Papers

Most of the laws of Nature involve derivatives up to the second order. Ostrogradski was the first to seek a formulation of the equations of higher-order derivatives. He extended Hamilton's equations by considering Lagrangians that depend on higher-order derivatives of generalized coordinates. The Hamilton-Ostrogradski formulation served as the basis for later studies with higher-order derivatives. However, the Hamilton-Ostrogradski formalism is rarely discussed in textbooks or the pedagogical literature. This motivated us to show how the Hamilton-Ostrogradski formalism can be applied it to the Pais-Uhlenbeck oscillator. We hope that the approach presented in this work can serve as a basis for discussion in advanced classical mechanics courses.

In this paper we propose a novel unified cosmological model that connects a pre-inflationary epoch, starting at the Planckian time, with the onset of inflation within a single scalar-field framework. The pre-inflationary phase is characterized by a decelerated expansion with an increasing comoving Hubble horizon, followed by a gradually transition to an accelerated inflationary regime. This early dynamics leads to a modified causal structure that naturally accounts for the suppression of large-angle $(\theta \gtrsim 60^\circ)$ correlations in the cosmic microwave background (CMB) reported by the satellite PLANCK. We study the quantum fluctuations of the scalar field using the Mukhanov-Sasaki formalism and a canonical quantization procedure based on energy minimization. We find that the vacuum state is well-defined only for sub-horizon modes at the onset of inflation, which induces a natural cutoff in the primordial power spectrum. The resulting spectrum exhibits a suppression at large scales while remaining nearly scale-invariant at small scales. In the appropriate limit, the model recovers the standard de Sitter result, in agreement with current observational constraints. These results highlight the relevance of pre-inflationary dynamics for addressing large-scale anomalies within a consistent inflationary framework.

In the present work we investigate the multiscale dynamics of enstrophy in homogeneous isotropic turbulence by exploiting the two-point formalism provided by the Kármán-Howarth-Monin-Hill approach. The study is conducted on direct numerical simulations with a Taylor-based Reynolds number in the range of $140 \lesssim Re_{\lambda} \lesssim 400$. The two-point enstrophy budget at scales $r > 10 \eta$ appears to be entirely determined by production via vortex stretching, which balances enstrophy destruction, and to be dominated by the diffusive transport at smaller scales, thus preventing the emergence of a range dominated by the inertial transport of enstrophy. The decomposition in longitudinal and transverse contributions also highlights a dual nature of the inertial enstrophy flux. In particular, enstrophy appears to be transferred across scales through a non-trivial combination of direct and reverse interscale transfer. It is shown that the dual nature of this transfer is strictly related to the vortex stretching mechanism, which, in addition to producing enstrophy through vorticity amplification, also transfers longitudinal vorticity towards larger scales (by stretching the vortical elements) and transverse vorticity towards smaller scales (by contracting these vortical elements in the radial direction). The sum of these two contributions results in an overall transfer of enstrophy from large towards small scales. We propose the use of the pressure transport term as a proxy to obtain some information on the dynamics of relevant events of inertial energy and enstrophy transport. The new findings highlight the relevance of inertial compression events in longitudinal energy transport. At the same time, a good correlation between transverse energy transport events and the radial contraction of vortical elements due to vortex stretching mechanisms is also found.

The present study considers the operation of a laser that incorporates a photonic time crystal (PTC), the purpose of which is to generate a field characterised by multiple widely separated optical frequencies. This laser is the subject of both a proposal and theoretical investigation. The laser comprises an active medium and a PTC within a small cavity constructed from two photonic crystals that are positioned in an overlapping configuration. PTC is modulated by an external field. The spikes in the laser field spectrum are separated by the PTC modulation frequency. The development of a quantum model of the laser with PTC has been achieved, and the analysis of a lasing mode with multi-frequency spikes has been made. The investigation focused on the study of lasing conditions, output power, and the lasing field spectra. The experimental realization of the multi-frequency laser with PTC under realistic conditions is discussed.

