Papers

Ultrasound-based elasticity imaging is a non-invasive technique for estimating tissue stiffness fields from displacement fields obtained by comparing ultrasound signals before and after compression. While recent deep learning approaches have enabled faster and more accurate elasticity estimation compared to traditional methods, several challenges remain for clinical translation. In this study, we employ finite element simulations of free-hand palpation to investigate the applicability of the Fourier neural operator (FNO). Four practical scenarios were investigated: (1) prediction across diverse lesion morphologies, (2) generalization to cases with lesion counts differing from those in the training data, (3) robustness to noise in measured displacement fields, and (4) resilience to variations in ultrasound device resolution. Across these tasks, FNO consistently outperformed baseline models such as U-Net and DeepONet in predictive accuracy and generalization, while maintaining robustness under noise and resolution changes. Validated through in silico simulations, these findings demonstrate the potential of FNO as a framework that could facilitate translation of elasticity imaging toward clinical practice.

Inverse design is a commonly used methodology for creating devices that manipulate electromagnetic (EM) waves by algorithmically modifying device parameters to achieve a desired functionality. Utilizing plasma, a dynamically tunable medium, allows the optimization of the design process to be conducted directly on the experimental hardware (in-situ). A key advantage of this method is the creation of devices that are inherently switchable and dynamically reconfigurable. Bayesian optimization is used to tune the plasma density of 91 independent discharges that make up a plasma metamaterial (PMM) device to steer incoming EM waves to desired exit waveguides. Measurements were conducted in an automated loop where a vector network analyzer records the PMM transmission characteristics for each device setting. By relying only on measured scattering parameters, this gradient-free approach is robust to experimental drift and noise and does not require complex full-wave models. Significant performance improvements over traditional simulation-based (in-silico) inverse design are demonstrated, with in-situ Bayesian optimization achieving up to 10,000x higher isolation between ports than the best in-silico design at the same target frequency. This work also presents guidelines for applying Bayesian optimization to noisy, high-dimensional physical systems.

The transient Pauli blocking effect offers a promising route for achieving ultrafast optical switching in semiconductors, enabling a rapid switching from an initially opaque state to a relatively transparent state upon photoexcitation. Herein, we demonstrate broadband ultrafast optical switching in degenerate InN thin films, spanning the visible to near-infrared spectral range, using pump-probe transient transmittance measurements. To elucidate the underlying physical mechanism, we perform probe-energy-resolved analysis for ultrafast dynamics, and develop a theoretical model based on a quasi-equilibrium Fermi-Dirac distribution. The model successfully captures the experimental transients and yields an electron-phonon coupling constant of $1.0\times10^{17}\,\mathrm{W\,m^{-3}\,K^{-1}}$, along with an electronic specific heat coefficient ranging from 1.52 to 2.02 $\mathrm{mJ\,mol^{-1}\,K^{-2}}$, which allow direct prediction of the spectral switching window. Notably, we demonstrate that the Pauli blocking effect can be induced solely by a laser-excitation driven rise in electronic temperature, without requiring significant carrier injection into the conduction band in degenerate semiconductors. These findings offer new insights for designing ultrafast optical modulators, shutters, and photonic devices for next-generation communication and computing technologies.

Intensity-modulated proton therapy (IMPT) employs proton radiation rather than conventional X-rays to treat cancerous tumors. This approach offers significant advantages by minimizing the radiation exposure of surrounding healthy tissue, leading to improved patient outcomes and reduced side effects compared to traditional X-ray therapy. To ensure patient safety, each treatment plan must be experimentally validated before clinical implementation. However, current dosimetry devices face limitations in performing angled beam measurements and obtaining multi-depth assessments, both of which are essential for verifying IMPT treatment plans. In this study, the performance of a \beta-Ga$_2$O$_3$-based metal-semiconductor-metal (MSM) detector with a low-noise amplifier is studied and evaluated under various proton radiation doses and energy levels delivered by a MEVION S250i proton accelerator. The detector performance is also compared with that of an ionization chamber. The \beta-Ga$_2$O$_3$ detector exhibits a linear response with proton dose for single-spot irradiations, and its response to varying proton energies closely matches both the ion chamber data and simulated dose distributions. These findings highlight the potential of \beta-Ga$_2$O$_3$-based detectors as robust dosimetry devices for IMPT applications.

