arXiv Physics @arxiv_physics@qoto.org

A current-biased kinetic inductance detector (CB-KID) is a novel superconducting detector to construct a neutron transmission imaging system. The characteristics of a superconducting neutron detector have been systematically studied to improve spatial resolution of our CB-KID neutron detector. In this study, we investigated the distribution of spatial resolutions under different operating conditions and examined the homogeneity of spatial resolutions in the detector in detail. We used a commercial standard Gd Siemens-star pattern as a conventional method to estimate the spatial resolution, and a lab-made 10B-dot array intended to examine detailed profiles on a distribution of spatial resolutions. We found that discrepancy in propagation velocities in the detector affected the uniformity of the spatial resolutions in neutron imaging. We analyzed the ellipsoidal line profiles along the circumferences of several different test circles in the Siemens-star image to find a distribution of spatial resolutions. Note that we succeeded in controlling the detector temperature precisely enough to realize stable propagation velocities of the signals in the detector to achieve the best spatial resolution with a delay-line CB-KID technique.

Suspensions of motile microorganisms can spontaneously give rise to large scale fluid motion, known as bioconvection, which is characterized by dense, cell-rich downwelling plumes interspersed with broad upwelling regions. In this study, we investigate bioconvection in shallow suspensions of Chlamydomonas reinhardtii cells confined within spiral-shaped boundaries, combining detailed experimental observations with 3D simulations. Under open liquid-air interface conditions, cells accumulate near the surface due to negative gravitaxis, forming spiral shaped density patterns that subsequently fragment into lattice-like structures and give rise to downwelling plumes. Space-time analyses reveal coherent rotational dynamics, with inward-moving patterns near the spiral core and bidirectional motion farther from the center. Introducing confinement by sealing the top boundary with an air-impermeable transparent wall triggers striking transitions in the bioconvection patterns, driven by oxygen depletion: initially stable structures reorganize into new patterns with reduced characteristic wavelengths. Complementary 3D simulations, based on the incompressible Navier-Stokes equations and incorporating negative buoyancy and active stress from swimming cells, capture the initial pattern formation and its subsequent instability, reproducing the fragmentation of spiral-shaped accumulations into downwelling plumes and the emergence of strong vortical flows, nearly an order of magnitude faster than individual cell swimming speeds. However, these models do not capture the oxygen-driven pattern transitions observed experimentally. Our findings reveal that confinement geometry, oxygen dynamics, and metabolic transitions critically govern bioconvection pattern evolution, offering new strategies to control microbial self-organization and flow through environmental and geometric design.

Background: Adnexal masses are heterogeneous and have varied sonographic presentations, making them difficult to diagnose correctly. Purpose: Our study aimed to develop an innovative hybrid artificial intelligence/computer aided diagnosis (AI/CADx)-based pipeline to distinguish between benign and malignant adnexal masses on ultrasound imaging based upon automatic segmentation and echogenic-based classification. Methods: The retrospective study was conducted on a consecutive dataset of patients with an adnexal mass. There was one image per mass. Mass borders were segmented from the background via a supervised U-net algorithm. Masses were spatially subdivided automatically into their hypo- and hyper-echogenic components by a physics-driven unsupervised clustering algorithm. The dataset was separated by patient into a training/validation set (95 masses; 70%) and an independent held-out test set (41 masses; 30%). Eight component-based radiomic features plus a binary measure of the presence or absence of solid components were used to train a linear discriminant analysis classifier to distinguish between malignant and benign masses. Classification performance was evaluated using the area under the receiver operating characteristic curve (AUC), along with sensitivity, specificity, negative predictive value, positive predictive value, and accuracy at target 95% sensitivity. Results: The cohort included 133 patients with 136 adnexal masses. In distinguishing between malignant and benign masses, the pipeline achieved an AUC of 0.90 [0.84, 0.95] on the training/validation set and 0.93 [0.83, 0.98] on the independent test set. Strong diagnostic performance was observed at the target 95% sensitivity. Conclusions: A novel hybrid AI/CADx echogenic components-based ultrasound imaging pipeline can distinguish between malignant and benign adnexal masses with strong diagnostic performance.

