Papers

Bright squeezed light has entered strong-field atomic physics, creating an immediate need to characterize the quantum field delivered to the target on subcycle timescales. Here we show that attosecond streaking maps the coherent displacement and phase-sensitive covariance of a displaced squeezed field onto distinct harmonics: the mean photoelectron momentum follows an $\omega$-periodic shift of the spectral center, whereas the momentum variance exhibits a $2\omega$-periodic breathing. A Feynman–Vernon formulation represents Gaussian quantum light by stochastic vector-potential trajectories, enabling Coulomb-resolved TDSE simulations with a finite XUV gate and without explicit photon-state propagation. A coherent-state reference calibrates the streaking phase and variance background, enabling retrieval of the coherent amplitude and phase together with the squeezed-noise amplitude and squeezing phase. This establishes attosecond streaking as an in situ, gas-phase diagnostic of bright squeezed light.

Here, we describe Monthly Diffusion at 1.5-degree grid spacing (MD-1.5 version 0.9), a climate emulator that leverages a spherical Fourier neural operator (SFNO)-inspired Conditional Variational Auto-Encoder (CVAE) architecture to model the evolution of low-frequency internal atmospheric variability using latent diffusion. MDv0.9 was designed to forward-step at monthly mean timesteps in a data-sparse regime, using modest computational requirements. This work describes the motivation behind the architecture design, the MDv0.9 training procedure, and initial results.

Variational quantum algorithms on bosonic quantum processors are an emerging paradigm for quantum chemistry calculations, exploiting the natural alignment between molecular structure and harmonic oscillator-based hardware. We introduce the qumode-based variational quantum deflation framework (QumVQD) for finding both electronic and vibrational excited state energies on qumode-based architectures. We validate the approach through electronic structure calculations on H$_{\text{2}}$ and linear H$_{\text{4}}$, where we introduce Hamming-weight filtering of the Fock basis to enforce particle number conservation and eliminate spurious eigenstates by reducing the required Hilbert space, which reduces the required number of qumodes in turn. We achieve agreement with full configuration interaction (FCI) using the STO-3G basis set within the chemical accuracy threshold at most points along the potential energy surfaces. Extending to the vibrational structure, we combine QumVQD with an existing Hamiltonian fragmentation approach based on Cartan subalgebra, allowing us to compute the vibrational eigenenergies of CO$_{\text{2}}$ and H$_{\text{2}}$S to spectroscopic accuracy with per-fragment circuits that scale as $O(N)$ in single-qumode gates and $O(N^2)$ in beam-splitter gates for $N$ qumodes. For the case of CO$_{\text{2}}$, we get total gate counts more than an order of magnitude smaller than those reported for qubit-based vibrational algorithms at this system size. These results demonstrate that bosonic quantum devices are a viable platform for excited-state quantum chemistry, particularly for vibrational problems where qubit-based methods incur substantial boson-to-qubit mapping overhead.

Coherent photonic computing uses both the phase and amplitude of light to implement linear operations such as dot products and matrix multiplication but requires phase stability between the interfering paths. This poses a challenge for such strategies when optical data is generated at a remote source due to environmental phase variations in fiber. Conventional approaches to distributed computing rely on optical-to-electrical conversion and buffering, limiting truly real-time and distributed computation. Here, we propose a new strategy via optical injection locking to enable distributed, real-time coherent optical processing without unnecessary conversions in the optical-to-electrical or analog-to-digital domains. Using a semiconductor laser rate-equation model, we explore the conditions required for stable operation by sweeping the power injection ratio, frequency detuning, and modulation conditions of the remote and injected lasers. Our results indicate that higher injection powers broaden the locking margin but more readily exhibit frequency-selective features associated with relaxation oscillations and increased amplitude-phase mixing, whereas lower injection powers yield a narrower, but more predictable operating window which remains stable under large modulation depth. End-to-end symbol-sequence simulations with balanced detection and temporal integration further confirm that reducing the injection ratio suppresses residual remote-modulation components in the injected laser output and improves computational accuracy. Overall, our study provides guidance and design trade-offs for remote coherent detection and distributed coherent photonic computing enabled by injection locking.

