Normal mode analysis (NMA) provides a mathematical framework for exploring the intrinsic global dynamics of molecules through the definition of an energy function, where normal modes correspond to the eigenvectors of the Hessian matrix derived from the second derivatives of this function. The energy required to 'trigger' each normal mode is proportional to the square of its eigenvalue, with six zero-eigenvalue modes representing universal translation and rotation, common to all molecular systems. In contrast, modes associated with small non-zero eigenvalues are more easily excited by external forces and are thus closely related to molecular functions. Inspired by the anisotropic network model (ANM), this work establishes a novel connection between normal mode analysis and sheaf theory by introducing a cellular sheaf structure, termed the anisotropic sheaf, defined on undirected, simple graphs, and identifying the conventional Hessian matrix as the sheaf Laplacian. By interpreting the global section space of the anisotropic sheaf as the kernel of the Laplacian matrix, we demonstrate a one-to-one correspondence between the zero-eigenvalue-related normal modes and a basis for the global section space. We further analyze the dimension of this global section space, representing the space of harmonic signals, under conditions typically considered in normal mode analysis. Additionally, we propose a systematic method to streamline the Delaunay triangulation-based construction for more efficient graph generation while preserving the ideal number of normal modes with zero eigenvalues in ANM analysis.

Revealing the role of quantum entanglement in charge-transport in the Photosystem II reaction center (PSII RC) is of great significance. In this work, we theoretically demonstrate that the robust quantum entanglement provides regulatory benefits to the charge-transport via a quantum heat engine (QHE) model with two absorbed photon channels. The calculation results manifest that the dynamic charge-transport and the steady-state photosynthetic properties of the PSII RC were enhanced by the intensity of quantum entanglement. Insight into the role of quantum entanglement in photosynthesis could motivate new experimental strategies for biomimetic photosynthetic devices in the future.

Recent evidence suggests that the multi charge-separation pathways can contribute to the photosynthetic performance. In this work, the influence of coupled-dipoles on the photosynthetic performance was investigated in a two-charge separation pathways quantum heat engine (QHE) model. And the population dynamics of the two coupled sites, j-V characteristics and power involving this photosynthetic QHE model were evaluated for the photosynthetic performance. The results illustrate that the photosynthetic performance can be greatly enhanced but quantum interference was deactivated by the coupled-dipoles between the two-charge separation pathways. However, the photosynthetic performance can also be promoted by the deactivated quantum interference owing to the coupled-dipoles. It is a novel role of the coupled-dipoles in the energy transport process of biological photosynthetic and some artificial strategies may be motivated by this photosynthetic QHE model in the future.

We study exciton quantum transfer along a molecular chain whilst accounting for the effects of permanent dipoles that are induced by charge displacements in the molecular orbitals. These effects are typically neglected as they do not arise in atomic quantum optics; however, they can play an important role in molecular systems. We also consider novel collective photon-assisted transport and compare it against the scaling of phonon-assisted transport in chains featuring permanent dipoles, and determine a linear scaling with the number of dipoles, akin to single-excitation superradiance. We further demonstrate how permanent dipoles, dipoles can preferentially arrange energy eigenstates to support excitation transport. Finally, we show how permanent dipoles can enhance the ability of the molecular chain to support excitation transport compared to that of systems that do not possess permanent dipoles across a range of environmental and system configurations.

The IceCube Neutrino Observatory includes low energy extensions such as the existing DeepCore subarray and the upcoming IceCube Upgrade, which will consist of seven new strings of photosensors with denser instrumentation than the existing array. The setup will allow for the study of neutrino oscillations with greater sensitivity compared to the existing instrumentation, improve neutrino mass ordering studies, and test for the unitarity of the PMNS mixing matrix with high precision. A critical component in these low-energy physics analyses is the accurate reconstruction of event information, particularly the zenith angle of incoming neutrinos. In this study, we discuss the processes that limit the zenith resolution, which include the transverse spread of the hadronic shower, in-ice photon scattering, module resolutions, and module noise. By considering approximations to these processes, we aim to approach the intrinsic zenith resolution limits for purely hadronic events.

A new physics-based model for analytical calculation of Soft Error Rate (SER) in digital memory circuits under the influence of heavy ions in space orbits is proposed. This method is based on parameters that are uniquely determined from the results of ground tests under nor-mal ion incidence. It is shown that preliminary averaging over the total solid angle within the standard inverse cosine model allows one to take into account the effect of isotropic flow, which increases the effective SER. The model includes the ability to estimate the contribution to SER of the low LET spectrum region, which is very important for modern ICs with low Single Event Upset tolerance.

Classical studies as the conservation laws and the radiation fields are investigated in the pseudo-electrodynamics. We explore the action symmetry under infinitesimal transformations to obtain the energy-momentum, the Belinfante-Rosenfeld, and the general angular momentum tensors for this nonlocal planar electrodynamics. Through the results such as the retarded potentials and fields generated by a point particle in an arbitrary motion, we study the radiation of an electric dipole and it radiated power in 1+2 dimensions. In addition, we propose a way to introduce magnetic monopoles in pseudo-electrodynamics, in which the solutions and conservation laws are also presented.

Perpendicular magnetic tunnel junctions (pMTJs) actuated by nanosecond pulses are emerging as promising devices for true random number generation (TRNG) due to their intrinsic stochastic behavior and high throughput. In this work, we study the tunability and quality of random-number distributions generated by pMTJs operating at a frequency of 104 MHz. First, changing the pulse amplitude is used to systematically vary the probability bias. The variance of the resulting bitstreams is shown to follow the expected binomial distribution. Second, the quality of uniform distributions of 8-bit random numbers generated with a probability bias of 0.5 is considered. A reduced chi-square analysis of this data shows that two XOR operations are necessary to achieve this distribution with p-values greater than 0.05. Finally, we show that there is a correlation between long-term probability bias variations and pMTJ resistance. These findings suggest that variations in the characteristics of the pMTJ underlie the observed variation of probability bias. Our results highlight the potential of stochastically actuated pMTJs for high-speed, tunable TRNG applications, showing the importance of the stability of pMTJs device characteristics in achieving reliable, long-term performance.

When time and velocities are dynamically rescaled relative to the instantaneous turnover time, the Sabra shell model acquires another (hidden) form of scaling symmetry. It has been shown previously that this symmetry is statistically restored in the inertial interval of developed turbulence. In this paper we answer the question: what is the general consequence of the restored hidden symmetry for the original variables? We derive the self-similarity rule stating that any (single or multi-time) observable that is time-scale homogeneous of degree $p$ is self-similar with the Holder exponent $h = \zeta_p/p$, where $\zeta_p$ is the usual anomalous exponent. As applications, we formulate self-similarity rules for multi-time structure functions and multi-time Kolmogorov multipliers and verify them numerically.

This work presents a large language model (LLM)-based agent OpenFOAMGPT tailored for OpenFOAM-centric computational fluid dynamics (CFD) simulations, leveraging two foundation models from OpenAI: the GPT-4o and a chain-of-thought (CoT)-enabled o1 preview model. Both agents demonstrate success across multiple tasks. While the price of token with o1 model is six times as that of GPT-4o, it consistently exhibits superior performance in handling complex tasks, from zero-shot case setup to boundary condition modifications, turbulence model adjustments, and code translation. Through an iterative correction loop, the agent efficiently addressed single- and multi-phase flow, heat transfer, RANS, LES, and other engineering scenarios, often converging in a limited number of iterations at low token costs. To embed domain-specific knowledge, we employed a retrieval-augmented generation (RAG) pipeline, demonstrating how preexisting simulation setups can further specialize the agent for sub-domains such as energy and aerospace. Despite the great performance of the agent, human oversight remains crucial for ensuring accuracy and adapting to shifting contexts. Fluctuations in model performance over time suggest the need for monitoring in mission-critical applications. Although our demonstrations focus on OpenFOAM, the adaptable nature of this framework opens the door to developing LLM-driven agents into a wide range of solvers and codes. By streamlining CFD simulations, this approach has the potential to accelerate both fundamental research and industrial engineering advancements.

Prosecutors are essential in combating organized crime, making key decisions about prosecution, target selection, and structuring imputation strategies. Despite their importance, the configuration of these strategies remains empirically underexplored. This study analyzes cases investigated by the International Commission Against Impunity in Guatemala (CICIG) using a multilevel network approach to examine legal interventions targeting criminal networks. The research employs a multilevel Exponential Random Graph Model (ERGM), integrating three networks: the criminal network of actors involved in illegal activities, the legal framework network represents offenses, and the prosecution network connects actors to offenses. This approach identifies structural patterns in prosecutorial strategies for individual actors and co-offenders. Findings show a strong tendency for triangular configurations, where two co-offenders are linked to a shared offense. Additionally, individuals are more likely to be involved in diverse offenses, spanning corruption-related and non-corruption-related activities, than in similar types of offenses. This highlights a strategic focus on addressing varied criminal behaviors within interconnected networks. Notably, by capturing the interplay between legal framework, criminal network, and prosecution strategy, the findings of this study emphasize the value of multilevel network analysis for enhancing the effectiveness of legal interventions, underscore the critical role of prosecutors in dismantling complex criminal networks, and offers a novel framework for improving prosecution strategies in combating organized crime.

Colloidal two-dimensional lead chalcogenide nanocrystals represent an intriguing new class of materials that push the boundaries of quantum confinement by combining a crystal thickness down to the monolayer with confinement in the lateral dimension. In particular flat PbSe quantum dots exhibit efficient telecommunication band-friendly photoluminescence (1.43 - 0.83 eV with up to 61% quantum yield) that is highly interesting for fiber-optics information processing. By using cryogenic scanning tunneling microscopy and spectroscopy, we probe distinct single layer-defined PbSe quantum dot populations down to a monolayer with in-gap state free quantum dot-like density of states, in agreement with theoretical tight binding calculations. Cryogenic ensemble photoluminescence spectra reveal mono-, bi-, and trilayer contribution, confirming the structural, electronic and theoretical results. From larger timescale shifts and ratio changes in the optical spectra we infer Ostwald ripening in solution and fusing in deposited samples of thinner flat PbSe quantum dots, which can be slowed down by surface passivation with PbI2. By uncovering the interplay between thickness, lateral size and density of states, as well as the synthetic conditions and post-synthetic handling, our findings enable the target-oriented synthesis of two-dimensional PbSe quantum dots with precisely tailored optical properties at telecom wavelengths.

The precise optical, label-free, measurement of mass at the nanoscale has been significantly advanced by techniques based on interferometric scattering, such as mass photometry (MP). These methods exploit the interference between a scattered and reference field to achieve a high signal-to-noise ratio (SNR) for weakly scattering objects (e.g. proteins) and are currently limited to masses bigger than 40 kDa. Standard MP employs a mask that attenuates the reference field, allowing for the increase in illumination power without saturation of the detector. In this theoretical study, we examine how the SNR evolves when extending reference attenuation beyond conventional levels: entering the low-reference regime. Our simplified model finds that a substantial SNR enhancement can be achieved when the magnitude of reference matches that of the scattered field and investigate refractive index tuning as a potential method to reach the required attenuation in practice. The accomplishable SNR improvement can be tailored to a given mass region, i.e. allowing the detection of masses smaller than 40 kDa.

Polaritonic states, which arise from strong coupling between light and matter, show great promise in modifying chemical reactivity. However, reproducible enhancement of chemical reactions with polaritons is challenging due to a lack of understanding on how to launch wavepackets along productive reactive coordinates while avoiding unproductive local minima in the multidimensional potential energy landscape. Here we employ resonance Raman intensity analysis to quantify mode-specific nuclear displacement values in pentacene thin films and pentacene exciton-polaritons. We find that coupling significantly changes the potential energy landscape, including both enhancement and suppression of nuclear displacements. We demonstrate that controlling cavity parameters enables selective steering of vibronic wavepackets. Our approach provides a quantitative methodology for screening polaritonic catalysts and opens new avenues for designing reproducible and effective cavity-controlled chemistry.

With the expansion of silicon photonics from datacom applications into emerging fields like optical I/O, quantum and programmable photonics there is an increasing demand for devices that combine ultra-compact footprints, low losses, and broad bandwidths. While inverse design techniques have proven very efficient in achieving small footprints, they often underutilize physical insight and rely on large parameter spaces that are challenging to explore, thereby limiting the performance of the resulting devices. Here we present a design methodology that combines inverse design with a topology based on cells, each of which contains a subwavelength metamaterial. This approach significantly reduces the parameter space, while the inherent anisotropy of the subwavelength structures yields shorter devices. We experimentally demonstrate our technique with an ultra-compact spot size converter that achieves a x24 expansion ratio times (from 0.5{\mu}m to 12{\mu}m) over a length of only 7{\mu}m, with insertion losses of 0.8 dB across a measured bandwidth of 160nm (up to 300nm in simulation), surpassing the state-of-the-art by a wide margin.

We present an implicit collision method with on-the-fly multiplicity adjustment based on the forward weight window methodology for efficient Dynamic Monte Carlo (MC) simulation of reactivity excursion transport problems. Test problems based on the Dragon experiment of 1945 by Otto Frisch are devised to verify and assess the efficiency of the method. The test problems exhibit the key features of the Dragon experiment, namely nine orders of magnitude neutron flux bursts followed by significant post-burst delayed neutron effects. Such an extreme reactivity excursion is particularly challenging and has never been solved with Dynamic MC. The proposed implicit collision multiplicity adjustment, in conjunction with a simple forced delayed neutron precursor decay technique, profitably trades simulation precision for reduced runtime, leading to an improved figure of merit, enabling efficient Dynamic MC simulation of extreme reactivity excursions.

Chaotic microcavities play a crucial role in several research areas, including the study of unidirectional microlasers, nonlinear optics, sensing, quantum chaos, and non-Hermitian physics. To date, most theoretical and experimental explorations have focused on two-dimensional (2D) chaotic dielectric microcavities, while there have been minimal studies on three-dimensional (3D) ones since precise geometrical information of a 3D microcavity can be difficult to obtain. Here, we image 3D microcavities with submicron resolution using X-ray microcomputed tomography (micro CT), enabling nondestructive imaging that preserves the sample for subsequent use. By analyzing the ray dynamics of a typical deformed microsphere, we demonstrate that a sufficient deformation along all three dimensions can lead to chaotic ray trajectories over extended time scales. Notably, using the X-ray micro CT reconstruction results, the phase space chaotic ray dynamics of a deformed microsphere are accurately established. X-ray micro CT could become a unique platform for the characterization of such deformed 3D microcavities by providing a precise means for determining the degree of deformation necessary for potential applications in ray chaos and quantum chaos.

