In this article, a quantum variant of chess is introduced, which can be played on a traditional board, without using computers or other electronic devices. The rules of the game arise naturally by combining the rules of conventional chess with key quantum-physical effects such as superposition and entanglement. Niel's Chess is recommended for ages 10 and above, to everyone who wishes to play a creative game with historical roots and at the same time gain intuition about the foundational quantum effects that power cutting-edge technologies like quantum computing and quantum communication, which are poised to revolutionize our society in the coming decades. Takeaways from a pilot educational session that was carried out with 10-to-12-year-old children are also presented.

Interest in science fiction's (SF's) potential science communication use is hindered by concerns about SF misrepresenting science. This study addresses these concerns by asking how SF media reflects scientific findings in exoplanet science. A database of SF exoplanets was analysed using a Bayesian network to find interconnected interactions between planetary characterisation features and literary data. Results reveal SF exoplanets designed after the discovery of real exoplanets are less Earth-like, providing statistical evidence that SF incorporates rapidly-evolving science. Understanding SF's portrayal of science is crucial for its potential use in science communication.

Data are given, commentary is supplied and explanations are provided with regard to the technical, the organizational and, of course, the human history connected to the time of research, which resulted to the paper entitled "Soil sampling and Cs-137 analysis of the Chernobyl fallout in Greece", written by late Professor S.E. Simopoulos. This paper has been provided in Greek translation within an issued honorary volume (ISBN 978-960-254-714-4). Reasonably, the narration starts with the review of the political, the financial and the social situation of Greece around 1986. Subsequently, an analysis is given on the then available means, the persons involved, the methods used, the lessons learned and any other connection with the oral history of the NTUA's Nuclear Engineering Laboratory and other relevant Greek Laboratories. For this history, written proof is now scarce and the persons available to pass it on are growing less and less. N.P. Petropoulos, now Laboratory member and then student of Professor S.E. Simopoulos was in charge of preparation of this text.

The formation of protein precursors, due to the condensation of atomic carbon under the low-temperature conditions of the molecular phases of the interstellar medium, opens alternative pathways for the origin of life. We perform peptide synthesis under conditions prevailing in space and provide a comprehensive analytic characterization of its products. The application of 13C allowed us to confirm the suggested pathway of peptide formation that proceeds due to the polymerization of aminoketene molecules that are formed in the C + CO + NH3 reaction. Here, we address the question of how the efficiency of peptide production is modified by the presence of water molecules. We demonstrate that although water slightly reduces the efficiency of polymerization of aminoketene, it does not prevent the formation of peptides.

Compact autonomous marine vehicles, both surface and submersible, are now commonly used to conduct observations of ocean velocities using Acoustic Doppler Current Profilers (ADCPs). However, in the inevitable presence of surface waves, ADCP measurements conducted by these platforms are susceptible to biases stemming from wave-coherent orbital motion and platform tilting. In typical ocean conditions, the magnitude of the bias can reach tens of centimeters per second. This paper presents analytical derivation of the depth-dependent bias formulas for a variety of scenarios, encompassing surface and subsurface platforms, upward- and downward-looking ADCPs, free-drifting and self-propelled vehicles. The bias is shown to be a function of the wave field properties, platform response dynamics, and the ADCP configuration (particularly, orientation and beam angle). In all cases, the wave-induced biases show parametric scaling similar to that of the Stokes drift, albeit with a number of critical nuances. Analytical derivations are validated with a semi-analytical model, which can also be used to estimate the biases for more complex measurement configurations. Further analysis reveals unexpected fundamental differences between the upward- and downward-looking ADCP configurations, offering insights for experimental design aimed at minimizing and mitigating wave-induced biases in autonomous oceanographic observations.

The ability to control the behavior of fluid instabilities at material interfaces, such as the shock-driven Richtmyer--Meshkov instability, is a grand technological challenge with a broad number of applications ranging from inertial confinement fusion experiments to explosively driven shaped charges. In this work, we use a linear-geometry shaped charge as a means of studying methods for controlling material jetting that results from the Richtmyer--Meshkov instability. A shaped charge produces a high-velocity jet by focusing the energy from the detonation of high explosives. The interaction of the resulting detonation wave with a hollowed cavity lined with a thin metal layer produces the unstable jetting effect. By modifying characteristics of the detonation wave prior to striking the lined cavity, the kinetic energy of the jet can be enhanced or reduced. Modifying the geometry of the liner material can also be used to alter jetting properties. We apply optimization methods to investigate several design parameterizations for both enhancing or suppressing the shaped-charge jet. This is accomplished using 2D and 3D hydrodynamic simulations to investigate the design space that we consider. We also apply new additive manufacturing methods for producing the shaped-charge assemblies, which allow for experimental testing of complicated design geometries obtained through computational optimization. We present a direct comparison of our optimized designs with experimental results carried out at the High Explosives Application Facility at Lawrence Livermore National Laboratory.

The concept of Hydrodynamic Cavitation (HC) has emerged as a promising method for wastewater treatment, bio-diesel production and multiple other environmental processes with Venturi-type cavitation reactors showing particular advantages. However, numerical simulations of a venturi-type reactor with an elucidated explanation of the underlying flow physics remain inadequate. The present study numerically investigates and analyzes the flow inside a venturi-type reactor from both global cavity dynamics and localized turbulence statistics perspectives. Some models in the Detached Eddy Simulation (DES) family are employed to model the turbulence with the study initially comparing 2D simulations before extending the analysis to 3D simulations. The results show that while URANS models show significantly different dynamics as a result of grid refinement, the DES models show standard flow dynamics associated with cavitating flows. Nevertheless, signifi- cant discrepancies continue to exist when comparing the turbulence statistics on the local scale. As the discussion extends to 3D calculations, the DES models are able to well predict the turbulence phenomena at the local scale and reveal some new insights regarding the role of baroclinic torque into the cavitation-vortex interaction.The findings of this study thus contribute to the fundamental understandings of the venturi-type reactor.

Understanding the dynamic nature of the semiconductor-water interface is crucial for developing efficient photoelectrochemical water splitting catalysts, as it governs reactivity through charge and mass transport. In this study, we employ ab initio molecular dynamics simulations to investigate the structural and dynamical properties of water at the $\beta$-TaON (100) surface. We observed that a well-defined interface is established through the spontaneous dissociation of water and the reorganization of surface chemical bonds. This leads to the formation of a partially hydroxylated surface, accompanied by a strong network of hydrogen bonds at the TaON-water interface. Consequently, various proton transport routes, including the proton transfer through "low-barrier hydrogen bond" path, become active across the interface, dramatically increasing the overall rate of the proton hopping at the interface. Based on our findings, we propose that the observed high photocatalytic activity of TaON-based semiconductors could be attributed to the spontaneous water dissociation and the resulting high proton transfer rate at the interface.

Starting from the assumption that saturation of plasma turbulence driven by temperature-gradient instabilities in fusion plasmas is achieved by a local energy cascade between a long-wavelength outer scale, where energy is injected into the fluctuations, and a small-wavelength dissipation scale, where fluctuation energy is thermalized by particle collisions, we formulate a detailed phenomenological theory for the influence of perpendicular flow shear on magnetized-plasma turbulence. Our theory introduces two distinct regimes, called the weak-shear and strong-shear regimes, each with its own set of scaling laws for the scale and amplitude of the fluctuations and for the level of turbulent heat transport. We discover that the ratio of the typical radial and poloidal wavenumbers of the fluctuations (i.e., their aspect ratio) at the outer scale plays a central role in determining the dependence of the turbulent transport on the imposed flow shear. Our theoretical predictions are found to be in excellent agreement with numerical simulations of two paradigmatic models of fusion-relevant plasma turbulence: (i) an electrostatic fluid model of slab electron-scale turbulence, and (ii) Cyclone-base-case gyrokinetic ion-scale turbulence. Additionally, our theory envisions a potential mechanism for the suppression of electron-scale turbulence by perpendicular ion-scale flows based on the role of the aforementioned aspect ratio of the electron-scale fluctuations.

The verification of whether small-scale turbulence is isotropic remains a grand challenge. The difficulty arises because the presence of small-scale anisotropy is tied to the dissipation tensor, whose components require the full three-dimensional information of the flow field in both high spatial and temporal resolution, a condition rarely satisfied in turbulence experiments, especially during field scale measurement of atmospheric turbulence. To circumvent this issue, an \emph{intermittency-anisotropy} framework is proposed through which we successfully extract the features of small-scale anisotropy from single-point measurements of turbulent time series by exploiting the properties of small-scale intermittency. Specifically, this framework quantifies anisotropy by studying the contrasting effects of burst-like activities on the scale-wise production of turbulence kinetic energy between the horizontal and vertical directions. The veracity of this approach is tested by applying it over a range of datasets covering an unprecedented range in the Reynolds numbers ($Re \approx 10^{3}$ to $10^{6}$), sampling frequencies (10 kHz to 10 Hz), surface conditions (aerodynamically smooth surfaces to typical grasslands to forest canopies), and flow types (channel flows, boundary layer flows, atmospheric flows, and flows over forest canopies). For these diverse datasets, the findings indicate that the effects of small-scale anisotropy persists up to the integral scales of the streamwise velocity fluctuations and there exists a universal relationship to predict this anisotropy from the two-component state of the Reynolds stress tensor. This relationship is important towards the development of next-generation closure models of wall-turbulence by incorporating the effects of anisotropy at smaller scales of the flow.

