dataesr/scientific-paragraphs-categorization huggingface dataset:
To benchmark the completeness of our map, we calculated the ratio between the area of primary forest in our database at country level, and the estimated area of "forest undisturbed by man" from the indicator 4. 3 in the Forest Europe report  10  or, for those countries where this information is not available, from FAO's Forest Resources Assessment  48. Although the definition of "forest undisturbed by man" in Forest Europe is consistent with our definition of primary forest  10  , it must be noted that these country-level estimates stem from national inventories or other studies, and data quality varies from country to country  49. The comparison presented here should, therefore, be taken with caution (Fig. 4 ). Forest Europe reports no primary forest for some western European countries (Spain, France, Belgium, Netherlands, Germany, United Kingdom and Ireland), although for most of these countries we did find information on at least a handful of primary forest sites.
Assessing recent human disturbance with remote sensing. Since our data were collected continuously over the last two decades, we cannot exclude that some forest patches may have undergone human disturbance after data collection. This is particularly relevant for areas where primary forests are lost at high rates, such as the Carpathians, Russian Karelia, or Northern Fennoscandia  [18] [19] [20]. To assess to what extent this might be an issue, we used the open-access Landsat archive and the LandTrendr disturbance detection algorithm  50, 51  , using Google Earth Engine 52 (Fig. 5 ). Specifically, we 1) quantified the proportion of polygons in our map that underwent disturbance between 1985 and 2018, i. e. , Landsat 5 operating time, 2) visually checked a stratified random selection of these disturbed polygons to quantify the prevalence of anthropogenic vs.
These estimates should be considered as lower bounds, because only the disturbance events with a magnitude sufficient to be captured with LandTrendr and occurring in 1985-2018 could be identified. Not being this a formal validation, the results presented here should not be extrapolated to primary forests not included in our map. Finally, being our database built with a bottom-up approach, we are unable to exclude the existence of remaining bias or interpretation error, which might have propagated through the successive steps required to build it. As such, we warn the users against possible heterogeneity in data quality, accuracy and completeness across datasets.
Usage Notes All data files are referenced in a geographic coordinate system (lat/long, WGS 84 -EPSG code: 4326). The provided files are in a personal geodatabase, and can be accessed and displayed using standard GIS software such as: QGIS (www. qgis. org/en). All datasets listed in Online-only     †  The number of disturbed polygons is higher than the total number of polygons because some polygons expanding over more than one biogeographical region were split. PF -Primary Forest. from the dataset 'Strict Forest Reserves in Switzerland' (ID 30, custodian: Jonas Stillhard). In the case of the dataset ' Ancient and Primeval Beech Forests of the Carpathians and Other Regions of Europe'  40, 41  (ID 34, Custodian: UNESCO), this data is freely available online, but its copyright does not allow redistribution.
code availability The code to reproduce the composite layers, for post-processing and for assessing recent human disturbance with remote sensing is available together with the database in Figshare (https://doi. org/10. 6084/m9. figshare. 13194095. v1)  33. We included seven scripts: Python (. py) scripts were run in ESRI ArcGIS (v10. 5) and are available also as ArcGIS Models inside the Geodatabase. R (. R) scripts were run using R (v 3. 6. 1)  46. The remaining. txt scripts were run in Google Earth Engine.
00_ComposeMap. R -Identifies overlapping polygons across individual datasets. • 01_CreateComposite_Points. py -Creates the composite point feature class. • 02_CreateComposite_Polygons. py -Creates the composite polygon feature class. • 03_PostProcessing. R -Extracts additional information on each primary forest. • 04_Add_Postprocessing. py -Imports post-processing output into the geodatabase. • 05_Summary_stats. R -Calculates summary statistics of primary forests • 06_DisturbanceAssessment_Step1_exportIntermediateChangeImg. txt -Runs LandTrendr in Google Earth Engine, tiles the area of interest, creates Change-Images for each tile, and exports these as intermediate. tif files containing the LandTrendr metrics. • 07_DisturbanceAssessment_Step2_extractPolygonValuesFromChangeImg. txt -Extracts LandTrendr metrics for each forest polygon from Change-Images and exports as.
