Physics and Society
Equity Implications of Federal-Local Cost-Sharing in Flood Buyouts: A Game-Theoretic Analysis with Heterogeneous Homeowners
Yuqun Zhou
Climate-driven flood risk increasingly necessitates managed retreat through government buyout programmes, yet empirical evidence documents substantial racial and economic disparities in programme implementation. Here we develop a three-level Stackelberg game to analyse how federal-local cost-sharing arrangements generate inequitable outcomes through strategic interactions among federal authorities, local governments, and heterogeneous homeowners. Our model reveals three distinct mechanisms driving inequity: differential discount rates across income groups, local governments' tax-base preservation incentives, and participation thresholds that exclude fiscally constrained communities. Numerical analysis of 34,493 households across nine flood-prone US regions demonstrates that the current Federal Emergency Management Agency 75/25 cost-sharing arrangement produces a relocation ratio gap of 0.26–low-income households relocate at roughly one-quarter the rate of high-income households. Achieving near-equity requires federal cost shares of at least 85%, though equity-weighted mechanisms can attain similar outcomes at 25% lower cost. These findings provide a theoretical foundation for understanding observed disparities and identify policy levers for more equitable
climate adaptation.
Read the full abstract ▾
General Physics
Schrodinger Was Right!
W. David Wick
Now that we have reached the centennial of Erwin Schrodinger's seminal paper introducing the wavefunction theory of matter, it is right and proper to inquire as to its legacy. It is undeniable that today every paper in atomic physics cites his 1926 equation in the first paragraph. But the philosophy undergirding the wavefunction seems to have fallen into the shadows. And Schrodinger left his program incomplete. I will argue here that recent developments in nonlinear mathematics, including so-called "chaos theory", permit finishing the task. It turns out that one nonlinear addition to his equation from 1926 can resolve both the Measurement Problem and the Randomness Problem. With this emendation, the wavefunction alone suffices to explain the outcomes of many experiments (and it is particles that can be relegated to the shadows).
Read the full abstract ▾
Geophysics
Predicting Neutron Attenuation from Bulk Density and Moisture for Soil Carbon Measurement
William Larsen, Valerie Smykalov, Cristina Castanha et al. (+4 more)
Inelastic
neutron scattering (INS) enables rapid, non-destructive in situ measurements of soil elemental composition over large soil volumes. Standard INS yields bulk elemental concentrations, but spatially resolved measurements require techniques such as Associated Particle Imaging (API), which pairs
neutron detection with coincident alpha detection to reconstruct the location of the
neutron interaction. One of the unique advantages of API is its capability to measure all major soil components simultaneously, allowing for the estimation of both bulk density and water content directly from the measured
neutron-induced gamma-ray spectra. Accurate interpretation of bulk INS-API data depends on correcting for both gamma-ray and
neutron attenuation in soil. Although gamma attenuation can be calculated from known mass attenuation coefficient data and density,
neutron attenuation is more complex, depending on
neutron energy, soil composition, bulk density, and hydrogen content from water and organic matter. We use Monte Carlo simulations of soils with varied compositions, bulk densities, and water contents to model
neutron attenuation and develop a simple predictive model requiring only dry bulk density and volumetric water content. We validate this model experimentally using an INS-API system with controlled soil columns, finding agreement within 10 percent at 30 cm depth. This approach enables practical, field-ready correction of INS-API measurements for
neutron attenuation, laying the groundwork for a self-consistent measurement framework that can address the elemental composition of soil carbon assessments.
Read the full abstract ▾
All authors William Larsen, Valerie Smykalov, Cristina Castanha, Eoin Brodie, Mauricio Ayllon Unzueta, Bernhard Ludewigt, Arun Persaud
Computational Physics
A complete phase-field fracture model for brittle materials subjected to thermal shocks
Bo Zeng and John E. Dolbow
Brittle materials subjected to thermal shocks experience strong temperature gradients that in turn give rise to mechanical stresses that can be large enough to induce fracture. This work presents a complete model for phase-field fracture for coupled thermo-mechanical problems, wherein the bulk material properties, the material strength, and the fracture toughness are specified independently. The capabilities of the model are assessed across a wide span of scenarios in thermo-mechanical fracture, from the propagation of large pre-existing cracks to crack nucleation under spatially uniform states of stress. In particular, we revisit the controlled quenching of glass plates, and demonstrate how the model captures experimentally observed crack patterns across a range of thermal loads. Ceramic disks subjected to infrared radiation are also examined, with the model reproducing both straight cracks in notched specimens and branching in intact specimens. Finally, ceramic pellets subjected to rapid power pulses are examined, with the model explaining experimental transitions from intact to fractured pellets and the important role of material strength. The results demonstrate that the complete phase-field model unifies the treatment of distinct fracture scenarios under thermal shock, surpassing classical approaches and enabling more reliable prediction of brittle fracture in extreme environments.