In the standard picture of fully-developed turbulence, highly intermittent hydrodynamic fields are nonlinearly coupled across scales, where local energy cascades from large scales into dissipative vortices and large density gradients. Microscopically, however, constituent fluid molecules are in constant thermal (Brownian) motion, but the role of molecular fluctuations on large-scale turbulence is largely unknown, and with rare exceptions, it has historically been considered irrelevant at scales larger than the molecular mean free path. Recent theoretical and computational investigations have shown that molecular fluctuations can impact energy cascade at Kolmogorov length scales. Here we show that molecular fluctuations not only modify energy spectrum at wavelengths larger than the Kolmogorov length in compressible turbulence, but they also significantly inhibit spatio-temporal intermittency across the entire dissipation range. Using large-scale direct numerical simulations of computational fluctuating hydrodynamics, we demonstrate that the extreme intermittency characteristic of turbulence models is replaced by nearly-Gaussian statistics in the dissipation range. These results demonstrate that the compressible Navier-Stokes equations should be augmented with molecular fluctuations to accurately predict turbulence statistics across the dissipation range. Our findings have significant consequences for turbulence modeling in applications such as astrophysics, reactive flows, and hypersonic aerodynamics, where dissipation-range turbulence is approximated by closure models.

The Derivative Source Method (DSM) takes derivatives of a particle transport equation with respect to selected parameters and solves them via the standard Monte Carlo random walk simulation along with the original transport problem. The Monte Carlo solutions of the derivative equations make the sensitivities of quantities of interest to the selected parameters. In this paper, we show that DSM can be embedded in Monte Carlo simulation to simultaneously calculate sensitivities of transport phase-space solution to multiple object dimensions and material densities. We verify and assess the efficiency of DSM by solving a multigroup neutronic system of a source-driven fuel-moderator-absorber slab lattice and calculating the fast and slow flux sensitivity coefficient distributions to the fuel and absorber dimensions, as well as the fuel, moderator, and absorber densities. The results are compared to those obtained from conventional finite difference sensitivity calculations. A figure of merit, defined as the product inverse of runtime and square of relative error, is used to assess method efficiencies. For a couple of the calculated sensitivities, well-configured finite difference calculations are the most efficient, followed closely by DSM. However, since seeking such well-configured finite difference is not always practical, in the rest of the cases, including the all-parameter simultaneous sensitivity calculation, DSM has the highest efficiency, demonstrating its robustness.

The application of the standard quasi-steady-state approximation to the Michaelis--Menten reaction mechanism is a textbook example of biochemical model reduction, derived using singular perturbation theory. However, determining the specific biochemical conditions that dictate the validity of the standard quasi-steady-state approximation remains a challenging endeavor. Emerging research suggests that the accuracy of the standard quasi-steady-state approximation improves as the ratio of the initial enzyme concentration, $e_0$, to the Michaelis constant, $K_M$, decreases. In this work, we examine this ratio and its implications for the accuracy and validity of the standard quasi-steady-state approximation. Using standard tools from the analysis of ordinary differential equations, we show that while $e_0/K_M$ provides an indication of the standard quasi-steady-state approximation's asymptotic accuracy, the standard quasi-steady-state approximation's predominance relies on a small ratio of $e_0$ to the Van Slyke-Cullen constant, $K$. We conclude that the magnitude of $e_0/K$ offers the most accurate measure of the validity of the standard quasi-steady-state approximation.

Cooperative behaviors are deeply embedded in structured biological and social systems. Networks are often employed to portray pairwise interactions among individuals, where network nodes represent individuals and links indicate who interacts with whom. However, it is increasingly recognized that many empirical interactions often involve triple or more individuals instead of the massively oversimplified lower-order pairwise interactions, highlighting the fundamental gap in understanding the evolution of collective cooperation for higher-order interactions with diverse scales of the number of individuals. Here, we develop a theoretical framework of evolutionary game dynamics for systematically analyzing how cooperation evolves and fixates under higher-order interactions. Specifically, we offer a simple condition under which cooperation is favored under arbitrary combinations of different orders of interactions. Compared to pairwise interactions, our findings suggest that higher-order interactions enable lower thresholds for the emergence of cooperation. Surprisingly, we show that higher-order interactions favor the evolution of cooperation in large-scale systems, which is the opposite for lower-order scenarios. Our results offer a new avenue for understanding the evolution of collective cooperation in empirical systems with higher-order interactions.

The pairing hierarchy of perfect pairing (PP), perfect quadruples (PQ) and perfect hextuples (PH) are sparsified coupled cluster models that are exact in a pairing active space for 2, 4, and 6 electron clusters, respectively. We describe and implement three extensions for radicals. First is the trivial generalization that does not correlate radical orbitals. The second model (PQr, PHr) includes terms that entangle pair indices and radical indices such that their maximum total number is 2 for PQ and 3 for PH (like their closed-shell versions). The third family of extended radical models (PPxr, PQxr, and PHxr) include cluster amplitudes that entangle up to 1, 2, and 3 pair indices with up to 1, 2, and 3 radical indices. Notably, PPxr and PQxr are exact for (3e,3o) and (5e,5o), respectively, while still having only $O(N)$ and $O(N^{2}$) amplitudes like their parent models (for $N$ paired electrons). Orbital optimization is considered for PPxr. A series of large-scale numerical tests of these models are presented for spin gaps, and ionization energies of polyenes and polyenyl radicals, ranging in size from ethene and allyl radical up to C$_{22}$H$_{24}$ in full-valence active spaces up to (122e,122o). The xR models perform best.

This work introduces a novel application for predicting the macroscopic intrinsic permeability tensor in deformable porous media, using a limited set of micro-CT images of real microgeometries. The primary goal is to develop an efficient, machine-learning (ML)-based method that overcomes the limitations of traditional permeability estimation techniques, which often rely on time-consuming experiments or computationally expensive fluid dynamics simulations. The novelty of this work lies in leveraging Convolutional Neural Networks (CNN) to predict pore-fluid flow behavior under deformation and anisotropic flow conditions. Particularly, the described approach employs binarized CT images of porous micro-structure as inputs to predict the symmetric second-order permeability tensor, a critical parameter in continuum porous media flow modeling. The methodology comprises four key steps: (1) constructing a dataset of CT images from Bentheim sandstone at different volumetric strain levels; (2) performing pore-scale simulations of single-phase flow using the lattice Boltzmann method (LBM) to generate permeability data; (3) training the CNN model with the processed CT images as inputs and permeability tensors as outputs; and (4) exploring techniques to improve model generalization, including data augmentation and alternative CNN architectures. Examples are provided to demonstrate the CNN's capability to accurately predict the permeability tensor, a crucial parameter in various disciplines such as geotechnical engineering, hydrology, and material science. An exemplary source code is made available for interested readers.

Plasma, which constitutes 99\% of the visible matter in the universe, is characterized by a wide range of waves and instabilities that play a pivotal role in space physics, astrophysics, laser-plasma interactions, fusion research, and laboratory experiments. The linear physics of these phenomena is described by kinetic dispersion relations (KDR). However, solving KDRs for arbitrary velocity distributions remains a significant challenge, particularly for non-Maxwellian distributions frequently observed in various plasma environments. This work introduces a novel, efficient, and unified numerical framework to address this challenge. The proposed method rapidly and accurately yields all significant solutions of KDRs for nearly arbitrary velocity distributions, supporting both unstable and damped modes across all frequencies and wavevectors. The approach expands plasma species' velocity distribution functions using a series of carefully chosen orthogonal basis functions and employs a highly accurate rational approximation to transform the problem into an equivalent matrix eigenvalue problem, eliminating the need for initial guesses. The efficiency and versatility of this framework are demonstrated, enabling simplified studies of plasma waves with arbitrary distributions. This advancement paves the way for uncovering new physics in natural plasma environments, such as spacecraft observations in space plasmas, and applications like wave heating in fusion research.

The first experimental evidence of quantum Drude oscillator behavior in liquids is determined using probabilistic machine learning-augmented iterative Boltzmann inversion applied to noble gas radial distribution functions. Furthermore, classical force fields for noble gases are shown to be reduced to a single parameter through simple empirical relations linked to atomic dipole polarizability. These findings highlight how neutron scattering data can inspire innovative force field design and offer insight into interatomic forces to advance molecular simulations.

Electric vehicle batteries have a proven flexibility potential which could serve as an alternative to conventional electricity storage solutions. EV batteries could support the balancing of supply and demand and the integration of variable renewable energy into the electricity system. The flexibility potential from electric vehicles, in distinction to conventional battery storage, depends on the vehicle user's willingness and opportunity to make their vehicle available for flexibility. This rate of participation is often not considered in studies, despite the impact electric vehicle flexibility could have on the electricity system. This work presents a modelling study of the Norwegian electricity system, demonstrating how a future net-zero electricity system can benefit from electric vehicles in terms of integrating renewables and balancing supply and demand, while considering the rate of participation. Our findings show electric vehicles' potential to eliminate the need for stationary battery storage with just 50% participation in vehicle-to-grid. We find that the flexibility of electric vehicles contributes to relative reductions in the total cost of the electricity system by almost 4% and 15% assuming 100% participation in flexible charging and vehicle-to-grid, respectively.

Piecewise-deterministic Markov processes combine continuous in time dynamics with jump events, the rates of which generally depend on the continuous variables and thus are not constants. This leads to a problem in a Monte-Carlo simulation of such a system, where, at each step, one must find the time instant of the next event. The latter is determined by an integral equation and usually is rather slow in numerical implementation. We suggest a reformulation of the next event problem as an ordinary differential equation where the independent variable is not the time but the cumulative rate. This reformulation is similar to the H\'enon approach to efficiently constructing the Poincar\'e map in deterministic dynamics. The problem is then reduced to a standard numerical task of solving a system of ordinary differential equations with given initial conditions on a prescribed interval. We illustrate the method with a stochastic Morris-Lecar model of neuron spiking with stochasticity in the opening and closing of voltage-gated ion channels.

We examine computationally the two-dimensional flow of elastoviscoplastic (EVP) fluids around a cylinder symmetrically placed between two plates parallel to its axis. The Saramito-Herschel-Bulkley fluid model is solved via the finite-volume method using the OpenFOAM software. As in viscoplastic materials, unyielded regions arise around the plane of symmetry well ahead or behind the cylinder, as two small islands located above and below the cylinder and as polar caps at the two stagnation points on the cylinder. Most interestingly, under certain conditions, an elongated yielded area around the midplane is predicted downstream of the cylinder, sandwiched between two unyielded areas. This surprising result appears, for example, with Carbopol 0.1% when considering a blockage ratio of 0.5 (the ratio of the cylinder's diameter to the channel's width) and above a critical elastic modulus (G>30 Pa). An approximate semi-analytical solution in the region mentioned above, reveals that it is caused by the intense variation of the stress magnitude there, which may approach the yield stress asymptotically either from above or below, depending on material elasticity. The drag coefficient on the cylinder increases with yield stress and blockage ratio but decreases with material elasticity. The unyielded regions expand as the yield stress increases. They also expand when material elasticity increases because this allows the material to elastically deform more before yielding. Furthermore, by decreasing the elastic modulus or increasing the yield stress beyond a critical value, the yield surface may exhibit damped oscillations, or irregular shapes even without a plane of symmetry, all under creeping flow conditions.

Astronomy, of all the sciences, is possibly the one with the most public appeal across all age groups. This is also evidenced by the existence of a large number of planetaria and amateur astronomy societies, which is unique to the field. Astronomy is known as a `gateway science', with an ability to attract students who then proceed to explore their interest in other STEM fields too. Astronomy's link to society is therefore substantive and diverse. In this white paper, six key areas are analysed, namely outreach and communication, astronomy education, history and heritage, astronomy for development, diversity, and hiring practices for outreach personnel. The current status of each of these areas is described, followed by an analysis of what is needed for the future. A set of recommendations for institutions, funding agencies, and individuals are evolved for each specific area. This work charts out the vision for how the astronomy-society connection should take shape in the future, and attempts to provide a road-map for the various stakeholders involved.

Enhanced Geothermal Systems (EGS) can provide constant, reliable electricity and heat with minimal emissions, but high drilling costs and uncertain cost reductions leave their future unclear. We explore scenarios for the future adoption of EGS in a carbon-neutral, multi-sector European energy system. We find that in a net-zero system, heat (co-)generating EGS at current cost can support 20--30 GWth of capacity in Europe, primarily driven by district heating demands. When drilling costs decrease by approximately 60%, EGS becomes competitive in electricity markets, expanding its market opportunity by one order of magnitude. However, the spatially dispersed rollout of district heating contrasts with the confined overlap of high geological potential and low potential for other renewables, which conditions the competitiveness of electricity-generating EGS. This results in a challenge where the majority of EGS market potential depends on pan-European technology learning for cost reductions, emphasising coordination is crucial in stakeholders' efforts to reduce EGS cost.

The high-energy repulsive interaction between nuclei at distances much smaller than the equilibrium bond length is the key quantity determining the nuclear stopping power and atom scattering in keV and MeV radiation events. This interaction is traditionally modeled within orbital-free density functional theory with frozen atomic electron densities, following the Ziegler-Biersack-Littmark (ZBL) model. In this work, we calculate atom pair specific repulsive interatomic potentials with the ZBL model, and compare them to two kinds of quantum chemical calculations - second-order M{\o}ller-Plesset perturbation theory in flexible Gaussian basis sets as well as density functional theory with numerical atomic orbital basis sets - which go well beyond the limitations in the ZBL model, allowing the density to relax in the calculations. We show that the repulsive interatomic potentials predicted by the two quantum chemical models agree within $\sim$ 1% for potential energies above 30 eV, while the ZBL pair-specific potentials and universal ZBL potentials differ much more from either of these calculations. We provide new pair-specific fits of the screening functions in terms of 3 exponentials to the calculations for all pairs $Z_1$-$Z_2$ for $1 \leq Z_i \leq 92$, and show that they agree within $\sim 2$% with the raw data. We use the new potentials to simulate ion implantation depth profiles in single crystalline Si and show very good agreement with experiment. However, we also show that under channeling conditions, the attractive part of the potential can affect the depth profiles. The full data sets of all the calculated interatomic potentials as well as analytic fits to the data are shared as open access.