Separate absorption charge multiplication germanium tin on silicon avalanche photodiode offers a viable solution to achieve CMOS compatible, high sensitivity detection technology in SWIR or extended SWIR range, leveraging the excellent k-factor of Si as multiplication layer and SWIR or e-SWIR band absorption of GeSn. However, unlike well-established growth of GeSn on Si with thick Ge buffer in-between to reduce threading dislocation density due to lattice mismatch, GeSn on Si APD design requires relatively thin Ge buffer to limit electric field drop through the background p-doped buffer and efficiently transporting photocarrier from GeSn absorber to Si multiplication layer, therefore making growth of high Sn content APD for e-SWIR coverage very challenging. In this work, we experimentally demonstrate GeSn on Si APD up to 12.7 percent Sn, monolithically grown on Si substrate with 122-nm-thick Ge buffer in between, which is considerably thinner than widely used 700-900 nm thick Ge buffer. Stronger relaxation of GeSn absorber via thin Ge buffer favors Sn incorporation, leading to higher Sn content than the nominal target of 8 percent Sn. Device detection range is significantly improved compared to previous work - with cutoff wavelength increased up to 2.7 micro at 300 K, in parallel with high avalanche gain at 77 K up to 21 at 1.55 micro and up to 52 at 2 micro, and good responsivity in SWIR or e-SWIR range, up to 1.45 AW-1 at 1.55 micro and 0.66 AW-1 at 2 micro.

Toroidal rotation is crucial for maintaining stable and high performance plasmas in tokamak fusion reactors. Among its driving mechanisms, the neoclassical toroidal viscosity (NTV) torque–induced by three-dimensional magnetic perturbations–is particularly significant due to its strong impact and controllability, especially for reactor-scale devices like ITER where conventional momentum injection method becomes less effective. However, traditional first-principle NTV modeling is computationally expensive, as it requires solving the drift kinetic equation (DKE) in high-dimensional phase space, therefore precluding any real-time applications such as active control or nonlinear integrated modeling of tokamak plasma. Although surrogate solver shows promising ability for accelerating scientific computations, obtaining the data required to train such model is still very challenging. In this work, we present a novel, data-free approach for developing fast surrogate solver of DKE, by training neural network solely based on physical constraints. Such physical constraints are implemented in two ways: First, the loss function is defined based on physical governing equations; Second, the boundary condition is hard-coded into the predicting model. The proposed model is validated against the dataset generated by first-principle numerical solver, which is found to achieve accurate DKE solution with significantly reduced time consuming. In particular, physics-driven surrogate shows higher physical consistency than data-driven surrogate. In general, our study provides a new idea for developing surrogate solvers in data-scarce scenarios, and demonstrates the potential of purely physics-driven neural networks to accelerate demanding scientific computations.

Recent U.S. Environmental Protection Agency (EPA) analyses have argued that greenhouse gas emissions from U.S. on-road vehicles contribute negligibly to global mean sea-level rise (GMSLR). Here, I replicate and extend the EPA's modeling framework using the FaIR climate model coupled with the BRICK sea-level model, incorporating a probabilistic weighting approach and a longer model timescale to better represent joint climate-sea-level uncertainty. In addition to the baseline SSP2-4.5 scenario and an EPA-consistent emissions reduction case, I examine alternative scenarios reflecting stalled technological progress and a counterfactual pre-regulation vehicle fleet. Results reproduce EPA estimates of approximately 1-2 cm of GMSLR reduction by 2100 under vehicle emissions mitigation but show that these differences grow substantially over multi-century timescales, exceeding 6 cm by 2200. Downscaling to U.S. coastlines reveals larger local effects, particularly along the Gulf of Mexico Coast. These findings highlight the long-term and regionally amplified benefits of emissions reductions from the transportation sector.