Read the full abstract ▾
Computational Physics
UniPhy: Unifying Riemannian-Clifford Geometry and Biorthogonal Dynamics for Planetary-Scale Continuous Weather Modeling
Ruiqing Yan, Haoyu Deng, Yuhang Shao et al. (+3 more)
While data-driven weather models have achieved remarkable deterministic accuracy, they fundamentally rely on discrete-time mappings and closed-system assumptions, failing to capture the multi-scale continuous dynamics and thermodynamic openness of the atmosphere. To address these limitations, we propose UniPhy, a continuous-time non-Hermitian neural
stochastic partial differential equation (SPDE) solver. Geometrically, we employ Riemannian-Clifford gauge transformations to flatten planetary heterogeneity, enabling globally consistent operations. Dynamically, we construct non-Hermitian biorthogonal spectral operators integrated with a global flux tracker to capture transient energy growth and open-system exchange. Computationally, by identifying the algebraic associativity of the analytic solution, we reformulate adaptive physical integration as a parallel prefix-sum problem, achieving log-linear sequence parallelism. UniPhy establishes a physically complete foundation model architecture that unifies
geometric adaptivity, thermodynamic consistency, and computational efficiency. Our code is available at <https://github.com/yrqUni/UniPhy>.
Read the full abstract ▾
All authors Ruiqing Yan, Haoyu Deng, Yuhang Shao, Xingbo Du, Jingyuan Wang, Zhengyi Yang
Medical Physics
Time-Resolved Multi-Spectral X-ray Computed Tomography of Cryoprotectant Diffusion Into Biomimetic Material
Alaa M. Ali, Jason T. Parker, Anthony N. Consiglio et al. (+4 more)
Cryopreservation via vitrification requires loading cryoprotective cocktails. Insufficient loading may lead to freezing, precluding successful recovery; overloading is toxic. Yet, existing in situ measurements of cryoprotectant permeation remain largely unvalidated and do not resolve individual cryoprotectant concentrations. We introduce multi-spectral X-ray computed
tomography (MSCT) to noninvasively quantify the spatiotemporal distribution of cryoprotectants diffusing into a tissue mimicking phantom. A developed
photon-energy bin selection
algorithm achieves sensitivity to low contrast cryoprotectants without contrast
agents or fluorescence edges. The technique is validated with a dimethyl sulfoxide, glycerol, and water solution, resolving cryoprotectant volume fractions to within 5% accuracy. We observe heterogeneous
diffusion of the cryoprotectants into the tissue mimicking hydrogel, a phenomenon not observable with conventional techniques. MSCT improves upon existing X ray CT methods because it is not underdetermined for multicomponent solutions and does not implicitly assume homogeneous
diffusion. These advancements enable the systematic development of cryoprotectant loading protocols and provide diagnostics to assess vitrifiability before cryopreservation.
Read the full abstract ▾
All authors Alaa M. Ali, Jason T. Parker, Anthony N. Consiglio, Brooke S. Chang, Linnea Warburton, Boris Rubinsky, Simo A. Mäkiharju
Computational Physics · Cross-listed from stat.ME
Parameter Estimation for Differential Equation Models Using Generalized Profiling: A Computational Tutorial
Matthew J Simpson, James S Bennett and Ruth E Baker
Parameter estimation connects mathematical models to real-world data and decision making across many scientific and industrial applications. Standard approaches such as maximum likelihood estimation and Markov chain Monte Carlo estimate parameters by repeatedly solving the model, which often requires numerical solutions of differential equation models. In contrast, generalized profiling (also called parameter cascading) focuses directly on the governing differential equation(s), linking the model and data through a penalized likelihood that explicitly measures both the data fit and model fit. Despite several advantages, generalized profiling is relatively rarely used in practice. This tutorial-style article outlines a set of self-directed computational exercises that facilitate skills development in applying generalized profiling to a range of ordinary differential equation models. All calculations can be repeated using reproducible open-source Jupyter notebooks that are available on GitHub.
Read the full abstract ▾
Physics and Society
The Spectral Topology of Global Imbalances:A Graph-Theoretic Framework for Systemic Risk in the Balance of Payments
Chandrasekhar Gokavarapu, Sanjeev Kumar Chejarla, Balayya Rajana et al. (+2 more)
We model global balance-of-payments linkages as a directed weighted exposure network and study systemic fragility through the spectrum of the associated operators. From a signed bilateral-flow
matrix, we construct a nonnegative exposure lift, derive a spectral stability criterion for linear propagation, and define node- and edge-level risk measures using Perron-Frobenius geometry, PageRank, and marginal spectral impact. We further give two complementary extensions: a directed
Laplacian formulation for shock
diffusion and a non-backtracking threshold for cascade formation on sparse exposure networks. The framework identifies systemic risk as a topological property of the global network rather than a collection of isolated country indicators, and it yields mathematically explicit diagnostics for resilience, concentration of stress, and targeted stabilization.