Confronting the escalating threat of climate change requires innovative and large-scale interventions. This paper presents a bold proposal to employ a buried nuclear explosion in a remote basaltic seabed for pulverizing basalt, thereby accelerating carbon sequestration through Enhanced Rock Weathering (ERW). By precisely locating the explosion beneath the seabed, we aim to confine debris, radiation, and energy while ensuring rapid rock weathering at a scale substantial enough to make a meaningful dent in atmospheric carbon levels. Our analysis outlines the parameters essential for efficient carbon capture and minimal collateral effects, emphasizing that a yield on the order of gigatons is critical for global climate impact. Although this approach may appear radical, we illustrate its feasibility by examining safety factors, preservation of local ecosystems, political considerations, and financial viability. This work argues for reimagining nuclear technology not merely as a destructive force but as a potential catalyst for decarbonization, thereby inviting further exploration of pioneering solutions in the fight against climate change.

We present the first analytic-derivative-based formulation of vibrational circular dichroism (VCD) atomic axial tensors for second-order Moller-Plesset (MP2) perturbation theory. We compare our implementation to our recently reported finite-difference approach and find close agreement, thus validating the new formulation. The new approach is dramatically less computationally expensive than the numerical-derivative method with an overall computational scaling of $O(N^6)$. In addition, we report the first fully analytic VCD spectrum for (S)-methyloxirane at the MP2 level of theory.

As a fundamental QED process, linear Breit-Wheeler (LBW) pair production predicted 90 years ago has not yet been demonstrated in experiments with real photons. Here, we propose an experimentally advantageous scheme to detect the LBW signal by irradiating a foil target with a single 10 PW-level laser. Our integrated QED particle-in-cell simulations demonstrate that the LBW signal can be explicitly distinguished from the Bethe-Heitler (BH) signal by comparing positron energy spectra behind the target at varying target thicknesses. The LBW positrons are created at the front of the target and subsequently experience both laser vacuum acceleration and sheath field acceleration to gain high energies, while BH positrons, originating within the target bulk, are only subjected to sheath field acceleration. As a result, the invariance of the high-energy tail of positron spectra with respect to the target thickness serves as a distinct signature of the LBW process. Notably, this scheme remains viable even when the BH yield dominates over the LBW yield.

This study investigates the aerodynamic performance of the symmetric NACA 0018 airfoil under harmonic pitching motions at low Reynolds numbers, a regime characterized by the presence of laminar separation bubbles and their impact on aerodynamic forces. The analysis encompasses oscillation frequencies of 1 Hz, 2 Hz, and 13.3 Hz, with amplitudes of 4{\deg} and 8{\deg}, along with steady-state simulations conducted for angles of attack up to 20{\deg} to validate the numerical model. The results reveal that the Transition SST turbulence model provides improved predictions of aerodynamic forces at higher Reynolds numbers but struggles at lower Reynolds numbers, where laminar flow effects dominate. The inclusion of the 13.3 Hz frequency, relevant to Darrieus vertical-axis wind turbines, demonstrates the model's effectiveness in capturing dynamic hysteresis loops and reduced oscillations, contrasting with the \(k-\omega\) SST model. Comparisons with XFOIL further highlight challenges in accurately modeling laminar-to-turbulent transitions and dynamic flow phenomena. These findings offer valuable insights into the aerodynamic behavior of thick airfoils under low Reynolds number conditions and contribute to advancing the understanding of turbulence modeling, particularly in applications involving vertical-axis wind turbines.

As integrated photonic systems grow in scale and complexity, Photonic Design Automation (PDA) tools and Process Design Kits (PDKs) have become increasingly important for layout and simulation. However, fixed PDKs often fail to meet the rising demand for customization, compelling designers to spend significant time on geometry optimization using FDTD, EME, and BPM simulations. To address this challenge, we propose a data-driven Eigenmode Propagation Method (DEPM) based on the unitary evolution of optical waveguides, along with a compact model derived from intrinsic waveguide Hamiltonians. The relevant parameters are extracted via complex coupled-mode theory. Once constructed, the compact model enables millisecond-scale simulations that achieve accuracy on par with 3D FDTD, within the model's valid scope. Moreover, this method can swiftly evaluate the effects of manufacturing variations on device and system performance, including both random phase errors and polarization-sensitive components. The data-driven EPM thus provides an efficient and flexible solution for future photonic design automation, promising further advancements in integrated photonic technologies.

Recently, researchers demonstrated numerous types of the metasurfaces with non-radiating states including the anapole ones. These metasurfaces have promising properties for various applications in optics which are mainly connected with the optical field confinement. But by itself, the existence of the anapole state does not make one available to exploit this feature. For example, Ospanova et al. [\textit{Laser \& Photonics Rev.}, 2018, vol. 12, p. 1800005] presented a grouped nanohole array in silicon slab possessing anapole mode in visible range. In this work, it is proved that despite of the existence of the anapole mode in the proposed nanostructure the valuable electric field confinement is not reached. Thus most expected uses of the anapole state cannot be gained through this nanostructure. As demonstrated here, the aforementioned nanostructure may be replaced by the square nanohole array having the anapole state with different meta-atom and much larger electric field confinement.

Near-field radiative heat transfer (RHT) can surpass Planck's blackbody limit by several orders of magnitude due to the tunneling effect of thermal photons. The ability to understand and regulate RHT is of great significance in contactless energy transfer. In this work, we construct a rotating system with a hexagonal boron nitride (h-BN) cylinder for actively regulating the RHT between two nanoparticles (NPs). The results show that when the two NPs are located directly above the cylinder, energy can be directionally transmitted along the cylindrical channel in the form of low-loss surface waves, which can significantly enhance RHT. In addition, we find that the RHT can be regulated by actively manipulating the excitation of cylindrical surface modes. When the rotation point is located in the middle of the line connecting the two NPs, the modulation contrast approaches five orders of magnitude, higher than that of cylinders made of other materials under the same conditions. When its diameter is slightly less than the distance between NPs, the h-BN cylinder shows excellent tunability in the heat exchange. The present work may offer a theoretical possibility for actively regulating and controlling near-field RHT between arbitrary objects based on cylindrical waveguides.

The conefield variant of a Converse KAM method for 3D vector fields, identifying regions through which no invariant 2-tori pass transverse to a specified direction field, is tested on some helical perturbations of an axisymmetric magnetic field in toroidal geometry. This implementation computes bounds on the slopes of invariant tori of a given class and allows to apply a subsidiary criterion for the extension of the non-existence region, saving significant computation time. The method finds regions corresponding to magnetic islands and chaos for the fieldline flow.

This article aims to solve the local geothermal development problem by conducting geothermal exploration in Xiao wang zhuang Town, Ban qiao Depression, Bo hai Bay Basin.

In blasting engineering, borehole utilization is a key metric for evaluating blasting performance. While previous studies have examined the effects of expansion space, cutting design, in-situ stress conditions, and rock properties on borehole utilization, research on the intrinsic relationship between extra-depth defined as the portion of the cut hole extending beyond the depth of auxiliary holes and borehole utilization remains insufficient. This gap in understanding has hindered the resolution of issues such as residual boreholes and unbroken rock at the borehole bottom in deep-hole blasting, thereby limiting improvements in borehole utilization. This study employs a simplified double-hole model for extra-depth cut blasting to conduct two-dimensional numerical simulations and three-dimensional cement mortar model experiments. It systematically investigates the blasting damage characteristics, fractal damage, and energy evolution under varying extra-depth as a single variable. Experimental parameters such as borehole utilization, cavity diameter, cavity volume, and fragment size distribution were obtained to comprehensively analyze the nonlinear effects of extra-depth on post-blast rock damage and its mechanisms. Both simulation and experimental results indicate that blasting damage parameters exhibit a nonlinear trend of initially increasing and then decreasing with increasing extra-depth. Appropriately increasing the extra-depth improves rock breakage efficiency, while excessive extra-depth reduces efficiency due to confinement effects at the borehole bottom. Adjusting the extra-depth can optimize the distribution of explosive energy between rock fragmentation and rock ejection.

Erbium (Er) ions are the gain medium of choice for fiber-based amplifiers and lasers, offering a long excited-state lifetime, slow gain relaxation, low amplification nonlinearity and noise, and temperature stability compared to semiconductor-based platforms. Recent advances in ultra-low-loss silicon nitride (Si$_3$N$_4$) photonic integrated circuits, combined with ion implantation, have enabled the realization of high-power on-chip Er amplifiers and lasers with performance comparable to fiber-based counterparts, supporting compact photonic systems. Yet, these results are limited by the high (2 MeV) implantation beam energy required for tightly confined Si$_3$N$_4$ waveguides (700 nm height), preventing volume manufacturing of Er-doped photonic integrated circuits. Here, we overcome these limitations and demonstrate the first fully wafer-scale, foundry-compatible Er-doped photonic integrated circuit-based tunable lasers. Using 200 nm-thick Si$_3$N$_4$ waveguides, we reduce the ion beam energy requirement to below 500 keV, enabling efficient wafer-scale implantation with an industrial 300 mm ion implanter. We demonstrate a laser wavelength tuning range of 91 nm, covering nearly the entire optical C- and L-bands, with fiber-coupled output power reaching 36 mW and an intrinsic linewidth of 95 Hz. The temperature-insensitive properties of erbium ions allowed stable laser operation up to 125$^{\circ}$C and lasing with less than 15 MHz drift for over 6 hours at room temperature using a remote fiber pump. The fully scalable, low-cost fabrication of Er-doped waveguide lasers opens the door for widespread adoption in coherent communications, LiDAR, microwave photonics, optical frequency synthesis, and free-space communications.

Networks of coupled nonlinear optical resonators have emerged as an important class of systems in ultrafast optical science, enabling richer and more complex nonlinear dynamics compared to their single-resonator or travelling-wave counterparts. In recent years, these coupled nonlinear optical resonators have been applied as application-specific hardware accelerators for computing applications including combinatorial optimization and artificial intelligence. In this work, we rigorously prove a fundamental result showing that coupled nonlinear optical resonators are Turing-complete computers, which endows them with much greater computational power than previously thought. Furthermore, we show that the minimum threshold of hardware complexity needed for Turing-completeness is surprisingly low, which has profound physical consequences. In particular, we show that several problems of interest in the study of coupled nonlinear optical resonators are formally undecidable. These theoretical findings can serve as the foundation for better understanding the promise of next-generation, ultrafast all-optical computers.

We use the helicoidal Peyrard-Bishop model of DNA in the current work. We solve a dynamical equation of motion using a continuum approximation, resulting in kink-solitary waves that travel along the chain. We demonstrate that, whereas supersonic kink solitons are not stable, subsonic ones are. Moreover, we demonstrate the importance of viscosity by showing that no wave is stable in the absence of viscosity.

Here we analyse ways to achieve deep subthreshold parametric squeezing of fluctuations beyond the $-6$~dB limit of single degree-of-freedom parametric resonators. One way of accomplishing this is via a lock-in amplifier feedback loop. Initially, we calculate the phase-dependent parametric amplification with feedback of an added ac signal. In one approach, we use the averaging method to obtain the amplification gain, while in the second approach, we obtain the ac response of the parametric amplifier with feedback using the harmonic balance method. In this latter approach, the feedback is proportional to an integral term that emulates the cosine quadrature output of a lock-in amplifier multiplied by a sine at the same tone of the lock-in. We find that the gain obtained via these two methods are the same whenever the integration time span of the integral is a multiple of the tone period. When this is not the case, we can obtain considerable deamplification. Finally, we analyse the response of the parametric resonator with feedback, described by this integro-differential model, to an added white noise in the frequency domain. Using this model we were able to calculate, in addition to squeezing, the noise spectral density in this resonator with feedback. Very strong squeezing or cooling can be obtained.

Traditional microperforated panels (MPPs) and metamaterial-based sound absorbers rely on local resonances or multi-resonator designs, which limit their bandwidth, angular applicability, and ease of fabrication. Leveraging the reciprocity theorem and cavity resonances, we introduce a new class of robust MPP absorbers, termed meta-MPPs, capable of achieving ultrabroadband near-total sound absorption across a range of 0.37 to 10 kHz. These absorbers demonstrate average performance exceeding that of traditional MPPs by over 100%, approaching the theoretical causality limit. Notably, their absorption performance can be tuned between angularly asymmetric and omnidirectional modes and remains highly robust to variations in MPP parameters and geometrical configurations. Validated through simulations and experiments, our findings present a simpler, more robust, and highly adaptable solution for noise control.

We study the saturation of three-dimensional unstable perturbations on a fast rotating turbulent flow using direct numerical simulations (DNSs). Under the effect of Kolmogorov forcing, a transition between states dominated by coherent two-dimensional modes to states with three-dimensional variations (quasi-two-dimensional) is observed as we change the global rotation rate. We find this akin to a critical phenomenon, wherein the order parameter scales with the distance to the critical point raised to an exponent. The exponent itself deviates from the predicted mean field value. Also, the nature of the fluctuations of the order parameter near the critical point indicate the presence of on-off intermittency. The critical rotation rate at which the transition occurs exhibits a linear scaling behaviour with the forcing wave number. A reduced model based on linear stability analysis is used to find the linear threshold estimates; we find these to be in good agreement with the 3D nonlinear DNS results.

