dataesr/scientific-paragraphs-categorization huggingface dataset:
For indoor environments, the RANS k ε -RNG  (Yakhot, Orszag, Thangam, Gatski, & Speziale, 1992)  turbulence model is frequently adopted. In this paper, we then considered this turbulence model, such as implemented in the commercial code Ansys Fluent 15. 0 with a finite volume approach. In the present case, the use of a realizable closure model was also enforced by the presence of a round jet. Comparison between predicted airflows and turbulence parameters with the LDA measurements confirm the relevance of this choice for the present application, as highlighted in the result section. The mesh size was constrained by the deposition formulation for the aerosol described in Section 3. 3, Eq. (  12 ), namely the sizes of the first cells near the wall were chosen so that the non-dimensional cell-wall distances were close to y > 30 +.
7 , p T p B + +2 -2/3 -1 (15) where κ is the Von Kàrmàn constant equal to 0. 41, τ L ( ). In practice, the bulk concentration n L t ( , ) b appearing in Eq. (  12 ) is chosen in the first near-wall cell. The size of this cell is specified as its center follows the constraint y > 30 + , where y + is the dimensionless wall distance, according to the practical recommendation of  Nerisson et al. (2011). The implementation of the complete model into a commercial CFD solver is further detailed in  Guichard et al. (2014a). In the present paper, this model was implemented in the commercial code ANSYS Fluent 15. 0. p.
Conclusion The CFD-QMOM three-dimensional modeling of the turbulent transport of airborne nanoparticles undergoing aggregation has been assessed on a comprehensive experimental dataset. The experiment was designed to provide space-resolved measurements of particles and airflow properties in a ventilated chamber operating in steady-state regime. In a first step, mean velocity, turbulence kinetic energy and mean age of air profiles, obtained from experiments and simulations have been compared to verify the modeled airflow. Then the simulation of the dynamics of two different aerosols has been validated by comparing measured and computed characteristics of the aerosols particle size distributions. The modeling approach was found to provide satisfactory predictions of the particle size distributions for all considered cases, given the experimental uncertainties.
Implementation of the THB1 potential within the LAMMPS package THB1 is an empirical potential developed to model Mg 2 SiO 4 forsterite and its high-pressure polymorphs  (Price and Parker 1987). It includes a long-range electrostatic term described through a classical Coulombic energy term, a short range repulsive term based on a Buckingham form, a harmonic three-body term to model the directionality of Si-O bonding, and a core-shell approximation to include electronic polarizability of ions. For computational efficiency, we implemented the THB1 potential into a new inter-atomic potential library freely available for the LAMMPS package. Our implementation currently relies on the following energy decomposition: U U U U U. 1 Total Coulomb Short Spring THB = + + + ( ) The long-range Coulombic energy is calculated by application of a Wolf summation  (Wolf et al 1999) , which allows calculations on periodic or non-periodic systems.
Acknowledgements This work was supported by funding from the  European Research Council  under the  Seventh Framework Programme  ( FP7) ,  ERC  grant  N°290424-RheoMan. Computational resources have been provided by the  CRI-Université de Lille 1. The authors are grateful for fruitful discussions with  Dr Xiao-Yu Sun  and his help in the implementation of the atomistic-tocontinuum cross over method. SG acknowledges the  MIRACLE  Project at the  University of Basel  funded by the  Werner Siemens Foundation, Zug/Switzerland.
VII. ACKNOWLEDGEMENTS This work has been carried out within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom  research and training programme  2014-2018  under grant agreement No  633053 , from the  RCUK Energy Programme  [grant number  EP/I501045 ], and from the  Engineering and Physical Sciences Research Council  under grant numbers  EP/L027682/1  and  EP/P002188/1. Computing facilities were provided by the  Scientific Computing Research Technology Platform of the University of Warwick  and the  EUROfusion Marconi supercomputer facility. The views and opinions expressed herein do not necessarily reflect those of the  European Commission.
