Discrete-time quantum walks are powerful platforms for simulating quantum transport and information processing. Here we introduce a walker on a one-dimensional lattice whose motion is controlled by three entangled coins, each initialized with the Hadamard gate, aiming to maximize information flow. The walker moves only when all three coins yield the same outcome (HHH or TTT), thus coupling the 8-dimensional coin Hilbert space to the position degree of freedom. By analyzing fully separable, fully entangled (GHZ-type) and intermediate initial states, and using the von Neumann entropy of reduced subsystems, we compute the mutual information $I(C;P;t)$ between coin and position. The results show that initial three-partite entanglement accelerates the growth of mutual information by up to 18\% after ten steps (when compared to the lower of the two separable states), although it exhibits short-term non-monotonic dynamics due to quantum interference. For the first time, we introduce a tunable parameter $α$ (amplitude of non-displacement states) and show that the GHZ state reaches a maximum of mutual information at $α\approx 0.71$ - a key finding for optimal control of information flow. Finally, an integrated photonic implementation using polarization, spatial modes and time bins is proposed, where $α$ can be tuned with nonlinear or electro-optic elements. A scalable numerical framework (Python code) for simulations up to $t = 5$ steps is provided. Our findings establish three-partite entanglement as a dynamical resource for maximizing information flow and spatial spreading, with direct applications in quantum state transfer, entanglement-assisted sensing and programmable photonic quantum processors.

Photonic graph states with advanced topologies can enable measurement-based quantum computing, distributed quantum sensing, and quantum interconnects. However, the efficient generation of photonic graph states is limited by the probabilistic nature of photonic entangling operations and the exponential dependence of generation rate on resource cost. In this work, we study photonic graph state synthesis as a cost-aware decomposition problem, exploiting local Clifford (LC) equivalence to identify more synthesis-friendly representations of the target graph state before decomposition. Specifically, we propose Cost-aware Fusion-based Decomposition (CFD), a three-stage heuristic framework that decomposes a target graph state into ring, star, and linear motifs, and assembles them via Type-I fusion operations to minimize fusion overhead and physical-qubit consumption. We further show that selecting the LC-equivalent graph state with the minimum number of edges provides a highly effective proxy for near-optimal synthesis: in many cases it matches the best generation rate observed within the LC equivalence class under CFD, and in most remaining cases it remains close to it. Numerical evaluations on graph state orbit data and 2D and 3D lattice graph states demonstrate that CFD achieves up to 84.6\% reduction in resource overhead compared to baseline constructions, and yields improvements in photonic generation rate spanning multiple orders of magnitude. These results suggest that combining structure-aware motif decomposition with LC equivalence is a practical and scalable strategy for photonic graph state synthesis.

An accelerated point charge can emit photons with an apparent Fermi-Dirac spectrum, even though the radiation is bosonic and its occupation numbers are not constrained to 0 or 1. The effect arises from a special class of acceleration kinematics and does not rely on thermal equilibrium, horizons, or statistical ensembles.

Identifying a materials platform for creating qubits that are both tunable and resilient towards environmental noise is one of the main hurdles that need to be overcome to realize quantum computation that is practically useful. One pursued avenue to this end is to use superconducting qubits with intrinsic spin-dependent interactions, such as spin-orbit coupling or magnetism. However, the recently discovered class of materials known as altermagnets remain largely unexplored in this context. We here use microscopic calculations to determine how the properties of superconducting qubits are modified when altermagnetic Josephson junctions are included. The key qubit performance parameters, including splitting, anharmonicity, decoherence, and single/coupled-qubit gate operation times, display rich behavior depending on the characteristic properties of the altermagnetic material, such as the strength of the Néel field and the crystallographic orientation of the altermagnetic relative interfaces in the system. We focus in particular on the transmon design and show that the qubit is very well protected against decoherence and simultaneously shows superior anharmonicity both near 0-$π$ transition points and when it is in a $ϕ$-state. We propose that by using strain, the altermagnetic qubit can be moved out of its protected regime to enable faster gate-operation times, and then moved back to its protected state. We also discuss how the altermagnetic properties influence flux qubits and fluxonium. Our results suggest that integration of altermagnetic materials into existing superconducting qubit design can substantially improve their performance due to the unique properties of the altermagnetic band-structure.

We present a novel method for full vector magnetometry using nitrogen-vacancy (NV) centers. In contrast to conventional optically detected magnetic resonance techniques, our method employs two distinct broadband microwave pulses and measures them after transmission through the NV sensor medium, thus capturing the line splitting of the ground state triplet due to the Zeeman effect. Two orthogonally polarized microwave pulses allow resolving all magnetic field components independently by reading out differently oriented NV centers. Simulated data is analyzed using deep neural networks, whose efficacy we expect to translate very well to experiments. Our method yields sensitivities between $5~\mathrm{pT}/\sqrt{\mathrm{Hz}}$ and $100~\mathrm{pT}/\sqrt{\mathrm{Hz}}$ across different magnetic field vector components, while achieving approximately $\mathrm{nT}$ accuracy at a signal-to-noise (SNR) ratio of $70~\mathrm{dB}$. By being capable of accurately measuring magnetic fields down to $25~\mathrm{μT}$, the need for a bias field beyond Earth's magnetic field is eliminated.