Purpose: To develop an accurate myocardial T1 mapping technique at 5T using Look-Locker-based multiple inversion-recovery with the real-time spoiled gradient echo (GRE) acquisition. Methods: The proposed T1 mapping technique (mIR-rt) samples the recovery of inverted magnetization using the real-time GRE and the images captured during diastole are selected for T1 fitting. Multiple-inversion recoveries are employed to increase the sample size for accurate fitting. Furthermore, the inversion pulse (IR) was tailored for cardiac imaging at 5T, optimized to maximize the inversion efficiency over specified ranges of B1 and off-resonance. The T1 mapping method was validated using Bloch simulation, phantom studies, and in 16 healthy volunteers at 5T. Results: The optimized IR pulse based on the tangent/hyperbolic tangent pulse was found to outperform the conventional hyperbolic secant IR pulse within a limited peak amplitude of 10.6 {\mu}T at the 5T scanner. This optimized IR pulse achieves an average inversion factor of 0.9014 within a B0 range of +/-250Hz and a B1 range of -50% to 20%. In both simulation and phantom studies, the T1 values measured by mIR-rt closely approximate the reference T1 values, with errors less than 3%, while the conventional MOLLI sequence underestimates T1 values. The myocardial T1 values at 5T are 1553 +/- 52 ms, 1531 +/- 53 ms, and 1526 +/- 60 ms (mean +/- standard deviation) at the apex, middle, and base, respectively. Conclusion: The proposed method is feasible for myocardial T1 mapping at 5T and provides better accuracy than the conventional MOLLI sequence. Keywords: Myocardial T1 mapping, 5T, Look-Locker

Understanding microstructure-property relationships (MPRs) is essential for optimising the performance of multiphase composites. Image-based poro/micro-mechanical modelling provides a non-invasive approach to exploring MPRs, but the randomness of multiphase composites often necessitates extensive 3D microstructure datasets for statistical reliability. This study introduces a cost-effective machine learning framework to reconstruct numerous virtual 3D microstructures from limited 2D exemplars, circumventing the high costs of volumetric microscopy. Using feedforward neural networks, termed the statistics-encoded neural network (SENN), the framework encodes 2D morphological statistics and infers 3D morphological statistics via a 2D-to-3D integration scheme. Statistically equivalent 3D microstructures are synthesised using Gibbs sampling. Hierarchical characterisation enables seamless capture of features across multiple scales. Validation on three composites demonstrates strong statistical equivalence between reconstructed and reference microstructures, confirmed by morphological descriptors and simulated macroscopic properties (e.g., stiffness, permeability). The SENN-based framework is a high-fidelity tool for efficiently and accurately reconstructing multiphase microstructures.

We obtain a two-dimensional nonlinear system of equations for the electrostatic potential envelope and the low-frequency magnetic field perturbation to describe the interaction of the upper hybrid wave propagating perpendicular to an external magnetic field with the dust-ion-magnetoacoustic (DIMA) wave in a magnetized dusty plasma. The equations contain both scalar and vector nonlinearities. A nonlinear dispersion relation is derived and the decay and modulation instability thresholds and growth rates are obtained. Numerical estimates show that instability thresholds can easily be exceeded in real dusty plasmas. In the static (subsonic) approximation, a two-dimensional (2D) soliton solution (ground state) is found numerically by the generalized Petviashvili relaxation method. The perturbations of the magnetic field and plasma density in the soliton are nonmonotonic in space and, along with the perturbation in the form of a well, there are also perturbation humps. Such peculiar radial soliton profiles differ significantly from previously known results on 2D solitons. The key point is that the presence of a gap in the DIMA wave dispersion due to the Rao cutoff frequency causes the nonlinearity to be nonlocal. We show that due to nonlocal nonlinearity the Hamiltonian is bounded below at fixed energy, proving the stability of the ground state.

Refraction is the predominant mechanism causing spatially inhomogeneous surface gravity wave fields. However, the complex interplay between depth- and current-induced wave refraction remains poorly understood. Assuming weak currents and slowly varying bathymetry, we derive an analytical approximation to the wave ray curvature, which is validated by an open-source ray tracing framework. The approximation has the form of linear superposition of a current- and depth-induced component, each depending on the gradients in the ambient fields. This separation enables quantification of their individual and combined contributions to refraction. Through analysis of a few limiting cases, we demonstrate how the sign and magnitude of these components influence the wave refraction, and identify conditions where they either amplify or counteract each other. We also identify which of the two plays a dominant role. These findings provide physically resolved insights into the influence of current- and depth-gradients on wave propagation, and are relevant for applications related to remote sensing and coastal wave forecasting services.

We present our computational implementation of the equation-of-motion (EOM) coupled-cluster (CC) singles, doubles, and triples (SDT) method for computing doubly ionized (DIP) and two-electron attached (DEA) states within Q-CHEM. These variants have been implemented within both the (conventional) double precision (DP) and the single precision (SP) algorithms and will be available in the upcoming major release of {\sl Q-CHEM}. We present here the programmable expressions and some pilot application of $CH_2$ for DIP and DEA EOM-CCSDT.

Hydraulic structures such as dams have a direct or indirect influence on the hydro-sedimentary functioning of rivers. They can impact the sediment continuity of the river and can create a sediment imbalance with zones of sediment accumulation upstream and a lack of sediment downstream from the dam. Upstream deposits are a major issue for managers as they can impact the operation and/oraffect the safety of the structure and induce extra-flood hazards. Operators therefore seek to determine the physical characteristics of these deposits to determine their remobilization potential.Usually, these sediment deposits are described using bathymetric data, sonar images or samples (dredging, coring). For this study, new measurements from two geophysical methods (acoustic and electromagnetic) were used in addition to these current monitoring methods. The contribution of geophysical techniques is undeniable to improve the characterization of these deposits. These measurements make it possible to: i) define the internal structures of the sediments, ii) determine the spatial distribution of these structures over the entire development, iii) evaluate the volumes deposited when little bathymetric data is available on the reservoir. The coupling of these methods thus makes it possible to reconstruct the evolution and the structuring events of the sedimentary deposits. This approach provides complementary and important elements for managers in terms of exploitation and optimization of sedimentary deposits management scenarios.

The lack of cooperation can easily result in inequality among members of a society, which provides an increasing gap between individual incomes. To tackle this issue, we introduce an incentive mechanism based on individual strategies and incomes, wherein a portion of the income from defectors is allocated to reward low-income cooperators, aiming to enhance cooperation by improving the equitable distribution of wealth across the entire population. Moreover, previous research has typically employed network structures or game mechanisms characterized by homogeneity. In this study, we present a network framework that more accurately reflects real-world conditions, where agents are engaged in multiple games, including prisoner's dilemma games in the top-layer and public good games in the down-layer networks. Within this framework, we introduce the concept of ``external coupling'' which connects agents across different networks as acquaintances, thereby facilitating access to shared datasets. Our results indicate that the combined positive effects of external coupling and incentive mechanism lead to optimal cooperation rates and lower Gini coefficients, demonstrating a negative correlation between cooperation and inequality. From a micro-level perspective, this phenomenon primarily arises from the regular network, whereas suboptimal outcomes are observed within the scale-free network. These observations help to give a deeper insight into the interplay between cooperation and wealth disparity in evolutionary games in large populations.

The local point group symmetry of luminescent centers, such as defects, impurities, and dopant ions within a crystal, dictates their optical transition properties. While conventional low-temperature spectroscopy quantifies point group symmetry by enumerating the splitting of optical transitions, it fails to capture vectorial symmetry information, such as the orientation of symmetry axes. Here, we establish a quantitative link between point group symmetry, crystal symmetry, optical transition dipole orientation, and polarized luminescence in rare-earth-doped single-particle crystals using computational electromagnetism. Combined with room-temperature single-particle polarization spectroscopy, we resolve the magnetic dipole orientations from individual optical transition of dopant ions in single microcrystal, enabling precise identification of point group symmetry and its axes. We observe single-particle chirality arising from spontaneous symmetry breaking of intrinsically chiral space group during crystallization. Based on point group and crystal symmetries, we present a practical scheme to optical sensing the 3D orientation of rare-earth-doped single particles in dynamic environments by using two arbitrarily transition bands from single-view polarization spectra. Our computational electromagnetism framework not only elucidates polarized luminescence but also provides insights into optimizing dipole orientation-dependent energy transfer for designing ultra-bright rare-earth-doped nanocrystals, such as F\"orster resonance energy transfer, and can interpret enhanced linear polarization in extreme optical phenomena like stimulated emission, superradiance, and superfluorescence following dipole-coherent radiation in single emitters.

Quantum Selected Configuration Interaction (QSCI) methods (also known as Sample-based Quantum Diagonalization, SQD) have emerged as promising near-term approaches to solving the electronic Schr\"odinger equation with quantum computers. In this work, we show that QSCI methods face fundamental limitations that severely hinder their practical applicability. Using the nitrogen molecule and the iron-sulfur cluster [2Fe-2S] as examples, we demonstrate that while QSCI can, in principle, yield high-quality CI expansions similar to classical SCI heuristics in some cases, the method struggles with inefficiencies in finding new determinants as sampling repeatedly selects already seen configurations. This inefficiency becomes especially pronounced when targeting high-accuracy results or sampling from an approximate ansatz. In cases where the sampling problem is not present, the resulting CI expansions are less compact than those generated from classical heuristics, rendering QSCI an overall more expensive method. Our findings suggest a fatal flaw in QSCI methods as the inescapable trade-off between finding sufficiently many determinants and generating compact, accurate CI expansions. This ultimately hinders utility in quantum chemistry applications as QSCI falls behind more efficient classical counterparts.

Social institutions are systems of shared norms and rules that regulate people's behaviors, often emerging without external enforcement. They provide criteria to distinguish cooperation from defection and establish rules to sustain cooperation, shaped through long-term trial and error. While principles for successful institutions have been proposed, the mechanisms underlying their emergence remain poorly understood. Here, we introduce the evolutionary dynamical-systems game, a framework that couples game actions with environmental dynamics and explores the evolution of cognitive frameworks for decision-making. We analyze a minimal model of common-pool resource management, where resources grow naturally and are harvested. Players use decision-making functions to determine whether to harvest at each step, based on environmental and peer monitoring. As these functions evolve, players detect selfish harvesting and punish it by degrading the environment through harvesting. This process leads to the self-organization of norms that classify harvesting actions as cooperative, defective, or punitive. The emergent norms for ``cooperativeness'' and rules of punishment serve as institutions. The environmental and players' states converge to distinct modes characterized by limit-cycles, representing temporal regularities in socio-ecological systems. These modes remain stable despite slight variations in decision-making, illustrating the stability of institutions. The evolutionary robustness of decision-making functions serves as a measure of the evolutionary favorability of institutions, highlighting the role of plasticity in responding to diverse opponents. This work introduces foundational concepts in evolutionary dynamical-systems games and elucidates the mechanisms underlying the self-organization of institutions by modeling the interplay between ecological dynamics and human decision-making.

Problem-based learning benefits from situations taken from real life, which arouse student interest. The shooting of Rwanda president aircraft on April 6th, 1994 is still unsolved. We discuss the methods to infer informations and conclusions about where the aircraft was shot and its trajectory during its fall, as well as about the place from which the missiles were launched, their trajectory and type. To this goal, we compile expert reports, witness indications and other public sources, then translate plain language sentences into quantitative equalities and inequalities applied to geometry and mechanics at undergraduate level. The precision of each result is discussed and propagated in order to ensure a proper assessment of the hypotheses and a traceability of their consequences. Overall, the precision discussion can train the students critical mind, and teach inference methods which are routinely used in several fields of physics research. In addition, it demonstrates the importance and limits of scientific expertise during a judiciary process.

Biosensing based on optically trapped fluorescent nanodiamonds is an intriguing research direction potentially allowing to resolve biochemical processes inside living cells. Towards this goal, we investigate infrared near (NIR) laser irradiation at 1064 nm on fluorescent nanodiamonds (FNDs) containing nitrogen-vacancy (NV) centers. By conducting comprehensive experiments, we aim to understand how NIR exposure influences the fluorescence and sensing properties of FNDs and to determine the potential implications for the use of FNDs in various sensing applications. The experimental setup involved exposing FNDs to varying intensities of NIR laser light and analyzing the resultant changes in their optical and physical properties. Key measurements included T1 relaxation times, optical spectroscopy, and optically detected magnetic resonance (ODMR) spectra. The findings reveal how increased NIR laser power correlates with alterations in ODMR central frequency but also that charge state dynamics under NIR irradiation of NV centers plays a role. We suggest protocols with NIR and green light that mitigate the effect of NIR, and demonstrate that FND biosensing works well with such a protocol.

Quantitative phase imaging is a method of choice to observe structure and dynamics of unlabelled biological cells. Fourier ptychographic microscopy (FPM) reconstructs high resolution images over a wide field of view, but requires capturing many images, thus limiting acquisition speed. On the other hand, differential phase contrast (DPC) is fast but limited in resolution, as it only exploits brightfield measurements. In this study, we propose perturbative Fourier ptychographic microscopy (pFPM) as a darkfield extension of DPC for fast, high-resolution, wide-field quantitative phase imaging. We interpret DPC as a single step of the Gauss-Newton algorithm with quadratic regularization, leading to a natural generalization of multiple steps and more general regularizers. In addition, we show that phase imaging with a minimal number of illumination patterns can be achieved using the DPC brightfield illumination patterns and annular darkfield illumination patterns. Our methodology combines both a perturbative phase retrieval algorithm and annular darkfield illumination patterns.

Understanding the solvation structure of electrolytes is critical for optimizing the electrochemical performance of rechargeable batteries, as it directly influences properties such as ionic conductivity, viscosity, and electrochemical stability. The highly complex structures and strong interactions in high-concentration electrolytes make accurate modeling and interpretation of their ``structure-property" relationships even more challenging with spectroscopic methods. In this study, we present a machine learning-based approach to predict dynamic $^7$Li NMR chemical shifts in LiFSI/DME electrolyte solutions. Additionally, we provide a comprehensive structural analysis to interpret the observed chemical shift behavior in our experiments, particularly the abrupt changes in $^7$Li chemical shifts at high concentrations. Using advanced modeling techniques, we quantitatively establish the relationship between molecular structure and NMR spectra, offering critical insights into solvation structure assignments. Our findings reveal the coexistence of two competing local solvation structures that shift in dominance as electrolyte concentration approaches the concentrated limit, leading to anomalous reverse of $^7$Li NMR chemical shift in our experiment. This work provides a detailed molecular-level understanding of the intricate solvation structures probed by NMR spectroscopy, leading the way for enhanced electrolyte design.

Carbonyl complexes of $d^6$ metals with an alpha-diimine ligand exhibit both emission and ligand-selective photodissociation from MLCT states. Studying this photodissociative mechanism is challenging for experimental approaches due to an ultrafast femtosecond timescale and spectral overlap of multiple photoproducts. The photochemistry of a prototypical system Cr(CO)$_4$bpy is investigated with non-adiabatic dynamic simulations. Obtained 86 fs lifetime of the bright $S_3$ state and 13% quantum yield are in good agreement with experimental data. The present simulations suggest a ballistic mechanism of photodissociation, which is irrespective of the occupied electronic state. This is in contrast to the previously established mechanism of competitive intersystem crossing and dissociation. Selectivity of axial photodissociation is shown to be caused by the absence of an avoided crossing in the equatorial direction.