Bayesian filtering is a well-known problem that aims to estimate plausible states of a dynamical system from observations. Among existing approaches to solve this problem, particle filters are theoretically exact for non-linear dynamics and observations, but suffer from poor scalability in high dimensions. In this work, we show that diffusion-based emulators of dynamical systems can be used to implement, without additional training, an optimal variant of particle filters that has remained largely unexplored due to implementation challenges with classical numerical solvers. Experiments on nonlinear chaotic systems, including atmospheric dynamics, demonstrate that the proposed approach successfully scales particle filtering to high-dimensional settings.

High-power structured light has a wide range of applications, from material processing and high-capacity optical communications to programmable electron beams, plasmas, and nuclear states. On-demand generation of structured light and adaptive control of beam profiles are essential for many practical applications, but are difficult to achieve at high power. Here, we demonstrate reconfigurable generation of structured light from a high-power multimode-fiber laser amplifier through full-field control of a low-power seed. An efficient nonlinear beam shaping scheme based on a local linear approximation of the complex nonlinear input–output relation is developed and realized in situ. Diverse structured beams such as Gaussian, Bessel, vector and orbital angular momentum beams are directly generated from the fiber amplifier at powers exceeding 500 W. Our scheme enables real-time programmability of structured light and is readily scalable to even higher power levels. This work provides a general framework for controlling high-dimensional nonlinear systems without accurate knowledge or tractable model.

This study has presented a comprehensive computational fluid dynamics (CFD) analysis of combustion flow in a realistic can combustor, evaluating the influence of various turbulence models on flow, thermal, and species fields. The non-premixed combustion modeling is performed using a presumed (beta) PDF approach in conjunction with a steady laminar flamelet model employing the San Diego reaction mechanism, and the turbulence is modeled using the RANS approach. The influence of turbulence models (standard $k-\epsilon$, realizable $k-\epsilon$, SST $k-\omega$, LPS-RSM) on the velocity field, such as the mean axial velocity, mean transverse velocity, turbulent kinetic energy (TKE) and shear stress, is analyzed, besides their influence on temperature and species (\ce{C3H8}, \ce{CO2}, and \ce{CO}) concentration. Analysis showed that despite the shortcomings of the isotropic turbulent viscosity formulation of the SST $k-\omega$ model being evident, it predicted the mean axial velocity, mean transverse velocity, turbulent kinetic energy and shear stress more accurately. Additionally, it predicted the flow features expected in a can combustor, such as the central recirculation zone (CRZ) and central vortex core (CVC), more accurately than other models. Besides, the model predicted a higher temperature in the primary zone, which is supported by a lower prediction of \ce{C3H8}, and elevated TKE, both of which support strong mixing and efficient heat release. Furthermore, the SST $k-\omega$ model predicted the most compact stoichiometric mixture fraction bubble, encompassing CRZ and shear layers, indicating that the majority of the combustion occurs in the primary zone. The corresponding progress variables also indicated high values in the primary zone and shear layers, confirming near completion of the reaction, supported by negligible prediction of \ce{C3H8} and \ce{CO} at the outlet.

The constitutive behavior of materials is modeled through relationships between stress, strain, and possibly additional internal variables. This results in relatively high-dimensional feature spaces for machine learning models rendering the efficient generation of informative datasets essential as brute force methods suffer from the curse of dimensionality. This work introduces a diversity-aware batch-mode query-by-committee active-learning strategy to generate datasets of maximum information content at minimum cost. In contrast to existing methods, this novel method selects multiple informative, non-redundant queries per iteration, enabling concurrent generation of informative datasets and reducing the number of machine-learning retraining cycles. A central component of this method is a cosine-similarity-based metric that complements the uncertainty criterion based on committee variance by promoting within-batch diversity. The query selection is guided by committee variance and a diversity-promoting criterion. The approach is benchmarked for efficient stress-space sampling in data-driven constitutive modeling. In this setting, a committee of support vector classifiers approximates the so-called yield surface, which is a manifold dividing the six-dimensional stress space into an elastic and plastic domain. We demonstrate that the method handles different batch sizes robustly, maintains high within batch diversity, and rapidly reduces committee uncertainty. The resulting machine learning yield surfaces achieve predictive accuracy comparable to sequential active learning, while requiring substantially fewer retraining cycles. This makes the proposed approach an efficient strategy for stress space sampling in data driven constitutive modeling and for reducing time to solution via concurrent data collection in each iteration.

