dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, direct numerical simulation (DNS) of the 3D pore-scale model, providing the pore-scale concentration fields, can be performed with another COMSOL Multiphysics code to compute the current delivered by the electrode. This solution, once averaged, may be compared to that of the macroscale problem to validate the upscaled model, as reported in  [1]. Without any restriction, the physico-chemical process is illustrated in the following by considering the reduction of O 2 at the cathode in which bilirubin oxidase (BOD) has been adsorbed at the pore surface. Current production results, at the cathode, from diffusion of O 2 within the open pores of the porous medium and reduction within the BOD catalytic layer, the electron transfer to the pore surface occurring in DET mode. The same modeling can be generalized to any other catalyzed reaction for which electron transfer occurs in DET mode.
1  ) and initial conditions. The mass balance of the reduced enzyme at the solid/fluid interface is written as ∂ c Re ∂t = ( k 1 c + k 2 a ) c t E -k 1 c + k 1 a + k 2 a + k 2 c c O 2 c Re at s f  (5)  supplemented by the initial condition. In the above equations, c O 2 and D O 2 are the oxygen bulk molar concentration and diffusion coefficient in the fluid saturating the electrode, whereas c Re and c t E are the reduced and total surface enzyme concentrations. Note that c t E is constant. Moreover, f and s f denote the fluid domain and the solid-fluid interface, which unit normal directed from the fluid to the solid is n. The total current produced by the electrode is given by I = s f F k 1 a c Re -k 2 c c t E -c Re dS (6) Here, F is Faraday's constant. • Direct numerical simulation The direct numerical simulation of the microscopic model can be performed in a 3D geometry using the software COMSOL Multiphysics.
The macroscopic domain to be considered at this level is now 1D, as schematized in Fig. 3  Eqs. (  7 ) and (  8   c  t E as the reference length, time, oxygen and enzyme surface concentrations, respectively, with the dimensionless variables denoted with the superscript * (see  [1]  for the details). The COMSOL program 1D_DET_comparison. mph to solve the macroscopic problem can be found here https://www. researchgate. net/publication/356, 907, 533 _ Codes _ dedicated _ to _ MethodsX _ paper The reduced enzyme and oxygen average concentration profiles along the electrode can be obtained versus time and compared with DNS results, once numerically averaged using a moving averaging domain having the size of a unit cell, for validation purposes. A comparison is illustrated in Fig. 4  (see  [1]  for details on the parameters used for the simulations), showing excellent agreement.
Parameters identification and prediction of the electrode behavior • Parameters identification Fig. 5. Current versus potential obtained from a 3D DNS and predicted from the solution of the 1D upscaled model. See  [1]  for details on the parameters used for the simulations. The 1D macroscopic model can be first used as a tool to identify electrochemical parameters characteristic of an electrode/reagents system operating in DET mode. To do so, a COMSOL Multiphysics program to solve the macroscopic equations with given parameter values was implemented in a cylindrical system of coordinates, namely 1D_model_Livelink. mph. The output of the program is the total current according to the scanning potential, E, applied to the electrode, whereas the input refers to the reaction rates k 0 , k 2 c , the electron transfer coefficient α, the standard potential of the redox center E 0 , the total enzyme concentration c t E , and the diffusion layer thickness L N.
Note that, due the quasi irreversible character of the oxygen reduction, k 2 a can be reasonably taken equal to zero. The parameters α and E 0 are involved in the electron transfer rate coefficients that can be expressed as k 1 a = k 0 Exp ( n 1 (1 -α) F (E -E 0 ) RT ) and k 1 c = k 0 Exp (-n 1 αF (E -E 0 ) RT ) , R being the ideal gas constant, T the temperature and n 1 the number of exchanged electrons that is known a priori. A Matlab function, namely Comsol_fun_NO_bubbling. m , is used to manage the input and output of the COMSOL Multiphysics program. This function is used in the main Matlab program. This program, named Optimization_NO_bubbling. m , allows the above-mentioned parameters identification by fitting experimental data. As an example, experimental data stored in a text file (see Exp_result_NO_bubbling.
