Papers

Self-reproducing automata, so-called von Neumann machines, have been repeatedly estimated to be capable of traversing the Galaxy many times given its age. Our mere existence thus seems to exclude an aggressive variant of such a probe having ever been launched in the Milky Way. The Hart-Tipler conjecture considers this to represent contra-positive evidence to the hypothesis that other extra-terrestrial technological entities have emerged in our galaxy. Recently, several authors have extended interstellar colonization calculations to cosmological volumes, but these models are loaded with specific assumptions about behavior and emergence times. Here, we present a bare-bones model of generic artificial infections (such as but not limited to von Neumann probes) at cosmological scale in order to maximize interpretability, an approach closer to the original spirit of the Hart-Tipler calculations. Our model has just three parameters, a spontaneous spawn rate, a propagation speed (u) and a start time for the calculation. Accounting for cosmological expansion, we find that half the Universe is infected by today for u=0.1c propagation starting 4.5 Gyr after the Big Bang if the spawn rate exceeds approximately once per million galaxies. For near-c propagation, this becomes a billion galaxies. Over 99.9% of cosmological volumes are filled with 0.1c if even 1-in-100,000 galaxies have ever spawned an infection. The "cosmological Hart-Tipler" problem therefore offers a remarkably sharp minimal-model constraint on the prevalence of aggressive, self-propagating technological behavior. We explore its implications, such as how anthropic reasoning implies such infections occur and its fine-tuning nature.

This study presents the first clinical integration and experimental demonstration of a nozzle-mounted Compton camera prompt gamma imaging (PGI) system for in vivo proton range verification. Four position-sensitive solid-state Compton camera modules, each containing four cadmium zinc telluride (CdZnTe) detector crystals, were integrated into a modified range shifter mounted directly on the treatment nozzle of a clinical proton therapy gantry. This compact fixed-geometry configuration maintained alignment with the proton beam axis throughout irradiation and enabled stable synchronized data acquisition during pencil-beam scanning delivery. The system was evaluated under realistic clinical proton beam delivery conditions using single-energy and spread-out Bragg peak (SOBP) irradiations at gantry angles of 90{\deg} and 270{\deg}, delivered doses of 2 Gy and 7.5 Gy, and controlled distal range shifts of up to 10 mm. Prompt gamma events were reconstructed into three-dimensional emission distributions using a physics-based Compton scatter reconstruction framework. The system operated reliably during all irradiations and produced reproducible prompt-gamma localization across repeated measurements. Reconstructed emission distributions remained geometrically consistent across gantry angles and demonstrated sensitivity to controlled distal range perturbations, with measurable upstream shifts of the emission hotspot corresponding to reduced proton penetration depth. These results demonstrate the feasibility of a clinically integrated nozzle-mounted quad-camera Compton PGI system for detecting millimeter-scale proton range variations during beam delivery and represent an important step toward clinically deployable prompt gamma-based in vivo treatment verification in proton therapy.

The International Astronomical Union's Office of Astronomy for Development (IAU OAD) uses astronomy as a tool to address societal challenges and contribute to sustainable development. Building on more than a decade of project funding and implementation, the OAD has developed a portfolio of flagship projects that represent tested and scalable applications of Astronomy for Development across thematic areas including socio-economic development, science diplomacy, skills development, inequality reduction, and technology transfer. To support the growth and long-term sustainability of these initiatives, the OAD has established the Flagship Ecosystem, a framework built around four interconnected pillars: Resources, Training, Community, and Implementation. This paper presents an overview of the OAD Flagship projects, the structure and components of the Flagship Ecosystem, and explores how it supports the translation of astronomy-based interventions into sustainable development outcomes. The ecosystem provides open-access resources, capacity-building opportunities, communities of practice, funding mechanisms, and evidence-generation activities that enable individuals and organizations to implement and scale astronomy-for-development initiatives. Grounded in principles of inclusivity, openness, sustainability, participation, and evidence-informed practice, the ecosystem aims to strengthen the global impact of Astronomy for Development while fostering collaboration across diverse sectors and regions.

