dataesr/scientific-paragraphs-categorization huggingface dataset:
Conversely, shades of red (blue) indicate that 20-year discharges from ERA5 actually correspond to smaller (larger) return periods when used in predictive mode. 90% confidence intervals around central estimates of flood thresholds are used to assess statistically significant differences between flood thresholds stemming from different input datasets, and at different lead times. Hence, assuming a confidence interval of 90%, we show in Fig. 8  the number of subsequent weeks over which ERA5-based 20-year thresholds fall within the distribution of range specific thresholds derived from reforecasts. We found that only in 27% of grid points, are ERA5-based 20-year thresholds statistically consistent with the entire 6week streamflow forecasts. On the other hand, in 44% of grid points ERA5-based 20-year thresholds are not consistent with those derived from reforecasts already from a forecast range of 1 week.
Among these six stations, 2 cases (Dniepr and Murray-Darling) retain low variability of flood thresholds over the entire 6-week range. Two cases (Cauvery and Mobile River) show a significantly rising trend, while two cases (Itapicuru and Congo River) a decreasing trend. An interesting example is that of the Congo River, where thresholds are almost constant through the entire forecast range, though they are significantly smaller than those derived from ERA5. Such differences are due to a number of extreme events simulated by ERA5 in 1986-1996, which are therefore not included in the shorter series derived from the reforecasts dataset,  (i. e. , 1997-2017) , hence causing the type of discontinuity described in Section 2. 3.
Discussion Results of our analyses show a complex spatial pattern of sign and magnitude of the differences between thresholds derived from a reanalysis dataset (i. e. , ERA5) and those derived from a forecast-consistent dataset (i. e. , the reforecasts). Differences do not appear to have clear linear dependence with a number of considered variables, such as return period of the flood thresholds, weekly forecast range, latitude, basin upstream area and mean discharge. Instead the pattern of such differences follows more that of the differences in precipitation statistics (see Fig. 3  and 5 ), hence it is specific of the chosen atmospheric datasets. However, we found a tendency for larger discrepancies between ERA5-and reforecasts-based flood thresholds in smaller and drier river basins, over longer forecast ranges.
Conclusions In this research we have tested a new method to estimate discharge thresholds for medium-range flood forecasting, for potential use in the Global Flood Awareness System. We have provided evidence that flood thresholds for medium-range forecasting can take on significantly different values from thresholds derived from the reference baseline run forced by ERA5 data, used to initialize the forecasts. In addition, thresholds often vary throughout consecutive weekly forecast ranges with a complex pattern of change, mostly driven by differences in the atmospheric datasets used as forcing to the hydrological model. Therefore, we propose a modified framework for the early detection of floods, based on range-dependent flood thresholds, which are able to compensate for forecast drifts in the extreme values, particularly useful over long ranges.
Numerical model A uniform discretisation is considered for space and time intervals. The discretisation parameters are denoted using ∆t ⋆ for the time and ∆x ⋆ for the space. The discrete values of the function u ( x ⋆ , t ⋆ ) are denoted by u n j def := u ( x j , t n ) with j = 1 ,. , N x and n = 1 ,. , N t. It is important to note that the numerical model is built for the dimensionless problem using the Matlab TM environment. The so-called Du Fort-Frankel scheme is used. It is an explicit numerical scheme with an increased stability domain. Interested readers are invited to consult  [18]  for the original work,  [19, 20]  for the results on the stability analysis and  [20, 21]  for further details and its applications for heat and moisture transfer in building porous materials. Since a complete description is provided in  [21] , only the main steps are recalled here.
Numerical Model The algorithm of the lumped RC model is developped in the Matlab TM environment. As performed in many works of literature  [6] [7] [8] , the numerical model is not written in a dimensionless form. The physical dimensional variables are used. As for the previous numerical model, the discrete values of T ( t ) are denoted using T n j def := T j ( t n ) , j ∈ 1 , 2 , 3. The time discretisation parameter is designated by ∆t. The ordinary differential Equation (  7 ) is approximated using an explicit Euler time scheme: T n+1 2 = T n 2 + k c • ℓ 2 • ∆t • T n 3 -2 • T n 2 + T n 1. This choice of time discretisation scheme imposes a stability condition and the choice of the time step ∆t : ∆t 1 2 • ℓ 2 • c k. To compute the temperatures T 1 and T 3 at the boundaries, the following equations are used: h L + k ℓ • T n+1 1 - k ℓ • T n+1 2 = h L • T ∞ , L , h R + k ℓ • T n+1 3 - k ℓ • T n+1 2 = h R • T ∞ , R.
