Fundamental Quantities Research Articles

Beyond i.i.d. in the Resource Theory of Asymmetry: An Information-Spectrum Approach for Quantum Fisher Information.

Energetic coherence is indispensable for various operations, including precise measurement of time and acceleration of quantum manipulations. Since energetic coherence is fragile, it is essential to understand the limits in distillation and dilution to restore damage. The resource theory of asymmetry (RTA) provides a rigorous framework to investigate energetic coherence as a resource to break time-translation symmetry. Recently, in the independent and identically distributed (i.i.d.) regime where identical copies of a state are converted into identical copies of another state, it was shown that the convertibility of energetic coherence is governed by a standard measure of energetic coherence, called the quantum Fisher information (QFI). This fact means that QFI in the theory of energetic coherence takes the place of entropy in thermodynamics and entanglement entropy in entanglement theory. However, distillation and dilution in realistic situations take place in regimes beyond i.i.d., where quantum states often have complex correlations. Unlike entanglement theory, the conversion theory of energetic coherence in pure states in the non-i.i.d. regime has been an open problem. In this Letter, we solve this problem by introducing a new technique: an information-spectrum method for QFI. Two fundamental quantities, coherence cost and distillable coherence, are shown to be equal to the spectral QFI rates for arbitrary sequences of pure states. As a consequence, we find that both entanglement theory and RTA in the non-i.i.d. regime are understood in the information-spectrum method, while they are based on different quantities, i.e., entropy and QFI, respectively.

Integral Turbulent Length and Time Scales of Higher Order Moments

Turbulent length and time scales represent a fundamental quantity for analysing and modelling turbulent flows. Although higher order statistical moments have been conveniently used for decades to describe the mean behaviour of turbulent fluid flow, the definition of the integral turbulent scales seems to be limited to the velocity or its fluctuation itself (i.e. the first moment). Higher order moments are characterized by smaller integral scales and a framework is proposed for estimating autocorrelation functions and integral turbulent length or time scales of higher order moments under the assumption that the probability distribution of the velocity field is Gaussian. The new relations are tested for synthetic turbulence as well as for DNS data of a turbulent plane jet at Reynolds number 10000. The present results in particular suggest that the length or time scales of higher order moments can be markedly smaller than those of the turbulent variable itself, which has implications for statistical uncertainty estimates of higher order moments.

Evolution Operator Can Always Be Separated into the Product of Holonomy and Dynamic Operators.

The geometric phase is a fundamental quantity characterizing the holonomic feature of quantum systems. It is well known that the evolution operator of a quantum system undergoing a cyclic evolution can be simply written as the product of holonomic and dynamical components for the three special cases concerning the Berry phase, adiabatic non-Abelian geometric phase, and nonadiabatic Abelian geometric phase. However, for the most general case concerning the nonadiabatic non-Abelian geometric phase, how to separate the evolution operator into holonomic and dynamical components is a long-standing open problem. In this Letter, we solve this open problem. We show that the evolution operator of a quantum system can always be separated into the product of holonomy and dynamic operators. Based on it, we further derive a matrix representation of this separation formula for cyclic evolution, and give a necessary and sufficient condition for a general evolution being purely holonomic. Our finding is not only of theoretical interest itself, but also of vital importance for the application of quantum holonomy. It unifies the representations of all four types of evolution concerning the adiabatic/nonadiabatic Abelian/non-Abelian geometric phase, and provides a general approach to realizing purely holonomic evolution.