Two-dimensional higher-order topological insulators are typically classified either as dipolar or quadrupolar depending on the relevant invariant. These two classes were previously considered non-overlapping. Here we put forward an example system exhibiting dipolar and quadrupolar higher-order topology simultaneously, suggest its implementation using the arrays of laser-written evanescently coupled optical waveguides and support our conclusions by the full-wave numerical simulations.

Attosecond pulse generation in solids remains challenging due to the complex phase structure arising from multiple electronic pathways in high harmonic generation (HHG). Here, we identify the macroscopic conditions that enable the synthesis of attosecond pulse trains in graphene via HHG. Using numerical simulations that combine microscopic and macroscopic HHG in single-layer graphene, we show that harmonic contributions associated with different emission times acquire distinct far-field beam-divergence properties, analogous to the short- and long-trajectory contributions in gas-phase HHG. Our results identify how transverse phase-matching can be engineered in solid-state HHG to select short-time or long-time electronic contributions through proper tailoring of the driving-field waveform. In particular, the macroscopic suppression of long-time contributions associated with delayed electron-hole recombinations or imperfect recollisions (usually removed in semiconductor Bloch equation calculations by introducing artificial decoherence times) leads to clean, positively chirped, attosecond pulse trains with a temporal quality comparable to that achieved in gas-phase HHG. These results establish a general framework for controlling attosecond emission in solid-state HHG and provide a route toward compact solid-state attosecond sources.

Neural operator surrogates (NO) approximate PDE solutions orders of magnitude faster than numerical solvers, but suffer from spectral bias: high-frequency content is systematically attenuated, limiting reliability where fine-scale structure matters. Sparse sensor measurements of the field are often available too, offering pointwise accuracy without spectral distortion but covering only a small fraction of the domain. We address this by treating NO predictions as auxiliary observations in a diffusion posterior sampling framework. Our method, FreqNO-DPS (https://github.com/niccoloperrone/FreqNO-DPS), combines an unconditional score-based diffusion prior, trained on high-fidelity simulations, with diffusion posterior sampling (DPS) conditioned on sparse observations and guided by a frozen neural operator. Naive integration reintroduces the surrogate's spectral bias; we resolve this with a closed-form, spectrally shaped guidance score that weights the surrogate by its frequency-dependent accuracy and needs no denoiser backpropagation. A distribution-free analysis bounds the approximation error across the frequency-diffusion-time plane and shows the guidance's frequency dependence is preserved regardless of distributional assumptions. On 3D elastic wavefield prediction at 5% and 2% sensor coverage, the method reaches near-zero spectral bias across all bands, where both the surrogate and sensor-only DPS show systematic high-frequency attenuation. Isotropic guidance, the natural baseline, improves pointwise accuracy but carries the bias into the posterior nearly intact, confirming that frequency-dependent calibration is essential, not merely beneficial. The framework needs only paired surrogate/reference data and exploits no problem-specific structure beyond the residual's approximate spectral diagonality, verifiable for new surrogates via the coherence diagnostic we provide.

Continuously replenished optical tweezer arrays will unlock unlimited-depth quantum circuits with neutral atom qubits. A key bottleneck limiting the cycle time of these systems is removing atoms from tweezers initially loaded with more than one atom. In the conventional technique of light-assisted collisions, slow collisional dynamics limit the timescale for removing excess atoms to several milliseconds. Here, we propose and demonstrate a scheme for selectively removing one atom at a time from multiply occupied tweezers on a microsecond timescale, using intra-tweezer Rydberg blockade and autoionization. We demonstrate the protocol in $^{171}$Yb in two complementary regimes. With two-photon Rydberg excitation from the ground state, we reduce multi-atom probability to 1% in 64.8 $\mu$s, while retaining single atoms in 58.2(2)% of the tweezers, which is comparable to the filling fraction achieved with light-assisted collisions under the same experimental conditions, but over two orders of magnitude faster. With single-photon excitation from the metastable state $^3P_0$, reduced single-atom loss enables a higher filling fraction of 74.8(3)%, at the cost of additional temporal overhead to prepare the atoms in $^3P_0$. The final filling fraction is limited by an unexplained two-body loss mechanism, which, if solved, could enable fast, quasi-deterministic loading.

