dataesr/scientific-paragraphs-categorization huggingface dataset:
New approach to parametric identification of the DCM The approach allows the calculation of all the DCM parameters using the following steps: -Determination of the electrical parameters (rotor resistance and the torque constant) by classical methods (see section 3). -Determination of the DCM transfer function using MATLAB Identification toolbox. The MATLAB Identification toolbox has the graphical user interface (GUI), presented in Fig. 4. This interface offers several types of models (transfer function, state-space representation, polynomial approximations…etc. ). But to carry out the identification using this toolbox, it is necessary to fix the order of the model which is not known in advance and which remains a major problem in the identification of all physical systems. In our case, the DCM can be approximated by a second-order transfer function.
Experimental results To test the models of the DCM obtained, we have carried out a no-load test. The measurements obtained are stored in a MATLAB file of type (. mat) thanks to the map dSPACE. The results obtained are presented in Figs. 8 9 10. From Figs. 9 and 10, we can say that the proposed identification approach allows obtaining a model that is very close to the real DCM. The estimated speed and currents waveforms are very close to the measured ones with a good correlation coefficient of the order (99%) between the DCM real system and the estimated model.
Conclusion In this paper, we have studied the contribution of techniques identifications such as classical methods and identification methods available in the software MATLAB (toolbox Ident), applied to the identification of DCM parameters. Then an experimental platform based on the dsPACE 1104 board was used to validate the models obtained. The results clearly show that the proposed approach offers several advantages for the identification of the DCM parameters. Another benefit is the risk minimization of damaging the actual system and saving time (a low number of tests on the machine). In addition, this solution remains simple, it is accurate and easy to implement (we can always get models very close to the real model). Indeed, this parametric identification method can be an interesting tool to the design of controllers (get an idea of the values initials of the parameters of the correctors used for the DCM control).
Ultraliser is a neuroscience-specific software framework capable of creating accurate and biologically realistic 3D models of complex neuroscientific structures at intracellular (e. g. mitochondria and endoplasmic reticula), cellular (e. g. neurons and glia) and even multicellular scales of resolution (e. g. cerebral vasculature and minicolumns). Resulting models are exported as triangulated surface meshes and annotated volumes for multiple applications in in silico neuroscience, allowing scalable supercomputer simulations that can unravel intricate cellular structure-function relationships. Ultraliser implements a high-performance and unconditionally robust voxelization engine adapted to create optimized watertight surface meshes and annotated voxel grids from arbitrary non-watertight triangular soups, digitized morphological skeletons or binary volumetric masks.
The interdependency of structure and function produces a kind of metaplasticity  [7]  that requires performing simulations within geometrically realistic subdomains at ultrastructural resolutions in which molecular reactions can be contained  [8]. Structural neuroscientific datasets are either acquired from a wide spectrum of imaging modalities, such as imaging scanners and microscopes  [9] [10] [11]  or digitally synthesized in supercomputer simulations to yield similar structural characteristics of biological counterparts  [12] [13] [14] [15]. 3D models of structural data (on the scale of 100 nm to 1 mm) can be classified according to their digital representations into four principal formats: morphology skeletons, surface meshes, volumetric meshes and volumetric grids. Each representation (Supplementary Figure  S1 ) is convenient for a specific category of simulation.
This applies to manually segmented neuropil structures  [11]  or even those segmented with state-of-the-art deep neural networks  [46]. Machine learning (ML) has helped automating the process  [47]  allowing the generation of a massive amount of neuro-glia-vasculature (NGV) reconstructions that are disseminated online as demonstrated by several research programs such as MICrONS  [48] , FAFB-FFN1  [49] , and FlyEM Hemibrain  [50]. Nevertheless, the majority of the resulting reconstructions requires many person-years of effort for proofreading. Online collaborative efforts are introduced to accelerate the process  [51] , which could be effective in resolving fragmentation artifacts, but watertightness remains lacking. On another level, there is a huge diversity of NGV morphological models that have been released publicly to central databases, for instance, NeuroMorpho.