Plasma-based acceleration (PBA) has emerged as a promising candidate for the accelerator technology used to build a future linear collider and/or an advanced light source. In PBA, a trailing or witness particle beam is accelerated in the plasma wave wakefield (WF) created by a laser or particle beam driver. The distance over which the drive beam evolves is several orders of magnitude larger than the wake wavelength. This large disparity in length scales is amenable to the quasi-static approach. Three-dimensional (3D), quasi-static (QS), particle-in-cell (PIC) codes, e.g., QuickPIC, have been shown to provide high fidelity simulation capability with 2-4 orders of magnitude speedup over 3D fully explicit PIC codes. We describe a mesh refinement scheme that has been implemented into the 3D QS PIC code, QuickPIC. We use a very fine (high) resolution in a small spatial region that includes the witness beam and progressively coarser resolutions in the rest of the simulation domain. A fast multigrid Poisson solver has been implemented for the field solve on the refined meshes and a Fast Fourier Transform (FFT) based Poisson solver is used for the coarse mesh. The code has been parallelized with both MPI and OpenMP, and the parallel scalability has also been improved by using pipelining. A preliminary adaptive mesh refinement technique is described to optimize the computational time for simulations with an evolving witness beam size. Several test problems are used to verify that the mesh refinement algorithm provides accurate results. The results are also compared to highly resolved simulations with near azimuthal symmetry using a new hybrid QS PIC code QPAD that uses a PIC description in the coordinates ($r$, $ct-z$) and a gridless description in the azimuthal angle, $\phi$.

The visible spectrum of Mo$^{15+}$ ions was measured using a high-temperature superconducting electron-beam ion trap at the Shanghai EBIT Laboratory, with an electron beam energy $E_{e}$=400 eV, significantly lower than the ionization potential (IP=544.0 eV) of Mo$^{14+}$ ions in the ground state. To expound on the experiment, the energy level structure, radiative transition properties, electron-impact excitation, and electron-impact ionization cross section for both the ground state and low-lying excited state of the Mo$^{14+}$ ions were calculated using Dirac-Fock-Slater method with a local central potential and distorted wave approximation. The results demonstrated reasonable agreement with both available experimental and theoretical data. Through an analysis of the related atomic processes of Mo$^{14+}$ ion, a scenario involving the stepwise ionization of the metastable state 3p$^{6}$3d$^{9}$4s was proposed to explain the presence of the Mo$^{15+}$ ions with a lower energy of the incident electron. Finally, the significance of the metastable levels in ionizing Mo$^{14+}$ ions is highlighted.

Accurate prediction of the dynamics and deformation of freely moving drops is crucial for numerous droplet applications. When the Weber number is finite but below a critical value, the drop deviates from its spherical shape and deforms as it is accelerated by the gas stream. Since aerodynamic drag on the drop depends on its shape oscillation, accurately modeling the drop shape evolution is essential for predicting the drop's velocity and position. In this study, 2D axisymmetric interface-resolved simulations were performed to provide a comprehensive dataset for developing a data-driven model. Parametric simulations were conducted by systematically varying the drop diameter and free-stream velocity, achieving wide ranges of Weber and Reynolds numbers. The instantaneous drop shapes obtained in simulations are characterized by spherical harmonics. Temporal data of the drag and modal coefficients are collected from the simulation data to train a Nonlinear Auto-Regressive models with eXogenous inputs (NARX) neural network model. The overall model consists of two multi-layer perceptron networks, which predict the modal coefficients and the drop drag, respectively. The drop shape can be reconstructed with the predicted modal coefficients. The model predictions are validated against the simulation data in the testing set, showing excellent agreement for the evolutions of both the drop shape and drag.

The problem of light waves interaction with charged particles becomes more and more complex starting with the case of plane waves, where the analytical solution is well known, to more natural, though more complicated situations which include focused or structured laser beams. Internal structure may introduce a new degree of freedom and qualitatively change the dynamics of interacting particles. For certain conditions, namely for the dilute plasma, description of single-particle dynamics in the focused structured laser beams is the first step and may serve as a good approximation on the way of understanding the global plasma response. Moreover, the general problem of integrability in complex systems starts from consideration of the integrals of motion for a single particle. The primary goal of this work is an understanding of the physics of the orbital angular momentum (OAM) absorption by a single particle in a focused structured light. A theoretical model of the process, including solutions of Maxwell equations with the required accuracy and a high-order perturbative approach to electron motion in external electromagnetic fields, is developed and its predictions are examined with numerical simulations for several exemplary electromagnetic field configurations. In particular, it was found that for the particles distributed initially with the azimuthal symmetry around the beam propagation direction, the transferred OAM has a smallness of the fourth order of the applied field amplitude, and requires an accurate consideration of the temporal laser pulse envelope.

In this study, we investigate second- and third-harmonic generation processes in Au nanorod systems using the real-time time-dependent density functional tight binding (RT-TDDFTB) method. Our study focuses on computation of nonlinear signals based on the time dependent dipole response induced by linearly polarized laser pulses interacting with nanoparticles. We systematically explore the influence of various laser parameters, including pump intensity, duration, frequency, and polarization directions, on the harmonic generation. We demonstrate all the results using Au nanorod dimer systems arranged in end-to-end configurations, and disrupting the spatial symmetry of regular single nanorod systems crucial for second harmonic generation processes. Furthermore, we study the impact of nanorod lengths, which lead to variable plasmon energies, on the harmonic generation, and estimates of polarizabilities and hyper-polarizabilities are provided.

Ultrafast electron diffraction (UED) instruments typically operate at kHz or lower repetition rates and rely on indirect detection of electrons. However, these experiments encounter limitations because they are required to use electron beams containing a relatively large number of electrons (>>100 electrons/pulse), leading to severe space-charge effects. Consequently, electron pulses with long durations and large transverse diameters are used to interrogate the sample. Here, we introduce a novel UED instrument operating at a high repetition rate and employing direct electron detection. We operate significantly below the severe space-charge regime by using electron beams containing 55 to 140 electrons per pulse at 30-kHz. We demonstrate the ability to detect time-resolved signals from thin film solid samples with a difference contrast signal, {\Delta}I/I0, and an instrument response function as low as 10-5 and 243-fs (FWHM), respectively, without temporal compression. Overall, our findings underscore the importance of increasing the repetition rate of UED experiments and adopting a direct electron detection scheme. Our newly developed scheme enables more efficient and sensitive investigations of ultrafast dynamics in photoexcited samples using ultrashort electron beams.

We present a comprehensive set of theoretical results for differential, integrated, and momentum transfer cross sections for the elastic scattering of electrons by beryllium, magnesium and calcium, at energies below 1 keV. In addition, we provide Sherman function values for elastic electron scattering from calcium in the same energy range. This study extends the application of our method of calculations, already employed for barium and strontium, to all stable alkaline-earth-metal atoms. Our semi-empirical approach to treating target polarization has produced in our earlier work a satisfactory agreement with experimental values and precise theoretical results such as convergent close-coupling calculations for barium. The present data are expected to be of similar high accuracy, based on our previous success in similar calculations for barium and all inert gases.

Recently, two-dimensional terahertz spectroscopy (2DTS) has attracted increasing attention for studying complex solids. A number of recent studies have applied 2DTS either with long pulses or away from any material resonances, situations that yield unconventional 2DTS spectra that are often difficult to interpret. Here, we clarify the generic origins of observed spectral features by examining 2DTS spectra of ZnTe, a model system with a featureless optical susceptibility at low terahertz frequencies. These results also reveal possible artifacts that may arise from electro-optic sampling in collinear 2DTS experiments, including the observation of spurious rectified or second harmonic signals.

In this work, we investigated the spatial evolution of optical power in a closed-form optical waveguide configuration consisting of six passive waveguides and each of the waveguides exhibits equal strength of Kerr nonlinearity. We considered only nearest neighbor interaction between the waveguides. We found that in the case of low Kerr nonlinearity, evolution of optical power shows synchronization behavior. But when we increased the strength of Kerr nonlinearity, we discovered that spatial evolution of optical power in all waveguides shows independent characteristics. On the other hand, we have studied the impact of the coupling constant on the synchronization dynamics of our system. Our findings showed us that strong coupling can strengthen the collective dynamics in the presence of strong Kerr nonlinearity. From our results, we can conclude that Kerr nonlinearity in our system plays the role of disorder parameter that destroys as well as alters the synchronization behavior of evolution of optical power in the waveguides and coupling constant plays the role of an antagonist and restores synchronization in the model.

We study high quality-factor (high Q) resonances supported by periodic arrays of Mie resonators from the perspectives of both Bloch wave theory and multiple scattering theory. We reveal that, unlike a common belief, the bound states in the continuum (BICs) derived by the Bloch-wave theory do not directly determine the resonance with the highest Q value in large but finite arrays. Higher Q factors appear to be associated with collective resonances formed by nominally guided modes below the light line associated with strong effect of both electric and magnetic multipoles. Our findings offer valuable insights into accessing the modes with higher Q resonances via bonding modes within finite metastructures. Our results underpin the pivotal significance of magnetic and electric multipoles in the design of resonant metadevices and nonlocal flat-band optics. Moreover, our demonstrations reveal that coupled arrays of high-Q microcavities do not inherently result in a stronger light-matter interaction when compared to coupled low-Q nanoresonators. This result emphasizes the critical importance of the study of multiple light-scattering effects in cavity-based systems.

Cross-sections for dissociative recombination and electron-impact vibrational excitation of the BF$^+_2$ molecular ion are computed using a theoretical approach that combines the normal modes approximation for the vibrational states of the target ion and use of the UK R-matrix code to evaluate electron-ion scattering matrices for fixed geometries of the ion. Thermally-averaged rate coefficients are obtained from the cross-sections for temperatures in the 10-3000 K range.