Understanding and anticipating scientific change requires models that distinguish between endogenous consolidation and exogenous diffusion of scientific concepts. Using the quantum computing subtree of concepts in OpenAlex, we construct a temporally resolved concept co-occurrence network and track each concept pair through its upstream citation lineage and downstream diffusion. We train LightGBM models on distributional and diversity-aware features to predict four outcomes: endogenous reinforcement, exogenous diffusion, their ratio, and diffusion entropy. After controlling for overall publication growth of the scientific body, endogenous reinforcement proves largely unpredictable in the primary quantum-computing benchmark. In contrast, exogenous diffusion and entropy are strongly predictable ($R^2$ up to $0.78\`a) and are driven by upstream heterogeneity, citation breadth, and distributional dispersion, as shown by SHAP analyses; replications on robotics, advanced materials, and neuro implants confirm that exogenous diffusion remains the top-ranked target across fields ($R^2_test \sim 0.60-0.87$), while endogenous predictability rises markedly in neuro implants (R^2_test = 0.83), indicating that the quantum-computing asymmetry does not generalise uniformly. Case studies reveal that sharp entropy increases coincide with the opening of new conceptual frontiers, while entropy collapses signal technological convergence or paradigm displacement. These results demonstrate that conceptual diffusion is governed by stable structural regularities embedded in semantic and citation environments. By identifying early diversity-based signals of cross-domain uptake, the approach provides a scalable foundation for anticipatory scientometrics, technology foresight, and innovation-oriented policy analysis in rapidly evolving research fields.

As Moore's law reaches its limits, Ising machines offer a promising alternative computing approach for difficult optimization problems. However, many analog, time-continuous Ising machines rely on gradient-descent-like dynamics to find solutions, which can limit speed and robustness. We investigate whether momentum and Adam optimization can improve these systems. Since these optimizers are traditionally formulated in discrete time, we derive continuous-time versions suitable for analog, time-continuous Ising-machine dynamics. On Max-Cut benchmarks, we find that Adam-based dynamics substantially reduce time-to-target and improve solution quality compared with gradient-descent- and momentum-based dynamics. We further introduce a first-order continuous-time approximation of Adam that is intended as a simpler starting point for future physical implementations and while performing better than the full Adam formulation in a continuous-time setting. We also study a purely algorithmic discrete-time setting, where the performance gap is reduced on easier problem instances, while the Adam-based update rule performs best on harder weighted problem instances. These results identify continuous-time Adam dynamics as a powerful design principle for analog Ising machines.

Quantum Erasure Imaging (QEI) turns delayed-choice erasure into a practical imaging protocol. Entangled photon pairs encode two classical modalities, absorption $T(x,y)$ and a phase-sensitive cosine quadrature of $\phi(x,y)$, reconstructed from a single run of time-tagged coincidences by retrospective sorting on a remote ancilla. Measuring the ancilla in H/V yields $T$ via which-path information; D/A yields interference visibility $\propto \frac{2\sqrt{T}}{T+1}\cos\phi$; and a rotated orthonormal analyzer continuously trades between them. We derive balanced two-port estimators whose denominators are analyzer independent (completeness / no signaling), together with Fisher information (FI) and Cramér–Rao bounds (CRBs) that establish an equivalence to time division under labeled randomization. The advantages of QEI are operational: single-run acquisition, perfect co-registration, and remote / delayed mode choice. We illustrate the protocol with Monte-Carlo simulations and open source our code.