Contrary to traditional remeshing applications that use geometry-based methods  [57]  to repair the geometric topology of non-watertight meshes, the core engine of Ultraliser is designed based on efficient voxelization kernels that create intermediate high resolution proxy volumes, with which we can extract continuous surface meshes that are adaptively optimized and watertight, refer to Figure  1  and Supplementary Figure  S2. The core library of Ultraliser is exploited to develop several applications that can be part of a large-scale software ecosystem for establishing fully automated neuroscientific pipelines involving multimodal simulation systems. The current version of Ultraliser integrates the following applications: (i) ultraMesh2Mesh, restructures an input polygonal surface mesh composed of a set of unorganized triangles with no defined connectivity-i.
This can be demonstrated with the Soma Reconstruction Toolbox in NeuroMorphoVis  [19] , where users can fine tune the parameters of the soma reconstruction algorithm and validate the resulting profile with respect to a segmented ground-truth mesh. Supplementary Figure  S81  provides an example of creating a watertight mesh of a spiny neuron from a set of input meshes, each representing a single component of the neuron (soma, arbor or even a spine). ultraMeshes2Mesh can therefore be seen as a complementary or post-processing application that can ensure the watertightness of resulting mesh models of NGV cellular structures created by other software applications.
What is missing then? RTE was extended to model light interaction with low-and high-scattering f luorescent participating media  [35, 80] , recent experiments were able to build accurate 3D brain atlases mapping the different optical properties of the tissue  [81] , and f luorescence databases are available online, where spectral properties of common f luorescent dyes are provided. The only missing element is a robust application capable of creating detailed biologically and optically accurate volume models of cortical circuits. To address this issue, we implemented ultraCir-cuit2Volume. Our implementation uses the information retrieved from the circuits that are digitally reconstructed by the Blue Brain Project  [2, 82]. These circuits identify neuron types, their coordinates and orientation.
Annotated volumetric tissue models are also used in in silico imaging studies, where we can simulate optical imaging experiments with brightfield or f luorescence microscopy  [10]. Ultraliser's workf low is graphically illustrated in Figure  1  and a detailed schematic showing the ecosystem and relationship between its different modules is illustrated in Supplementary Figure  S2. The code is open sourced under the GNU General Public License version 3. 0 (GPL), and is available for free on GitHub at https://github. com/BlueBrain/Ultraliser. Documentation and tutorials are available online at https://github. com/BlueBrain/ Ultraliser/wiki. Quantitative and qualitative analysis scripts used in this study are also open sourced and integrated into NeuroMorphoVis  [19]  (github. com/BlueBrain/NeuroMorphoVis).
Morphology skeletons Neuroscientific 3D models with branching topologies-that are traced from optical microscopy stacks, for example: neurons, astrocytes and vasculature-are segmented, digitized and typically stored as morphology skeletons. 3D models acquired with electron microscopy  [11, 58]  are further processed and converted into morphology skeletons using skeletonization  [83, 84]. Neuronal morphologies are commonly stored in the standardized SWC file format  [16]. This format is also used to store processes of astroglial cells, but it does not account for any endfeet information, which is typically stored as trianglular patches and requires custom file formats that can combine branching and surface data  [15, 25]. The SWC format is adopted by the NeuroMorpho. Org  [16]  database, which contains hundreds of thousands of neuronal and astrocytic morphologies collected from a huge diversity of experiments.
The SWC format has been also adapted to store cerebral arterial arborizations, for instance the datasets of the Brain Vasculature (BraVa)  [18]  database (cng. gmu. edu/brava). Ultraliser has full support to load SWC morphologies of neurons, astrocytes and cerebral vasculature. Moreover, it supports loading customized file formats such as (i) H5 morphologies that were defined within the scope of the Human Brain Project  [85]  and (ii) the VMV format that is used to store vascular morphologies supported by VessMorphoVis  [26]. Morphology skeletons are represented by a list of connected morphological samples, where each sample has a unique identifier, 3D Cartesian coordinate, cross-sectional radius, and optionally an index characterizing the type of the branch it belongs to. A pair of adjacent samples defines a segment or an edge, and a concatenated list of adjacent edges between two branching points defines a section or strand.