Read the full abstract ▾
All authors Chandrasekhar Gokavarapu, Sanjeev Kumar Chejarla, Balayya Rajana, V. N. S. R. Murthy Duggirala, Srinivasulu Chittaru
Physics and Society
CoolPath Tool: A Thermal Comfort Path Planning Tool for Urban Mobility
Aditya Patel, Naveen Sudharsan, Trevor Brooks et al. (+5 more)
Extreme heat poses a growing challenge for active transportation in cities where conventional weather reporting (e.g. limited air temperature measurement for the whole city) fails to capture the large microclimate variations that pedestrians and cyclists experience. We present a novel walking and biking route planning tool (”CoolPath Tool”) that selects paths based on thermal comfort using the Universal Thermal
Climate Index (UTCI) rather than just distance or travel time. This system combines high-resolution thermal modeling with real-time route mapping. We generate city-scale UTCI maps using GPU version of
Solar and LongWave Environmental Irradiance Geometry (SOLWEIG) model, to account for urban features (buildings, trees,) and weather conditions. The urban features are pre-mapped using satellite data products and the routes are the roadways. For any given origin and destination, our tool calculates the average UTCI along each possible route and recommends the ”coolest” route, i.e. the path with the lowest heat stress (often the most shaded or otherwise thermally comfortable), while still being reasonably direct. We demonstrate this in a case study for Austin, Texas. The approach identifies routes that significantly reduce pedestrians' heat exposure (often recommending routes with a much larger proportion of shade). Such thermally-informed route planning has important public well-being, and economic implications: by helping people avoid dangerous heat hotspots and sun-exposed areas, it can reduce the risk of heat-related illness and make walking or biking a safer choice even on hot days. This tool is part of the Austin Digital Twin efforts developed as part the UT-City CoLab needs. The work while demonstrated for Austin, TX is scalable, and transferrable to other cities globally.
Read the full abstract ▾
All authors Aditya Patel, Naveen Sudharsan, Trevor Brooks, Harsh Kamath, Dru Crawley, Marc Coudert, Zach Baumer, Dev Niyogi
Instrumentation and Detectors
Development and extension of a monochromatic neutron beamline for neutron polarimetry device characterization at the Spallation Neutron Source
Kavish Imam, Vince Cianciolo, Brad Filippone et al. (+8 more)
The precise manipulation and analysis of
neutron spin states are foundational for a wide range of physics experiments, from fundamental
symmetry tests to materials science. To enable systematic characterization of
neutron polarimetry devices, we have constructed and extended a monochromatic
neutron beamline at the Spallation
Neutron Source, Oak Ridge National Laboratory. The beamline delivers monochromatic
neutrons and provides a flexible platform for deploying and evaluating advanced
neutron spin manipulation instruments. We describe the design and commissioning of the extended beamline and present a proof-of-concept
neutron polarimetry study using three devices: a supermirror
neutron polarizer, a Mezei
spin flipper, and an in situ
neutron 3He
spin analyzer system. Performance metrics, optimization strategies, and systematic effects are discussed, demonstrating the beamline utility for
neutron instrumentation testing. These results establish the extended monochromatic beamline as a useful resource for the development and validation of
neutron polarimetry technologies.
Read the full abstract ▾
All authors Kavish Imam, Vince Cianciolo, Brad Filippone, Nadia Fomin, Geoffrey Greene, Chenyang Jiang, Jordan O'Kronley, Seppo Penttila, Josh Pierce, John Ramsey, Isaiah Wallace
Computational Physics · Cross-listed from astro-ph.IM
aurel: A Python package for automatic relativistic calculations
Robyn L. Munoz, Christian T. Byrnes and Will J. Roper
aurel is an open-source Python package designed to automatically calculate relativistic quantities. It uses an efficient, flexible and user-friendly caching and dependency-tracking system, ideal for managing the highly nonlinear nature of general relativity. The package supports both symbolic and numerical calculations. The symbolic part extends SymPy with additional tensorial calculations. The numerical part computes a wide range of tensorial quantities, such as
curvature, matter kinematics and much more, directly from any spacetime and matter data arrays using finite-difference methods. Inputs can be either generated from analytical expressions or imported from Numerical Relativity (NR) simulations, with helper functions provided to read in data from standard NR codes. Given the increasing use of NR, aurel offers a timely post-processing tool to support the popularisation of this field.
