Papers

Phase matching is a fundamental problem in nonlinear optics that is normally constrained by material dispersion. The limited operation wavelengths within phase matching window limits the application, including the precise metrology using Kerr combs. Demanding applications like compact optical clock and astronomical spectroscopy requires atomic reference around 800 nm, where the natural phase matching is challenging. Here we revisit the concept of cavity phase matching (CPM), and fully reveal its advantage to engineer artificial phase matching beyond material dispersion. With the access of CPM condition in a monolithic high-Q fiber Fabry-Pérot resonator featuring a macroscopic cavity length, we achieve low noise Kerr comb generation around 800 nm, within the power budget of a single-mode laser diode. Inside a pump-integrated package of 19 cm3, low phase noise of -125 dBc/Hz at 100 kHz offset frequency is achieved for a 10.1 GHz repetition rate. Most importantly, the generated Kerr comb has been directly referenced to rubidium atomic transition for long-term stable operation. This result not only opens a new way for Kerr comb generation at arbitrary wavelengths, but also can be generalized to any other nonlinear optical frequency conversion application.

True random number generators (TRNGs) based on the dark noise of perimeter-gated single-photon avalanche diodes (pg-SPADs) naturally produce outputs that follow a tunable Bernoulli distribution. For applications requiring non-uniform random numbers, multiple such units can be coupled, or the inherent Poisson statistics of the accumulated dark events from a single pg-SPAD can be exploited. In this work, we present the principles for extending pg-SPAD-based TRNGs from the uniform to the non-uniform regime and investigate the impact of the perimeter-gate voltage on the underlying probability distributions. The proposed concepts were experimentally validated using a prototype 64 x 64 pg-SPAD chip, fabricated in a standard 0.35 $\mu$m CMOS process. Collectively, we showed that common non-uniform distributions such as Poisson, exponential, and approximated Gaussian can be achieved and tuned in real-time using pg-SPAD TRNGs.

Molecular systems have many degrees of freedom, but their metastable behavior can often be described by a few collective variables. Identifying these variables and estimating free energies along them from limited simulation data remains a challenging, important problem. Separate short trajectories may sample different metastable states without capturing transitions or establishing their relative equilibrium populations. For unbiased trajectories generated with the same Hamiltonian at a single temperature, alternate methods based on histogram reweighting cannot correct this imbalance. Here we present SPIBER, which combines the State Predictive Information Bottleneck (SPIB) with Generative Flow Networks (GFlowNets). SPIB uses deep learning to approximate slow degrees of freedom through a past-future information bottleneck, retaining information needed to predict future metastable states. We show that this compression limits conditional entropy variations in populated regions, allowing conditional mean potential energies, which are much easier to calculate, to be used to approximate free energy differences. Given sufficient local sampling to estimate these energies, they define the target distribution for GFlowNets, energy-based generative samplers that sample according to estimated thermodynamic stability rather than observed populations. For a particle in a radial double-well potential, for alanine dipeptide, and for the nine-residue peptide AIB9, SPIBER recovers free energy differences between sampled metastable states to within one thermal energy unit of reference values. The method combines collective-variable learning and free energy estimation in up to four latent dimensions, without requiring converged state populations or additional molecular dynamics simulations.

Physics-Informed Neural Networks (PINNs) provide a mesh-free framework for solving partial differential equations but often struggle with sharp gradients and shock-dominated solutions. Extended Physics-Informed Neural Networks (XPINNs) address this challenge through domain decomposition; however, the effects of decomposition design, overlap, and local network capacity on solution accuracy remain insufficiently characterized. This work presents a systematic assessment of domain decomposition configurations for XPINNs applied to the one-dimensional viscous Burgers equation ($\nu=0.01/\pi$). We compare a standard PINN, a conventional XPINN, a shock-focused XPINN with increased network capacity in the shock-containing subdomain, and overlapping XPINNs with different overlap widths. All configurations are trained using a common protocol based on Adam, L-BFGS refinement, and ten Residual-based Adaptive Refinement (RAR) cycles. Performance is evaluated against a reference solution using global and shock-region relative $L_2$ and $L_\infty$ errors, maximum PDE residuals, and interface continuity errors. The results show that decomposition design strongly affects XPINN accuracy and shock resolution. Among the overlapping configurations, the smallest overlap provides the best accuracy, while larger overlaps degrade performance. The shock-focused XPINN achieves the lowest overall solution errors, indicating that increased local network capacity can provide greater benefit than increasing overlap alone. RAR produces the largest improvements during early refinement stages, followed by increasingly non-monotonic changes in accuracy. These findings highlight the importance of jointly considering decomposition, interface coupling, network capacity, and adaptive refinement for shock-dominated problems.

