dataesr/scientific-paragraphs-categorization huggingface dataset:
Particle-in-cell simulations for the characterization of the VADIS electrostatic cavity The software VSim from Tech-X Corporation  [15, 16]  is used in the present work to model the electrostatic field distribution within the VADIS cavity and gain an insight into the RILIS-mode ion dynamics. It contains a well validated implementation of the electrostatic particlein-cell (PIC) algorithm with Monte Carlo treatment for the field ionization, impact ionization as well as elastic and inelastic scattering processes. In this study we focus on the electric field distribution caused by the electrons reaching the anode cavity, combined with the voltages applied to the ion source walls and external electrostatic fields reaching the extraction zone. When the rate of electron supply to the anode (electrons incident from the cathode, plus those generated through collisions with the atoms and anode walls) matches the rate of electron absorption on the walls of the anode, a quasi-steady state is reached for the electron population and, accordingly for the electric field distribution.
The results are in broad agreement with earlier studies  [8]  which used the CPO software  [9]. The ion source efficiency depends on the ionization efficiency, ion survival and extraction probability. Since the anode exit aperture is small compared to its internal surface area it is particularly important for the electrostatic field distribution to provide ion confinement (preventing anode wall collisions) and to result in ion flow towards the exit aperture. The consequence of this, however, is that satisfying this requirement typically results in a region of the potential well in the centre of the anode volume where ions are confined from reaching the anode walls and the exit aperture. They are therefore trapped. This is illustrated in Fig. 3  where the central blue area corresponds to this trapped region.
VADLIS with improved extraction characteristics of laser-ions The electrical field distribution inside the anode due to the potential difference between the anode body and the locally grounded extraction plates, has an effect on the ion extraction. Here we investigate the recovery of an effective potential difference, that can be optimized for maximum ion extraction probability while operating in RILIS-mode. This can be achieved by applying a negative voltage to the extraction plates aperture of the ion source. The present study was performed using the VSim software, leading to the construction of a VADLIS prototype that presents improved ion extraction characteristics as demonstrated experimentally.
Conclusions and outlook Previous studies have demonstrated that the anode cavity of the VADIS ion source serves as an efficient laser-atom interaction region. We used the VSim particle-in-cell software to study the electrostatic field distribution inside the anode cavity and the ion time structure. The general features observed in the calculated time structure with the help of VSim simulation code matched the experimentally measured values and the interpretations of previous work. The simulated electrostatic field distribution within the anode   8a  and Fig. 8b  were taken during different measurement periods and consistency in terms of all experimental conditions (laser alignment, power, ion beam purity, electron emission etc) could not be guaranteed. Therefore, the intensity ratios observed when going from region I to region II do not correspond to the ion signal ratios observed in the anode scan shown in 2.
Acknowledgements This work has been funded by  FWO-Vlaanderen (Belgium) , by  GOA/ 2010/ 010  ( BOF KU Leuven ), by the  IAP Programme  initiated by the  Belgian Science Policy Office (BriX network   P7/12 ), by the  European Commission  within the  European Nuclear Science and Application Research ENSAR2  (Grant agreement No. 654002 ) and by a Grant from the  European Research Council  ( ERC-2011-AdG-291561-HELIOS ). The authors would like to thank the support received from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 642889 MEDICIS-PROMED.
GUTS models can be used to predict survival rate under untested exposure conditions. DEBtox models explore the effects on growth and reproduction of toxicants over time, even over the entire life cycle. TKTD model for primary producers and pesticides have been developed for algae, Lemna and Myriophyllum. For all TKTD model calibration, both toxicity data on standard test species and/or additional species can be used. For validation, substance and species-specific data sets from independent refined-exposure experiments are required. Based on the current state of the art (e. g. lack of documented and evaluated examples), the DEBtox modelling approach is currently limited to research applications. However, its great potential for future use in prospective ERA for pesticides is recognised. The GUTS model and the Lemna model are considered ready to be used in risk assessment.
The 'Add-my-Pet' 6  (AmP) database is a collection of parameters of the standard DEB model, as well as of its different variants, for more than 1,000 species, including numerous aquatic species. Conceptual overviews of the DEB theory are given in  Jager et al. (2014)  and  Baas et al. (2018). From the formulation of the standard DEB model, a simplification for standard laboratory toxicity tests has been derived; it is based on the following assumptions: size is a perfect proxy for maturity; size at puberty remains constant; costs per egg are constant and thus not affected by the reserve status of the mother; egg costs can only be affected by a direct chemical stress on the overhead costs; and the reserve is always in steady-state with the food density in the environment. Under this framework, the kappa (j) rule should not be affected by a toxicant.
