dataesr/scientific-paragraphs-categorization huggingface dataset:
We then constructed an aggregate QI measure for each country by assigning a score of 1 to every 75th percentile score or above and an extra 1 to every 90th percentile score or above. We used this aggregate score to compare the overall level of deficiency in quality of care of the 59 facilities and the 8 countries in SHELTER. A country or facility will only be compared with other countries or facilities on a LTCFQI, if the actual number of cases with a positive score on the QI or the predicted number of cases with a positive score is 5 or more. A ranking of the countries or facilities is possible by dividing the aggregate QI measure by the number of LTCQIs for which a score was calculated. Analyses were conducted using SPSS and Microsoft Excel, see Appendix. Research ethics approval for the SHELTER study was received for all participating countries and specifically from the following ethics committees: METC VUmc Amsterdam, METC Universita Universita Cattolica del Sacro Cuore Rome, METC National Committee, THL Helsinki, METC Hospital Saint Périne Paris, METC Haifa University, Kaplan Medical Center, The Schools Research Committee Ethical Panel, University of Kent Canterbury, METC University of Ulm, Multi-centric Ethics Committee, General Faculty Hospital Prague.
Iron area is therefore imposed and number of turns (primary and secondary) can be calculated. But for highpower applications, magnetic core does not exist. Therefore, the designer has to impose the ratio between copper area and iron area by imposing the geometry of magnetic cores or by adjusting number of primary turns, for example. In this design methodology, we suppose that the geometry of magnetic cores is free and the number of primary turns is imposed. The algorithm-aided design has been realized with MATLAB. It is based on classical technique of design of high-frequency transformers and considers magnetizing current and voltage drop. Their magnitudes depend on final inductive parameters (calculated from the 3-D analytic model due to a reluctance network), which can be estimated only when the geometrical characteristics of the magnetic component are known.
The more the parameters are low, the more the computation time is long because of computation step that they imposed, but they have no consequences on the solution. The fields "Design specifications" and "Conductor specifications" correspond to classical electrical constraints for the design of a magnetic component. The field "Algorithm parameters" corresponds to the parameters dedicated to this design methodology. The function "Calc_AcAi" calculates suitable primary and secondary products of areas (AcAi1 and AcAi2). "Calc_Geom" determines geometries of magnetic cores. From AcAi1 and the number of primary turns (imposed by user), the program calculates the primary iron area (Ai1). From the figure in front of the "Mechanical constraints" in Fig. 9 , we can deduce three expressions (  8 )-(  10 ) with three unknowns: p (the depth of magnetic cores), nc (the width of the central leg of magnetic cores), and L (the width of winding windows) Ai1=p • nc (8) PG max = 2 • L • (1 + δL)+p (9) LG max = 2 • L +2• nc.
Numerical solution for transient state A simulation algorithm has been developed in Matlab s. It allows the evaluation of the permeate flux in function of the operation variables; this flux is determined for the individual coefficients in each resistance layer. In the first step, the operating conditions (flows rates of aqueous and gaseous solutions, composition, pressure and temperature) and the structural parameters (inside and outside diameters, membrane characteristics: length, porosity and tortuosity) are defined, while in the second step the program computes the properties of the phases and then calculates the dimensionless numbers and individual coefficients of mass transfer. Finally, in the last step the simulation program generates the estimation of transmembrane fluxes performing iteration of the concentrations at the interphases with the Regula Falsi method.
To estimate the magnitude of the host galaxy, we obtained a series of observations from Gemini-S (u, g, and r bands on 2012 June 24) and Gemini-N (J band on 2012 November 6). These provide magnitudes of u = 25. 36 ± 0. 25, g = 25. 72 ± 0. 07, r = 24. 94 ± 0. 06, and J > 24. 0 (all AB). Given the flatness of the u band between these epochs, we assume that all the magnitudes measured here are of the host galaxy. However, we note that while this seems likely for the u band (unless the afterglow has exhibited a prolonged plateau), the g-and r-band data remain on the extrapolation of the earlier afterglow (see Figure  6 ) and so formally these magnitudes provide only an upper limit on the host galaxy brightness.