We propose a computationally inexpensive algorithm to determine whether an opaque spherical body eclipses a luminous one, using only classical geometrical tools. We apply this method to the Sun-Earth-Moon system and predict solar eclipses, including their type, beginning, and duration, using the three-body problem. For predictions over a one-year period, the resulting errors are in the order of minutes.

Holographic dark energy ties the dark-energy density to an infrared length. We study new Tsallis holographic dark energy (NTHDE), built on the exponential Tsallis entropy, with the local Granda-Oliveros (GO) cutoff $L^{-2}=\alpha H^2+\gamma\dot H$ in a spatially curved universe. Writing the background as an autonomous system for $(\Omega_d,u,\Omega_k)$, we obtain four exact results. (i) In the closed-form solution without the Tsallis deformation, the dark energy splits into a matter-like and a curvature-like part. (ii) Because the GO cutoff lacks the term $k/a^2$ of the Ricci scalar, the coefficient of $(1+z)^2$ in $H^2/H_0^2$ is $R_k\Omega_{k0}$ with $R_k=(1-\alpha+\gamma)^{-1}>1$, while distances and the Clarkson-Bassett-Lu test return $\Omega_{k0}$; the Ricci completion restores $R_k=1$. (iii) Big-bang nucleosynthesis locks the matter-era dark-energy fraction to $1/(1+3|w_{\rm GO}|)$, where $w_{\rm GO}$ is the equation of state of the GO mode. (iv) The finite-time fold singularity of the flat model survives curvature. Against Pantheon+SH0ES supernovae, 32 cosmic chronometers and DESI DR2 BAO, curved GO-NTHDE gives $\Omega_{k0}=-0.001^{+0.041}_{-0.043}$, consistent with flatness, and $\Delta\chi^2=-2.82$ relative to flat $\Lambda$CDM, but the Bayesian evidence prefers flat $\Lambda$CDM ($\ln B=-2.66\pm0.24$), and allowing curvature costs $\ln B=-1.39\pm0.25$. The data cannot yet measure $R_k$: the GO and Ricci cutoffs fit equally well ($\ln B=+0.28\pm0.26$). With Union3 supernovae the model is on a par with $\Lambda$CDM ($\ln B=+0.53$). A fraction 0.91 of the posterior reaches the finite-time fold.

Operators for interfacial problems are trained on reference solutions produced by the solver they are intended to replace. This work develops a data-free physics-informed neural operator for level-set interface advection, in which the interface is the equation's unknown and the operator maps an initial interface to the full spatiotemporal trajectory under a prescribed flow. Training uses only the transport residual and a geometric constraint; no reference solution enters the objective at any point. A spacetime Fourier backbone emits the entire trajectory in one pass, and the initial condition is imposed by construction rather than by penalty, which removes the competition between the anchoring term and the residual that otherwise arises when no solution data are available. Supervised and hybrid operators are trained under an identical architecture, family, budget and test set, and are reported throughout as baselines that quantify what refusing labels costs. On a reversed single vortex the data-free operator reaches 1.614 +/- 0.067% relative L2 error on 100 held-out initial interfaces against 0.369 +/- 0.035% for the supervised baseline, a factor of 4.4; on solid-body rotation the corresponding figures are 3.804 +/- 1.075% and 2.576 +/- 0.159%, a factor of 1.5. The two benchmarks rank the arms differently, and the difference is attributable to the eikonal constraint: the exact solution violates |grad phi| = 1 over 0.3% of the domain under rotation and 86.9% under the vortex. Where the constraint is valid the physics-trained operator conserves enclosed area 2.7 times better than the supervised baseline despite a larger field error, and a hybrid arm using eight reference solutions outperforms a supervised arm using sixteen.