This experimental particle-physics thesis reports the latest developments on the GasPM, a novel gaseous photodetector aimed at suppressing beam-induced backgrounds in the electromagnetic calorimeter for a potential upgrade of the Belle~II experiment. The GasPM technology is based on combining a photocathode with a resistive-plate chamber offering high efficiency, excellent time resolution, and cost-effective scalability. A further advantage is that, combined with a radiator, the GasPM offers precise Cherenkov-based charged-particle identification. As part of a project launched in 2017, this work aims at addressing the degradation in time resolution observed in a previous beam test over what was achieved earlier with laser light. I focus specifically on ultraviolet-photon emission during excitation and de-excitation of the gas molecules, which leads to a secondary signal that in turn spoils time resolution (photon feedback). I design and execute an improved beam test that, along with several configuration changes, newly introduces single-vs-multiple electron discrimination and high-frequency signal readout. In addition, I probe through a cosmic-ray test the quantum efficiency of a new LaB$_6$ photocathode resistant to damage from ions drifting backwards, for use in future beam tests. The principal results are the development of an algorithm to efficiently suppress photon feedback; a preliminary calibration of a novel digitiser; the achievement of discrimination between single- and multiple-electron events; and an early qualification of a LaB$_6$ photocathode. These results are being prepared for showing at the 7th International Workshop on New Photon Detectors organized in Bologna in December 2025 and pave the way for an upcoming beam test of an improved GasPM prototype.

Antireflection coatings are ubiquitous in optical systems, where they maximize transmission and suppress undesirable reflections by impedance-matching uniform interfaces. Extending this principle to metasurfaces, however, is fundamentally more challenging because wavefront control requires a library of geometrically distinct meta-atoms, each locally imposing a prescribed phase that is tethered to its transmittance. Here, we show that vertical integration resolves this constraint by allowing bilayer meta-atoms to operate simultaneously as a phase shifter and an impedance-matching stack. Using an effective thin-film model, we derive a design rule that links the effective indices of two independently patterned layers and identifies antireflective bilayer libraries with full $0$-$2\pi$ transmission-phase coverage. We realize this concept in a free-standing TiO$_2$/TiO$_2$ metalens operating at 1310 nm, which suppresses reflectance below that of bare glass while preserving diffraction-limited focusing. These results establish bilayer metasurfaces as a framework for co-engineering optical impedance and wavefront response at the meta-atom level.

We present PEPSKit$.$jl, a Julia package for simulating two-dimensional quantum many-body systems with infinite projected entangled-pair states (iPEPS). PEPSKit$.$jl builds on the TensorKit$.$jl package for tensor computations and provides high-level algorithms for iPEPS simulations that support both Abelian and non-Abelian symmetries, as well as fermionic systems. This work gives an overview of the main package features, which include support for ground-state, time-evolution, and finite-temperature simulations in systems with different physical symmetries and lattice geometries. These capabilities are illustrated through various examples and technical benchmarks.

We present a novel parallel implementation for large-scale three-dimensional electromagnetic inversion based on a Gauss-Newton framework combined with a rational near-best approximation of the matrix exponential for transient simulations. The method employs parallel direct solvers for the shifted linear systems arising from the partial fraction representation of the rational approximation and demonstrates efficient parallel execution on a shared-memory architecture using MPI. A key property of the approach is that the time dependence is entirely contained in the residuals of the employed rational functions, such that the computation of forward responses and sensitivities becomes effectively independent of the number of desired observation times. Model regularization is done with smoothness constraints, formulated with Raviart-Thomas elements. The linearized inverse problems are solved using LSQR, using an implicit parallel Jacobian operator. Numerical experiments demonstrate the successful recovery of a synthetic 3-D conductivity structure with approximately 700,000 degrees of freedom. The study further discusses computational bottlenecks related to memory consumption and shared-memory scalability arising from the simultaneous storage of multiple sparse matrix factorizations. Possible improvements based on preconditioned iterative solvers and distributed high-performance computing architectures are outlined. The implementation in the Julia programming language is released as open-source software to support reproducible research and further development by the geophysical inversion community.