Step 1: generation of experimental observations The observations are generated with a numerical model based on pseudo-spectral approach using the Matlab TM open source toolbox Chebfun  [26]. The model employs the function pde23t of Chebfun to compute a numerical reference solution u ref of the partial derivative equation (3) based on the Chebyshev polynomials representation in space. The reference solution is computed using the real value of the parameter p r. It is directly obtained for the sensor location x obs and the observation time set Ω t. Then, to obtain the K observations u obs , a noise is added to simulate the experimental uncertainties due to the sensor design and location: u obs : Ω x × Ω t × Ω p -→ Ω obs , x ⋆ obs , t ⋆ k , p r -→ u ref x ⋆ obs , t ⋆ k + η ω , where η ∼ N 0 , σ obs is a noise following a Gauß normal distribution with zero mean and standard deviation σ obs.
Metrics for the evaluation of the reliability Several metrics are defined for the evaluation of the reliability of the two models to estimate one unknown parameter among c , k , h L. Since the estimation of the unknown parameter is realized for N s samples of observations, it is possible to compute classical statistical metrics. The expectation Eand the standard deviation σof the random variable y are defined by: E y def := 1 N s N s s = 1 y s , σ 2 y def := E y -E y 2. These metrics can be applied to the ratio p • p r between the estimated and real parameters and to the number of iterations N m or the computational (CPU) time t CPU required for the algorithm to estimate the parameter. The latter is measured using Matlab TM environment on a computer equipped with Intel i7 -6820HQ CPU, 2. 70GHz and 32 GB of RAM.
Acknowledgments The authors acknowledge the  Junior Chair Research program  "Building performance assessment, evaluation and enhancement" from the  University of Savoie Mont Blanc  in collaboration with The  French Atomic and Alternative Energy Center (CEA)  and  Scientific and Technical Center for Buildings (CSTB). The authors also would like to acknowledge  Dr. Celine Labart  ( LAMA UMR 5127 ,  University Savoie Mont Blanc, France ) for her precious discussions on numerical matters.
A code computing consistently the evolution of stars, gas and dust, as well as the energy they radiate, is required to derive reliably the history of galaxies by fitting synthetic spectral energy distributions (SEDs) to multiwavelength observations. The new code Pégase. 3 described in this paper extends to the far-infrared/submillimeter the ultraviolet-to-near-infrared modeling provided by previous versions of Pégase. It first computes the properties of single stellar populations at various metallicities. It then follows the evolution of the stellar light of a galaxy and the abundances of the main metals in the interstellar medium (ISM), assuming some scenario of mass assembly and star formation. It simultaneously calculates the masses of the various grain families, the optical depth of the galaxy and the attenuation of the SED through the diffuse ISM in spiral and spheroidal galaxies, using grids of radiative transfer precomputed with Monte Carlo simulations taking scattering into account.
The code determines the mean radiation field and the temperature probability distribution of stochastically heated individual grains. It then sums up their spectra to yield the overall emission by dust in the diffuse ISM. The nebular emission of the galaxy is also computed, and a simple modeling of the effects of dust on the SED of star-forming regions is implemented. The main outputs are ultraviolet-to-submillimeter SEDs of galaxies from their birth up to 20 Gyr, colors, masses of galactic components, ISM abundances of metallic elements and dust species, supernova rates. The temperatures and spectra of individual grains are also available. The paper discusses several of these outputs for a scenario representative of Milky Way-like spirals. Pégase. 3 is fully documented and its Fortran 95 source files are public.
