Papers

The two promising methods for capturing high-speed flows are local artificial diffusivity (LAD) and centralised gradient-based reconstruction (C-GBR), the former being computationally economical and the latter being more robust and stable but expensive. While the LAD approach captures discontinuities by adding artificial fluid transport coefficients, C-GBR employs a wave appropriate discontinuity sensor to obtain cleaner results and utilises the HLLC approximate Riemann solver for computing inviscid fluxes. The efficacy of these schemes is initially demonstrated in single-species 1D and 2D test cases. Moreover, the shock-capturing capability is assessed for 3D supersonic and hypersonic turbulent boundary layers. The accuracy of LAD predictions is comparable to that of C-GBR for the test case of a supersonic turbulent boundary layer. From the stability front, all simulations are found to be stable with the C-GBR scheme, whereas the LAD-based simulations are observed to abruptly diverge for supersonic and hypersonic flows over compression corners with stronger shocks and larger flow separations. From the computational front, the LAD-based schemes are $1.17 - 2.32 \times$ faster than the monotonicity-preserving explicit/implicit C-GBR schemes. A hybrid approach leveraging the strengths of LAD and GBR schemes is proposed as a promising solution for high-speed turbulent flows with strong shock-boundary layer interactions. The efficacy of the hybrid LAD-GBR solver is demonstrated for the compressible triple-point and supersonic compression ramp test cases. For the M2.9, $24^{\circ}$ case, the hybrid solver was stable and achieved a notable $1.67 \times$ speed-up over the C-GBR scheme.

Subglacial blisters form due to the rapid drainage of supraglacial lakes into grounded ice sheets, and are characterised by elastic ice uplift and transient ice-velocity anomalies. Although blister occurrence is confirmed by observations, the dynamics of blisters and their impacts on ice flow remain poorly represented in current subglacial hydrology models, as typical cavity-channel system models cannot capture short-timescale blister formation, propagation, and relaxation. Here we present a unified, self-consistent modelling framework that directly couples blister evolution with the subglacial drainage system, extending existing subglacial hydrology models to account for transient responses to rapid lake drainage events. Numerical simulations, motivated by field observations of wintertime lake drainages, reveal distinct seasonal behavior: during summer, lake drainage generates short-lived blisters that rapidly leak water into a pre-existing drainage system of efficient, channelised water pathways, whereas winter drainage results in persistent blisters that propagate slowly and serve as the primary meltwater pathway at the ice-bed interface. The dynamics of blister propagation and leakage in our model are governed by effective viscosity and a characteristic leakage length scale, which reflects the connection between the blister and the surrounding hydrological network. This unified model offers a valuable tool for investigating blister dynamics and their interplay with subglacial hydrology, facilitating the interpretation of observed surface uplift and ice-velocity variations following supraglacial lake drainage events.

Many biological, culinary, and engineering processes lead to the co-encapsulation of several soft particles within a liquid interface. In these situations the particles are bound together by the capillary forces that deform them and influence their biological or rheological properties. Here, we introduce an experimental approach to encapsulate a controlled number of soft beads within aqueous droplets in oil. These droplet-encapsulated gels are manipulated in a deformable microfluidic device to merge them and modify the liquid fraction. In the dry limit the contact surface between the hydrogels is found to be determined by the elastocapillary number $E_c$, with the contact radius following a $E_c^{1/3}$ dependence, indicating that the deformation increases for soft or small particles. When multiple beads are co-encapsulated within a single droplet they can be arranged into linear or three-dimensional aggregates that remain at a local energy minimum.

A new efficient and accurate method is presented for simulating frequency-domain borehole electromagnetic (EM) measurements acquired in the presence of three-dimensional (3D) variations of anisotropic subsurface conductivity. The method is based on solving the quasi-static Maxwell equations with a finite-volume discretization via goal-oriented block-quadrature reduced-order modeling. Discretization is performed with a Lebedev grid that enables accurate and conservative solutions in the presence of any form of anisotropic electrical conductivity. Likewise, the method makes use of a new effective-medium approximation to locally account for non-conformal boundaries and large contrasts in electrical conductivity, especially in the vicinity of EM sources and receivers. The finite-volume discretization yields a large symmetric linear system of equations, which is reduced to a set of smaller structured problems via block Lanczos recursion. The formulation also enables the efficient calculation of the adjoint solution, which is necessary for gradient-based inversion of the measurements to estimate the associated spatial distribution of electrical conductivity, i.e., to solve the inverse problem. The method is applied and verified for borehole ultra-deep azimuthal resistivity (UDAR) measurements commonly used in subsurface geosteering and well navigation. Efficiency, robustness, and scalability are demonstrated using synthetic UDAR data generated in a three-dimensional formation inspired by North Sea geology. The numerical results confirm suitability for integration into real-time UDAR processing frameworks.