This algorithm is designed to perform numerical transforms to convert data from the temporal domain into the spectral domain. This algorithm obtains the spectral magnitude and phase by studying the Coefficient of Determination of a series of artificial sinusoidal functions with the temporal data, and normalizing the variance data into a high-resolution spectral representation of the time-domain data with a finite sampling rate. What is especially beneficial about this algorithm is that it can produce spectral data at any user-defined resolution, and this highly resolved spectral data can be transformed back to the temporal domain.

Scanning helium microscopy (SHeM) is attractive for imaging delicate and insulating surfaces because it combines a non-destructive neutral-atom probe with strong surface sensitivity. However, large-working-distance pinhole instruments have so far been limited in spatial resolution. Here we report sub-micron resolution in a large-working-distance pinhole SHeM, with an intrinsic beamwidth of 340nm achieved at working distances of 770 {\mu}m to 850 {\mu}m. This sixfold improvement over our previous long-working-distance configuration is enabled by constrained optimisation of the atom optics together with a redesigned high-resolution pinhole-plate, a reduced pinhole diameter, an increased source–pinhole distance and a larger detector aperture. Beamwidth measurements agree well with the modified optimisation model and show that geometric, source-size and diffraction terms now contribute on a similar footing, placing the instrument in a near-optimised regime. The resulting combination of sub-micron beam size, useful depth of field and practical sample access is demonstrated on bacterial specimens and eroded diamond. The work establishes large-working-distance pinhole SHeM as a viable sub-micron imaging platform and extends its usefulness for topographic imaging and micro-diffraction applications.

We investigate two-point velocity-gradient correlation functions in homogeneous isotropic turbulence using exact relations and direct numerical simulations. The second-order gradient correlation is shown to be exactly related to the Laplacian of the velocity correlation, implying inertial-range scaling $C_2^{1,1}(r)\sim r^{-4/3}$. At higher orders, we uncover a parity-dependent organization of gradient correlations: odd-odd correlations exhibit scaling close to $r^{-4/3}$ with weak dependence on order, whereas even-even correlations display systematically different exponents. We show that this distinction originates from the sign structure of the gradient field: sign decorrelation suppresses intermittent contributions in odd-odd sectors, while even-even correlations retain them and remain sensitive to the spatial organization of intense structures. The measured even-even exponents are quantitatively consistent, across two Reynolds numbers, with independently measured box-counting dimensions of intermittent gradient structures. These results identify parity under sign reversal as a fundamental organizing principle for higher-order turbulent correlations and establish a direct connection between sparse intermittent geometry and scaling exponents in turbulence.

Can a closed quantum system generate persistent time-crystal-like dynamics without external driving? Within the Bateman dual oscillator framework, we show that the answer is affirmative. We consider a nonrelativistic (2+1)-dimensional system in which spin-induced spatial deformation generates an effective Bateman oscillator structure. After quantization, the system is governed by a time-independent Hermitian Hamiltonian describing coherent coupling between damped and amplified oscillator sectors while preserving the total energy of the global doubled system. Tracing over the amplified sector, we derive an effective non-Markovian reduced dynamics for the observable subsystem. The resulting memory effects sustain persistent oscillations of subsystem observables and generate emergent time-crystal-like temporal ordering without external periodic driving or equilibrium spontaneous symmetry breaking. Since the oscillatory behavior originates from nonequilibrium reduced subsystem dynamics rather than equilibrium expectation values of the full Hamiltonian, the mechanism lies outside the assumptions of conventional no-go theorems for equilibrium time crystals. The same dynamics further exhibits logarithmic-spiral trajectories and self-similar fractal scaling, revealing a direct connection between coherent dissipative dynamics, non-Markovian memory effects, and emergent temporal ordering in a globally unitary quantum system. In this specific sense, "watching the growth" of these self-similar structures corresponds to observing the gradual formation of time-crystal-like ordering.