The onset of decoherence in open many-body systems lacks a dynamical timescale grounded in the loss of bipartite entanglement. Here, we introduce the $geometric$ $decoherence$ $time$, defined as the earliest moment the monotone relation between logarithmic negativity and Rényi-$\tfrac{1}{2}$ entropy -- exactly equal across any bipartition for pure states -- breaks down under open-system evolution, signaling entropy growth without accompanying entanglement growth. We establish this criterion in both single-particle Gaussian dynamics and many-body Lindbladian evolution. We show that quantum mutual information provides a complementary long-time diagnostic: its asymptotic vanishing is equivalent to factorization of the steady state across the bipartition, a condition strictly stronger than separability, and whenever a product steady state is approached exponentially in trace norm, negativity and mutual information share the same decay rate. In the presence of a strong symmetry, this tracking can fail -- residual classical correlations can survive after entanglement has vanished. In the Kitaev chain with balanced gain and loss, we derive a closed-form solution and show that the topological phase sustains longer coherence times than the trivial phase at identical dissipation, with a local minimum at the chiral-symmetric point. In the interacting XXZ chain, exact many-body evolution shows that local $Z$-dephasing preserves residual classical correlations, whereas gain and loss restore the mutual-information tracking of negativity. Our results establish the geometric decoherence time as a dynamical scale tracking the onset of decoherence.

Quantum transit times have a long history of inequivalent definitions, including phase times, dwell times, and quantum-clock constructions. In this context we revisit the Salecker--Wigner--Peres stationary quantum clock as a phase-sensitive scattering observable, with clock time defined by the energy derivative of the transmission phase shift across the interaction region. For real compactly supported one-dimensional potentials, we show that the raw stationary Peres clock generically contains a universal \(1/\sqrt{E}\) continuum-edge term whose coefficient is fixed by low-energy scattering data. For the attractive square well, this threshold singularity is inherited from the vanishing exterior momentum and the associated scattering matching, rather than from resonant delay itself. We derive the exact stationary clock time for the square well and introduce a new threshold-subtracted clock observable. Away from exceptional zero-energy tuning, the subtraction removes the universal low-energy term and isolates the resonant contribution. Comparison with the dwell time and the transmission Wigner phase delay shows that the threshold-subtracted clock acquires the expected local Lorentzian form near isolated transmission resonances. Near the continuum edge, if \(\varepsilon\) denotes the detuning from threshold, the resonant peak grows only as \(\varepsilon^{-1/2}\), whereas the unsubtracted threshold background grows as \(\varepsilon^{-3/2}\). A symmetric barrier--well--barrier cavity and a numerical asymmetric two-step attractive well provide complementary controls. The result is a new threshold-subtracted stationary-clock candidate that separates universal threshold kinematics from pole-sensitive resonant delay.

The scalability of physical qubit numbers is a central challenge toward a universal fault-tolerant quantum computer. The inherent scalability of atom array quantum computers stems from the identical nature of atomic qubits, so the available qubit resource is primarily limited by the number of atoms that can be trapped and controlled. Here, we robustly trap 11,000 individual atoms in a tweezer array, thereby enabling the available qubit resource to reach the tens-of-thousands scale for the first time among all quantum computation platforms. This advance is enabled by a single metasurface, approximately 2 cm in diameter, that generates the entire tweezer array without the need for microscope objectives, thereby maximizing laser-power efficiency. The large aperture ensures a working distance of about 1.5 cm, allowing the metasurface to be placed outside the vacuum cell and avoiding the technical complications of in-vacuum operation. We further characterize the randomly loaded atom array using the statistical theory of percolation phase transitions. This work takes an important first step toward a quantum computer at the 10,000-qubit scale.

Machine Learning-based quantum error mitigation (ML-QEM) has emerged as a promising approach for improving the performance of noisy quantum algorithms. However, existing ML-QEM methods often have restricted applicability to variational circuits and rely on inaccessible noiseless training data. In this work, we propose a practical ML-QEM protocol tailored to variational quantum algorithms, which generates training data by simulating (near-)Clifford circuits. This data is used for model selection and training, producing a mitigation model that can correct variational circuits with arbitrary parameters and transfer across different target Hamiltonians of similar structure. We benchmark the proposed method on the Variational Quantum Eigensolver (VQE) task for the Sherrington-Kirkpatrick Hamiltonian of up to $n=12$ qubits under various noise models, analyzing its effect on trainability and comparing its performance against standard Zero-Noise Extrapolation (ZNE). The results demonstrate consistent several-fold error suppression across all tested settings and superior performance over ZNE in the high-noise regime, providing evidence for the applicability of the proposed protocol to present-day NISQ processors.