Read the full abstract ▾
Biological Physics · Cross-listed from cond-mat.soft
Plasticity, hysteresis, and recovery mechanisms in spider silk fibers
Renata Olivé, José Pérez-Riguero and Noy Cohen
Spider silk is a remarkable biomaterial with exceptional stiffness, strength, and toughness stemming from a unique microstructure. While recent studies show that silk fibers exhibit plasticity, hysteresis, and recovery under cyclic loading, the underlying microstructural mechanisms are not yet fully understood. In this work, we propose a mechanism explaining the loading-unloading-relaxation response through microstructural
evolution: initial loading distorts intermolecular bonds, resulting in a linear elastic regime. Upon reaching the yield stress, these bonds dissociate and the external load is transferred to the polypeptide chains, which deform entropically to allow large deformations. Unloading is driven by entropic shortening until a traction free state with residual stretch is achieved. Subsequently, the fiber recovers as chains reorganize and bonds reform, locking the microstructure into a new stable
equilibrium that increases stiffness in subsequent cycles. Following these mechanisms, we develop a microscopically motivated, energy-based model that captures the macroscopic response of silk fibers under cyclic loading. The response is decoupled into two parallel networks: (1) an elasto-plastic network of inter- and intramolecular bonds governing the initial stiffness and yield stress, and (2) an elastic network of entropic chains that enable large deformations. The model is validated against experimental data from Argiope bruennichi dragline silk. The findings from this work are three-fold: (1) explaining the mechanisms that govern hysteresis and recovery and linking them to microstructural
evolution; (2) quantifying the recovery process of the fiber, which restores and enhances mechanical properties; and (3) establishing a predictive foundation for engineering synthetic fibers with customized properties.
Read the full abstract ▾
Biological Physics
Effects of PLGA coating on biological and mechanical behaviors of tissue engineering scaffolds
A.M. Maadani, F. Davoodian and E. Salahinejad
Scaffolds have a key role in the clinical success of tissue engineering for the regeneration of damaged tissues. Their bio-performance is often described as the extent to which they can provide an extracellular
matrix-like environment for
cells embedded where their function and growth can effectively continue. For this purpose, tissue engineering scaffolds should exhibit biodegradability, biocompatibility, bioactivity, delivery, and mechanical performance. The use of
polymer coatings, especially poly(lactic-co-glycolic acid) (PLGA), on tissue engineering scaffolds has been found to be one of the most effective methods to improve the scaffold properties. This paper reviews the techniques used to coat tissue engineering scaffolds with PLGA and its effects on the mechanical characteristics, biodegradability, biocompatibility, Molecular delivery, and osteointegration of the scaffolds. It is concluded that apart from apatite-formation ability, all bio-functionalities can be tuned through PLGA coatings. This reflects the great potential of this modification approach to be used in tissue regeneration and therapeutic delivery applications.
Read the full abstract ▾
Data Analysis and Statistics
Comparison of Image Processing Models in Quark Gluon Jet Classification
Daeun Kim, Jaeyoon Cho, Jiwon Lee et al. (+5 more)
Quark-
gluon discrimination provides a useful test case for studying how different machine-learning architectures learn the spatial structure of QCD radiation. In this work, we compare convolutional neural network (CNN),
Vision Transformers (ViT), and hierarchical Swin
Transformers using the same three-channel jet-image representation, consisting of charged-particle momentum, neutral-particle momentum, and charged-particle multiplicity from PYTHIA 8 jets. We study their performance for different training-set sizes and fine-tuning configurations, with particular
attention to the role of local and global information in the jet images. CNN and Swin models consistently perform better than ViT in the cases studied. Since both CNN and Swin retain a strong local component in their architectures, this suggests that local jet substructure plays an important role in
quark-
gluon discrimination. The performance of the hierarchical Swin model also suggests that combining local features over larger spatial scales is useful. Block-wise fine-tuning improves the performance of the
Transformer models, although the improvement becomes smaller and the training less stable as more blocks are unfrozen. We also find that self-supervised Momentum Contrast (MoCo) pretraining improves the model initialization, particularly when the amount of labeled training data is limited. Based on these observations, we developed a smaller Swin model adopted to the jet-image representation used in this study. It achieves comparable performance with substantially fewer parameters. The results show that it is important to adapt the model architecture and training procedure to the specific input characteristics of High Energy Physics (HEP) data when applying
vision models in HEP.
Read the full abstract ▾
All authors Daeun Kim, Jaeyoon Cho, Jiwon Lee, Wonjun Jeong, Hyeongwoo Noh, Giyeong Kim, Seunghwan Yang, MinJung Kweon
Physics and Society · Cross-listed from cs.CL
Culturally Grounded Personas in Large Language Models: Characterization and Alignment with Socio-Psychological Value Frameworks
Candida M. Greco, Lucio La Cava and Andrea Tagarelli
Despite the growing utility of Large
Language Models (LLMs) for simulating human behavior, the extent to which these synthetic personas accurately reflect world and moral value systems across different cultural conditionings remains uncertain. This paper investigates the
alignment of synthetic, culturally-grounded personas with established frameworks, specifically the World Values Survey (WVS), the Inglehart-Welzel Cultural Map, and Moral Foundations Theory. We conceptualize and produce LLM-generated personas based on a set of interpretable WVS-derived variables, and we examine the generated personas through three complementary lenses: positioning on the Inglehart-Welzel map, which unveils their interpretation reflecting stable differences across cultural conditionings; demographic-level consistency with the World Values Survey, where response distributions broadly track human group patterns; and moral profiles derived from a Moral Foundations questionnaire, which we analyze through a culture-to-morality mapping to characterize how moral responses vary across different cultural configurations. Our approach of culturally-grounded persona generation and analysis enables evaluation of cross-cultural structure and moral variation.