We extend the Harrow-Hassidim-Lloyd (HHL) algorithm, which is well-studied in the qubit framework, to its qutrit counterpart (which we call qutrit HHL, as opposed to qubit HHL, which is HHL using qubits), and develop a program for its implementation. We design Weyl-Heisenberg gadgets, the qutrit equivalents of Pauli gadgets, and come up with a practical implementation scheme for qutrit HHL. We test HHL with qutrits for simple matrices and verify the results against the expected outcomes. We apply the algorithm to quantum chemistry, and in particular, to the potential energy curve calculations of the model problem of the Hydrogen molecule in the split valence basis. We do so for two cases: 1-qutrit and 2-qutrit input states, where the latter makes use of our gadgets. We compare the number of qudits and the number of gates required between qubit and qutrit HHL implementations. In general, we find that for a fixed precision, the qutrit HHL circuit requires fewer number of qudits and comparable number of two-qudit gates than its qubit counterpart.

We examined the asymmetric deformation in collisions and the transition conditions from oblique to normal collisions and non-collisions to address the problem of oblique collisions of rigid bodies in classical mechanics. A closed solution satisfying the fundamental equations and adhering to the energy conservation law without introducing new material parameters was derived. The solution exhibited a nonlinear relationship between post-collision velocity and initial state parameters, contrasting with the linear results of existing studies. This solution avoided the fallacy in Whittaker's hypothesis that kinetic energy might increase after a collision. Consequently, the solution presented herein fundamentally resolves Kane's puzzle, previously overlooked in classical mechanics.

Growing energy demands of modern digital devices necessitate alternative, low-power computing mechanisms. When incident loads take the form of acoustic or vibrational waves, the ability to mechanically process information eliminates the need for transduction, paving the way for passive computing. Recent studies have proposed systems that learn and execute mechanical logic through buckling, bistability, and origami-inspired lattices. However, owing to the large timescales of shape morphing, such concepts suffer from slow operation or require active stimulation of adaptive materials. To address these limitations, we present a novel approach to mechanical logic, leveraging the rich dynamics of wave propagation in elastic structures. In lieu of traditional forward-design tools, such as band diagrams and transmission spectra, we employ a multi-faceted topology optimization approach, enabling us to identify candidate waveguide configurations within an extremely large design space. By incorporating voids within an otherwise uniform substrate, the optimized waveguides are able to precisely manipulate wave propagation paths, triggering desirable interferences of the scattered wavefield that culminate in energy localization at readouts corresponding to a given logic function. An experimental setup is used to demonstrate the efficacy of such logic gates and their resilience to non-uniform loading. By implementing these building blocks into a mechanical adder, we demonstrate the scalable deployment of more sophisticated mechanical computing circuits, opening up new avenues in mechanical signal processing and physical computing.

Optical kernel machines offer high throughput and low latency. A nonlinear optical kernel can handle complex nonlinear data, but power consumption is typically high with the conventional nonlinear optical approach. To overcome this issue, we present an optical kernel with structural nonlinearity that can be continuously tuned at low power. It is implemented in a linear optical scattering cavity with a reconfigurable micro-mirror array. By tuning the degree of nonlinearity with multiple scattering, we vary the kernel sensitivity and information capacity. We further optimize the kernel nonlinearity to best approximate the parity functions from first order to fifth order for binary inputs. Our scheme offers potential applicability across photonic platforms, providing programmable kernels with high performance and low power consumption.