Full Waveform Inversion (FWI) is a promising technique for achieving high-resolution imaging in medical ultrasound. Traditional FWI methods suffer from issues related to computational efficiency, dependence on initial models, and the inability to quantify uncertainty. This study introduces the Stein Variational Gradient Descent (SVGD) algorithm into FWI, aiming to improve inversion performance and enhance uncertainty quantification. By deriving the posterior gradient, the study explores the integration of SVGD with FWI and demonstrates its ability to approximate complex priors. In-silico experiments with synthetic data and real-world breast tissue data highlight the advantages of the SVGD-based framework over conventional FWI. SVGD-based FWI improves inversion quality, provides more reliable uncertainty quantification, and offers a tighter bound for the prior distribution. These findings show that probabilistic inversion is a promising tool for addressing the limitations of traditional FWI methods in ultrasonic imaging of medical tissues.

Photochemistry in the earth's atmosphere is driven by the sun, continuously altering the concentration and spatial distribution of pollutants. Precisely monitoring their atmospheric abundance relies predominantly on optical sensing, which requires the knowledge of exact absorption cross sections. One key pollutant which impacts many photochemical reaction-pathways is formaldehyde. Agreement on formaldehyde absolute absorption cross section remains elusive in the photochemically-relevant ultraviolet spectral region, hampering sensitive concentration tracking. Here, we introduce free-running ultraviolet dual comb spectroscopy, combining high spectral resolution (1 GHz), broad spectral coverage (12 THz), and fast acquisition speed (500 ms), as a novel method for absolute absorption cross section determination with unprecedented fidelity. Within this bandwidth, our method uncovers almost one order of magnitude more rovibrational transitions than detected before which leads to refined rotational constants for high-level quantum simulations of molecular eigenstates. This ultra-resolution method can be generalized to provide a universal tool for fast electronic fingerprinting of atmospherically-relevant species, both for sensing applications and to benchmark improvements of ab-initio quantum theory.

The microscopic organization of dynamical systems coupled via higher-order interactions plays a pivotal role in understanding their collective behavior. In this paper, we introduce a framework for systematically investigating the impact of the interaction structure on dynamical processes. Specifically, we develop an hyperedge overlap matrix whose elements characterize the two main aspects of the microscopic organization of higher-order interactions: the inter-order hyperedge overlap (non-diagonal matrix elements) and the intra-order hyperedge overlap (encapsulated in the diagonal elements). This way, the first set of terms quantifies the extent of superposition of nodes among hyperedges of different orders, while the second focuses on the number of nodes in common between hyperedges of the same order. Our findings indicate that large values of both types of hyperedge overlap hinder synchronization stability, and that the larger is the order of interactions involved, the more important is their role. Our findings also indicate that the two types of overlap have qualitatively distinct effects on the dynamics of coupled chaotic oscillators. In particular, large values of intra-order hyperedge overlap hamper synchronization by favoring the presence of disconnected sets of hyperedges, while large values of inter-order hyperedge overlap hinder synchronization by increasing the number of shared nodes between groups converging on different trajectories, without necessarily causing disconnected sets of hyperedges.

Strong numerical hints exist in favor of a universal blowup scenario in the Sabra shell model, perhaps the most popular cascade models of 3D turbulence, which features complex velocity variables on a geometric progression of scales $\ell_n \propto \lambda ^{-n}$. The blowup is thought to be of self-similar type and characterized by the finite-time convergence towards a universal profile with non-Kolmogorov (anomalous) small-scale scaling $\propto \ell_n^{x}$. Solving the underlying nonlinear eigenvalue problem has however proven challenging, and prior insights mainly used the Dombre-Gilson renormalization scheme, transforming self-similar solutions into solitons propagating over infinite rescaled time horizon. Here, we further characterize Sabra blowups by implementing two strategies targeting the eigenvalue problem. The first involves formal expansion in terms of the bookkeeping parameter $\delta = (1-x)\log \lambda$, and interpretes the self-similar solution as a (degenerate) homoclinic bifurcation. Using standard bifurcation toolkits, we show that the homoclinic bifurcations identified under finite-truncation of the series converge to the observed Sabra solution. The second strategy uses machine-learning optimization to solve directly for the Sabra eigenvalue. It reveals an intricate phase space, with the presence of a continuous family of non-universal blowup profiles, characterized by various number $N$ of pulses and exponents $x_N\ge x$.

The cancellation of high-frequency laser phase noise using feedforward techniques, as opposed to feedback methods, has achieved significant advancements in recent years. However, directly applying existing feedforward techniques to laser systems based on nonlinear conversion still faces substantial challenges. Here, we propose and demonstrate a feedforward scheme that suppresses phase noise in frequency-doubled light by utilizing phase noise information of its fundamental pump. This scheme is enabled by the fact that the phase jitter of the frequency-doubled light is simply twice that of the pump, except for a first-order low-pass filtering effect introduced by the SHG enhancement cavity. Testing this method on a 420-nm frequency-doubled laser system, we realize a 25-dB suppression of the servo noise bump near 1 MHz on the 420-nm light, and an average suppression of 30 dB for strong injected noise ranging from 100 kHz to 20 MHz. This scheme shows promising potential for applications requiring blue or ultraviolet light with minimal high-frequency phase noise, such as precision control of atoms and molecules.

Recent research indicates that the use of multiple external representations MERs has the potential to support learning, especially in complex scientific areas, such as quantum physics. In particular, the provision of informationally redundant external representations can have advantageous effects on learning outcomes. This is of special relevance for quantum education, where various external representations are available and their effective use is recognised as crucial to student learning. However, research on the effects of informationally redundant external representations in quantum learning is limited. The present study aims to contribute to the development of effective learning materials by investigating the effects of learning with informationally redundant external representations on students' learning of quantum physics. Using a between-subjects design, 113 students were randomly assigned to one of four learning conditions. The control group learnt with a traditional multimedia learning unit on the behaviour of a single photon in a Mach-Zehnder interferometer. The three intervention groups received redundant essential information in the Dirac formalism, the Bloch sphere, or both. The use of eye tracking enabled insight into the learning process depending on the external representations provided. While the results indicate no effect of the study condition on learning outcomes (content knowledge and cognitive load), the analysis of visual behaviour reveals decreased learning efficiency with the addition of the Bloch sphere to the multimedia learning unit. The results are discussed based on current insight in learning with MERs. The study emphasises the need for careful instructional design to balance the associated cognitive load when learning with informationally redundant external representations.

We present a direct lifetime measurement of the $5s5p~^1P_1$ state of strontium using time-correlated single-photon counting of laser induced fluorescence in a hot atomic beam. To achieve fast switch-off times and a high signal-to-noise ratio, we excite the strontium atoms with a femtosecond pulsed laser at $\approx$461 nm and collect the fluorescence onto a hybrid single-photon detector. Analysis of the measured exponential decay gives a lifetime of the $^1P_1$ state of $\tau = (5.216 \pm 0.006_{stat} \pm 0.013_{sys})$ ns, where all the systematic effects have been thoroughly considered.

Antiferromagnetism, characterized by magnetic moments aligned in alternating directions with a vanished ensemble average, has garnered renewed interest for its potential applications in spintronics and axion dynamics. The synergy between antiferromagnetism and topology can lead to the emergence of an exotic topological phase unique to certain magnetic order, termed antiferromagnetic topological insulators (AF TIs). A hallmark signature of AF TIs is the presence of a single surface Dirac cone--a feature typically associated with strong three-dimensional (3D) topological insulators--only on certain symmetry-preserving crystal terminations. However, the direct observation of this phenomenon poses a significant challenge. Here, we have theoretically and experimentally discovered a 3D photonic AF TI hosting a single surface Dirac cone protected by the combined symmetry of time reversal and half-lattice translation. Conceptually, our setup can be viewed as a z-directional stack of two-dimensional Chern insulators, with adjacent layers oppositely magnetized to form a 3D type-A AF configuration. By measuring both bulk and surface states, we have directly observed the symmetry-protected gapless single-Dirac-cone surface state, which shows remarkable robustness against random magnetic disorders. Our work constitutes the first realization of photonic AF TIs and photonic analogs of strong topological insulators, opening a new chapter for exploring novel topological photonic devices and phenomena that incorporate additional magnetic degrees of freedom.

Two samples of silicon pad sensors and two samples of GaAs sensors are studied in an electron beam with 5 GeV energy from the DESY-II test-beam facility. The sizes of the silicon and GaAs sensors are about 9$\times$9 cm$^2$ and 5$\times$8 cm$^2$, respectively. The thickness is 500 micrometer for both the silicon and GaAs sensors. The pad size is about 5$\times$5 mm$^2$. The sensors are foreseen to be used in a compact electromagnetic sampling calorimeter. The readout of the pads is done by metal traces connected to the pads and the front-end ASICs at the edges of the sensors. For the silicon sensors, copper traces on a Kapton foil are connected to the sensor pads with conducting glue. The pads of the GaAs sensors are connected to bond-pads via aluminium traces on the sensor substrate. The readout is based on a dedicated front-end ASIC, called FLAME. Pre-processing of the raw data and deconvolution is performed with FPGAs. The whole system is orchestrated by a Trigger Logic Unit. Results are shown for the signal-to-noise ratio, the homogeneity of the response, edge effects on pads, and for signals due to the readout traces.

Room-temperature metals and semi-metals which consist of a gas of bound electrons in a near-continuum band structure can be classified as cold quantum plasmas. This insight suggests that Particle-in-Cell (PIC) simulations, traditionally used for modeling classical plasmas, may be adapted for the next generation of nanoscopic simulations in photonics, plasmonics, and beyond. This article introduces four key physics modules implemented in two open-source PIC codes that can be applied to condensed matter calculations. These modules include (I) the incorporation of Fermi-Dirac (FD) electrons, (II) material structure boundary conditions, (III) a bound particle model for linear dispersive materials, and (IV) the inclusion of massless Dirac carriers for simulating graphene-like materials. By integrating these modules into existing PIC frameworks, we provide a versatile and self-consistent approach for simulating condensed matter systems, opening new avenues for modeling dynamic phenomena in photonics and plasmonics.

We present efficient and reliable molecular dynamics simulations of the photodissociation of dichloromethane followed by Coulomb explosion. These simulations are performed by calculating trajectories on accurate potential energy surfaces of the low-lying excited states of the neutral dichloromethane molecule. The subsequent time-resolved Coulomb explosions are simulated on the triply charged ionic state, assuming Coulomb interactions between ionic fragments. Both the neutral state trajectories and the simulated Coulomb explosion observables indicate that intra-molecular photoisomerization of dichloromethane is unlikely to occur. Estimating the kinetic energy release using \textit{ab initio} ionic potential reveals a discrepancy of approximately 5-8 eV compared to our simulated values using Coulomb potential. The molecular structural changes during photodissociation are clearly mapped to the ionic-fragment coincidence signals, demonstrating the Coulomb explosion imaging technique as a powerful tool to probe the time-resolved reaction dynamics.

Accelerating Airy beams, known for their non-diffracting nature, self-healing properties, and curved propagation trajectories, are solutions to the paraxial wave equation. In this work, we theoretically and experimentally investigate nonuniform (radially and azimuthally) polarized vector Airy beams. We provide an analytical representation of their spatial spectra and examine their electromagnetic field distributions in space. To validate the theoretical model, we have used a nanograting inscribed inside a glass volume by a femtosecond laser to create geometrical phase elements, suitable for the realization of high-power ultrafast radially and azimuthally polarized vector Airy beams. We have conducted experiments that confirm the successful generation of high-power vector beams and have performed Stokes parameter measurements of these beams. Additionally, we explore both theoretically and experimentally their topology, identifying particle-like formations such as skyrmionic and antiskyrmionc accelerating lattices within the high-power ultrafast electric fields and their Stokes parameters.

To clean or not to clean? The solution to this dilemma is related to understanding the plasticiser migration which has a few practical implications for the state of museum artefacts made of plasticised poly(vinyl chloride) - PVC and objects stored in their vicinity. The consequences of this process encompass aesthetic changes due to the presence of exudates and dust deposition, an increase in air pollution and the development of mechanical stresses. Therefore, this paper discusses the plasticiser migration in PVC to provide evidence and support the development of recommendations and guidelines for conservators, collection managers and heritage scientists. Particularly, the investigation is focused on the migration of the ortho-phthalates representing the group of the most abundant plasticisers in PVC collections. The predominance of inner diffusion or surface emission (evaporation) determining the rate-limiting step of the overall migration process is considered a fundament for understanding the potential environmental and mechanical risk. According to this concept, general correlations for various ortho-phthalates are proposed depending on their molar mass with the support of molecular dynamics simulations and NMR diffusometry. The study reveals that for the majority of the PVC objects in collections, the risk of accelerated migration upon mild removal of surface plasticiser exudate is low. Thus, surface cleaning would allow for diminishing dust deposition and air pollution by phthalate-emitting objects in a museum environment. Bearing in mind simplicity and the need for fast decision-supporting solutions, the step-by-step protocol for non-destructive identification and quantification of plasticisers in objects made of or containing plasticised PVC, determination of the physical state of investigated artefacts and rate-limiting process of plasticiser migration is proposed.

We present a tensorization algorithm for constructing tensor train representations of functions, drawing on sketching and cross interpolation ideas. The method only requires black-box access to the target function and a small set of sample points defining the domain of interest. Thus, it is particularly well-suited for machine learning models, where the domain of interest is naturally defined by the training dataset. We show that this approach can be used to enhance the privacy and interpretability of neural network models. Specifically, we apply our decomposition to (i) obfuscate neural networks whose parameters encode patterns tied to the training data distribution, and (ii) estimate topological phases of matter that are easily accessible from the tensor train representation. Additionally, we show that this tensorization can serve as an efficient initialization method for optimizing tensor trains in general settings, and that, for model compression, our algorithm achieves a superior trade-off between memory and time complexity compared to conventional tensorization methods of neural networks.

Being widely adopted by the transportation and planning practitioners, the fundamental diagram (FD) is the primary tool used to relate the key macroscopic traffic variables of speed, flow, and density. We empirically analyze the relation between vehicular space-mean speeds and flows given different signal settings and postulate a parsimonious parametric function form of the traditional FD where its function parameters are explicitly modeled as a function of the signal plan factors. We validate the proposed formulation using data from signalized urban road segments in Salt Lake City, Utah, USA. The proposed formulation builds our understanding of how changes to signal settings impact the FDs, and more generally the congestion patterns, of signalized urban segments.