Acknowledgments Financial support from  ANR  through the project  MOMA  (  ANR-17-CE08-0 0 05  ) is gratefully acknowledged. L. Zhang ,  C. Carucci ,  S. Gounel ,  S. Bichon ,  F. Lorenzutti ,  A. Kuhn ,  T. Šafarik  and  N. Mano  are acknowledged for the experimental data used to illustrate the procedures reported here.
Aspen HYSYS is a typical tool used in some Master in Chemical Engineering courses at the University of Castilla-La Mancha like "Analysis and Optimization of Chemical Processes" and "Dynamic of Process: Regulation of Chemical Plants. " Automation is the process of linking commercial software to thirdparties applications built in EXCEL-Visual Basic for Applications. The capability of automation as a powerful tool for simulating unprecedented complex processes suggests that it could be a relevant background complement to the classical one offered by a university. In this sense, a seminar about automation based on the study of two cases: a refrigeration process and a production one taken from literature, has been proposed at the University of Castilla-La Mancha. The analysis of the survey performed for the subject evaluation resulted to be very positive.
| INTRODUCTION Chemical process engineers deal with two types of tasks  [8] : the design of new processes and the analysis of existing ones aiming, for example, to modify and/or optimize them. The use of process simulation software has had a great impact on the chemical engineering (ChE) curricula during the last few years, being a very powerful tool for the design and operation of chemical plants. The most popular commercial process simulators are Aspen Plus, Aspen HYSYS, Unisim, VMGSim, ProMax, ChemCad, and so forth. They are very robust program packages capable of solving complex tasks related to process engineering and give chemical engineers the opportunity to make fast and complex calculations. They offer chemical engineers the opportunity to make fast and complex calculations. However, the use of simulators requires assumptions, they have restrictions, and, eventually, cannot satisfactorily be a replacement for both complex situations not specifically envisaged by them and engineering judgment  [5].
Particularly, Aspen HYSYS is a process simulator widely used at an industrial level, especially for performing the conceptual design, control, optimization, and process monitoring in different stages of any project. Despite all the advantages that simulators provide to process engineers, several requirements and restrictions related to their use can be identified. To make effective use of the simulators, process engineers must know the guidelines and assumptions of the models provided by each simulator  [6]. Thus, it is important to understand in which cases they are useful and how to extract information from them. A solution to the limitations to the analysis of processes, not contemplated by commercial simulators, but that could be performed through interaction with them is to automate the simulation process with third-party software like MS Excel-Visual Basic for Applications (VBA).
The use of the Excel spreadsheet and VBA results is a powerful computational tool for engineering students and practitioners for solving real-life projects in different fields, for instance, hydraulic engineering  [11]. Automation permits programmers to expose objects within a program to be used by other applications. By using an automation client like VBA, embedded inside MS Excel, the end user can write the code to access these objects and interact with Aspen HYSYS. The end user does not need to see the Aspen HYSYS source code or even understand what was required to expose the objects. With a visual basic code, it is possible to send and receive information to and from HYSYS, respectively. This way, end users can introduce algorithms for solving specific problems not considered by simulators and take advantage of their extraordinary capability to estimate physical properties.
Some examples of automation can be found in the literature. A straightforward method for calculating the physical and chemical energies of the material stream was proposed by linking Aspen HYSYS with MS Excel-VBA codes  [1]. VBA tools were also linked with Aspen HYSYS for the safer design of heat exchanger networks  [13]. An interface between the commercial software Aspen HYSYS and MS-Excel VBA was carried out to study the effects of the environmental relative humidity on the performance of the natural gas liquefaction process  [15]. Very recently, two further papers have shown the capability of automation procedures based on using Aspen HYSYS for effectively simulating processes: a transcritical heat-driven compression refrigeration machine with CO 2 as the working fluid  [10] , and a whole green-field saturated gas plant  [2].