For over a decade, photonic time crystals have promised access to novel and exotic optical phenomena, offering fundamentally new ways to manipulate classical and quantum light. Central to these capabilities is the emergence of momentum bandgaps – the counterpart of the more familiar frequency bandgaps in spatial crystals – which have proven difficult to observe experimentally due to the combined need for high modulation speed and strength. To date, these requirements have all but hindered the development of time crystals at optical frequencies. Here, we show that the stringent modulation-speed requirement is a direct consequence of the Manley-Rowe relations governing conventional modulation schemes. We further demonstrate that modulating the plasma frequency of a Lorentz-dispersive material overcomes this limitation. Incorporating a specific form of spatial nonlocality (spatial dispersion) into this already temporally nonlocal (frequency dispersive) framework removes all remaining constraints, enabling momentum bandgaps of infinite extent – in both frequency and momentum – with arbitrarily small modulation speeds and strengths.

Coherent control of quantum materials has progressed along two major fronts: nonlinear phononics, which reshapes lattices to induce emergent states, and Floquet engineering, which tailors electronic band reconstruction via time-periodic driving. Both mechanisms face fundamental limitations at terahertz (THz) frequencies: phononic nonlinearities are intrinsically weak in standard lattices, while electronic Floquet states are often constrained by rapid decoherence upon light-off and by a scarcity of coherence-resolved, multi-correlation probes beyond (quasi-)stationary band structures. Here we report an extreme THz nonlinear-phononics mechanism in $\text{Ta}_\text{2}\text{NiSe}_\text{5}$, where a highly susceptible non-equilibrium electronic correlation bath dramatically amplifies lattice nonlinearities under coherent driving. Utilizing THz two-dimensional spectroscopy as a coherence-tomography tool, we resolve an exceptionally rich landscape of approximately 30 distinct multi-order quantum pathways, including high-harmonic phonon generation, multi-quantum coherences, and multi-wave anharmonic cross-mode mixing. The density and complexity of this extreme manifold establishes a new benchmark for THz nonlinear phononics, as the multi-order quantum pathways surpass the limits of conventional lattice responses. These high-order signals collapse above ~100~K, defining an electronic correlation scale of a coherence-imprinted hybrid electronic-phonon order that governs the sustainability of high-order quantum correlations and nonlinear pathways beyond linear and equilibrium responses. Our results establish a route for correlation-boosted, phonon-anchored periodic Hamiltonian engineering and for certifying such periodically-driven states via multi-correlation coherence tomography.

Membrane wrapping controls nanoparticle uptake during endocytosis, whereas the reverse process of membrane unwrapping accompanies particle expulsion and membrane fusion events. Existing theoretical descriptions typically focus on adhesion and bending energies within the particle membrane contact region and often neglect the deformation energy of the membrane outside the contact zone. This approximation is valid only in the limit of vanishing membrane tension, where the non contact membrane assumes a catenoid like configuration with negligible bending energy. However, at finite tension the deformation of the non contact membrane becomes a dominant energetic contribution. Here we show that this tension dependent non contact energy governs the progression of particle wrapping. By analysing the variation of the total membrane energy with wrapping degree, we uncover a competition between particle adhesion, membrane tension and particle size that determines whether wrapping proceeds, stalls, or reverses into spontaneous unwrapping. This framework reveals a stalling boundary separating regimes of particle uptake and expulsion. To capture the non contact deformation efficiently, we derive a compact phenomenological approximation that accurately reproduces the full numerical solution of the membrane shape. The resulting energetic map provides a unified physical description of particle wrapping and unwrapping, with implications for endocytosis, membrane fusion, and nanoparticle design.

The lattice Boltzmann (LB) equation with a third-order scheme can be regarded as a unified and self-consistent framework of the pseudopotential LB model for multiphase flows. In this work, we theoretically analyze pseudopotential LB simulations of two-phase Poiseuille flow at the discrete level. The finite-difference velocity equation is derived for both grid-aligned and grid-oblique cases. The terms responsible for spurious velocity oscillations near the phase interface are identified. Based on this discrete-level analysis, an improved third-order scheme is proposed to suppress spurious velocity oscillations. This scheme does not introduce any additional conceptual or computational complexity compared with the original one and reduces to the original scheme under static conditions. Numerical simulations of two-phase Poiseuille flow validate the present theoretical analysis and demonstrate the effectiveness of the improved scheme. Then, annular shear flow with a curved phase interface is considered to show that spurious velocity oscillations can also be effectively suppressed by the improved scheme in cases with such interfaces. Finally, the falling of a droplet in a vertical channel is simulated, and the results show that spurious velocity oscillations can lead to an overestimation of the drag force and distinct falling patterns. These results highlight the necessity of using the improved third-order scheme to suppress spurious oscillations and obtain reliable results.