Read the full abstract ▾
Medical Physics
Body Fat, Skin Tone, and the Accuracy of Smartwatch Caloric Expenditure Estimates
Jason Kostrna, Ekaterina Oparina, Andres J. Rodriguez et al. (+4 more)
Smartwatches are widely used to estimate caloric expenditure for weight management, clinical decision making, and public health monitoring. These devices combine photoplethysmography, accelerometry, and proprietary
algorithms. However, prior studies report substantial error, and the influence of moderators such as skin tone and body fat percentage (BF) remains underexamined. This study tested whether smartwatch brand, BF, and Fitzpatrick skin type (III to V) predict caloric expenditure error relative to indirect calorimetry. Fifty eight Hispanic adults completed a single laboratory visit including a ten minute recumbent cycling protocol with alternating two minute moderate and vigorous intensity intervals, bracketed by rest and recovery. Participants wore four consumer devices: Apple Watch Series 8, Fitbit Sense 2, Samsung
Galaxy Watch 5, and Garmin Forerunner 955. Energy expenditure was measured using a COSMED K5 metabolic system. After device specific data quality filtering, valid participant device pairings ranged from 44 to 52 per brand. One sample tests showed significant mean bias for three devices: Apple, Garmin, and Samsung. Fitbit showed no significant overall bias, although this depended on device specific outlier removal. Mean bias varied by brand, with Garmin and Samsung showing the largest overestimations. Mixed effects models revealed significant effects of device and BF, as well as a device by BF interaction, with physical activity energy expenditure error increasing as adiposity increased. Overall, common smartwatches substantially misestimate caloric expenditure compared with indirect calorimetry. Error varies by brand and worsens with higher body fat, highlighting limitations of current consumer wearables and the need for improved accuracy across diverse body types.
Read the full abstract ▾
All authors Jason Kostrna, Ekaterina Oparina, Andres J. Rodriguez, JunZhu Pei, Ajmal Ajmal, Cristina Palacios, Jessica C. Ramella-Roman
Physics and Society
Convergent Discovery of Critical Phenomena Mathematics Across Disciplines
Bruce Stephenson and Robin Macomber
Techniques for detecting critical phenomena – phase transitions where correlation length diverges and small perturbations have large effects – have been developed across multiple fields over nine decades. We survey between six and twelve disciplines (depending on classification criteria) where researchers derived functionally corresponding measures of correlation scaling, with little documented awareness of each other's work. The physicist's correlation length $\xi$, the cardiologist's DFA scaling exponent $\alpha$, the financial analyst's Hurst exponent $H$, and the machine learning engineer's spectral radius $\chi$ all detect critical signatures under different notation. We classify each surveyed domain as independent derivation, domain transfer, or empirical precursor, and present citation network evidence that cross-domain citations remained significantly below random-mixing expectations (under a simple null model of domain mixing) during the formative period (1987–2010). The framework that motivated this investigation – derived from distributed systems engineering and presented in the Supplementary Material – is excluded from the survey count given the authors' dual role. Building on prior syntheses – notably Sornette's 2004 textbook – this paper contributes a taxonomy of discovery types and quantitative documentation of the convergence pattern.
Read the full abstract ▾
Medical Physics
PPG-Based Heart Rate Accuracy in Diverse Populations: Investigating Inequities Across Body Composition and Skin Tones
Kostrna, Jason, Oparina et al. (+11 more)
Wearable devices are widely used for heart rate (HR) monitoring, yet their accuracy across diverse body compositions and skin tones remains uncertain. This study evaluated four wrist worn devices (Apple, Fitbit, Samsung, Garmin) in 58 Hispanic adults with Fitzpatrick skin types III to V during a cycling protocol alternating moderate (0.64 to 0.76 HRmax) and vigorous (0.77 to 0.95 HRmax) intensities. Criterion HR was obtained using a Polar H10 ECG, and accuracy was assessed using mean absolute error, mean absolute percentage error (MAPE), bias, and intraclass correlation coefficients. All devices showed significant deviation from criterion measures. Apple and Garmin demonstrated the lowest error, whereas Fitbit and Samsung exhibited greater inaccuracies. Higher BMI and darker skin tones were associated with increased MAPE. These biases disproportionately affect higher risk populations, underscoring the need for improved
algorithms to ensure equitable health monitoring.
Read the full abstract ▾
All authors Kostrna, Jason, Oparina, Ekaterina, Palacios, Cristina, Rodriguez, Andres J, Pei, JunZhu, Ajmal, Ajmal, Ramella-Roman, Jessica C
Atomic Physics
Low energy elastic scattering of hydrogen, deuterium and tritium on helium isotopes
B.J.P. Jones, A. Negi and A. Semakin
Motivated by the needs of atomic tritium sources for
neutrino mass experiments and Doppler-free two-
photon 1S-2S spectroscopy in atomic deuterium and tritium, we present calculations of energy-dependent elastic scattering cross sections of hydrogen isotopes (H, D and T) on helium isotopes ($^3$He and $^4$He) in the temperature range 1~mK to 300~K. The tritium-on-helium cross sections are found to be enhanced over their hydrogen-on-helium counterparts by a near-threshold resonant s-wave bound state at low energy, similar to one that has been predicted in the triplet T-T system. While the energy-dependent cross sections span a wide range at low energy due to this s-wave enhancement, they tend toward a common value at high energy where the scattering becomes effectively
geometric in nature.