Polygonal surface meshes Ultraliser has native support to import and export polygonal surface meshes with multiple file formats including those that are commonly used for visual analytics with Blender  [86]  (blender. org) or MeshLab  [87]  (meshlab. net), such as OBJ and PLY, and also those required for 3D printing, molecular simulations and conversion into tetrahedral meshes by tetrahedralization applications  [38, 39]  such as STL and OFF file formats. The meshes downloaded from the MICrONS program  [48]  are stored in H5 files based on the HDF5  [88]  library. Although this format is not standard, it is straightforward to reconstruct the triangulation of the mesh. Ultraliser implements two complementary data structures to store 'triangular' surface meshes. The first data structure Ultraliser:: Mesh is a light version that only stores vertices and triangles.
With such resolutions, the reconstructed mesh is excessively tessellated, possibly with tens of millions of polygons, whose practicality is undoubtedly questionable. Ultraliser integrates a mesh optimization module that can adaptively refine highly tessellated meshes to create optimized counterparts with preserved features. The mesh optimization module extends an existing implementation  [99]  that uses an angle-based approach for adaptive tessellation and normalbased smoothing to guarantee the quality of the resulting surface. The optimization process includes: surface smoothing, normal smoothing, f lat coarsening, dense coarsening and also adaptive optimization.
Acknowledgments We thank  Grigori Chevtchenko  and  Samuel Lapere  for the impactful discussions on watertight meshing,  Pawel Podhajski  on technical assistance to deploy the software on Blue Brain 5. We also thank  Karin Holm  and  Judit Planas  for their valuable comments on the manuscript. We acknowledge the artistic touch of  Elvis Boci  to improve the quality of figures.
We provide a detailed step-by-step guide as well as the related Stata codes on conducting variance decomposition analysis with multilevel modeling techniques. Using a 10-year (2009-2018) dataset comprising 7281 distinct European B2B firms operating in 348 industries and 29 countries, we empirically examine the relative importance of firm, industry, country, year, and residual effects in driving firm performance for B2B firms. Our analysis shows that firm-specific factors have the highest relative importance for B2B firms' performance, followed by home country and industry effects.
Our study also shows how one can model data structures that are not perfectly hierarchical but rather cross-nested (e. g. , firms are simultaneously nested within counties and industries). Such data structures are common in management and marketing research  (Andersson, Cuervo-Cazurra, & Nielsen, 2014). Examining the relative importance of firm, industry, country, and year on the financial performance of B2B firms, the present study relies on a 10-year  (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018)  dataset comprising 7281 distinct European B2B firms operating in 348 industries and 29 countries. We decompose the variance in firm return on assets (ROA) accounting for a cross-nested data structure: repeated observations of ROA are nested within firms, which are cross-nested within both industries and countries.
Makino et al. (2004)  significantly extended prior variance decomposition studies that focused on a single country by adding a new level, namely the host country. Specifically, the authors focused on multinational corporations and examined the relative importance of host country effects in explaining variation in foreign affiliates' performance. They relied on a database consisting of a panel of foreign affiliates of Japanese multinational corporations observed over 1996-2001. Makino and colleagues found that country factors (equal to nearly 6%) were as vital as industry factors (7%), following affiliate (31%) and corporate parent (11%) factors. The results also suggested that corporate parent and affiliate factors explained the most variance in the foreign affiliates' performance in the developed countries, equating to 13% and 28%, respectively.