Matter under high pressure often exhibits attractive properties, which, unfortunately, are typically irretrievable when released to ambient conditions. Intuitively, nanostructure engineering might provide a promising route to contain high-pressure phase of materials because of the exceptional mechanical strength at nanoscale. However, there is no available theoretical model that can analyze this possibility, not to mention to quantitatively evaluate the pressure-bearing capability of nano-cavities. Here, a physical model is proposed to appraise the absolute theoretical limit of various nanotubes/nanospheres to preserve high-pressure materials to ambient conditions. By incorporating with first-principles calculations, we screen and select four types of representative nanomaterials: graphene, hexagonal boron nitride (h-BN), biphenylene, and {\gamma}-graphyne, and perform systematic investigations. The results indicate that nanotube/nanosphere of graphene exhibits the best pressure-bearing capability, followed by h-BN, biphenylene and {\gamma}-graphyne. Our model reveals that the structure with the largest average binding energy per bond and the highest density of bonds will have the highest absolute limit to contain pressure materials, while electron/hole doping and interlayer interactions have minor effects. Our finding suggests that one can utilize nanotube/nanosphere with multiple layers to retrieve compressed material with higher pressures. For example, a single layer graphene sphere can retrieve compressed LaH10 with a volume size of 26 nm3 that corresponding to a pressure of 170 GPa and with a near room temperature superconductor transition of Tc=250 K. Similarly, in order to retrieve the metastable atomic hydrogen or molecular metallic hydrogen at about 250 GPa, it requires only three layers of a nanosphere to contain a volume size of 173 nm^3.

High-fidelity shock experiments were performed on copper powders with controlled porosity via improved target fabrication and assembly. Optical velocimetry and multi-channel pyrometry were used to obtain Hugoniot data, isentropic release paths, and interface temperature histories. The results validate a modified two-phase equation of state (EOS) for copper based on the framework of Greeff et al. The measured Hugoniot shows good agreement with the present model but exhibits significant softening above ~156 GPa relative to the original Greeff EOS, indicating that reduction in lattice specific heat becomes essential when shock temperatures exceed three times the melting point (T > 3Tm). Unloading behavior matches hydrodynamic simulations incorporating the recalibrated EOS, confirming its accuracy for off-Hugoniot states. Theoretical analysis of temperature release profiles suggests that the thermal conductivity of shocked copper powders may be considerably higher than first-principles predictions. Crucially, despite heterogeneity in shock heating, the macroscopic dynamic response of copper powders with a porosity of ~1.7 is well captured by an average-density EOS model, supporting the use of porous material experiments for EOS validation under extreme conditions.

Natural hyperbolic materials have attracted significant interest in the field of photonics due to their unique optical properties. Based on the initial successful explorations on layered crystalline materials, hyperbolic dispersion was associated with extreme structural anisotropy, despite the rarity of natural materials exhibiting this property. Here we show that non cubic electrides are generally promising natural hyperbolic materials owing to charge localization in interstitial sites. This includes elemental and binary electrides, as well as some two-dimensional materials that show prominent in-plane hyperbolic dispersion. They exhibit low plasma frequencies and a broad hyperbolic window spanning the infrared to the ultraviolet. In semiconductor electrides, anisotropic interband transitions provide an additional mechanism for hyperbolic behaviour. These findings remove the previously held prerequisite of structural anisotropy for natural hyperbolic materials, and open up new opportunities, which might change the current strategy for searching and design photonic materials.

Dissipation anomaly-the persistence of finite energy dissipation in the inviscid limit-is a hallmark of turbulence, sometimes regarded as the "zeroth law" of turbulent flows. Here, we demonstrate that this phenomenon is not exclusive to turbulence. Using fluctuating hydrodynamics, we show that a simple gradient-driven nonequilibrium steady state, in which a fluid is subjected to a constant scalar gradient but remains macroscopically quiescent, also exhibits dissipation anomaly. Direct numerical simulations and self-consistent mode-coupling theory reveal that the anomaly originates from giant, long-range nonequilibrium fluctuations amplified by the imposed gradient. While linear theory predicts a divergent dissipation in the inviscid limit, nonlinear mode coupling regularizes the divergence, yielding a finite anomalous dissipation. Our findings identify a new, non-turbulent arena for dissipation anomaly and establish the interplay between thermal noise and nonequilibrium driving as a fundamental route to singular behavior in hydrodynamics.