The numerical simulation of rarefied gas mixtures with disparate mass and concentration is a huge research challenge. Based on our recent kinetic modelling for monatomic gas mixture flows, this problem is tackled by the general synthetic iterative scheme (GSIS), where the mesoscopic kinetic and macroscopic synthetic equations are alternately solved by the finite-volume discrete velocity method. Three important features of GSIS are highlighted. First, the synthetic equations are precisely derived from the kinetic equation, naturally reducing to the Navier-Stokes equations in the continuum flow regime; in other flow regimes, the kinetic equation provides high-order closure of the constitutive relations to capture the rarefaction effects. Second, these synthetic equations, which can be solved quickly, help to adjust the kinetic system to relax rapidly toward the steady state. Furthermore, in such a two-way coupling, the constraint on the spatial cell size is relieved. Third, the linear Fourier stability analysis demonstrates that the error decay rate in GSIS is smaller than 0.5 for various combinations of mass, concentration and viscosity ratios, such that the error can be reduced by three orders of magnitude after 10 iterations. The efficiency and accuracy of GSIS are demonstrated through several challenging cases covering a wide range of mass ratio, species concentration, and flow speed.

One of the most promising approaches for the next generation of neutrino experiments is the realization of large hybrid Cherenkov/scintillation detectors made possible by recent innovations in photodetection technology and liquid scintillator chemistry. The development of a potentially suitable future detector liquid with particularly slow light emission is discussed in the present publication. This cocktail is compared with respect to its fundamental characteristics (scintillation efficiency, transparency, and time profile of light emission) with liquid scintillators currently used in large-scale neutrino detectors. In addition, the optimization of the admixture of wavelength shifters for a scintillator with particularly high light emission is presented. Furthermore, the pulse-shape discrimination capabilities of the novel medium was studied using a pulsed particle accelerator driven neutron source. Beyond that, purification methods based on column chromatography and fractional vacuum distillation for the co-solvent DIN (Diisopropylnaphthalene) are discussed.

Liquid argon detectors are ubiquitous in particle, astroparticle, and applied physics. They reached an unprecedented level of maturity thanks to more than 20 years of R&D and the operation of large-scale facilities at CERN, Fermilab, and the Gran Sasso laboratories. This article reviews such an impressive advance - from the grounding of the experimental technique up to cutting-edge applications. We commence the review by describing the physical and chemical properties of liquid argon as an active and target medium for particle detection, together with advantages and limitations compared with other liquefied noble gases. We examine the opportunities and challenges of liquid argon detectors operated as calorimeters, scintillators, and time projection chambers. We then delve into the core applications of liquid argon detectors at colliders (ATLAS), accelerator neutrino beams (SBN, DUNE), and underground laboratories (DarkSide, DEAP, ICARUS) for the observation of rare events. We complete the review by looking at unconventional developments (pixelization, combined light-charge readout, Xe-doped devices, all-optical readout) and applications in medical and applied physics to extend this technology's scope toward novel research fields.

We apply the political stress index as introduced by Goldstone (1991) and implemented by Turchin (2013), to the case study of Poland. The approach quantifies political and social unrest as a single quantity based on a multitude of economic and demographic variables. The present-day data allow us to directly apply index without the need of simulating the elite component, as was done previously. Neither model version shows appreciable unrest levels for the present, while the simulated model applied to partial historical data yields the index in remarkable agreement with the fall of communism in Poland. We next analyze the model's sensitive dependence on its parameters (the hallmark of chaos), which limits its utility and application to other countries. The original equations cannot, by construction, describe the elite fraction for longer time-periods; and we propose a modification to remedy this problem. The model still holds some predictive power, but we argue that some components should be reinterpreted if one wants to keep its dynamical equations.

This paper discusses some features of the spectral line profile theory used in the treatment of measured atomic transitions. It is shown that going beyond the established linear approximation for the spectral line contour in the case of its nonresonant extension, the potential for a more accurate extraction of atomic characteristics from experimental data arises. Using the example of the Lyman-$\alpha$ (Ly$_\alpha$) transition in hydrogen, a simple analysis of the observed spectral line distorted by a possible interfering transitions is given. In particular, the results obtained in the present work clearly demonstrate that the processing of the same experimental data at different settings can provide an accurate determination of the transition frequency, the centre of gravity as well as the hyperfine splitting of the ground state in hydrogen-like atomic systems. The latter is especially important for setting up precision spectroscopic experiments on the antihydrogen atom.

Walking is the most sustainable form of urban mobility, but is compromised by uncomfortable or unhealthy sun exposure, which is an increasing problem due to global warming. Shade from buildings can provide cooling and protection for pedestrians, but the extent of this potential benefit is unknown. Here we explore the potential for shaded walking, using building footprints and street networks from both synthetic and real cities. We introduce a route choice model with a sun avoidance parameter $\alpha$ and define the CoolWalkability metric to measure opportunities for walking in shade. We derive analytically that on a regular grid with constant building heights, CoolWalkability is independent of $\alpha$, and that the grid provides no CoolWalkability benefit for shade-seeking individuals compared to the shortest path. However, variations in street geometry and building heights create such benefits. We further uncover that the potential for shaded routing differs between grid-like and irregular street networks, forms local clusters, and is sensitive to the mapped network geometry. Our research identifies the limitations and potential of shade for cool, active travel, and is a first step towards a rigorous understanding of shade provision for sustainable mobility in cities.

Integrated frequency combs promise transformation of lab-based metrology into disruptive real-world applications. These microcombs are, however, sensitive to stochastic thermal fluctuations of the integrated cavity refractive index, with its impact becoming more significant as the cavity size becomes smaller. This tradeoff between microcomb noise performance and footprint stands as a prominent obstacle to realizing applications beyond a controlled lab environment. Here, we demonstrate that small footprint and low noise become compatible through the all-optical Kerr-induced synchronization (KIS) method. Our study unveils that the phase-locking nature of the synchronization between the cavity soliton and the injected reference pump laser enables the microcomb to no longer be limited by internal noise sources. Instead, the microcomb noise is mostly limited by external sources, namely, the frequency noise of the two pumps that doubly pin the microcomb. First, we theoretically and experimentally show that the individual comb tooth linewidths of an octave-spanning microcomb remain within the same order-of-magnitude as the pump lasers, contrary to the single-pumped case that exhibits a more than two order-of-magnitude increase from the pump to the comb edge. Second, we theoretically show that intrinsic noise sources such as thermorefractive noise in KIS are quenched at the cavity decay rate, greatly decreasing its impact. Experimentally, we show that even with free-running lasers, the KIS microcomb can exhibit better repetition rate noise performance than the predicted thermorefractive noise limitation in absence of KIS.

We implemented the Bayesian analysis to the polarised neutron reflectivity data. Reflectivity data from a magnetic TbCo thin film structure was studied using the bundle of a Monte-Carlo Markov-chain algorithm, likelihood estimation, and error modeling. By utilizing the Bayesian analysis, we were able to investigate the uniqueness of the solution beyond reconstructing the magnetic and structure parameters. This approach has demonstrated its expedience as several probable reconstructions were found (the multimodality case) concerning the isotopic composition of the surface cover layer. Such multimodal reconstruction emphasizes the importance of rigorous data analysis instead of the direct data fitting approach, especially in the case of poor statistically conditioned data, typical for neutron reflectivity experiments. The analysis details and the discussion on multimodality are in this article.

The Dirac exchange interaction is derived from recent quantum kinetic theory for collisionless plasmas. For this purpose, the kinetic equation is written in the semiclassical and long wavelength approximations. The validity of the model for real systems is worked out, in terms of temperature and density parameters. Within the region of applicability, the correlation potential energy is shown to be always smaller than the exchange contribution. From the moments of the quantum kinetic equations, macroscopic, hydrodynamic equations are found, for an electron-ion plasma. The Dirac exchange term is explicitly derived, in the case of a completely degenerate electron gas. These results show, within quantum kinetic theory for charged particle systems, a new view of the Dirac exchange interaction frequently used in density functional theory parametrization. Finally, a simpler form of the quantum plasma exchange kinetic theory is also found.

Solving the wave equation is essential to seismic imaging and inversion. The numerical solution of the Helmholtz equation, fundamental to this process, often encounters significant computational and memory challenges. We propose an innovative frequency-domain scattered wavefield modeling method employing neural operators adaptable to diverse seismic velocities. The source location and frequency information are embedded within the input background wavefield, enhancing the neural operator's ability to process source configurations effectively. In addition, we utilize a single reference frequency, which enables scaling from larger-domain forward modeling to higher-frequency scenarios, thereby improving our method's accuracy and generalization capabilities for larger-domain applications. Several tests on the OpenFWI datasets and realistic velocity models validate the accuracy and efficacy of our method as a surrogate model, demonstrating its potential to address the computational and memory limitations of numerical methods.

This paper presents a novel method for low maintenance, low ambiguity in-situ drift velocity monitoring in large volume Time Projection Chambers (TPCs). The method was developed and deployed for the 40m^3 TPC tracker system of the NA61/SHINE experiment at CERN, which has a one meter of drift length. The method relies on a low-cost multi-wire proportional chamber (MWPC) placed downstream of the TPCs to be monitored. The drift velocity is then determined by matching the reconstructed tracks in the TPC to the hits of the pertinent monitoring chamber, called Geometry Reference Chamber (GRC), which is then used as a differential length scale. An important design requirement on the GRC was minimal added complexity to the existing system, in particular, compatibility with Front-End Electronics (FEE) cards already used to read out the TPCs. Moreover, the GRC system was designed to operate both in large and small particle flux. The system is capable of monitoring the evolution of the in-situ drift velocity down to a one permil precision, with a few minutes of time sampling.

