Papers

Twisted bilayer graphene reconstructs from weak breathing corrugation to large common bending near the magic angle, but the origin of this collective crossover has remained unclear. Here we show that the crossover is a soft-mode condensation of layer-symmetric $A_1$ moiré flexural phonons: these modes soften on the breathing branch, lose stiffness near the magic angle, and freeze into the bending morphology. We call this mechanism Moiré Phonon Condensation (MPC). At $\theta=1.08^\circ$, it is extremely surprising that displacements of all 11164 atoms in the moiré supercell, with a maximum atomic position shift of 2.30 Angstrom, is captured by only two $A_1$ phonon modes at more than $99.5\%$ spectral weight. A first-harmonic continuum theory identifies a dimensionless control parameter of the phenomenon, showing that as the twist approaches the magic angle, the growing moiré length scale amplifies a smooth stress-bending competition until the flexural stiffness changes sign. Mode-resolved tight-binding calculations further show that the condensed phonon coordinates are electronically active. This work identifies MPC as a twist-controlled structural order parameter for moiré reconstruction.

The paper is devoted to the study of the connection between the numerical dispersion arising in FDTD modeling of electromagnetic signal propagation in nondispersive homogeneous media optically different from vacuum and the Courant number in the 2D case. The main results are formulated in the form of four statements, as well as a number of corollaries and remarks that determine the nature of the numerical dispersion, the optimal value of the Courant number and the limitations of the method. It is proved that the optimal choice of the Courant number eliminates the numerical dispersion and extends the capabilities of the developed numerical algorithm to media, which refractive index lesser than refractive index of vacuum, as well as media with negative refraction.

Surface characterization is essential for revealing the structural, chemical, and physical properties of materials. Yet high-resolution methods such as atomic force microscopy (AFM) require complex equipment and delicate skillsets, making them particularly challenging for applications involving soft and biological materials in liquids. Here, we propose and validate an innovative motion-enabled sensing scheme that uses untethered micromotors as robotic probes to interact with surfaces or objects, with their motion responses serving as sensing signals for characterization. This sensing concept is validated by employing 3D electrokinetic tweezers, which control micro/nanoparticles with up to 20 nm positioning precision in solution, to drive Au microsphere motors along designed scanning paths. When the motors encounter local chemical or structural variations, their locomotion changes, allowing motion itself to serve for the detection. This effort enables untethered motors, for the first time, to detect biomolecular patterns and lithographically defined microridge arrays in liquid environments. The work establishes robotic locomotion as a new sensing modality, opening a wireless, solution-compatible, and low-cost technical pathway to standard surface sensing.

Penning ion traps as quantum platforms have primarily utilized global control and symmetric Dicke states for quantum simulation and sensing experiments. The introduction of local control greatly increases the power of the platform as a quantum simulator but is technically challenging due to the rapid rotation of the ion crystals. Here we use an ultraviolet-compatible spatial light modulator (SLM) to imprint programmable AC Stark shift patterns with different azimuthal symmetries and gradients that co-rotate with the ion crystals, demonstrating localized coherent control of single plane crystals with greater than 100 ions. Comparisons of the measured ion qubit populations with calculations from independent measurements of the applied AC Stark shift patterns show good agreement, validating the technique and providing a path, with a higher format SLM, for parallelizable, coherent individual ion addressing in Penning traps.

A method is proposed for solving the two-dimensional problem of electromagnetic wave scattering by a cluster of an arbitrary number of parallel, infinitely long, homogeneous, non-overlapping right circular cylinders. The cylinders may have arbitrary radii and complex permittivities, and their axes, while remaining parallel, may occupy arbitrary positions in the transverse plane. The solution is constructed using an analytical expansion of the electromagnetic field in cylindrical harmonics. Multiple scattering is taken into account by Graf's addition theorem, which leads to a system of linear equations for the expansion coefficients. This system is solved numerically with condition number monitoring and, when necessary, extended-precision arithmetic, followed by a multistage verification of convergence. The method provides numerically verified solutions with controlled accuracy over a wide range of parameters, including densely packed subwavelength configurations. As an example, scattering of a normally incident, linearly polarized monochromatic plane wave by a subwavelength cluster of three identical aluminum nanocylinders (nanowires) is studied. The scattering, absorption, and extinction cross sections, as well as the scattering indicatrix, are computed and analyzed. Streamlines of the Poynting vector field are constructed, demonstrating redistribution of the energy flux between the cylinders of the cluster and the formation of localized regions of field enhancement near their surfaces.