We introduce an explainable machine-learning approach that forecasts the structural precursors of scientific breakthroughs – the emergence and intensification of links between research concepts – by modelling how OpenAlex concept networks evolve over time. Using 59 semantic and topological features, a two-stage LightGBM model jointly predicts the formation and the future weight of concept pairs, adding a regression stage that quantifies expected intensity to prior link-existence forecasts. Relative to the state of the art, the approach improves accuracy and explainability at once: comparative validation across four technology and biomedical domains yields ROC-AUC in [0.954, 0.967] at all horizons without re-tuning, exceeding the roughly 0.90 of prior models, while every forecast rests on structural, auditable features rather than opaque embeddings. Classification performance is high (AUC about 0.95) and regression remains stable (RMSLE 0.45 to 0.6 over one to five years). Feature attribution shows that structural factors – particularly Adamic-Adar similarity and degree-based Hadamard measures – consistently drive accuracy, suggesting that breakthrough-relevant recombinations emerge in tightly connected sub-networks. Two expert-anchored cases, quantum annealing and AI-enabled quantum architectures, show the model surfacing technological convergence consistent with expert expectations. We then outline a three-layer decision architecture – detection, expert translation, institutional integration – that turns these forecasts into evidence-based research strategy and policy, anchored in open data and explainable features.

The quantum-computing speedup theory considers the symmetric structures and properties of quantum systems as the fundamental Quantum-Computing-Speedup (QCS) resources which are responsible for exponentially speeding up quantum computing and simulating. At present a large and important problem is how to make use of the fundamental QCS resources to speed up essentially quantum computing and simulating. Here the author makes a great effort toward solving this important problem. The theoretical research work in this paper is mainly divided into the two Parts I and II. The Part I investigates mainly the multiple-quantum operator algebra spaces. And the relationships are analyzed among the multiple-quantum operator algebra spaces, quantum simulating for the unitary time-evolutional processes, and the fundamental QCS resources which exist in the different kinds of basic quantum spaces: the multiple-quantum operator algebra spaces, the density operator spaces, and the Hilbert spaces. It concludes that the multiple-quantum operator algebra space must be positioned as the central place where the fundamental QCS resources are exploited to speed up quantum computing and simulating. The Part II investigates mainly the subspace-selective unitary manipulation based on the Hilbert-space symmetric structures. Recognize that the multiple-quantum operator algebra space is the central place. Then those fundamental QCS resources original from the Hilbert space (a quantum-state space) must be explicitly taken into account in the multiple-quantum operator algebra space (a linear operator space). This is an important problem. The subspace-selective unitary manipulation is able to solve this problem. It aims to harness the fundamental QCS resources original from the Hilbert space to speed up quantum computing and simulating in the multiple-quantum operator algebra space.

This study presents a hybrid experimental and computational framework that couples in vitro 4D phase-contrast magnetic resonance imaging (4D-flow MRI) measurements with data assimilation and linear modeling to characterize the flow linear amplification mechanisms. We manufacture an idealized stenosis phantom with a cosine-shaped contraction and acquire three-dimensional (3D) mean velocity measurements at Reynolds number 3960 using 4D-flow MRI. To overcome the inherent displacement artifact, we perform data assimilation via a two-step optimization strategy using physics-informed neural network (PINN). This approach first corrects measurement artifacts before extracting the unknown mean pressure and eddy viscosity fields. The RANS-compatible mean flow then serves as the base state for global linear stability analysis (LSA) and resolvent analysis. The global LSA reveals stationary eigenmodes located in the recirculation bubble that exhibit a positive growth rate for azimuthal wavenumbers m=2 and m=3. The forced dynamics of this eigenmode dominates the low-frequency dynamics. Resolvent analysis identifies a broadband pseudo-resonance associated with the convective instability of the separated shear-layer, with maximal amplification for m=0. This methodology demonstrates how integrating sparse experimental MRI data with physics-based modeling enables the identification of mean fields and coherent structures. By leveraging the capabilities of 4D-flow MRI to non-invasively measure 3D velocity fields without requiring physical or optical access, this approach is a first step in the application of linear analysis to cardiovascular flows.