The transport-of-intensity equation (TIE), namely the continuity equation associated with a coherent paraxial optical wavefield, is widely used for phase retrieval. It is a second-order partial differential equation which may be solved for the phase of a coherent paraxial field such as a monochromatic scalar optical beam, given the intensity and longitudinal intensity derivative in a plane perpendicular to the optical axis. We show how the coherent flow associated with the TIE may be augmented by a diffusive flow associated with a scalar or tensor diffusion field. Such diffusive flow can arise via scattering from unresolved spatially random microstructure in an illuminated sample, blurring effects of an extended chaotic source that illuminates the sample, the resolution-reducing effect of shot noise in detected intensity images of the sample, and the sharpening effect (negative diffusion) associated with scattering from sharp sample edges. Augmenting the TIE's modeling of coherent flow with a diffuse-flow channel leads to a Fokker-Planck extension to this equation. Two different augmentations are obtained, using several complementary derivations. The inverse problems of phase retrieval and diffusion-field retrieval are then considered, for defocus-based imaging and mask-based imaging. When symmetric overfocus and underfocus images are used for phase retrieval, the diffusive term drops out and our Fokker-Planck formalism implies that any ensuing TIE-based phase-retrieval method needs no modification in light of our formalism. However, the same focal-series dataset—typically an infocus image, a weakly overfocused image, and a weakly underfocused image—may also be employed to access the additional channel of information associated with the Fokker-Planck diffusion field. Our formalism is applicable to visible light, x-ray, electron, and neutron imaging.

How skeletal architecture and landing posture shape the immediate post-impact viscoelastic response of the foot remains incompletely understood, in part because cadaveric specimens are ill-suited to repeated impact testing across postures. In this study, we developed an anthropomimetic foot joint structure aimed at replicating the skeletal geometry of the human foot. Using a vertical drop apparatus that simulates landing and a viscoelastic system-identification model, we investigated how skeletal structure and posture modulate the apparent post-impact viscoelastic response. The results show that the multi-jointed anthropomimetic structure exhibited a higher damping ratio than simplified flat and rigid feet. Moreover, ankle dorsiflexion and toe extension systematically shifted the identified parameters, reducing the damping ratio under the tested conditions. Taken together, these findings indicate that an arch-like, multi-jointed skeletal architecture can enhance impact attenuation in an anthropomimetic mechanical foot, and that morphology and passive posture alone can tune the trade-off between attenuation and rebound. The observed trends are qualitatively consistent with reported differences in human landing strategies, and highlight the engineering advantage of anatomically informed skeletal design for achieving tunable impact attenuation through postural adjustment.

Contact tracing (CT) is a frontline measure against emerging epidemics, yet in practice it is never complete. The quantitative impact of missing information – such as untraced cases or unnotified contacts – on the effectiveness of CT remains insufficiently understood. Using a stochastic agent-based model with sociodemographics from metropolitan areas in South Korea, we simulate how different forms of information loss affect epidemic spreading dynamics. We construct information-loss scenarios based on two types: infector-omission (IO), the omission of infected individuals from the tracing process, and contact-omission (CO), the omission of specific contact events even when the infected individuals themselves are identified. The sensitivity of epidemic dynamics to increasing omission rates differs markedly between the two types: IO produces substantially stronger and more abrupt changes in transmission structure and epidemic outcomes, whereas CO produces more gradual effects. Notably, CT effectiveness breaks down beyond a city-specific threshold – an IO rate of approximately 4% in Seoul but about 10% in less populous Busan – underscoring that CT strategies must be tailored to regional population and mobility structure. Both IO and CO scenarios also lead to an increase in the transmission network diameter as information loss grows, indicating that a small network diameter reflects effective contact tracing that limits the depth of transmission chains. Collectively, our results offer threshold estimates and practical guidance for designing robust CT systems in the real world.

This letter proposes a physics-aware multi-modal contrastive learning framework designed to transform complex seismic wavefields into human-readable physical representations. Traditional data-driven inversion methods often focus on pixel-wise mapping, which lacks physical grounding and interpretability. To address this, we introduce a novel framework that jointly aligns seismic shot gathers, subsurface velocity models, and explicit physical descriptors (e.g., mean velocity and gradients) in a shared latent space. By introducing these descriptors as a third modality, our approach encourages the learned embeddings to capture intrinsic geological semantics rather than superficial signal correlations. Experiments on the OpenFWI dataset demonstrate that the proposed method not only achieves robust seismic-to-velocity retrieval but also preserves meaningful physical semantics, enabling cross-modal inference of interpretable attributes. This representation-centric perspective provides a flexible foundation for expert-guided subsurface characterization.