Laser light with squeezed quantum uncertainty is a powerful tool for interferometric sensing. A routine application can be found in gravitational wave observatories. A significant quantum advantage is only achievable if a large fraction of the photons are actually measured. For this reason, quantum-enhanced vibrational measurements of strongly absorbing or scattering surfaces have not been considered so far. Here we demonstrate the strongly quantum-enhanced measurement of the frequency characteristics of surface vibrations in air by measuring the air pressure wave instead. Our squeezed laser beam, which simply passes the vibrating surface, delivers a sensitivity that an ultra-stable conventional light beam in the same configuration can only achieve with ten times the power. The pressure amplitude of a ultrasonic wave of just 0.12 mPa/ Hz was clearly visible with a spatial resolution in the millimetre range and a 1 kHz resolution bandwidth. We envision applications in sensor technology where distant, highly absorbing or optically inaccessible surface vibrations in air are to be measured with limited, e.g. eye-safe, light powers.

Precise, accurate neutrino flux predictions for neutrino beam experiments are crucial for physics results. Flux predictions for the Booster Neutrino Beam at Fermilab were first published in 2009 by the MiniBooNE collaboration. It is no longer possible to run the simulation used to create these predictions due to outdated software versions. In this exciting period for the Short-Baseline Neutrino Program at Fermilab, with both the far detector (ICARUS) and the near detector (SBND) operating, an updated flux model for the BNB is necessary. For the purpose of using a dynamic, up-to-date beam model, the SBN program has created a new simulation named G4BNB. This framework contains new features, such as a full neutrino ancestry to handle hadron production systematics with more precision, and the inclusion of neutral mesons from the proton-beryllium scatter to allow the study of exotic BSM scenarios.

Monoclonal antibodies are among the most promising therapeutic agents in modern medicine, yet their formulation into high-concentration solutions for subcutaneous self-administration poses a major challenge. A key obstacle is the marked increase in viscosity often observed under these conditions. To gain deeper insights into this phenomenon, coarse-grained models derived from soft matter physics have been widely employed. However, these models have yet to be fully leveraged for analyzing the rheological collective properties of such systems. In this study, using molecular dynamics simulations, we directly compute the antibody solution viscosity by starting from commonly used models in which electrostatic interactions are treated through effective screened Coulomb potentials. We demonstrate that this approach fails to reproduce experimental evidence and we show, by analyzing stress correlations in the system, that it is necessary to treat the heterogeneously charged domains explicitly, also including counterions and salt ions, and to properly account for the long-ranged nature of Coulomb interactions. By thoroughly analyzing the microscopic structure of the system, we further reveal the presence of transient strongly correlated antibodies which would not be present if charges were treated implicitly, thus pointing to a prominent role of electrostatics in determining the increase in viscosity at high concentrations. By taking advantage of our realistic treatment, new approaches can be devised to ensure that antibody solutions exhibit the desired characteristics for their intended broad use and effective deployment.

Scanning electron microscopy (SEM), a century-old technique, is today a ubiquitous method of imaging the surface of nanostructures. However, most SEM detectors simply count the number of secondary electrons from a material of interest, and thereby overlook the rich material information contained within them. Here, by simple modifications to a standard SEM tool, we resolve the momentum and energy information of secondary electrons by directly imaging the electron plume generated by the electron beam of the SEM. Leveraging these spectroscopic imaging capabilities, our technique is able to image lateral electric fields across a prototypical silicon p-n junctions and to distinguish differently doped regions, even when buried beyond depths typically accessible by SEM. Intriguingly, the sub-surface sensitivity of this technique reveals unexpectedly strong surface band bending within nominally passivated semiconductor structures, providing useful insights for complex layered component designs, in which interfacial dynamics dictate device operation. These capabilities for non-invasive, multi-modal probing of complicated electronic components are crucial in today's electronic manufacturing but is largely inaccessible even with sophisticated techniques. These results show that seemingly simple SEM can be extended to probe complex and useful material properties.

Global solar photospheric magnetic maps play a critical role in solar and heliospheric physics research. Routine magnetograph measurements of the field occur only along the Sun-Earth line, leaving the far-side of the Sun unobserved. Surface Flux Transport (SFT) models attempt to mitigate this by modeling the surface evolution of the field. While such models have long been established in the community (with several releasing public full-Sun maps), none are open source. The Open Source Flux Transport (OFT) model seeks to fill this gap by providing an open and user-extensible SFT model that also builds on the knowledge of previous models with updated numerical and data acquisition/assimilation methods along with additional user-defined features. In this first of a series of papers on OFT, we introduce its computational core: the High-performance Flux Transport (HipFT) code (github.com/predsci/hipft). HipFT implements advection, diffusion, and data assimilation in a modular design that supports a variety of flow models and options. It can compute multiple realizations in a single run across model parameters to create ensembles of maps for uncertainty quantification and is high-performance through the use of multi-CPU and multi-GPU parallelism. HipFT is designed to enable users to easily write extensions, enhancing its flexibility and adaptability. We describe HipFT's model features, validations of its numerical methods, performance of its parallel and GPU-accelerated code implementation, analysis/post-processing options, and example use cases.

We present a realizability-preserving numerical method for solving a spectral two-moment model to simulate the transport of massless, neutral particles interacting with a steady background material moving with relativistic velocities. The model is obtained as the special relativistic limit of a four-momentum-conservative general relativistic two-moment model. Using a maximum-entropy closure, we solve for the Eulerian-frame energy and momentum. The proposed numerical method is designed to preserve moment realizability, which corresponds to moments defined by a nonnegative phase-space density. The realizability-preserving method is achieved with the following key components: (i) a discontinuous Galerkin (DG) phase-space discretization with specially constructed numerical fluxes in the spatial and energy dimensions; (ii) a strong stability-preserving implicit-explicit (IMEX) time-integration method; (iii) a realizability-preserving conserved to primitive moment solver; (iv) a realizability-preserving implicit collision solver; and (v) a realizability-enforcing limiter. Component (iii) is necessitated by the closure procedure, which closes higher order moments nonlinearly in terms of primitive moments. The nonlinear conserved to primitive and the implicit collision solves are formulated as fixed-point problems, which are solved with custom iterative solvers designed to preserve the realizability of each iterate. With a series of numerical tests, we demonstrate the accuracy and robustness of this DG-IMEX method.

Understanding the dynamics of photoexcited carriers is essential for advancing photoelectronic device design. Photon absorption generates electron-hole pairs, and subsequent scatterings can induce ultrafast thermalization within a picosecond, forming a quasi-equilibrium distribution with overheated electrons. The high-energy tail of this distribution enables carriers to overcome energy barriers, thereby enhancing quantum efficiency--a phenomenon known as photo-thermionic emission (PTE). Despite its importance, the onset and mechanisms of PTE remain under debate. Using real-time time-dependent density functional theory (rt-TDDFT), we investigate ultrafast carrier thermalization in two-dimensional materials graphene and PtTe2, and the results reveal distinct differences. In graphene, both electrons and holes thermalize into Fermi-Dirac distributions with good agreement to experiment, while PtTe2 exhibits anomalous high-energy tails for both electrons and holes, deviating significantly from Fermi-Dirac behavior. We attribute this anomaly to differences in orbital coupling between the two materials, from which we derive design principles for identifying optimal PTE candidates and, ultimately, improving photodetector performance.

The so-called Geometric Trinity of Gravity includes General Relativity (GR), based on spacetime curvature; the Teleparallel Equivalent of GR (TEGR), which relies on spacetime torsion; and the Symmetric Teleparallel Equivalent of GR (STEGR), grounded in nonmetricity. Recent studies demonstrate that GR, TEGR, and STEGR are dynamically equivalent, raising questions about the fundamental structure of spacetime, the under-determination of these theories, and whether empirical distinctions among them are possible. The aim of this work is to show that they are equivalent in many features but not exactly in everything. In particular, their relationship with the Equivalence Principle (EP) is different. The EP is a deeply theory-laden assumption, which is assumed as fundamental in constructing GR, with significant implications for our understanding of spacetime. However, it introduces unresolved conceptual issues, including its impact on the nature of the metric and connection, its meaning at the quantum level, tensions with other fundamental interactions and new physics, and its role in dark matter and dark energy problems. In contrast, TEGR and STEGR recover the EP but do not rely on it as a foundational principle. The fact that GR, TEGR, and STEGR are equivalent in non-trivial predictions, but the EP is not necessary for TEGR and STEGR, suggests that it may not be a fundamental feature but an emergent one, potentially marking differences in the empirical content of the three theories. Thus, the developments within the Geometric Trinity framework challenge traditional assumptions about spacetime and may help to better understand some of the unresolved foundational difficulties related to the EP.

The high-pressure growth technique is a vital approach that facilitates the stabilization of new phases and allows for meticulous control of structural parameters, which significantly impact electronic and magnetic properties. We present a short review of our ongoing investigations into various families of iron-based superconductors (IBS), employing the high-gas pressure and high-temperature synthesis (HP-HTS) method. This technique is capable of producing the gas pressures up to 1.8 GPa and a heating temperature of up to 1700 {\deg}C through a three-zone furnace within a cylindrical chamber. Different kinds of IBS samples are prepared using HPHTS and characterized through various measurements to reach the final conclusions. The results demonstrate that the high-pressure growth technique significantly enhances the properties of IBS, including the transition temperature, critical current density, and pinning force. In addition, the quality of the samples and their density are improved through the intergrain connections. Furthermore, the comprehensive evaluations and investigations prove that a growth pressure of 0.5 GPa is sufficient for producing high-quality IBS bulks under the optimized synthesis conditions.

The achievement of selectivity in the formation of cubic diamond is challenging due to the emergence of competing phases such as its hexagonal polymorph or clathrates possessing similar free energy. Although both polymorphs exhibit a complete photonic bandgap, the cubic diamond exhibits it at lower frequencies than the hexagonal counterpart, positioning him as a promising candidate for photonic applications.Herein, we demonstrate that the 1:1 mixture of identical patchy particles cannot selectively form the cubic diamond polymorph due to the frustrations present in the system that are manifested in the primary adsorption layer and propagate as the film grows. We provide a plausible} explanation why the binary system under confinement, resembling interactions between the complementary DNA bases cannot yield the selectivity in the formation of cubic diamond crystals which is based on the similarities to the antiferromagnetic systems. We always observe the mixture of both hexagonal and cubic diamonds, however, the formation of such stacking hybrids is observed for a wider range of patch sizes compared to the one-component system.

We show how random feature maps can be used to forecast dynamical systems with excellent forecasting skill. We consider the tanh activation function and judiciously choose the internal weights in a data-driven manner such that the resulting features explore the nonlinear, non-saturated regions of the activation function. We introduce skip connections and construct a deep variant of random feature maps by combining several units. To mitigate the curse of dimensionality, we introduce localization where we learn local maps, employing conditional independence. Our modified random feature maps provide excellent forecasting skill for both single trajectory forecasts as well as long-time estimates of statistical properties, for a range of chaotic dynamical systems with dimensions up to 512. In contrast to other methods such as reservoir computers which require extensive hyperparameter tuning, we effectively need to tune only a single hyperparameter, and are able to achieve state-of-the-art forecast skill with much smaller networks.

Deep learning models for anticipating the products of organic reactions have found many use cases, including validating retrosynthetic pathways and constraining synthesis-based molecular design tools. Despite compelling performance on popular benchmark tasks, strange and erroneous predictions sometimes ensue when using these models in practice. The core issue is that common benchmarks test models in an in-distribution setting, whereas many real-world uses for these models are in out-of-distribution settings and require a greater degree of extrapolation. To better understand how current reaction predictors work in out-of-distribution domains, we report a series of more challenging evaluations of a prototypical SMILES-based deep learning model. First, we illustrate how performance on randomly sampled datasets is overly optimistic compared to performance when generalizing to new patents or new authors. Second, we conduct time splits that evaluate how models perform when tested on reactions published in years after those in their training set, mimicking real-world deployment. Finally, we consider extrapolation across reaction classes to reflect what would be required for the discovery of novel reaction types. This panel of tasks can reveal the capabilities and limitations of today's reaction predictors, acting as a crucial first step in the development of tomorrow's next-generation models capable of reaction discovery.

BATSRUS, our state-of-the-art extended magnetohydrodynamic code, is the most used and one of the most resource-consuming models in the Space Weather Modeling Framework. It has always been our objective to improve its efficiency and speed with emerging techniques, such as GPU acceleration. To utilize the GPU nodes on modern supercomputers, we port BATSRUS to GPUs with the OpenACC API. Porting the code to a single GPU requires rewriting and optimizing the most used functionalities of the original code into a new solver, which accounts for around 1% of the entire program in length. To port it to multiple GPUs, we implement a new message passing algorithm to support its unique block-adaptive grid feature. We conduct weak scaling tests on as many as 256 GPUs and find good performance. The program has 50-60% parallel efficiency on up to 256 GPUs, and up to 95% efficiency within a single node (4 GPUs). Running large problems on more than one node has reduced efficiency due to hardware bottlenecks. We also demonstrate our ability to run representative magnetospheric simulations on GPUs. The performance for a single A100 GPU is about the same as 270 AMD "Rome" CPU cores, and it runs 3.6 times faster than real time. The simulation can run 6.9 times faster than real time on four A100 GPUs.

Hyperuniform states of matter exhibit unusual suppression of density fluctuations at large scales, contrasting sharply with typical disordered configurations. Various types of hyperuniformity emerge in multicomponent disordered systems, significantly enhancing their functional properties for advanced applications. This paper focuses on developing a theoretical framework for two-component hyperuniform systems. We provide a robust theoretical basis to identify novel conditions on structure factors for a variety of hyperuniform binary mixtures, classifying them into five distinct types with seven unique states. Our findings also offer valuable guidelines for designing multihyperuniform materials where each component preserves hyperuniformity, added to the overall hyperuniformity.

The exploration of ultrafast phenomena is a frontier of condensed matter research, where the interplay of theory, computation, and experiment is unveiling new opportunities for understanding and engineering quantum materials. With the advent of advanced experimental techniques and computational tools, it has become possible to probe and manipulate nonequilibrium processes at unprecedented temporal and spatial resolutions, providing insights into the dynamical behavior of matter under extreme conditions. These capabilities have the potential to revolutionize fields ranging from optoelectronics and quantum information to catalysis and energy storage. This Roadmap captures the collective progress and vision of leading researchers, addressing challenges and opportunities across key areas of ultrafast science. Contributions in this Roadmap span the development of ab initio methods for time-resolved spectroscopy, the dynamics of driven correlated systems, the engineering of materials in optical cavities, and the adoption of FAIR principles for data sharing and analysis. Together, these efforts highlight the interdisciplinary nature of ultrafast research and its reliance on cutting-edge methodologies, including quantum electrodynamical density-functional theory, correlated electronic structure methods, nonequilibrium Green's function approaches, quantum and ab initio simulations.