Empirical regularities described by parametrized families of probability distributions can shape how complex systems are interpreted and explained. A recurring problem is to determine the extent to which these descriptions are supported by observations, and how they can be used to characterize the underlying system. Here, we introduce a general framework for constructing constrained surrogates capable of representing arbitrary families of continuous distributions. We demonstrate its utility by providing explicit algorithms for several canonical distributions in statistical physics, including continuous powerlaw, lognormal, exponential, and truncated Gaussian distributions. These constrained surrogates enable unbiased estimation and accurate tests of model validity based on arbitrary (physically meaningful) sample statistics, without requiring specification or estimation of model parameters. We show how these theoretical advantages translate into more informative and nuanced conclusions in applications ranging from bushfire magnitudes and word usage in texts to brain connectomes and city populations.

The persistence of beta-blockers like atenolol in aquatic environments necessitates efficient remediation strategies, such as enzymatic biodegradation. This study employs reactive molecular dynamics (rMD) simulations to investigate the initial degradation steps of atenolol by the oxidoreductase 9CL6 ammonia monooxygenase. Using the ReaxFF reactive forcefield within a simulated aqueous environment, the degradation processes were modeled at 300 K and 350 K over 6.5 nanoseconds. Results indicate initial degradation rates of 24% at 300 K and 18% at 350 K, with each enzyme molecule degrading approximately 20 atenolol molecules before reaching a saturation point. Mass spectrum analysis reveals the primary formation of C8 and C6 fragments at both temperatures. However, at 300 K, additional intermediate fragments (C1, C3, C11, C13) are observed, which are completely bypassed at the higher temperature of 350 K. Furthermore, increasing the temperature to 350 K accelerates the initial degradation phase from 2 ns to 0.5 ns, despite yielding a slightly lower overall degradation rate. Ultimately, these atomistic insights provide a foundational computational methodology for predicting biodegradation rates and pathways of pharmaceutical contaminants at the molecular scale.

Quantum tunnelling plays a central role in the structure and spectroscopy of hydrogen-bonded systems, and its sensitivity to isotopic substitution provides a stringent probe of the underlying potential-energy landscape. Despite extensive numerical studies, many high-level approaches tend to obscure the simple physical relationships linking effective mass, barrier geometry, and tunnelling amplitudes. Here, we develop a Cornell-type analytical–numerical framework to describe proton and deuteron tunnelling, combining a semi-analytical localized wavefunction ansatz with numerical solutions of the one-dimensional Schrödinger equation. The resulting tunnelling splittings exhibit an exponential dependence on the square root of the effective isotope mass,$\ln(\Delta E)\propto -\sqrt{\mu_{\mathrm{eff}}}$, in agreement with semiclassical Wentzel–Kramers–Brillouin (WKB) theory. Comparison with multidimensional reaction-space calculations for the formic acid dimer shows that this scaling persists in fully coupled 3D and 5D quantum models, yielding an empirical relation $\ln(\Delta E)= -1.75\sqrt{\mu_{\mathrm{eff}}}+2.60$. The present framework provides a transparent and computationally efficient approach for quantifying mass-scaling and tunnelling dynamics in hydrogen-bonded and other double-well systems.

A species-specific weighting scheme for the Natural-Sample-Size collision model within the Direct Simulation Monte Carlo method is developed and implemented in the open-source gas and plasma solver PICLas. The implemented weighting scheme places particular emphasis on chemical reactions and ionization processes of trace species in order to guarantee physically accurate and low-noise simulations of atmospheric entry processes. The successful implementation and the conservation of momentum and energy during relaxation processes and chemical reactions are verified through a reservoir simulation of a reacting air mixture. As a more sophisticated validation case, atmospheric entry simulations of a cylinder at an altitude of 108 km are investigated. In a final step, the developed weighting scheme is employed to investigate ionization processes in the shock layer surrounding the RAM-C II reentry probe during atmospheric entry. The simulations showed very good agreement with experimental flight data and reduced noise in the rarefied regions for the charged species.

This 30 years old book is posted for free download by the generosity of Oxford University Press that granted permission to allow access to future generations of young scientists. A printed copy can be purchased from the publisher. The authors received for this book "The 1999 INFORMS Computing Society Prize for Research Excellence in the Interface Between Operations Research and Computer Science" from the Institute for Operations Research and the Management Sciences (INFORMS). For a book review by Janos D. Pinter go to: Journal of Global Optimization, 16: 107-108, 2000.