This article provides a concise review of quantum phase space theory, beginning with its foundational principles and the properties of standard quantum quasi-probability distributions, specifically the Wigner, Husimi Q, and Glauber--Sudarshan P functions. We discuss the intrinsic limitations of these distributions, such as the appearance of negative values and phase-space blurring. A significant portion of this review highlights recent theoretical developments, particularly the quantum Wannier basis. This approach establishes a unitary mapping between the Hilbert space and a discretized phase space, yielding a genuine probability distribution in phase space and thereby providing a basis-dependent entropy for pure quantum states. Furthermore, we examine Bourgain's nonperiodic basis as a theoretical framework to circumvent the constraints imposed by the Balian--Low theorem. These developments provide practical tools for numerical studies based on the quantum Wannier basis, as well as conceptual benchmarks for understanding the localization limits of orthonormal phase-space representations.

We derive and analyze the exact solutions of the inverted Dirac-Moshinsky oscillator (IDMO) in $(1+1)$ dimensions, obtained from the standard model via the substitution $p \to p + imωβx$. The upper spinor component satisfies a Weber equation with complex spectral parameter $λ= (E^2-m^2)/(2mω)+i/2$, whose solutions are parabolic cylinder functions $D_ν(ξ)$ with complex order $ν= λ- 1/2$. The physical spectrum is purely continuous ($|E|>m$), with no discrete bound states. Three normalization schemes are developed, and the discrete Gamow resonances at $E_n^\pm = \pm\sqrt{m^2+(2n+1)mω-imω}$ are identified as poles of the resolvent. The negative-energy sector describes antiparticle anti-resonances whose positive imaginary part signals vacuum instability and spontaneous pair production, analogous to the Schwinger effect. The algebraic structure is governed by the principal series of $SU(1,1)$, and the Hamiltonian is $\mathcal{PT}$-symmetric with unbroken symmetry for $|E|>m$.

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° and 270°, 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.

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.

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 $μ$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.

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.

Online debate platforms offer a unique window into the mechanisms driving opinion formation: they capture both explicit political preferences and the peer environment in which those preferences are expressed. In this work, I develop a Bayesian logistic regression model, inspired by ideal point models from political science, to disentangle two competing mechanisms of voting behaviour in online debates: conviction, driven by prior ideological beliefs, and conformity, driven by peer influence. I apply this framework to the Debate.org dataset, comprising approximately 341k votes across 78k debates on 48 socio-political topics. As the debate platform does not provide predefined topic labels for each debate, I infer the topic and stance from the debate text using large language models, and, with a Bayesian approach, I quantify the relative contribution of each mechanism. I find substantial heterogeneity across topics: conviction dominates on issues tied to personal freedoms and lifestyle choices, such as drug legalisation and legalised prostitution, while conformity dominates on several topics widely regarded as paradigmatic cases of moral conviction, including abortion, gun rights, and global warming. These results have implications for the stability of online political discourse and the design of deliberative platforms.

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.

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.

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.

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.

Beam Intercepting Devices (BIDs) include targets, scrapers, collimators, protection absorbers and beam dumps. They enable secondary particle production, shape or clean beams, and protect sensitive components by concentrating beam losses into shielded locations. In high-power proton machines, BIDs operate close to thermo-mechanical limits under intense radiation fields, and their reliability directly impacts accelerator availability. This review summarizes the dominant design drivers (energy deposition, temperature gradients, thermal stress, fatigue, radiation damage and activation), outlines a pragmatic design workflow combining energy-deposition assessment with coupled thermal/structural and fluid dynamic analyses, and reviews representative BIDs at PSI's High Intensity Proton Accelerator (HIPA), including current hardware and developments for the IMPACT project (Isotope and Muon Production using Advanced Cyclotron and Target technology).

Deep learning has become a prevailing paradigm across a wide range of geophysical applications. Yet most existing studies concentrate on methodological refinements -- novel network architectures, physics-informed constraints, or taskspecific loss functions -- while paying comparatively little attention to a more fundamental challenge of any data-driven approach: the availability and representativeness of high-quality training data. This limitation is especially pronounced in geophysics. Unlike computer vision, which benefits from large-scale, well-curated benchmarks such as ImageNet, comparably abundant and reliably labelled geophysical data are prohibitively expensive to acquire and, in most field settings, lack accessible ground-truth supervision. To alleviate this data deficiency, we propose GeoVolDiff, a generative framework for three-dimensional geological volumes. It comprises three coupled stages: (i) constructing a foundational training corpus through physics-based forward simulation; (ii) training a Latent Diffusion Model (LDM) to capture the statistical distribution of 3D geological structures; and (iii) synthesizing diverse, structurally plausible volumes at scale for downstream geophysical tasks. We examine the utility of the synthesized data on a representative downstream task, seismic impedance inversion. Without incorporating any additional physical or geological prior, inversion networks pre-trained exclusively on synthesized data attain competitive performance on both synthetic and field datasets, indicating that data synthesised by the generative model can serve as an effective surrogate for costly field-acquired labels.