There exist many iterative methods for computing the maximum likelihood estimator but most of them suffer from one or several drawbacks such as the need to inverse a Hessian matrix and the need to find good initial approximations of the parameters that are unknown in practice. In this paper, we present an estimation method without matrix inversion based on a linear approximation of the likelihood equations in a neighborhood of the constrained maximum likelihood estimator. We obtain closed-form approximations of solutions and standard errors. Then, we propose an iterative algorithm which cycles through the components of the vector parameter and updates one component at a time. The initial solution, which is necessary to start the iterative procedure, is automated. The proposed algorithm is compared to some of the best iterative optimization algorithms available on R and MATLAB software through a simulation study and applied to the statistical analysis of a road safety measure.
In addition to this, it must be noted that obtaining explicit expressions of standard errors is not generally easy. In this paper, we present an estimation method without matrix inversion based on a linear approximation of the likelihood equations in a neighborhood of the constrained maximum likelihood estimator. We obtain closed-form approximations of solutions and standard errors. Then, we propose a partial linear approximation (PLA) algorithm which cycles through the components of the vector parameter and updates one component at a time. The initial solution is automated and standard errors are obtained in a closed-form. The PLA is compared to some of the best available algorithms on R and MATLAB software through a simulation study and applied to real crash data. The remainder of the paper is organized as follows.
The accidents data for the control area over the same period is given by the non-random vector ( ) T 1 , , r Z z z =  where j z denotes the ratio of the number of accidents of type j registered in the period after to the number of accidents of type j registered in the period before. Following N'Guessan et al. [18] , we assume that the vector Y has the multinomial distribution ( )  (  ) , Y M n π φ ∼ where n is the total number of crashes recorded at the experimental site and ( ) 11 1 21 2 , , , , , r r π π π π π =   is a vector of class probabilities whose components are ( ) 1 1 1 if 1; 1, , 1 if 2; 1, ,. 1 j r m m m ij r j m m m r m m m i j r z z i j r z β θ β π φ θβ β θ β = = =  = =  +  =   = =  +  ∑ ∑ ∑   (9) By construction, the parameter vector ( ) ( ) 0, with 1 , 1 r C C φ φ β = = - (10) where ( ) 1 1, ,1 r r = ∈  .
1 stems from the cyclic algorithm of N'Guessan and Geraldo  [20]. The PLA proceeds as follows: step 1 allows to estimate φ alternating between its two components θ et β. To start the procedure, we initialize ( ) 0 θ. Then we compute ( ) ( ) 0 1, , j j r β =  and define ( ) ( ) ( ) ( ) 0 0 0 , φ θ β =. But, we could also initialize ( ) 0 β using the problem's data and get ( ) 0 θ. The process is repeated until the stopping criterion is satisfied. We note that our algorithm is automated and can be started as soon as the problem's data y D is entered. We can also note that the second partial derivatives of the log-likelihood function are no longer used in our algorithm. The aim of this paper is not to carry out a theoretical study of the convergence of the proposed algorithm. We rather focus on the numerical aspect of this convergence using simulated datasets.
Numerical Results This subsection deals with the numerical convergence of the partial linear approximation algorithm. As usual in the study of iterative algorithms, we analyse the influence of the initial solution The methods selected for comparison are the Newton-Raphson's method, Nelder-Mead's (NM) simplex algorithm  [21] , quasi-Newton BFGS algorithm (from the names of its authors Broyden, Fletcher, Goldfarb and Shanno  [22]  [23] [24]  [25] ), Interior Point (IP) algorithm  [26] , the Lenverberg-Marquardt (LM) algorithm  [27]  [28] and Trust Region (TR) algorithms  [29]. In our work, the BFGS and NM algorithms are implemented using the package developed by Varadhan  [30]. The simulation process was performed on many simulated crash datasets. For By combining the two values of n and the three ways to initialize ( ) 0 β , we get six scenarios and for each one, we performed 1000 replications.
[18]  used to estimate the average effect of a road safety measure and the different accident risks when road conditions are improved. We prove that the assumptions are all satisfied and we obtain simple expressions of the estimators and their asymptotic variance. Afterwards, we give a practical version of our algorithm. The numerical performance of the proposed algorithm is examined through a simulation study and compared to those of classical methods available in R and MATLAB software. The choice of these other methods is dictated not only by the fact that they are relevant and integrated in most statistical software but also by the fact that some need second order derivatives and others do not. The comparisons suggest that not only the partial linear approximation algorithm is competitive in statistical accuracy and computational speed with the best currently available algorithm, but also it is not disturbed by the initial guess.