This study introduces GraphShed, a data-derived group-finder that applies top-down watershed segmentation to Voronoi-induced graphs, identifying galaxy systems directly from the density field via a GraphShed-derived linking length, with no density thresholds or tunable parameters. A GraphShed galaxy group catalog compared to a Friends-of-Friends (FoF) catalog built from the IllustrisTNG100-1 simulation with a box size of $\sim 75$ Mpc/h yielding $1067$ and $987$ systems, respectively. The GraphShed-derived linking length adapts to the global clustering power of the sample, varying by $\sim 18 \%$ relative to FoF across different sample selections; thus, catalog differences originate in the watershed segmentation. Cross-catalog comparison shows that $\sim 58 \%$ of FoF systems are identically reproduced by GraphShed, $\sim 5 \%$ are completely removed, and the remainder are partially shaved and/or split into multiple systems. While the $M_{200}$ distributions of the two catalogs are statistically consistent, other structural properties including, $R_{200}$, sphericity, compactness, spin, and centroid shift, differ significantly in at least some richness ranges. The unweighted two-point correlation function of GraphShed systems exhibits a higher amplitude on small scales, $r \lesssim 1$ Mpc/h, and agrees with FoF for $r\gtrsim 5$ Mpc/h. A velocity-based classification reveals that GraphShed resolves more interacting pairs than FoF, demonstrating its ability to distinguish nearby overdense structures that position-only methods merge into single systems. These results demonstrate that GraphShed preserves cosmological statistics while providing a more resolved detection of galaxy systems and their dynamical interactions.

Online debate platforms offer a window into whether exposure to diverse crowds changes how people reason: they capture political preferences, the perceived quality of arguments, and the ideological diversity of the audience. Drawing on theories of deliberative democracy and cross-cutting exposure, I develop a Bayesian logistic regression model that decomposes voting behaviour in online debates into three components: conviction (agreement between a user's prior beliefs and a debate's stance), persuasiveness (perceived argument quality), and cross-cutting exposure (the ideological diversity of a debate's prior voters). I apply this framework to the DDO dataset: approximately 341k votes across 78k debates on 48 socio-political topics. As the platform provides no predefined topic labels, I infer topic and stance from debate text using large language models. I test whether a more ideologically diverse prior audience weakens a voter's conviction, a central claim from the cross-cutting exposure literature. Persuasiveness is the strongest and most consistent predictor of vote choice, never outweighed by conviction, though conviction remains a credible predictor on several topics tied to personal freedoms. Cross-cutting exposure moderates conviction on 4 of 47 topics, though these results are sensitive to topic classification and sparse coverage, with implications for the design of deliberative platforms.

Determining the neutrino mass ordering remains a central open problem in particle physics. While next-generation long-baseline experiments are expected to resolve this question, current data provide limited sensitivity because the spectral differences between normal and inverted ordering are subtle and entangled with parameter degeneracies. We investigate a machine-learning strategy for mass-ordering determination using a feed-forward neural-network classifier trained on synthetic long-baseline datasets generated with three-flavour oscillation probabilities, matter effects, and statistical fluctuations. We evaluate the classifier against standard $\chi^2$ and $\log\mathcal{L}$ approaches using common discrimination metrics, including receiver-operating-characteristic curves, to quantify sensitivity and to illustrate how operating points can be selected to prioritise purity or efficiency. We find that the neural network achieves performance comparable to conventional fits for the scenarios studied, providing a flexible, independent cross-check of established analyses. The framework can be extended to incorporate systematic uncertainties and to explore joint inference of oscillation parameters, and it may also serve as a pedagogical tool for introducing machine-learning methods in neutrino physics.

