testable science founded on open verification, reproducibility, and physical understanding.
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All authors Qinghua Lei, Didier Sornette, William W. Chadwick Jr., Scott L. Nooner, Maochuan Zhang, William S. D. Wilcock
Optics
Video-rate gigapixel ptychography via space-time neural field representations
Ruihai Wang, Qianhao Zhao, Zhixuan Hong et al. (+14 more)
Achieving gigapixel space-bandwidth products (SBP) at
video rates represents a fundamental challenge in imaging science. Here we demonstrate
video-rate ptychography that overcomes this barrier by exploiting spatiotemporal correlations through neural field representations. Our approach factorizes the space-time volume into low-rank spatial and temporal features, transforming SBP scaling from sequential measurements to efficient correlation extraction. The architecture employs dual networks for decoding real and imaginary field components, avoiding phase-wrapping discontinuities plagued in amplitude-phase representations. A gradient-domain loss on spatial derivatives ensures robust convergence. We demonstrate
video-rate gigapixel imaging with centimeter-scale coverage while resolving 308-nm linewidths. Validations span from monitoring sample dynamics of
crystals, bacteria, stem
cells, microneedle to characterizing time-varying probes in extreme ultraviolet experiments, demonstrating versatility across wavelengths. By transforming temporal variations from a constraint into exploitable correlations, we establish that gigapixel
video is tractable with single-sensor measurements, making ptychography a high-throughput sensing tool for monitoring mesoscale dynamics without lenses.
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All authors Ruihai Wang, Qianhao Zhao, Zhixuan Hong, Qiong Ma, Tianbo Wang, Lingzhi Jiang, Liming Yang, Shaowei Jiang, Feifei Huang, Thanh D. Nguyen, Leslie Shor, Daniel Gage, Mary Lipton, Christopher Anderton, Arunima Bhattacharjee, David Brady, Guoan Zheng
Optics
Deep-ultraviolet ptychographic pocket-scope (DART): mesoscale lensless molecular imaging with label-free spectroscopic contrast
Ruihai Wang, Qianhao Zhao, Julia Quinn et al. (+11 more)
The mesoscale characterization of biological specimens has traditionally required compromises between resolution, field-of-view, depth-of-field, and molecular specificity, with most approaches relying on external labels. Here we present the Deep-ultrAviolet ptychogRaphic pockeT-scope (DART), a handheld platform that transforms label-free molecular imaging through intrinsic deep-ultraviolet spectroscopic contrast. By leveraging biomolecules' natural absorption fingerprints and combining them with lensless ptychographic microscopy, DART resolves down to 308-nm linewidths across centimeter-scale areas while maintaining millimeter-scale depth-of-field. The system's virtual error-bin methodology effectively eliminates artifacts from limited temporal coherence and other optical imperfections, enabling high-fidelity molecular imaging without lenses. Through differential spectroscopic imaging at deep-ultraviolet wavelengths, DART quantitatively maps nucleic acid and
protein distributions with femtogram sensitivity, providing an intrinsic basis for explainable virtual staining. We demonstrate DART's capabilities through molecular imaging of tissue sections, cytopathology specimens, blood
cells, and neural populations, revealing detailed molecular contrast without external labels. The combination of high-resolution molecular mapping and broad mesoscale imaging in a portable platform opens new possibilities from rapid clinical diagnostics, tissue analysis, to biological characterization in space exploration.
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All authors Ruihai Wang, Qianhao Zhao, Julia Quinn, Liming Yang, Yuhui Zhu, Feifei Huang, Chengfei Guo, Tianbo Wang, Pengming Song, Michael Murphy, Thanh D. Nguyen, Andrew Maiden, Francisco E. Robles, Guoan Zheng
History and Philosophy of Physics
A computational framework for evaluating an edge-integrated, multi-ramp construction model of the Great Pyramid of Giza
Vicente Luis Rosell Roig
Despite decades of study, a quantitative, integrated framework to evaluate minutescale throughput,
geometric control, and a zero external footprint for Khufu's pyramid has been lacking. We test the Integrated Edge-Ramp (IER) model-a helical path formed by omitting and backfilling perimeter courses-using a unified, end-to-end pipeline coupling parametric geometry, discrete-event logistics, and staged finite-element analysis (FEA). An adaptive multiramp strategy can sustain 4-6-minute dispatches and yields a median on-site duration of 13.8-20.6 years (95% CI); including quarrying, river transport, and seasonal pauses gives 20-27 years. FEA indicates that stresses and settlements remain within plausible limits for Old Kingdom limestone under self-weight. The model's geometry is also consistent with internal voids identified by
muon imaging (a hypothesis-generating result). The IER helps reconcile throughput, survey access, and zero-footprint closure, and produces falsifiable predictions (edge-fill signatures, corner wear). Our study provides a transferable, open-data/code framework for testing construction hypotheses for ancient megastructures.