Chern insulator systems are realizable in numerous physical systems and can support robust nonreciprocal transmission of energy. A routing functionality constructed from two counter-oriented Chern insulator regions, using coupled Haldane type systems is proposed. By adjusting the strength of a magnetic field and the frequency of an antenna source, it possible to steer the flow of energy: completely to the left, completely to the right, or split. Alternatively, two sources can be used to direct the flow of energy. This formulation has the potential to serve as a robust and reconfigurable component in optical transmission.

The phenomena of subthreshold photoemission and absorption under coherent and entangled-photon-pair illumination are reviewed, and the generation and properties of entangled-photon pairs are surveyed. Three prominent forms of subthreshold photoemission are examined: one-photon Fermi-tail photoemission (FTP), two-photon photoemission (TPP), and entangled-two-photon photoemission (ETPP). Experimental methods for measuring subthreshold photocurrents and photoelectron count rates are discussed, along with strategies for enhancing selected contributions. Experimental observations of FTP from a CsK$_2$Sb photocathode in a photomultiplier tube (PMT), under both coherent and entangled-photon-pair illumination, are reviewed, and the role of FTP as a noise source in two-photon measurements is elucidated. TPP from Na and CsK$_2$Sb photocathodes in a PMT under classical-light illumination is considered, as are TPP and ETPP from a CsK$_2$Sb photocathode in a channel photomultiplier (CPM) under coherent and entangled-photon-pair illumination. The observation of ETPP is facilitated by the use of a CPM, which suppresses FTP, and by low-intensity illumination, which minimizes TPP. Quantum models of TPP and ETPP accord well with experiment. Entangled-two-photon absorption (ETPA) is analyzed, as are its applications in entangled-two-photon fluorescence microscopy (ETPFM) and entangled-two-photon spectroscopy (ETPS). The three principal forms of subthreshold absorption parallel those of subthreshold photoemission: singleton-induced Boltzmann-tail absorption; cousin-induced/singleton-pair-induced two-photon absorption; and twin-induced ETPA. Heuristic particle and fully quantum models of these processes are compared, and experimental studies of ETPA and ETPFM, together with methods for enhancing their observability, are summarized.

We report measurements of superradiant optical transition radiation in the 550-800 nm range produced by ultrashort relativistic electron bunches at a dielectric boundary. In the measured optical spectra, we observe photon production with quadratic charge dependence in the visible range, consistent with optical frequency coherence determined by the longitudinal electron bunch form factor. The measured spectral envelope is reproduced by a theoretical model of coherent transition radiation (CTR), which is consistent with a sub-femtosecond longitudinal feature within the electron bunch with characteristic scale $\tau_{\mathrm{FWHM}} = 1.2~\mathrm{fs}$. These results extend CTR from the terahertz into the visible spectrum without the use of undulators or externally seeded microbunching. This superradiant boundary emission in the optical range opens a route to tunable coherent radiation from charged particle beams and provides a platform for broadband coherent light generation, enabling new opportunities for phase-sensitive optical experiments.

Experiments on attractive Bose-Einstein condensates (BECs) have unlocked many intriguing out-of-equilibrium dynamics through the interplay between matter-wave dispersion and nonlinear attractive interaction. Competition between these effects leads to fascinating phenomena such as wave collapse, modulational instability, and formation of multidimensional bright solitons. This chapter reviews experimental studies on attractive condensates, with a primary focus on alkali atoms featuring two-body contact interactions. We review recent experimental advances in optical trapping and interaction control techniques, which have enabled new studies on attractive condensates in three and also in lower dimensions. Specifically, we discuss pioneering and recent experimental observations on the dynamics and stability of attractive BECs, including the formation of bright solitons, their collisions, and excitations in quasi-one-dimensional traps. Recent observations of the elusive two-dimensional Townes solitons and vortex solitons are also discussed in this Chapter. We then highlight an experimental technique revealing the nonclassical signatures of modulational instability in an attractive condensate.

