dataesr/scientific-paragraphs-categorization huggingface dataset:
Using these packages, the agentbased model needs to be previously coded in the NetLogo software using the NetLogo language. Then, the model is launched and controlled by an R script but it still runs in NetLogo. Both the R extension of the NetLogo software and the RNetLogo and nlrx R packages require the user to code his/her model in the NetLogo software using the NetLogo language and the model always runs inside NetLogo. Here, we present the R package NetLogoR. This package provides new R classes, which are extensions of core R classes, and functions to build and run SE-ABMs in R using only the R language. The framework used for this package is similar to NetLogo, but the implementation of SE-ABMs do not call or interact with the NetLogo software or any NetLogo code. NetLogoR allows the implementation of SE-ABMs, from building to running, benefiting from the efficient NetLogo framework and dictionary, while staying only in the R environment and using only R language.
At least seven main advantages result from the use of the single R platform to implement SE-ABMs, over the use of the single NetLogo software and/or of the NetLogo R extension or the RNetLogo/nlrx R packages. First, coding and running SE-ABMs only in the R environment reduces the cognitive switching cost associated with navigating between two software and their different syntaxes and paradigms. Users wanting to use the R extension of NetLogo or the RNetLogo/nlrx packages to benefit from the R performances need to learn and use both pieces of software, NetLogo and R, and switch between the two different languages. NetLogoR brings new vocabulary to R, not a new language; users do not need to learn NetLogo language and software utilization to implement SE-ABMs using NetLogoR. Second, building SE-ABMs in R allows using classes not available for models coded in NetLogo such as data.
frames (R Core Team), data. tables (Dowle and Srinivasan 2019) , spatial object classes (e. g. for use with shapefiles), or links to database servers like PostgreSQL and PostGIS for very large data and spatial problems. For example, in a model simulating animal movement, shapefiles of roads can be used directly in a model coded in R, instead of being transformed in a raster-grid format to be used in a model coded in NetLogo. Third, powerful functions, using R classes not available in NetLogo language, can be used to build SE-ABMs coded with NetLogoR. For example, the well-known dplyr  (Wickham et al. 2018 ) and data. table (Dowle and Srinivasan 2019)  packages vastly improve the use and speed of data management. Numerous GIS packages (e. g. raster  (Hijmans 2018) , rgdal  (Bivand et al. 2018) , rgeos  (Bivand and Rundel 2018) ) help manipulating vector and raster data formats.
The generic predict() method is also extremely useful and relevant as a user can predict from any arbitrary statistical model, thereby taking into account variance-covariance structures. Sophisticated random number generators from different packages that go well beyond random uniform, such as truncated pareto distributions (rtruncpareto() in VGAM package (Yee 2010)) or multivariate normal (rmvnorm() in MASS package  (Venables and Ripley 2002) ) can simulate data to better depict real-world complex distributions. Fourth, with the aim of optimizing the models, SE-ABMs running in R can calculate complex statistics iteratively while simulating. It allows an efficient regular adaptation of the model behavior and/or parameters. To do so in NetLogo, users would have to transform NetLogo classes into R classes to perform complex statistics to then, back transform the results into NetLogo classes to continue simulations when using the R wrapper for NetLogo with the RNetLogo or nlrx packages.
Fifth, even though NetLogoR does not provide function for visualizations, R has a huge arsenal of these functions (e. g. ggplot2 package  (Wickham 2016 )) that can reproduce the live-visual of the agents' movement in the landscape as well as the plots displayed on the NetLogo interface. Sixth, there are relatively new R packages (e. g. SpaDES  (Chubaty and McIntire 2018) ) that allow decentralized integration of discrete event simulations. So, a model made with NetLogoR using SpaDES to iterate over time (rather than the simple for() or while() loops) means that a model can interface dynamically with an ever growing set of simulation models built by other users around the world. Finally, modern web application development tools (e. g. shiny package  (Chang et al. 2018 ) and other javascript wrappers) help to easily share R models online with an allowance of some control of the model by external users online.