Post-growth defect engineering via ion implantation provides a powerful pathway for spatial optical profiling and colour patterning in III-V alloying photonic integration. However, the comprehensive recombination kinetics governing deep-level defect saturation and excitonic recovery under high-temperature annealing remain insufficiently understood. Here, we present a systematic study on the optical dynamics, rate-equation kinetics, and chromaticity evolution of indium-implanted InGaN/GaN multiple quantum wells (MQWs) across implantation doses (5E14 to 5E16 ions cm^-2) and subsequent thermal annealing stages (500 to 1100 deg C). Photoluminescence (PL) spectrum analysis reveals that a dose of <= 5E14 ions cm^-2 induces a modification of the optical response that does not further change upon high-dose implantation. A two-channel coupled rate-equation model is fitted to the data, optimised via differential evolution, allowing the extraction of transition rate constants. This demonstrates that thermal processing at 1000 deg C suppresses the carrier capture rate into deep-level states and also reduces its radiative recombination rate. This kinetic bottleneck drives an order-of-magnitude extension in the channel-specific radiative lifetime. Leveraging the excitation power density dependence of the differential recombination kinetics, where deep defects saturate whilst MQW emission scales near-linearly, we achieve a universal and power density-tunable chromaticity trajectory from warm yellow to cool white-blue emission. These insights enable microscopic defect physics to be linked to the macro-scale colour tailoring observed.

Surface nanoscale axial photonic resonators (SNAPRs) provide an axially structured whispering-gallery-mode platform in which nanoscale effective-radius variations produce spatially localized optical resonances. Here, we demonstrate cascaded Brillouin-Kerr microcomb generation in a SNAPR fabricated from 660 micrometer diameter silica fiber. Under continuous-wave pumping, cascaded stimulated Brillouin scattering generates first- and second-order Stokes fields that seed Kerr four-wave mixing within the cavity. The resulting nonlinear spectrum contains two comb families with a common azimuthal spacing of 100.2 GHz and a relative offset of 9.6 GHz, consistent with the Brillouin shift in silica. A low-power transmission spectrogram provides a spatially resolved modal analysis of the resonator, revealing localized resonances with loaded quality factors on the order of ten million, and optical resonance pairs compatible with the measured Brillouin spacing. These results establish SNAPRs as a platform for studying cascaded stimulated Brillouin scattering, Kerr four-wave mixing, and comb formation in axially structured whispering gallery resonators.

A set of experiments on merging dynamics of localized magnetized plasma pressure filaments in close proximity has been carried out in a large linear magnetized plasma device. A two filament configuration is used to determine the separation scale of cross-field interaction. Fluctuation analysis of the ion saturation current confirms the presence of unstable drift-Alfvén modes on each filament which are predominantly driven by the steep electron temperature gradient. When the filaments are separated a distance of approximately five times the size of a single filament or greater, no interaction is observed and the drift-Alfvén modes remain uncoupled. When brought closer together, significantly stronger interaction occurs with transfer of charge and energy, leading to the generation of inter-filament electric fields. This has the effect of rotating the filaments and influencing the merging dynamics. When the filaments are asymmetrically heated, the cooler filament becomes wrapped around the other, and the drift-Alfvén modes become fully coupled in a global mode structure. Transport between the filaments is explained in terms of E x B drift and the local potential wells of the filaments, and drift-Alfvén mode coupling occurs when the filaments are brought within the distance of a collisionless skin depth.

We perform direct numerical simulations (DNS) to investigate how cubic-lattice porous substrates influence momentum and heat transfer in turbulent channel flows. The simulations span friction Reynolds numbers from 260 to 1500, Prandtl numbers of 0.5, 1, and 2, and substrate porosities of 50%, 71%, and 87%. We show that theories developed for rough- wall turbulence can be extended to porous surfaces by replacing the roughness height with the inverse of the streamwise Forchheimer coefficient. The shifts in the mean velocity and temperature profiles follow existing fully rough momentum and thermal theories, enabling their prediction with rough-wall models. Combining these models with synthetic temperature and velocity profiles, we derive analytical formulas for the friction coefficient and Stanton number that agree with our DNS data to within 5%. The performance enhancement factor, which measures heat-transfer augmentation relative to the pressure-drop penalty at constant pumping power, is comparable to that obtained for rough surfaces. This suggests that porous substrates provide an alternative method for enhancing heat transfer in turbulent flows.