Acknowledgements This project was funded by  Frankfurt Zoological Society  (FZS; project  ETN-WIE-FZS-001 ) and the  European Commission  ( Marie Sklodowska-Curie  fellowship to FMS, project  FORESTS & CO , # 658876 ). The Italian dataset was supported by funding from the  Department for Nature Protection of the Italian Ministry of the Environment, Land and Sea Protection. The GEVFP's French dataset was supported by funding from  EU, RF  and ' Conseil Régional Midi-Pyrénées '. MM gratefully acknowledges the  Czech Science Foundation  (Grant GACR no. 21-27454S ), as well as the institutional project EVA No. CZ. 02. 1. 01/0. 0/0. 0/16_019/0000803. The Serbian dataset was supported by the  Ministry of Education, Science and Technological Development of the Republic of Serbia , grant number  451-03-9/2021-14/ 200197.
This work would not have been possible without all those who responded to the questionnaires and those who collected all the data presented here. A special thanks goes to all data contributors, especially those who are not coauthors of this paper (in alphabetic order):  Paloma Hannonen  (Dataset  39 ),  Matti Liimatainen  (Dataset  38 ),  Simone Mayrhofer  (Dataset  49 ),  Daniel Vallauri  ( Dataset 13 ) and  Juraj Vysoky  (Dataset  26 ). FMS also acknowledges the financial support within the funding programme Open Access Publishing by the  German Research Foundation (DFG). We also thank  Teresa De Marzo  for support during the assessment of recent human disturbance in primary forest polygons. We thank the editor and two anonymous reviewers for very helpful and constructive comments on prior manuscript versions.
• 00_ComposeMap. R -Identifies overlapping polygons across individual datasets. • 01_CreateComposite_Points. py -Creates the composite point feature class. • 02_CreateComposite_Polygons. py -Creates the composite polygon feature class. • 03_PostProcessing. R -Extracts additional information on each primary forest. • 04_Add_Postprocessing. py -Imports post-processing output into the geodatabase. • 05_Summary_stats. R -Calculates summary statistics of primary forests • 06_DisturbanceAssessment_Step1_exportIntermediateChangeImg. txt -Runs LandTrendr in Google Earth Engine, tiles the area of interest, creates Change-Images for each tile, and exports these as intermediate. tif files containing the LandTrendr metrics. • 07_DisturbanceAssessment_Step2_extractPolygonValuesFromChangeImg. txt -Extracts LandTrendr metrics for each forest polygon from Change-Images and exports as.
11* 0, 1, 25, 32, 33, 35 Russian Federation 3,082\3 37,417. 69* 0, 51 Serbia 14\4 7. 78 0, 35, 36, 44, 45 Slovakia 290\4 10. 98 0, 9, 26 Slovenia 170\1 9. 53 0, 27 Spain 44\58 9. 4* 0, 41, 52 Sweden 0\51 32. 81* 0, 29, 35 Switzerland 5\5 2. 29 0, 30, 35 Ukraine 8,966\3 97. 8* 0, 1, 32 United Kingdom 0\2 0. 1 9 Total 18,411\299 41,136. 53* of primary forest data across European countries. Dataset IDs correspond to those in Online-only Table1. * Some point features have no information on forest patch area.
Introduction oduction Finite element simulation of thin sheet metal structures has played an incredible role in product design and manufacture processes. Quantitative analyses of accuracy of numerical results show that the selection of a proper element plays a vital role in the accuracy and efficiency of the simulation, in particular, the complicated ones, such as sheet metal forming. Recently, considerable effort has been devoted to the development of solid-shell finite elements  [1, 2, 3]. They combine the advantages of the traditional solid and shell elements since they are three dimensional displacement/velocity based elements with reduced integration and enhanced strain fields. In this work, one of the solid-shell elements family (SHB) is presented. A prismatic element is selected as it can be integrated into our selected software FORGE.