In the case of a radio-frequency (RF) cavity with a mismatched input coupler, a direct calculation of the power dissipation in the cavity and the intrinsic quality factor from continuous-wave (CW) measurements may have uncertainty due to systematic errors. Formulae for an indirect calculation of these quantities are derived for the case of a cavity with two couplers of fixed coupling strength. In this approach, the signal from the pickup coupler is used to infer the amplitude of the "emitted wave" from the input coupler. A graphical method for self-consistency assessment is evaluated. The impact of frequency offsets is considered. Applications of these methods are presented, drawing on cold tests of superconducting cavities produced for the Facility for Rare Isotope Beams.

The Hodgkin-Huxley equations have described neuronal excitability for seventy years, yet their mathematical structure-gating exponents m3h and n4, exponential voltage dependencies, and bounded activation variables, has remained empirically justified rather than theoretically derived. Hodgkin and Huxley introduced voltage-dependent conductances controlled by gating variables. While these equations reproduce experimental observations, they were derived through curve-fitting without theoretical justification. Modern theoretical physics derives governing equations from symmetry principles through Lie group theory. We prove that the complete Hodgkin-Huxley equations necessarily follow from three fundamental symmetries: (1) compact conformational state spaces, (2) multiplicative conductance scaling, and (3) temporal translation invariance. These symmetries uniquely determine a Lie group structure isomorphic to SO(2) semidirect product with R2. From representation theory, we derive: boundedness from SO(2) compactness, exponential Boltzmann factors from scale invariance, specific integer exponents m3h and n4 from irreducible representations, and first-order kinetics from Lie algebra flows. This demonstrates that the HH equations are not empirical curve-fits but the unique mathematical structure mandated by fundamental symmetries. We reveal why gating variables must be bounded, voltage dependencies must be exponential, sodium requires three activation gates and one inactivation gate, potassium requires four activation gates, and kinetics must be first-order. This establishes that neural electrophysiology obeys the same theoretical framework as modern physics, where symmetries determine dynamics, providing a foundation for understanding channel mutations and network dynamics through group theory.

The temporal degree-of-freedom of light is incredibly powerful for modern quantum technologies, enabling large-scale quantum computing architectures and record key-rates in quantum key distribution. However, the generalized measurement of large and complex quantum superpositions of the time-of-arrival of a photon remains a unique experimental challenge. Conventional methods based on unbalanced Franson-type interferometers scale poorly with dimension, requiring multiple cascaded devices and active phase stabilization. In addition, these are limited by construction to a restricted set of phase-only superposition measurements. Here we show how the coupling of spatial and temporal information inside a single multi-mode fiber can be harnessed to program completely generalized measurements for high-dimensional superpositions of photonic time-bin. Using the multi-spectral transmission matrix of the fiber, we find special sets of spatial modes that experience distinct dispersive delays through the fiber. By exciting coherent superpositions of these spatial modes, we engineer the equivalent of large, unbalanced multi-mode interferometers inside the fiber and use them to perform high-quality measurements of arbitrary time-bin superpositions in up to dimension 11. The single fiber functions as a scalable, common-path interferometer for time-bin qudits that significantly eases the experimental overheads of standard approaches based on unbalanced Franson-type interferometers, serving as an essential tool for quantum technologies that harness the temporal properties of light.

Convection is ubiquitous in stellar and planetary interiors where it likely plays an integral role in the generation of magnetic fields. As the interiors of these objects remain hidden from direct observation, numerical models of convection are an important tool in the study of astrophysical dynamos. In such models, unrealistic large values of the viscous ($\nu$) and thermal ($\kappa$) diffusivity are routinely used as an ad-hoc representation of the effects of subgrid scale turbulence which is otherwise too small-scale to resolve numerically. However, the functional forms of these diffusion coefficients can vary greatly between studies, complicating efforts to compare between results and against observations. We explore this issue by considering a series of non-rotating, non-magnetic, solar-like convection models with varying radial functions for the diffusivities and differing boundary conditions. We find that the bulk kinetic energy scales similarly regardless of the diffusivity parameterization. This scaling is consistent with a free-fall scaling, wherein viscosity plays a subdominant role in the force balance. We do not, however, observe such diffusion-free behavior in the convective heat transport. Our results also indicate that the functional form adopted for the diffusion coefficients can impact the distribution of turbulence within the convective shell. These results suggest that some care should be taken when comparing solar convection models directly against helioseismic observations.

