dataesr/scientific-paragraphs-categorization huggingface dataset:
Electromechanics Model The deformation of water voids in cubic cells is described by a 3D electromechanics model. Initially, a single water void in a cubic cell was modelled and its deformation studied with varying voltage across the cell. Water trees are formed as a result of the combination of water voids present in the insulator which leads to its degradation. Therefore, it is important to study the merging of water voids under the effect of an electric field. Thus, a cubic cell with two water voids was modelled to determine the critical distance that leads to formation of a single geometry. The governing equations of the electromechanics module in COMSOL Multiphysics are the equilibrium equation of continuum mechanics Equation (  1 ) and Gauss law of electricity Equation (2): -∇ • σ = F (1) ∇ • D = ρ ( 2 ) where σ is the Cauchy stress tensor, F is body force, D is electric displacement, and ρ is free charge density.
In  (C. Pelletier et al. , 2019) , an analysis of  RNN and Temporal Convolutional Neural Networks (TempCNNs)  for the classification of Sentinel-2 image time series is provided. In  (Emmanuel Maggiori, 2017) , RNNs are used to correct satellite image classification maps. In this work, we exploit both temporal and spatial information provided by multi-temporal Sentinel-2 images to generate accurate and up-to-date land cover maps. For this purpose, we combine a U-Net with a RNN. The remainder of this paper is organized as follows. In Section 2. , we present our methodology, including the design of the U-Net and RNN. In Section 3. , we present the dataset we built and that was one of the key points of this work. Finally, experimental results are presented in Section 1.
DATASET A key point of our study was to create a dataset. For that we needed to identify which geographic areas to include and which land cover classes to consider. We focused on regions where both the images and the reference data are available. In addition, we required the data to be public and free. Given these constraints, we used online map data from OpenStreetMap database which provides semantic labeling for many places in the World. We selected places where the data are well annotated. These data were generated by volunteers who labeled aerial or satellite images, or by national mapping agencies that donated their labeled data to make it available to a wider public. For each area, the database has historical files that contain the most recent updates in the For this study we considered two semantic classes: roads and not roads.
For this we had to extract the road shapefiles from the OSM database. While there are other classes present (e. g: trees, buildings, rails), the roads class is the only one that is consistent across large areas. For example Buildings are usually not aligned with the structure or not represented at all. This makes it difficult to derive a pixelwise semantic labeling. The next step was to select a number of candidate areas for the dataset. For this purpose we visually inspected road labels accross several areas and assess whether the roads are properly aligned with the images. In some regions (e. g. , Africa, Asia, South America), data were poorly annotated and therefore were excluded from the dataset. After extensive research, we found that certain cities in North America and Europe satisfy our needs.
16 cities were chosen in Canada, USA, France and Germany. The corresponding Sentinel-2 images were downloaded from ESA website. The download folder contains the envelope of all resolutions including 10m, 20m and 60m. We used to the True Color Image (TCI) at 10m resolution, built from the B02 (Blue), B03 (Green), and B04 (Red) bands. Fig. 7  shows an example of a TCI image and its corresponding reference label obtained by rasterizing OSM shapefile. We set the sequence length of the RNN to 3. So for each city, 3 images corresponding to 3 different acquisition days were downloaded. Each image has a spatial resolution of 10m/pixel and a size of 10900 x 10900 pixels. We also used vertical/horizontal flips and 0/90/180/270 degrees rotations to augment data. Data augmentation helps in building a strong model which is less dependent on input image orientation.
Background: Tuberculosis (TB) depicts heterogeneous spatial patterns with geographical aggregation of TB cases due to either ongoing person-to-person transmission or reactivation of latent infection in a community sharing risk factor. In this regard, we aimed to assess the spatiotemporal aggregation of drug-resistant TB (DR-TB) patients notified to the national TB program (NTP) from 2015 to 2018 in selected districts of Karnataka, South India. Methods: This was a cross-sectional study among DR-TB patients notified from Dakshina Kannada, Udupi, and Chikamagalur districts of the state of Karnataka. Clinico-demographic details were extracted from treatment cards. The registered addresses of the patients were geocoded (latitude and longitude) using Google Earth. Using the QGIS software, spot map, heat maps and grid maps 25 km 2 with more than the expected count of DR-TB patients were constructed.