Microtubules (MTs) are dynamic protein filaments essential for intracellular organization and transport, particularly in long-lived cells such as neurons. The plus and minus ends of neuronal MTs switch between growth and shrinking phases, and the nucleation of new filaments is believed to be regulated in both healthy and injury conditions. We propose stochastic and deterministic mathematical models to investigate the impact of filament nucleation and length-regulation mechanisms on emergent properties such as MT lengths and numbers in living cells. We expand our stochastic continuous-time Markov chain model of filament dynamics to incorporate MT nucleation and capture realistic stochastic fluctuations in MT numbers and tubulin availability. We also propose a simplified partial differential equation (PDE) model, which allows for tractable analytical investigation into steady-state MT distributions under different nucleation and length-regulating mechanisms. We find that the stochastic and PDE modeling approaches show good agreement in predicted MT length distributions, and that both MT nucleation and the catastrophe of large-length MTs regulate MT length distributions. In both frameworks, multiple mechanistic combinations achieve the same average MT length. The models proposed can predict parameter regimes where the system is scarce in tubulin, the building block of MTs, and suggest that low filament nucleation regimes are characterized by high variation in MT lengths, while high nucleation regimes drive high variation in MT numbers. These mathematical frameworks have the potential to improve our understanding of MT regulation in both healthy and injured neurons.

A new model to study the dynamics of relativistic quantum plasmas using the quantum electrodynamical (QED) approach has been constructed to analyze the quantum effects, relativistic corrections, and electromagnetic interactions. Considering the covariant Lagrangian function and EulerLagrange equation, the equations of motion have been established describing the interaction of strong electromagnetic waves in plasma. These equations of motion constitute a model for the propagation of relativistic laser pulse through high density quantum plasma. Our model specifically takes the effects of four spin and four velocity into account during the interaction process. This model is applicable to high density plasmas in all ranges of electromagnetic fields which includes astrophysical environments, high power laser plasma interactions, etc.

This study investigates the radial densification of spruce wood using explicit Finite Element Method simulations, focusing on the effects of various densification protocols. These protocols include quasi-static compression, oscillatory excitation, and self-densification through shrinking hydrogel fillings and their impact on the morphogenesis of folding patterns across different tissue types. The simulations incorporate the an isotropic mechanical behavior of wood tracheid walls and account for moisture and delignification effects using a hierarchical approach. Our results reveal the technological potential of targeted densification in creating tailored density profiles that enhance stiffness and strength. These insights offer valuable guidance for optimizing densification processes in practical applications.

Purpose: Accurately classifying tissue margins during cancer surgeries is crucial for ensuring complete tumor removal. Rapid Evaporative Ionization Mass Spectrometry (REIMS), a tool for real-time intraoperative margin assessment, generates spectra that require machine learning models to support clinical decision-making. However, the scarcity of labeled data in surgical contexts presents a significant challenge. This study is the first to develop a foundation model tailored specifically for REIMS data, addressing this limitation and advancing real-time surgical margin assessment. Methods: We propose FACT, a Foundation model for Assessing Cancer Tissue margins. FACT is an adaptation of a foundation model originally designed for text-audio association, pretrained using our proposed supervised contrastive approach based on triplet loss. An ablation study is performed to compare our proposed model against other models and pretraining methods. Results: Our proposed model significantly improves the classification performance, achieving state-of-the-art performance with an AUROC of $82.4\% \pm 0.8$. The results demonstrate the advantage of our proposed pretraining method and selected backbone over the self-supervised and semi-supervised baselines and alternative models. Conclusion: Our findings demonstrate that foundation models, adapted and pretrained using our novel approach, can effectively classify REIMS data even with limited labeled examples. This highlights the viability of foundation models for enhancing real-time surgical margin assessment, particularly in data-scarce clinical environments.

Plasmonic nanostructures provide local field enhancement to be used as efficiency-boosting tools in fluorescence-based applications. For photostable quantum dots (QDs) to have enhanced emission, their size and exact location in the proximity of plasmonic nanostructure become key parameters while constructing light emitting devices. However, plasmonic nanostructures mostly suffer from non-radiative energy transfer at close proximity, which hinders the ultimate performance of fluorophores. In this work, we provided critical interparticle distances through finite difference time domain (FDTD) simulations, where the radiative decay rate is equalized to the non-radiative counterpart for light emitting QD-based technologies. To show the promises of the QD placement at a critical distance, we demonstrate an optical switch for the fluorescence efficiency of a CdSe/ZnS core-shell QD (CSQD) by optically exciting the silver nanoparticle (AgNP) placed at a critical distance. While the provided single particle spectroscopy allows for the observation of heterogeneity in CSQD-AgNP coupling that is often masked in ensemble measurements, our benchmark study serves as a base reference for the development of QD-based light emitting technologies by resolving the optically switchable active tuning of radiative decay rates.