Very recently, the values of the binary interaction parameters of the e-NRTL model for different salts in the water included in the Aspen Plus database were refined  [19]. The e-NRTL equation was solved by using a procedure implemented in Aspen Plus by linking the process simulator and the Excel VBA code via automation as the third-party software. The present study reports the pedagogical methodology followed in the seminar of "Automation with Aspen HYSYS" integrated into the courses of Master in ChE at the University of Castilla-La Mancha "Analysis and Optimization of Chemical Processes" and "Dynamic of Process: Regulation of Chemical Plants. " Two independent study cases were considered: a refrigeration process (similar to the process which has appeared in tutorials of the different Aspen HYSYS releases), and the production of biphenyl whose data were taken from the literature  [18].
Five hours is the time required to develop all the contents proposed in the seminar. First, EXCEL-VBA concepts explained in previous courses are quickly reviewed in order for the students can better understand the code used in the applications. Furthermore, students must individually work on a couple of proposed cases: evaluation of the relative gain array of a process and determination of the existence and composition of homogeneous azeotropes. These latter examples plus others like the optimization of a distillation tower with side draws, determination of the temperature and concentration profiles inside a catalyst particle by considering the reaction, and the simultaneous nonisothermal internal and external diffusion phenomena and solution of a multistage separation problem could be considered for further potential papers.
| Motivation and learning outcomes The aim of the seminar was to introduce the students to the concept of automation with the commercial software Aspen HYSYS and its capability for tackling in a more or less simple way the simulation of complex chemical processes by accessing the exposed objects of the simulator with other commercial software (third party software) like Mathlab or homemade codes designed by users. This methodology would allow to simulation of processes that the simulator in its standard configuration could not. The main objectives or learning outcomes of the seminar were: • To introduce the concept of automation of commercial simulators taking Aspen HYSYS as the reference one. • To develop the concept through different cases of study. • To know the objects required by VBA to interact with the simulator.
Regarding the block diagram, different modules must be defined: In module WAYIN, the input data are used to interact with Aspen HYSYS. If units of the transferred variables are not defined, by default Aspen HYSYS considers them what it defines as an SI units system. It is highly recommended to define the SI units system in this simulation case and then check the actual units the simulator is handling. Thus: Figures S3-S8 show the complete VBA code for buttons and modules used in the application associated with Case of Study 1. Finally, Figures  3  and 4  show the components, the fluid package, the streams, the equipment, and the file allocation of the Aspen HYSYS simulation (sheet HYSYS) and the set of data used, and the corresponding results obtained from the Aspen HYSYS interaction (sheet CÁLCULOS), respectively.
The block diagram used for designing the Excel-VBA application is shown in Figure  7. Again, students can use a file template which allowed them to build the final application (Figures  S9  and S10 ). Two sheets were also defined with the same purpose as that of Case 1: CÁLCULOS and HYSYS. Regarding the block diagram, different modules must be also defined. Modules BORRAR1 y HYCONEXION in Case 2 is equal to that of Case 1 so they will be not described next. This way: -WAYIN: This is the module that makes the Automation. It calls module BORRAR which clears the cells in sheet CÁLCULOS. Then, it calls module DEFHYSYS and module FALPOSICION which performs the iterative procedure. -DEFHYSYS: This is the module where, apart from the definition of public variables used by the application, the different instructions and objects are also defined: F I G U R E 5 Aspen HYSYS flowsheet of Case 2: Biphenyl production  [18] , with the data required for reaching a convergence -FALPOSICION: It is the module that allows running the Regula-Falsi method.