Spherical colloids with harsh repulsive forces have long been used as experimental analogs of the hard sphere model, with demonstrated good agreement with computer simulations for bulk and structural properties of the fluid, glass and crystal phases. However, an enigmatic discrepancy remains for the crystal-melt stiffness coefficient. Here we perform computer simulations of colloidal hard spheres under tunable buoyant mass and show that the long-standing discrepancy can be traced to a hitherto unrecognized gravity dependent contribution of the stiffness coefficient. This effect is one practical realization of a more general result for the external field dependence of stiffness coefficients of arbitrary interfaces.

Time-varying metamaterials involve a rapid temporal modulation of the permittivity, often at frequencies comparable to the oscillation frequency of light. However, pronounced physical effects at low modulation amplitudes are observed only when resonances sustained in the metamaterials are utilized. This requires an additional spatial structuring. Here, we demonstrate the first exploitation of bound states in the continuum (BICs) in such spatio-temporal metamaterials consisting of a multilayered metasurface. Leveraging Fabry-Perot BICs in a metasurface-based cavity, we realize polarization-insensitive scattering anomalies such as exceptional points (EPs), coherent perfect absorption (CPA), and lasing at extremely small modulation amplitudes. In a second example, by utilizing symmetry-protected BICs and breaking time-reversal symmetry of a multilayered metasurface, we obtain strong nonreciprocal behavior. Harnessing nonreciprocity, we further demonstrate a device capable of one-way monochromatic light transmission at perturbative modulation amplitudes. Our contribution establishes BIC-enabled spatio-temporal metamaterials as a scalable platform for low-power, tunable light-matter interactions, opening new pathways toward practical nonreciprocal photonic devices, dynamic wave control, and on-chip optical signal processing.

We extend the classical 1D nonlinear relativistic plasma wakefield paradigm to evaluate the pristine fluid profiles driven by intense relativistic proton trains under the comoving coordinate approximation. Using an intense two-bunch scheme, our adaptive integration model is validated down to machine precision against exact closed-form cold-fluid invariants, confirming that interface boundary corners represent physical step discontinuities in the second potential derivative. Scaling to an $N=100$ micro-bunch train, we resolve macroscopic phase-accumulation and subsequent nonlinear detuning by deriving a closed-form, parameter-free piecewise analytical envelope expression. This solution successfully maps the initial unattenuated quadratic coherent growth alongside the flat asymptotic horizontal saturation horizon governed by nonlinear energy conservation. Furthermore, the framework analytically evaluates the transient envelope overshoot, tracking its decay onto the saturation boundary via an exact phase-slip integration length scale. Providing immediate, ready macro-envelope bounds, this computationally inexpensive tool eliminates empirical adjustments and yields a robust mathematical baseline designed to guide and accelerate complex, multi-dimensional particle-in-cell (PIC) simulations through rapid parameter space exploration before deploying heavy computational assets. It establishes an efficient foundation to optimize asymmetric, custom-shaped driver profiles required to break the fundamental symmetric transformer ratio limit of 2 to make current particle accelerators more efficient.