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Space Physics
Forecasting Thermospheric Density with Transformers for Multi-Satellite Orbit Management
Cedric Bös, Alessandro Bortotto and Mohamed Khalil Ben-Larbi
Accurate thermospheric density prediction is crucial for reliable satellite operations in Low Earth Orbits, especially at high
solar and geomagnetic activity. Physics-based models such as TIE-GCM offer high fidelity but are computationally expensive, while empirical models like NRLMSIS are efficient yet lack predictive power. This work presents a
transformer-based model that forecasts densities up to three days ahead and is intended as a drop-in replacement for an empirical baseline. Unlike recent approaches, it avoids spatial reduction and complex input pipelines, operating directly on a compact input set. Validated on real-world data, the model improves key prediction metrics and shows potential to support mission planning.
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Optics
Super-resolution with Fourier measurements
S. A. Wadood, Shaurya Aarav, Kevin Liang et al. (+1 more)
Resolving sources beyond the diffraction limit is important in imaging, communications, and metrology. Current image-based methods of super-resolution require phase information (either of the source points or an added filter) and perfect
alignment with the centroid of the object. Both inhibit the practical application of these methods, as uniform motion and/or relative jitter destroy their assumptions. Here, we show that measuring intensity in the
Fourier plane enables super-resolution without any of the issues of image-based methods. We start with the shift-invariance of the
Fourier transform and the observation that the two-point position problem ${x_1,x_2}$ in the near field corresponds to the single-point wavenumber problem $k\ =2\pi/(x_2-x_1)$ in the far field. We consider the full range of mutual coherence and show that for fully coherent sources, the
Fourier method saturates the
quantum limit, i.e. it gives the best possible measurement. Similar results hold for sub-Rayleigh constellations of $N$ sources, which can act collectively as a spatially averaged metasurface and/or individually as elements of a phased-array
antenna. The theory paves the way to merge
Fourier optics with super-resolution techniques, enabling experimental devices that are both simpler and more robust than previous designs.
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All authors S. A. Wadood, Shaurya Aarav, Kevin Liang, Jason W Fleischer
Computational Physics
NeuroPINNs: Neuroscience Inspired Physics Informed Neural Networks
Shailesh Garg and Souvik Chakraborty
We introduce NeuroPINNs, a neuroscience-inspired extension of Physics-Informed Neural Networks (PINNs) that incorporates biologically motivated spiking
neuron models to achieve energy-efficient PDE solving. Unlike conventional PINNs, which rely on continuously firing activations and therefore incur high computational and energy costs, NeuroPINNs leverage Variable Spiking
Neurons (VSNs) to enable sparse, event-driven communication. This makes them particularly well-suited for deployment on neuromorphic hardware and for scenarios with constrained computational resources, such as embedded and edge devices. A central challenge, however, lies in reconciling the discontinuous dynamics of spiking
neurons with the smooth residual-based loss formulation required in PINNs. Direct smoothing introduces systematic biases, leading to inaccurate PDE learning. To overcome this, we employ a novel
stochastic projection method inspired from upscaled theory that faithfully captures spiking behavior while maintaining compatibility with gradient-based optimization. Standard surrogate backpropagation is used for parameter updates, ensuring computational tractability. We demonstrate the effectiveness of NeuroPINNs on four representative PDE problems across both regular and irregular domains. Furthermore, application of NeuroPINN for linear elastic micromechnics in three dimensions was also explored. Results show that NeuroPINNs achieve high accuracy while substantially reducing communication and energy demands, marking a step toward scalable, neuromorphic-ready scientific machine learning.