Mathematical-Based CFD Modelling and Simulation of Mushroom Drying in Tray Dryer

In this study, CFD simulations that incorporate the inherent coupling between the moisture content of the mushroom and hot air flow in the tray dryer were performed. Conservation principles were applied to the fundamental quantities of mass, momentum, and heat. The source terms due to the moisture evaporation, the viscous and inertial resistance, and continuous evaporative cooling were determined through experimental results. Experiments were conducted to study and select the drying kinetics model at the optimum drying conditions and moisture sorption isotherm model at 30, 40, and 50°C temperatures. The best model describing the drying kinetics of mushrooms and moisture sorption isotherm model was chosen based on the lowest RMSE values and the highest R 2 value. Midilli et al.’s drying kinetics model and the modified Henderson sorption isotherm model were adopted in CFD modelling. The CFD software ANSYS Fluent was used for the 3D modelling of mushroom drying in a tray dryer. The mass and energy source term equations were added to the ANSYS Fluent software using a user-defined function (UDF). The parameter permeability of medium ( α ) and pressure-jump coefficient ( C 2 ) appearing in the momentum source term were directly introduced in the Fluent setup as cell zone conditions. The simulation results of the moisture removal and drying temperatures were validated against experimental data. Both results are in good agreement with the experimental data, with R 2 values of 0.9906 for moisture contents and 0.926 for drying temperature. Thus, simulation can be an option to study the drying mechanisms and alleviate some drawbacks of doing experiments.

Stress fluctuations and adiabatic speed of sound in liquids: a simple way to estimate it from ab initio simulations

One of the fundamental quantities in dynamics of the liquid state, the adiabatic speed of sound c_s, is extremely difficult to predict from computer simulations, especially in ab initio simulations. Here we derive an expression for the instantaneous correlator of fluctuations of longitudinal component of stress tensor, which contains c_s along with others quantities easy accessible via classical and ab initio computer simulations. We show that the proposed methodology works well in the case of Lennard-Jones and soft-sphere simple fluids, Kr–Ar liquid mixture in connection with simulations with effective pair interactions as well as for liquid Sb, fluid Hg and molten NaCl from ab initio simulations.

The phonon quantum of thermal conductance: Are simulations and measurements estimating the same quantity?

The thermal conductance quantum is a fundamental quantity in quantum transport theory. However, two decades after its first reported measurements and calculations for phonons in suspended nanostructures, reconciling experiments and theory remains elusive. Our massively parallel calculations of phonon transport in micrometer-sized three-dimensional structures suggest that part of the disagreement between theory and experiment stems from the inadequacy of macroscopic concepts to analyze the data. The computed local temperature distribution in the wave ballistic nonequilibrium regime shows that the spatial placement and dimensions of thermometers, heaters, and supporting microbeams in the suspended structures can noticeably affect the thermal conductance's measured values. In addition, diffusive transport assumptions made in the data analysis may result in measured values that considerably differ from the actual thermal conductance of the structure. These results urge for experimental validation of the suitability of diffusive transport assumptions in measuring devices operating at sub-kelvin temperatures.

Multiscale Velocity Gradients in Turbulence

Understanding and predicting turbulent flow phenomena remain a challenge for both theory and applications. The nonlinear and nonlocal character of small-scale turbulence can be comprehensively described in terms of the velocity gradients, which determine fundamental quantities like dissipation, enstrophy, and the small-scale topology of turbulence. The dynamical equation for the velocity gradient succinctly encapsulates the nonlinear physics of turbulence; it offers an intuitive description of a host of turbulence phenomena and enables establishing connections between turbulent dynamics, statistics, and flow structure. The consideration of filtered velocity gradients enriches this view to express the multiscale aspects of nonlinearity and flow structure in a formulation directly applicable to large-eddy simulations. Driven by theoretical advances together with growing computational and experimental capabilities, recent activities in this area have elucidated key aspects of turbulence physics and advanced modeling capabilities.

Mixing times and hitting times for general Markov processes

The hitting and mixing times are two fundamental quantities associated with Markov chains. In Peres and Sousi [PS15] and Oliveira [Oli12], the authors show that the mixing times and “worst-case” hitting times of reversible Markov chains on finite state spaces are equal up to some universal multiplicative constant. We use tools from nonstandard analysis to extend this result to reversible Markov chains on general state spaces that satisfy the strong Feller property. Finally, we show that this asymptotic equivalence can be used to find bounds on the mixing times of a large class of Markov chains used in MCMC, such as typical Gibbs samplers and Metropolis–Hastings chains, even though they usually do not satisfy the strong Feller property.