The constraints for GRB 101225A are weak, since it was not detected after the rapid decay. For GRB 111209A and GRB 121027A, the XRT observations continue until ∼2 × 10 6 s and suggest that any jet break occurs after this time. In practice, the data are only weakly sensitive to breaks close to the end of these observations (e. g. ,  Racusin et al. 2009) , due to the limited lever arm provided at late times. Coupled with this, more realistic simulations of GRB jet evolution suggest that for many viewing angles (those not directly on-axis), the jet break is effectively smeared out, making its detection extremely challenging  (van Eerten et al. 2011; van Eerten & MacFadyen 2012). Nonetheless, for canonical jet parameters in simple models (e. g. ,  Frail et al. 2001) , it is possible to place limits on the jet opening angle of θ j > 12 deg (111209A) and θ j > 10 deg (121027A).
We thank  D. A. Kann  and the  GROND team  for sharing their photometric calibration and  Jamie Kennea  for providing information on the Swift slew strategy. A. J. L. acknowledges support from the  Science and Technology Facilities Council (STFC , under grant  ID ST/I001719/1 ) and is grateful to the  Leverhulme Trust  for a  Philip Leverhulme Prize award. N. R. T. , K. W. , and P. T. O. thank  STFC  for support (grant ID  ST/H001972/ 1 ). R. L. C. S. is supported by a  Royal Society Dorothy Hodgkin Fellowship. The  Dark Cosmology Centre  is supported by the  DNRF. Based on observations made with  ESO Telescopes at the La Silla Paranal Observatory  under programme ID 288. D-5027. Support for programs 11734,  12438 , and 12786 was provided by  NASA  through a grant from the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc.
These observations were obtained as part of programme IDs GN-2010B-Q-7, GN-2011A-Q-4, GS-2011B-Q-7, GN-2011B-Q-34, and  GS-2012A-Q-25. This work was made possible by contributions from  Iniciativa Cientifica Milenio  grant  P10-064-F  ( Millennium Center for Supernova Science ), with input from " Fondo de Innovación para la Competitividad, del Ministerio de Economía, Fomento y Turismo de Chile," and Basal-CATA  ( PFB-06/2007 ). R. A. M. J. W. is supported by the  ERC  through advanced investigator grant no. 247295. Partly based on observations made with the  Gran Telescopio Canarias (GTC)  at the  Spanish Observatorio del Roque de los Muchachos  of the  Instituto de Astrofsica de Canarias ,  La Palma  under programme  GTC43-11B , PI:  C. C. Thöne. A. dUP acknowledges support by the  European Commission  under the  Marie Curie Career Integration Grant programme  ( FP7-PEOPLE-2012-CIG 322307 ).
C. C. T. , J. G. , and R. S. R. acknowledge support from  AYA2010-21887-C04-01  (Estallidos) and  AYA2011-24780/ESP. Observations reported here were obtained at the  MMT Observatory , a joint facility of the  Smithsonian Institution  and the  University of Arizona. This work made use of data supplied by the  UK Swift Science Data Centre at the University of Leicester. S. C. and G. T. acknowledge support from  ASI  grant  I/004/11/0. S. B. C. acknowledges generous financial assistance from  Gary & Cynthia Bengier , the  Richard and Rhoda Goldman Fund , the  Christopher R. Redlich Fund, NASA/Swift  grants  NNX10AI21G  and  NNX12AD73G , the  TABASGO Foundation , and  NSF  grant  AST-1211916. J. P. O. acknowledges support from the  UK Space Agency. E. B. acknowledges support from the  National Science Foundation  under Grant  AST-1107973  and from  NASA/Swift   AO7  grant  NNX12AD69G.