Read the full abstract ▾
Instrumentation and Detectors
High-resolution calorimetric sample platforms for cryogenic thermodynamic studies with multimodal synchrotron x-ray compatibility
U. Patel, H. Zheng, J. L. McChesney et al. (+3 more)
X-ray calorimetric sample platforms combining specific heat and synchrotron x-ray measurements provide a powerful means to investigate fundamental material properties. Calorimeter
cell designs featuring a compact heater and thermometer arranged in a sidecar geometry, with the sample positioned directly above the heater at the center of a silicon nitride membrane, are presented. High-yield, wafer-level batch fabrication of precision calorimetric sensor chips, beamline and laboratory cryostat plugins with sensor mounting and packaging are described. Using our calorimetric sensors, we present specific heat measurements on samples with masses ranging from 4 {\mu}g to 145 {\mu}g. The sample and reference
cells are characterized with relaxation and ac steady-state measurements. The thermal response is captured using lock-in detection at carefully optimized measurement frequencies, with phase-lag correction ensuring precise extraction of heat capacity. The reference
cell's background heat capacity was measured to be under 320 nJ/K at 300 K, decreasing to just 0.4 nJ/K at 0.7 K. The calorimeter performance is illustrated by studying the specific heat of small samples of superconducting Nb and a 4 {\mu}g piece of superconducting Al under different
magnetic field strengths. The determination of fundamental thermodynamic quantities from low-temperature electronic and
lattice specific heat measurements is discussed. These versatile, high-throughput sample platforms are engineered for small-sample calorimetry across a broad cryogenic temperature range, and they support scalable integration with a wide range of cryostats, including beamline cryostats at the Advanced
Photon Source. They accommodate
multimodal geometries and enable operation under ultra-high
vacuum, millikelvin temperatures,
magnetic fields, and x-ray illumination.
Read the full abstract ▾
All authors U. Patel, H. Zheng, J. L. McChesney, U. Welp, Z. Islam, A. Miceli
Computational Physics · Cross-listed from gr-qc
Revisiting the energy-momentum squared gravity
Mihai Marciu
In this paper we have revisited the energy-momentum squared
gravity theory, by taking into account the second derivative of the matter Lagrangian with respect to the metric, encapsulating relations originated from thermodynamical grounds. After obtaining the scalar tensor representation of the energy-momentum squared
gravity with the new corrections, we have analyzed the physical implications by relying on the linear stability theory. The results show that the current cosmological system is compatible with the expansion of the Universe for some specific matter Lagrangians, explaining the emergence of matter domination era, approaching the late time accelerated expansion era close to the de-Sitter phenomenology.
Read the full abstract ▾
Physics and Society
Correcting temporal bias in mobility data using time-use surveys
Sarah A. Sanchez, Hamish Gibbs, Takahiro Yabe et al. (+2 more)
GPS mobility data is a valuable source of behavioral measurement which is subject to systematic biases including the over- or under-representation of demographic groups, and variations in the quality of location sampling across time. In this paper, we address the challenge of temporal bias in mobility data, which can skew the representation of mobility behaviors due to the event-based nature of location data sampling. We use the American Time Use Survey (ATUS) to assess the accuracy of a place-based measure of economic segregation drawn from large-scale mobility data across 11 U.S. cities. We show that comparisons with high quality time use surveys such as the ATUS can validate behavioral insights from mobility data, while quantifying uncertainty and highlighting areas of relative instability in analytical findings. We also propose a temporal re-weighting method that can complement existing bias-mitigation techniques to improve the accuracy of conclusions drawn from GPS-based mobility data.