BCD spectrophotometry for massive stars in transition phases

Context. Stars in transition phases, such as those showing the B[e] phenomenon and luminous blue variables (LBVs), undergo strong, often irregular mass-ejection events. The prediction of these phases in stellar evolution models is therefore extremely difficult, if not impossible. As a result, their effective temperatures, their luminosities, and even their true nature are not fully known. Aims. A suitable procedure to derive the stellar parameters of these types of objects is to use the BCD spectrophotometric classification system, which is based on the analysis of the Balmer discontinuity (BD). The BCD parameters (λ1, D) have the advantage that they are independent of interstellar extinction and circumstellar contributions. Methods. We obtained low-resolution spectra for a sample of 14 stars with the B[e] phenomenon and LBVs. Using the BCD classification system, we derived the stellar and physical parameters. The study was complemented with the information provided by the JHK colour-colour diagram. Results. For each star under investigation, the BCD system gives a complete set of fundamental parameters and related quantities such as luminosity and distance. Among the 14 studied stars, we confirmed the classification of HK Ori, HD 323771, and HD 52721 as pre-main sequence HAeB[e] stars, AS 202 and HD 85567 as FS CMa-type stars, and HD 62623 as sgB[e]. We also classified Hen 3- 847, CD-245721, and HD 53367 as young B[e] stars or FS CMa-type candidates, and HD58647 as a slightly evolved B[e] star. In addition, Hen 3–1398 is an sgB[e] and MWC 877, CPD–59 2854, and LHA 120-S 65 are LBV candidates. The stellar parameters of the latter two LBVs are determined for the first time. We also used the size-luminosity relation to estimate the inner radius of the dust disc around the pre-main sequence and main sequence B[e] stars. Conclusions. Our results emphasise that the BCD system is an important and highly valuable tool with which to derive the stellar parameters and physical properties of B-type stars in transition phases. This method can be combined with near-infrared (NIR) colour-colour diagrams to determine or confirm the evolutionary stage of emission-line stars with dust discs.

A Scalar Product for Computing Fundamental Quantities in Matter

We introduce a systematic way to obtain expressions for computing the amount of fundamental quantities such as helicity and angular momentum contained in static matter, given its charge and magnetization densities. The method is based on a scalar product that we put forward which is invariant under the ten-parameter conformal group in three-dimensional Euclidean space. This group is obtained as the static restriction (frequency ω=0) of the symmetry group of Maxwell equations: The fifteen-parameter conformal group in 3+1 Minkowski spacetime. In an exemplary application, we compute the helicity and angular momentum squared stored in a magnetic Hopfion.

A stochastic evaluation of quantum Fisher information matrix with generic Hamiltonians

Quantum Fisher information matrix (QFIM) is a fundamental quantity in quantum physics, which closely links to diverse fields such as quantum metrology, phase transitions, entanglement witness, and quantum speed limit. It is crucial in quantum parameter estimation, central to the ultimate Cramér-Rao bound. Recently, the evaluation of QFIM using quantum circuit algorithms has been proposed for systems with multiplicative parameters Hamiltonian. However, systems with generic Hamiltonians still lack these proposed schemes. This work introduces a quantum-circuit-based approach for evaluating QFIM with generic Hamiltonians. We present a time-dependent stochastic parameter-shift rule for the derivatives of evolved quantum states, whereby the QFIM can be obtained. The scheme can be executed in universal quantum computers under the family of parameterized gates. In magnetic field estimations, we demonstrate the consistency between the results obtained from the stochastic parameter-shift rule and the exact results, while the results obtained from a standard parameter-shift rule slightly deviate from the exact ones. Our work sheds new light on studying QFIM with generic Hamiltonians using quantum circuit algorithms.

Flavor Symmetry of Hydrogen Atoms Potentially Affecting the Proton Radius Deduced from the Electron-Hydrogen Scattering