The land-atmosphere coupling strength has been defined as the percentage of precipitation variability explained by the variation of soil moisture in the Global Land-Atmosphere Coupling Experiment (GLACE). While it is useful to identify global hotspots of land-atmosphere interaction, this coupling strength is different from coupling sensitivity, which directly quantifies how precipitation generation responds to the perturbation of soil moisture and is essential for our understanding of the global water cycle. To disentangle these two quantities, here we theoretically explore the relationships among coupling strength, sensitivity, and soil moisture variances. We use climate model outputs to show that the largest soil moisture variances are located in the transitional climate zones and the variations of soil moisture largely account for the geographical patterns of coupling hotspots. The coupling sensitivity is not necessarily low in non-hotspot regions, which could impose great impacts on the development of extreme climate events. We therefore call for more research attention on coupling sensitivity to improve our understanding of the climate system.

The fusion research facility ITER is currently being assembled to demonstrate that fusion can be used for industrial energy production, while several other programmes across the world are also moving forward, such as EU-DEMO, CFETR, SPARC and STEP. The high engineering complexity of a tokamak makes it an extremely challenging device to optimise, and test-based optimisation would be too slow and too costly. Instead, digital design and optimisation must be favored, which requires strongly-coupled suites of High-Performance Computing calculations. In this context, having surrogate models to provide quick estimates with uncertainty quantification is essential to explore and optimise new design options. Furthermore, these surrogates can in turn be used to accelerate simulations in the first place. This is the case of Parareal, a time-parallelisation method that can speed-up large HPC simulations, where the coarse-solver can be replaced by a surrogate. A novel framework, Neural-Parareal, is developed to integrate the training of neural operators dynamically as more data becomes available. For a given input-parameter domain, as more simulations are being run with Parareal, the large amount of data generated by the algorithm is used to train new surrogate models to be used as coarse-solvers for future Parareal simulations, leading to progressively more accurate coarse-solvers, and thus higher speed-up. It is found that such neural network surrogates can be much more effective than traditional coarse-solver in providing a speed-up with Parareal. This study is a demonstration of the convergence of HPC and AI which simply has to become common practice in the world of digital engineering design.

We study the classical dynamics of a system of a pair of Kerr-Duffing nonlinear oscillators coupled by a nonlinear interaction and subject to a parametric drive. Within a rotating wave approximation (RWA), we analyze the steady-state solutions for the oscillation amplitude of the two oscillators. In the most relevant case of identical oscillators, we separately investigate configurations in which only one oscillator is parametrically driven, or both of them are simultaneously driven. In the latter regime, special attention is paid to the symmetric case where the parametric drives acting on the two oscillators are equal: for an increasing value of the detuning of the parametric drive, a transition to a multi-stable, symmetry-breaking regime is found, where the two oscillators display different oscillation amplitudes and phases.

Recent developments in numerically exact quantum dynamics methods have brought the dream of calculating the dynamics of chemically complex open systems closer to reality. Path-integral-based methods, hierarchical equations of motion (HEOM) and quantum analog simulators all require the spectral density of the environment to describe its effect on the system. Here we find that the rate of slow population relaxation is sensitive to the precise functional form used to describe the spectral density peaks. This finding highlights yet another challenge to obtaining accurate spectral densities. In the context of quantum information science, we give a simple recipe to adjust the results of analog simulation for this difference assuming both the simulator and the target spectral densities are known.

Intermolecular charge-transfer is a highly important process in biology and energy-conversion applications where generated charges need to be transported over several moieties. However, its theoretical description is challenging since the high accuracy required to describe these excited states must be accessible for calculations on large molecular systems. In this benchmark study, we identify reliable low-scaling computational methods for this task. Our reference results were obtained from highly accurate wavefunction calculations that restrict the size of the benchmark systems. However, the density-functional theory based methods that we identify as accurate can be applied to much larger systems. Since targeting charge-transfer states requires the unambiguous classification of an excited state, we first analyze several charge-transfer descriptors for their reliability concerning intermolecular charge-transfer and single out DCT as an optimal choice for our purposes. In general, best results are obtained for orbital-optimized methods - and among those, IMOM proved to be the most numerically stable variant - but optimally-tuned range-separated hybrid functionals combined with rather small basis sets proved to yield surprisingly good results. This makes these fast calculations attractive for high-throughput screening applications.

The Low-Gain Avalanche Diode (LGAD) is a new silicon detector and holds wide application prospects in particle physics experiments due to its excellent timing resolution. The LGAD with a pixel size of 1.3 mm $\times$ 1.3 mm was used to construct a High Granularity Timing Detector (HGTD) in ATLAS experiments to solve the pile-up problem. Meanwhile, the Circular Electron Positron Collider (CEPC) also proposes detectors using the LGAD. However, pixel LGAD exhibits higher readout electronics density and cost, which somewhat limits the application of LGADs. To decrease the readout electronics density, the Institute of High Energy Physics (IHEP) of the Chinese Academy of Sciences has designed strip LGADs with larger areas. These strip LGADs are all 19 mm in length but with different widths of 1.0 mm, 0.5 mm, and 0.3 mm. This article provides a detailed introduction to the design parameters of these strip LGADs and tests their electrical characteristics, including leakage current, break-down voltage, depletion capacitance, etc. The timing resolution and signal-to-noise ratio of the three strip LGAD sensors were investigated using a beta source test system. The position resolution parallel to the strip direction was tested and analyzed for the first time using a pico-second laser test system. Tests have demonstrated that the timing resolution of strip LGADs can reach about 37.5 ps, and position resolution parallel to the strip direction is better than 1 mm.

This study evaluates the precision of widely recognized quantum chemical methodologies, CCSD(T), DLPNO-CCSD(T) and localized ph-AFQMC, for determining the thermochemistry of main group elements. DLPNO-CCSD(T) and localized ph-AFQMC, which offer greater scalability compared to canonical CCSD(T), have emerged over the last decade as pivotal in producing precise benchmark chemical data. Our investigation includes closed-shell, neutral molecules, focusing on their heat of formation and atomization energy sourced from four specific small molecule datasets. Firstly, we selected molecules from the G2 and G3 datasets, noted for their reliable experimental heat of formation data. Additionally, we incorporate molecules from the W4-11 and W4-17 sets, which provide high-level theoretical reference values for atomization energy at 0 K. Our findings reveal that both DLPNO-CCSD(T) and ph-AFQMC methods are capable of achieving a root-mean-square deviation (RMSD) of less than 1 kcal/mol across the combined dataset, aligning with the threshold for chemical accuracy. Moreover, we make efforts to confine the maximum deviations within 2 kcal/mol, a degree of precision that significantly broadens the applicability of these methods in fields such as biology and materials science.

We present theoretical and experimental evidence of high-gain far-detuned nonlinear frequency conversion, extending towards both the visible and the mid-infrared, in a few-mode graded-index silica fiber pumped at 1.064 $\mu$m, and more specifically achieving gains of hundreds of dB per meter below 0.65 $\mu$m and beyond 3.5 $\mu$m. Our findings highlight the potential of graded-index fibers in terms of strong modal confinement over an ultrabroad spectral range for enabling high-gain wavelength conversion. Such advancements require an accurate interpretation of intramodal and intermodal four-wave mixing processes.

The reconstruction of photon conversions is importantin order to improve the reconstruction efficiency of the physics measurements involving photons. However, there are significant number of conversions in which only one of the two tracks emitted electrons is reconstructed in the detector due to very asymmetric energy sharing between the electron-positron pair. The momentum determination of the parent photon can be improved by estimating the missing energy in such conversions. In this study, we propose a simple statistical method that can be used to determine the mean value of the missing energy. By using simulated minimum bias events at LHC conditions and a toy detector simulation, the performance of the method is tested for several decay channels commonly used in particle physics analyses. A considerable improvement in the mass reconstruction precision is obtained when reconstructing particles decaying to photons whose energies are less than 20 GeV.

Ground-based laser interferometric gravitational wave detectors consist of complex multiple optical cavity systems. An arm-length stabilization (ALS) system has played an important role in bringing such complex detector into operational state and enhance the duty cycle. The sensitivity of these detectors can be improved if the thermal noise of their test mass mirror coatings is reduced. Crystalline AlGaAs coatings are a promising candidate for this. However, traditional ALS system with frequency-doubled 532 nm light is no longer an option with AlGaAs coatings due to the narrow bandgap of GaAs, thus alternative locking schemes must be developed. In this letter, we describe an experimental demonstration of a novel ALS scheme which is compatible with AlGaAs coatings. This ALS scheme will enable the use of AlGaAs coatings and contribute to improved sensitivity of future detectors.

Neural network interatomic potentials (NNPs) have recently proven to be powerful tools to accurately model complex molecular systems while bypassing the high numerical cost of ab-initio molecular dynamics simulations. In recent years, numerous advances in model architectures as well as the development of hybrid models combining machine-learning (ML) with more traditional, physically-motivated, force-field interactions have considerably increased the design space of ML potentials. In this paper, we present FeNNol, a new library for building, training and running force-field-enhanced neural network potentials. It provides a flexible and modular system for building hybrid models, allowing to easily combine state-of-the-art embeddings with ML-parameterized physical interaction terms without the need for explicit programming. Furthermore, FeNNol leverages the automatic differentiation and just-in-time compilation features of the Jax Python library to enable fast evaluation of NNPs, shrinking the performance gap between ML potentials and standard force-fields. This is demonstrated with the popular ANI-2x model reaching simulation speeds nearly on par with the AMOEBA polarizable force-field on commodity GPUs (GPU=Graphics processing unit). We hope that FeNNol will facilitate the development and application of new hybrid NNP architectures for a wide range of molecular simulation problems.