Sample collection and DNA sequencing We assembled a database of previously published mtDNA control region sequences collected from biopsy samples or sloughed skin from 3009 individuals across 14 sampling sites representing all the known breeding stocks in the Southern Hemisphere and Arabian Sea (Fig. 1 ; no genetic samples were available from BSC4 at the time for inclusion, and so this substock is not included in this analysis). All research undertaken followed local regulations and guidelines. Sample collection, preservation methods, total genomic DNA extraction, amplification, and sequencing of the mtDNA control region are described elsewhere  (Olavarría et al. 2007 , Rosenbaum et al. 2009 , Anderson 2013) , and sample information is detailed in Table  1. Rigorous error-checking procedures were carried out by each respective research group, enabling the integration of these data sets for the subsequent analysis  (Morin et al.
We investigated population structure at 2 different spatial scales. The data were first partitioned according to breeding stock (A, B, C, D, E1, E [comprising E2 and E3], F, G, ASHW) and then into subpopulations within breeding stocks (A, B1, B2, C1, C2, C3, D, E1, E2, E3, F [CI], F [FP], G, and ASHW; Fig. 1 ). The spatial hierarchical structure of the data among breeding stocks and subpopulations was evaluated through an analysis of molecular variance (AMOVA)  (Excoffier et al. 1992 ) in Arlequin v. 3. 5. 1. 2 (Excoffier et al. 2005). Pairwise genetic differentiation between stocks and substocks was calculated using fixation indices based on haplotype frequencies (F ST  ; Weir & Cockerham 1984)  and genetic divergence (Φ ST  ; Excoffier et al. 1992). Statistical significance was evaluated using the null distribution generated from 10 000 non-parametric random permutations of the data at the 0.
e. average number of nucleotide differences per site between randomly chosen sequences); θ: sample-specific theta (genetic diversity accounting for population size and gene flow) estimated by the program MIGRATE. Standard error for H r25 , standard deviation for h and π, and 95% confidence intervals for θ are shown in parentheses. Because the consensus sequence of the combined data set is of a shorter length (311 bp) than each of the contributing data sets (470-474 bp), genetic diversity measures were lower than those reported in  Olavarría et al. (2007) ,  Rosenbaum et al. (2009), and Anderson (2013). ASHW: Arabian Sea humpback whale lation genetic model and therefore offer a complementary approach to fixation indices. The 2 methods were implemented in the ade4 v. 1. 7-3 (Dray & Dufour 2007)  and the adegenet v.
Migration rates between breeding stocks To estimate the degree and directionality of matrilineal gene flow between contiguous breeding stocks, we used the maximum likelihood procedures implemented in MIGRATE software  (Beerli & Felsenstein 2001). MIGRATE provides estimates of M (m/µ) and θ (2N e µ), where M is the mutation-scaled immigration rate, θ is the mutation-scaled population size, m is the immigration rate, µ is the mutation rate, and N e is the effective population size. The product θ M results in the number of im migrants per generation 2N e m. We adopted a maximum likelihood analysis strategy and migration matrix model allowing for asymmetric migration rates and variable θ between breeding stocks and variable sub population sizes (without subsampling). We se lected an inheritance scaler selected for θ of 1.
00. Our data were first tested with default starting values for the population size and M parameters in 20 in dependent replicates of the Markov chain scheme: each replicate employed a static heating scheme and comprised 15 short chains (dememorization: 10 000 genealogies; recorded genealogies: 100; sampling increment: 100) and 3 long chains (dememorization: 10 000 genealogies; recorded genealogies: 1000; sampling increment: 100). We monitored acceptance rates of genealogies (and other parameter updates), effective sample sizes, and autocorrelations between parameters throughout the run. Using as initial parameters the consis-tent resulting values from these 20 initial runs, and employing the same Markov chain scheme, we launched 100 new runs to estimate the degree and directionality of gene flow between our stocks of interest at the breeding stock level.