The diode-pumped Nd: Vanadate laser is one of the most widely used lasers in the near-infrared range. Using passive Q-switching with a Cr:YAG saturable absorber (SA), it generates nanosecond pulses at tens of kilohertz. This laser is known to exhibit low-dimensional deterministic chaos. In this letter, we present experimental evidence of a strange nonchaotic attractor (SNA). SNAs exhibit complex dynamical behavior that is neither periodic nor chaotic, making them difficult to detect in real-world systems. They are typically observed in systems modeled by equations and arise through a route to chaos. By experimentally obtaining time series of two laser variables, we reconstruct the underlying attractor. For a certain position of the SA, we find that all Lyapunov exponents are negative and that the energy spectra exhibit a broad frequency range, indicating that the attractor is nonchaotic and lacks periodicity. Using Higuchi's method, we estimate the fractal dimension df and find that 13% of the nonchaotic time series exhibit a well-defined non-integer df, confirming the presence of an SNA. To the best of our knowledge, this is the first experimental observation of a strange nonchaotic attractor in a complex optical system.

Accurate prediction of the freezing level (FZL) is essential for hydrometeorological forecasting systems and precipitation phase estimation, and it influences runoff generation and reservoir management decisions. In this study, we develop a deep learning based postprocessing framework using the Unet convolutional neural network (CNN) architecture to refine the FZL forecasts from the West Weather Research and Forecasting (West-WRF) model. The proposed framework leverages reforecast data from West WRF and FZL estimates from the California Nevada River Forecast Center (CNRFC) to train a deterministic Unet model over the Yuba-Feather watershed, a hydrologically critical basin in northern California. We introduce two variants of our model, Unet-Log and Unet-GMM, which utilize the logarithm of the hyperbolic cosine of Error and Gaussian Mixture Model loss functions, respectively, to enhance FZL forecast accuracy beyond an RMSE based benchmark. Results indicate that the Unet based postprocessing framework significantly improves FZL forecast skill across diverse atmospheric conditions and complex topography. Compared to the raw West-WRF output, our model achieves reductions in RMSE of up to 25% and increases the forecast observation correlation by about 10% over the Yuba-Feather watershed. Furthermore, it effectively captures the spatiotemporal variability of the FZL across different elevations, mitigating systematic biases inherent in the West-WRF model. This novel deep learning based postprocessing approach demonstrates a promising pathway for integrating machine learning into hydrometeorological forecasting and decision support within the Forecast Informed Reservoir Operations (FIRO) framework.

Within the framework of the theory of irreducible tensor operators, using well-known general analytical results for double sums ($\sum_{jm}$) of products of two $3j$-Wigner symbols, analytical expressions for single sums ($\sum_m$) for the values $j_1 = j_2 = 1$ and $j = 2$ parameters of the upper row $3j$-Wigner symbol are specified. The expressions obtained supplement the well-known analytical results of the theory of angular momentum and are in demand in solving, in particular, such problems of atomic physics as the construction of a nonrelativistic quantum theory of single and double bremsstrahlung when a photon is scattered by an atom (atomic ion) (Hopersky et al [6,7,8]) and two-photon resonance single ionization of the deep shell of an atomic ion (Hopersky et al [9]).

The CMS quality measure for CT mandates compliance with dose and image quality across 18 exam categories, yet the method for calculating global noise (GN) remains undefined, referencing only the Duke and Wisconsin methods. This ambiguity raises concerns about standardization and clinical applicability. We retrospectively analyzed 719 CT exams across seven protocols, comparing five GN metrics derived from the Duke and Wisconsin approaches. Results showed significant variability across metrics and protocols, with Wisconsin_tissue_mean consistently yielding the highest values. Correlation strength varied by protocol, and mode-based metrics demonstrated higher agreement, suggesting dose dependence. In the Chest PE protocol, GN values differed significantly based on reconstruction kernel. These findings underscore the need for a standardized, clinically meaningful GN metric to ensure consistent image quality evaluation and effective implementation of the CMS measure.