Surrogate modeling of body-driven fluid flows where immersed moving boundaries couple structural dynamics to chaotic, unsteady fluid phenomena remains a fundamental challenge for both computational physics and machine learning. We present AeTHERON, a heterogeneous graph neural operator whose architecture directly mirrors the structure of the sharp-interface immersed boundary method (IBM): a dual-graph representation separating fluid and structural domains, coupled through sparse cross-attention that reflects the compact support of IBM interpolation stencils. This physics-informed inductive bias enables AeTHERON to learn nonlinear fluid-structure coupling in a shared high-dimensional latent space, with continuous sinusoidal time embeddings providing temporal generalization across lead times. We evaluate AeTHERON on direct numerical simulations of a flapping flexible caudal fin, a canonical FSI benchmark featuring leading-edge vortex formation, large membrane deformation, and chaotic wake shedding across a 4x5 parameter grid of membrane thickness (h* = 0.01-0.04) and Strouhal number (St = 0.30-0.50). As a proof-of-concept, we train on the first 150 timesteps of a representative case using a 70/30 train/validation split and evaluate on the fully unseen extrapolation window t=150-200. AeTHERON captures large-scale vortex topology and wake structure with qualitative fidelity, achieving a mean extrapolation MAE of 0.168 without retraining, with error peaking near flapping half-cycle transitions where flow reorganization is most rapid – a physically interpretable pattern consistent with the nonlinear fluid-membrane coupling. Inference requires milliseconds per timestep on a single GPU versus hours for equivalent DNS computation. This is a continuously developing preprint; results and figures will be updated in subsequent versions.

Epitaxial aluminum nitride (AlN) thin-film bulk acoustic resonators (FBARs) enable low loss filtering for future 6G systems. They also provide a compact approach for qubit sensing at cryogenic temperatures. However, these devices are rarely characterized systematically from room temperature to cryogenic temperatures, and the mechanisms that limit their cryogenic performance remain unclear. In this work, we study a 15.6 GHz epitaxial AlN FBAR from room temperature to cryogenic temperatures to identify losses from the AlN film and those introduced by the electrodes, anchors, and other device layers. Small signal RF measurements from 294 K down to 6.5 K show an increase in the raw Qmax from 363 to 1589. A temperature dependent model that includes phonon phonon scattering, thermoelastic damping, dielectric loss, electrical loss, and anchor loss helps explain the measured Q(T) trend and identifies a transition from the Landau Rumer to the Akhiezer regime near 270 K. The model indicates that acoustic energy leakage through the anchors limits Q at cryogenic temperatures, while electrical loss dominates at higher temperatures. These results point to two routes toward higher cryogenic Q: better acoustic isolation of the anchors and lower loss electrodes, including superconducting electrodes. Improved anchor design benefits both high frequency 6G filters and cryogenic quantum microwave circuits, while superconducting electrodes are particularly useful for cryogenic operation.

Classical vehicle dynamics contains several widely adopted misconceptions that, while intuitively appealing, may lead to inconsistencies when examined under a rigorous mechanical framework. This paper revisits a number of such misconceptions, clarifying their domains of validity and highlighting potential sources of misunderstanding. The aim is not to dismiss established models, but to promote a more precise and scientifically grounded interpretation of key concepts.

Solid-state cooling via electroluminescent emission from light-emitting diodes is a promising alternative to thermoelectric and vapor-compression refrigeration, but practical performance remains limited by nonradiative losses and unfavorable tradeoffs between efficiency and cooling power. Thermophotonic (TPX) architectures partially address this by recycling PV-generated power back to the LED, improving the coefficient of performance (COP) but introducing a parasitic backward photon flux from the PV that reduces the cooling power density. Here we show that this tradeoff can be circumvented by inserting a nonreciprocal semi-transparent intermediate layer that violates Kirchhoff's law of thermal radiation. The layer permits unity transmission from the LED to the PV while fully absorbing the backward PV flux, functioning as a radiative heat shield that re-emits toward the LED at a lower intermediate temperature. In the idealized limit for $\Delta$ T = 50 K between the hot and cold side, the nonreciprocal filter improves the cooling power density by nearly an order of magnitude over the unfiltered TPX case while preserving the COP benefit, while a reciprocal filter provides no improvement. Incorporating Shockley-Read-Hall and Auger recombination into GaAs and InP-based LED device models, we find enhancements of approximately 50% in both cooling power density and COP persisting across temperature differences from $\Delta$ T = 50 K to 100 K. These results highlight the potential importance of electromagnetic nonreciprocity in improving the real-world performance of thermophotonic cooling devices.