Precise knowledge of such fundamental quantity as the proton charge radius rp is extremely important both for the quantum chromodynamics (for quark-gluon structure) and for atomic physics (for atomic hydrogen spectroscopy). Yet the ambiguity in measuring rp persists for over a dozen of years by now—from the time when in 2010 the muonic hydrogen spectroscopy experiment yielded rp ≈ 0.84 fm in contrast to the form factor experiment by the Mainz group that produced rp ≈ 0.88 fm. Important was that this difference corresponded to about seven standard deviations and therefore was inexplicable. In the intervening dozen of years, more experiments of various kinds were performed in this regard. Nevertheless, the controversy remains, which is why several different types of new experiments are being prepared for measuring rp. In one of our previous papers, we pointed out the factor that was never taken into account by the corresponding research community: the flavor symmetry of electronic hydrogen atoms, whose existence was confirmed by four kinds of atomic or molecular experiments and also evidenced by two kinds of astrophysical observations. Specifically, in that paper there was discussed the possible presence of the second flavor of muonic hydrogen atoms (in the corresponding experimental gas) and its effect on the shift of the ground state of muonic hydrogen atoms due to the proton finite size. In the present paper we analyze the effect of the flavor symmetry of electronic hydrogen atoms on the corresponding elastic scattering cross-section and on the proton charge radius rp deduced from the cross-section. As an example, we use our analytical results for reconciling two distinct values of rp obtained in different elastic scattering experiments: 0.88 fm and 0.84 fm (which is by about 4.5% smaller than 0.88 fm). We show that if the ratio of the second flavor of hydrogen atoms to the usual hydrogen atoms in the experimental gas would be about 0.3, then the extraction of rp from the corresponding cross-section would yield by about 4.5% smaller value of rp compared to its true value. We also derive the corresponding general formulas that can be used for interpreting the future electronic and muonic experiments.

Studies of three-and four-body hypernuclei with heavy-ion beams, nuclear emulsions and machine learning

Interests on few-body hypernuclei have been increased by recent results of experiments employing relativistic heavy ion beams. Some of the experiments have revealed that the lifetime of the lightest hypernucleus, hypertriton, is significantly shorter than 263 ps which is expected by considering the hypertriton to be a weakly-bound system. The STAR collaboration has also measured the hypertriton binding energy, and the deduced value is contradicting to its formerly known small binding energy. These measurements have indicated that the fundamental physics quantities of the hypertriton such as its lifetime and binding energy have not been understood, therefore, they have to be measured very precisely. Furthermore, an unprecedented Λnn bound state observed by the HypHI collaboration has to be studied in order to draw a conclusion whether or not such a bound state exists. These three-body hypernuclear states are studied by the heavy-ion beam data in the WASA-FRS experiment and by analysing J-PARC E07 nuclear emulsion data with machine learning.

Tropical Cyclone Rainfall Climatology, Extremes, and Flooding Potential from Remote Sensing and Reanalysis Datasets over the Continental United States

Abstract Tropical cyclones (TCs) are high-impact events responsible for devastating rainfall and freshwater flooding. Quantitative precipitation estimates (QPEs) are thus essential to better understand and assess TC impacts. QPEs based on different observing platforms (e.g., satellites, ground-based radars, and rain gauges), however, may vary substantially and must be systematically compared. The objectives of this study are to 1) compute the TC rainfall climatology, 2) investigate TC rainfall extremes and flooding potential, and 3) compare these fundamental quantities over the continental United States across a set of widely used QPE products. We examine five datasets over an 18-yr period (2002–19). The products include three satellite-based products, CPC morphing technique (CMORPH), Integrated Multi-satellitE Retrievals for GPM (IMERG), and Tropical Rainfall Measuring Mission–Multisatellite Precipitation Analysis (TRMM-TMPA); the ground-radar- and rain-gauge-based NCEP Stage IV; and a state-of-the-art, high-resolution reanalysis (ERA5). TC rainfall is highest along the coastal region, especially in North Carolina, northeast Florida, and in the New Orleans, Louisiana, and Houston, Texas, metropolitan areas. Along the East Coast, TCs can contribute up to 20% of the warm season rainfall and to more than 40% of all daily and 6-hourly extreme rain events. Our analysis shows that Stage IV detects far higher precipitation rates in landfalling TCs, relative to IMERG, CMORPH, TRMM, and ERA5. Satellite- and reanalysis-based QPEs underestimate both the TC rainfall climatology and extreme events, particularly in the coastal region. This uncertainty in QPEs is further reflected in the TC flooding potential measured by the extreme rainfall multiplier (ERM) values, whose single-cell maxima are substantially underestimated and misplaced by the satellite and reanalysis QPEs compared to that using NCEP Stage IV. Significance Statement Tropical cyclones (TCs) can produce extreme rainfall and widespread flooding. Improvements in our preparedness, mitigation, and adaptation efforts rest on a better understanding of TC rainfall and its impact. Assessment of various rainfall products and uncertainties is urgently needed for decision making and other applications. Different quantitative precipitation estimate (QPE) datasets have been developed over the past decades. This study uses 18 years of data to better understand how key properties of TC rainfall, including its contribution to extreme events and flooding potential, are represented across five widely used QPE products. Our analysis shows that satellite-based, and to a lesser extent, reanalysis-based QPEs underestimate TC rainfall properties both in terms of climatology and extreme events compared to ground-radar-gauge-based QPE. The uncertainty increases in the coastal region and is reflected in the estimated TC flooding potential.