Roaming reactions are organized not by potential-energy saddles but by transition states that are unstable invariant objects in phase space, periodic orbits in the two degrees of freedom studied here. To ask what controls roaming, we modify the Chesnavich model of a barrierless ion–molecule dissociation: its orientation-dependent angular hindrance is replaced by a transverse-stiffness ridge whose angular frequency peaks at an interior radius, and the classical dynamics are studied at a fixed energy just above the dissociation threshold. Comparing two ensembles that differ only in this angular interaction (same radial channel, energy, and inward initial conditions) isolates its effect. The ridge gates entry into the inner well, cutting inner capture from $57\%$ to $15\%$ and returning most of the incoming flux directly to reactants; it does not eliminate roaming but relocates it outward, suppressing it inside the ridge and switching it on farther out. The model retains analogues of the original model's three transition states (tight, free-rotor, and outer orbiting orbits), which we locate as unstable periodic orbits. The tight orbit spans a dividing surface that coincides, within numerical accuracy, with the variational minimum-flux surface, and it carries no barrier along the reaction coordinate: a deep entropic bottleneck placed at an interior radius by the stiffness maximum. Its entropic character is shared with the original model. Strength-matched monotone controls show that the gating tracks the hindrance strength at the bottleneck radius; what the interior maximum supplies is placement, concentrating that strength where it gates most effectively. The trajectories it admits roam nonstatistically, with nonexponential gap-time distributions: the entropic bottleneck governs how much is captured, not the dynamics that follow.

The simulation of high-frequency acoustic wave propagation in unbounded domains with local heterogeneous materials and high-contrast interfaces poses significant challenges to numerical methods. The volume-surface integral equation (VSIE) method is an attractive approach as it automatically satisfies the radiation condition at infinity via Green's functions, handles heterogeneous materials via Newton potentials, and models scattering at high-contrast interfaces via surface integral operators. However, its effectiveness in practical simulations has been limited by high computational costs, sensitivity to sharp interfaces, and insufficient computational verification. This study extends the applicability of VSIE by deriving integral formulations for nested heterogeneous materials with parameter jumps at interfaces. We also develop extensive benchmarks against coupled finite-element and boundary-element methods to verify the VSIE's accuracy and mesh convergence. The various benchmarks using open-source software demonstrate the effectiveness of VSIE for large-scale acoustic simulations.

Free-energy surfaces govern the populations of metastable states and the barriers that control transitions between them, making their direct optimization a central challenge in molecular and materials design. In this work, we introduce Gradient-Based Free Energy Surface Optimization (GB-FESO), an inverse design framework that uses a trained conditional diffusion model as a differentiable surrogate for the ensemble distribution. After training, the diffusion model is frozen, and the conditioning variables defining the system are optimized so that the generated ensemble reproduces a prescribed target free-energy surface. The optimization is carried out by backpropagating a distribution-level loss, based on kernel density estimates of the Kullback-Leibler divergence, through a deterministic diffusion sampling trajectory. We first validate GB-FESO on one-dimensional Gaussian ensembles, demonstrating that both continuous and relaxed discrete conditioning variables can be optimized to recover target distributions, including those outside the training domain. We then apply the method to a four-particle Lennard-Jones toy peptide exhibiting multiple metastable conformational states. In this more physically motivated setting, GB-FESO successfully optimizes the interaction parameters to reproduce target free-energy landscapes in the majority of test cases, with optimization performed either in the full internal-coordinate space or in a reduced collective-variable representation. These results establish GB-FESO as a promising first step toward an ensemble-level inverse design framework for molecular systems with prescribed thermodynamic and kinetic behavior.

Disordered materials such as glasses exhibit complex structural dynamics that are challenging to probe with conventional spectroscopies. We demonstrate that terahertz-driven four-wave mixing (FWM) at glass surfaces provides direct access to low-frequency vibrational modes and structural evolution in amorphous solids. Applied to a compositional series of PbO-silicate glasses (20-54 mol% PbO), this technique resolves distinct contributions from collective Boson-peak excitations and Pb-O / Si-O network stretching modes, and tracks their systematic evolution across structurally distinct compositional regimes. The dominant vibrational frequency blueshifts with PbO content, reflecting the progressive evolution of the Pb$^{2+}$ network role from silicate-modifier to ward network-former. A pronounced enhancement of the FWM signal near 44 mol% PbO coincides with the emergence of medium-range Pb-Pb correlations, while in-plane-to-out-of-plane FWM intensity ratio ($I_{\rm SS}/I_{\rm PS}$) tracks $\chi^{(3)}$ tensor anisotropy tied to Pb$^{2+}$ lone-pair spatial correlations. The non-monotonic peak in both observables at 44 mol% PbO - a composition where NMR finds no change in local Pb-O coordination and Pb-O-Pb free-oxide linkages are negligible - provides direct evidence that a collective lone-pair reorganization occurs in the medium-range structure independently of nearest-neighbor bonding. These results establish terahertz-driven FWM as a bulk-sensitive, near-surface depth-confined ($\sim$50 nm) nonlinear spectroscopy sensitive to vibrational and electronic structural fingerprints inaccessible to linear infrared, Raman, and terahertz time-domain probes.