We demonstrate that multipoint Bayesian algorithm execution can overcome fundamental computational challenges in storage ring design optimization. Dynamic (DA) and momentum (MA) optimization is a multipoint, multiobjective design task for storage rings, ultimately informing the flux of x-ray sources and luminosity of colliders. Current state-of-art black-box optimization methods require extensive particle-tracking simulations for each trial configuration; the high computational cost restricts the extent of the search to $\sim 10^3$ configurations, and therefore limits the quality of the final design. We remove this bottleneck using multipointBAX, which selects, simulates, and models each trial configuration at the single particle level. We demonstrate our approach on a novel design for a fourth-generation light source, with neural-network powered multipointBAX achieving equivalent Pareto front results using more than two orders of magnitude fewer tracking computations compared to genetic algorithms. The significant reduction in cost positions multipointBAX as a promising alternative to black-box optimization, and we anticipate multipointBAX will be instrumental in the design of future light sources, colliders, and large-scale scientific facilities.

The solution of potential-driven steady-state flow in large networks is required in various engineering applications, such as transport of natural gas or water through pipeline networks. The resultant system of nonlinear equations depends on the network topology, and its solution grows more challenging as the network size increases. We present an algorithm that utilizes a given partition of a network into tractable sizes to compute a global solution for the full nonlinear system through local solution of smaller subsystems induced by the partitions. When the partitions are induced by interconnects or transfer points corresponding to networks owned by different operators, the method ensures data is shared solely at the interconnects, leaving network operators free to solve the network flow system corresponding to their own domain in any manner of their choosing. The proposed method is shown to be connected to the Schur complement and the method's viability demonstrated on some challenging test cases.

Machine learning (ML) has emerged as a powerful tool for accelerating the computational design and production of materials. In materials science, ML has primarily supported large-scale discovery of novel compounds using first-principles data and digital twin applications for optimizing manufacturing processes. However, applying general-purpose ML frameworks to complex industrial materials such as steel remains a challenge. A key obstacle is accurately capturing the intricate relationship between chemical composition, processing parameters, and the resulting microstructure and properties. To address this, we introduce a computational framework that combines physical insights with ML to develop a physics-informed continuous cooling transformation (CCT) model for steels. Our model, trained on a dataset of 4,100 diagrams, is validated against literature and experimental data. It demonstrates high computational efficiency, generating complete CCT diagrams with 100 cooling curves in under 5 seconds. It also shows strong generalizability across alloy steels, achieving phase classification F1 scores above 88% for all phases. For phase transition temperature regression, it attains mean absolute errors (MAE) below 20 {\deg}C across all phases except bainite, which shows a slightly higher MAE of 27 {\deg}C. This framework can be extended with additional generic and customized ML models to establish a universal digital twin platform for heat treatment. Integration with complementary simulation tools and targeted experiments will further support accelerated materials design workflows.

This work proposes a geometric-statistical reinterpretation of the dark sector, grounded in a discrete spacetime framework composed of non-material spatial units termed hodons. Unlike particle-based dark matter models, hodons are kinematically inert and possess ultra-light effective mass derived from vacuum energy density and holographic volume bounds. We introduce a covariant scalar field $\mathcal{N}(x^\mu)$ representing local hodon density and derive an entropy-driven evolution equation consistent with causal structure and general relativity. The resulting stress-energy contribution from hodon fluctuations yields gravitational clumpiness without invoking new particles or modified gravity. A virial-based toy model demonstrates that baryonic matter surrounded by hodons forms stable, cored halo profiles, consistent with galactic rotation curves and low-mass halo observations. The framework naturally suppresses small-scale structure via spatial uncertainty relations, aligning with constraints from the Lyman-$\alpha$ forest and weak lensing. By integrating Bousso's covariant entropy bound and distinguishing between strong and weak holography, we situate the model within a broader epistemological context. These results suggest that dark sector phenomenology may emerge from the statistical geometry of space itself, offering a falsifiable alternative to particle dark matter.

Urban Building Energy Modeling (UBEM) plays a central role in understanding and forecasting energy consumption at the city scale. In this work, we present a UBEM pipeline that integrates EnergyPlus simulations, high-performance computing (HPC), and open geospatial datasets to estimate the energy demand of buildings in Bologna, Italy. Geometric information including building footprints and heights was obtained from the Bologna Open Data portal and enhanced with aerial LiDAR measurements. Non-geometric attributes such as construction materials, insulation characteristics, and window performance were derived from regional building regulations and the European TABULA database. The computation was carried out on Leonardo, the Cineca-hosted supercomputer, enabling the simulation of approximately 25,000 buildings in under 30 minutes.