Integrated quantum photonics holds significant promise for scalable photonic quantum information processing, quantum repeaters, and quantum networks, but its development is hindered by the mismatch between materials hosting high-quality quantum emitters and those compatible with mature photonic technologies. Heterogeneous integration offers a potential solution to this challenge, yet practical implementations have been limited by inevitable insertion losses at material interfaces. Here, we present a self-aligned heterogeneous quantum photonic integration approach that can deterministically achieve near-unity coupling efficiency at the interface. To showcase our approach, we demonstrate Purcell enhancement of a silicon vacancy (SiV) center in diamond induced by a heterogeneous photonic crystal cavity defined by titanium dioxide (TiO2), as well as optical spin control and readout via a TiO2 photonic circuit. We further show that, when combined with inverse photonic design, our approach enables efficient and broadband collection of single photons from a color center into a heterogeneous waveguide. Our approach is not restricted to SiV centers or TiO2; it can be broadly applied to integrate diverse solid-state quantum emitters with thin-film photonic devices where conformal deposition is possible. Together, these results establish a practical route to scalable quantum photonic integrated circuits that combine high-quality quantum emitters with technologically mature photonic platforms.

This paper argues that several canonical puzzles in quantum mechanics, including spin measurement, the double slit, entanglement correlations, and Wigner's friend, share a common origin in a semantic error and the illicit promotion of interaction conditional outcomes to intrinsic properties. I introduce four principles that license only configuration relative predication, grounding outcomes in physical measurement geometry while preserving objectivity. Applying these principles uniformly dissolves each puzzle without new physics or ad hoc interpretive machinery. Bell's theorem and the Kochen-Specker theorem are reframed not as dynamical mysteries but as constraints on permissible explanatory structure, evidence that intrinsic-outcome semantics is incompatible with empirical reality. The result is a relational objectivity that avoids both naive property realism and observer subjectivism.

The spherical harmonics $Y_{\ell m}(\theta,\varphi)$ are complex-valued functions on the surface of a sphere, and have found widespread application in physics and astronomy. Every physics students knows them from quantum mechanics and electromagnetic theory, where they form the basis of hydrogen orbitals and of the multipole expansion, respectively. More advanced applications include the physics of the cosmic microwave background, gravitational lensing, and gravitational waves. In this paper I aim to contrast their usual $3d$ visualisation with Peirce's quincuncial projection, a conformal projection of the sphere onto a $2d$ unfolded square dihedron, where the projection respects the fundamental rotational symmetries and preserves angles. With this mapping, I guide the reader through the properties of the spherical harmonics in a pedagogical way and show that many of their mathematical relations have an intuitive visualisation on Peirce's $2d$ map, which might be useful for people challenged by processing $3d$ shapes, or which people might appreciate aesthetically.

Large-scale neutrino observatories operate under unavoidable detector deadtime and signal pile-up, leading to systematic inefficiencies in conventional coincidence-based trigger systems. Such triggers typically rely on binary temporal windows and assume continuous sensor availability, causing partial or complete loss of correlated signal information during non-live intervals. We introduce Recursive Manifold Coherence (RMC), a geometric framework that reformulates distributed trigger logic as a continuous state estimation problem in a low-dimensional information space defined by correlated charge and timing observables. Instead of applying hard vetoes during deadtime, the proposed method employs a recursive update rule that propagates a coherence state across sensor nodes, allowing partially obscured signals to be retained and evaluated consistently. Using simulation studies representative of large optical detector arrays, we demonstrate that RMC successfully recovers event-level coherence for high-multiplicity topologies even when direct coincidence chains are broken. By treating the detector response as a smooth manifold rather than discrete hits, the framework achieves superior robustness against data fragmentation compared to standard binary logic. The framework is detector-agnostic and compatible with software-defined trigger pipelines, providing a flexible foundation for deadtime-aware analysis and triggering strategies in future distributed detector systems.