Revisiting mass–radius relationships for exoplanet populations: a machine learning insight

ABSTRACT The growing number of exoplanet discoveries and advances in machine learning techniques have opened new avenues for exploring and understanding the characteristics of worlds beyond our Solar system. In this study, we employ efficient machine learning approaches to analyse a data set comprising 762 confirmed exoplanets and eight Solar system planets, aiming to characterize their fundamental quantities. By applying different unsupervised clustering algorithms, we classify the data into two main classes: ‘small’ and ‘giant’ planets, with cut-off values at Rp = 8.13R⊕ and Mp = 52.48M⊕. This classification reveals an intriguing distinction: giant planets have lower densities, suggesting higher H–He mass fractions, while small planets are denser, composed mainly of heavier elements. We apply various regression models to uncover correlations between physical parameters and their predictive power for exoplanet radius. Our analysis highlights that planetary mass, orbital period, and stellar mass play crucial roles in predicting exoplanet radius. Among the models evaluated, the Support Vector Regression consistently outperforms others, demonstrating its promise for obtaining accurate planetary radius estimates. Furthermore, we derive parametric equations using the M5P and Markov Chain Monte Carlo methods. Notably, our study reveals a noteworthy result: small planets exhibit a positive linear mass–radius relation, aligning with previous findings. Conversely, for giant planets, we observe a strong correlation between planetary radius and the mass of their host stars, which might provide intriguing insights into the relationship between giant planet formation and stellar characteristics.

Genomic epidemiology offers high resolution estimates of serial intervals for COVID-19

Serial intervals – the time between symptom onset in infector and infectee – are a fundamental quantity in infectious disease control. However, their estimation requires knowledge of individuals’ exposures, typically obtained through resource-intensive contact tracing efforts. We introduce an alternate framework using virus sequences to inform who infected whom and thereby estimate serial intervals. We apply our technique to SARS-CoV-2 sequences from case clusters in the first two COVID-19 waves in Victoria, Australia. We find that our approach offers high resolution, cluster-specific serial interval estimates that are comparable with those obtained from contact data, despite requiring no knowledge of who infected whom and relying on incompletely-sampled data. Compared to a published serial interval, cluster-specific serial intervals can vary estimates of the effective reproduction number by a factor of 2–3. We find that serial interval estimates in settings such as schools and meat processing/packing plants are shorter than those in healthcare facilities.

Quantifying Internal Stress and Demagnetization Effects for Natural Multidomain Magnetite and Magnetite‐Ilmenite Intergrowths

AbstractDemagnetizing effects and internal stress are difficult to distinguish in natural magnetite samples, but quantitative stress estimates can provide valuable information about microstructure formation, surface oxidation, impacts, tectonic stresses, or interface properties in exsolution structures. Quantifying demagnetizing effects informs about magnetite particle shape, magnetostatic interaction, or anisotropic texture. Here, we establish an improved measurement workflow to separate demagnetizing effects from internal stress for natural magnetite. The method is based on temperature‐dependent hysteresis measurements, and for natural samples require accurate estimates of Curie temperature and temperature‐dependent saturation magnetization to ensure that near‐end‐member magnetite is the dominant magnetic mineral, and to calibrate the temperature‐dependent scaled reversible work (SRW). SRW is the fundamental quantity to determine stress and demagnetizing factor. The improved SRW method is applied to three natural samples with different stress histories where it proves that large magnetite crystals in the metamorphosed Modum complex (Norway) have low internal stress (<100 MPa), while in highly exsolved magnetite‐ilmenite intergrowths from Taberg (Sweden) and Bushveld (South Africa) the magnetite component is highly stressed (>230 MPa). This confirms experimentally that interface strain in complex microstructures due to spinodal decomposition and partial oxidation creates large average internal stress in the magnetite minerals. Because sister specimens have similar internal stress but noticeably (>20%) different demagnetizing factors, textural, and shape anisotropy contribute substantially to SRW in these samples.