The Hessian of the ring polymer spring potential in the standard Trotter path integral is a $P\times P$ symmetric circulant matrix with a centroid eigenvalue of zero. All such matrices commute and are diagonalised by the same bead to normal mode transformation matrix, and their eigenvalues contain $\lceil P/2\rceil-1$ degenerate pairs by symmetry. However, this still leaves some freedom to improve on the Trotter approximation: one can optimise the remaining $\lfloor P/2\rfloor$ independent non-zero normal mode frequencies to fit the exact quantum mechanical radii of gyration of harmonic ring polymers with frequencies in the range $0\le\omega\le\omega_{\rm max}$, where $\omega_{\rm max}$ is the maximum physical frequency in the problem of interest. The optimisation involves solving a simple least squares problem for the optimum (economised or "Eco") internal mode frequencies. The remainder of the calculation then proceeds in the same way as a Trotter path integral calculation. An example application to hexagonal ice shows that the convergence of the Eco path integral is comparable to that of the 4th order Suzuki-Chin path integral, but with purely 2nd order Trotter effort. There is no need to calculate the projected Hessians that arise in the Suzuki-Chin method by finite differences, there is no need to develop any new estimators for observables, and once the Eco frequencies have been calculated the implementation of the Eco path integral involves changing just a few lines of a Trotter path integral code. To provide a more impressive example we have implemented the Eco method in GPUMD and used it to converge the (negative) thermal expansion coefficient and the constant pressure heat capacity of MOF-5 with a machine-learned neuroevolution potential.

Bright dissipative Kerr solitons in optical microresonators provide chip-scale sources of ultrashort pulses and frequency combs. Their properties are defined by the cavity dispersion for which fundamentally conflicting requirements exist: short pulses and broadband spectra require weak dispersion, whereas strong dispersion is associated with predictable dynamics. Here, we resolve this conflict by introducing a localized strong-dispersion section spanning several modes around the pump resonance within an otherwise weakly dispersive system. We implement this hybrid-dispersion scheme in a photonic crystal microresonator and reveal a new soliton attractor of backward-propagating solitons, accessible at low pump power in a thermally stable manner within the blue-detuned regime. The conflicting requirements for broadband spectra and low-noise single-soliton formation are reconciled, even in microwave-repetition-rate resonators, which otherwise are prone to uncontrollable multi-soliton formation. These results highlight the potential to achieve previously incompatible characteristics in nonlinear photonic systems through hybrid-dispersion attractor shaping.

Attosecond metrology is the ability to measure ultrafast optical light-wave oscillations, yet its approach has been limited to classical fields. Hence, the influence of the fluctuations of a quantum field on attosecond measurements has remained unexplored. Here, we close this gap by showing that the attosecond streaking measurement of bright quantum light is sensitive to quantum fluctuations of the optical field on the attosecond timescale. The distinct sub-cycle modulations allow to extract the properties of the squeezed field quadrature in regimes where conventional state tomography approaches reach their limitation. With the full quantum optical attosecond streaking scheme developed here, we provide a certification method that can measure quantum squeezing below the shot noise limit, thereby overcoming the problem of tomographically measuring bright quantum light. This opens the way towards quantum optical metrology of field fluctuations with attosecond temporal resolution.