We examine log-optimal portfolio allocation when the long-run price of an asset follows a power-law trajectory, $P(t)=At^\alpha$, and its instantaneous return variance decays as $\sigma^2(t)=\sigma_0^2 t^{-2\gamma}$. Under a zero risk-free-rate benchmark, the continuous-time Kelly fraction scales as $K^\ast(t)=(\alpha/\sigma_0^2)t^{2\gamma-1}$. Exact temporal invariance therefore occurs when $\gamma=1/2$, whereas deviations from this value produce systematic age dependence in the allocation. We propose a scaling hypothesis connecting growth in network participation, effective market liquidity, and declining volatility. Under a specified set of scaling assumptions, this model predicts the benchmark exponent $\gamma=1/2$. Using historical daily Bitcoin prices, we estimate the power-law price exponent and examine the sensitivity of the volatility exponent to the length of the rolling window. For windows of four to nine years, the estimated volatility exponents have an arithmetic mean of 0.53 and a cross-window standard deviation of approximately 0.03. Because these estimates are obtained from overlapping observations and the same underlying price history, this spread is interpreted as a measure of model sensitivity rather than a formal confidence interval. Finally, we show that a time-dependent multiplicative contribution to return variance generally breaks exact Kelly invariance. We illustrate this result using a scenario in which Bitcoin transaction-fee variability affects the effective variance process. The results identify the conditions under which log-optimal allocation can remain stable under non-stationary power-law asset dynamics and clarify the assumptions required when applying this result to Bitcoin.

In the realm of material analysis, identifying different material phases is of key importance. Artificial intelligence in the form of neural networks provides a very fast and, once trained, computationally inexpensive method for analysing large amounts of image data, like the sets of diffraction patterns generated during four-dimensional scanning transmission electron microscopy (4DSTEM) dataset acquisition. In this work, we train multiple network architectures on images of this type to distinguish between the phases of LiNiO2 and NiO, an important and challenging distinction in the lithium-ion battery community, since the formation of NiO limits battery capacity. We test both classical convolutional neural networks (CNNs) and different forms of vision transformers (ViTs). Our networks are trained on synthetic images and tested on experimentally recorded diffraction patterns. Additionally, we also investigate the decision-making of our networks using the GradCAM method. We test our networks on both synthetic as well as experimental diffraction patterns. Our networks exhibit very robust results, especially when dealing with highly varying data, an area where traditional template-matching methods typically struggle.

Generative artificial intelligence (Gen AI) is increasingly shaping how students access information and engage with physics content, yet many instructional approaches still assume a pre-AI classroom. In this paper, we describe a practical framework for integrating Gen AI into physics instruction through the use of context prompts: instructor-designed prompts that define the role, task, and boundaries of the AI interaction. We focus on AI-supported exploration assignments implemented in an asynchronous introductory physics course enrolling approximately 70 students. In these activities, students used AI as a guided tutor to explore selected topics, ask follow-up questions, request explanations and analogies, and connect physics concepts to their personal interests and everyday experiences. We argue that this structure can help move students beyond answer-seeking and toward more dialogic and personally meaningful engagement with course content. We also discuss reflection as a natural extension of the same framework, while distinguishing it from the implemented exploration activities described here. Finally, we highlight practical considerations that emerged in use, including uneven access to AI platforms, grading workload, the need to teach students how to engage AI conversationally, and the growing importance of AI literacy in physics education.