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Optics
Entropy mode driven gas optics
P. Michel, A. Oudin, H. Rajesh et al. (+9 more)
We propose a novel class of gaseous diffractive optical elements created by imprinting an
entropy mode in a gas. Previous approaches to gaseous diffractive optics relied on the simultaneous excitation of a standing acoustic wave and an
entropy mode to produce one-dimensional periodic structures. However, the presence of acoustic oscillations in the gas imposes stringent constraints on some operational parameters of these optical elements, such as their lifetime and diffraction angle. In this work, we introduce a new approach that eliminates the acoustic mode, relying solely on the
entropy mode. This enables control of the lifetime and temporal profile of gaseous optical elements, and also allows the creation of arbitrary structures with greater contrast, including non-periodic patterns such as chirped gratings or lenses. This approach should allow operation over a wider parameter space, including larger diffraction angles and compatibility with
laser pulse durations ranging from femtoseconds to microseconds.
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All authors P. Michel, A. Oudin, H. Rajesh, K. Ou, D. Chakraborty, S. Cao, E. Kur, L. Lancia, D. Ghosh, C. Riconda, J. S. Wurtele, M. R. Edwards
Optics
Multiscale aperture synthesis imager
Ruihai Wang, Qianhao Zhao, Tianbo Wang et al. (+7 more)
Synthetic aperture imaging has enabled breakthrough observations from radar to astronomy. However, optical implementation remains challenging due to stringent wavefield synchronization requirements among multiple receivers. Here we present the multiscale aperture synthesis imager (MASI), which utilizes parallelism to break complex optical challenges into tractable sub-problems. MASI employs a distributed array of coded sensors that operate independently yet coherently to surpass the diffraction limit of single receiver. It combines the propagated wavefields from individual sensors through a computational phase synchronization scheme, eliminating the need for overlapping measurement regions to establish phase coherence. Light diffraction in MASI naturally expands the imaging field, generating phase-contrast visualizations that are substantially larger than sensor dimensions. Without using lenses, MASI resolves sub-micron features at ultralong working distances and reconstructs 3D shapes over centimeter-scale fields. MASI transforms the intractable optical synchronization problem into a computational one, enabling practical deployment of scalable synthetic aperture systems at optical wavelengths.
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All authors Ruihai Wang, Qianhao Zhao, Tianbo Wang, Mitchell Modarelli, Peter Vouras, Zikun Ma, Zhixuan Hong, Kazunori Hoshino, David Brady, Guoan Zheng
Atomic and Molecular Clusters · Cross-listed from cond-mat.mtrl-sci
Strain-Tunable Spin Filtering and Valley Splitting Coexisting with Anomalous Hall Effect in 2D Half-Metallic VSe2/VN Heterostructure: Toward a Unified Spintronic-Valleytronic Platform
Vivek Chowdhury and Ahmed Zubair
Rapid progress in valleytronics and spintronics is limited by the scarcity of two-dimensional materials that simultaneously provide robust valley splitting and strong
spin selectivity. Here we showed that a van der Waals
heterostructure (VSe2/VN) built from hexagonal VSe2 and hexagonal VN addressed this gap. Using first-principles density functional theory,
phonon, ab initio molecular dynamics stability tests, Bader charge analysis, and Wannier-based Berry-
curvature calculations, we demonstrated an energetically and dynamically stable
heterostructure that exhibited interlayer charge transfer and a work function intermediate between the constituent monolayers. The electronic structure showed small indirect PBE gap (108.9 meV), with HSE06 indicating a half-metallic tendency; a sizable conduction-band valley splitting (\Delta CKK' = 22.9 meV for
spin-up and \Delta CKK' = 61.3 meV for
spin-down); and pronounced
spin asymmetry, where the
spin-down channel showed a wide semiconducting gap (0.64 eV) while the
spin-up channel was nearly gapless. These features yielded a high zero-strain
spin-filter efficiency P = 75.4%, tunable to 82.5% under +4% biaxial tensile strain. The
heterostructure also supported non-zero, valley-contrasting Berry
curvature, and a large anomalous
Hall conductivity (peak sigmaxy = 568.33 S/cm). Importantly, mean-field estimation placed the
ferromagnetic Curie temperature near room temperature at zero strain (Tc = 284.04 K), while Tc decreased to 183.9 K at +4% strain, the
magnetic order remained robust to cryogenic temperatures, providing a beneficial tuning knob to balance
spin-filter performance with thermal stability in device-relevant regimes. These results identified VSe2/VN as a practical, strain-tunable platform for integrated valleytronic, spintronic devices, and for exploring anomalous
Hall and valley-dependent transport phenomena.