Separating waves and mean flows is a fundamental challenge in ocean dynamics. Lagrangian filtering of passive-tracer time series into high-frequency wave and low-frequency mean-flow components provides a practical route, as the relevant time scales are often cleanly split in the Lagrangian frame. Here we show that Lagrangian filtering can be applied to surface drifter observations, providing a powerful approach to quantify wave and mean-flow contributions to surface kinetic energy statistics. A key methodological choice is to implement the filtering in a generalized Lagrangian mean (GLM) framework, attributing filtered velocities to mean rather than particle trajectories; this produces more physically interpretable diagnostics. Using Gulf of Mexico drifter data, we compute second-order velocity structure functions (SF2s) for waves and mean flow components across spatial scales. With these filtered SF2s as a benchmark, we illustrate that Helmholtz decomposition of unfiltered SF2s alone should not be interpreted as a dynamical wave-mean decomposition. Applying Helmholtz decomposition to the filtered SF2s further illuminates seasonal dynamics. Mean-flow surface kinetic energy is rotationally dominated at scales larger than O(1) km, while at and below O(1) km, divergent and rotational contributions are approximately equipartitioned in both summer and winter, suggesting low-frequency divergent motions and possible associated vertical exchange. Winter mean flows are more active than summer mean flows over 500 m-10 km. Super-inertial motions are broadly consistent with linear waves. In winter, wave kinetic energy is concentrated at smaller spatial scales than in summer, possibly reflecting enhanced downscale transfer by stronger submesoscale mean flows.

Iron-rhodium (FeRh) has a first-order phase transition near room temperature between antiferromagnetic (AFM) and ferromagnetic (FM) phases, making it a promising material for magnetic memory technologies like heat-assisted magnetic recording (HAMR). It has a comparatively sharper phase transition and lower writing temperature than alternative materials, implying less thermal engineering constraints and an increase in write/read head lifetime. Despite great effort, however, AFM-based magnetic memory using FeRh has not yet been realized. Here, we employ both wide-field and scanning nanoscale quantum diamond microscopes (QDMs) to image directly the magnetic field of a patterned FeRh thin film structure under ambient conditions, demonstrating a magnetic recording technique that is reliable and robust. We experimentally identify coupling between the Néel and magnetization vector directions; and also, that the magnetic orientation of the FM phase uniquely determines the Néel vector in the AFM phase, due to pinned uncompensated magnetic moments (UMMs) in the FeRh structure. Thus, the magnetic orientation is maintained when the system is cycled between AFM and FM phases, providing the foundation for a practical, AFM-based magnetic memory.

We demonstrated hybrid free-space optics (FSO) and D-band (110-170GHz) millimetre wave transmitter enabled by a single phase-locked laser pair, simultaneously enabling ultra-low RF phase noise and optical linewidth for communications. Based on this, we further study combined capacity with beam angle misalignment using >100Gb/s signalling.

We investigate electrostatic ion-acoustic precursor solitons in a plasma flow past an absorbing charged obstacle using two-dimensional (2D) electrostatic PIC simulations. A key outcome of the present formulation is that ion-temperature anisotropy can enable precursor formation even in regimes where isotropic plasmas, due to Landau damping, cannot sustain such structures. Specifically, temperature anisotropy in the 2D drifting flow along the x-direction, arising from a reduction in the transverse thermal velocity (y-direction) relative to the parallel thermal velocity (x-direction), favors the generation of coherent upstream structures whose effectiveness increases with stronger anisotropy. Both positive and negative obstacle polarities are considered to identify the conditions for upstream nonlinear structure formation. A negatively biased plate produces only a wake-like response, whereas a positively biased plate generates upstream density pulses. This study offers physical insight into nonlinear wave formation in streaming plasmas over charged objects and could be useful for plasma-based debris detection in the low Earth orbit (LEO) region.

We generalize the augmented Roothaan-Hall (ARH) Hessian formalism to the self-consistent field (SCF) optimization of spin-restricted open-shell (RO) wavefunctions, encompassing high-spin, low-spin, and two-determinant electronic states. A detailed ARH formulation is presented. We demonstrate that ARH is a highly efficient optimization algorithm for rapidly identifying accurate SCF solutions, primarily owing to its systematic construction of an effective Hessian, particularly in the case of Euclidean quadratic energy functions. The ARH is built upon a universal energy formulation, including grid-based integration, for spin-restricted closed-shell, spin-unrestricted and RO density functional theory (DFT), thereby unifying and simplifying their numerical implementation. The performance of the present method is evaluated using two benchmarking studies. First, for a series of iron-sulfur clusters exhibiting different spin states, which represent notoriously challenging SCF problems, the ARH algorithm demonstrates superior convergence efficiency relative to L-BFGS and truncated Newton methods, requiring much fewer RO-SCF iterations to achieve convergence. Second, the ARH approach tends to avoid convergence to higher-energy stationary points in two-determinant RO-SCF calculations for singlet excited states of selected photoactive compounds. Finally, an application of the ARH-based RO-SCF is illustrated by an investigation of the mechanistic origin of the spin-crossover phenomenon in Ni(II)-porphyrin complex utilized as a contrast agent.