It should be kept in mind that the definition of 'mode of action' in a DEB context (DEBMoA, as stated by  Baas et al. , 2018)  is not the same as the definition of 'mode of action' in a general toxicity context. In a DEB context, the mode of action is mathematically formulated as a change on how the chemical affects the physiological processes accounted for in a DEB model. Hence, chemicals with different biochemical modes of action as defined by molecular target sites for, e. g. pesticides, may exhibit a similar DEB mode of action when evaluating how the pesticide affects the DEB model parameters. Even though some tools are already available to perform parameter estimation (e. g. DEBtoxM 7 and BYOM 8 packages for Matlab, home-made scripts in the R software), DEBtox models in their current form require advanced statistical skills to be fitted to experimental data.
Definition and testing of GUTS This section contains specific information about GUTS itself and its implementation, but not about a specific application or data set. It contains as separate subsections information about: • the formal model, that is mathematical (differential) equations and other detailed information about the model; • the model implementation, that is which programming language or environment was used, which settings of key values have been chosen, etc. ; • the verification of the model implementation, that is basic tests to show that the code works as it should for some selected cases; • Sensitivity analysis of the model, that is the effects of changes in parameter values on the model output; • General information about the way model parameters have been estimated, that is how calibration routines are performed, which function is used as target for optimisation routine, etc.
; • The definition of model output and how uncertainties in model predictions are handled; • Criteria for the validation of a calibrated model on an external data set. All the above subsections are exemplified hereafter with implementations of GUTS both in the Mathematica and the R programming languages. The simplest GUTS version (e. g. for an aquatic invertebrate like Daphnia magna without measurements of internal concentrations) assumes a one-compartment model and links external concentrations directly to the scaled damage (see Section 2. 3 for an introduction to the scaled damage concept). The choice of this model, called 'reduced GUTS' (GUTS-RED) implies that the dominant rate constant k D (time À1 ) is determined directly from the raw observed survival data (without internal concentration measurements).
Test results for two model implementations The implementation of a model is software-specific. The documentation of the implementation should contain an overview of the source code files, and the version and necessary packages of the used programming environment. Testing of the implementation ('implementation verification') needs to be performed and documented for any model implementation, while the reasonable behaviour of the model ('sensitivity analysis') has been performed within this opinion for the example implementations (with Mathematica and R) and is not necessarily part of the documentation of a model implementation. The following sections contain a short overview of examples for an implementation of the GUTS TKTD model in Mathematica  (Section 4. 1. 2. 1)  and in R (Section 4. 1.
Model implementation in Mathematica The different GUTS-RED versions have been implemented in Mathematica (Wolfram Research, version 11. 0, http://www. wolfram. com/mathematica/). Mathematica is proprietary software for performing mathematics on a computer. It provides comprehensive methods for computation. The GUTS implementation in Mathematica uses mainly the functionality to calculate numerical solutions for ordinary differential equations (method NDSolve), to find the minimum of a given objective function (NMinimize), to read and write files of various formats (Import/Export) and to operate with lists and matrices of data. Mathematica is under continuous development; the implementation is steadily tested and verified. The GUTS Mathematica notebooks contain applications for the single modelling steps, i.
e. model calibration, model validation, predictions of surviving individuals for the exposure scenarios and probabilistic model simulations, together with all necessary data import and export functionality (see Appendix A). The source code of the Mathematica notebooks is not write-protected, that can, however, be achieved if necessary. Mathematica notebooks come in general in a specific format that contains both the program code and the output. Providing all notebooks and input files gives the opportunity to look into the source code and to see at the same time the output. A Mathematica installation and license will be necessary to run the program code and to redo and test calculations. The GUTS Mathematica code has not been optimised for providing user-friendly software. The structure of the model code enables, however, given a running Mathematica installation, to redo all steps that have been performed in the scope of the GUTS implementation, by loading and executing the GUTS-methods notebook, followed by one of the application notebooks.
Model implementation in R (package 'morse') GUTS-RED versions have also been implemented within the R-package 'morse  ' 3. 1. 0 (R Core Team, 2016; Baudrot et al. , 2018a). R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS (https://www. r-projec t. org/). It can be downloaded from any CRAN mirror (https://cran. r-project. org/mirrors. html). Package 'morse' provides tools for the analysis of survival/reproduction data collected from standard toxicity tests. It can be used to explore/visualise experimental data, and to perform estimation of LC x /EC x values by fitting concentration-response curves, or estimation of NEC values by fitting GUTS-RED-SD and/or -IT models. The LC x /EC x /NEC as well as model parameters are provided along with a quantification of their uncertainty.