Prospective and Future Work In this section, the prospective of the ongoing work along with future work will be discussed in brief. Currently, anisotropic yield criteria have been developed in external library called MPCP at CEMEF. The aim afterward is to solve MPF with anisotropic yield criteria and notice the change in the results. This comes in handy with the fact that the workpiece used in MPF is very thin and might inherit anisotropic plastic behaviour. Moreover, the work on remeshing process for SHB element in going in full swing. The main idea is to try to utilize remeshing features of Forge and apply them on the new element. This is expected to improve the efficiency of the code as the simulation could be started with coarse mesh and mesh is refined when needed by the remesher. The newly developed featurs are supposed to be implemented and tested with benchmark problems whether MPF or other problems.
Conclusion Conclusion In this paper, linear prismatic solid-shell (SHB) element has been used in the application of magnetic pulse forming. The resulting element formulation was implemented in finite element software FORGE which is based on tetrahedral element (MINI). The compatibility issues were resolved by means of prism division technique to introduce the prism element as a combination of tetrahedral elements to be able to utilize all the subroutines available in FORGE. The performance of the proposed element was assessed by simulating the MPF process. The obtained results from SHB element were compared to its counterpart MINI element originally implemented in FORGE. Also the numerical results were compared to experimental results carried out in CEMEF. The overall results show that SHB element is relatively more accurate than MINI element even when using very low number of elements.
A larger effect on the global COS biosphere sink was found by changing the fixed COS mole fraction of 500 pmol mol -1 to values that vary spatially and temporally. The reduction in the COS sink strength is most pronounced in regions with large biomass such as the tropics. This analysis highlights the importance of accounting for variations in atmospheric COS mole fractions, which has not yet been adopted as standard practice. We make a number of recommendations for future improvements of the model, including re-calibration of the COS model parameters. However, we are limited by site-and leaf-level data coverage in being able to accurately constrain the model over different PFTs and seasons. More campaigns and long-term observations in underrepresented PFTs, biomes, and soil types and more laboratory measurements such as for CA sensitivity in leaves and soils would be key to continued improvement of the model.
In our previous publication, we presented a new method based on recursive random forest approaches to predict aqueous solubility values of drug and drug-like molecules  [4]. It was based on the development of two novel recursive machine-learning approaches used for data cleaning and variable selection, and a consensus model generated by the combination of regression and classification algorithms. This model was able to provide good solubility prediction compared to many of the models described in the literature. Considering that our model was developed from a database of aqueous solubility values with limited information on the experimental conditions of the solubility assay, could our model successfully predict the intrinsic solubility values of the two sets of drugs used in the second "Solubility Challenge"? The present study describes the performance of our model with the molecules of the second "Solubility Challenge" and the comparison of the results with those obtained with the best performing models of the competition.
Software The Konstanz Information Miner (KNIME) is a free and public software tool that has become one of the main analytical platforms for innovation, data mining and machine learning. The flexibility of workflows developed in KNIME to include different tools allows users to read, create, edit, train and test machine learning models, greatly facilitating the automation of predictions and application by any user  [5, 6]. In this study, we used the open source software KNIME Analytical Platform version 4. 0. 2 [7]  and its free complementary extensions for transformation, analysis, modelling, data visualization and data prediction. For the generation of molecular descriptors from structures, the "Descriptor" node from "alvaDesc" extension  [8]  and the "RDKit Descriptor" node  [9]  were employed.
Modelling dataset To predict the molecules of the second "Solubility Challenge", we used as the training set the curated set of aqueous solubility published in our previous paper. This set consists of two large aqueous solubility databases  [10, 11]. For each molecule, taking the SMILES (Simplified Molecular Input Line Entry Specification) code as input format, a structure cleaning, standardization, and duplicate removal protocol was developed. The InChi (IUPAC International Chemical Identifier) code was used for duplicate identification and the standard deviation among experimental measurements was computed. A detailed description of this procedure has been shown in our previous article  [4]. Although the hypothesis that -the quality of the experimental data is the main limiting factor in predicting aqueous solubility-has been challenged  [12] , any variability in the experimental protocol is always "noise" for in silico modelling purposes.