Photonic neuromorphic computing offers a promising route to overcoming the limitations of conventional Von Neumann architectures by exploiting the high bandwidth, low latency, and massive parallelism of optical systems. However, most existing implementations rely on fixed dynamical substrates such as classic reservoir computing, where learning is restricted to external readout layers and memory is limited to transient fading effects. This work proposes a Reconfigurable Nonlinear Photonic Decision Network (RNPDN), a physically grounded neuromorphic framework in which computation, memory, and learning emerge directly from driven-dissipative dynamics. Through numerical simulations, this work demonstrates the simultaneous realization of key properties: local physical learning rules enabling adaptive state evolution, a tunable stability-plasticity tradeoff governed by decay and hysteresis mechanisms, controlled memory formation and erasure via bistable photonic states, fading memory, in-situ learning, and hardware-faithful nonlinear dynamics incorporating saturation and dissipation. In contrast to conventional approaches, the proposed system enables intrinsic adaptation within the physical layer while supporting both transient and persistent memory. These results establish a unified framework for adaptive photonic information processing and provide a pathway toward scalable and energy-efficient neuromorphic photonic hardware.

The simulation of quantum transport in nanodevices requires the solution of the Dyson and Keldysh equations, a task dominated by the inversion of massive, block-tridiagonal matrices. While the Recursive Green's Function (RGF) method has long been the standard $O(N)$ solver for quasi-1D systems, its formulation has typically been restricted to sequential execution and nearest-neighbor interactions. In this work, we carefully reformulate RGF through the lens of Domain Decomposition and Schur Complement theory. This allows us to extend the recursive formalism to block $n$-diagonal systems (handling higher-order stencils) and to derive a parallel algorithm, Domain-Decomposition based RGF (DDRGF), which stitches macroscopic domains via reduced interface systems. We explore data dependencies in DDRGF in detail, by means of block-sparse structures and tracing back to the desired output as a block tridiagonal approximation, giving a clear, reproducible and extensible formulation. We validate these algorithms using LibNEGF.jl, a Julia-based implementation, demonstrating that the structural insights of domain decomposition provide a robust pathway for high-performance quantum transport simulations on modern multi-core clusters. The theory presented here lays down the base for tackling the Keldysh problem, to be similarly handled in future stages of our work. Although the target here is the acceleration of kernels in the non-equilibrium Green's function method, the algorithms and the implementations presented can be immediately used in any application involving block $n$-diagonal systems.

We present new multielement NanoSIMS isotopic measurements (C, N, Si, Mg-Al, Ti, and Ni) for four putative nova SiC grains and 79 AB SiC grains from the Murchison meteorite to reassess their stellar origins. High-resolution imaging and a revised Mg/Al relative sensitivity factor for SiC yield substantially improved 26Al/27Al ratios and the most reliable multielement characterization to date for 13C-rich presolar SiC grains. To interpret these data, we computed an expanded suite of hydrodynamic CO, ONe, and recurrent nova models spanning a range of white-dwarf masses and pre-enrichment parameters. When all isotopic systems are considered together (C, N, Mg-Al, Si, Ti, and Ni), the CO nova models provide the closest and most self-consistent match to both the putative nova grains and the subset of AB grains lacking s-process signatures. CO novae of low- to intermediate-mass naturally reproduce the observed 14N/15N-26Al/27Al trend, the Si isotope compositions of AB grains which dominantly reflect Galactic chemical evolution (GCE), and the mild Si isotope shifts in putative nova grains relative to the GCE trend defined by AB grains. In contrast, ONe and recurrent nova models fail multiple isotopic constraints simultaneously. These results demonstrate that low- to intermediate-mass CO novae (0.6-1.0 Msun) are the most plausible stellar sources of 13C-rich SiC dust lacking s-process signatures (1-2% of all presolar SiC), and they establish a multielement, model-anchored framework for quantifying nova contributions to the dust reservoir in the interstellar medium.

Contributions from connected quintuple excitations in coupled cluster theory can reach the 0.5 kcal/mol range, important enough to matter in accurate computational thermochemistry, yet the very steep $\propto N^{12}$ CPU time scaling impedes routine evaluation. We show that for the differential contribution of quintuples, convergence of a frozen natural orbital (FNO) expansion with respect to the NO cutoff is rapid enough to make FNO-CCSDTQ(5)$_\Lambda$ with cutoffs of 0.0025 or 0.001 viable alternatives. A naive extrapolation to zero cutoff from \{0.005,0.0025\} works surprisingly well as a low-cost option. Interestingly, FNO convergence is definitely slower for second-row than for first-row compounds.