Use of the variance decomposition analysis in marketing Despite being common in strategic management and other related disciplines, marketing scholars have only recently started to employ variance decomposition analysis to examine the relative role of different factors in driving firm behavior and outcomes. One of the few examples is  Zhang et al. (2020)  who applied the variance decomposition approach to study the contribution of the firm-, industry-and country-level innovation in explaining variance in firm performance. Zhang et al. (2020)  compiled a dataset comprising 4530 firms operating in 794 industries with headquarters in 39 countries. They utilized hierarchical linear multilevel modeling techniques to test the effects of innovation at the three levels on performance. The technique was appropriate due to the hierarchical nature of the study's data and the possibility of simultaneously partitioning the variance-covariance components.
Step 1: Defining categories of explanatory variables The steps follow several guidelines established in the previous variance decomposition studies  (Guo, 2017; McGahan & Porter, 1997; McGahan & Victer, 2010; Misangyi et al. , 2006). First, we have to clearly define categories of explanatory variables. The aim of variance decomposition studies is mainly to capture categorical effects, e. g. , industry, country, corporate parent, and business unit as a whole  (McGahan & Porter, 1997 , 2002). We define a firm as an independent entity as indicated in the Orbis database (i. e. , no shareholder with more than 25% direct or total ownership). Such firms are not prone to external interference in their decision-making, having more freedom in their strategy development and implementation. The firm's industry membership is based on its primary 4-digit NACE Rev.
Step 2: Initial data selection We then continue by constructing a dataset. Variance decomposition studies normally require a sizeable amount of lower-level observations (e. g. , firms nested in industries or countries). Also, to separate residual effects from firm effects, the data should have a panel structure  (Guo, 2017; Misangyi et al. , 2006). In variance decomposition studies, residual effects can also be referred to as "error" or "unexplained variance," i. e. , "the performance variance potentially attributable to transient factors"  (Misangyi et al. , 2006: 580). We obtained all data for the analyses from the Orbis Europe database, which identifies both listed and nonlisted European firms (both within and outside of the European Union), the core industry in which they operate, and their home country.
Step 4: Data cleaning Our total initial sample consisted of 1,322,458 observations of yearly firm performance for the years 2009-2018. This dataset is further screened following the steps initially reported in  McGahan and Porter (1997)  and then adopted by subsequent variance decomposition studies (e. g. ,  Guo, 2017; Karniouchina et al. , 2013; Misangyi et al. , 2006). From our original total, we drop: (a) 62,102 observations of firms conducting financial and insurance activities and (b) 30,024 observations of firms with activities in public administration and defense, compulsory social security, and not elsewhere classified. Variance decomposition research often excludes firms from these industries as their returns are not comparable with those of other industries according  (McGahan & Porter, 1997).
We further proceed to eliminate (c) 1,161,740 observations of small firms with sales or assets less than €10 million and (d) 1390 observations of firms that have been in this dataset for only one year. Single-year appearances may have anomalous performance and are normally excluded from the analysis  (McGahan & Porter, 1997 , 2002; Misangyi et al. , 2006). Unless small firms are of interest to a researcher, they are also often excluded from the analysis for a reason similar  (McGahan & Porter, 1997 , 2002). We also drop (e) 787 observations of firms that are the only ones in an industry in a given year, as these entities may be analogous to monopolies  (McGahan & Porter, 1997). In addition, (f) 619 firm observations are removed due to a limited number of them in a given country as opposed to other countries.
Step 5: Model specification and estimation The next step is to specify and estimate a model. We proceed by decomposing variance in firm ROA by fitting multilevel models to the dataset. In the dataset, repeated observations of ROA (Level 1) are nested within firms (Level 2), which are cross-nested within both industries (Level 3) and countries (Level 3). There are certain specifics in modeling such data structures as they are not characterized by perfect hierarchical nesting. Industries and countries represent imperfect hierarchies because lower-level units (firms) simultaneously belong to multiple higher-level units (industries and home countries). In other words, industries and home countries are not hierarchically nested, as not all firms from a particular home country compete in the same industry, nor do all firms competing in a particular industry originate from the same home country.