Read the full abstract ▾
All authors Sarah A. Sanchez, Hamish Gibbs, Takahiro Yabe, Daniel T. O'Brien, Esteban Moro
Space Physics · Cross-listed from astro-ph.SR
Conversion Layer Controls the Evolution of Magnetic Deflections Near the Alfven Surface
Dominic Payne, Mojtaba Akhavan-Tafti, Joshua Goodwill et al. (+10 more)
We examine the statistics of Alfvenic deflections in both sub-Alfvenic and super-Alfvenic
solar wind with particular focus on a common parameter that underlies the definition of switchbacks: the
magnetic deflection angle. Our findings are in general agreement with earlier studies that suggest
magnetic deflection angles > 90 degrees are very unlikely to occur in sub-Alfvenic regimes. We find that their upper limit exhibits an identifiable trend with the Alfven Mach number Ma, suggesting that gradual steepening of Alfvenic deflections with increasing Ma is a plausible mechanism controlling deflection angles in the young
solar wind. Further analysis reveals that large velocity fluctuations tend to be important in the largest sub-Alfvenic
magnetic deflections with increasing contributions from the parallel component very close to Ma = 1, while virtually no
magnetic deflections in the super-Alfvenic regime exhibit such large velocity perturbations. We also determine the local ratio of radial Poynting flux SR to kinetic energy flux KR and find that large sub-Alfvenic deflection angles tend to be dominated by SR, while super-Alfvenic deflections are eventually dominated by the KR associated with the radial
solar wind flow. Our results show that within the vicinity of the Alfven surface (where Ma = 1), there is a critical region of parameter space within which velocity deflections approach the Alfven velocity and KR/SR is close to unity. We refer to this region (where | log10(Ma)| < 0.2) as the conversion layer. The conversion layer may play a significant role in the
evolution of
magnetic defections by providing the medium for converting
magnetic energy to particle energy and likely driving the formation of
magnetic switchbacks in super-Alfvenic
solar wind.
Read the full abstract ▾
All authors Dominic Payne, Mojtaba Akhavan-Tafti, Joshua Goodwill, Samuel Badman, Riddhi Bandyopadhyay, Subash Adhikari, William Matthaeus, Gary Zank, Chen Shi, Michael Stevens, Roberto Livi, Yeimy Rivera, Kristoff Paulson
Geophysics · Cross-listed from math.AP
A Generalized Analytical Heat Transfer Model for Enhanced Geothermal Systems: Capturing Fracture Interactions and Correcting Classical Optimistic Predictions
Nelson Barros-Galvis or Christine Ehlig-Economides or Cristi Darley Guevara
Numerical analytical heat transfer models play a critical role in geothermal design and feasibility studies. Classical solutions, such as those proposed by Gringarten et al. 1975, rely on simplified assumptions and systematically overestimate thermal performance, which can lead to unrealistic engineering decisions. This study presents a generalized analytical model for enhanced geothermal systems that explicitly captures thermal interactions between fractures while preserving analytical tractability. The formulation is based on Greenś functions and reproduces realistic thermal behavior under conditions representative of fractured geothermal reservoirs. The resulting solution is computationally efficient and sufficiently simple to be implemented directly in standard spreadsheets, without requiring Laplace space transformations or numerical inversion
algorithms. The model is validated against numerical simulations performed using CMG
STARS and Volsung software, showing close agreement in temperature
evolution, including the effects of interacting fractures. Compared with classical analytical approaches, the proposed model corrects optimistic bias and provides more reliable predictions of production temperature and energy recovery. These results have direct implications for geothermal feasibility studies, well design, and power
forecasting, effectively bridging the gap between legacy analytical models and numerical or commercial engineering tools. Building on the analytical framework originally introduced by Gringarten et al. 1975, the proposed formulation generalizes classical heat transfer solutions to account for fracture interaction while retaining analytical simplicity and practical applicability.
Read the full abstract ▾
Optics
Toward all-optical unsupervised Hebbian learning in deep photonic neuromorphic networks
Xi Li, Disha Biswas, Peng Zhou et al. (+4 more)
We propose a deep photonic neuromorphic network (PNN) architecture based on phase-change material (PCM) synapses and local optical feedback for online, unsupervised Hebbian learning. The proposed architecture combines optical vector-
matrix multiplication, non-volatile PCM synaptic weighting, and local coincidence-driven synaptic adaptation within a multilayer photonic crossbar framework compatible with photonic integrated
circuits. Unlike conventional PNNs that rely on externally computed gradients, repeated optical-electrical-optical conversions, or global backpropagation, the proposed framework employs local Hebbian learning governed directly by correlated pre- and post-synaptic optical activity. To investigate the feasibility of the proposed learning mechanism, we implemented the PNN design using fiber-optic components, programmable variable optical attenuators, and real-time software control that incorporates PCM thermal dynamics. Supervised and unsupervised learning behaviors were experimentally evaluated under both offline and online learning conditions using representative image-recognition tasks. The experimental results demonstrate adaptive synaptic
evolution, successful optical inference, and autonomous pattern encoding through local Hebbian learning under realistic fiber-optic hardware conditions. These results establish a pathway toward future integrated photonic neuromorphic systems capable of scalable and energy-efficient online Hebbian learning.