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Optics
Unveiling architectural and optoelectronic synergies in lead-free perovskite/perovskite/kesterite triple-junction monolithic tandem solar cells
Md. Faiaad Rahman, Md. Ashaduzzaman Niloy, Ehsanur Rahman et al. (+1 more)
The widespread use of lead-based materials in tandem
solar cells raises critical environmental and health concerns due to their inherent toxicity and risk of contamination. To address this challenge, we focused on lead-free tandem architectures based on non-toxic, environmentally benign materials such as tin-based perovskites and kesterites, which are essential for advancing sustainable photovoltaic technologies. In this study, we present the proposition, design, and optimization of two distinct lead-free monolithic tandem
solar cell architectures - an all-perovskite dual-junction device employing potassium tin iodide (KSnI3) and formamidinium tin triiodide (FASnI3) as absorbers for the top and bottom subcells, respectively, and a triple-junction monolithic tandem structure incorporating KSnI3, FASnI3, and Ag-doped copper zinc tin selenide (ACZTSe) as absorbers for the top, middle, and bottom subcells, respectively. We simulated the optical and electrical characteristics of these devices using the finite-difference time-domain and finite element methods, explicitly considering radiative, non-radiative, and surface recombination mechanisms. The optimized all-perovskite dual-junction
solar cell achieved a power conversion efficiency (PCE) of 27.3%, with short-
circuit current density (Jsc) of 14.74 mA/cm2, open-
circuit voltage (Voc) of 2.227 V, and fill factor (FF) of 83.14%. Conversely, the optimized triple-junction hybrid perovskite-kesterite architecture secured an elevated PCE of 30.69%, along with Jsc of 13.184 mA/cm2, Voc of 2.766 V, and FF of 84.18%. These findings reveal the strong potential of lead-free perovskite and kesterite material based absorbers in promoting high-performance hybrid tandem
solar cells, highlighting their importance in advancing sustainable and efficient photovoltaic technologies.
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All authors Md. Faiaad Rahman, Md. Ashaduzzaman Niloy, Ehsanur Rahman, Ahmed Zubair
Optics
Broadband Achromatic Metalens for the Short-Wave Infrared
Yan He and Adetunmise C. Dada
The 1.8-2.3 {\mu}m band lies within the short-wavelength infrared (SWIR) region and serves as a key window for a wide range of applications, including
quantum sensing, molecular spectroscopy, and free-space
quantum and classical optical communication. Despite its significance, optical devices operating in this band still face two major challenges: chromatic aberration across the spectral range and difficulty of integration due to bulky optical elements. Metalenses are composed of subwavelength nanostructures that locally control the phase and group delay of light, enabling wavefront shaping and broadband dispersion compensation. These capabilities make them promising for infrared optical systems, particularly in focusing and imaging for compact devices. In this study, we propose a metalens design based on a CaF$_2$ substrate, where each nanocell consists of a silicon bar structure. These nanocells are periodically arranged with a 900~nm period, enabling control of dispersion and phase. By systematically finetuning the bar length and width, the design enables simultaneous dispersion compensation and phase modulation, achieving stable focusing performance over a broad spectral range. Finite-Difference Time-Domain (FDTD) simulations demonstrate effective suppression of chromatic aberration across 1800 - 2300 nm, with focal-length variation within 6% of the target value. We further analyze the polarization distribution across the focal spot and find a weak wavelength dependence of the degree of polarization (DoP), which we attribute to the spatially varying polarization state in the high-NA focal region together with the wavelength-dependent anisotropic response of the nanostructures. This design offers a compact, broadband, and high-performance approach for beam collimation and wavefront shaping in the SWIR band, showing promising potential for
quantum communication and sensing systems.