Community Supported Agriculture (CSA) has been recognized globally as a promising framework that embeds agriculture within social relations, yet its diffusion remains limited in contexts such as Japan. Existing studies have largely focused on either consumer or producer participation in isolation, offering fragmented insights and leaving unexplored how their reciprocal processes jointly shape CSA communities. This study addresses this gap by integrating the trajectories of both groups into a comprehensive account of CSA community formation. Drawing on semi-structured interviews with ten CSA producers and ten consumers, we employed the Modified Grounded Theory Approach (M-GTA) to inductively theorize processes of participation and practice. The analysis showed that producers advance CSA through internal adjustments and sense-making to cope with uncertainties, while consumers are guided by life events, practical skills, and prior purchasing experiences toward participation. Synthesizing these insights, we propose a six-phase model of CSA community formation, dispersed interest, awareness, interest formation, motivation, practice, and co-creative continuation, that demonstrates how producers, consumers, and intermediaries interact across stages. The model highlights the pivotal role of key players in sustaining engagement and provides a new perspective for institutionalizing CSA as a durable component of sustainable food systems.

Extreme gust encounters by finite wings with disturbance velocity exceeding their cruise speed remain largely unexplored, while particularly relevant to miniature-scale aircraft. This study considers extreme aerodynamic flows around a square wing and the large, unsteady forces that result from gust encounters. We analyse the evolution of three-dimensional, large-scale vortical structures and their complex interactions with the wing by performing direct numerical simulations at a chord-based Reynolds number of 600. We find that a strong incoming positive gust vortex induces a prominent leading-edge vortex (LEV) on the upper surface of the wing, accompanied by tip vortices (TiVs) strengthened through the interaction. Conversely, a strong negative gust vortex induces an LEV on the lower surface of the wing and causes a reversal in TiV orientation. In both extreme vortex gust encounters, the wing experiences significant lift fluctuations. Furthermore, we identify two opposing effects of the TiVs on the large lift fluctuations. First, the enhanced or reversed TiVs contribute to significant lift surges or drops by generating large low-pressure cores near the wing. Second, the TiVs play a part in attenuating lift fluctuations through enhanced downwash or upwash, formation of an arch vortex, and distortion of vortical structure around the wing corners. The second effect outweighs the first, resulting in smaller transient lift changes on the finite wing compared to the 2D wing. We also show that flying above a positive gust vortex or flying below a negative one can mitigate lift fluctuations during encounters. The current findings provide potential guidance on how TiV dynamics and wing positions could be leveraged to alleviate large transient lift fluctuations experienced by finite wings in severe gust conditions.

Negative information often exerts a disproportionately strong impact on human decision-making, a phenomenon known as the negativity bias. In behavioral economics, this effect is formally captured by Prospect Theory, which posits that losses loom larger than equivalent gains. For example, a single negative product review can outweigh numerous positive ones, reflecting this principle of loss aversion in consumer behavior. While this psychological effect has been widely documented, its implications for collective opinion dynamics, critical for understanding market stability and reputation dynamics, remain poorly understood. Here, we generalize the $q$-voter model with independence by introducing opinion-dependent influence group sizes, $q_+$ and $q_-$, which represent the social reinforcement needed to change an opinion from negative to positive and from positive to negative, respectively. We study two versions of this asymmetric model: a baseline model that reduces to the standard $q$-voter model when $q_+ = q_- = q$, and an extended model that incorporates an additional asymmetry expressed as a preference for one opinion. In its reduced version, this represents a minimal model in terms of non-linearity within the $q$-voter framework that allows for discontinuous phase transitions and hysteresis. Using mean-field analysis and computer simulations, we show that these modifications lead to rich collective behaviors, including double hysteresis, one form of which is irreversible, providing a mechanism for path-dependence and the sustained, irrecoverable damage to collective sentiment, brand equity, or market confidence.