Package 'morse' can be downloaded at https://cran. rproject. org/web/packages/morse/index. html; a step-by-step explanation of its use is provided at https://cran. r-project. org/web/packages/morse/vignettes/tutorial. html, while a more formal description of the underlying estimation procedures is provided at https://cran. r-project. org/web/packages/morse/ vignettes/modelling. pdf. The full description of all functions provided by the R-package 'morse' can be found at https://cran. r-project. org/web/packages/morse/morse. pdf. The source code of these functions is accessible within R from instruction 'morse:::function_name'. Moreover, the R-package 'morse' uses the R-package 'rjags'  (Plummer, 2013)  as an R interface to the JAGS library for Bayesian model estimation. Note that package 'rjags' does not include a copy of the JAGS library that needs to be installed separately (http://mcmc-jags.
sourceforge. net/). Note that basic features of the R-package 'morse' are also available online within the user-friendly web-platform MOSAIC  (Charles et al. , 2017)  which offers a new GUTS module specifically dedicated to the online fitting of GUTS-RED-SD and GUTS-RED-IT (http://pbil. univ-lyon1. fr/software/mosaic/guts, Baudrot et al. , 2018c). R notebooks are usually provided as readable stand-alone text files directly usable as R scripts (. R extension). They contain all necessary code lines associated with comments, to run and get output results [See Appendix E for the code archive].
Verification of model implementation In order to verify the model code, it would be ideal to show that the code is correctly implemented with respect to the conceptual and the formal model and that the code is error-free. Model code verification is an important tool to build trust to model results. A rigorous verification of the complete implementation code, in the sense of a complete check of the computer code would mean an extensive effort and an enormous documentation. In order to keep the balance between effort and results, model results have been produced for a set of scenarios, representing specific classical and extreme cases, and the model outcomes have been checked for reasonable results. Results below are given side by side both for Mathematica and R implementations in order to illustrate that both independent implementations show the same results.
Conclusive statement on the formal and computer model Both implementations, Mathematica and R, finally give exactly the same results when verifying the source code (Section 4. 1. 2. 3)  or analysing the sensitivity of model outputs to changes in parameter values  (Section 4. 1. 2. 4). The results corroborate the correct implementation of the formal model for both model implementations. Future implementations of GUTS could do the same checks to show correct implementation, in addition to the required ring-test performance (see also  Section 4. 2, and Appendix B. 6 and B. 7). Moreover, OAT sensitivity analysis of the GUTS-RED models has been performed and the results indicate that model parameters have almost equal influence on the model output, hence sensitivity analyses of the GUTS-RED models are not required in future model applications.
g. Levenberg-Marquardt) because of their capability to find global minima and not to get stuck in non-trivial optimum parameter searches. Implementations of such algorithms are available and can be used in software such as Mathematica, where the Nminimize method gives the opportunity to choose from a variety of optimisation algorithms. Also, the OpenModel software (http://openmodel. info/) provides the opportunity of using a MCMC algorithm to find best parameters. The convergence of optimisation algorithms is checked in the mentioned software implementations automatically, the principle is that the optimisation is starting for a number of different starting conditions (so-called 'chains'), and by the evaluation of differences within and between the different chains, the convergence of the algorithm is checked, e.
Several tools are available to automatically perform these computations like: JAGS for Just Another Gibbs Sampler  (Plummer, 2003)  as used in some parts of this opinion in combination with the R-package rjags which provides an interface from R to the JAGS library for Bayesian data analysis; STAN, a state-of-the-art platform for statistical modelling and high-performance statistical computation  (Carpenter et al. , 2017) ; Winbugs, based on the BUGS (Bayesian inference Using Gibbs Sampling) project  (Lunn et al. , 2000). Today, the performance of these tools allows a wide use of the Bayesian approach, even if it is still challenging to deal with ordinary differential equations.
Modelling results Two model implementations were run in parallel: one under a frequentist framework with the Mathematica software (Section 4. 1. 2. 1); another one under a Bayesian framework with the R software (Section 4. 1. 2. 2). More implementation details are given in Appendix A Testing the implementations in Section 4. 1. 1. 2 already indicated that both implementations give very similar outputs. The results obtained under the Bayesian framework are presented below; additional plots and results from the frequentist approach implemented in Mathematica are given in Appendix B for comparison.
Results of model calibration The model parameters have been calibrated in a Bayesian framework as implemented either in the R-package 'morse'  (Baudrot et al. , 2018a)  or directly online within the 'GUTS' module of the MOSAIC web-platform  (Baudrot et al. , 2018c). All details about the underlying implementation, verifications and sensitivity are given in Section 4. 1. 2, while details about the parameter estimation process are given in Section 4. 1. 3. Additional details about the parameter estimation are given in Appendix A. 1 (Mathematica) and A. 2 (R-package 'morse'). The estimated parameter values and corresponding credible intervals are reported together with the optimal log-likelihood values in Table  3. The background mortality rate was considered in the estimation process together with the other parameters.