Due to traits of CMOS compatibility and scalability, HfO2-based ferroelectrics are promising candidates for next-generation memory devices. However, their commercialization has been greatly hindered by reliability issues, with fatigue being a major impediment. We report the fatigue-free behavior in interface-designed Hf0.5Zr0.5O2-based heterostructures. A coherent CeO2-x/Hf0.5Zr0.5O2 heterointerface is constructed, wherein CeO2-x acts as an oxygen sponge, capable of reversibly accepting and releasing oxygen vacancies. This design effectively alleviates defect aggregation at the electrode-ferroelectric interface, enabling improved switching characteristics. Further, a symmetric capacitor architecture is designed to minimize the imprint, thereby suppressing the cycling-induced oriented defect drift. The two-pronged technique mitigates oxygen-voltammetry-generated chemical/energy fluctuations, suppressing the formation of paraelectric phase and polarization degradation. The design ensures a fatigue-free feature exceeding 10^11 switching cycles and an endurance lifetime surpassing 10^12 cycles for Hf0.5Zr0.5O2-based capacitors, along with excellent temperature stability and retention. These findings pave the way for developing ultra-stable hafnia-based ferroelectric devices.

Deep learning techniques have evolved rapidly in recent years, significantly impacting various scientific fields, including experimental particle physics. To effectively leverage the latest developments in computer science for particle physics, a strengthened collaboration between computer scientists and physicists is essential. As all machine learning techniques depend on the availability and comprehensibility of extensive data, clear data descriptions and commonly used data formats are prerequisites for successful collaboration. In this study, we converted open data from the Large Hadron Collider, recorded in the ROOT data format commonly used in high-energy physics, to pandas DataFrames, a well-known format in computer science. Additionally, we provide a brief introduction to the data's content and interpretation. This paper aims to serve as a starting point for future interdisciplinary collaborations between computer scientists and physicists, fostering closer ties and facilitating efficient knowledge exchange.

Materials emitting circularly polarized light (CPL) are highly sought after for applications ranging from efficient displays to quantum information technologies. Established methods for time-resolved CPL characterization have significant limitations, preventing in-depth photophysical insight necessary for materials development. We have designed and built a high-sensitivity (noise level 10^-4), broadband (ca. 400-900 nm), transient (ns resolution, ms range) full-Stokes (CPL and linear polarizations) spectroscopy setup. We demonstrate its broad applicability by measuring compounds with low dissymmetry factors across various timescales, as well as tracking the temporal evolution of linear polarization components alongside associated CPL artefacts. We have written open-source software and used stock optical components to make transient CPL spectroscopy practically accessible to a wide audience, enabling the study of chiral materials in numerous diverse applications.

We introduce Neural Discrete Equilibrium (NeurDE), a machine learning (ML) approach for long-term forecasting of flow phenomena that relies on a "lifting" of physical conservation laws into the framework of kinetic theory. The kinetic formulation provides an excellent structure for ML algorithms by separating nonlinear, non-local physics into a nonlinear but local relaxation to equilibrium and a linear non-local transport. This separation allows the ML to focus on the local nonlinear components while addressing the simpler linear transport with efficient classical numerical algorithms. To accomplish this, we design an operator network that maps macroscopic observables to equilibrium states in a manner that maximizes entropy, yielding expressive BGK-type collisions. By incorporating our surrogate equilibrium into the lattice Boltzmann (LB) algorithm, we achieve accurate flow forecasts for a wide range of challenging flows. We show that NeurDE enables accurate prediction of compressible flows, including supersonic flows, while tracking shocks over hundreds of time steps, using a small velocity lattice-a heretofore unattainable feat without expensive numerical root finding.

Understanding and predicting the diverse conformational states of molecules is crucial for advancing fields such as chemistry, material science, and drug development. Despite significant progress in generative models, accurately generating complex and biologically or material-relevant molecular structures remains a major challenge. In this work, we introduce a diffusion model for three-dimensional (3D) molecule generation that combines a classifiable diffusion model, Diffusion Transformer, with multihead equivariant self-attention. This method addresses two key challenges: correctly attaching hydrogen atoms in generated molecules through learning representations of molecules after hydrogen atoms are removed; and overcoming the limitations of existing models that cannot generate molecules across multiple classes simultaneously. The experimental results demonstrate that our model not only achieves state-of-the-art performance across several key metrics but also exhibits robustness and versatility, making it highly suitable for early-stage large-scale generation processes in molecular design, followed by validation and further screening to obtain molecules with specific properties.

Both quantum phase transitions and thermodynamic phase transitions are probably induced by fluctuations, yet the specific mechanism through which fluctuations cause phase transitions remains unclear in existing theories. This paper summarizes different phases into combinations of three types of network structures based on lattice models transformed into network models and the principle of maximum entropy. These three network structures correspond to ordered, boundary, and disordered conditions, respectively. By utilizing the transformation relationships satisfied by these three network structures and classical probability, this work derive the high-order detailed balance relationships satisfied by strongly correlated systems. Using the high-order detailed balance formula, this work obtain the weights of the maximum entropy network structures in general cases. Consequently, I clearly describe the process of ordered-disordered phase transitions based on fluctuations and provide critical exponents and phase transition points. Finally, I verify this theory using the Ising model in different dimensions, the frustration scenario of the triangular lattice antiferromagnetic Ising model, and the expectation of ground-state energy in the two-dimensional Edwards-Anderson model.

Complex CME/ICME structures in the solar wind often arising in the heliosphere as a result of interaction between two or more CMEs are very important due to their enhanced geoefficiency, but their modeling is difficult due to lack of observational data outside the solar corona. The outstanding evidence of such complex structure occurred on May 10-11,2024, when the strongest geomagnetic storm was caused by a series of successive CMEs emerged from the same solar AR13664. The complex formed from the first 4 CMEs of the series triggered a drop of the Dst-index to -412nT. The aim of this study is to consider propagation of these ICMEs in the heliosphere using observations at three stages: at the starting point observed with the LASCO, in the middle heliosphere by the IPS method and in situ at the L1 point with the ACE. The IPS observations were carried out on May 9 and 10 with the BSA LPI, which enables to build in the scanning mode 2D map of scintillation index m2 associated with enhancements of the integrated over the LOS plasma density over a range of the heliocentric distances 0.4-0.8AU. Evolution of the ICMEs in the heliosphere was described using a cone model in the selfsimilar approximation and kinematics according to the DBM. The initial spatial distributions of density at the cone base were determined from the LASCO images of the CMEs at the heights of 20Rsun, and then were rescaled according to radial distances to the position corresponding to the time of the BSA observations. It was shown that the modeled distribution of the ICME complex LOS density N2 over heliocentric distance is consistent with distribution of m2. Some discrepancies may characterize variation of the density structure inside the complex due to CME interaction. The modeled mean volume density of the ICME plasma at the L1 position agreed with in situ ACE measurements, which validates the developed expansion model.

For semiconductor device fabrication, Pulsed Laser Annealing (PLA) offers significant advantages over conventional thermal processes. Notably, it can provide ultrafast (~ns) and high temperature profiles ($>1000^\circ$C). When the maximum temperature exceeds the melting point, a solid-liquid phase transition is observed, immediately followed by rapid recrystallization. This unique annealing mechanism gives raises questions about dopant diffusion and residual defects, in not only in the recrystallized region, but also just below it. As power devices require micrometer-sized junctions, high laser energy densities are needed, which were proved to promote the incorporation of complex impurities from the surface and the creation of defects at the liquid/solid interface. This paper reports on the impact of laser annealing at high energy densities (up to 8.0 J/cm$^2$) on the leakage current, using Schottky and PN diodes, and DLTS measurements. Various laser annealing conditions were used: energy densities between 1.7 and 8.0 J/cm$^2$ with 1 to 10 pulses. Our results suggest that the liquid and solid solubility of vacancies in silicon are fixed by the maximum temperature reached, so to the energy density. Increasing the number of laser pulses allows, not only to reach this maximum vacancy concentration but also to promote their diffusion towards the surface. Concomitantly, the in-diffusion of complex impurities inside the melted region allows the coupling between both defect types to create trap centers, responsible for the degradation of the leakage current.

We study the ground states of CFTs with a global $U(1)$ symmetry on $\mathbb{R}\times S^2$ in the regime of large charge $Q$ and large angular momentum $J$, using large charge EFT. We find that in the range $Q \ll J \ll Q^2$, the ground state solution is a superfluid densely populated with vortices rotating at a constant angular velocity $\Omega$. This is a relativistic generalization of the known (non-relativistic) rigid rotation phase, which corresponds to the small $\Omega$ limit of our solution. In the regime $Q^{3/2}\ll J\ll Q^2$, our solution achieves lower energy than previously identified states. In this regime, most of the vortices move near the speed of light, and we obtain the chiral fluctuation modes propagating at the speed of light. Interestingly, we find that our ground state can be interpreted as a zero temperature charged normal fluid rotating at a constant angular velocity $\Omega$. We rederive this solution purely from the fluid dynamics. Based on the (already established) applicability of fluid description to large non-supersymmetric extremal AdS black holes, we find that the boundary stress tensor and $U(1)$ current of extremal AdS Kerr-Newman black hole align with those of our solution.

A faster than real time forecast system for solar wind and interplanetary magnetic field transients that is driven by hourly updated solar magnetograms is proposed to provide a continuous nowcast of the solar corona ($<0.1$AU) and 24-hours forecast of the solar wind at 1 AU by solving a full 3-D MHD model. This new model has been inspired by the concept of \textit{relativity of simultaneity} used in the theory of special relativity. It is based on time transformation between two coordinate systems: the solar rest frame and a boosted system in which the current observations of the solar magnetic field and tomorrow's measurement of the solar wind at 1 AU are simultaneous. In this paper we derive the modified governing equations for both hydrodynamics (HD) and magnetohydrodynamics (MHD) and present a new numerical algorithm that only modifies the conserved quantities but preserves the original HD/MHD numerical flux. The proposed method enables an efficient numerical implementation, and thus a significantly longer forecast time than the traditional method.

We demonstrate the merits of an event-based camera (EBC) for tracking of passive and, in particular, active matter. For passive matter, we tracked the Brownian motion of different microparticles and estimated their diffusion coefficient. For active matter, we explored the case of tracking murine spermatozoa and aimed to extract motility parameters from the motion of cells. This has applications in enhancing outcomes for clinical fertility treatments. Using the EBC, we obtain results equivalent to those from an sCMOS camera, yet achieve a reduction in file size of up to two orders of magnitude. This is important in the modern computer era, as it simplifies data acquisition and analysis. We believe the EBC is an excellent choice, particularly for long-term studies of active matter.

Collectively-encoded qubits, involving ensembles of atomic or solid-state emitters, present many practical advantages for quantum technologies. However, they suffer from uncontrolled inhomogeneous dephasing which couples them to a quasi-continuum of dark states. In most cases, this process cannot be encompassed in a standard master equation with time-independent coefficients, making its description either tedious or inaccurate. We show that it can be understood as a displacement in time-frequency phase space and accurately included in resource-efficient numerical simulations of the qubit's dynamics. This description unveils a regime where the qubit becomes protected from dephasing through a combination of strong driving and non-Markovianity. We experimentally investigate this regime using a Rydberg superatom and extend its coherent dynamics beyond the inhomogeneous-dephasing characteristic time by an order of magnitude.

In this work, we have performed a critical review of the thermophysical properties of sulfuric acid-water mixtures. The thermophysical properties analyzed are density, viscosity, refraction index, thermal expansion coefficient, and mass expansion coefficient. The density of the sulfuric acid-water mixture has been measured for different mass fractions, w = 0.1-0.4, over a broad temperatura range, T = 273.15-333.15 K. A new parametrization of the density has been performed using a least-squares method. In this parametrization, the number of coefficients has been reduced with respect to previous works. Moreover, this new equation has been analyzed in an extended region of temperature and concentration in the following ranges: w = 0-0.1 and T = 233.15-373.15 K. The coefficients of thermal and mass expansion have also been calculated experimentally, and a linear relation of the density has been determined as a function of the variations in temperature and concentration. In addition, viscosity and refraction index measurements have been performed at 298.15 and 293.15 K, respectively. Our results have been checked against measurements in the literature (review) and show good correspondence.

Entanglement distribution in quantum networks will enable next-generation technologies for quantum-secured communications, distributed quantum computing and sensing. Future quantum networks will require dense connectivity, allowing multiple parties to share entangled states in a reconfigurable manner, while long-distance connections are established through the teleportation of entangled states, namely entanglement swapping. However, developing flexible physical platforms that can distribute entanglement through a high-capacity and scalable architecture remains a significant challenge. Here we realise a multiplexed programmable network where entanglement is routed and teleported between four parties through a reconfigurable multi-port circuit operating on the transverse-spatial photonic degree-of-freedom. We harness the natural mode-mixing process inside a multi-mode fibre and place it between two programmable phase planes to implement high-dimensional operations for two independent photons carrying eight transverse-spatial modes. This complex-medium-based circuit allows for the control of a four-party state where high-fidelity entangled states can be simultaneously distributed over multiple channels with different on-demand configurations. Our design allows us to break away from the limited planar geometry and bypass the control and fabrication challenges of conventional integrated platforms. Our demonstration showcases the potential of this architecture for enabling quantum networks with scalable and versatile connectivity that is fully compatible with existing communications infrastructure.

We coarse-grain a model of closely-packed ellipses that can vary their aspect ratio to derive continuum equations for materials comprising confluent deformable particles such as epithelial cell layers. We show that contractile nearest neighbour interactions between ellipses can lead to their elongation and nematic ordering. Adding flows resulting from active hydrodynamic stresses produced by the particles also affects the aspect ratio and can result in active turbulence. Our results, which agree well with multi-phase field simulations of deformable isotropic cells, provide a bridge between models which explicitly resolve cells and continuum theories of active matter.