Free energy landscapes encode the kinetics, intermediates, and transition states that govern molecular processes and are thus a key target of single biomolecule research. Typical approaches to deriving optimal, error-minimizing, non-equilibrium driving protocols for estimating these landscapes require a priori knowledge of the landscape. Here, we present an alternative: an iterative algorithm for optimizing full free energy landscape reconstructions which can be used alongside experiments on unknown landscapes. Our approach (i) takes experimental or simulated trajectory data; (ii) reconstructs an `approximate' energy landscape; (iii) derives optimal control protocols from low-dimensional differentiable Brownian dynamics simulations on the candidate landscape using automatic differentiation; (iv) re-runs the experiment or simulation using the updated protocol; and (v) iterates until convergence. Using this approach, we recover known benchmarks from the literature and probe far-from-equilibrium regimes for symmetric, asymmetric, and triple-well energy landscapes under both 1- and 2-dimensional control. Our control protocols – derived with no a priori knowledge of the energy landscape – yield substantially reduced variance and bias in free energy landscape reconstructions compared to naive linear protocols.

This study address the computational determination of catalytic reaction rates by moving beyond traditional Transition State Theory (TST), addressing its limitations in complex systems. The Hill relation framework, integrated with Adaptive Multilevel Splitting (AMS), offers exact rate constants for stochastic dynamics, overcoming TST's assumptions and limitations such as recrossings and post-transition state bifurcations. Two case studies validate the approach: water formation on {\gamma}-alumina and protonated isobutanol dehydration in the gas phase, demonstrating consistency with DFT results and highlighting the importance of dynamical effects. This framework provides a robust, computationally feasible methodology for studying complex catalytic processes.

The IceCube Neutrino Observatory is a cubic-kilometer Cherenkov detector embedded in the Antarctic ice at the South Pole. Its densely instrumented sub-array and dedicated low-energy analyses provide sensitivity to neutrinos in the 5-100 GeV range, enabling precision studies of neutrino oscillations and searches for new physics. This work focuses specifically on this low-energy regime, where sparse hit patterns limit the performance of topology-based reconstruction and classification methods. We introduce Waveform-based Particle Identification (WavePID), a statistically rigorous and interpretable likelihood-ratio discriminator for track-cascade separation, built from Monte Carlo templates in timing-aware, physics-motivated observables and validated through dedicated simulations. Applied to both Monte Carlo and 11.1 years of IceCube data, WavePID suggests improved cascade purity by about 5 percentage points at a fixed 20% down-selection rate relative to the current leading cascade selection, while maintaining Data-MC agreement within detector systematics. The approach is compact and robust to sparse observations, demonstrating the value of waveform-level timing for low-energy reconstruction.

Deterministic Lateral Displacement (DLD) devices are widely used in microfluidics for label-free, size-based separation of particles and cells, with particular promise in isolating circulating tumor cells (CTCs) for early cancer diagnostics. This study focuses on the optimization of DLD design parameters, such as row shift fraction, post size, and gap distance, to enhance the selective isolation of lung cancer cells based on their physical properties. To overcome the challenges of rare CTC detection and reduce reliance on computationally intensive simulations, machine learning models including gradient boosting, k-nearest neighbors, random forest, and multilayer perceptron (MLP) regressors are employed. Trained on a large, numerically validated dataset, these models predict particle trajectories and identify optimal device configurations, enabling high-throughput and cost-effective DLD design. Beyond trajectory prediction, the models aid in isolating critical design variables, offering a systematic, data-driven framework for automated DLD optimization. This integrative approach advances the development of scalable and precise microfluidic systems for cancer diagnostics, contributing to the broader goals of early detection and personalized medicine.

Circular Rydberg states offer advantages for quantum information and quantum simulation platforms due to their long lifetimes and strong dipole-dipole interactions. Unfortunately, current techniques for the production of these states remain technically challenging. Here we investigate the ability of twisted electron collisions to produce circular Rydberg states. Twisted electrons carry quantized orbital angular momentum that can be transferred to the electronic state of the atom, potentially providing an efficient means to generate circular Rydberg states. Using a fully quantum mechanical framework, we compute total excitation cross sections for circular Rydberg states of hydrogen, rubidium, and cesium targets using Bessel electron beams. Our models account for the full Bessel-beam structure of the incident electron and incorporate macroscopic target effects to model experimentally-relevant conditions. Our results show that twisted electrons with large opening angles produce significant enhancements in the excitation probability relative to plane-wave electrons, particularly for large opening angles and low energies. We trace this enhancement to contributions from projectiles with large values of orbital angular momentum. These findings demonstrate that twisted-electron excitation may provide a feasible and potentially advantageous pathway for generating circular Rydberg states.