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History and Philosophy of Physics · Cross-listed from quant-ph
Quantum-Like Correlations from Local Hidden-Variable Theories Under Conservation Law
Alejandro J. Garza and Jonte R. Hance
The precision with which we can measure operators that do not commute with conserved quantities is limited by the need to preserve the associated global
symmetries. We show how to construct a local hidden-variable model that violates Bell inequalities by interpreting this measurement error as altering the measure space of the hidden variables. This provides a physically-motivated example of a supermeasured model, where the statistical independence assumption used to form Bell inequalities is violated without there being a causal dependence of the measurement settings on the hidden variables (superdeterminism) or a causal dependence of the hidden variables on the measurement settings (retrocausality). The model also gives subtly different predictions to
quantum mechanics, that could be tested experimentally.
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History and Philosophy of Physics
Nearly forgotten results in development of physical cosmology
Alexander F. Zakharov
It would be reasonable to recall some critical issues in physical cosmology development. GR was created by A. Einstein in 1915. In 1917 Einstein proposed the first (static) cosmological model. Soon after the A. Eddington proved that the model is unstable therefore it can not be realizable in nature. In 1922 and 1924 A. A. Friedmann found non-stationary solutions for cosmological equations written in the framework of GR. In 1927 G. Lemaitre obtained very similar results and, in addition, he derived the Hubble law (E. Hubble obtained this law from observations). Unfortunately, G. Lemaitre published his paper in not very popular Belgium journal. In 1931 Lemaitre proposed the first version of hot Universe model (he called it hypothesis of the primeval atom). In his book Lemaitre predicted even a background radiation as a signature of his model. One of the important property of the Lemaitre – Gamow model was a prediction of CMB radiation with a temperature around a few K. It was recalled that the discovery of CMB radiation was done by T. Shmaonov in 1956 and his paper was published in 1957 (several years before Penzias and Wilson). In 1965, 1970 E. B. Gliner proposed
vacuum like equation of matter which could correspond to exponential explosion of the Universe which was later called inflation. For decades in USSR, Friedmann's cosmological non-stationary models were treated as purely mathematical results without cosmologocal applications. On September 16, 1925 passed away untimely and it would be reasonable to remind today his great contribution in physical cosmology since the authors of book on Friedmann wrote that "similarly to Copernicus who forced the Earth to move Friedmann forced the Universe to expand".
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Atomic Physics · Cross-listed from nucl-th
Microscopic study of low-lying states in odd-mass nuclei for atomic electric dipole moment searches
E. F. Zhou and J. M. Yao
We present a microscopic study of the low-lying states of five odd-mass
nuclei of particular interest for experimental searches of atomic electric dipole moments (EDMs): $^{129}$Xe, $^{199}$Hg, $^{225}$Ra, $^{229}$Th, and $^{229}$Pa. The analysis is performed within the recently developed multi-reference covariant density functional theory (MR-CDFT), which incorporates
symmetry restoration and configuration mixing based on self-consistent mean-field solutions. The calculated energy spectra and electromagnetic observables of these
nuclei are reasonably well reproduced without introducing any parameters beyond those of the underlying universal relativistic energy density functional. The results demonstrate the reliability of MR-CDFT in describing the structure of these
nuclei and in providing essential input on
nuclear Schiff moments relevant to ongoing EDM searches.
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Geophysics
PGDM: Physically guided diffusion model for land surface temperature downscaling
Huanyu Zhang, Bo-Hui Tang, Tian Hu et al. (+2 more)
Land surface temperature (LST) is a fundamental parameter in thermal infrared remote sensing, while current LST products are often constrained by the trade-off between spatial and temporal resolutions. To mitigate this limitation, numerous studies have been conducted to enhance the resolutions of LST data, with a particular emphasis on the spatial dimension (commonly known as LST downscaling). Nevertheless, a comprehensive
benchmark dataset tailored for this task remains scarce. In addition, existing downscaling models face challenges related to accuracy, practical usability, and the capability to self-evaluate their uncertainties. To overcome these challenges, this study first compiled three representative
datasets, including one
dataset over mainland China containing 22,909 image patches for model training and evaluation, as well as two
datasets covering 40 heterogeneous regions worldwide for external evaluation. Subsequently, grounded in the surface energy balance (SEB)-based geophysical
reasoning, we proposed the physically guided
diffusion model (PGDM) for LST downscaling. In this framework, the downscaling task was formulated as an inference problem, aiming to sample from the posterior distribution of high-spatial-resolution (HR) LST conditioned on low-spatial-resolution (LR) LST observations and a suite of HR geophysical priors. Comprehensive evaluations demonstrate the effectiveness of PGDM, which generates high-quality downscaling results and outperforms existing representative interpolation, kernel-driven, hybrid, and deep learning approaches. Finally, by exploiting the inherent stochasticity of PGDM, the scene-level standard deviation of multiple generations was computed, revealing a strong positive linear correlation with the actual downscaling error...