It calls module FUNUNA where the function to make zero is defined. This way: The value to be optimized by the Regula-Falsi method is updated hycell. CellValue = X A pause of 1 second is introduced to leave time the simulation to converge Application. Wait (Now + TimeValue("00:00:01")) The temperature of stream A computed by the application is collected in Fahrenheit degrees TACALC = hystreamA. Temperature. GetValue("F") The function to make 0 (F) is computed, where TA is the objective temperature (1100°F) F = TA -TACALC In this case, WAYIN is defined as follows: Definition of the row (rw) and column (col) in the spreadsheet where the variable to be modified is defined, the object and data reading (objective temperature):   8  and 9  show the components, the fluid package, the streams, the equipment, and the file allocation of the Aspen HYSYS simulation (sheet HYSYS) and the objective temperature, the initial estimations of the C/E ratio and the results obtained from the Aspen HYSYS interaction (sheet CÁLCULOS), respectively.
analysis-mass spectrometer, steady-state and dynamic simulation, and modeling of chemical processes by using home-made computer applications and commercial software: HYSYS, ASPEN, UNISIM, and PROMAX and thermodynamics of electrolytes in solution and their application to the analysis of ion exchange systems. He has led more than 60 projects funded by the European Union, the Spanish Government, the Regional Government of Castilla-La Mancha, and contracts with private companies. Victor R. Ferro is currently a professor of chemical engineering at the Universidad Autonoma de Madrid, Spain. He was initially formed in physical chemistry acquiring experience in different experimental techniques of synthesis, electrochemistry, and spectroscopy. Later, he worked on themes related to the use of solar energy and, for several years on the molecular modeling and the design of molecules with specific properties by chemical-theoretical methods of quantum-mechanical nature.
In order to make the evaluations between different placement algorithms more convenient, we provide a network-based web-socket API which allows a custom placement algorithm to be connected over the network regardless of its programming language 1. We decided to make our NSPlacer tool public, offering the community a common tool to which they could compare their solutions. We believe that this can improve the reproducibility of the research results by providing a common environment for evaluating and comparing the algorithms. The demo paper is organized as follows. Section II introduces the problem of services' placement. Section III provides an understanding of the architectural solution and describes how the tool performs the placements, while Section IV gives a brief overview of the problem' parameters as well as the supported placement algorithms.
IV. Input/Output Parameters In order to evaluate the influence of the inputs of the problem, the tool allows to choose the values of a large number of parameters classified in 3 categories. 1) Substrate Network: It is the physical network hosting the network services. Our tool accepts network topologies represented in the Zoo Topology  [7]  XML file format. New topologies can be downloaded from the zoo topology dataset, or defined as an XML file and uploaded into our tool. A graph visualization section is provided for the network topologies in order to be able to check the connectivity of the network visually. Currently, we consider CPU and storage as two resource types for each network node, along with the bandwidth and the latency for the network links (although latency is considered as a QoS metric, we use the term resource for unification).
Summary and perspectives We provided for the first time the complete high-  Drell-Yan likelihood for the full set of energy-enhanced  ≤ 8 SMEFT operators, with arbitrary flavor indices. This was achieved by recasting the most recent run-II data sets by ATLAS and CMS in the mono-lepton (, , ) and dilepton channels (, , , , , ). Our results are presented in a package HighPT  [8] , which also supports some tree-level mediators scenarios, allowing us to explicitly check the validity of the EFT approximation. This study leaves room for many improvements: (i) the inclusion of additional experimental searches increasing the sensitivity, (ii) the joint determination of PDF and BSM couplings for better control of the uncertainties, (iii) the implementation of double-differential distributions, as well as (iv) a model-independent combination of our high-energy limits with electroweak and low-energy flavor data.
In addition, the long-term demographic trajectory of a species may also be retrieved from genetic data  (Cristofari et al. 2018 , Cole et al. 2019 , Cleary et al. 2021) , which can help to understand how species have responded to past major climatic and environmental changes. Thus, understanding, predicting, and managing biodiversity responses to rapid climate change demand a full consideration of the genetic differentiation within a species, an assessment of the species' evolutionary potential and knowledge of demographic trends. The Snowy Owl (Bubo scandiacus), declining according to the IUCN (BirdLife International 2020), provides a case study of how conservation actions can be informed and refined using genetic information. This owl is a highly mobile top predator which breeds exclusively on the Arctic tundra  (Holt et al.