All of these arguments are real advantages because they can be used within a simulation model when built in R. Using a wrapper around the NetLogo software would only allow for trivial use of these advantages either before a NetLogo simulation for data preparation or after for data analysis. It would be impossible to use these advantages within simulations without developing repeated back-and-forth sequence of calls to and interrupting the NetLogo simulation (i. e. iterate NetLogo, break out, change objects in R, estimate new parameters or GIS summaries, send back to NetLogo, restart at a new start point etc. ).
Methods and features NetLogoR can be used to code raster-based and individualbased models. It provides new R classes to represent model agents and functions to build and run SE-ABMs. The package also contains two vignettes (a programming guide and a dictionary) and three model examples. NetLogoR 0. 3. 5 is available on CRAN under the GNU General Public License  (ver. 3, 2007). There are 3 new R classes provided by NetLogoR: worldMatrix and worldArray to represent landscapes as a grid cell, and agentMatrix to represent moving individuals. Note for NetLogo users: worldMatrix and worldArray are two types of 'world' made of 'patches' (cells) and agentMatrix represent the 'turtles'. To represent landscapes in R, Raster*  (Hijmans 2018 ) and matrix classes (R Core Team), can be very effective and convenient.
The NetLogoR function createTurtles() creates an agentMatrix by specifying the number of individuals to create and their location (either precise coordinates or the world onto which they will develop). Note: moving individuals are called 'turtles' in NetLogo and other programming language (e. g. Logo, Python) hence the name of some NetLogoR functions (i. e. *turtles() or turtles*()). Additionally, the user can specify the heading (i. e. direction), breed name and color of the individuals when creating them. The NetLogoR function turtlesOwn() allows to later create new variables to store additional information on the individuals in the agentMatrix object. Along these new R classes, NetLogoR provides around a hundred functions to create and run SE-ABMs. Functions use world* and agentMatrix classes and act on landscape cells ('patches') and moving individuals ('turtles') to modify them or perform statistics using them.
NetLogoR also contains functions that did not exist in Netogo but which were essential to build models in R (e. g. createWorld()). On the other hand, there are also NetLogo primitives that we did not translate into NetLogoR functions. For some of these, functions from the R software or from other packages already existed to perform similar actions so we did not create duplicates (e. g. the R functions such as sample() and runif() can be used to replace the NetLogo primitives random and random-float). For some others, we felt that the NetLogo primitives were too specific to the software and their absence would not prevent users to build models. These primitives are mostly the ones and those related to the agent category 'link' that we did not implement. Links are connections between agents to represent relationships.
Examples There are many ways to build a SE-ABM but here is an example of how to construct a very simple movement model using NetLogoR. First, we define the model agents, in this case three moving individuals and the world they will exist on. Second, we define a simple movement model that runs inside a for() loop, where the individuals move with a step length dependent on the cell value under their position, and then they rotate with an angle dependent on the group mean heading. To illustrate a few of the advantages mentioned above, the movement model uses classes and functions that would be difficult to include in a NetLogo code. Here is the R script for this example. NetLogoR functions are underlined. The following code is also provided as an R script in the Supplementary material Appendix 1.
R script using NetLogoR (left side) and NetLogo script (right side) for the Butterfly-1 model  (Railsback and Grimm 2012). Model comments are in light grey. '[…]' in the R script represent comment sections that were removed for clarity of the presentation here but which are available in the original script when downloading the NetLogoR package. Similar sections between the two scripts are in bold and NetLogoR functions are underlined in the R script. ;;decide whether to move to the highest surrounding with probability q ifelse random-float 1 < q [uphill elevation] ;;move deterministically uphill [move-to one-of neighbors] ;;or move randomly end is the 'Ants' model (Ants. R, Wilensky 1999). This model simulates an ant colony with several individuals foraging for food: individuals move around to find food and when a food source is found, they carry pieces of it back to their nest and signal other individuals of their finding with chemical cue.