Modelling algorithm Due to the uncertainty of the database, we considered the importance of a rigorous protocol for data selection in the development of the original model, in order to discriminate those molecules with potential unreliability. As a first step, we selected a RELIABLE Test Set, consisting of molecules with more than one reported measurement and with inter-source standard deviation greater than 0 and less than 1 logarithmic unit. We used beyond 1 logarithmic unit as a threshold to discriminate unreliable samples. This RELIABLE Test Set was used for model optimization. From the QSPR perspective, it is necessary to select a set of descriptors that leads to the most predictive model and facilitates model interpretation. To this end, we developed a recursive variable selection algorithm based on regression random forest (RRF).
Second "Solubility Challenge" prediction First, we ensured that all test set molecules found in the initial source set used as the training set were removed. Since the model was previously validated using the RELIABLE Test Set and by 5-fold crossvalidation, we used the entire database (including the RELIABLE Test Set) to predict the test challenge samples. To analyse the performance of the solubility regression models, two types of coefficient of determination (r 2 ), root mean squared error (RMSE), mean absolute error (MAE), bias and the percent of molecules with an absolute error less than 0. 5 logarithmic units (% 0. 5 log) were calculated.
Conclusions The results obtained with the evaluation of the second "Solubility Challenge" reinforce the idea that data quality is not the major limiting factor for obtaining adequate solubility predictions if the implemented modelling methodology can cope with data uncertainty. In our case, the developed algorithm was able to overcome data variability to obtain acceptable aqueous solubility prediction results. The results published here are a blind prediction, since the experimental aqueous solubility values of the challenge test set were not accessible at the time of our model development and training. Although the achieved performance is comparable to those reported in the review of the second Solubility Challenge, our model is only based on public data compared to some of the best models of the second Solubility Challenge, which were based on the huge aqueous solubility databases available from pharmaceutical companies.
Furthermore, the algorithm of our model is global, as demonstrated by the use of generic data without the bias of "training close to the test data". The automation of the proposed methodology and its possible application on larger databases, collected under more homogeneous conditions, could be a step forward to improve solubility prediction during drug discovery and development stages. In attention to the importance of sharing data and methods to ensure reproducibility and applicability of QSPR models, we made the data publicly available along with our predictive model based on the KNIME Analytical Platform as a new free tool for the assessment of aqueous solubility of drug candidates.
A Borexino bound on non-standard contributions to neutrino-electron scattering  [162] [163] [164]  has also been used to constrain the L µ -L τ gauge boson  [166, 168, 169]. Our reproduction of the Borexino constraint is shown in Fig. 17  Fig. 17  Existing constraints and projected DUNE sensitivity in the Lµ -Lτ parameter space. Shown in green is the region where the (g -2)µ anomaly can be explained at the 2σ level. The parameter regions already excluded by existing constraints are shaded in gray and correspond to a CMS search  161]  ("BaBar"), a previous measurement of the trident cross section  [146, 151]  ("CCFR"), a measurement of the scattering rate of solar neutrinos on electrons  [162] [163] [164]  ("Borexino"), and bounds from Big Bang Nucleosynthesis  [165, 166]  ("BBN"). The DUNE sensitivity shown by the solid blue line assumes 6 years of data running in neutrino mode, leading to a measurement of the trident cross section with 40% precision.
While spectral information regarding E e could allow a search to distinguish between χe and ν µ e scattering, we expect that uncertainties in the ν µ flux (both in terms of overall normalization and shape as a function of neutrino energy) will make such an analysis very complicated. For this reason, we include a normalization uncertainty of 10% on the expected background rate and perform a counting analysis. Studies are ongoing to determine how such an analysis may be improved. For this analysis we have assumed 3. 5 years of data collection each in neutrino and antineutrino modes, analyzing events that occur within the fiducial volume of the DUNE near detector. We compare results assuming either all data is collected with the ND on-axis, or data collection is divided equally among all off-axis positions, 0.