Read the full abstract ▾
All authors Xi Li, Disha Biswas, Peng Zhou, Wesley H. Brigner, Anna Capuano, Joseph S. Friedman, Qing Gu
Atmospheric and Oceanic Physics · Cross-listed from cs.LG
Conformal Prediction for Generative Models via Adaptive Cluster-Based Density Estimation
Qidong Yang, Qianyu Julie Zhu, Jonathan Giezendanner et al. (+3 more)
Conditional generative models map input variables to complex, high-dimensional distributions, enabling realistic sample generation in a diverse set of domains. A critical challenge with these models is the absence of calibrated uncertainty, which undermines trust in individual outputs for high-stakes applications. To address this issue, we propose a systematic conformal prediction approach tailored to conditional generative models, leveraging density estimation on model-generated samples. We introduce a novel method called CP4Gen, which utilizes clustering-based density estimation to construct prediction sets that are less sensitive to outliers, more interpretable, and of lower structural complexity than existing methods. Extensive experiments on synthetic
datasets and real-world applications, including
climate emulation tasks, demonstrate that CP4Gen consistently achieves superior performance in terms of prediction set volume and structural simplicity. Our approach offers practitioners a powerful tool for uncertainty estimation associated with conditional generative models, particularly in scenarios demanding rigorous and interpretable prediction sets.
Read the full abstract ▾
All authors Qidong Yang, Qianyu Julie Zhu, Jonathan Giezendanner, Youssef Marzouk, Stephen Bates, Sherrie Wang
Biological Physics
Distinguishable spreading dynamics in microbial communities
Meiyi Yao, Joshua M. Jones, Joseph W. Larkin et al. (+1 more)
A packed community of exponentially proliferating microbes will spread in size exponentially. However, due to nutrient depletion, mechanical constraints, or other limitations, exponential proliferation is not indefinite, and the spreading slows. Here, we theoretically explore a fundamental question: is it possible to infer the dominant limitation type from the spreading dynamics? Using a continuum active fluid model, we consider three limitations to
cell proliferation: intrinsic growth arrest (e.g., due to sporulation), pressure from other
cells, and nutrient access. We find that memoryless growth arrest still results in superlinear (accelerating) spreading, but at a reduced rate. In contrast, pressure-limited growth results in linear (constant-speed) spreading in the long-time limit. We characterize how the expansion speed depends on the maximum growth rate, the limiting pressure value, and the effective fluid friction. Interestingly, nutrient-limited growth results in a phase transition: depending on the nutrient supply and how efficiently nutrient is converted to biomass, the spreading can be either superlinear or sublinear (decelerating). We predict the phase boundary in terms of these parameters and confirm with simulations. Thus, our results suggest that when an expansion slowdown is observed, its dominant cause is likely nutrient depletion. More generally, our work suggests that
cell-level growth limitations can be inferred from population-level dynamics, and it offers a methodology for connecting these two scales.
Read the full abstract ▾
All authors Meiyi Yao, Joshua M. Jones, Joseph W. Larkin, Andrew Mugler
Instrumentation and Detectors · Cross-listed from quant-ph
Three-dimensional squeezing of optically levitated nanospheres
Giacomo Marocco, David C. Moore and Daniel Carney
We propose a protocol to measure impulses beyond the standard
quantum limit. The protocol reduces noise in all three spatial dimensions and consists of squeezing a mechanical system's state via a series of jumps in the frequency of the harmonic potential. We quantify how decoherence in a realistic system of an optically levitated, dielectric nanoparticle limits the ultimate sensitivity. We predict that $\sim$10 dB of squeezing is achievable with current technology, enabling
quantum-enhanced detection of weak impulses.
Read the full abstract ▾
Optics
Time-domain optical coherence tomography at 2 $\mu\mathrm{m}$ using GaSb-based broadband superluminescent diode
Ifte Khairul Aam Bhuiyan, Alejandro Martinez Jimenez, Ramona Cernat et al. (+8 more)
We report a time-domain optical coherence
tomography (TD-OCT) system operating in the 2 $\mu\mathrm{m}$ spectral region, enabled by a GaSb-based superluminescent diode (SLD). The spectrum emitted by the SLD exhibits a full-width half-maximum (FWHM) of $\sim$80 nm centred near 2.1 $\mu\mathrm{m}$. For OCT operation, stable amplified spontaneous emission with low spectral ripple ($<20\%$) is maintained at drive currents below 150 mA. The SLD is fiber coupled and integrated into a fiber-based Michelson interferometer. In the OCT system, the measured coherence envelope yields an axial resolution of approximately 300 $\mu$m in air and enables depth-resolved imaging of scattering paint-based coating samples. In contrast to OCT implementations at 2 $\mu\mathrm{m}$ wavelength region that commonly rely on supercontinuum sources, the use of GaSb-based SLDs offers a compact practical alternative, leveraging the maturity and scalability of electrically driven
semiconductor light sources packaged in a standard "butterfly" module. This report represents the first demonstration of TD-OCT imaging at 2 $\mu\mathrm{m}$ using a GaSb-based SLD source and establishes its suitability for compact and scalable mid-IR OCT instrumentation targeting non-biological, low-water-content materials.
Read the full abstract ▾
All authors Ifte Khairul Aam Bhuiyan, Alejandro Martinez Jimenez, Ramona Cernat, Adrian Fernandez Uceda, Joonas Hilska, Markus Peil, Manuel Jorge Marques, George Dobre, Jukka Viheriala, Adrian Podoleanu, Mircea Guina