Conclusion NetLogoR enlarges the R vocabulary providing the necessary functions to build and run SE-ABMs in the R platform using only the R language. Doing agent-based modeling in R with NetLogoR combines the benefits of the R language, as well as the efficient NetLogo design. This package allows staying on the R platform for the different steps of the modeling process, from the data preparation, to the SE-ABM construction and running, with finally the analyses of the model outputs. People familiar with NetLogo will also find it very easy to build new models or translate existing NetLogo models into R with this package. To cite NetLogoR or acknowledge its use, cite this Software note as follows, substituting the version of the application that you used for 'version 0': Bauduin, S. ,  McIntire, E.
[2015]. A finite element DFN-VDF numerical model is implemented using COMSOL Multiphysics® software. We include the Boussinesq approximation in the COMSOL model to reduce nonlinearity and improve computational efficiency. In order to quantify the variability in model outputs resulting from the uncertain parameters, we use the global sensitivity analysis (GSA). GSA is more appropriate than local sensitivity analysis as it provides a robust and practical framework to explore the entire inputs space and to assess the key variables driving the model outputs uncertainty  [Saltelli, 2002; Sudret, 2008; De Rocquigny, 2012]. GSA is a powerful approach to fully understand the complex physical processes and assess the applicability of models. It is also important for risk assessment and decision-making.
DFN-VDF finite element model: COMSOL Multiphysics®: The DFN-VDF simulations are performed using a finite element model developed with COMSOL Multiphysics® software package. COMSOL is a comprehensive simulation software environment for various applications. The use of COMSOL in applications related to hydrogeology is increasingly frequent as this software is a user-friendly tool that facilitates all the modeling steps (preprocessing, meshing, solving and post-processing) and allows an easy coupling of different physical processes  [Ren et al. , 2017; Fischer et al. , 2018]. With the finite-element modeling framework, the common degrees of freedom at the triangle nodes in the matrix and at the 1D segments in the fractures are used to model the volumetric and mass fluxes between the matrix and the fractures.
As is obvious from Fig. 3 , excellent agreement is obtained between the COMSOL and the semi-analytical results. This highlights the accuracy of the developed COMSOL models and the related post-treatment procedure applied to obtain the SWI metrics. It also confirms the validity of the Boussinesq approximation for SWI in homogenous CAs. For FCAs, analytical or semi-analytical solutions do not exist. We compare the developed COMSOL models (SWI-COMSOL and SWI-COMSOL-Bq) against an in-house research code (TRACES) based on advanced space and time discretization techniques  [Younes et al. , 2009]. This code has been validated by comparison against several configurations of semianalytical solutions in Fahs et al. [2018]. It has proven to be a robust tool for the simulation of SWI in both homogeneous and heterogeneous domains.
DFN approach, which is based on average properties over the fracture width, is not available in TRACES. Thus, the fractures are modeled by considering heterogeneity of material without reduction of the dimensionality; i. e. fracture is a specific layer of the 2D domain with different assigned properties. We considered two validation cases which are based on a single horizontal and vertical fractures, respectively. The horizontal fracture is located at the aquifer middle-depth = 0. 5 while the vertical fracture is located near the seaside at x=1. 8m. The physical parameters are given in Table  1. The mesh used in the COMSOL simulations involves about 50,000 elements. In the in-house code we use a mesh with about 70,000 elements. The obtained main isochlors are given in Fig. 4  and the corresponding SWI metrics are summarized in Table  2.
In such a case the advection and buoyancy processes are very important and the corresponding numerical solution could be highly sensitive to the mesh size as it might suffer from unphysical oscillations or numerical diffusion. A mesh-independent solution is achieved for this case using a grid consisting of about 50,000 elements. This mesh is used for the 100 simulations required for computing the PCE expansions. For each SWI metric (or model output), the corresponding PCE surrogate model is calculated using the technique described in section 3. For the salt concentration distribution (multivariate output), component-wise PCE is constructed on each node of the regular 2D square grid defined for the control points (involving 5,000 control points). The MATLAB code developed by  Shao et al. [2017]  is used to compute the sparse PCE.