Nodes are shaded according to sampling location. Size of node corresponds to the frequency of that haplotype among sampled individuals. Internal white nodes represent reconstructed median haplotypes. Gray notches represent nucleotide differences between haplotypes. Haplotype frequencies are available on the Dryad Data Repository (doi:10. 5061/dryad. 8cs4f). Breeding stock and subpopulation abbreviations defined in Table  1  The sPCA investigated the sequence variability using allelic frequencies in a spatial context, which further resolved some of the inconsistencies de scribed for the PCA (Fig. 3 ). The first positive axis had the highest eigenvalue and was therefore retained. Both global and local tests for structure were not found to be statistically significant (simulated p-value: global = 0.
Error bars reflect 95% confidence intervals. Number of immigrants per generation (N e m) is obtained as M i θ i , where i is the individual breeding stock. Breeding stock and subpopulation abbreviations defined in Table  1  scale geographic context required for the interpretation of existing regional and local analyses. All pairwise comparisons between breeding stocks (i. e. BSA-BSG) based on haplotype frequencies (F ST ) and the majority of pairwise nucleotide differences (Φ ST ) were statistically significant (p < 0. 05) (Table  2 ; haplotype frequencies are available on the Dryad Data Repository, doi: 10. 5061/ dryad. 8cs4f), indicating that population structure at this scale is driven by maternally directed site fidelity to breeding areas  (Mackintosh 1942 , Baker & Medrano-Gonzalez 2002 , Baker et al.
2015). It is worth noting that all of the DPSs in the Southern Hemisphere are listed as Not at Risk under the US ESA, possibly due to a lack of resolution at the substock level  (Federal Register 2016). Our results demonstrate that the IWC stocks and subpopulations should represent the minimum number of discrete populations taken into consideration until further research has been conducted. Moreover, uncertainties in stock and subpopulation boundaries, and how these uncertainties affect estimates of recovery, need to be carefully and transparently accounted for in any regulatory review of this species. Data archive. GenBank accession numbers for the haplotypes included in this study are as follows: GQ913691-GQ913852  (Rosenbaum et al. 2009 ), DQ768307-DQ768421  (Olavarría et al. 2007), and MF174847-MF174851 (Anderson 2013).
Acknowledgements. For research conducted in the South Atlantic, and western and northern  Indian Oceans , funding to H. C. R. was provided by numerous individuals and foundations, as well as by The  International Whaling Commission Scientific Committee. We are grateful to the staff of the  American Museum of Natural History's Sackler Institute for Comparative Genomics , especially  George Amato  and  Rob DeSalle. Field  work on BSA in Brazil was sponsored by  Petrobas. In Gabon , the work was supported by the  Agence Nationale des Parcs Nationaux, WCS Gabon , and the staff of  Operation Loango. These  included  Lee White ,  Solange Ngouessonno ,  Richard Parnell ,  Bryan Curran ,  Linda Percy ,  Guy-Philippe Songuet ,  Nick Mbadinga ,  Christian Mbina ,  Francois Horent ,  Mike Fay , and the staff and interns of the  WCS Ocean Giants Program.
Samples from Eastern Australia were collected by  Wally & Trish Franklin  (The Oceania Project),  David Paton  (The  Cape Byron Whale Research Project ) and  Daniel Burns  (The Ballina Whale Research Project). Funding for sample collection and analyses was provided by The Oceania Project,  Southern Cross University  and the  Australian Department of the Environment and Energy (formerly the Department of the Environment and Heritage). Research in Oceania was coordinated and contributed by members of the  South Pacific Whale Research Consortium. Funding was provided by the  National Marine Mammal Laboratory ; the  Australian Department of Water, Heritage and Arts ; and the  International Whaling Commission Scientific Committee. We thank all our collaborators in and out of the field over the many years that this research has taken place, and the  International Fund for Animal Welfare  for their long-term support.