The resonant cavity system has been widely used to search for the electromagnetic response of dark photons, although its achievable detection sensitivity remains at a relatively low level. In this letter, we propose a feasible approach to significantly improve its achievable detection sensitivity by enhancing the detectability of the dark photon-photon dynamical effect, assisted with the steady-state excitation of the target mode in the cavity. Unlike in almost all the previous detection schemes, wherein where the cavity modes are kept in vacuum (and thus only the second-order energy signals can be detected), here the pre-excited steady-state field in the cavity can be used to achieve the coherent amplification of the dark photon response signal, thereby obtaining detectable first-order (rather than the conventional second-order) energy response signals of dark photons. Although the phase of the dark photon field and thus its electromagnetic response signal is stochastic, the amplitude of such a first-order energy response power signal can still be extracted by using mature IQ demodulation technology. As a consequence, we argue that, even considering the influence of the shot noise of the pre-excited steady-state field, the achievable detection sensitivity of this in-situ enhancement detectability, based on the steady-state excitation signal of the target mode, is still at least one order of magnitude higher than those of the current resonant cavity experiments with the same Q-quality factors. Based on existing microwave cavity and weak signal demodulation detection technologies, the feasibility of such a significantly enhanced detectability scheme is also discussed.

Accurate prediction of energy and forces for 3D molecular systems is one of fundamental challenges at the core of AI for Science applications. Many powerful and data-efficient neural networks predict molecular energies and forces from single atomic configurations. However, one crucial aspect of the data generation process is rarely considered while learning these models i.e. Molecular Dynamics (MD) simulation. MD simulations generate time-ordered trajectories of atomic positions that fluctuate in energy and explore regions of the potential energy surface (e.g., under standard NVE/NVT ensembles), rather than being constructed to steadily lower the potential energy toward a minimum as in geometry relaxations. This work explores a novel way to leverage MD data, when available, to improve the performance of such predictors. We introduce a novel training strategy called FRAMES, that use an auxiliary loss function for exploiting the temporal relationships within MD trajectories. Counter-intuitively, on two atomistic benchmarks and a synthetic system we observe that minimal temporal information, captured by pairs of just two consecutive frames, is often sufficient to obtain the best performance, while adding longer trajectory sequences can introduce redundancy and degrade performance. On the widely used MD17 and ISO17 benchmarks, FRAMES significantly outperforms its Equiformer baseline, achieving highly competitive results in both energy and force accuracy. Our work not only presents a novel training strategy which improves the accuracy of the model, but also provides evidence that for distilling physical priors of atomic systems, more temporal data is not always better.

Potential flow theory remains a cornerstone of unsteady aerodynamics due to its computational efficiency in modeling complex flow phenomena. This study presents a significant advancement by integrating a viscous unsteady theory with established numerical vortex methods, creating a hybrid computational tool for low-to-moderate Reynolds number flows. We develop a Viscous Discrete Vortex Method (VDVM) by replacing the classical inviscid Kutta condition with a closure derived from triple-deck boundary layer theory, allowing the model to account for Reynolds number dependencies and unsteady viscous effects. The framework utilizes a three-dimensional vortex ring scheme and an unsteady Bernoulli formulation for load calculation. The model is validated against experimental and high-fidelity CFD data, showing excellent agreement in thrust and torque across a wide operational envelope. Using this validated framework, we conduct a systematic parametric investigation into rotor blade design for electric vertical take-off and landing (eVTOL). A sophisticated optimization of the spanwise geometry was performed: twist distributions were calculated by iteratively solving for axial and tangential induction factors to maintain optimal local angles of attack, while chord distributions were derived using the Adkins and Liebeck framework to satisfy the Betz condition for maximum efficiency. Results demonstrate that this tapered chord and nonlinear twist profile significantly mitigate tip losses and manage spanwise loading. The optimized geometry achieved an 8.99% increase in the efficiency compared to the baseline. This work bridges the gap between high-fidelity viscous analysis and fast vortex methods, providing a versatile tool for the performance-driven design of lifting surfaces in unsteady flight regimes.