Summarising the model calibration for the example of propiconazole and G. pulex, the data appear appropriate because the concentration-response relation ranges from no up to full effects. The choice of the reduced GUTS models is appropriate because internal concentrations have not been measured so that the use of the full model cannot be recommended. The parameters in this example have been estimated under a Bayesian framework using the MCMC algorithm as implemented either in the R-package 'morse' or within the web-platform MOSAIC. Parameter values, credible limits and likelihood values have been reported (Table  3 ). The results of the model calibration are visualised and tested in different ways, including plotting the survival over time, the PPC and concentration-response curves.
Model implementation in the R software All endpoints (exposure, survival, growth and reproduction) were fitted simultaneously to estimate all 14 model parameters, since all corresponding observed data were linked together as shown in Figure  28. Parameters were estimated within a Bayesian framework via an R code based on JAGS and the R-package rjags (see Appendix A, Section A. 2. 2 for additional information). Prior probability distributions are those reported in Table  5  according to  Billoir et al. (2011). Because it is not possible to numerically integrate differential equations in JAGS, models were implemented in discrete time with one day as time step. Three independent MCMC chains were run in parallel. After an initial burn-in period of 5,000 iterations, the Bayesian algorithm was run 50,000 iterations and the corresponding sample of the joint parameter posterior distribution was recorded.
A key point is also the fact that the underlying implementation remains home-made as no user-friendly software today exist to perform DEBtox model calibration in a simply way. Under a Bayesian framework, one of the main reason for this, is that further research is still needed to automatise the choice of the priors for the whole set of parameters, as well as to deal with ordinary differential equation in combination with MCMC calculations. Nevertheless, the use of DEBtox model in the perspective of ERA is valuable. Particular attention should be paid to the choice of the DEBtox set of equations that needs to be argued based on knowledge on the mode of action of the chemical substance (see Table  1  and chapter 9 ).
Model implementation The model was implemented in R (Version 2. 15. 1), using the general solver for ordinary differential equations 'ode' of the R-package 'deSolve' described in  Soetaert et al. (2010)  for solving the differential equations. The complete code is provided in the supplementary information of  Schmitt et al. (2013). To validate the growth model, a stochastic simulation was performed varying the growth parameters in a Monte Carlo (MC) approach with 100 runs. Based on the comparison of literature data, showing very small variability in parameter values obtained in different studies, a standard deviation of 10% was used.
The Myriophyllum model As discussed in Section 2. 4. 3 the Myriophyllum model is not as completely described as the Lemna model, and source code is, to our knowledge, not publicly available. The model is described in three papers: The first one builds and parameterises the growth model, and describes how growth varies as a function of irradiance and temperature  (Heine et al. , 2014). The second paper focusses on describing and parameterising the TK-processes for several herbicides, including both sulfonylurea and photo-system II inhibitors. Also, they determine cuticular permeability for chemicals based on their log K ow  (Heine et al. , 2015). The final paper describes and parameterises the toxicodynamic model using the sulfonylurea iofensulfuron  (Heine et al. , 2016a). The fact that the model is described in three separate papers makes it difficult to get an overview of its entity, as is also discussed in Section 2.
g. an Excel sheet or a web-based shinyApphttp://lbbe-shiny. univ-lyon1. fr/guts-shinyapp/) should be used by the evaluator to test whether the output of the evaluated model implementation can be reproduced for some parameter sets. In addition, the computer code of the model implementation should be made available to allow further checks by experts. A ring-test equivalent to the one available for GUTS models is not available for DEBtox or primary producer models yet. Therefore Chapter 10. 4 of EFSA PPR Panel (2014) should be used to check the computer model code. Such an evaluation includes basically similar steps as described in Section 4. 1. 2 and needs expert assessment.
Evaluation of model use According to EFSA PPR Panel Opinion on good modelling practice (EFSA PPR Panel 2014), the risk assessor should be able to re-run the modelling using either the parameter values provided or alternative values as a way to both verify the submitted results and check whether the model behaves reasonably. A full access to the model allows the risk assessor to run alternative exposure scenarios in case they consider that the applicant has not provided the required exposure profiles. This requires that either the model source code is provided in case the model is run in a standard software (e. g. in R or Mathematica), or alternatively, an executable file or web-based access to the model is provided. Ideally, standard software should be available, so that applicants and Member State assessors can run the models, both for calibration and testing scenarios.