Scheduled maintenance is likely to be lengthy and therefore consequential for the economics of fusion power plants. The maintenance strategy that maximizes the economic value of a plant depends on internal factors such as the cost and durability of the replaceable components, the frequency and duration of the maintenance blocks, and the external factors of the electricity system in which the plant operates. This paper examines the value of fusion power plants with various maintenance properties in a decarbonized United States Eastern Interconnection circa 2050. Seasonal variations in electricity supply and demand mean that certain times of year, particularly spring to early summer, are best for scheduled maintenance. Seasonality has two important consequences. First, the value of a plant can be 15% higher than what one would naively expect if value were directly proportional to its availability. Second, in some cases, replacing fractions of a component in shorter maintenance blocks spread over multiple years is better than replacing it all at once during a longer outage, even through the overall availability of the plant is lower in the former scenario.

Time-dependent Hartree-Fock (TDHF) is one of the fundamental post-Hartree-Fock (HF) methods to describe excited states. In its Tamm-Dancoff form, equivalent to Configuration Interaction Singles, it is still widely used and particularly applicable to big molecules where more accurate methods may be unfeasibly expensive. However, it is rarely implemented in real space, mostly because of the expensive nature of the exact-exchange potential in real space. Compared to widely used Gaussian-type orbitals (GTO) basis sets, real space often offers easier implementation of equations and more systematic convergence of Rydberg states, as well as favorable scaling, effective domain parallelization, flexible boundary conditions, and ability to treat model systems. We implemented TDHF in the Octopus real-space code as a step toward linear-response hybrid time-dependent density-functional theory (TDDFT), other post-HF methods, and ensemble density-functional theory methods involving exact exchange. Calculation of HF's non-local exact exchange is very expensive in real space. We overcome this limitation with Octopus' implementation of Adaptively Compressed Exchange (ACE), and find the appropriate mixing and starting point to complete the ground-state calculation in a practical amount of time, to enable TDHF. We compared our results to those from GTOs on a set of small molecules and confirmed close agreement of results, though with larger deviations than in the case of semi-local TDDFT. We find that convergence of TDHF demands a finer real-space grid than semi-local TDDFT. We also present the subtleties in benchmarking a real-space calculation against GTOs, relating to Rydberg and vacuum states.

We propose a theoretical scheme for a non-Hermitian atomic grating within an ultra-cold rubidium-87 ($^{87}Rb$) atomic ensemble. The grating's diffraction properties depend on the polarization states of incident photons and are controlled non-locally through Rydberg interactions. Multiple types of polarization-dependent diffraction modes are generated, benefiting from no crosstalk atomic transition channels based on transition selection rules. Those polarization-dependent diffraction modes can be switched using dynamic optical pulse trains, exploiting the Rydberg blockade effect, and are tunable by non-Hermitian optical modulation. Our work will advance the application of asymmetric optical scattering by utilizing the polarization degree of freedom within continuous media and benefit the application of versatile non-Hermitian/asymmetric optical devices.

The detection of individual photons at cryogenic temperatures is of interest to many experiments searching for physics beyond the Standard Model. Silicon photomultipliers are often deployed in liquid argon or liquid xenon to detect scintillation light by either directly detecting the vacuum ultra-violet scintillation or by detecting light from fluorescent compounds that are used to shift the wavelength. Here we present results from an experimental setup that measures the photon detection efficiencies of silicon photomultipliers, sensitive to the visible spectrum, at liquid nitrogen temperature, 77 K. Results from a KETEK PM3325-WB-D0 and a Hamamatsu S13360-3050CS silicon photomultiplier exhibit a decrease in photon detection efficiency greater than 20% at liquid nitrogen temperature relative to room temperature for 562.5 nm light.

Experiments violating Bell's inequality appear to indicate deterministic models do not correspond to a realistic theory of quantum mechanics. The theory of pilot waves seemingly overcomes this hurdle via nonlocality and statistical dependence, however it necessitates the existence of "ghost waves". This manuscript develops a deterministic dynamical system with local interactions. The aggregate behavior of the trajectories are reminiscent of a quantum particle evolving under the Schr\"{o}dinger equation and reminiscent of Feynman's path integral interpretation in three canonical examples: motion in free space, double slit diffraction, and superluminal barrier traversal. Moreover, the system bifurcates into various dynamical regimes including a classical limit. These results illustrate a deterministic alternative to probabilistic interpretations and aims to shed light on the transition from quantum to classical mechanics.

Second-order topological insulators can be characterized by their bulk polarization, which is believed to be intrinsically connected to the center of the Wannier function. In this study, we demonstrate the existence of second-order topological insulators that feature a pair of partially degenerate photonic bands. These arise from the nonsymmorphic glide symmetry in an all-dielectric photonic crystal. The center of the maximally localized Wannier function (MLWF) is consistently located at the origin but is not equivalent with respect to the sum of constituent polarizations. As a result, topological corner modes can be identified by the distinctly hybridized MLWFs that truncate at the sample boundary. Through full-wave numerical simulations paired with microwave experiments, the second-order topology is clearly confirmed and characterized. These topological corner states exhibit notably unique modal symmetries, which are made possible by the inversion of the Wannier bands. Our results provide an alternative approach to explore higher-order topological physics with significant potential for applications in integrated and quantum photonics.

Using molecular simulation and continuum dielectric theory, we consider how electrochemical kinetics are modulated as a function of twist angle in bilayer graphene electrodes. By establishing an effective connection between twist angle and the screening length of charge carriers within the electrode, we investigate how tunable metallicity can result in modified statistics of the electron transfer energy gap. Constant potential molecular simulations show that the activation free energy for electron transfer is an increasing function of the screening length, or decreasing function the density of states at the Fermi energy in the electrode, and subsequently a non-monotonic function of twist angle. We find the twist angle alters the density of states, which tunes the number of thermally-accessible channels for electron transfer, as well as the reorganization energy by altering the stability of the vertically excited state through attenuated image charge interactions. Understanding these effects allows us to cast the Marcus rate of interfacial electron transfer as a function of twist angle, in a manner consistent with a growing body of experimental observations.

Computational electromagnetics (CEM) is employed to numerically solve Maxwell's equations, and it has very important and practical applications across a broad range of disciplines, including biomedical engineering, nanophotonics, wireless communications, and electrodynamics. The main limitation of existing CEM methods is that they are computationally demanding. Our work introduces a leap forward in scientific computing and CEM by proposing an original solution of Maxwell's equations that is grounded on graph neural networks (GNNs) and enables the high-performance numerical resolution of these fundamental mathematical expressions. Specifically, we demonstrate that the update equations derived by discretizing Maxwell's partial differential equations can be innately expressed as a two-layer GNN with static and pre-determined edge weights. Given this intuition, a straightforward way to numerically solve Maxwell's equations entails simple message passing between such a GNN's nodes, yielding a significant computational time gain, while preserving the same accuracy as conventional transient CEM methods. Ultimately, our work supports the efficient and precise emulation of electromagnetic wave propagation with GNNs, and more importantly, we anticipate that applying a similar treatment to systems of partial differential equations arising in other scientific disciplines, e.g., computational fluid dynamics, can benefit computational sciences

Quantification of chaos is a challenging issue in complex dynamical systems. In this paper, we discuss the chaotic properties of generalized Lotka-Volterra and May-Leonard models of biodiversity, via the Hamming distance density. We identified chaotic behavior for different scenarios via the specific features of the Hamming distance and the method of q-exponential fitting. We also investigated the spatial autocorrelation length to find the corresponding characteristic length in terms of the number of species in each system. In particular, the results concerning the characteristic length are in good accordance with the study of the chaotic behavior implemented in this work.

Scientists conduct large-scale simulations to compute derived quantities-of-interest (QoI) from primary data. Often, QoI are linked to specific features, regions, or time intervals, such that data can be adaptively reduced without compromising the integrity of QoI. For many spatiotemporal applications, these QoI are binary in nature and represent presence or absence of a physical phenomenon. We present a pipelined compression approach that first uses neural-network-based techniques to derive regions where QoI are highly likely to be present. Then, we employ a Guaranteed Autoencoder (GAE) to compress data with differential error bounds. GAE uses QoI information to apply low-error compression to only these regions. This results in overall high compression ratios while still achieving downstream goals of simulation or data collections. Experimental results are presented for climate data generated from the E3SM Simulation model for downstream quantities such as tropical cyclone and atmospheric river detection and tracking. These results show that our approach is superior to comparable methods in the literature.

The Kerr nonlinearity allows for exact analytic soliton solutions in 1+1D. While nothing excludes that these solitons form in naturally-occurring real-world 3D settings as solitary walls or stripes, their observation has previously been considered unfeasible because of the strong transverse instability intrinsic to the extended nonlinear perturbation. We report the observation of solitons that are fully compatible with the 1+1D Kerr paradigm limit hosted in a 2+1D system. The waves are stripe spatial solitons in bulk copper doped potassium-lithium-tantalate-niobate (KLTN) supported by the unsaturated photorefractive screening nonlinearity. The parameters of the stripe solitons fit well, in the whole existence domain, with the 1+1D existence curve that we derive for the first time in closed form starting from the saturable model of propagation. Transverse instability, that accompanies the solitons embedded in the 3D system, is found to have a gain length much longer than the crystal. Findings establish our system as a versatile platform for investigating exact soliton solutions in bulk settings and in exploring the role of dimensionality at the transition from integrable to non-integrable regimes of propagation.