Utilising physics-informed neural networks for optimisation of diffusion coefficients in pseudo-binary diffusion couples

ABSTRACT We propose a numerical inverse method based on physics-informed neural networks (PINN) for calculating composition-dependent diffusion coefficients in pseudo-binary (PB) diffusion couples in multicomponent alloys. Traditional methods rely solely on experimental diffusion profiles as design targets, which can lead to unreliable estimates. In contrast, PINN uses a combination of available data and physics-based constraints to obtain optimised design parameters and exact solutions for constrained governing differential equations. The constraints in PINN include governing partial differential equations, initial and boundary conditions, and any other equality/inequality relations obeyed by physical parameters. Our study shows the necessity of experimentally estimated intrinsic diffusion coefficients for the prediction of reliable composition-dependent mobility data. In the absence of such data, different combinations of unknown intrinsic diffusion coefficients can also produce reasonable approximations of diffusion profiles and interdiffusion coefficients while wrongly predicting more fundamental quantities (i.e. intrinsic diffusivities). Our method utilises PINN to simultaneously obtain optimised design parameters (diffusion coefficients) and exact solutions for governing diffusion equations. The implementation of PINN uses experimentally estimated diffusion coefficients obtained by the PB diffusion couple method, in addition to diffusion profiles, as design targets. The method is extended to non-ideal PB diffusion profiles, such as conventional diffusion couples in which all the components develop diffusion profiles, by incorporating additional constraints such as zero composition gradient of certain component(s) and experimentally estimated interdiffusion coefficients at extrema in the composition profiles. PINN is found to be a promising approach for obtaining reliable estimates of diffusion coefficients in multicomponent alloys.

Infoautopoiesis and consciousness

There is a need to demystify the concept of information to understand consciousness from a fundamental perspective. This is possible to do using the explanatory potential of infoautopoiesis or the process of self-production of information. Infoautopoiesis allows a human organism-in-its-environment to uncover the bountifulness of matter and/or energy as expressions of their environmental spatial/temporal motion/change, i.e., as information or Batesonian differences which make a difference. Leading to the realization that self-produced information is not a fundamental quantity of the Universe. Rather, it is internally generated and subsequently externalized information relevant to individuated satisfaction of physiological and/or relational needs of the human organism-in-its-environment. Sensorial percepts play an important role in making the external environment meaningful. Individuated, internal, inaccessible, semantic information is the essence of consciousness, and may be externalized or syntactically shared with others using gestures, pictographs, language, music, figurines, writing. We create and live in an environment surrounded by our syntactic, artificial creations, since self-produced information is the primary element that allows humans their unique existence.

Optical Forces on Multipoles Induced by the Belinfante Spin Momentum

AbstractThe Belinfante spin momentum (BSM) is a fundamental yet enigmatic quantity of electromagnetic fields. It vanishes from the global momentum of the field, but can be detected locally, manifesting itself as an optical force on small particles. Hitherto, however, the BSM force is a concept well established only within the dipole approximation, and there is no explicit experimental evidence for its action on multipoles. Here, a theoretical model for multipolar BSM forces, exerted on generic Mie particles supporting multipoles of arbitrary order, is developed. This multipolar mechanical action is observed experimentally from a structured light field, which suppresses the effect of spin–orbit coupling. It can also selectively enhance the multipolar BSM forces, while restraining the dipolar component on a probe particle (Au sphere). These results constitute a distinct chapter in the physics of high‐order interactions in light–matter systems and can facilitate additional progress in optomechanics, optical manipulation, and Mie‐tronics.