Simulations of the transitional flow in Taylor-Couette configuration are carried out to study the effect of the gap width on turbulent transition. The research results show that, under the same radius and the rotating speed of the inner cylinder, as the gap width increases, the flow becomes more stable. It is discovered that the average velocity distribution in the gap approaches the free vortex flow as the width increase and the stability of the flow is enhanced. It is found that, as the gap width increases, the maximum of the energy gradient function (from the energy gradient theory) in the gap decreases, which delays the turbulent transition. As such, the larger the gap width, the later the transition occurs. As the gap width increases, the Reynolds number based on the gap width alone is not able to characterize the flow behavior in Taylor-Couette flows, and the effect of the radius ratio should be taken into account.

Laser-driven, high-brilliance secondary sources (electrons, ions, neutrons, X-rays) open new perspectives for compact material probing and imaging of high-speed events. A key advantage is their ability to perform multiplexed probing, as these sources are generated simultaneously in a single shot using a single laser beam. Here, we report the first quantitative measurements of photon spectra (0.1–100 MeV) and angular distributions in the petawatt interaction regime, using an ultra-intense ($>10^{21}\,\rm W/cm^2$), ultra-short (24~fs) laser pulse. These results are complemented by the characterization of simultaneously produced MeV neutrons. We discuss how these neutrons, once moderated, can enable in-depth material identification via resonance transmission analysis. This work highlights the potential of compact, ultrashort-pulse PW lasers for dual neutron and X-ray radiography of dense materials.

Consciousness and quantum mechanics are among the most puzzling phenomena studied in the sciences. Some scholars suggest they are related, though others think this claim commits a "minimization of mystery" fallacy. The aim of this programmatic paper is to draw attention to a less widely discussed parallel between consciousness and quantum mechanics: both challenge the classical objectivist worldview of science. Under certain assumptions, they are each in tension with a package of metaphysical theses – "non-relationalism", "non-fragmentation", and "one world" – that jointly make up that worldview. This points to three distinct non-objectivist responses: the "relationalist", "fragmentalist", and "many-subjective-worlds" ones. We will map out their pros and cons.

Distributed Acoustic Sensing (DAS) has emerged as a promising tool for environmental and cryoseismological studies, yet its performance under the extreme conditions of the High Arctic remains poorly documented. Here we report on a multi-season DAS experiment conducted across tundra and glacier environments in Hornsund, Svalbard, using 9\,km of fiber-optic cable. The study combines a description of the deployment strategy, instrumentation, and operational constraints with an exploratory analysis of the recorded data to assess the types of cryospheric processes that can be captured with DAS. We document logistical, environmental, and technical challenges and provides guidelines for future experiments, including issues related to coupling, noise sources, cable integrity, and seasonal accessibility. Furthermore, we demonstrate how the dataset can be used for detecting permafrost freezing using noise interferometry, locating icequakes and calving events, as well as monitoring runoff from river-induced seismic noise. The experiment provides a field-based reference for the design and interpretation of future DAS studies in Arctic environments and highlights considerations relevant for long-term cryoseismological monitoring.

Balancing the societal costs of non-pharmaceutical interventions with epidemic suppression requires adaptive feedback control. Rather than relying on state-dependent operational caps, we formulate an infinite-horizon optimal control problem for a networked SIQR model that strictly enforces suppression via a hard spectral constraint on the transmission dynamics. We derive a safety-critical Model Predictive Control (MPC) approximation that embeds this spectral certificate stage-wise, yielding a tunable exponential decay rate. Furthermore, we construct a terminal set ensuring recursive feasibility and a feasible continuation that decays globally, proving positive invariance directly via the physical depletion of susceptibles rather than standard quadratic Lyapunov functions. To handle prediction uncertainty, we develop a robust counterpart that replaces nominal constraints by upper-envelope versions, recovering recursive feasibility and finite-horizon realized decay. We conclude by validating our approaches using simulation studies that leverage public data from counties in the state of Massachusetts.