High-resolution oceanographic fields are critical for resolving mesoscale and sub-mesoscale coastal dynamics, yet their generation remains constrained by both computational cost and observational sparsity. We present OcDiffSR, a conditional denoising diffusion probabilistic model (DDPM) for oceanographic super-resolution that reconstructs high-resolution sea-surface fields from coarse-resolution reanalysis inputs. The model is trained on ten years (2011-2020) of paired low-resolution (GLORYS12V1, 1/12) and high-resolution (Mediterranean Sea Physics Reanalysis, Med MFC, 1/24) data, and evaluated on an independent test year (2009) over the Adriatic Sea. OcDiffSR employs a conditional U-Net augmented with multi-scale low-resolution encoders, cross-attention bottleneck layers, and sinusoidal seasonal embeddings via Feature-wise Linear Modulation (FiLM), enabling joint super-resolution of sea-surface temperature (SST), salinity (SSS), and horizontal velocity components with visually coherent circulation patterns. Benchmarked against bilinear interpolation and the state-of-the-art residual diffusion model CorrDiff, OcDiffSR achieves substantially lower reconstruction errors for scalar fields (RMSESST=0.477 C, RMSESSS=0.346 psu), near-unity Pearson correlation (PCC >= 0.999), and high structural similarity (SSIM >= 0.964). For dynamical vector fields, OcDiffSR outperforms both baselines in absolute error and spatial coherence, though moderate correlation (PCC = 0.64) reflects the intrinsic stochasticity of oceanic velocity fields. Daily and monthly evaluations confirm temporal robustness across all seasons. These results establish OcDiffSR as a reliable framework for high-fidelity oceanographic downscaling and reanalysis enhancement, producing fields that are visually consistent with known ocean dynamics.

The ratio of a structure's first two natural frequencies depends on its boundary conditions but not on its size, material density, or absolute stiffness, which multiply every frequency equally and cancel in the quotient. We use this to identify the boundary conditions of a small-scale beam or plate from two ratio measurements, without knowing its exact dimensions or material. The paper first establishes how the ratio $r=\omega_2/\omega_1$, and the fractional shift $\Delta r$ it undergoes when a boundary condition changes, respond to the two effects that matter at small scale: transverse shear deformation, through Timoshenko beam and Mindlin plate theory, and small-scale elasticity, through Eringen's nonlocal Timoshenko model. Using a locking-free Rayleigh-Ritz beam solver and a finite-element Mindlin plate solver, each checked against benchmarks, we find an asymmetry. The absolute ratio moves appreciably with thickness, by 12 to 14 per cent over the shear-deformable range, and with orthotropy, by 6 to 9 per cent, but only weakly with nonlocality; the shift $\Delta r$, by contrast, varies by less than 0.2 per cent over the full nonlocal range and by a few per cent under thickness change. Because $\Delta r$ is largely independent of these details, a measured shift identifies which boundary-condition transition a deliberate change of support has produced, with a resolution we quantify and verify against molecular-dynamics data. Two spring continua then extend the method to estimating support compliance, with the non-uniqueness of the tip-support case stated explicitly. The method needs no value of the small-scale parameter or the material constants.

Solar wind protons exhibit a temperature anisotropy that deviates from the double-adiabatic prediction, suggesting an additional energy source. To investigate solar wind heating mechanisms, we analyze Helios 1 and 2 data and characterize the radial evolution of the adiabatic invariants and anisotropic heating rates of fast wind protons, considering both the bulk population and its core and beam components separately. Unlike previous studies, we account for proton heat fluxes and deviations from the Parker spiral caused by large-amplitude fluctuations, which causes significant differences in the parallel heating rates compared to earlier results. Although earlier work has questioned the reliability of power laws to estimate heating rates and proposed as a more robust approach the investigation of the radial evolution of adiabatic invariants, we show that the two approaches yield consistent results when applied to the same dataset. Our analysis shows that all proton populations considered require net perpendicular heating, consistent with earlier studies. In contrast, the parallel energy evolution differs between populations. Core protons require parallel heating, whereas beam protons undergo parallel cooling although the total proton population undergoes a net parallel cooling. This behavior is consistent with the combined action of turbulence and core-beam kinetic instabilities, which together can drive preferential perpendicular heating while simultaneously produce parallel cooling in the fast solar wind. Core proton parallel heating could instead be a signature of Alfvén wave decay.