As a first step, there is a need for assessing the geospatial distribution of drug-resistant TB locally to detect whether there is any geographical aggregation of DR-TB patients using routine program data. However, the inherent difficulties in geotagging the location of the patient and technical challenges in handling such data have limited the use of geospatial epidemiology in TB control efforts. Hence, as a desk review, we explored the possibility of geocoding the address of DR-TB patients using Google Maps and constructing the spatial heterogeneity maps. We geocoded all DR-TB patients treated under the Revised National Tuberculosis Control Program (RNTCP) in three selected districts of Karnataka from 2015 to 2018 to depict the spatiotemporal pattern of these patients.
Data variables, sources of data, and data collection Data on socio-demographic and clinical characteristics and drug-resistant pattern like PMDT number, age, gender (male/female/others), village/ward, taluk, district, name of TU, name of PHI, DOTS provider, date of DST, date of DST results, date of registration, type of TB (new/treatment after loss to follow-up/treatment after failure/recurrent/relapse), site of TB (pulmonary/extra-pulmonary), type of DST used for diagnosis (CDST/Xpert MTB/RIF assay), molecular test results (not done/Mtb+/Rif+/Mtb+/Rif-/Mtb -/MTB+ Rif indeterminate), type of DR-TB (RR-TB/ MDR-TB/XDR-TB), resistance to isoniazid (yes/no/ not available), resistance to ethambutol (yes/no/not available), resistance to pyrazinamide (yes/no/not available), resistance to streptomycin (yes/no/not available), resistance to fluoroquinolones (yes/no/not available), resistance to kanamycin (yes/no/not available), HIV status (yes/no/not available), ART status (yes/no/not available/not applicable), weight at initiation of treatment, tobacco use (yes/no/not available), and alcohol use (yes/no/not available) were extracted from DR-TB treatment card and DR-TB register maintained at DR-TB center.
Geocoding The geocode (latitude and longitude) of each patient was obtained based on the registered address using Google Earth pro version 7. 3. 2. 5776 application. The village/ ward/street of the patient was used to geocode the address  [15] [16] [17]. The details like taluk, district, and PHI were used to locate the village/ward/street on the geospatial map. In case of non-availability of the village/ ward/street, the support was sought from Senior Treatment Supervisor (STS) of the TU or PHI to which the patient was referred for DOTS-Plus. If the village/ward/ street provided in the address is not found on Google Maps, the nearest village as suggested by the STS or staff of PHI was geocoded. The geocodes were noted in decimal degrees format with a minimum of five digits after the decimal point  [18].
Data entry and analysis Data were double entered and validated using EpiData Entry software (EpiData Association, Odense, Denmark) and analyzed using EpiData analysis (version 2. 2. 2. 182, EpiData Association, Odense, Denmark) and Stata version 12. 0 (STATA Corp. , College, TX, USA). Sociodemographic and clinical characteristics and drug- resistant patterns were summarized using numbers and percentages. The QGIS version 2. 18. 15 (QGIS Developer team, Las Palmas (2016)) was used to plot the drug-resistant TB patients, and a map of geospatial distribution was constructed. The heat maps depicting spatial heterogeneity  [19]  were constructed for the year 2015 to 2018. The quadratic weight was used as a density measure  [20] , and the heat maps  [5, 20]  were constructed with a buffer radius appropriate to the layer units  [21].
Acknowledgements This research was conducted as a part of the "National Operational Research Training Course 2018-19" organized by  Project Axshya , funded by  The Global Fund , and implemented by The  International Union Against Tuberculosis and Lung Diseases (The Union ),  South-East Asia Regional Office, New Delhi, India. The training course was conducted in collaboration with  Revised National Tuberculosis Control Program, Ministry of Health and Family Welfare, Government of India and National Institute for TB and Respiratory Diseases, New Delhi, India. The training is based on "The  Union/Medécins sans Frontières (MSF) " model OR course and which has been adapted as SORT IT ( Structured Operational Research and Training Initiative ) coordinated by  TDR , the  Special Programme for Research and Training in Tropical Diseases  at the  World Health Organization Mentorship , and facilitation for this course was provided through  The Union South-East Asia Office, New Delhi ; the  Centre for Operational Research, The Union, Paris, France ;  Baroda Medical College, Vadodara ;  Médecins Sans Frontières, New Delhi ;  ESIC Medical College and PGIMSR, Bengaluru; North Delhi Municipal Corporation Medical College, Hindu Rao Hospital, New Delhi; GMERS Medical College, Vadodara ;  Postgraduate Institute of Medical Education and Research, Chandigarh, India ; and  Yenepoya Medical College, Mangalore.