Interplanetary links (IPL) serve as crucial enablers for space exploration, facilitating secure and adaptable space missions. An integrated IPL with inter-satellite communication (IP-ISL) establishes a unified deep space network, expanding coverage and reducing atmospheric losses. The challenges, including irregularities in charged density, hardware impairments, and hidden celestial body brightness are analyzed with a reflectarray-based IP-ISL between Earth and Moon orbiters. It is observed that $10^{-8}$ order severe hardware impairments with intense solar plasma density drops an ideal system's spectral efficiency (SE) from $\sim\!38~\textrm{(bit/s)/Hz}$ down to $0~\textrm{(bit/s)/Hz}$. An ideal full angle of arrival fluctuation recovery with full steering range achieves $\sim\!20~\textrm{(bit/s)/Hz}$ gain and a limited beamsteering with a numerical reflectarray design achieves at least $\sim\!1~\textrm{(bit/s)/Hz}$ gain in severe hardware impairment cases.

Liquid argon is an excellent medium for detecting particles, given its yields and transport properties of light and charge. The technology of liquid argon time projection chambers has reached its full maturity after four decades of continuous developments and is, or will be, used in world class experiments for neutrino and dark matter searches. The collection of ionization charge in these detectors allows to perform a complete tridimensional reconstruction of the tracks of charged particles, calorimetric measurements, particle identification. This work proposes a novel approach to the problem of charge recombination in liquid argon which moves from a microscopic model and is applied to the cases of low energy electrons, alpha particles and nuclear recoils. The model is able to describe precisely several sets of experimental data available in the literature, over wide ranges of electric field strengths and kinetic energies and can be easily extended to other particles.

Astronomical (or Milankovi\'c) forcing of the Earth system is key to understanding rhythmic climate change on time scales >~ 10 kyr. Paleoceanographic and paleoclimatological applications concerned with past astronomical forcing rely on astronomical calculations (solutions), which represent the backbone of cyclostratigraphy and astrochronology. Here we present state-of-the-art astronomical solutions over the past 3.5 Gyr. Our goal is to provide tuning targets and templates for interpreting deep-time cyclostratigraphic records and designing external forcing functions in climate models. Our approach yields internally consistent orbital and precession-tilt solutions, including fundamental solar system frequencies, orbital eccentricity and inclination, lunar distance, luni-solar precession rate, Earth's obliquity, and climatic precession. Contrary to expectations, we find that the long eccentricity cycle (previously assumed stable and labeled ''metronome'', recent period ~405 kyr), can become unstable on long time scales. Our results reveal episodes during which the long eccentricity cycle is very weak or absent and Earth's orbital eccentricity and climate-forcing spectrum are unrecognizable compared to the recent past. For the ratio of eccentricity-to-inclination amplitude modulation (frequently observable in paleorecords) we find a wide distribution around the recent 2:1 ratio, i.e., the system is not restricted to a 2:1 or 1:1 resonance state. Our computations show that Earth's obliquity was lower and its amplitude (variation around the mean) significantly reduced in the past. We therefore predict weaker climate forcing at obliquity frequencies in deep time and a trend toward reduced obliquity power with age in stratigraphic records. For deep-time stratigraphic and modeling applications, the orbital parameters of our 3.5-Gyr integrations are made available at 400-year resolution.

This study introduces a systematic framework to compare the efficacy of Large Language Models (LLMs) for fine-tuning across various cheminformatics tasks. Employing a uniform training methodology, we assessed three well-known models-RoBERTa, BART, and LLaMA-on their ability to predict molecular properties using the Simplified Molecular Input Line Entry System (SMILES) as a universal molecular representation format. Our comparative analysis involved pre-training 18 configurations of these models, with varying parameter sizes and dataset scales, followed by fine-tuning them on six benchmarking tasks from DeepChem. We maintained consistent training environments across models to ensure reliable comparisons. This approach allowed us to assess the influence of model type, size, and training dataset size on model performance. Specifically, we found that LLaMA-based models generally offered the lowest validation loss, suggesting their superior adaptability across tasks and scales. However, we observed that absolute validation loss is not a definitive indicator of model performance - contradicts previous research - at least for fine-tuning tasks: instead, model size plays a crucial role. Through rigorous replication and validation, involving multiple training and fine-tuning cycles, our study not only delineates the strengths and limitations of each model type but also provides a robust methodology for selecting the most suitable LLM for specific cheminformatics applications. This research underscores the importance of considering model architecture and dataset characteristics in deploying AI for molecular property prediction, paving the way for more informed and effective utilization of AI in drug discovery and related fields.

Space Weather is the study of the conditions in the solar wind that can affect life on the surface of the Earth, particularly the increasingly technologically sophisticated devices that are part of modern life. Solar radio observations are relevant to such phenomena because they generally originate as events in the solar atmosphere, including flares, coronal mass ejections and shocks, that produce electromagnetic and particle radiations that impact the Earth. Low frequency solar radio emission arises in the solar atmosphere at the levels where these events occur: we can use frequency as a direct measure of density, and an indirect measure of height, in the atmosphere. The main radio burst types are described and illustrated using data from the Green Bank Solar Radio Burst Spectrometer, and their potential use as diagnostics of Space Weather is discussed.

There is a growing interest in reconstructing the density matrix of photoionized electrons, in particular in complex systems where decoherence can be introduced either by a partial measurement of the system or through coupling with a stochastic environment. To this, end, several methods to reconstruct the density matrix, quantum state tomography protocols, have been developed and tested on photoelectrons ejected from noble gases following absorption of XUV photons from attosecond pulses. It remains a challenge to obtain model-free, single scan protocols that can reconstruct the density matrix with high fidelities. Current methods require extensive measurements or involve complex fitting of the signal. Faithful single-scan reconstructions would be of great help to increase the number of systems that can be studied. We propose a new and more efficient protocol - rainbow-KRAKEN - that is able to reconstruct the continuous variable density matrix of a photoelectron in a single time delay scan. It is based on measuring the coherences of a photoelectron created by absorption of an XUV pulse using a broadband IR probe that is scanned in time and a narrowband IR reference that is temporally fixed to the XUV pulse. We illustrate its performance for a Fano resonance in He as well as mixed states in Ar arising from spin-orbit splitting. We show that the protocol results in excellent fidelities and near-perfect estimation of the purity.

Large language models (LLMs) have shown remarkable potential in various domains, but they often lack the ability to access and reason over domain-specific knowledge and tools. In this paper, we introduced CACTUS (Chemistry Agent Connecting Tool-Usage to Science), an LLM-based agent that integrates cheminformatics tools to enable advanced reasoning and problem-solving in chemistry and molecular discovery. We evaluate the performance of CACTUS using a diverse set of open-source LLMs, including Gemma-7b, Falcon-7b, MPT-7b, Llama2-7b, and Mistral-7b, on a benchmark of thousands of chemistry questions. Our results demonstrate that CACTUS significantly outperforms baseline LLMs, with the Gemma-7b and Mistral-7b models achieving the highest accuracy regardless of the prompting strategy used. Moreover, we explore the impact of domain-specific prompting and hardware configurations on model performance, highlighting the importance of prompt engineering and the potential for deploying smaller models on consumer-grade hardware without significant loss in accuracy. By combining the cognitive capabilities of open-source LLMs with domain-specific tools, CACTUS can assist researchers in tasks such as molecular property prediction, similarity searching, and drug-likeness assessment. Furthermore, CACTUS represents a significant milestone in the field of cheminformatics, offering an adaptable tool for researchers engaged in chemistry and molecular discovery. By integrating the strengths of open-source LLMs with domain-specific tools, CACTUS has the potential to accelerate scientific advancement and unlock new frontiers in the exploration of novel, effective, and safe therapeutic candidates, catalysts, and materials. Moreover, CACTUS's ability to integrate with automated experimentation platforms and make data-driven decisions in real time opens up new possibilities for autonomous discovery.

A fundamental problem associated with the task of network reconstruction from dynamical or behavioral data consists in determining the most appropriate model complexity in a manner that prevents overfitting, and produces an inferred network with a statistically justifiable number of edges. The status quo in this context is based on $L_{1}$ regularization combined with cross-validation. As we demonstrate, besides its high computational cost, this commonplace approach unnecessarily ties the promotion of sparsity with weight "shrinkage". This combination forces a trade-off between the bias introduced by shrinkage and the network sparsity, which often results in substantial overfitting even after cross-validation. In this work, we propose an alternative nonparametric regularization scheme based on hierarchical Bayesian inference and weight quantization, which does not rely on weight shrinkage to promote sparsity. Our approach follows the minimum description length (MDL) principle, and uncovers the weight distribution that allows for the most compression of the data, thus avoiding overfitting without requiring cross-validation. The latter property renders our approach substantially faster to employ, as it requires a single fit to the complete data. As a result, we have a principled and efficient inference scheme that can be used with a large variety of generative models, without requiring the number of edges to be known in advance. We also demonstrate that our scheme yields systematically increased accuracy in the reconstruction of both artificial and empirical networks. We highlight the use of our method with the reconstruction of interaction networks between microbial communities from large-scale abundance samples involving in the order of $10^{4}$ to $10^{5}$ species, and demonstrate how the inferred model can be used to predict the outcome of interventions in the system.

Realism about quantum theory naturally leads to realism about the quantum state of the universe. It leaves open whether it is a pure state represented by a wave function, or an impure one represented by a density matrix. I characterize and elaborate on Density Matrix Realism, the thesis that the universal quantum state is objective but can be impure. To clarify the thesis, I compare it with Wave Function Realism, explain the conditions under which they are empirically equivalent, consider two generalizations of Density Matrix Realism, and answer some frequently asked questions. I end by highlighting an implication for scientific realism.