We propose an inverse biomechanical design framework for sectorial customized corneal crosslinking in keratoconus. The cornea is modeled as an anisotropic reduced shell with spatially varying crosslinking-induced stiffening, enabling the optimization of localized treatment patterns rather than uniform irradiation profiles. Numerical simulations show that sectorial stiffening can redistribute curvature, reduce localized steepening, and improve corneal regularity in decentered keratoconus models while preserving biomechanical plausibility. These results support the use of patient-specific computational planning for customized crosslinking protocols and provide a basis for future integration with corneal tomography and programmable ultraviolet delivery systems.

Characterization of the decomposition products of energized Criegee intermediates is essential for assessing their impact on the chemical evolution of the atmosphere. Here, a generic and microscopically resolved approach is used to determine the molecular fragmentation pathways and products for CF$_3$CHOO. They include, among others, direct formation of CO$_2$ + CHF$_3$ (HFC-23), HF + CO$_2$ + CF$_2$, and fragmentation routes that are not evident from static reaction path calculations alone. The computed probability for formation of HFC-23 of 14 % qualitatively agrees with a value of $(7.9^{+0.4}_{-0.2})$ % from recent measurements, given the differences in the two approaches. Non-statistical dynamics is found for almost all decomposition pathways and the simulations show that excess energy can redirect reaction outcomes away from minimum-energy pathways. The results highlight the power of machine-learned PESs to elucidate multi-step reaction mechanisms of atmospherically relevant intermediates beyond traditional Master equation/electronic structure approaches to provide molecular-level understanding of the role of dynamics.

Membrane-deforming protein lattices play a central role in essential and pathogenic remodeling processes, including clathrin-mediated endocytosis and viral budding. Simulating these systems at biologically relevant length and time scales requires mesoscale approaches that preserve structural detail. We present a hybrid simulation framework that couples a particle-based flexible protein lattice to a continuum membrane model, enabling systematic investigation of how lattice geometry and rigidity influence membrane remodeling. We validate the coupled model by comparing simulation results with theoretical predictions for membranes under increasing tension. Using buckling-based deformations of pre-assembled clathrin lattices, we quantify the lattice flexural rigidity and establish a direct relationship between the force constants in the coarse-grained energy function and the emergent lattice mechanics. We then compare this flexural rigidity, $\kappa_{l,\mathrm{flex}}$, to an effective rigidity, $\kappa_{l,\mathrm{eff}}$, commonly used in continuum descriptions of sphere-forming protein assemblies. Although $\kappa_{l,\mathrm{flex}}$ is set solely by the energy function, $\kappa_{l,\mathrm{eff}}$ depends on lattice size and connectivity, with the two measures converging for weakly-connected lattices. As a result, with lattice growth, the effective rigidity relevant for spherical bud formation increases and the relaxation timescale decreases. This size-dependent stiffening highlights the importance of structural details in interpreting lattice mechanics and cautions against assuming a single constant stiffness. We demonstrate the generality of the method by applying it to pre-assembled viral lattices generated with reaction-diffusion simulations. This work provides a validated framework for simulating deformable protein assemblies of diverse geometry coupled to membrane dynamics and remodeling.

We investigate the effect of a high concentration (32.5 g.L$^{-1}$) of sodium chloride (NaCl) on TTAB (tetradecyltrimethylammonium bromide) vertical soap films also called foam films, pulled out of a bath under controlled humidity conditions. We observe that the film lifetime increases with relative humidity, both in the presence and absence of salt. At any given humidity, the presence of NaCl systematically enhances film stability. Our film thickness measurements show that the thinning dynamics with or without salt are nearly identical down to 100 nm. Down to that thickness, the effect of evaporation can be rationalised by a constant evaporation rate, which becomes non-negligible compared to the drainage rate at film thicknesses below 400 nm. The main effect of salt is the stabilisation of a Newton black film at a thickness of approximately 5~nm, whereas in the absence of salt, the film ruptures upon reaching a critical thickness of about 10 nm.