Using programmable analog quantum annealing processors, we implement a sampling-based magnetic hysteresis protocol to probe the counterintuitive notion of magnetic memory in antiferromagnetic models. A key component of this protocol responsible for the hysteresis is a transverse field, which enables state transitions, while the longitudinal magnetic field sweep is done via a longitudinal control field. We observe intriguing non-monotonic magnetization, alongside full saturation and reversal of the hysteresis curve, Barkhausen-like noise, as well as emergent magnetic domains mediated by quantum fluctuations that give rise to the magnetic memory effect in antiferromagnets.

The closed-off structure of the Fermilab Drift Tube Linac precludes a robust array of instrumentation from directly monitoring the H- beam that is accelerated from 750 keV to 116 MeV. To improve beam tuning and operational assessment of Drift Tube Linac performance, scintillator-based loss monitors were previously installed along the exterior of the first two accelerating cavities to assess low energy beam losses. Here we present a recent upgrade to the loss monitor system, including significant improvements in analog signal processing to address baseline-interfering noise; digitization of the signals to enable regular operational use and tuning; and a new remote operation upgrade of the translating loss monitor with precise positioning of the loss monitor along its nine-foot track. Data from the fixed and translating detectors collected under varying beam conditions validate the utility of the upgrade.

Production and manipulation of orbital angular momentum (OAM) of coherent soft x-ray beams is demonstrated utilizing consecutive diffractive optics. OAM addition is observed upon passing the beam through consecutive fork gratings. The OAM of the beam was found to be decoupled from its spin angular momentum (SAM). Practical implementation of angular momentum control by consecutive devices in the x-ray regime opens new experimental opportunities, such as direct measurement of OAM beams without resorting to phase sensitive techniques, including holography. OAM analyzers utilizing fork gratings can be used to characterize the beams produced by synchrotron and free electron lasers sources; they can also be used in scattering experiments.

Evolutionary algorithms for molecular design require computationally efficient yet accurate fitness functions. We systematically benchmark Hartree-Fock and density functional theory for predicting molecular first hyperpolarizability ($\beta$), evaluating five functionals (HF, PBE0, B3LYP, CAM-B3LYP, M06-2X) across six basis sets against experimental data from five organic push-pull chromophores. For this dataset, HF/3-21G achieves 45.5% mean absolute percentage error with perfect pairwise ranking in 7.4 minutes per molecule. All 30 tested combinations of functional and basis sets maintain perfect pairwise agreement, validating their use as evolutionary fitness functions despite moderate absolute errors. Larger basis sets yield a lower percentage error compared to the experimental values than the difference with the functional. The preservation of pairwise rankings across all combinations of functionals and basis sets provides crucial guidance for evolutionary optimization of nonlinear optical materials.

Deterministic Lateral Displacement (DLD) devices enable liquid biopsy for cancer detection by separating circulating tumor cells (CTCs) from blood samples based on size, but designing these microfluidic devices requires computationally expensive Navier-Stokes simulations and particle-tracing analyses. While recent surrogate modeling approaches using deep learning have accelerated this process, they often inadequately handle the critical periodic boundary conditions of DLD unit cells, leading to cumulative errors in multi-unit device predictions. This paper introduces a periodicity-enforced surrogate modeling approach that incorporates periodic layers, neural network components that guarantee exact periodicity without penalty terms or output modifications, into deep learning architectures for DLD device design. The proposed method employs three sub-networks to predict steady-state, non-dimensional velocity and pressure fields (u, v, p) rather than directly predicting critical diameters or particle trajectories, enabling complete flow field characterization and enhanced design flexibility. Periodic layers ensure exact matching of flow variables across unit cell boundaries through architectural enforcement rather than soft penalty-based approaches. Validation on 120 CFD-generated geometries demonstrates that the periodic layer implementation achieves 0.478% critical diameter error while maintaining perfect periodicity consistency, representing an 85.4% improvement over baseline methods. The approach enables efficient and accurate DLD device design with guaranteed boundary condition satisfaction for multi-unit device applications.