Theory and empirical science should be in constant dialogue, but often find it hard to understand one another. Here we describe a graduate-level university course we developed to improve matters. The course was designed to help empirically focused biology graduate students read and understand theory papers, despite little prior mathematical training. It uses several evidence-based principles of modern teaching: backwards design, active learning, and just-in-time teaching. This curricular content, emphasizing the nature of evidence and the role of theory in science, aims to improve critical thinking as a means of accelerating scientific progress.

In this investigation, we conduct a systematic computational search for potential singularities in 3D Navier-Stokes flows on a periodic domain $\Omega$ based on the Ladyzhenskaya-Prodi-Serrin conditions. They assert that for a solution $\mathbf{u}(t)$ of the Navier-Stokes system to be regular on an interval $[0,T]$, the integral $\int_{0}^T \|\mathbf{u}(t)\|_{L^q}^p\,dt$, where $2/p+3/q=1,\;q>3$, and the expression $\sup_{t \in [0,T]} \|\mathbf{u}(t)\|_{L^3}$ must be bounded. Flows which might become singular and violate these conditions are sought by solving a family of variational PDE optimization problems where we identify initial conditions $\mathbf{u}_{0}$ with the corresponding flows $\mathbf{u}(t)$ locally maximizing the integral $\int_{0}^T \|\mathbf{u}(t)\|_{L^q}^p\,dt$ for a range of different values of $q$ and $p$ or the norm $\|\mathbf{u}(T)\|_{L^3}$ for different time windows $T$ and increasing sizes $\| \mathbf{u}_0 \|_{L^q}$ of the initial data. We consider two formulations where these expressions are maximized over appropriate Lebesgue spaces $L^q(\Omega)$ or the largest Hilbert-Sobolev spaces $H^s(\Omega)$ embedded in them. The lack of Hilbert-space structure in the first case necessitates development of a novel computational approach to solve the problem. While no evidence of unbounded growth of the quantities of interest, and hence also for singularity formation, was detected, we were able to quantify how "close" the flows realizing such worst-case scenarios come to forming a singularity. A comparison of these results with estimates on the rate of growth of the norms $||\mathbf{u}(t)||_{L^q}$ and of the enstrophy $\mathcal{E}(t)$ indicates that the extreme flows do enter a regime where these quantities are amplified at a rate consistent with singularity formation in finite time, but this growth is not sustained long enough for singularities to form.

Overlapping fish-scale architectures are among nature's most distinctive surface adaptations, combining protection, contact regulation, hydrodynamics, optical and directional mechanical response within a thin textured integument. Here, we show that their biomimetic structural analogues can host deterministic chaos. Biomimetic scale substrates develop chaotic flexural vibrations at modest amplitudes because bending activates unilateral contact and progressive jamming, while built-in asymmetry from unequal texturing biases the restoring response and shifts the onset of chaos. From continuum mechanics, we derive a singular reduced-order model (sROM) that reduces the scale-covered beam to a nonlinear oscillator whose parameters map directly to overlap, scale inclination, damping, forcing, and substrate stiffness. Finite element (FE) simulations validate the model in quasi-static bending and long-time forced response. Stroboscopic regime maps reveal a period-doubling cascade from period-1 to period-2 and period-4, ultimately chaos. Overlap and inclination determine the strength of post-engagement nonlinearity, whereas damping bounds the chaotic operating window. Unequal top-bottom scale distributions break the antisymmetry of the restoring response, generating offset force-displacement laws. This reduced symmetry does not accelerate instability; instead, it delays the onset of chaos and fragments the response into intermittent periodic windows, whereas restoring symmetry can paradoxically widen the chaotic regime. When the texture is sufficiently sparse or steep on one side, it remains dynamically inactive, and the beam behaves as a fully asymmetric one-sided system. The results identify biomimetic scale substrates as a distinct class of contact-rich architectured metasurfaces in which chaos is programmable through geometry rather than large deflection or constitutive nonlinearity.