To identify the species, we then mapped the shotgunsequencing data to the chicken (Gallus gallus; GenBank: NC_001323) mitogenome. Out of a total of 85,408,658 reads, 3,663 (<0. 01%) mapped against the chicken mitogenome. While the reconstructed mitogenome was incomplete, we extracted a partial COI gene (232 bp), which is routinely used for species identification. We then searched for matches with this gene in GenBank avian genetic databases using BLASTn  13, 14  and found a 100% identity match with the horned lark (Eremophila alpestris). To estimate the overall endogenous DNA content of the ancient horned lark extract, we then mapped the shotgunsequencing data to a nuclear genome assembly from a modern horned lark (Sigeman et al. 15  ; N50 = 20 kb; subspecies E. a. flava). Out of 8,5408,658 reads, 757,883 (0.
Methods Sample collection and radiocarbon dating. Following retrieval of the nearcomplete bird carcass (Figs. 1  and 4 ) from the permafrost tunnel located along the Tirekhtyakh River 2), we excised ~120 mg of feather from the specimen and used this for radiocarbon dating at the Oxford Radiocarbon Accelerator Unit (ORAU) using Oxcal computer program (v4. 3)  12. In addition, a small piece of tissue (~300 mg) was collected and stored at -20 °C for subsequent DNA analysis. The carcass itself has been accessioned the collections at the Sakha Academy of Sciences in Yakutsk (accession ID: 2018-Tir-Shorelark-01). DNA extraction and sequencing. After an overnight digestion of ~50 mg of bird tissue in a buffer optimised to digest keratin-rich tissues  32  , we extracted DNA using the DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany).
Xp kit (validated method) at the SciLifeLab sequencing facility in Stockholm. Extractions and library preparation were conducted in a separate aDNA lab and appropriate precautions were taken to minimize the risk of contamination of the ancient sample  36. Data processing, genome reconstruction and analyses. First, raw aDNA data was demultiplexed using bcl2Fastq v2. 17. 1 with default settings (Illumina Inc. ). We then trimmed adapters and merged paired-end reads using SeqPrep v1. 1  37  with default settings but with a minor modification in the source code, allowing us to choose the best quality scores of bases in the merged region instead of aggregating the scores following Palkopoulou et al. 11. We then merged sequencing reads and mapped them against the reference mitogenomes for chicken (Gallus gallus; GenBank: NC_001323) mitogenome using the BWA v0.
7. 13 aln algorithm  38  and slightly modified default settings with deactivated seeding (-l 16,500), allowing more substitutions (-n 0. 01) and allowing up to two gaps (-o 2). The BWA samse command was then used to generate alignments in SAM format. The resulting reads were then processed in SAMtools v1. 9  39  , converted to BAM format coordinate sorted and indexed. We removed duplicates from the alignments using a custom python script to avoid inflation of length distribution for loci with deep coverage  11. Next, we used Picard v1. 141 (http://broadinstitute. github. io/picard) to assign read group information including library, lane and sample identity to each bam file. Reads were then re-aligned around indels using GATK v3. 4. 0  40. Only reads/alignments with mapping quality ≥30 were kept for subsequent analysis.