2009 ). These authors estimated the global species effective population size (Ne) at ca 14,000 individuals. This estimate, derived from mtDNA, corresponds to the number of breeding females and does not equate to the whole population size as it is considered by IUCN (BirdLife International 2020). Indeed, not only is Ne not a direct proxy for census population size (e. g. ,  Ferchaud et al. 2016) , but this estimate disregards the number of breeding males or the variance in reproductive success among individuals  (Storz et al. 2001 , Wang et al. 2016). Therefore, further research using genome-wide nuclear data would provide a better understanding of the genetic structure of the population and a more accurate estimate of the current effective population size of Snowy Owls. Snowy Owls exhibit a diversity of movement strategies throughout the year  (McCabe et al.
Population Genetic Structure, Genetic Distance, and Isolation-by-distance We used complementary analyses to quantifying population genetic structure. Unless mentioned, all analyses were in R*3. 6. 2 (R Core Team 2019). We first used Principal Component Analysis (PCA), considering the a priori defined wintering regions with the function glPCA using the package Adegenet *2. 1. 1  (Jombart 2008). Next, to compute pairwise F ST among regions according to  Weir and Cockerham (1984)  we used the gl. fst. pop in the dartR package  (Gruber et al. 2018 ; See the Supplementary Material Appendix). Also, we performed a Discriminant Analysis of Principal Components (DAPC;  Jombart et al. 2010 ; also implemented in the package Adegenet  (Jombart 2008 )) to infer the number of genetic clusters that best fit our data.
In addition, we used a Maximum Likelihood approach using ADMIXTURE*1. 3 software  (Alexander et al. 2009)  to estimate the number of populations underlying the genetic dataset based on individual relatedness without our a priori wintering regions. Finally, we tested for Isolation-bydistance at the individual level by investigating the between the natural logarithm of Euclidean distance and relatedness (i. e. beta, according to  Weir and Goudet 2017)  distance matrices between each pair of individuals. An extended description of the methods is provided in Supplementary Material Appendix.
Descriptive Genetic Diversity We calculated the observed heterozygosity (Ho) and the expected heterozygosity (He) for each locus using the function gl. Ho and gl. Hs in the dartR  (Gruber et al. 2018 ) package in R*3. 6. 2 (R Core Team 2019). We also computed Ho and Hs for each wintering area. To test whether a significant difference in Hs among wintering areas can exist, we performed a Linear Mixed Model (LMM) with wintering area as the fixed effect and the loci ID as a random effect. We verified linearity assumptions of model residuals, and we then computed a post-hoc Tukey test to contrast wintering areas.
Population Trajectory Reconstruction We reconstructed the population history of Snowy Owls using the Stairway Plot 2 method  (Liu and Fu 2020). The Stairway Plot infer detailed population demographic history using the site frequency spectrum (SFS: Liu and Fu 2015) from DNA sequence data. This method uses a flexible, multi-epoch model as used in the Skyline Plot method  (Strimmer and Pybus 2001, Navascués et al. 2017) , and based on the expected composite likelihood of a given SNP frequency spectrum (SFS). It provides information on the history of population effective size (Ne) over time until the recent past. We estimated the folded SFS using the VCF file and a Python script, EasySFS (https://github. com/isaacovercast/easySFS; Covercast 2017) and included the total number of observed nucleic sites (both polymorphic and monomorphic sites).
SNPs Genotyping After the de novo SNP calling and filtering procedure, the data set encompassed 13,793 SNPs distributed over 5,987 loci. We only kept the SNPs that had less than 15% of missing data, i. e. , that were typed for at least 85% of birds. Individuals scored on average 5,767 SNPs ± 198 (SD)  [4,826; 5,921] , resulting in 3. 7% missing data in the genotype matrix. Error rates estimated on the 13,793 SNPs obtained from replicated samples were 0. 004 +/-0. 003 and dropped to 0. 003 +/-0. 002 when considering single SNP per loci (n=5,987 SNPs). For subsequent analyses, we only considered a single SNP per locus, for a final dataset consisting of 5,987 SNPs genotyped on 150 Snowy Owl individuals.