The stability of perovskite solar cells is closely related to the defects in perovskite crystals, and there are a large number of crystal defects in the perovskite thin films prepared by the solution method, which is not conducive to the commercial production of PSCs. In this study, resveratrol(RES), a green natural antioxidant abundant in knotweed and grape leaves, was introduced into perovskite films to passivate the defect. RES achieves defect passivation by interacting with uncoordinated Pb2+ in perovskite films. The results show that the quality of the perovskite film is significantly improved, and the energy level structure of the device is optimized, and the power conversion efficiency of the device is increased from 21.62% to 23.44%. In addition, RES can hinder the degradation of perovskite structures by O2- and CO2- free radicals, and the device retained 88% of its initial PCE after over 1000 hours in pure oxygen environment. The device retains 91% of the initial PCE after more than 1000 hours at 25{\deg}C and 50+5% relative humidity. This work provides a strategy for the use of natural and environmentally friendly additives to improve the efficiency and stability of devices, and provides an idea for the development of efficient, stable and environmentally friendly PSCs.

The quantum dynamics of a dense and dipole-dipole coupled ensemble of two-level emitters interacting via their environmental thermostat is investigated. The static dipole-dipole interaction strengths are being considered strong enough but smaller than the transition frequency. Therefore, the established thermal equilibrium of ensemble's quantum dynamics is described with respect to the dipole-dipole coupling strengths. We have demonstrated the quantum nature of the spontaneously scattered light field in this process for weaker thermal baths as well as non-negligible dipole-dipole couplings compared to the emitter's transition frequency. Furthermore, the collectively emitted photon intensity suppresses or enhances depending on the environmental thermal baths intensities.

Thin layers can lead to unfavorable meshes in a finite element (FE) analysis. Thin shell approximations (TSAs) avoid this issue by removing the need for a mesh of the thin layer while approximating the physics across the layer by an interface condition. Typically, a TSA requires the mesh of both sides of the TSA interface to be conforming. To alleviate this requirement, we propose to combine mortar methods and TSAs for solving the heat equation. The mortar TSA method's formulation is derived and enables an independent discretization of the subdomains on the two sides of the TSA depending on their accuracy requirements. The method is verified by comparison with a reference FE solution of a thermal model problem of a simplified superconducting accelerator magnet.

The low efficiency of conventional liquefaction technologies based on the Joule-Thomson expansion makes liquid hydrogen currently not attractive enough for large-scale energy-related technologies that are important for the transition to a carbon-neutral society. Magnetocaloric hydrogen liquefaction has great potential to achieve higher efficiency and is therefore a crucial enabler for affordable liquid hydrogen. Cost-effective magnetocaloric materials with large magnetic entropy and adiabatic temperature changes in the temperature range of 77 $\sim$ 20 K under commercially practicable magnetic fields are the foundation for the success of magnetocaloric hydrogen liquefaction. Heavy rare-earth-based magnetocaloric intermetallic compounds generally show excellent magnetocaloric performances, but the heavy rare-earth elements (Gd, Tb, Dy, Ho, Er, and Tm) are highly critical in resources. Yttrium and light rare-earth elements (La, Ce, Pr, and Nd) are relatively abundant, but their alloys generally show less excellent magnetocaloric properties. A dilemma appears: higher performance or lower criticality? In this review, we study how cryogenic temperature influences magnetocaloric performance by first reviewing heavy rare-earth-based intermetallic compounds. Next, we look at light rare-earth-based, "mixed" rare-earth-based, and Gd-based intermetallic compounds with the nature of the phase transition order taken into consideration, and summarize ways to resolve the dilemma.

Mixtures with many components can segregate into coexisting phases, e.g., in biological cells and synthetic materials such as metallic glass. The interactions between components dictate what phases form in equilibrium, but quantifying this relationship has proven difficult. We derive scaling relations for the number of coexisting phases in multicomponent liquids with random interactions and compositions, which we verify numerically. Our results indicate that interactions only need to increase logarithmically with the number of components for the liquid to segregate into many phases. In contrast, a stability analysis of the homogeneous state predicts a power-law scaling. This discrepancy implies an enormous parameter regime where the number of coexisting phases exceeds the number of unstable modes, generalizing the nucleation and growth regime of binary mixtures to many components.

This work explores the hypothesis that subjectively attributed meaning constitutes the phenomenal content of conscious experience. That is, phenomenal content is semantic. This form of subjective meaning manifests as an intrinsic and non-representational character of qualia. Empirically, subjective meaning is ubiquitous in conscious experiences. We point to phenomenological studies that lend evidence to support this. Furthermore, this notion of meaning closely relates to what Frege refers to as "sense", in metaphysics and philosophy of language. It also aligns with Peirce's "interpretant", in semiotics. We discuss how Frege's sense can also be extended to the raw feels of consciousness. Sense and reference both play a role in phenomenal experience. Moreover, within the context of the mind-matter relation, we provide a formalization of subjective meaning associated to one's mental representations. Identifying the precise maps between the physical and mental domains, we argue that syntactic and semantic structures transcend language, and are realized within each of these domains. Formally, meaning is a relational attribute, realized via a map that interprets syntactic structures of a formal system within an appropriate semantic space. The image of this map within the mental domain is what is relevant for experience, and thus comprises the phenomenal content of qualia. We conclude with possible implications this may have for experience-based theories of consciousness.

Understanding pedestrian dynamics is crucial for appropriately designing pedestrian spaces. The pedestrian fundamental diagram (FD), which describes the relationship between pedestrian flow and density within a given space, characterizes these dynamics. Pedestrian FDs are significantly influenced by the flow type, such as uni-directional, bi-directional, and crossing flows. However, to the authors' knowledge, generalized pedestrian FDs that are applicable to various flow types have not been proposed. This may be due to the difficulty of using statistical methods to characterize the flow types. The flow types significantly depend on the angles of pedestrian movement; however, these angles cannot be processed by standard statistics due to their periodicity. In this study, we propose a comprehensive model for pedestrian FDs that can describe the pedestrian dynamics for various flow types by applying Directional Statistics. First, we develop a novel statistic describing the pedestrian flow type solely from pedestrian trajectory data using Directional Statistics. Then, we formulate a comprehensive pedestrian FD model that can be applied to various flow types by incorporating the proposed statistics into a traditional pedestrian FD model. The proposed model was validated using actual pedestrian trajectory data. The results confirmed that the model effectively represents the essential nature of pedestrian dynamics, such as the capacity reduction due to conflict of crossing flows and the capacity improvement due to the lane formation in bi-directional flows.

Tomography of single-particle-resolved detectors is of primary importance for characterizing particle correlations with applications in quantum metrology, quantum simulation and quantum computing. However, it is a non-trivial task in practice due to the unavoidable presence of noise that affects the measurement but does not originate from the detector. In this work, we address this problem for a three-dimensional single-atom-resolved detector where shot-to-shot atom number fluctuations are a central issue to perform a quantum detector tomography. We overcome this difficulty by exploiting the parallel measurement of counting statistics in sub-volumes of the detector, from which we evaluate the effect of shot-to-shot fluctuations and perform a local tomography of the detector. In addition, we illustrate the validity of our method from applying it to Gaussian quantum states with different number statistics. Finally, we show that the response of Micro-Channel Plate detectors is well-described from using a binomial distribution with the detection efficiency as a single parameter.

In this work, we investigate the localization of targets in the presence of multiple scattering. We focus on the often omitted scenario in which measurement data is affected by multiple scattering, and a simpler model is employed in the estimation. We study the impact of such model mismatch by means of the Misspecified Cram\'er-Rao Bound (MCRB). In numerical simulations inspired by tomographic inspection in ultrasound nondestructive testing, the MCRB is shown to correctly describe the estimation variance of localization parameters under misspecification of the wave propagation model. We provide extensive discussion on the utility of the MCRB in the practical task of verifying whether a chosen misspecified model is suitable for localization based on the properties of the maximum likelihood estimator and the nuanced distinction between bias and parameter space differences. Finally, we highlight that careful interpretation is needed whenever employing the classical CRB in the presence of mismatch through numerical examples based on the Born approximation and other simplified propagation models stemming from it.

We present new calculations of atomic data needed to model autoionizing states of Fe XVI. We compare the state energies, radiative and excitation data with a sample of results from previous literature. We find a large scatter of results, the most significant ones in the autoionization rates, which are very sensitive to the configuration interaction and state mixing. We find relatively good agreement between the autoionization rates and the collisional excitation rates calculated with the R-matrix suite of programs and autostructure. The largest model, which includes J-resolved states up to n=10, produces ab-initio wavelengths and intensities of the satellite lines which agree well with solar high-resolution spectra of active regions, with few minor wavelength adjustements. We review previous literature, finding many incorrect identifications, most notably those in the NIST database. We provide several new tentative identifications in the 15-15.7 A range, and several new ones at shorter wavelengths, where previous lines were unidentified. Compared to the previous CHIANTI model, the present one has an increased flux in the 15--15.7 A range at 2 MK of a factor of 1.9, resolving the discrepancies found in the analysis of the Marshall Grazing Incidence X-Ray Spectrometer (MaGIXS) observation. It appears that the satellite lines also resolve the long-standing discrepancy in the intensity of the important Fe XVII 3D line at 15.26 A.