In confined turbulent rotating convection, the largest vertical velocities are found near the sidewalls in the form of wave-like structures known as wall modes. These structures persist deep into the turbulent regime, bias heat transport, and disrupt bulk flow organisation through radial jets. Controlling or suppressing wall modes is, therefore, essential for accessing bulk dynamics free from wall-induced effects. Here, we combine experiments and direct numerical simulations to investigate wall modes control in cylindrical cells equipped with ring-shaped sidewall barriers. Barriers suppress vertical-velocity maxima near the sidewall and disrupt the characteristic wave-like pattern. Simulations further show that the barriers reduce the wall-mode-induced enhancement of heat transport, shifting it towards values characteristic of laterally periodic domains. The suppression efficiency is governed by the ratio of the barrier width to the wall-mode scale and is enhanced by the addition of a second barrier. In the horizontal plane, radial jet ejections are attenuated, while the time-averaged flow reveals suppression of the boundary zonal flow (BZF), a ring-shaped region of positive azimuthal velocity near the sidewall, provided measurements are taken away from the immediate vicinity of the barriers. In this region, isotherms bend toward the poorly conducting barrier, creating a local misalignment with the isobars and inducing a baroclinic flow adjacent to the barrier faces. This effect weakens with increasing barrier conductivity or smoother geometry. These results demonstrate that sidewall barriers provide a robust route for suppressing wall modes signatures in experimental turbulent rotating convection, while locally inducing secondary baroclinic flows near the barriers. Their use enables access to extreme rotating-convection regimes with reduced sidewall influence.

We introduce a physics-informed framework for learning finite-dimensional embeddings of solution families of partial differential equations. The method uses a multihead Physics-Informed Neural Network in which a shared body learns a latent manifold representing the solution space, while linear heads reconstruct individual solutions associated with different initial conditions. A head-orthogonalization penalty removes degeneracies in the latent representation and stabilizes the principal-component spectrum across training realizations. Because the initial condition is built into the network output by construction, these principal components measure the additional variability the network learns on top of the initial profile, not the full solution itself. We apply the method to the one-dimensional viscous Burgers equation, with the heat and wave equations as robustness checks. For a latent dimension $n_b=20$, the learned manifolds exhibit pronounced effective dimensional reduction: for Burgers dynamics, only $2$-$4$ principal components capture about $95\%$ of the latent-space variance, while $4$-$7$ capture about $99\%$, depending on the initial-condition family; the same qualitative compression holds for the heat and wave equations. We also split the wavenumber axis into bands (“Fourier shells”) and measure how much each band contributes to every principal component. The resulting frequency profile is invariant under the change-of-basis freedom that the orthogonalization penalty leaves in the latent space, and is therefore reproducible across independent training runs. More broadly, this establishes the learned spectral profiles and principal components as robust observables of solution-manifold geometry.

Our experimental and numerical study of filamentation of the 248 nm 100 fs pulses in air, nitrogen, and oxygen at atmospheric pressure shows that the densities of photoelectrons in the filaments are not sufficient to limit Kerr self-focusing. The simulations explain this result, showing that the nonlinear refractive index growth with the UV intensity is saturated due to transient photoexcitation of a molecule, not the photoionization. Correct simulation of the observed filament intensity ratio in different gases validates the suggested mechanism of the femtosecond UV pulses filamentation.

The multifractal detrended cross-correlation coefficient $\rho_q(n)$ is widely used to investigate scale-dependent interactions, but its application to negative fluctuation orders is affected by numerical instabilities, unbounded values, and interpretational difficulties. We propose a Signed Multifractal Detrended Cross-Correlation Coefficient, $\rho_{\mathrm{SMFDCCA}}(n,q)$, an amplitude-conditioned correlation observable for multifractal detrended analysis, based on locally normalized detrended correlations and regularized fluctuation amplitudes. The proposed coefficient preserves the sign of local interactions, remains strictly bounded within $[-1,1]$ for both positive and negative values of $q$, and eliminates the corrective procedures required by previous approaches. Validation using independent fractional Gaussian noise confirms the absence of spurious cross-correlations and the numerical stability of the method. Applications demonstrate that the proposed observable resolves how cross-correlations evolve jointly with temporal scale and fluctuation amplitude, revealing scale- and amplitude-dependent correlation structures, including stronger synchronization during large fluctuations in stock-market indices and heterogeneous coupling patterns in temperature records.