Twisted light carries orbital angular momentum (OAM) and can drive excitations of confined, interacting electrons that are dark to uniform dipolar probes. Here we show how this “beyond-Kohn's-Theorem” optical channel can become a concrete control primitive for quantum computing. Correlation sectors in few-electron quantum dots – characterized by the relative angular momentum quantum number – form a tunable ladder of many-body states that are robust in the limited sense of symmetry-protected selection rules and persistent chiral spectroscopic fingerprints; full topological gap protection requires three or more electrons. A twisted-light pulse with prescribed OAM index and polarization provides fast optical write, read, and scalable addressing of these sectors via the selection rule $\Delta|m|=\pm(l+\sigma)$. In the analytically solvable Calogero ($1/r^2$) interaction limit, both the energy spectrum and the twisted-light matrix elements are closed-form functions of the interaction strength, allowing gate parameters (Rabi frequency, qubit frequency, anharmonicity, and leakage rates) to be written down explicitly. We map these results onto a universal single-qubit gate set, propose a concrete two-qubit entangling mechanism via state-dependent Coulomb coupling between adjacent dots, and identify the dominant decoherence channel (quadrupolar charge noise). A semi-analytic $N=3$ extension using the $1/N$ expansion provides a design-level scaffold for the topological roadmap, including quasihole sector addressing. The central operational message is that twisted light enables WRITE (pulse-create a correlation sector), READ (spectroscopically diagnose correlations), and SCALE (optical addressing via spatial light modulator) in a unified photonic control layer. Throughout, screened and Coulomb interactions preserve the same qualitative chiral fingerprints established in the solvable limit.

We extend the CCSDTQ implementation in CFOUR to UHF and ROHF references and demonstrate its efficiency. We apply it to basis set convergence of post-CCSDT(Q) corrections for the W4-08 thermochemical dataset. Convergence of (Q)$_\Lambda$–(Q) is relatively rapid. For difficult species (e.g., B2, O3), CCSDTQ–CCSDT(Q)$_\Lambda$ may converge more slowly than (5)$_\Lambda$, but the effects and and basis-set trends oppose each other. Consequently, a single-shot CCCSDTQ(5)$_\Lambda$-CCSDT(Q)$_\Lambda$ correction appears most efficient. For radicals with bifurcating UHF solutions, energetics of the `less spin-contaminated' solution are clearly more well-behaved. Alternatives to a single-shot CCSDTQ(5)$_\Lambda$–CCSDT(Q)$_\Lambda$ correction are evaluating (5)$_\Lambda$ either in a truncated cc-pVDZ(p,s) basis set, or by means of frozen natural orbitals. Our best computed adiabatic electron affinity of ozone is in excellent agreement with experiment.

We present an accelerated “on-the-fly” coupled-cluster path-integral molecular dynamics (PIMD) method for finite-temperature simulations in which electron correlation and nuclear quantum effects are treated simultaneously. The approach is based on our quantum ring-polymer contraction (qRPC) technique, in which the inexpensive Hartree-Fock potential is evaluated on the full ring-polymer, while the expensive coupled-cluster correction is evaluated on the centroid only. This qRPC decomposition is combined with a second-generation Car-Parrinello-like dynamics of the Hartree-Fock reference and a basis-consistent extrapolation of the coupled-cluster and de-excitation amplitudes. The combination of all three acceleration layers is essential for making correlated PIMD calculations feasible. We apply the method to a proton shared by water and formaldehyde. Relative to classical nuclei, nuclear quantum effects broaden covalent X–H bond-length distributions, reduce the bias of the shared proton toward formaldehyde, and shift the mean proton-transfer coordinate from 0.206 to 0.135A. The probability of finding the proton closer to formaldehyde decreases from 81.7$\%$ to 61.1$\%$. The corresponding nuclear magnetic shielding tensors show that electron correlation and nuclear quantum effects are of comparable magnitude and can act in opposite directions. These results demonstrate that predictive simulations of asymmetric hydrogen bonds require a simultaneous treatment of correlated electronic structure and nuclear quantum fluctuations.