This problem was solved by  Charlot & Bruzual (1991)  through the method of isochrones, also used since the first version of code Pégase  (Fioc & Rocca-Volmerange 1997). Another solution, based on the fuel consumption theorem (see  Renzini & Buzzoni 1983 , for instance), was implemented by  Maraston (2005). Many other developments occurred meanwhile. To cite a few,  Guiderdoni & Rocca-Volmerange (1987)  modeled the metallicity-dependent attenuation of a galaxy SED by dust grains distributed in a slab;  Worthey (1994)  considered the effect of non-solar abundance ratios of metals on the spectrum of a single stellar population;  Fioc & Rocca-Volmerange (1999b; Pégase. 2) computed consistently the metallicity and the spectral evolution, from the UV to the near-IR, using metallicitydependent stellar spectra, evolutionary tracks and yields;  Devriendt et al.
The variety of the inputs used to compute galaxy models, the wealth of its outputs and its modular structure should make it particularly suited to cosmological simulations. Its source files, written in Fortran 95, are moreover entirely public 1  and may be freely adapted by the user. For technical details, see the documentation  (Fioc & Rocca-Volmerange 2019). Section 2 of this paper first describes the system considered by Pégase. 3, and how the code consistently computes the spectral evolution of a galaxy's stellar component and the chemical evolution of its interstellar medium (ISM) for a given scenario of mass assembly and star formation. We detail in particular the inputs used to model single stellar populations. Section 3 is devoted to the characteristics of grains -their optical properties and size distribution -and to their evolution in the ISM.
The system The system considered in the code is formed of the galaxy proper, of reservoirs furnishing the gas falling onto the galaxy (infall), and of interstellar matter ejected by the galaxy into the intergalactic medium (outflow): so, the galaxy is modeled as an open box, but the total mass of the system, M sys , is constant. The evolution of the galaxy from its initial state is determined by a scenario providing, among others, the parameters from which the star formation, infall and outflow rates are computed as a function of time. All the parameters defining a scenario are de-M. Fioc & B. Rocca-Volmerange: Pégase. 3: spectrochemical evolution of galaxies with dust scribed in detail in the code's documentation  (Fioc & Rocca-Volmerange 2019) ; they are organized in trees for convenience.
For the evolution of the mass of metals, for in-M. Fioc & B. Rocca-Volmerange: Pégase. 3: spectrochemical evolution of galaxies with dust stance, d(M ism Z ism ) dt = -(ψ Z ism )(t) + n res j=1 Ṁin, j (t) Z in, j -( Ṁout Z ism )(t) + t t =0 ψ(t -t ) Ṁssp ej, Z (t , Z[t -t ]) dt , (4) where M ism and Z ism are the mass of matter and the metallicity in the ISM, n res is the number of reservoirs, Ṁin, j and Z in, j are the infall rate from reservoir j and its metallicity, Ṁout is the outflow rate into the intergalactic medium, and Ṁssp ej, Z is the mass ejection rate of metals by an SSP into the ISM. The code computes in the same way the evolution of the ISM abundances of He, C, N, O, Ne, Mg, Si, S, Ca and Fe. These equations assume that stellar ejecta are instantaneously and homogeneously mixed with the ISM once in it, and that the composition of galactic outflows is the same as that of the ISM.
Nebular emission in the dust-free case The modeling of nebular emission implemented in previous versions of Pégase dated back, for most of the emission lines, to  Guiderdoni & Rocca-Volmerange (1987). The intensities of these lines were taken from  Stasińska (1984) , and only UV-tonear-IR lines at solar metallicity were considered. An upgrade was clearly needed. We therefore computed a grid of models of dust-free H ii regions with version c17. 01 of the code Cloudy  (Ferland et al. 2017 ) as a function of the metallicity of the ISM, Z mod , and of the number rate of Lyman continuum (LC) photons emitted by the central ionizing source, N mod. The values considered ranged from ≈ 0 to 0. 1 for Z mod , and from 10 46 to 10 53 s -1 for N mod. The geometry adopted in these computations was radiationbounded, spherical and with an inner cavity of radius r c = 1 pc centered on the ionizing source.
Temperatures and SEDs of grains Pégase. 3 may optionally provide the temperature probability distribution of stochastically heated individual grains and their emission spectrum. As an illustration, Fig. 9  shows these properties for silicate grains with various radii in the diffuse ISM of the Milky Way model at 13 Gyr. This figure highlights that small grains have a large range of temperatures and emit significantly at short wavelengths. For bigger and bigger grains, the temperature distribution narrows around the equilibrium value, and the emission spectrum becomes similar to that of a blackbody peaking in the far-IR. The SEDs of the several species of dust grains -silicates, graphites and PAHs -are plotted in Fig. 10  for the same model. A&A proofs: manuscript no. article_Pegase. 3 While PAH features are prominent in the mid-IR, graphites dominate at longer wavelengths.