This numerical study examines a strain-rate inconsistency in the conventional flamelet/progress-variable (FPV) formulation for non-premixed combustion and proposes an alternative coupling based on the turbulence kinetic energy dissipation rate, $\varepsilon$. Two-dimensional Reynolds-averaged Navier-Stokes (RANS) simulations of a transonic accelerating reacting mixing layer are performed using one-step kinetics, a conventional FPV model, and the proposed $\varepsilon$-$Z$ flamelet model. The analysis focuses on the relation between the RANS-computed mean strain-rate field and the local strain rate imposed on the flamelet through the coupling between the flow computation and the flamelet library. In the FPV formulation, the flamelet state is selected through a transported progress variable, whose evolution is governed by advection, diffusion, and chemical production rather than by the local strain-rate environment. The present results show that this can lead to preferential sampling of near-equilibrium flamelet states in high-strain regions, thereby weakening the intended connection between the computed flow field and the strain-rate-controlled flamelet response. In the $\varepsilon$-$Z$ formulation, $\varepsilon$ is used to infer the imposed flamelet strain rate, $S^*$, so that the local flamelet state is directly constrained by the modeled turbulence field and the pressure-dependent flammability limit. Selected species are transported explicitly, allowing products to persist through locally quenched regions, while a reactant-availability scaling limits tabulated source terms when the transported composition departs from the flamelet manifold.

Similarly to Thomson, we consider the thermoelectric generator at open circuit as a classical heat engine. It is shown that, as long as the Thomson coefficient is nonzero, the operation generates entropy and is therefore irreversible. By expanding Thomson's approach we show that the voltage produced can be described by the usual Guy–Stodola equation for classical heat engines.

Pulse-echo speed-of-sound (SoS) imaging based on minute misalignments between consecutively acquired ultrasound images traditionally relies on images beamformed on Cartesian grids. Existing SoS imaging developments do not allow for real-time imaging and typically do not prioritize feasibility in conventional ultrasound systems that have limited resources and rigid processing structures. In this work, we propose a resource-efficient approach based on radial beamforming with virtual source transmits for implementation within an on-the-fly beamformer. We also introduce alternating transmissions with fast pair-alternating beamforming for motion-robust displacement tracking with typical line-based beamformers. We tested these methods comparatively on numerical simulations, tissue-mimicking phantom experiments, and in vivo data from breast lesion examinations. We demonstrate that the proposed radial grid beamforming approach performs comparably to a Cartesian grid approach, while allowing implementation on standard hardware for beamforming. Our proposed sequences would allow for SoS data acquisition frame rates of more than 20 fps in parallel to conventional B-mode imaging. The proposed speckle-shift based radial approach with fast alternation between congruent beamforming lines is a major step towards real-time SoS imaging on standard ultrasound systems with moderate resources.

Photonic integrated circuits (PICs) enable optical systems with dramatically increased performance, cost-effectiveness, and scalability through enhanced light-matter interactions, high-density integration, and mass production. Due to the significant mode mismatch between various integrated photonic platforms and optical fibers, spot-size conversion interfaces with low-loss, compact footprint, and high manufacturability are essential. Conventional spot-size converters based on intuitive designs often require multi-layer tapering structures and tiny waveguide tips to adiabatically expand the eigenmodes. These rigid design constraints commonly lead to large device footprints and the requirements of multiple high-precision lithography steps. In this paper, we overcome these limitations using inverse design methods, which optimize the coupling efficiency over a large parameter space beyond traditional eigenmode evolution limits. Specifically, we demonstrate efficient and ultra-compact photonic interfaces on the thin-film lithium niobate (TFLN) platform, where the partially etched rib waveguides and non-vertical sidewalls have previously hindered the achievement of low-loss waveguide tapers in single-layer configurations. Our inverse-designed photonic structures achieve simulated and experimentally measured coupling efficiencies as low as 1 dB and 3 dB per facet between TFLN waveguides and lensed/ultra-high numerical aperture (UHNA) fiber, with broad 1-dB bandwidths exceeding 120 nm. The inverse-designed interfaces are highly compatible with standard TFLN PIC components and require only a single high-resolution lithography step. More importantly, the design concept transcends traditional eigenmode evolution theories and is broadly applicable to a variety of material platforms and application scenarios.