We construct a wind-driven forest-fire model in one dimension in which a fire can jump gaps between trees to ignite disjoint downwind forests. The size of a gap that a fire can jump depends on the fire intensity, which increases as the fire propagates through trees and diminishes as the fire jumps gaps. Trees grow on empty sites at rate $r$ and lightning strikes each site with rate $f$. When $f\ll r/L$, where $L$ is the system length, lightning is sufficiently rare that quasi-deterministic dynamics arises where all trees are consumed when a lightning-induced fire occurs. For $f\gg L^{-\mu}$ with $\mu\approx 0.8$, lightning is sufficiently frequent that a steady state is reached, but with unexpected behaviors for the forest- and gap-size distributions. Intermittency arises in between these regimes, with coexisting temporal domains of deterministic and chaotic dynamics.

Quantitative characterization of the mechanical properties of cells and tissues is essential for understanding disease progression and tissue regeneration. Optical tweezers (OT) enable the direct application of biologically relevant forces; however, OT has been limited to single axial indentations of cells, thereby restricting throughput. Furthermore, the use of quadrant photodiodes is insufficient for assessing the large displacements required for biomechanical characterization of tissues. We present light-sheet optical tweezers as a force transducer (LOFT), an approach that improves the throughput by at least 3x through simultaneous multiparticle trapping and parallelized characterization under sub-nN forces. LOFT is achieved by uniquely integrating light-sheet illumination for extended trapping, femtosecond-pulsed lasers to augment the optical gradient force, and videography-based particle tracking for observation of force transduction. The platform is validated through single-cell indentation experiments. We then apply LOFT to myocardial tissue, revealing significant biomechanical differences between healthy and infarcted regions; the interpretation of which is further supported by quantitative multiphoton imaging using the same optical source and platform. This work represents the first demonstration of OT for the mechanical testing of intact soft tissues, and establishes LOFT as a versatile, multifunctional platform for high-throughput, minimally invasive, mechanical characterization of complex biological systems in situ.

The interplay between optical spin and crystal symmetry provides a powerful mechanism to control light-matter interaction, enabling advances in imaging, holography, sensing, signal processing, and more. Here we reveal that this interplay can also be exploited as a programmable mechanism for selecting and shaping nonlinear frequency-conversion pathways. Using a z-cut lithium niobate thin film, we experimentally demonstrate spin-selective switching between complementary three-wave mixing pathways, where co- and cross-circular-polarization excitations activate sum- and difference-frequency generation, respectively. We further generalize this concept to establish a quantitative spin-to-frequency mapping, that encodes the pump spin composition into the generated frequencies, and enables the reverse retrieval of the pump spin state. Finally, we leverage this concept also to demonstrate mapping of spin patterns to a combination of frequency-dependent real space images, and Fourier-space diffraction patterns. These results pave the way for all-optical programable frequency-conversion and spatiotemporal nonlinear signal manipulation in free-space and integrated photonic systems.

We investigate the impact of a monolithic three-dimensional (M3D) integration process-critical $400^\circ$C post-processing forming gas anneal (FGA) on the electrical performance, reliability, and defect evolution of oxide-channel thin-film transistors (TFTs), combining systematic experiments with density-functional-theory (DFT)-based liquid-quench molecular-dynamics (MD) simulations. Indium tungsten oxide (IWO) TFTs are employed as a model system and encapsulated with a thin 3 nm Al$_2$O$_3$ / 3 nm HfO$_2$ hybrid layer that effectively suppresses external hydrogen ingress. We reveal a non-monotonic evolution of device behavior during FGA, governed by initial densification followed by partial crystallization. Short-duration FGA (10 min) induces channel densification and the formation of shallow, delocalized defect states, leading to pronounced positive bias temperature instability (PBTI) degradation and the emergence of a characteristic transfer-curve "kink." With prolonged annealing (>40 min), partial crystallization of the oxide channel occurs, stabilizing hydrogen in deep, localized defect states, suppressing hydrogen mobility, and restoring device reliability. As a result, the PBTI shift is reduced to 10.4 mV after 2000 seconds of stress, accompanied by complete elimination of the transfer-curve kink. These findings provide a mechanistic understanding of hydrogen-defect interactions during high-temperature post-processing FGA and demonstrate that appropriate hydrogen-blocking encapsulation enables oxide-channel TFT integration without compromising electrical performance or reliability.