Conclusion and prospects This paper presents Pégase. 3, a new version of the code Pégase specifically aimed to model the spectrochemical evolution of galaxies at all redshifts. The most important outputs of the code are synthetic SEDs from the far-UV to the submillimeter, from which colors may be determined in a large number of photometric systems. Because the star formation history and the chemical evolution are computed consistently, this large wavelength range should help to lessen the degeneracy between the parameters (e. g. , stellar mass, current SFR, age, metallicity) derived from fits of model SEDs to galaxy observations. The main improvement, with respect to previous versions of the code, is the extension to the mid-and far-infrared, which required to model the evolution of the dust content and its effects on the light radiated by a galaxy.
3 to all these codes, but, to highlight the specifities of ours, let us just consider a recent one, Cigale  (Boquien et al. 2019, in its last version). The working principle of this code is quite different from that of Pégase. 3, despite similar goals, as, when fitting M. Fioc & B. Rocca-Volmerange: Pégase. 3: spectrochemical evolution of galaxies with dust modeled SEDs to an object's observed spectral data, Cigale employs a powerful Bayesian method to derive the likelihood of the input parameters. However, in this procedure, the various galactic components -stars, gas and dust -are treated independently. For instance, the metallicity of stellar populations is constant, and the star formation history is quite arbitrary since neither the evolution of the ISM nor the history of mass assembly of the galaxy are considered.
The only theoretical constraint is that the energy balance between the absorption of stellar and nebular light by dust and its re-emission must be respected. This datadriven approach provides a lot of suppleness but does not ensure the internal consistency of the models. On the other hand, although Pégase. 3 does not incorporate a fitting procedure of the code's scenarios to observed data 7 , it strives to model simultaneously the evolution of stellar populations and of gas and dust in the ISM, in particular the abundances of various metals and types of grains, as well as the contributions of these components to the SED. Finally, Cigale relies on libraries of single stellar populations computed by either  Bruzual & Charlot (2003)  or  Maraston (2005) ; in contrast to Pégase, users of this code do not, therefore, have full control of these inputs and are restricted to a limited number of initial mass functions.
Codes more sophisticated than Pégase in some respects also exist. For instance, Grasil  (Granato et al. 2000)  takes into account the radiation field at each point in the galaxy and the self-absorption by dust grains. This, however, requires users to run anew Monte Carlo simulations of radiative transfer at all ages, for each modeled galaxy, which seems impractical to simulate the evolution of a large number of objects on cosmological timescales. To fulfill this aim, the choice made in Pégase. 3 was rather to try to strike the right balance between computational efficiency and physical correctness, while still maintaining consistency of the modeling. The code provides a wealth of outputs, besides SEDs and derived quantities (colors, equivalent widths of emission lines, etc. ): among others, the masses in stars, compact stellar remnants, the ISM and dust grains; the abundances of the most important elements; the rates of star formation, ionizing photon emission and supernova explosions, both for core-collapse and type Ia objects.
(2016)  and  Podigachoski et al. (2016)  also used Pégase. 3, in combination with models of AGNs, to disentangle the contributions of an active nucleus and of dust heated by young stars to the mid-and far-IR emission. 7 However, the code Z-Pég of Le Borgne & Rocca-Volmerange (  2002 ) (available at www. iap. fr/pegase/), which currently uses Pégase. 2 templates from  Fioc & Rocca-Volmerange (1999b)  to estimate by χ 2 -minimization both the photometric redshift of an object and the bestfitting model, might easily be updated to process Pégase. 3 outputs. Because of its large spectral coverage, the code provides a nearly "bolometrically"-complete modeling for standard galaxies. This will make it possible to study in a more consistent way the star formation history, the dust attenuation and emission and the chemical evolution in these objects, and to build more-reliable indicators of the recent star formation rate, stellar mass, age, metallicity and dust content.