Multi-photon dressing and interference in atomic systems is a key to several cutting edge technologies like Rydberg atom radio frequency sensors, clocks and magnetometers because it enables the engineering of atomic properties. Rydberg atom sensors are attracting significant interest because they can be used for applications where it is difficult or impossible to use conventional antennas, opening a number of new opportunities in fields like communications, test and measurement and radar. To date, radio frequency field amplitude detection is well-established in Rydberg electrometry. Phase detection, which is crucial for encoding radio frequency signals, typically requires an external heterodyning field or an atomic closed-loop interferometer. The heterodyne method compromises the intrinsic transparency of the sensor to the radio frequency wave and its inherently broad carrier bandwidth, in addition to increasing its complexity by introducing a local oscillator. In prior theoretical work, aimed at overcoming the disadvantages of the heterodyne method, we theoretically investigated the possibility of using the oscillatory dynamics of an all-optical five-level closed loop to sense the phase and amplitude of the target radio frequency fields. In this work, we experimentally demonstrate the scheme. We determine the coherence time of the loop to be on the order of ms and show that in-phase and quadrature signals can be extracted from a radio frequency signal.

We characterise position-error behaviour of Two-Line Element (TLE) propagation against operator-updated truth on Starlink, sweeping 24,641 next-TLE-truth pairs across 501 satellites stratified by altitude shell (540, 550, 560 km) and platform generation (v1.0, v1.5, v2-mini) over April 2026. Each pair is propagated with SGP4 and GMAT at high fidelity (EGM2008 $70\times70$, NRLMSISE-00 drag, Sun and Moon third-body gravity, conical-shadow SRP), then compared against the operator's next TLE as proxy truth. Three findings: First, position error follows a per-cell power law $\lVert\Delta\mathbf{r}(\Delta t)\rVert \approx A\,\Delta t^{k}$ with fitted exponents in $(1,2)$ on every v2-mini cell and on the high-fidelity v1.x cells at 540 and 560 km, while SGP4 v1.x and high-fidelity v1.x at 550 km are sub-linear ($k \lesssim 1$); the cohort-specific mix of mean-motion bias and unmodelled in-track acceleration sets the per-cell exponent. Pooled $L_{2}$ medians grow from $\sim 1$ km at 6 h to $\sim 38$ km (SGP4) / $\sim 76$ km (high-fid) at 7 d. Second, high-fidelity propagation from public-TLE inputs does not improve over SGP4 at any of the four staleness horizons; SGP4 wins on $\sim 65$–$75\%$ of pairs, with v2-mini at long $\Delta t$ the one regime where high-fidelity wins on a majority of pairs at both populated shells. The negative result reflects operator-OD residual dominance at epoch, SGP4-vs-SGP4 truth-construction kernel alignment, and spacecraft-property bias amplification on the high-fidelity arm. Third, the per-satellite SGP4 staleness coefficient regressed against F10.7 returns a positive slope clearing conventional significance at one shell (560 km) on the 30-day, $\sim 17$ sfu window – direction-consistent with the LEO density-gradient expectation, not a calibrated F10.7-modulation measurement.

Based on quantum field theory of linearized gravity, we formulate the Wigner function for right- and left-handed gravitons. By applying the Wigner transformation to the second-order metric perturbations in the graviton energy-momentum tensor obtained from the Einstein-Hilbert action, we demonstrate the emergence of the spin Hall effect of gravitons in curved spacetime. This effect originates from the Berry curvature of gravitons, which has opposite signs for right- and left-handed helicities, and leads to a helicity-dependent splitting of the graviton energy Hall current. The magnitude of this splitting is found to be exactly twice that of the corresponding spin Hall current for photons.