Cross-nested data structures like this are common to marketing and management research and need to be appropriately modeled  (Andersson et al. , 2014). We utilize two existing approaches to performance variance decomposition: multilevel modeling with maximum likelihood estimation (e. g. ,  Hough, 2006; Misangyi et al. , 2006)  and multilevel modeling with MCMC estimation in a Bayesian framework (e. g. ,  Castellaneta & Gottschalg, 2016; Guo, 2017). The former approach has been more standard in the variance decomposition research, and it can be easily implemented using standard statistical software, such as using the "mixed" command in Stata. The latter approach has been introduced to the management literature relatively recently; one of its benefits is that it allows obtaining reference statistics, such as standard error, for both the absolute effects (variances) and relative effects (percentages) of different variance components  (Browne, 2017).
The model assumes e tij ~N(0, σ e 2 ). σ e 2 is therefore denoted as residual (also referred to as "across-time") variance. At Level 2, the over-time mean ROA, α 0ij , is simultaneously modeled as an outcome varying randomly around the industry mean: α 0ij = β 00j + u ij, (1. 2) where β 00j is the mean ROA of all firms in industry j and u ij is the between-firm residual that is distributed as u ij ~N(0, σ u 2 ). σ u 2 thus denotes between-firm variance. At Level 3, the mean ROA of all firms in industry j, β 00j , is simultaneously modeled as an outcome varying randomly around the grand mean: β 00j = γ 000 + v j , (1. 3) where γ 000 is the grand-mean ROA of all firms in the dataset and v j is the between-industry residual distributed as v j ~N(0, σ v 2 ). σ v 2 is between-industry variance.
The additional advantage of a Bayesian MCMC estimation approach is that it reports the means and standard deviations of the parameter monitoring chains  (Browne, 2017; Goldstein, 2011) , thus providing inference statistics for estimated absolute (variances) and relative (percentages) effects. Importantly, this approach can provide more precise estimates in the presence of cross-classified structures  (Rasbash & Goldstein, 1994). Finally, it is possible to implement in Stata quickly, yet it requires the MLwiN software  (Rasbash, Charlton, Browne, Healy, & Cameron, 2009)  to be additionally installed. We fit Models 4 and 5 using the MCMC estimation procedure with a Bayesian estimation  (Browne, 2017)  by running the MLwiN software  (Rasbash et al. , 2009)  in Stata using the "runmlwin" command  (Leckie & Charlton, 2012)  with the cross-classification option.
Because the software requires us to specify starting values for the model parameters, we first estimate a naive hierarchical linear model by iterative generalized least squares (IGLS). Even though the resulting IGLS estimates are not substantively interpretable because the effects are not perfectly nested, they typically provide good starting values for correctly fitting a crossnested model. The procedure to estimate variances and their percentages, therefore, replicates  Guo (2017). Table  3  below specified the Stata commands that can be used to estimate Models 4 and 5 to obtain the variance components.
HPLC-MS analyses HPLC-MS analyses were carried out on an Agilent 1260 Infinity HPLC system, coupled with an Agilent 6120 mass spectrometer [electrospray ionization (ESI) + mode]. The multiply charged envelope was deconvoluted using the charge deconvolution tool in Agilent OpenLab CDS ChemStation software to obtain the average [M] value.
AlphaFold2 models The AlphaFold2 model of the DTX2:E2 complex shown in Fig. 3C  was produced using ColabFold (AlphaFold2 + MMseqs2)  (65)  with default settings (3 recycles, etc. ) except for one optional feature that was included: AMBER relaxation. Sequences of the previously crystallized human DTX2 RING-DTC fragment (residues 390 to 622 of DTX2_HUMAN) and the full-length human UBCH5A (UB2D1_ HUMAN) were used. The five generated models, one of which is shown in Fig. 3C , converged on an almost identical structure. The AlphaFold2 model of the human RNF213 fragments 4545 to 4596 was created using the same settings as above. The AlphaFold2 models of human UBE2J2 and HOIL1 are shown according to the AlphaFold Protein Structure Database (alphafold. ebi. ac. uk), except for the position of His 510 of HOIL1, which was adjusted according to the models of murine and zebrafish HOIL1 from the same database.