Acknowledgements. Michel Fioc  is grateful to the  NASA/Goddard Space Flight Center (Greenbelt, Maryland)  for its hospitality during the early phases of this work. In particular, he warmly thanks  Eli Dwek , his supervisor during this stay, for his help in the modeling of dust grains and, much more importantly, for his kindness. Brigitte Rocca-Volmerange  acknowledges financial support from the  CNES-PRSS program  to her works on the analysis with Pégase. 3 of spatial observations of distant radiogalaxies. The authors also thank the referee for insightful questions on the modeling of star-forming regions and nebular emission: they helped to clarify the paper and incited them to significantly improve the code.
In this paper, a modelling investigation of water droplet evaporative freezing was conducted in order to better understand the snowmaking process and hence to optimise the design of the artificial snowmaking device. To this end, mass and heat transfer theoretical models of a single water droplet cooling in an air space were established and implemented in a numerical model developed using the software COMSOL Multiphysics. The effects of the air temperature, relative humidity and velocity and the water droplet initial diameter and temperature on this process were identified and analysed, and their appropriate ranges for the snowmaking were determined.
elachkar@tjcu. edu. cn surfaces by molecular dynamics simulation. The kinetics and time evolution of pure water and brine freezing were studied. The experiments showed that the total freezing time of the brine is longer than that of the pure water. The authors attributed this phenomenon to the influence of the ion clusters on the cluster formation ability. Nelson  [17]  provided a model that was improved by estimating the diffusion field adjacent to the growing ice crystal and using various ice crystal growth data. He experimentally obtained a large amount of data to establish an ice crystal growth prediction model to analyse the ice below -22 • C. Evans  [18]  studied the growth and fragmentation of ice crystals under electric field, especially the growth rate of ice crystals in the direction of electric field under the supersaturation temperature.
Acknowledgments Financial supports from 13th  Five-Year Plan National Key Research & Development Initiative  (N •  2018YFD0401304 ),  Tianjin Natural Science Foundation  (N •  18JCQNJC84600 ),  Foundation of Tianjin Educational Committee  (N • 2017KJ177) and  Program for Innovation Research Team of Tianjin Colleges and Universities  (N •  TD13-5088 ) are gratefully acknowledged.
As a representative example, the link between masculinity and attractiveness in male faces is subject to significant inter-and intra-subject differences  4, 5, 7, 18. An evolutionary explanation is that exaggerated masculinity could be perceived as denoting a lack of some personality facets such as honesty or expressiveness  15. In this context, the so called multiple fitness or multiple motive model  4, 11, 19  proposes that attractiveness varies according to a variety of motives, each one evoking a different abstract attribute of the person whose face is evaluated. On the other hand, an impressive amount of work is committed to the automatic facial beauty rating. This is tackled as a supervised inference problem whose training database is composed of natural facial images codified by vectors of facial coordinates in face-space  3, 20, 21  , along with (inter-subject averaged) numerical ratings assigned to them by human subjects, to be inferred.
By means of a dedicated software, based on image deformation and genetic algorithms, we focus on inter-subject differences in aesthetic criterion and let several subjects sculpt their favorite variation of a reference portrait, parametrized by a vector of geometric facial coordinates. We observe how different subjects tend to systematically sculpt facial vectors in different regions of the face-space, which we call attractors, pointing towards a strong subjectivity in the perception of facial beauty. In addition, the facial vectors sculpted by different subjects exhibit strong correlations for pairs of facial distances, which is a manifestation of the underlying universality and complexity of the cognitive process of facial image discrimination. The correlations contain information regarding the different sources of variability in the dataset of selected vectors.
In a first experiment (E1), we have let S 1 = 95 subjects sculpt their facial variations of reference portrait RP1 (see 1-A). This results in a final population, S 1 = {f (s,n) } S 1 ,N s=1,n=1 of N = 28 facial vectors for each subject. Starting from N initial random facial vectors, the FACEXPLORE software generates pairs of facial images that are presented to the subject, who selects the one that he/she prefers. Based on N left/right choices, a genetic algorithm produces a successive generation of N vectors, in a constant feedback loop of offspring generation and selection operated by the subject. The iteration of this process leads to a sequence of T generations of facial vectors, each one more adapted than the last to the subject's selection criteria, eventually converging to a pseudo-stationary regime in which the populations are similar to themselves and among consecutive generations.