We then estimated the endogenous mitochondrial DNA content (i. e. , the proportion of reads mapping to the mitogenome) and coverage with SAMtools. Secondly, we reconstructed a consensus mitogenome using a majority consensus rule and a minimum of 5X coverage in Geneious® v7. 0. 336  41  and the raw data mapped to chicken. We extracted a partial COI gene (232 bp) from this mitogenome and used it as input for species identification in BLASTn  13, 14. Third, after identifying a 100% identity match with the horned lark (Eremophila alpestris), we generated a de novo mitogenome for a modern horned lark (E. a. flava), using whole genome sequencing data from Sigeman et al. 15  , with Mitobim v1. 9. 0 16 using the 'quick' option and the Mongolian lark (Melanocorypha mongolica; GENBANK: NC_036760), which is the closest-related species with a compete mitogenome  17  as bait reference.
Reconstruction of this mitogenome was done after four iterations. Next, we remapped the raw data for the ancient horned lark to the modern lark mitogenome, built its consensus genome in Geneious® using the same parameters as above and estimated the endogenous mitochondrial DNA content, as described above. We also extracted the cytb (987 bp) and COI (683 bp) genes to confirm species identity in BLASTn  13, 14. Fourth, we investigated the phylogenetic relationships between the ancient horned lark and modern horned larks by including it in a mitochondrial data set consisting of 2034 bp (996 bp cytochrome b and 1038 bp ND2) from Ghorbani et al. 20. We only included specimens without missing data, making for a total of 31 modern larks. The evolutionary relationship was inferred by implementing a Bayesian relaxed clock model in BEAST2 v2.
Acknowledgements Genome sequencing and radiocarbon dating was funded through a grant from  FORMAS  (grant no. 2018-01640 ). The authors acknowledge  Boris Berezhnov  and  Spartak Khabrov  for access to the bird remains. The authors also acknowledge support from the  Uppsala Multidisciplinary Centre for Advanced Computational Science  for assistance with massively parallel sequencing and access to the UPPMAX computational infrastructure. Sequencing was performed by the  Swedish National Genomics Infrastructure (NGI)  at the  Science for Life Laboratory , which is supported by the  Swedish Research Council  and the  Knut and Alice Wallenberg Foundation. The field work was supported by  Renegade Pictures UK Ltd. Open access funding provided by  Stockholm University.
Advanced FE models were developed taking into account the effect of temperature on the friction between the metallic sheet and the tools, allowing to estimate the shape and the quality of the final product after experiencing large deformations. These models allow the simulation of a single stroke of the forming process. For multiple strokes, given the complexity of the FE models and the high computation time for one stroke, the full simulation of a real industrial part until reaching the steady state is not possible. Therefore, a speed-up method was developed by M2i and ESI, and implemented using two different FE commercial software. It's a method based on extrapolation, it was initially used in fatigue damage computations in which a lot of cycles of simulations are needed  [1]. The proposed method, called jump in strokes, allows a CPU reduction up to 60%.
This method was applied for two industrial use cases: the first use-case was proposed by the Philips (small parts) and the second by Opel (large panels). In this paper, the authors will present the proposed jump in strokes method and its application for the two use cases. In the first section, the theory and main assumptions behind the jump-in-strokes method are presented. In the next sections, the method is applied for the two use cases. For each application a full description is provided and the FE model for a single stroke is described. Implementation of the jump in strokes method within the FE software is detailed: MSC. Marc for the first application and ESI PAM-STAMP [2] for the second application.
The finite element model for a single stroke In this section, we describe the finite element model used to simulate Philips' deep drawing use case: Geometry and mesh, behavior laws, temperature dependent friction model. Geometry and mesh: the tools (blank holder, punch, die and ejector) and blank are represented. The two steps are described by axisymmetric FE models in the FE software MSC. Marc, using fully meshed tools, which allow to capture their temperature distribution. Both the tools and the blank are modelled as deformable bodies. Fig. 4  shows the geometry and the mesh of the tools and the blank for the first and the second step. The shape of the blank after each step is also showed. The blank geometry and its state variables (including the temperature field) are transferred from the step-1 model to the step-2 model using the so-called section file in MSC.
The finite element model for a single stroke Thermo-mechanical modelling: to capture the effect of temperature on the formability of the deep drawn part, a thermalmechanical coupled simulation is created within the ESI PAM-STAMP software. The mechanical and transient thermal problems are solved by an explicit time integration scheme. Shell elements of type Belytschko-Tsay with 5 integration points over the thickness are used to mesh the blank. The tools are rigid, only their thermal evolution is considered. For thermomechanical simulations in which cooling channels are considered, the state of the art prescribes to use a volumetric tool mesh. Meanwhile the gradient temperature inside the tool can be neglected and the relevant quantity is the surface temperature, which is the input of the frictional model.