In this work, the SC is performed with the DREAM(ZS) software  [16]  based on the Markov Chain Monte Carlo process (MCMC). DREAM(ZS) computes multiple sub-chains in parallel to thoroughly explore the parameter space. Taking the last 25% of individuals of the MCMC (when the chains have converged) yields multiple sets of parameters that adequately fit the model onto observations. These sets can then be used to estimate the updated parameter distributions and therefore the best estimates of the parameters and the associate confidence intervals.
In this work, the pdf is performed using the DREAM(ZS) software  [16]  based on the Markov Chain Monte Carlo (MCMC) sampler. DREAM(ZS) generates random sequences of parameter sets that asymptotically converge toward the target joint posterior dis-tribution  [27]. Thus, if the number of runs is sufficiently high, the generated samples allows exploring the entire parameter space of the posterior distribution of the parameters and provides the pairwise parameter correlations and the uncertainty of model predictions. The Bayes theorem states that the probability density function of the model parameters conditioned onto data can be expressed as:       | | mes mes p p p  ξ y y ξ ξ , ( 27 ) where    A synthetic pumping test is generated by running the analytical solution using the reference parameter values given in Table  1  for     2 2 ξ ξy h h N mes h h SS p | exp        , (28) 0 f K , 0 f S , 0  , a and b.
CONCLUSION A new analytical solution for interference hydraulic pumping tests in fractal fractured porous media is developed using the dual-porosity concept and power laws in space for all hydrodynamic parameters. The developed analytical solution is compared to a numerical one obtained with the FV method using different time step sizes. Discrepancies are observed between analytical and numerical solutions when the latter is used with large time steps. These discrepancies are more pronounced with dual fractal media than with homogeneous dual media. When the size of the time step decreases, the numerical solution converges toward the analytical one for both homogeneous and fractal dual media. The applicability of the fractal dual-porosity model was then assessed by investigating identifiability of the hydraulic parameters from a synthetic interference pumping test with a set of noisy data using the MCMC sampler.
Acknowledgements This work was partially supported by the  GdR MoMaS (PACEN/CNRS ,  ANDRA ,  BRGM ,  CEA , EDF, IRSN) France and by the  French National Research Agency (ANR)  through the  program AAP Blanc -SIMI 6 project RESAIN  (n• ANR-12-BS06-0010-02 ).
Introduction Modern personal computing is mediated by desktop, mobile, and web applications (apps for short). This model of software has become pervasive since the introduction of the Macintosh and Windows desktop environments. ¹ The app model shows its limitation when confronted with the natural complexity of human activity: while it is common to add, remove or change collaborators, tools, procedures, or working materials in the physical world, these dimensions of flexibility are limited or nonexistent in computer-mediated activity. These limitations are especially evident as software has become ubiquitous in our social and professional lives. In general, apps model procedures, in the sense that they couple what users can do, e. g. , changing color, with what they can do it to, e. g. , text.
Critiquing Software The limitations of apps are inherent to the established model for building interactive software. Apps are made up of compiled programs, executed by operating systems. The structure of an app is embedded in a program that users have no ability to inspect or manipulate. As a result, software is made up of the app, which is static, and Note, however, that the Xerox Star, which spearheaded graphical user-interfaces, was document-based, not app-based. This paper draws heavily on the first author's master's thesis [ ]. documents, which are dynamic. ³ This distinction is somewhat arbitrary: Most apps only let users change the files they work with, but some let users change the cosmetics of the app with stylesheets, or extend the app with new features through plugins. Apps are tightly isolated from the environment in which they are used.
To work on a document stored in a file, an app has to load the file and create an internal representation which it can change. This strictly limits how apps can be combined. Apps can be sequenced, i. e. a file output by one app can be loaded by another (if the formats are compatible), but they cannot concurrently work with the same file. Some apps share content through a remote database, but then bear the burden of maintaining consistency between the database and their internal state. This is more akin to a distributed app than an open environment. The app model is the result of common architectures and software engineering principles that have good properties for engineers and developers, but not for users. From an engineering perspective, the app model is very reasonable: The separation between things that can change and things that cannot prevents users from messing them up, and allows expert designers to maintain a lot of control over how their software is used.