Achieving low-voltage, nanosecond multi-level programming and non-destructive read-out of ferroelectric non-volatile memories (NVM) is critical for analog in-memory computing architectures relying on ferroelectric capacitive devices (FeCap). We integrate HfO2/ZrO2 ferroelectric nanolayers concurrently in the BEOL of CMOS and on SiO2/Si, achieving nanosecond multilevel switching with programming voltages below 5 V. Partial ferroelectric switching enhances FeCap endurance above 1011 cycles, leading to MemCapacitance (MC) states with non-destructive read-out and 10-year retention. However, experiments reveal the collapse of the MC window for read frequencies above 1 MHz. To overcome this speed limit, we introduce a novel, non-destructive readout methodology. Using electrical pulses with widths down to 20 ps, below the RC time constant of the FeCaps, we enable measurement of the polarization-dependent leakage current, providing ultrafast and non-destructive read operations at only 14 fJ.

To migrate, cells exert traction forces on the extracellular matrix (ECM) – a biopolymer network that often exhibits nonlinear strain-stiffening elasticity. Cellular tractions can therefore stiffen the ECM. At the same time, cells exert stronger tractions on stiffer ECM. Here, we show theoretically that this traction-stiffness feedback can produce traction bistability and hysteresis. As a result, increasing either the ECM's nonlinear elasticity or cellular contractility leads to a discontinuous transition from low to high tractions. This traction jump might trigger collective cell migration as the ECM stiffens, for example during development and tumor progression. Moreover, the bistable behavior might provide robustness to cellular traction forces when cells migrate through mechanically heterogeneous environments.

A forefront research question is how energy evolves in weakly collisional plasmas for which departures from local thermodynamic equilibrium (LTE) are significant. The standard approach is studying the terms in the non-LTE energy evolution equation derived by taking the second moment of the Boltzmann equation, but the resultant fluid metrics do not retain information about which particles at which velocities drive energy evolution. A widely studied channel for internal energy density evolution is the pressure-strain interaction. Here we employ the kinetic pressure-strain [S. A. Conley et al., ${\it Phys. Plasmas,} {\bf 31}$, 122117 (2024)], a phase space diagnostic whose velocity-space integral recovers the pressure-strain interaction to disambiguate the contributions to pressure-strain interaction from disparate particle populations in composite phase-space densities. We develop phase-space analogs of the pressure-strain interaction decompositions to provide the phase-space origins of normal vs. sheared flow. We introduce the "kinetic strain-rate" tensor, the phase-space analog of strain-rate tensor, which we argue is needed to interpret phase-space origins of pressure-strain interaction. To demonstrate the utility of these quantities, we investigate them for composite electron distributions near the electron diffusion region in two-dimensional particle-in-cell simulations of antiparallel symmetric magnetic reconnection. We find that the phase space-based diagnostics isolate the roles of distinct populations. These results contribute to a growing body of work providing new methods for quantifying phase space energy evolution for a broad array of processes, from magnetic reconnection to collisionless shocks and turbulence, opening new pathways for answering longstanding problems of particle energization in weakly collisional plasmas.

Beam instrumentation is known as the 'eyes and ears of a particle accelerator'. It provides the data required for the most basic operating functions such as beam steering and intensity measurement, but also for diagnosing problems and optimising performance. This paper describes some of the many techniques used in the field, with an emphasis on mechanical designs, issues and current research trends.