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All authors Huanyu Zhang, Bo-Hui Tang, Tian Hu, Yun Jiang, Zhao-Liang Li
Biological Physics · Cross-listed from quant-ph
Photodiode quantum efficiency for 2-{\mu}m light in the signal band of gravitational wave detectors
Julian Gurs, Nils Sueltmann, Christian Darsow-Fromm et al. (+2 more)
Quantum technologies with
quantum correlated light require photodiodes with near-perfect `true'
quantum efficiency, the definition of which adequately accounts for the photodiode dark noise. Future squeezed-light-enhanced gravitational wave detectors could in principle achieve higher sensitivities with a longer
laser wavelength around 2 {\mu}m. Photodiodes made of extended InGaAs are available for this range, but the true
quantum efficiency at room temperature and the low frequency band of gravitational waves is strongly reduced by dark noise. Here we characterize the change in performance of a commercial extended-InGaAs photodiode versus temperature. While the dark noise decreases as expected with decreasing temperature, the detection efficiency unfortunately also decreases monotonically. Our results indicate the need for a dedicated new design of photodiodes for gravitational wave detectors using 2-{\mu}m
laser light.
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All authors Julian Gurs, Nils Sueltmann, Christian Darsow-Fromm, Sebastian Steinlechner, Roman Schnabel
Optics
Spatiotemporally Localized Optical Links and Knots
Yaning Zhou, Nianjia Zhang, Ao Zhou et al. (+7 more)
Optical links and
knots have attracted growing
attention owing to their exotic topologic features and promising applications in next-generation information transfer and storage. However, current protocols for optical
topology realization rely on paraxial propagation of spatial modes, which inherently limits their three-dimensional topological structures to longitudinal space-filling. In this work we propose and experimentally demonstrate a scheme for creating optical
knots and links that are localized in space within a transverse plane of a paraxial field, as well as in time. These spatiotemporal topological structures arise from polychromatic wave fields with tightly coupled spatial and temporal degrees of freedom that can be realized in the form of superpositions of toroidal light
vortices of opposite topological charges. The (2+1)-dimensional nature of a toroidal light
vortex imparts spatiotemporally localized wave fields with nontrivial topological textures, encompassing both individual and nested links or
knots configurations. Moreover, the resulting topological textures are localized on an ultrashort timescale propagate at the group velocity of the wave packets and exhibit remarkable topological robustness during propagation as optical carriers. The nascent connection between spatiotemporally localized fields and
topology offers exciting prospects for advancing space-time photonic
topologies and exploring their potential applications in high-capacity informatics and communications.
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All authors Yaning Zhou, Nianjia Zhang, Ao Zhou, Zhao Zhang, Jinsong Liu, Chunhao Liang, Sergey A. Ponomarenko, Qiwen Zhan, Yangjian Cai, Xin Liu
Chemical Physics · Cross-listed from cs.SI
The Role and Mechanism of Deep Statistical Machine Learning In Biological Target Screening and Immune Microenvironment Regulation of Asthma
Pengwei Zhu
As an important source of small molecule drugs, natural products show remarkable biological activities with their rich types and unique structures. However, due to the limited number of samples and structural complexity, the rapid discovery of lead compounds is limited. Therefore, in this study, natural inhibitors of phosphodiesterase 4 (PDE4) and Phosphodiesterase 7 (PDE7) were screened by combining computer aided drug design (CADD) technology and deep learning method, and their activities were verified by enzyme activity experiment and enzymo-linked immunoassay. These two enzymes have important application potential in the treatment of inflammatory diseases such as chronic obstructive pulmonary disease and asthma, but PDE4 inhibitors may cause adverse reactions, so it is particularly important to develop both effective and safe dual-target inhibitors. In addition, as a potential target of hyperuricemia, the development of natural inhibitors of xanthine oxidase (X0) is also of great value. We used pharmacophore technology for virtual screening, combined with molecular docking technology to improve accuracy, and finally selected 16 potential natural inhibitors of PDE4/7, and verified their binding stability through molecular dynamics simulation. The results of this study laid a foundation for establishing an efficient dual-target inhibitor screening system and exploring the lead compounds of novel X0 inhibitors.