Conclusion In this work a speed-up method called "Jump in strokes method" was proposed and implemented within two FEM software packages, being MSC. Marc and ESI PAM-STAMP. The accuracy of the method was checked against the incremental method, which showed the potential of the method. The method was also used to model two challenging use-cases at different scales: small parts used for consumer goods proposed by Philips and large ones used for cars industry proposed by Opel. Experimental measurements were performed during the industrial runs for the two processes and compared to the corresponding FE models predictions together with the Jump in stroke method. The validity of the numerical predictions is related to the accuracy of the assumptions and approaches considered in each single stroke FE models, such as modeling of the friction behavior and the definition of the thermal boundary conditions.
Then, the total number of wings used to analyze overall variation and the number of complete individuals used to analyze asymmetric variation. In order to remove the proximal part of the wing, which is usually deteriorated during the removal of the wing from the thorax and may bias wing-size estimates, two landmarks were placed in ImageJ 1. 51j8  (Rasband, 2012) , that correspond to landmarks 1 and 3 in  Fraimout et al. (2018)  and a line between them was traced to discard all the proximal part of the wing. Then, a semi-automatized script in Python 3. 6. 1 was written to extract the wing and spot contours.
TA B L E 2 Their respective areas were estimated by the Green's theorem in the function contourArea of the OpenCV library  (Bradski & Kaehler, 2000)  as the number of pixels within the wing and spot contours. Our script was based on the manual adjustment of the picture color contrast. Because the color difference between the wing and the background is sharp, the wing outline was easily and robustly extracted. To the contrary, because the limits of the spot are fuzzy, a subtle difference in the image contrast can induce a relatively large difference in the spot outline. The manual adjustment of the picture contrast might thus be an important source of measurement error, particularly so for the spot size. We thus repeated the estimation of the wing and spot sizes in all the pictures and measurement error was quantified using an ANOVA for each population, assessing individual variation and measurement residuals.
| Natural populations We applied a linear model to assess the relationship between absolute spot size and wing size, differences among natural populations on the spot size and whether there was natural variation in the spot-wing relationship (i. e. , spot size ~ wing size + population + wing size × population). To have an estimate of natural variation for the other two phenotypic traits, we also assessed with one-way ANOVAs the differences among natural populations for wing size and the ratio. To explore the potential effect of temperature on those wild samples, we collected the average temperature during the month of collection in these places from the National Oceanic and Atmospheric Administration database (www. ncdc. noaa. gov/ IPS/mcdw/mcdw. html; Table  1 ) and ran a linear regression for each trait with temperature as a continuous variable.
The replicated measurements were included in the error term. Once the presence or absence of significant DA and FA was assessed in each particular group, we assessed the effect of temperature and geography with an ANOVA for DA, and a Levenne's test for FA. We checked for differences in the slope of the relationship between temperature and ratio asymmetry among populations with three pairwise t tests. As effect sizes, Cohen's d for differences among geographic populations and r 2 for temperature effects were reported due to their simplicity  (Rosenthal, Cooper, & Hedges, 1994). All analyses were run in R version 3. 5. 2 (R Core Team 2018) and graphs designed with ggplot2  (Wickham, 2016). All data, scripts, and figures can be found in Dryad and GitLab (https ://gitlab. com/cefer inovg ).
DATA AVA I L A B I L I T Y S TAT E M E N T All data, R scripts, and reports of R scripts can be found in Dryad: https ://doi. org/10. 5061/dryad. 66t1g 1jxp O RCI D Ceferino Varón-González https://orcid. org/0000-0002-4577-4187 Antoine Fraimout https://orcid. org/0000-0003-4552-3553 Vincent Debat https://orcid. org/0000-0003-0040-1181 R E FE R E N C E S Arnold, P. A. , Nicotra, A. B. , & Kruuk, L. E. B. (2019). Sparse evidence for selection on phenotypic plasticity in response to temperature.