The preservation of witness beam polarization in wakefield accelerators will be crucial for future collider applications. While extensive theoretical studies on the injection and initial acceleration of polarized electrons exist, a study concerning higher-energy regimes has been neglected thus far. Besides the spin precession usually considered in wakefield-related research, radiative effects could become increasingly relevant at higher energies as the witness electrons perform betatron oscillations during which they will emit photons. In the present study, we use particle-in-cell simulations extended with Monte-Carlo routines to study the influence of radiative spin-flips on beam polarization. We find that at high energies, the importance of radiative effects on beam polarization mainly comes down to the alignment of the witness beam with respect to the wakefield.

UiO-66 is the most widely studied metal-organic framework, on account of its structural tunability given by its capacity of sustaining high amounts of point defects in its structure. Its synthesis mechanism is largely unknown, with only a few works mostly focused on the formation of the Zr-oxide cluster. In this work, a partially reactive force field to model UiO-66, nb-UiO-FF, is introduced. This force field incorporates node–ligand reactivity via a Morse potential and the introduction of dummy atoms to reproduce the anisotropic charge distribution of the Zr atoms in the node. nb-UiO-FF reproduces structural features of both UiO-66 and its isoreticular analog UiO-67 as well as framework stability with or without defects, activated or filled with N,N-dimethylformamide or ethanol. Defect generation and node–ligand binding energies are comparable with density functional theory values. The force field is further employed within a molecular dynamics scheme to study the early stages of solvothermal node–ligand binding. Species with structures corresponding to motifs present in UiO-66 are observed, along with other metastable species. This force field enables studying the self-assembly of UiO-66, as well as the formation of its point defects.

Data-centric materials science is changing how materials are discovered, optimized, manufactured, and qualified, yet many deployment-limiting materials problems still depend on experimental, processing-rich, device-level, and field-relevant data that are difficult to capture in conventional materials databases. This perspective argues that the Great Plains and adjacent interior research corridor can make a distinctive national contribution by organizing distributed experimental assets into a trusted regional materials-data ecosystem. The proposed model emphasizes FAIR metadata, provenance, persistent sample identifiers, uncertainty-aware modeling, semi-closed-loop workflows, stackable workforce training, and tiered governance for academic, public, controlled-access, and industry-protected data. We identify five coupled barriers – fragmented data, weak algorithm–laboratory translation, uneven access to cyberinfrastructure and technical staff, workforce gaps at the materials–data interface, and insufficient incentives for sharing and reuse – and propose a staged roadmap for addressing them. A high-purity germanium pilot illustrates how regional strengths can be converted into reusable datasets, benchmark models, trained personnel, and decision-improving workflows. The broader message is that regional leadership in data-centric materials science will depend less on geographic concentration than on trustworthy data practices, interoperable infrastructure, cross-trained people, and application-driven materials challenges.

This article extends a recently introduced kinetic closure of turbulence by developing its theoretical framework, operational realizations, and validation. In contrast with filtered Navier–Stokes formulations, filtering the Boltzmann equation retains subgrid transport under the linear streaming operator, so that unresolved physics is concentrated on the collision side. We show that in dilute-gas large-eddy simulation, the main limitation of Bhatnagar–Gross–Krook (BGK)-type collision models is not the breakdown of molecular chaos, but the retention of a Markovian collision at a scale where filtering can induce finite temporal correlations in the collision product. In a BGK-type framework, the closure problem is dual: one must infer the filtered fine-grained equilibrium, not computable from filtered moments alone, and model the non-Markovian collision dynamics generated by the collision-product covariance. The present framework makes this dual structure explicit and represents the resulting collision-covariance source term through a BGK-like closure built from the equilibrium commutation residual, with the turbulent relaxation frequency given by a first phenomenological realization. The framework relies on a Chapman–Enskog analysis organized by the reference timescale ratio emerging from the nondimensionalization of the kinetic equation, performed in the classical sense, thereby avoiding artificial turbulent scale separations. We show that the Chapman–Enskog structure is not a pure one-parameter Knudsen scaling: the primary ordering is set by the kinetic-to-macroscopic timescale ratio, while higher moments retain an additional Mach dependence through the mixed scaling of particle velocity. The resulting kinetic closures are validated through lattice Boltzmann simulations and compared with the Smagorinsky model and regularization-based collision models.