Since the advent of Computer-Aided Design (CAD) and Geographical Information System (GIS) tools, project participants have been increasingly leveraging these tools throughout the different phases of a civil infrastructure project. In recent years the number of GIS software that provides tools to enable the integration of Building information in geo context has risen sharply. More and more GIS software are added tools for this purposes and other software projects are regularly extending these tools. However, each software has its different strength and weakness and its purpose of use. This paper provides a thorough review to investigate the software capabilities and clarify its purpose. For this study, Autodesk Revit 2012 i. e. BIM editor software was used to create BIMs. In the first step, three building models were created, the resulted models were converted to BIM format and then the software was used to integrate it.
INTRODUCTION Data conversion between these CAD/BIM and GIS systems is resulting in a enormous mismatch that are require a manual work to be undertaken, due to limitations associated with exchange of data and knowledge. In 3D, spanning a bridge between CAD and GIS is even more challenging  (Oosterom et al 2006 , Isikdag and Zlatanova 2009 , Emgard and Zlatanova 2008 , teghtmeijer et al 2009 , Wu, and Hsieh, 2007). This is due to the following facts: existing CAD and GIS platforms have been developed independently with different purposes resulting in significant differences in terms of data formats they support, terminology they used, semantics of concepts they represent, reasoning techniques on which they are based, different scale representations, and transformation of the local (CAD) coordinates into reference system for both the horizontal and vertical coordinates, geometry modelling in both systems, CAD software provides all kinds of primitives to create geometric (and their visual attributes), it represent objects with different representations such as CSG and Sweeping, however these primitives are not supported in GIS (e.
g. parametric primitives) while geospatial models mainly use BRep as the main geometrical representation method; Furthermore, CAD models usually do not store topologic information which is in fact an important characteristic of geospatial models (i. e. geospatial models use topology to store geometric information in a more efficient manner). , For these reasons currently CAD and GIS platforms are not interoperable. Recently and as a result of the developed building standard such as Industry Foundation Classes (IFC), the Integration between Geospatial information and BIM/CAD could have been achieved at semantic level  [6]. Efforts deals with bridging the gap between BIM/CAD and geospatial information models are providing harmonization of semantics that allowing the interoperability at the data level  (IFC explorer, 2008 , Paul and Borrmann 2008 , IFG, 2006)  and as web services  (Lapierre and Cote, 2008).
Current building models are capable to store semantic information and spatial relationships of building elements in an object oriented data structures. As a result different tools are being developed that enable the transformation of building models to be represented using 3D City data model standards  (BIM server 2009 , IFC explorer, 2008)  and thus enable the visualization within 3D geospatial context using GIS software which provide stand alone or add on extension which is limited for visualization purposes, and by doing so, neglects spatial analysis, the very core of a GIS system. This paper provide a review to a number of GIS software and investigate its capabilities to integrate CAD/BIM in GIS and perform spatial analysis, different software's have been revised including: AutoCAD Map, Google and ArcGIS.
Modelling of the buildings The software's evaluations are based on the integration of three BIM models using selected GIS software's. The models were created using Autodesk Revit 2012 software, Revit is specifically built for building information modeling (BIM) providing a complete architectural design and documentation solution, supporting all phases of design and all architectural drawings and schedules required for a building project, from massing and conceptual studies through the most detailed construction drawings and schedules (Revit, 2013).
Tests of import Revit offers multitude of possibilities of export. On one hand CAD's diverse formats such as dwg, dxf and dgn. On the other hand we have the format IFC, the great promise created by the International Alliance for Interoperability with the purpose of facilitating the interoperability between different actors in Architectural, construction and engineering industry (AEC)  (Mikael Laakso, 2012). This phase of the research has been based on testing each of these formats with different GIS software's. The objective of these tests is to analyze the result of each one of these integrations, since the purpose of the project is to manage the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-2/W2, ISPRS 8th 3DGeoInfo Conference & WG II/2 Workshop, 27 -29 November 2013, Istanbul, Turkey integration of the model with their database in a GIS system without any problem.