The diffusion model, the state-of-the-art generative machine-learning architecture, has shown promising results in airfoil inverse designs. In this study, we implement and trained a series of diffusion models on three different airfoil geometry data encoding formats – principal component weights, ordered $x$-$y$ coordinates, and 2D signed distance functions (SDF) – to generate 2D airfoils. By systematically comparing the performance of diffusion models trained on different data structures, it is found that for 2D airfoil design problems, the diffusion model performs the best when directly trained with coordinates. Training with latent space (principal component weights in this study) limits the model's design freedom and decreases the training effectiveness. Although the 2D SDF data appear to result in the least performing model, it proves its feasibility in aerodynamic shape generation, paving the way toward 3D problems where SDF is more favored. This study also investigated deploying the diffusion model in practical engineering applications. A multi-target optimization procedure is proposed based on the stochastic nature of the diffusion process, which drastically simplifies the procedure compared to conventional methods. The extrapolation performance of the model is also investigated by tasking the model with both aerodynamic and flow condition labels that are extrapolated beyond the training set boundaries.

We develop a rigorous analytical framework for metastable stochastic transitions in Landau-type gradient systems inspired by QCD phenomenology. The functional $F(\sigma;u)=\int_\Omega [\frac{\kappa}{2}|\nabla\sigma|^2+V(\sigma;u)]\,dx$, depending smoothly on a control parameter $u\in\mathcal U$, is analyzed through the Euler-Lagrange map $\mathcal{E}(\sigma;u)=-\kappa\Delta\sigma+V'(\sigma;u)$ and its Hessian $\mathcal{L}_{\sigma,u}=-\kappa\Delta+V''(\sigma;u)$. By combining variational methods, $\Gamma$- and Mosco convergence, and spectral perturbation theory, we establish the persistence and stability of local minima and index-one saddles under parameter deformations and variational discretizations. The associated mountain-pass solutions form Cerf-continuous branches away from the discriminant set $\mathcal D=\{u:\det\mathcal L_{\sigma,u}=0\}$, whose crossings produce only fold or cusp catastrophes in generic one- and two-parameter slices. The $\Gamma$-limit is taken with respect to the $L^2(\Omega)$ topology, ensuring compactness, convergence of gradient flows, and spectral continuity of $\mathcal L_{\sigma,u}$. As a consequence, the Eyring-Kramers formula for the mean transition time between metastable wells retains quantitative validity under both parameter deformations and discretization refinement, with convergent free-energy barriers, unstable eigenvalues, and zeta-regularized determinant ratios. This construction unifies the classical intuition of Eyring, Kramers, and Langer with modern variational and spectral analysis, providing a mathematically consistent and physically transparent foundation for metastable decay and phase conversion in Landau-QCD-type systems.

We present a self-contained theoretical analysis of a dark-QCD chiral transition in which the Polyakov-loop sector retains an explicit $Z(3)$ structure and couples consistently to the chiral order parameter.Working within a coupled chiral–Polyakov effective theory, we map the homogeneous vacuum landscape and identify a metastability window bounded by spinodal loss of stability.We then construct $Z(3)$ domain-wall solutions including chiral backreaction, extracting temperature-dependent wall profiles and surface tension.Finally, we connect homogeneous metastability and wall microphysics to thermal bubble nucleation by evaluating the critical radius $R_c(T)$ and the nucleation exponent $S_3(T)/T$ in the thin-wall regime, providing a compact set of reproducible diagnostics for the decay of the metastable phase.Our results establish a coherent pipeline from vacuum structure to nonperturbative interfaces and nucleation barriers, suitable for systematic extensions to full multi-field bounce calculations and dark-sector cosmological applications.

Physical neural networks typically train linear synaptic weights while treating device nonlinearities as fixed. We show the opposite - by training the synaptic nonlinearity itself, as in Kolmogorov-Arnold Network (KAN) architectures, we yield markedly higher task performance per physical resource and improved performance-parameter scaling than conventional linear weight-based networks, demonstrating ability of KAN topologies to exploit reconfigurable nonlinear physical dynamics. We experimentally realise physical KANs in silicon-on-insulator devices we term 'Synaptic Nonlinear Elements' (SYNEs), operating at room temperature, microampere currents, 2 MHz speeds and ~750 fJ per nonlinear operation, with no observed degradation over 10^13 measurements and months-long timescales. We demonstrate nonlinear function regression, classification, and prediction of Li-Ion battery dynamics from noisy real-world multi-sensor data. Physical KANs outperform equivalently-parameterised software multilayer perceptron networks across all tasks, with up to two orders of magnitude fewer parameters, and two orders of magnitude fewer devices than linear weight based physical networks. These results establish learned physical nonlinearity as a hardware-native computational primitive for compact and efficient learning systems, and SYNE devices as effective substrates for heterogenous nonlinear computing.