Once a neutron has oscillated to an antineutron in a nucleus, the antineutron has a 18/39 chance of annihilating with a proton in argon, and a 21/39 chance of annihilating with a neutron. The energies and momenta of the annihilation products are assigned randomly but consistently with four-momentum conservation. The products of the annihilation process follow the branching fractions (shown in Table  9 ) measured in low-energy antiproton annihilation on hydrogen  [254]. The default model in GENIE for the propagation of particles inside the nucleus is hA2015, an empirical, data-driven model that does not simulate the cascade of hadronic interactions step by step, but instead uses one effective interaction to represent the effect of final-state interactions (FSI). Hadron-nucleus scattering data is used to tune the predictions.
Acknowledgements This document was prepared by the DUNE collaboration using the resources of the  Fermi National Accelerator Laboratory (Fermilab) , a  U. S. Department of Energy, Office of Science, HEP User Facility. Fermilab  is managed by  Fermi Research Alliance, LLC (FRA) , acting under Contract No. DE-AC02-07CH11359. This work was supported by  CNPq ,  FAPERJ ,  FAPEG  and  FAPESP, Brazil ;  CFI ,  IPP  and  NSERC ,  Canada ; CERN; M ŠMT, Czech Republic; ERDF,  H2020-EU  and  MSCA ,  European Union ;  CNRS/IN2P3  and  CEA, France ;  INFN, Italy ; FCT, Portugal; NRF, South Korea;  CAM, Fundación "La Caixa " and  MICINN, Spain ;  SERI  and  SNSF, Switzerland ;  T ÜB İTAK ,  Turkey ;  The Royal Society  and  UKRI/STFC ,  United Kingdom ;  DOE  and  NSF ,  United States of America.
DATA COLLECTION AND ANALYSIS Data for the research was collected through observations, participant enquiries, and short videos recorded during the TechShops sessions. Each of the three facilitators wrote field notes based on observations in TechShops. We catalogued aspects of the TechShops that participants liked (providing them motivation and supporting their interests), the key skills and competencies they exhibited, and participation activities that were challenging due to lack of competency or interest. At the end of each session, the observational notes were shared and discussed in a debrief session amongst the facilitators and thematic patterns identified. The thematic patterns were then reflected on by the broader research team and inductively refined, guided by the principles of thematic analysis described in  [11] , and finalized as findings.
Interest in Social Media Participation The survey data revealed social media as the favourite and most commonly used ICT by participants. Most parti cipants were interested in enhancing their participation skills in social media apps that they were already using, and/or developing participation skills in using those they were not yet using, but desired to use. Participation statistics varied from YouTube to Instagram. Though everyone used YouTube, 19 participants used Facebook, while eight and seven participants used Snapchat and Instagram respectively  [5]. Because most participants were interested in developing their social media participation skills further, we engaged them through TechShops  [3] , to enhance their skills. The TechShops were focused on YouTube (which all participants used and wanted to enhance their skills at using), a nd Facebook (which most participants wanted to enhance their participation skills in and learn how to use).
However, once a useful video is found, it can be shared within the friend group and saved to playlists. Moreover, due to the value the DSO found in SkillsTube, their support staff started developing accessible videos on specific skill development of interest to participants such as cooking, making coffee and dressing beds. With available content, support staff envisaged running TechShops to introduce the app to other young adults in different centres, and supporting existing users to enhance their skills in using features such as playlist to archive their favourite videos. The app's iterative design process and TechShops supported and enhanced the development of participants' competencies that were not directly related to their social media participation. For example, the QR code scanning feature that the app's design used for logging in and adding friends was neither a YouTube or Facebook feature.