A workflow is introduced for constructing accurate, wavefunction-derived Kohn-Sham (KS) exchange-correlation (XC) energies across atomic and molecular species. Correlated total energies and densities are obtained from configuration interaction wave functions in a polarized triple-zeta basis, cc-pVTZ, and each wavefunction density is then mapped onto a KS determinant. A composite correction adds valence and core basis functions (cc-pVQZ and cc-pCVTZ), plus a two-point Riemann extrapolation to approach the complete-basis-set limit. The workflow is applied to 100 closed-shell atomic and molecular species composed of main group elements to form the XC100 data set. The resulting KS densities closely reproduce the correlated densities, with a median L2/Ne difference of 1.42 X 10^{-5}, while the composite corrections recover substantial correlation energy beyond the cc-pVTZ reference. Comparison with conventional and machine-learned density functional approximations gives insight into the quality of Exc across different types of models for XC. In all, this paper's workflow demonstrates a practical route for generating XC reference data for the assessment and development of density functionals.

Few fragments of Earth's earliest crust have survived, and so this formative period of our planet's history is shrouded in mystery. To leverage the limited data available, it is important to understand all the processes that could have shaped our young planet. Here, I explore a previously neglected aspect of early-Earth dynamics: rotation. Earth after the Moon-forming impact was rapidly rotating with its rotation rate slowing significantly during the first tens of millions of years after the impact as the Moon tidally receded. Using planetary structure and tidal evolution calculations, I show that the initially distorted structure of Earth would likely have led to significant latitudinal variations in primary crustal thickness. Further, the subsequent changes in Earth's shape in response to its slowing rotation rate could have driven extensive tectonic activity and modulated the response of the crust to other forces. There was extension of the crust in polar regions and convergence in the equatorial regions at rates potentially comparable to those forming the Himalayas today in high-angular momentum scenarios. Depending on the rheology of the earliest crust, such motions could have led to burial of hydrated and weathered surface material, extensive volcanism, and degassing of Earth's interior. Phase changes due to increases in internal pressure could have led to delamination of parts of the crust, producing early mantle heterogeneities. Hydrated secondary melting of the crust and the production of evolved magmas could explain the emergence of a diversity of rock types within tens of millions of years of Earth's formation.

We present TubeLab, a browser-based acoustic simulator and inverse design tool for wind instrument bores without tone holes. The direct problem is solved by a Transfer Matrix Method. Inverse design - finding a bore correction that shifts selected resonance frequencies toward specified musical targets - is formulated as a Tikhonov-regularized least-squares problem whose Jacobian is computed by a single adjoint backward pass per mode. Modes whose frequencies must be preserved are handled by a saddle-point formulation that enforces hard equality constraints regardless of the regularization strength. Resonance frequencies agree with state-of-the-art finite-element calculations to within 1.21 cent over eleven geometries and 132 mode pairs. Experimental validation against tap-tone measurements of 16 numerical control-machined didgeridoos spanning six distinct bore designs yields 9.6 cent RMS deviation over 189 matched mode pairs. Replicate specimens of the same nominal bore disagree with each other by 4.5 cent RMS, so about 36% of the residual variance is fabrication scatter.

A fundamental mechanism underlying collective phenomena in social, technological, and economic systems is decentralized decision-making, in which the behavior of individual agents follows from local interactions in the absence of central coordination. Consensus formation is a central feature of such systems, with relevance to blockchain networks, distributed artificial intelligence, autonomous multi-agent systems, distributed control, and collective decision networks. We investigate consensus formation using a stochastic consensus model on random networks and examine how initial conditions, network connectivity, and system size shape the emergence of unanimous states. We show that the stochastic dynamics strongly amplify small initial majorities, progressively suppress the competing state, and drive the system toward a predictable collective outcome. Network connectivity primarily controls the efficiency of this process: increasing connectivity accelerates the propagation of local agreement and reduces the likelihood that fluctuations reverse the initially dominant state, although these gains gradually saturate in highly connected networks. System size produces a complementary effect. Larger networks require more individual updates to reach unanimity, but they are also increasingly reliable in selecting the state favored by the initial majority. Finite-size analysis shows that the range of initial conditions associated with uncertain outcomes becomes progressively narrower as the network grows, decreasing approximately with the inverse square root of the system size. These results reveal a collective amplification mechanism by which weak initial asymmetries become increasingly decisive in large decentralized networks, and they provide a simple framework for understanding the efficiency, predictability, and reliability of consensus formation in distributed decision systems.