Meniscus oscillations at interfaces between liquids, solids, and air significantly impact fluid dynamics and control. While idealized models exist, experimental data on capillary-gravity wave scattering involving meniscus effects remain limited. In this study, we systematically measured wave transmission past a surface-piercing barrier, focusing on meniscus and contact-line effects. By varying the barrier's surface properties and the wave frequencies, we explored how meniscus deformation influences wave transmission. The results are compared with simulations and limiting-case theories. We find that the meniscus water column beneath the barrier enhances coupling and increases transmission, while surface bending suppresses it by constraining motion. These competing effects explain the observed frequency and contact angle dependencies. Our findings provide insights into how meniscus dynamics govern surface wave behavior.

Tensor networks, particularly the tensor train (TT) format, have emerged as powerful tools for high-dimensional computations in physics and computer science. In solving coupled differential equations, such as those arising from stochastic differential equations (SDEs) via duality relations, ordering the TT cores significantly influences numerical accuracy. In this study, we first systematically investigate how different orderings of the TT cores affect the accuracy of computed moments using the duality relation in stochastic processes. Through numerical experiments on a two-body interaction model, we demonstrate that specific orderings of the TT cores yield lower relative errors, particularly when they align with the underlying interaction structure of the system. Motivated by these findings, we then propose a novel quantitative measure, $score$, which is defined based on an ordering of the TT cores and an SDE parameter set. While the score is independent of the accuracy of moments to compute by definition, we assess its effectiveness by evaluating the accuracy of computed moments. Our results indicate that orderings that minimize the score tend to yield higher accuracy. This study provides insights into optimizing orderings of the TT cores, which is essential for efficient and reliable high-dimensional simulations of stochastic processes.

This study investigates ray geodesics and wave propagation on the Beltrami surface, with a particular emphasis on the effective potentials governing photon dynamics. We derive the geodesic equations and analyze the Helmholtz equation within this curved geometry, revealing that the resulting potentials are purely repulsive. For ray trajectories, the potential is determined by wormhole parameters such as the throat radius (\(\ell\)), radial optical distance (\(u\)), scale parameter (\(R\)), and the angular momentum of the test field. Near the wormhole throat, the potential remains constant, preventing inward motion below a critical energy threshold, whereas at larger radial distances, it decays exponentially, allowing free propagation. In the context of wave propagation, the potential exhibits a centrifugal barrier along with a constant repulsive term at large \(u\). The Beltrami surface, characterized by constant negative Gaussian curvature, serves as a model for graphene sheets and optical wormholes in condensed matter systems. These results allow us to determine the space- and frequency-dependent refractive index of the medium, providing a coherent framework for understanding photon behavior in such systems, with promising implications for material applications.

We discuss a model for the relativistic bound states of 3-quark baryons based on confining quantum chromoynamics (QCD) with general Yang-Mills symmetry. The model postulates that 3-quark states are formed by consecutive 2-body collisions. For a proton, d and u quarks get together first, and then they capture another u quark so that the d quark is at the core to form a stable proton state with intergral electric charge. The two u quarks form a quantum spheric shell and move in a confining potential $C(r)= Q' r$ of the core d quark. The confining potential $C(r)$ is a static solution of new `phase' fields satisfying the fourth-order equation based on general Yang-Mills symmetry. The two u quarks with the confining potentials $C(r)$ in the spherical shell can produce an effective quark Hooke potential $V_{qH}(r)=Qr^2 + V_o$ for the d quark at the core, where Q and $Q'$ are not independent. The proton mass is assumed to be approximately given by $ E(d) + 2E(u)$, which can be obtained analytically from Dirac Hamiltonians involving $V_{qH}(r)$ and $C(r)$ for d and two u quarks respectively. The model gives a reasonable understanding of roughly 120 baryon masses based on two different coupling constants and one free parameter $V_o$ for sub-spectra specfied by $J^P$. These results are roughly within 20\% in percent deviation, which appears to be independent of the assumption of color charges. The confining QCD model also gives the neutron-proton mass difference $\approx 0.6 MeV$.