Inverse Compton X-ray sources are laboratory-scale devices providing quasi-monochromatic synchrotron radiation which is generated by laser photons Compton-scattering off highly relativistic electrons. Since the shape and width of the X-ray spectrum are determined by the properties of the colliding beams, these must be carefully optimised. However, device compactness limits the space for diagnostics, rendering a complete characterisation challenging, especially if an electron storage ring is combined with a laser enhancement cavity. Here, a framework for laser, electron and X-ray beam parameter determination is proposed to address this issue. First, methods for determining the laser- and X-ray parameters are presented. Knowing these, electron beam parameters are retrieved from the shape of the X-ray spectrum. To this end, an analytical physical model enabling a rapid calculation of inverse Compton scattering spectra is developed and combined with a genetic algorithm. This strategy's effectiveness is demonstrated by applying the concept at the Munich Compact Light Source, a storage ring-based inverse Compton X-ray source facility. Since the analytical model is computationally very inexpensive, the proposed framework could enable real-time monitoring of inverse Compton X-ray sources or be used as a non-invasive diagnostic based on a single spectrum for the electron beam emittance of storage rings or accelerators.

Collective emotion is often inferred from the tone of mass media, but such emotion is not directly observed. One approximation is to extract sentiment from text and use sentiment indexes as proxies to study the temporal organization of news sentiment. Using a daily index of U.S. economic news sentiment from 24 newspapers (1980-2025), we examine whether the response time of this sentiment process has changed. Although the average balance of positive and negative coverage has remained broadly stable, the persistence of news sentiment states has increased substantially. In dynamical terms, this implies longer residence times in optimistic or pessimistic regimes and weaker short-run correction of sentiment shocks. Complementary statistics show declining sentiment volatility, fewer reversals, and increasing bimodality, i.e. a stronger separation between positive and negative sentiment states. We also find an asymmetry between bursts of negative and positive sentiment, with negative bursts tending to last longer. These patterns are consistent with a minimal endogenous-memory model in which a slowly evolving latent sentiment component becomes more persistent while short-range corrective feedback weakens. The findings indicate a change in the temporal response of the U.S. economic newspaper sentiment index over the last 45 years, with sentiment shocks leaving longer traces than expected under short-memory exponential decay. News-based sentiment is thus better modeled as persistent episodes rather than as daily reactions that reset after each event.

Computational modeling and simulation have emerged as powerful tools for assessing medical device performance and safety, particularly in silico clinical trials (ISCTs) for automated medical systems. In ventilation, where gas exchange, respiratory mechanics, and patient-ventilator interaction must be managed under evolving pathophysiology, clinical translation of automated control strategies remains slow and resource-intensive. These challenges are particularly relevant for AI-based therapy-control systems, whose data-driven decision-making must be evaluated across heterogeneous and safety-critical patient states that may be sparsely represented in clinical datasets. Mechanistic, physiology-based models provide a complementary and interpretable environment for testing such scenarios. This paper applies a standards-aligned framework for credibility assessment of a computational respiratory model, demonstrated using an automated weaning case study. The framework operationalizes ASME V&V 40 and FDA principles within a structured validation workflow. The model integrates respiratory mechanics, gas exchange, respiratory control, and a ventilator representation, with validation under a defined context of use and explicit questions of interest. Model credibility is assessed through calibration, physiological plausibility, population-based evaluation, and reproduction of emergent behavior. All model requirements derived from the intended context of use are addressed, and gaps are transparently reported. The resulting credibility argument supports applicability of the model for medium-low-risk preclinical ISCTs of automated weaning strategies. Residual limitations relate to the extent of in vivo evidence, population representativeness, and external validation. The validation procedure provides a blueprint for validation of this and similar models in mechanical ventilation and related use cases.