To give more confidence to the sparse PCE, we also compute total order PCE using the UQLAB software  [Marelli and Sudret, 2014]. As five input variables are considered and 100 samples are available, only third-order polynomial could be reached via the total order PCE expansion. The corresponding optimal number of samples is 56. With the sparse technique, implemented in this work, higher orders can be reached even if the optimal number of samples required for full PCE is not achieved. Sixth order PCE is reached for the salt concentration distribution and all SWI metrics except the width of the mixing zone for which the polynomial order is limited to five. The accuracy of the resulting sparse PCE surrogate model is checked by comparison against the physical COMSOL model. In Fig. 6 , we compare the values obtained with the sparse PCE with those of DFN-VDF physical model implemented with COMSOL for parameter inputs corresponding to the experimental design (i.
Acknowledgment Behzad Ataie-Ashtiani  and  Craig T. Simmons  acknowledge support from the  National Centre for Groundwater Research and Training ,  Australia. Behzad Ataie-Ashtiani  also appreciates the support of the Research Office of the  Sharif University of Technology, Iran. The authors are grateful for  Behzad Mozafari  from  Sharif University of Technology  for useful discussion on model development.
Scientists interested to work as part of interdisciplinary teams may be discouraged to do so when realizing that they will be at a significant disadvantage compared to others who prefer "flying solo. " McLeish and Strang identify "Individual Career Progression" as one of the crucial levels at which there is an immediate need for an effective evaluation method for interdisciplinarity  (28). Furthermore, from their experience as evaluators, the authors report enormous pressures on researchers to establish a distinct identity, fueling the claim that career progression is hampered by interdisciplinary research and potentiated by singlediscipline work. Nevertheless, some successful interdisciplinary translational researchers counter-argue that their aim is impact, a goal favored by several institutions.
Introduction With the advances in Internet technologies, such as data mining tools, personalized marketing services, and ubiquitous electronic commerce applications, the collection and analysis of personal information are becoming rampant. Online consumers are increasingly concerned about their privacy, as their personal information and online activities are often automatically tracked and analyzed without their consent or knowledge  (CIGI 2018; IDC 2017). Data breaches of large commercial databases, such as Facebook and Sony Pictures  (Chaykowski 2018; Lewis 2014) , also exacerbate consumers' privacy concerns. Such privacy concerns are further evident in the multimillion dollars lawsuits against Twitter  (Roberts 2017) , Facebook  (Meyer 2017) , and Google (Ruddick 2017). According to recent surveys, 91% of consumers agreed that they have lost control of their personal information and data  (Rainie 2016) , and 70% of consumers are more concerned about their privacy today than they were a few years ago  (IDC 2017).
2006; Singh and Hill 2003) , inclusion of privacy policy statements on the websites  (Awad and Krishnan 2006; Hui et al. 2007; Pollach 2005; Resnick and Montania 2003; Tsai et al. 2011; Turner and Dasgupta 2003) , utilization of privacy protection seals such as TRUSTe  (Caudill and Murphy 2000; Etzioni 2019; Hui et al. 2007; Singh and Hill 2003; Xu et al. 2005) , ensuring users' awareness of information collection  (Culnan and Bies 2003; Sheehan 2002; Sheehan and Hoy 2000; Singh and Hill 2003) , and removing identifiable personal information from databases  (Garfinkel et al. 2007; Li and Sarkar 2006). In general, prior research has found that privacy protection is effective in reducing IPC  (Xu et al. 2011a ). Thus, we hypothesize that privacy protection can help to reduce IPC (H7). Social Presence is defined as the extent to which a medium, e.