DWG to Google Earth Google Earth is a virtual globe, map and geographical information program it maps the Earth by the superimposition of images obtained from satellite imagery, aerial photography and GIS 3D globe. Many people use the applications to add their own data, Google Earth is able to show all kinds of images overlaid on the surface of the earth and is also a Web Map Service client  (Google Earth, 2013). Google Earth supports managing three -dimensional Geospatial data including 3d models such as buildings through Keyhole Markup Language (KML). The process of integrating a BIM model in Google Earth is not straightforward; first it is necessary to open the BIM model in format. dwg with the software Sketchup. After several operations we will be able to visualize it in Google Earth. The results nevertheless have not been satisfactory.
DWG to AutoCAD Map 3D Autocad Map 3D is software that provides access to GIS and mapping data to support planning, design, and data management. The software is capable of working with both technologies CAD and GIS  (Autocad Map, 2013). It can import most CAD format. Moreover, it provides the possibility to integrate, edit and analyze geospatial information. After the tests was finished, the result was been a change in the structure of the model. A BIM model is formed by elements, such as walls, doors or windows, nevertheless, after integrating it in Autocad Map 3D, the model has been transformed in a model formed by meshes of polylines, in the structure of CAD. After this change in the structure, it is impossible to export the model again with its associated attributes; therefore, AutoCAD Map 3D still do not provide a valuable solution.
Performing spatial quires After geo-referencing the 3D models were done. The models were visualized using Arc scene. Further we implement two queries, below is a description of these quires and its results Query 1: Select all the doors which made from wood? This query was performed using standard ArcGIS tools. It was done directly using select by attribute function on the door  This query was not possible to be undertaken using the standard ArcGIS tools. Therefore a special tool is developed using ArcPay. The query was to calculate the floor area of the building "Vauban" from ESTP campus. The floor area of a building refers to the sum of floor area closed and covered under a ceiling with height greater than 1. 80 m. Therefore, we use Python to develop a script that is able to perform the required operation.
EXPECTED RESULT Expected result The work should provide initial understanding for the available software for integrating BIM information in Geo-context, also the type of information that can be integrated i. e. geometric and semantic. The investigation should also implement a number of trial conversions that will be carried out in future to investigate further the capabilities of ArcGIS to perform this integration. Moreover, the work will investigate a new possible application for analyzing BIM in GIS. Also, this will include looking for developing scripts that automate the required analysis. The script prototype capabilities will allow us to develop a suitable user interface that can facilitate the using of GIS for BIM analysis.
Background: High-throughput deep-sequencing technology has generated an unprecedented number of expressed sequence reads that offer the opportunity to get insight into biological systems. Several databases report the sequence of small regulatory RNAs which play a prominent role in the control of transposable elements (TE). However, the huge amount of data reported in these databases remains mostly unexplored because the available tools are hard for biologists to use. Results: Here we report NucBase, a new program designed to make an exhaustive search for sequence matches and to align short sequence reads from large nucleic acid databases to genomes or input sequences. NucBase includes a graphical interface which allows biologists to align sequences with ease and immediately visualize matched sequences, their number and their genomic position.
NucBase identifies nucleic motives with strict identity to input sequences, and it capably finds candidates with one or several mismatches. It offers the opportunity to identify "core sequences" comprised of a chosen number of consecutive matching nucleotides. This software can be run locally on any Windows, Linux or Mac OS computer with 32-bit architecture compatibility. Conclusions: Since this software is easy to use and can detect reads that were undetected by other software, we believe that it will be useful for biologists involved in the field of TE silencing by small non-coding RNAs. We hope NucBase will be useful for a larger community of researchers, since it makes exploration of small nucleic sequences in any organism much easier.
Background Next-generation sequencing has recently emerged as a powerful technology that provides unprecedented insight into biological systems  [1]. With automated sequencing technologies becoming routinely used, the acquisition of millions of sequence reads per experiment leads researchers to the subsequent challenge of data analysis. Many of these data are now made available in databases that provide the scientific community with an invaluable shared resource. The number of databases reporting sequences of non-coding small RNAs has dramatically increased during the past five years and offers tremendous promise for elucidating the role of siRNAs, piRNAs and miRNAs in health and disease  [2]. The class of small RNAs called piRNAs is mainly involved in transposable element regulation in the germline cells of many organisms (mice, rats, Drosophila and other animal species).