Understanding the connection between active galactic nuclei and star-formation (the AGN-SF connection) is one of the longest standing problems in modern astrophysics. In the age of large Integral Field Unit (IFU) surveys, studies of the AGN-SF connection greatly benefit from spatially resolving AGN and SF contributions to study the two processes independently. Using IFU data for 54 local active galaxies from the S7 sample, we present a new method to separate emission from AGN activity and SF using mixing sequences observed in the [NII]$\lambda 6584$/H$\alpha$ vs. [OIII]$\lambda 5007$/H$\beta$ Baldwin-Phillips-Terlevich (BPT) diagram. We use the new decomposition method to calculate the H$\alpha$ star-formation rate and AGN [OIII] luminosity for the galaxies. Our new method is robust to outliers in the line-ratio distribution and can be applied to large galaxy samples with little manual intervention. We infer star-formation histories (SFHs) using pPXF, conducting detailed recovery tests to determine the quantities that can be considered robust. We test the correlation between the AGN Eddington ratio, using the proxy L[OIII]/$\sigma_*^4$, and star-formation properties. We find a moderately strong correlation between the Eddington ratio and the star-formation rate (SFR). We also observe marginally significant correlations between the AGN Eddington ratio and the light-weighted stellar age under 100 Myr. Our results point to higher AGN accretion being associated with young nuclear star formation under 100 Myr, consistent with timelines presented in previous studies. The correlations found in this paper are relatively weak; extending our methods to larger samples, including radio-quiet galaxies, will help better constrain the physical mechanisms and timescales of the AGN-SF connection.

Transcranial ultrasound must overcome the significant challenge of the human skull, limiting both imaging and therapeutic applications. While high-fidelity numerical simulations can compensate for skull-induced distortions, they require precise skull templates (typically derived from Computed Tomography, CT) and spatial alignment to the patient's anatomy. Current alignment relies on concurrent Magnetic Resonance Imaging (MRI) for registration, introducing financial, logistical, and throughput barriers. To overcome these challenges, we present Manifold Optimisation for Full-Waveform Inversion (MOFI), a method to register skull templates without using a guidance image. Our method aligns the skull template by minimising the difference between simulated and observed radio-frequency acoustic data. We demonstrate that MOFI accurately recovers the position of skull templates in silico and in vitro, offering an alternative to MRI guidance-based registration. These results indicate that MOFI has the potential to be a practical alternative to MRI-guided approaches, reducing the barriers to wider clinical adoption of transcranial ultrasound.

This work develops a unified, dimensionless framework for comparing geometrically similar reacting porous-flow systems across scale, with emphasis on hydrometallurgical heap leaching, when particle size distribution (PSD) and intraparticle pore structure differ. Under dynamic similarity, the dimensionless liquid residence-time distribution (RTD) is identical, but differences in PSD and internal porosity break microscopic similarity. Using the shrinking-core model (SCM), the analysis shows how a PSD in particle diameter maps to a distribution of particle-scale Damköhler numbers that governs heap-averaged conversion. Explicit PSD to Damköhler transformations are derived for (i) external film control, (ii) intraparticle diffusion control, and (iii) mixed control via additive rates. Dual-porosity hydrology relevant to sedimentary or strongly stratified ores is then incorporated by coupling SCM kinetics to mobile and immobile liquid domains, introducing additional dimensionless groups that describe interporosity exchange. Two numerical examples map a lognormal PSD into film- and diffusion-controlled Damköhler distributions and compare column/heap conversion for different PSDs. A practical workflow is outlined for hydrometallurgical column-test interpretation, combining tracer RTD calibration with PSD-aware kinetic fitting in dimensionless time. The framework clarifies why diffusion-controlled leaching is far more sensitive to PSD tails and dual-porosity structure than film-controlled leaching, and it identifies the compact set of dimensionless groups that must be matched to ensure similarity between laboratory columns and industrial heaps.