e. , interaction management and information management) and six first-order constructs (i. e. , collection, secondary usage, errors, improper access, control, and awareness), following  Hong and Thong's (2013)  conceptualization. We measured the six first-order constructs with three items each, adapted from  Hong and Thong (2013). A sample item for collection is "It usually bothers me when websites ask me for personal information. " A sample item for secondary usage is "I am concerned that when I give personal information to a website for some reason, the website would use the information for other reasons. " A sample item for errors is "I am concerned that websites do not take enough steps to make sure that my personal information in their files is accurate. " A sample item for improper access is "I am concerned that databases that contain my personal information are not protected from unauthorized access.
Privacy Concern (Errors) (Adapted from Hong and Thong 2013) ERR1 I am concerned that websites do not take enough steps to make sure that my personal information in their files is accurate ERR2 I am concerned that websites do not have ade- quate procedures to correct errors in my personal information ERR3 I am concerned that websites do not devote enough time and effort to verifying the accuracy of my per- sonal information in their databases Internet Privacy Concern (Improper Access) (Adapted from Hong and Thong 2013) ACC1 I am concerned that databases that contain my per- sonal information are not protected from unauthor- ized access ACC2 I am concerned that websites do not devote enough time and effort to preventing unauthorized access to my personal information.
A Gaussian process regression model for estimating the creep rupture stress of single crystal nickel-based superalloys is constructed. It is built and validated on data disclosed in patents as well as scientific and technical reports. This model is coupled with computational thermodynamics for the prediction of microstructural features, and a model for the estimation of density. Using this combination, the key characteristics of a large number of potential alloys are systematically computed as a function of their composition. Materials specifications targeted towards applications as turbine blades, which none of the alloys from the creep database follow, narrow down the search from 300 000 000 to 180 000 alloys. A large number of these candidates are predicted as featuring greater specific properties than existing single crystal nickel-based superalloys.
Creep resistance Gaussian processes regression (also known as "kriging") is used to model the dependencies of the creep rupture stress of superalloys on their composition. Gaussian processes are a supervised learning method allowing regression over an output as a function of multiple inputs. Such model is learnt from data, i. e. it assumes a consistent aggregation of data in the form of databases has been conducted. At the core of Gaussian processes lies the covariance function, which incorporates a measure of the distance between data points in the input space, and is used to estimate the value of an output given a new input by interpolation. Due to the flexible nature of the covariance function  [30] , Gaussian processes are able to capture nonlinear relationships in the data. Furthermore, predictions from Gaussian processes are tied to uncertainty estimates indicating the model confidence.
Both time to rupture and creep rupture stress are expressed in logarithmic units so that the distribution of data points is more uniform. The model is built using the Netlab library  [35]. Compositions and properties were normalised between 0 and 1. A squared exponential kernel was employed  [31] , with its hyperparameters optimised using scaled conjugate gradients based on the logarithm of the likelihood. Of note is the absence of heat treatments in the database. Instead, all alloys are considered to have an optimal microstructure when their creep lives are reported. Only when identical alloys were found in different sources is a choice made in favour of that displaying the highest resistance. This avoids the additional burden of dealing with incomplete reporting of heat treatment parameters or the difficulty of modelling the heat treatment effects.
This indirect consideration is reasonable in that finding the right heat treatments for an alloy is easier than tuning its composition, as fewer variables are involved. Besides, as the proposed design procedure exploits to their best the conventional metallurgical routes, heat treating should closely resemble that of existing single crystal nickel-based superalloys. Validation of the model is performed through five-fold cross-validation, which consists in randomly splitting the database in five subsets of identical length, using four subsets to train the model and the fifth to test its predictive capabilities. The process iterates so that each of the subset is used for validation. The whole operation is repeated ten times over which three metrics are averaged: the Mean Absolute Error (MAE, Equation  1 ), the Mean Absolute Percentage Error (MAPE, Equation  2 ) and the Root Mean Square Error (RMSE, Equation  3 ).