These Piwi interacting RNAs (piRNAs) are bound to proteins of the argonaute PIWI family. They recognize and destroy mRNA encoded by transposable elements, preventing their propagation  [3] [4] [5] [6]. In 2007, the GJ Hannon laboratory published the first large database of piRNAs from Drosophila with approximately 17,000 reads  [7]. One year later, more than one million reads were reported in a database from deep sequencing piRNAs  [8]. Yet, the huge amount of information contained in these databases remains mostly unexplored because the informatics tools currently available are difficult to use. An efficient, easy handling tool allowing small RNA mining and alignment would help to further elucidate the mechanism of mRNA recognition by small RNAs. Here we introduce NucBase, a new program designed to align short reads from large nucleic acid databases to reference sequences or genomes.
Mapping short reads can be achieved using other tools like SOAP  [9] , BWA  [10] , Bowtie  [11]  and vmatch  [12] , which are very fast, but omit potential matches due to their greedy algorithms. NucBase is an easy-to-use program with a graphical interface. It identifies nucleic motives with strict identity to input sequences and also finds candidates with one or several mismatches. Furthermore, this software can identify partial fragments from a read that match a target sequence. The user is able to select the number of consecutive nucleotides within these partial fragments, called "core sequences," as well as the number of possible nucleotide mismatches. These "core sequences" can be detected regardless of position within the read. Results are exported in a Generic Feature Format 3 (GFF3) output format, which permits the visualization of read alignments on target sequences and fast annotation on the GBrowse viewer for a better understanding of potential regulations.
Implementation NucBase is an easy-to-use program developed in C++ with a Qt graphical interface, whose aim is to align millions of reads on target sequences. For this purpose, it first computes the Burrows-Wheeler Transform (BWT)  [13]  of the target, as well as its suffix array, through the fast and lightweight LibDivSufSort library (Mory, 2010). These two elements, which form an FM-Index, are then used to find all the positions where there are exact read matches  [14, 15]. In addition, NucBase can allow a variable number of mismatched characters, which identify reads with imperfect matches  [16]. As a third alternative, NucBase offers the possibility to find a partial, consecutive sequence called a "core sequence" from a read. The size, L, of that core sequence is chosen by the user as well as the number of mismatches to introduce.
Results and discussion We compared NucBase performance to the very efficient Bowtie alignment program, which is widely used and among the fastest competing open source tools for aligning millions of reads. These tests were run on a Windows Vista x64 workstation with 16 cores and 48 GB of RAM. By mapping w 1118 ovary piRNAs from the database published by Brennecke et al. [8] ) (701,853 reads) onto the Drosophila genome, we evaluated the speed and sensitivity performance of NucBase compared to Bowtie. Since NucBase always reports all the alignments, we used the corresponding option in Bowtie during these tests. Table  1  reports the results. We found that the speed of Bowtie and NucBase is comparable: NucBase is 2. 6 times faster for perfect matches, but 1. 7 times slower with 2 mismatches. Speed and memory improvements of NucBase are planned for the future.
If the R character is present in a read, NucBase will align successively A and G, and identify reads with either of the two nucleotides. On the contrary, Bowtie will only test A. Due to this flexibility, short consensus sequences can be mapped appropriately by NucBase but not by Bowtie. Second, NucBase allows the identification of core sequences regardless of position within the read whereas Bowtie only identifies these sequences (called seeds) if they are positioned at the 5′ end of the read. Mapping Brennecke's database  [7]  to the nanos gene of Drosophila, we searched for a core sequence of 18 nucleotides with one mismatch allowed. The number of reads recovered by NucBase and Bowtie are 1,702 and 501, respectively. This indicates that NucBase allows alignments that are ignored by Bowtie.