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Chemical Physics
A reduced-cost third-order algebraic diagrammatic construction based on state-specific frozen natural orbitals: Application to the electron-attachment problem
Tamoghna Mukhopadhyay, Kamal Majee and Achintya Kumar Dutta
We have developed a reduced-cost non-Dyson third-order algebraic diagrammatic construction theory for the electron-attachment problem based on state-specific frozen natural orbitals. Density fitting and truncated natural auxiliary functions were employed to enhance computational efficiency. The use of state-specific frozen natural orbitals significantly decreases the virtual space and provides a notable speedup over the conventional EA-ADC(3) method with a systematically controllable accuracy. A perturbative correction for the truncated natural orbitals significantly reduces the error in the calculated electron affinity values. The method also shows sufficient accuracy in the case of non-valence correlation-bound anions, where the local approximation-based methods fail. The efficiency of the method is demonstrated by performing an EA-ADC(3) calculation with more than 1300 basis functions.
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Applied Physics
From cathode to anode: Understanding lithium loss in 21700-type Ni-rich NCM||Graphite-SiOx cells
Thien An Pham, Hannah Bosch, Giovanni Ceccio et al. (+5 more)
Moving to larger
cell formats in lithium-ion batteries increases overall useable energy but introduces inhomogeneities that influence aging. This study investigates degradation in 21700-type
cells with NCM cathodes and graphite/SiOx anodes under cyclic aging, using in operando
neutron diffraction,
neutron depth profiling, and X-ray computed
tomography. Prolonged cycling causes lithium loss, observed on the cathode side as reduced NCM unit
cell change during cycling. On the anode side, this loss appears as diminished formation of the fully lithiated LiC6 phase. Differential voltage analysis during aging reveals not only lithium inventory loss but also active anode material loss. Diffraction data confirm this through shifts in the LiC12 transition and LiC6 onset to lower capacities, requiring less lithium to trigger the transitions. Lithium concentration profiles across electrode positions show depletion in the cathode, while elevated concentrations in the anode indicate increased solid-electrolyte interphase formation, suggesting lithium consumed from the cathode deposits on the anode side. CT measurements show that intrinsic inhomogeneities inside the
cells have a stronger influence on the macroscopic structure than aging-induced changes, indicating that the observed capacity fade primarily originates from microscopic degradation processes within the electrodes. Overall, the combined techniques provide direct evidence of lithium loss, active material degradation, and spatially dependent aging mechanisms in large-format cylindrical
cells.
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All authors Thien An Pham, Hannah Bosch, Giovanni Ceccio, Lukas Keller, Hannes Wolf, Nicolas Bucher, Peter Müller-Buschbaum, Ralph Gilles
Computational Physics
Towards Unified AI-Driven Fracture Mechanics: The Extended Deep Energy Method (XDEM)
Yizheng Wang, Yuzhou Lin, Somdatta Goswami et al. (+8 more)
Physics-Informed Neural Networks (PINNs) have recently emerged as powerful tools for solving partial differential equations (PDEs), with the Deep Energy Method (DEM) proving especially effective in fracture mechanics due to its energy-based formulation. Despite these advances, existing DEM approaches require dense collocation near cracks, face stability challenges, and typically treat discrete and continuous fracture models separately. To overcome these limitations, we introduce the Extended Deep Energy Method (XDEM), a unified deep learning framework that incorporates both displacement discontinuities and crack-tip asymptotics in the discrete setting, while flexibly coupling displacement and phase fields in the continuous setting. This integration enables accurate fracture predictions using uniformly distributed, relatively sparse collocation points. Validation across
benchmark problems including stress intensity factor evaluation, straight and kinked crack growth, and complex crack initiation demonstrates that XDEM consistently outperforms standard DEM in accuracy and efficiency. By bridging discrete and phase-field models within a single framework, XDEM establishes a robust foundation for applying AI to fracture mechanics and opens new avenues for predictive modeling in engineering and materials science.