Humans spend over 90% of their time in buildings which account for 40% of anthropogenic greenhouse gas (GHG) emissions, making buildings the leading cause of climate change. To incentivize more sustainable construction, building codes are used to enforce indoor comfort standards and maximum energy use. However, they currently only reward energy efficiency measures such as equipment or envelope upgrades and disregard the actual spatial configuration and usage. Using a new hypergraph model that encodes building floorplan organization and facilitates automatic geometry creation, we demonstrate that space efficiency outperforms envelope upgrades in terms of operational carbon emissions in 72%, 61% and 33% of surveyed buildings in Zurich, New York, and Singapore. Automatically generated floorplans for a case study in Zurich further increase access to daylight by up to 24%, revealing that auto-generated floorplans have the potential to improve the quality of residential spaces in terms of environmental performance and access to daylight.

Analyzing local structures effectively is key to unraveling the origin of many physical phenomena. Unsupervised algorithms offer an effective way of handling systems in which order parameters are unknown or computationally expensive. By combining novel unsupervised algorithm (Pairwise Controlled Manifold Approximation Projection) with atomistic potential descriptors, we distinguish between various chemical environments with minimal computational overhead. In particular, we apply this method to silicon and water systems. The algorithm effectively distinguishes between solid structures and phases of silicon, including solid and liquid phases, and accurately identifies interstitial, monovacancy, and surface atoms in diamond structures. In the case of water, it is capable of identifying an ice nucleus in the liquid phase, demonstrating its applicability in nucleation studies.

Surface diffusion and surface electromigration may lead to a morphological instability of thin solid films and nanowires. In this paper two nonlinear analyzes of a morphological instability are developed for a single-crystal cylindrical nanowire that is subjected to the axial current. These treatments extend the conventional linear stability analyzes without surface electromigration, that manifest a Rayleigh-Plateau instability. A weakly nonlinear analysis is done slightly above the Rayleigh-Plateau (longwave) instability threshold. It results in a one-dimensional Sivashinsky amplitude equation that describes a blow-up of a surface perturbation amplitude in a finite time. This is a signature of a formation of an axisymmetric spike singularity of a cylinder radius, which leads to a wire pinch-off and separation into a disjoint segments. The scaling analysis of the amplitude spike singularity is performed, and the time-and-electric field-dependent dimensions of the spike are characterized. A weakly nonlinear multi-scale analysis is done at the arbitrary distance above a longwave or a shortwave instability threshold. The time-and-electric field-dependent Fourier amplitudes of the major instability modes are derived and characterized.

3-Hydroxypropenal (HOCHCHCHO) is the lower energy tautomer of malonaldehyde which displays a complex rotation-tunneling spectrum. It was detected tentatively toward the solar-type protostar IRAS 16293$-$2422 B with ALMA in the framework of the Protostellar Interferometric Line Survey (PILS). Several transitions, however, had large residuals, preventing not only their detection, but also the excitation temperature of the species from being determined unambiguously. We want to extend the existing rotational line list of 3-hydroxypropenal to shed more light on the recent observational results and to facilitate additional radio astronomical searches for this molecule. We analyzed the rotation-tunneling spectrum of 3-hydroxypropenal in the frequency regions between 150 and 330 GHz and between 400 and 660 GHz. Transitions were searched for in the PILS observations of IRAS 16293$-$2422. Local thermodynamic equilibrium (LTE) models were carried out and compared to the observations to constrain the excitation temperature. Additional transitions were searched for in other ALMA archival data of the same source to confirm the presence of 3-hydroxypropenal. More than 11500 transitions were assigned in the course of our investigation with quantum numbers $2 \le J \le 100$, $K_a \le 59$, and $K_c \le 97$, resulting in a greatly improved set of spectroscopic parameters. The comparison between the LTE models and the observations yields an excitation temperature of 125 K with a column density $N = 1.0 \times 10^{15}$ cm$^{-2}$ for this species. We identified seven additional lines of 3-hydroxypropenal that show a good agreement with the model in the ALMA archive data. The calculated rotation-tunneling spectrum of 3-hydroxypropenal has sufficient accuracy for radio astronomical searches. The detection of 3-hydroxypropenal toward IRAS 16293$-$2422 B is now secure.

Large-scale machines like particle accelerators are usually run by a team of experienced operators. In case of a particle accelerator, these operators possess suitable background knowledge on both accelerator physics and the technology comprising the machine. Due to the complexity of the machine, particular subsystems of the machine are taken care of by experts, who the operators can turn to. In this work the reasoning and action (ReAct) prompting paradigm is used to couple an open-weights large language model (LLM) with a high-level machine control system framework and other tools, e.g. the electronic logbook or machine design documentation. By doing so, a multi-expert retrieval augmented generation (RAG) system is implemented, which assists operators in knowledge retrieval tasks, interacts with the machine directly if needed, or writes high level control system scripts. This consolidation of expert knowledge and machine interaction can simplify and speed up machine operation tasks for both new and experienced human operators.

As spacecraft journey further from Earth with more complex missions, systems of greater autonomy and onboard intelligence are called for. Reducing reliance on human-based mission control becomes increasingly critical if we are to increase our rate of solar-system-wide exploration. Recent work has explored AI-based goal-oriented systems to increase the level of autonomy in mission execution. These systems make use of symbolic reasoning managers to make inferences from the state of a spacecraft and a handcrafted knowledge base, enabling autonomous generation of tasks and re-planning. Such systems have proven to be successful in controlled cases, but they are difficult to implement as they require human-crafted ontological models to allow the spacecraft to understand the world. Reinforcement learning has been applied to train robotic agents to pursue a goal. A new architecture for autonomy is called for. This work explores the application of Large Language Models (LLMs) as the high-level control system of a spacecraft. Using a systems engineering approach, this work presents the design and development of an agentic spacecraft controller by leveraging an LLM as a reasoning engine, to evaluate the utility of such an architecture in achieving higher levels of spacecraft autonomy. A series of deep space mission scenarios simulated within the popular game engine Kerbal Space Program (KSP) are used as case studies to evaluate the implementation against the requirements. It is shown the reasoning and planning abilities of present-day LLMs do not scale well as the complexity of a mission increases, but this can be alleviated with adequate prompting frameworks and strategic selection of the agent's level of authority over the host spacecraft. This research evaluates the potential of LLMs in augmenting autonomous decision-making systems for future robotic space applications.

GROMACS is a widely-used molecular dynamics software package with a focus on performance, portability, and maintainability across a broad range of platforms. Thanks to its early algorithmic redesign and flexible heterogeneous parallelization, GROMACS has successfully harnessed GPU accelerators for more than a decade. With the diversification of accelerator platforms in HPC and no obvious choice for a multi-vendor programming model, the GROMACS project found itself at a crossroads. The performance and portability requirements, and a strong preference for a standards-based solution, motivated our choice to use SYCL on both new HPC GPU platforms: AMD and Intel. Since the GROMACS 2022 release, the SYCL backend has been the primary means to target AMD GPUs in preparation for exascale HPC architectures like LUMI and Frontier. SYCL is a cross-platform, royalty-free, C++17-based standard for programming hardware accelerators. It allows using the same code to target GPUs from all three major vendors with minimal specialization. While SYCL implementations build on native toolchains, performance of such an approach is not immediately evident. Biomolecular simulations have challenging performance characteristics: latency sensitivity, the need for strong scaling, and typical iteration times as short as hundreds of microseconds. Hence, obtaining good performance across the range of problem sizes and scaling regimes is particularly challenging. Here, we share the results of our work on readying GROMACS for AMD GPU platforms using SYCL, and demonstrate performance on Cray EX235a machines with MI250X accelerators. Our findings illustrate that portability is possible without major performance compromises. We provide a detailed analysis of node-level kernel and runtime performance with the aim of sharing best practices with the HPC community on using SYCL as a performance-portable GPU framework.

A formalism of classical mechanics is given for time-dependent many-body states of quantum mechanics, describing both fluid flow and point mass trajectories. The familiar equations of energy, motion, and those of Lagrangian mechanics are obtained. An energy and continuity equation is demonstrated to be equivalent to the real and imaginary parts of the time dependent Schroedinger equation, respectively, where the Schroedinger equation is in density matrix form. For certain stationary states, using Lagrangian mechanics and a Hamiltonian function for quantum mechanics, equations for point-mass trajectories are obtained. For 1-body states and fluid flows, the energy equation and equations of motion are the Bernoulli and Euler equations of fluid mechanics, respectively. Generalizations of the energy and Euler equations are derived to obtain equations that are in the same form as they are in classical mechanics. The fluid flow type is compressible, inviscid, irrotational, with the nonclassical element of local variable mass. Over all space mass is conserved. The variable mass is a necessary condition for the fluid flow to agree with the zero orbital angular momentum for s states of hydrogen. Cross flows are examined, where velocity directions are changed without changing the kinetic energy. For one-electron atoms, the velocity modification gives closed orbits for trajectories, and mass conservation, vortexes, and density stratification for fluid flows. For many body states, Under certain conditions, and by hypotheses, Euler equations of orbital-flows are obtained. One-body Schroedinger equations that are a generalization of the Hartree-Fock equations are also obtained. These equations contain a quantum Coulomb's law, involving the 2-body pair function of reduced density matrix theory that replace the charge densities.

We observe an inverse turbulent-wave cascade, from small to large lengthscales, in a homogeneous 2D Bose gas driven isotropically on a lengthscale much smaller than its size. Starting with an equilibrium condensed gas, at long drive times we observe a nonthermal steady state. At increasing lengthscales, starting from the forcing one, the steady-state momentum distribution features in turn: (i) a power-law spectrum, with an exponent close to the analytical result for a particle cascade in weak-wave turbulence, and (ii) a spectrum intriguingly reminiscent of a nonthermal fixed point associated with universal coarsening in an isolated 2D gas. In further experiments, based on anisotropic driving, we also reveal the qualitative picture of the cascade-formation dynamics.