Developing epithelial tissues coordinate cell proliferation and mechanical forces to achieve proper size and shape. As epithelial cells tightly adhere together to form the confluent tissue, the distribution of cell areas significantly influences possible patterns of cellular packing and thereby also the mechanics of the epithelium. Therefore, it is important to understand the origin of cell area heterogeneity in developing tissues and, if possible, how to control it. Previous models of cell growth and division have been successful in accounting for experimentally observed area distributions in cultured cells and bacterial colonies, but developing tissues present additional complexity due to self-organized patterns of mechanical stresses that guide morphogenesis. Here, we address this challenge focusing on the D. melanogaster wing disc epithelium. We consider a simple model that couples cell cycle dynamics to tissue mechanics. From time-lapse imaging of the cellular network, we extract all model parameters - cell growth rates, division rates, and mechanical fluctuations - revealing that they all depend on cell size. With these independently measured parameters, our model quantitatively reproduces the observed cell area distribution without any fitting parameters and further predicts tissue pressure gradients, in quantitative agreement with previously published data. Importantly, we find that cell proliferation accounts for 85% of cell area variance, establishing it as the dominant source of packing disorder that influences tissue mechanics and organization.

Super-time-stepping (STS) methods provide an attractive approach for enabling explicit time integration of parabolic operators, particularly in large-scale, higher-dimensional kinetic simulations where fully implicit schemes are impractical. In this work, we present an explicit STS framework tailored for diffusion operators in gyrokinetic models, motivated by the fact that constructing and storing a Jacobian is often infeasible due to strong nonlocal couplings, high dimensionality, and memory constraints. We investigate the performance of several STS methods, including Runge-Kutta-Chebyshev (RKC) and Runge-Kutta-Legendre (RKL) schemes, applied to a diffusion equation discretized using both discontinuous Galerkin (DG) and finite-difference methods. To support time adaptivity, we introduce a novel error norm designed to more accurately track temporal error arising from DG spatial discretizations, in which degrees of freedom contribute unevenly to the solution error. Finally, we assess the performance of an automatic eigenvalue estimation algorithm for determining the required number of STS stages and compare it against an analytical estimation formula.

A central unresolved question in the theory of superconductivity is why only a small subset of metallic elements exhibit a superconducting state, whereas many others remain strictly normal. Neither the conventional Bardeen Cooper Schrieffer (BCS) framework nor its extensions involving charge density wave (CDW) or pair density wave (PDW) order provide a predictive or material-selective criterion capable of distinguishing superconducting metals from non-superconducting ones. In particular, the persistent absence of superconductivity in simple noble metals with well-defined Fermi surfaces poses a challenge for all traditional approaches. Here we address this problem using the Modulated Electron Lattice (MEL) Ginzburg Landau (GL) framework introduced in our previous work. In this formulation, a coarse-grained MEL charge field $\rho_{\mathrm{MEL}}(\mathbf{r})$ with momentum dependent stiffness $\alpha(q)$ is coupled to the superconducting (SC) order parameter $\psi(\mathbf{r})$. We show that metallic superconductivity emerges only when the system satisfies a specific “MEL enhancement window,” characterized by a negative minimum of $\alpha(q)$ at either a finite modulation wave vector $q^{\ast}$ or at $q=0$, together with sufficiently strong coupling between $\rho_{\mathrm{MEL}}$ and $\psi$. This unified criterion naturally partitions metallic elements into three universal classes: (i) MEL-enhanced superconductors with a finite-$q^{\ast}$ charge mode, (ii) conventional BCS superconductors as the homogeneous $q^{\ast}=0$ limit of the MEL framework, and (iii) metals for which $\alpha(q)$ remains positive for all $q$, suppressing all MEL modes and preventing any superconducting instability. By applying this criterion to simple metallic elements, we identify why some metals develop superconductivity while others do not, possibly resolving a selection problem long open within the BCS paradigm.

Atomically sharp 2D in-plane heterostructures with nanoscale interfaces provide a powerful platform for tailoring optical and electrical properties at the nanoscale, enabling novel device engineering and the exploration of new physical phenomena. However, direct experimental correlation between local dielectric response and excitonic properties across such interfaces has remained elusive. Here, we probed the nanoscale complex dielectric function and the corresponding localized photoluminescence (PL) modulations in heterostructure domains of lateral monolayer MoxW1-xS2 - WxMo1-xS2, synthesized using a liquid-phase precursor-assisted approach. Near-field nano imaging across the visible-near-infrared range enables real space mapping of sharp amplitude and phase changes at the heterointerface, resolving the local complex dielectric function with nano-meter scale spatial resolution. Excitation energy-dependent nano spectroscopy reveals a reversal of dielectric contrast between Mo-rich and W-rich domains at their respective excitonic resonances, consistent with Lorentz-oscillator fits. Complementary hyperspectral nano-PL mapping resolves the evolution of excitonic emission across the lateral heterointerface, with neutral-exciton intensities varying continuously from W-rich to Mo-rich regions. Effective-medium theory modeling of the imaginary part of the effective dielectric function of the heterostructure as a function of photon energy and Mo filling fraction reproduces the observed excitonic trends, linking the PL evolution to a composition-dependent dielectric response. Together, these results provide direct nanoscale correlation between dielectric and excitonic boundaries in laterally stitched monolayer heterostructures and establish a multimodal near-field spectroscopy framework for probing excitonic phenomena at the nanoscale.