Organic diradicals are envisioned as elementary building blocks for designing a new generation of spintronic devices and have been used in constructing prototypical field effect transistors and non-linear optical devices. Open-shell systems, however, are also reactive, thus requiring design strategies to protect their radical character from the environment, especially when they are embedded into solid-state devices. Here, we report the persistence on a metallic surface of the diradical character of a diindeno[b,i]anthracene (DIAn) core protected by bulky end-groups. Our scanning tunneling spectroscopy measurements on single-molecules detected singlet-triplet excitations that were absent for DIAn species packed in assembled structures. Density functional theory simulations unravel that molecular geometry on the metal substrate can crucially modify the value of the single-triplet gap via the delocalization of the radical sites. The persistence of the diradical character over metallic substrates is a promising finding for integrating radical-based materials in functional devices.

Kohn-Sham density functional theory (DFT) is widely used for screening color centers in semiconductors. While the Perdew-Burke-Ernzerhof (PBE) functional is efficient, it often lacks precision in describing defects. The Heyd-Scuseria-Ernzerhof (HSE) functional is more accurate but computationally expensive, making it impractical for large-scale screening. However, third-rung functionals of "Jacob's ladder" remain largely under explored in this context. This study evaluates the Strongly Constrained and Appropriately Normed (SCAN) family of meta-GGA functionals as potential alternatives to PBE for characterizing NV-like color centers in 4H-SiC using the Automatic Defect Analysis and Qualification (ADAQ) framework. We examine nitrogen, oxygen, fluorine, sulfur, and chlorine vacancies in 4H-SiC, focusing on applications in quantum technology. Our results show that SCAN and r2SCAN achieve greater accuracy than PBE, approaching HSE's precision at a lower computational cost. This suggests that the SCAN family offers a practical improvement for screening new color centers, with computational demands similar to PBE.

The advantages of temporal networks in capturing complex dynamics, such as diffusion and contagion, has led to breakthroughs in real world systems across numerous fields. In the case of human behavior, face-to-face interaction networks enable us to understand the dynamics of how communities emerge and evolve in time through the interactions, which is crucial in fields like epidemics, sociological studies and urban science. However, state-of-the-art datasets suffer from a number of drawbacks, such as short time-span for data collection and a small number of participants. Moreover, concerns arise for the participants' privacy and the data collection costs. Over the past years, many successful algorithms for static networks generation have been proposed, but they often do not tackle the social structure of interactions or their temporal aspect. In this work, we extend a recent network generation approach to capture the evolution of interactions between different communities. Our method labels nodes based on their community affiliation and constructs surrogate networks that reflect the interactions of the original temporal networks between nodes with different labels. This enables the generation of synthetic networks that replicate realistic behaviors. We validate our approach by comparing structural measures between the original and generated networks across multiple face-to-face interaction datasets.

A computationally efficient method to calculate the capillary pressure-saturation relations of immiscible multiphase flow on two-dimensional pore morphologies is presented here. The method is an extension of the Pore Morphology Method that includes wetting angle and trapped mechanism of the displaced fluid, and calculation of material properties by density functional theory. After validating the method with micro-chip fluid injection experiments, the method is used to relate pore morphology to capillary pressure-saturation relation using square-lattice pore morphologies. Because the method uses only morphological binary operations, it is more efficient than well-established high-resolution voxel dynamics methods such as Lattice Boltzmann Methods and Level-set computational fluid dynamics. Apart from pore morphology, only the material parameters related to contact angle (wettability) and interfacial tension are required to connect the pore-saturation relation and pore throat distribution. We investigate the effect on interfacial tension, wettability, sample size, and pore throat distribution on entry pressure and residual saturation.

Superconducting nanowire single-photon detectors (SNSPDs) are the current leading technology for the detection of single-photons in the near-infrared (NIR) and short-wave infrared (SWIR) spectral regions, due to record performance in terms of detection efficiency, low dark count rate, minimal timing jitter, and high maximum count rates. The various geometry and design parameters of SNSPDs are often carefully tailored to specific applications, resulting in challenges in optimising each performance characteristic without adversely impacting others. In particular, when scaling to larger array formats, the key challenge is to manage the heat load generated by the many readout cables in the cryogenic cooling system. Here we demonstrate a practical, self-contained 64-pixel SNSPD array system which exhibits high performance of all operational parameters, for use in the strategically important SWIR spectral region. The detector is an 8x8 array of 27.5 x 27.8 {\mu}m pixels on a 30 {\mu}m pitch, which leads to an 80 -- 85% fill factor. At a wavelength of 1550nm, a uniform average per-pixel photon detection efficiency of 77.7% was measured and the observed system detection efficiency (SDE) across the entire array was 65%. A full performance characterisation is presented, including a dark count rate of 20 cps per pixel, full-width-half-maximum (FWHM) jitter of 100 ps per pixel, a 3-dB maximum count rate of 645 Mcps and no evidence of crosstalk at the 0.1% level. This camera system therefore facilitates a variety of picosecond time-resolved measurement-based applications that include biomedical imaging, quantum communications, and long-range single-photon light detection and ranging (LiDAR) and 3D imaging.

Social media play a key role in mobilizing collective action, holding the potential for studying the pathways that lead individuals to actively engage in addressing global challenges. However, quantitative research in this area has been limited by the absence of granular and large-scale ground truth about the level of participation in collective action among individual social media users. To address this limitation, we present a novel suite of text classifiers designed to identify expressions of participation in collective action from social media posts, in a topic-agnostic fashion. Grounded in the theoretical framework of social movement mobilization, our classification captures participation and categorizes it into four levels: recognizing collective issues, engaging in calls-to-action, expressing intention of action, and reporting active involvement. We constructed a labeled training dataset of Reddit comments through crowdsourcing, which we used to train BERT classifiers and fine-tune Llama3 models. Our findings show that smaller language models can reliably detect expressions of participation (weighted F1=0.71), and rival larger models in capturing nuanced levels of participation. By applying our methodology to Reddit, we illustrate its effectiveness as a robust tool for characterizing online communities in innovative ways compared to topic modeling, stance detection, and keyword-based methods. Our framework contributes to Computational Social Science research by providing a new source of reliable annotations useful for investigating the social dynamics of collective action.

Accurate, interpretable, and real-time modeling of multi-body dynamical systems is essential for predicting behaviors and inferring physical properties in natural and engineered environments. Traditional physics-based models face scalability challenges and are computationally demanding, while data-driven approaches like Graph Neural Networks (GNNs) often lack physical consistency, interpretability, and generalization. In this paper, we propose Dynami-CAL GraphNet, a Physics-Informed Graph Neural Network that integrates the learning capabilities of GNNs with physics-based inductive biases to address these limitations. Dynami-CAL GraphNet enforces pairwise conservation of linear and angular momentum for interacting nodes using edge-local reference frames that are equivariant to rotational symmetries, invariant to translations, and equivariant to node permutations. This design ensures physically consistent predictions of node dynamics while offering interpretable, edge-wise linear and angular impulses resulting from pairwise interactions. Evaluated on a 3D granular system with inelastic collisions, Dynami-CAL GraphNet demonstrates stable error accumulation over extended rollouts, effective extrapolations to unseen configurations, and robust handling of heterogeneous interactions and external forces. Dynami-CAL GraphNet offers significant advantages in fields requiring accurate, interpretable, and real-time modeling of complex multi-body dynamical systems, such as robotics, aerospace engineering, and materials science. By providing physically consistent and scalable predictions that adhere to fundamental conservation laws, it enables the inference of forces and moments while efficiently handling heterogeneous interactions and external forces.

Previous studies have shown that there is considerable variation in the dust-to-gas density ratio in the vicinity of low-mass planets undergoing growth. This can lead to a significant change in the planetary momentum exerted by the gas and solid material. However, due to the low dust-to-gas mass ratio of protoplanetary disks, the back-reaction of the solid material, is often neglected. We study the effect of the back-reaction of solid material on the torques felt by low-mass planets. We perform locally isothermal, 2D hydrodynamic simulations of planet-disk interactions. Low-mass planets in the range of 0.1-10MEarth accrete only solid material. Simulations are compared with and without taking into account the back-reaction of the solid material on the gas. The solid component is assumed to have a fixed Stokes number in the range 0.01-10. In general, the inclusion of back-reaction results in a greater number of models with positive torque values compared to models that neglect back-reaction. It is clear, therefore, that the simulation of planetary growth and migration via hydrodynamic modeling requires the inclusion of solid-gas back-reaction. As a result of the back-reaction and accretion, a Mars-sized planetary embryo will experience positive total torques from the disk containing coupled solid components St<=0.01. Earth-mass planets also experience positive total torques from the disk containing boulder-sized solid components 2<=St<=5. The accretion of weakly coupled solid material tends to increase the positive torques and decrease the negative torques. Our results suggest that the combined effect of back-reaction and accretion is beneficial to the formation of planetary systems by reducing the likelihood of a young planet being engulfed by the central star.

The directional coupler is a fundamental element of wave-based circuits. The state of the art for the spin-wave directional couplers consists mostly of macroscopic waveguides or two-dimensional planar systems. In this Letter, we present the design of the nanoscale spin-wave vertical directional coupler with a very high efficiency exceeding 99.5%. We demonstrate that the operation of the coupler can be controlled by the magnitude of the external magnetic field. Moreover, it can perform multiplexing and demultiplexing of the spin-wave signal. Such a device ought to become an essential element of the three-dimensional magnonic circuits.

Using techniques from many-body quantum theory, we propose a framework for representing the evolution of observables of measure-preserving ergodic flows through infinite-dimensional rotation systems on tori. This approach is based on a class of weighted Fock spaces $F_w(\mathcal H_\tau)$ generated by a 1-parameter family of reproducing kernel Hilbert spaces $\mathcal H_\tau$, and endowed with commutative Banach algebra structure under the symmetric tensor product using a subconvolutive weight $w$. We describe the construction of the spaces $F_w(\mathcal H_\tau)$ and show that their Banach algebra spectra, $\sigma(F_w(\mathcal H_\tau))$, decompose into a family of tori of potentially infinite dimension. Spectrally consistent unitary approximations $U^t_\tau$ of the Koopman operator acting on $\mathcal H_\tau$ are then lifted to rotation systems on these tori akin to the topological models of ergodic systems with pure point spectra in the Halmos--von Neumann theorem. Our scheme also employs a procedure for representing observables of the original system by polynomial functions on finite-dimensional tori in $\sigma(F_w(\mathcal H_\tau))$ of arbitrarily large degree, with coefficients determined from pointwise products of eigenfunctions of $U^t_\tau$. This leads to models for the Koopman evolution of observables on $L^2$ built from tensor products of finite collections of approximate Koopman eigenfunctions. Numerically, the scheme is amenable to consistent data-driven implementation using kernel methods. We illustrate it with applications to Stepanoff flows on the 2-torus and the Lorenz 63 system. Connections with quantum computing are also discussed.

Traditional cosmic ray simulations make use of the Montecarlo method in a very naive way to randomise energy and direction for each simulated particle. The flux of cosmic rays is modelled as a rain coming from a plane above the object of interest (detectors in particle physics applications, planes in dosimetry studies, etc.) with an experimental angular and energy distributions. This strategy is very inefficient because many of the particles never touch the detector. Here a refined way of implementing the Montecarlo method is proposed in order to generate a sample of events that hit the target volume whose angular distribution coincides with the one from the naive implementation. It is based on the projection of a sphere containing the target volume onto a plane tangent to it with a fixed angle, we call it the secant method. This configuration allows to compute the probability of a cosmic particle hitting the sphere with this incoming angle as proportional to the area of the corresponding section of a cylinder. The performance of this method is faster in terms of computing time and identical physical results are achieved. It has been implemented in REST-for-Physics framework and it is tested with the geometry of a real detector, the IAXO-D0 Micromegas X-ray detector for the future axion helioscope BabyIAXO. Our method is 37 times more efficient than the traditional Montecarlo schema for the same accuracy, being more useful when the target volume departs from spherical shape

Political polarization, a key driver of social fragmentation, has drawn increasing attention for its role in shaping online and offline discourse. Despite significant efforts, accurately measuring polarization within ideological distributions remains a challenge. This study evaluates five widely used polarization measures, testing their strengths and weaknesses with synthetic datasets and a real-world case study on YouTube discussions during the 2020 U.S. Presidential Election. Building on these findings, we present a novel adaptation of Kleinberg's burst detection algorithm to improve mode detection in polarized distributions. By offering both a critical review and an innovative methodological tool, this work advances the analysis of ideological patterns in social media discourse.

We propose a high-order spacetime wavelet method for the solution of nonlinear partial differential equations with a user-prescribed accuracy. The technique utilizes wavelet theory with a priori error estimates to discretize the problem in both the spatial and temporal dimensions simultaneously. We also propose a novel wavelet-based recursive algorithm to reduce the system sensitivity stemming from steep initial and/or boundary conditions. The resulting nonlinear equations are solved using the Newton-Raphson method. We parallelize the construction of the tangent operator along with the solution of the system of algebraic equations. We perform rigorous verification studies using the nonlinear Burgers' equation. The application of the method is demonstrated solving Sod shock tube problem using the Navier-Stokes equations. The numerical results of the method reveal high-order convergence rates for the function as well as its spatial and temporal derivatives. We solve problems with steep gradients in both the spatial and temporal directions with a priori error estimates.

Music has always been central to human culture, reflecting and shaping traditions, emotions, and societal changes. Technological advancements have transformed how music is created and consumed, influencing tastes and the music itself. In this study, we use Network Science to analyze musical complexity. Drawing on $\approx20,000$ MIDI files across six macro-genres spanning nearly four centuries, we represent each composition as a weighted directed network to study its structural properties. Our results show that Classical and Jazz compositions have higher complexity and melodic diversity than recently developed genres. However, a temporal analysis reveals a trend toward simplification, with even Classical and Jazz nearing the complexity levels of modern genres. This study highlights how digital tools and streaming platforms shape musical evolution, fostering new genres while driving homogenization and simplicity.

We present a method to construct high-order polynomial approximate invariants (AI) for non-integrable Hamiltonian dynamical systems, and apply it to modern ring-based particle accelerators. Taking advantage of a special property of one-turn transformation maps in the form of a square matrix, AIs can be constructed order-by-order iteratively. Evaluating AI with simulation data, we observe that AI's fluctuation is actually a measure of chaos. Through minimizing the fluctuations with control knobs in accelerators, the stable region of long-term motions could be enlarged.