We study isotropic turbulence decay in the context of the k-epsilon model, which solves the dissipation and kinetic energy equations. In modeling the dissipation equation, the coefficient C_epsilon2, suggested by Hanjalic and Launder [Journal of Fluid Mechanics, 1972] [1], is related to the temporal decay power-law by n = 1/(C_epsilon2 -1 )) and is assumed to be a constant value. In this work, we perform high-fidelity numerical simulations to examine the mathematical terms responsible for the decay of isotropic turbulence, considering both scenarios of forced and decaying turbulence. Our data suggest that the instantaneous C_epsilon2 not only depends on the instantaneous Reynolds number but is also sensitive to the history of energy injection in turbulence. We attribute these observations to the finite time required for the cascade from energetic to dissipative scales. Considering data from both decaying and growing forced turbulence, we develop an evolution equation for C_epsilon2 with Reynolds-dependent coefficients. We demonstrate that this model accurately captures the time evolution of dissipation and kinetic energy over a wide range of Reynolds numbers under a wide range of forced and decay scenarios.

A spherical Penning trap has well-separated, clean microwave resonances, making it attractive for precision measurements of the electron magnetic moment and for dark-photon and axion searches with trapped electrons. We demonstrate single-electron trapping in a spherical Penning trap and characterize its microwave resonance structure. The design, single-electron detection, microwave mode characterization, and advantages of this geometry are presented.

Artificial spin ice (ASI) based on a square lattice with a topological defect are known to generate orbital angular momentum (OAM) in diffracted X-ray beams. A previous investigation of ASI with even-charge topological defects showed both charge and magnetic X-ray scattering yield photon OAM, but these were confined to integer OAM values. However, the period of the square ASI's antiferromagnetic ground state is twice the period of the structural ground state, which should lead to fractional OAM from magnetic scattering when the topological defect has odd-charge. We employed photoemission electron microscopy to confirm that these ASIs order into antiferromagnetic ground states with protected superdomain walls that provide the phase discontinuity required for fractional OAM. Resonant, coherent X-ray scattering from ASIs with topological defects of charge 1 yields integer-valued X-ray OAM at structural charge peaks and fractional X-ray OAM at magnetic peaks. For thermally active ASIs, the fractional OAM beam exhibits fluctuations in the position of the phase discontinuity and thus dynamic rotation of the beam.

The use of digital tools like educational video games and interactive simulations is of great importance to physics education. This study investigates the sequencing effects of an educational game, 'Photon Jump' and PhET Photoelectric Effect simulation on students' perception of such tools for learning about the photoelectric effect. Using a counterbalanced quasi-experimental crossover design, a total of 55 physics and engineering students from a calculus-based physics course were divided into two groups of comparable sizes and administered the game and the simulation in reverse order followed by 9 open ended reflection questions. The results show a clear preference for the game-based activity over the simulation. Additionally, gaming frequency showed no correlation with willingness to use similar tools, suggesting broad accessibility. Thematic analysis revealed that intuitive and explorative learning through the game was reinforced by the analytical aspect of the simulation.

The use of digital tools and multiple representations like educational games and interactive simulations is of great importance to physics education. This study investigates the sequencing effects of an educational video game 'Photon Jump' and the PhET Photoelectric Effect simulation on pre-service teachers' understanding of the photoelectric effect. Using a counterbalanced quasi-experimental crossover design, pre-service teachers enrolled in a physics course (N = 83) experienced both interventions in opposite orders. Conceptual understanding was measured across three standardized assessments, complemented by open-ended reflection questions on participants' preferences and willingness to use both tools for future learning. The simulation-first sequence yielded a greater significant improvement in performance p = 0.001 as compared with game-first sequence p = 0.06. participants' preferences for using the game as opposed to the simulation were dependent on the sequence that they were randomly assigned to. Findings underscore the complementary strengths of game-based and simulation-based instruction, highlighting the importance of choosing the right sequence when using multiple representations in teaching abstract physics phenomena to pre-service teachers.