The high-power helicon wave system in the DIII-D tokamak introduces new plasma–material interaction (PMI) challenges due to rectified RF sheath potentials forming near antenna structures and surrounding tiles. Using the STRIPE modeling framework-which integrates SOLPS-ITER, COMSOL, RustBCA, and GITR/GITRm-we simulate carbon erosion, re-deposition, and global impurity transport in two H-mode discharges with varying antenna–plasma gaps and RF powers. COMSOL predicts rectified sheath potentials of 1-5 kV, localized near the bottom of the antenna where magnetic field lines intersect at grazing angles. Erosion is dominated by carbon self-sputtering, with RF-accelerated D+ ions contributing up to 1 % of the total erosion flux. GITRm simulations show that in the small-gap case, only ~ 13 % of eroded carbon is re-deposited locally, with 58 % transported into the core. In contrast, the large-gap case exhibits lower total erosion, along with reduced core penetration (~ 35 %) and weaker re-deposition (~ 4 %), consistent with lower collisionality and limited plasma contact. The simulation trends are consistent with experimental observations, which have not shown elevated core impurity levels during helicon operation in the present graphite-wall configuration. However, under certain plasma conditions and magnetic configurations, the helicon antenna may still act as a finite source of net erosion and core-directed impurity transport, potentially influencing the overall core impurity balance. These findings emphasize the need for sheath-aware antenna designs and predictive impurity transport modeling to support future high-power RF systems with high-Z first wall materials in fusion devices.

Connecting cell and tissue mechanics is a fundamental challenge in the physics of morphogenesis. Conventional elasticity theory is generally inapplicable: cytoskeletal turnover precludes a fixed reference state, and the dominant forces are generated actively, independently of strain. Here, we study epithelia in quasi-static force balance between intracellular pressure and active interfacial tensions. The theory starts from a prescribed set of active tensions, represented as a Maxwell-Cremona tension triangulation dual to the cell tiling. This geometric formulation brings to bear the mathematics of discrete conformal geometry. Using generalized Voronoi diagrams, we construct the mechanically balanced cell tiling resulting from the given tensions and compute the tissue-scale stress. Conceptually, we treat cell interfaces as force dipoles whose embedding in physical space -the cell tiling- is determined by force balance. The tensions define an effective reference state in which the macroscopic stress vanishes as pressures and tensions cancel locally. Deformations from this reference result in macroscopic stress, constituting an emergent form of elasticity. Adiabatic tension dynamics change the reference shape. Thus, rather than fluidizing, tissues can deform -or "morph"-, while resisting external forces like a solid. We also define a conformal-geometric representation of cell network topology, remodeled by plastic cell rearrangement. We make experimentally testable predictions, notably that macroscopic stress is independent of local tension anisotropy. Overall, the unified description of emergent elasticity and plastic flow in epithelia is a step towards a theory of morphogenesis that puts active forces first and reflects the differences between inert and biological matter. Our framework also applies to 2D fluid foams and to frictionless granular media via a mechanical duality.

We present a comprehensive analysis and experimental study of THz hot-electron bolometer (HEB) mixers made from 40-nm-thick sputtered magnesium diboride (MgB2) thin films on high-resistivity silicon substrates. Using a lumped-element bolometric model, we achieve strong quantitative agreement with measurements of conversion gain, noise temperature, and local-oscillator (LO) coupling to the HEB devices. Our analysis shows that the sensitivity of current HEB devices is primarily limited by on-chip optical losses, with both Johnson and thermal-fluctuation noise contributing significantly to the overall noise temperature. Simulations of an optimized device with near-ideal optical coupling suggest that Johnson noise remains a substantial factor even with improved coupling. Further reduction of the noise temperature may require additional suppression of Johnson noise (via improved intrinsic conversion gain) beyond optimizing optical coupling efficiency. We emphasize the importance of accurate modeling to achieve good numerical agreement with experiments, thereby enabling understanding of the causes of sensitivity loss.