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All authors Yizheng Wang, Yuzhou Lin, Somdatta Goswami, Luyang Zhao, Huadong Zhang, Jinshuai Bai, Cosmin Anitescu, Mohammad Sadegh Eshaghi, Xiaoying Zhuang, Timon Rabczuk, Yinghua Liu
Optics
HAPS Communication Networks: A Tutorial-cum-Survey on Integration with Optical Atmospheric Sensing
Ali Elkhazraji, Mohamed-Slim Alouini and Aamir Farooq
High-Altitude Platform Stations (HAPS) are emerging as key enablers of future non-terrestrial networks (NTNs), supporting gigabit-class free-space optical (FSO) backhaul links while hosting
laser-based sensing payloads. This tutorial and survey reviews recent advances in HAPS optical communication and integration with atmospheric remote sensing via shared optical links. Among several sensing techniques, Differential Absorption
Lidar (DIAL) is identified as most promising due to its range-resolved sensitivity, spectral selectivity, and compatibility with HAPS constraints. A roadmap is outlined for implementing telecom-band DIAL on HAPS alongside high-throughput FSO systems. The paper analyzes architectural and atmospheric advantages of HAPS over terrestrial and satellite nodes, emphasizing spatial-temporal coverage, station-keeping ability, and support for compact
laser payloads such as DIAL, in-situ sensors, and multispectral imagers. It highlights the feasibility of co-locating sensing and communication within a shared optical and power envelope, especially in the telecom C-band (1.53-1.57 um), enabling trace-gas
retrieval (CO2, CH4, N2O, H2S, O3) while maintaining multi-Gbps downlinks. Suitable HAPS architectures (balloons,
UAVs, airships) and use cases are identified where integrated sensing and communication (
ISAC)-enabled HAPS outperform satellites and
UAVs, including greenhouse gas monitoring, disaster response, air-quality mapping, and 6G NTN extensions. A literature survey for 2005-2025 shows HAPS publications have tripled since 2014, indicating rapid growth. The results confirm that optical hardware, favorable transmission windows, and active R&D are positioning HAPS as a persistent stratospheric layer for 6G
ISAC communications and environmental observation.
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Instrumentation and Detectors
Compressive multi-beam scanning transmission electron microscopy
Akira Yasuhara, Takumi Sannomiya and Ryoichi Horisaki
We demonstrate a multi-beam scanning transmission electron microscopy (STEM) imaging that integrates down-sampling with super-resolution image reconstruction via a compressive sensing framework. A custom condenser aperture with six randomly positioned circular holes is employed to produce a multi-beam STEM probe, with the beam shape and distribution tuned through defocus. While the raw multi-beam images exhibit overlapping patterns, reconstruction using Adam optimization and total variation normalization yields high-fidelity images that closely reproduce the original sample structures, even from substantially down-sampled data. The proposed approach offers a pathway toward significant acceleration of such techniques through multibeam sparse sampling and computational reconstruction potentially useful for the analytical scanning methods in general.
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Accelerator Physics
Beam Halo Formation via Longitudinal-Transverse Coupling in Continuous-Wave Photoinjectors
Zhen Zhang, Yuantao Ding, David Cesar et al. (+3 more)
Beam
halo formation poses a critical challenge for high-repetition-rate continuous-wave (CW) free-electron
lasers (FELs), directly affecting beam quality and machine protection, as observed during the LCLS-II commissioning. We identify and experimentally validate a previously unrecognized three-step mechanism for
halo generation in the photoinjector, arising from coupled longitudinal-transverse dynamics in the low-energy beam. Theoretical analysis reveals that (i) the RF buncher induces an energy-radius correlation, (ii) velocity bunching transforms this correlation into hollowed density structures in the bunch head and tail, and (iii) differential overfocusing of these hollowed regions by downstream focusing forms the observed
halo. This mechanism is confirmed by particle-in-
cell simulations and direct experimental measurements, including controlled formation of a core-ring profile via solenoid tuning. The results establish the physical origin of the
halo and demonstrate a mitigation via buncher compression tuning that reduces
halo and downstream loss, supporting sustained high-repetition-rate FEL operation.
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All authors Zhen Zhang, Yuantao Ding, David Cesar, Feng Zhou, Ji Qiang, Zhirong Huang