dataesr/scientific-paragraphs-categorization huggingface dataset:
Generation of bi-phasic and tri-phasic structures. The MATLAB code developed for generating 3D structures was based on Tschopp MATLAB code  [20]. The code generated 3D microstructures composed of a population of non-overlapping ellipsoidal particles heterogeneously distributed in size and orientation, within a periodic RVE. Same code could be used for spheres. Bi-phasic structures. The composite structure was generated in a cuboid shaped representative volume element (RVE) defined by (x, y, z) ∈ [0, L x ] × [0, L y ] × [0, L z ], where z is the overall diffusion direction. The RVE was supposed periodic along its vertical faces, to represent an infinite repetitive structure along x and y axis. The cuboid shape was selected in order to keep as the [0, L z ] dimension, the whole thickness of th composite membrane studied.
It should be noted that if a particle intercepted one of the vertical faces of the RVE then, the particle section outside from the RVE was shifted to the opposite face, to ensure the periodicity of the RVE (Fig. 1a ). Tri-phasic structures. The tri-phasic composite structure was built by considering an interphase of fixed thickness around each particle of the already generated bi-phasic structures. The interphase volume was thus different for each particle, keeping constant the initial particle volume. For the interphase compartment, the periodicity at vertical faces of the RVE still applies. For particles close to the upper (resp. lower) face of the RVE, the interphase may intercept the boundary. In that case, the intercept with the exterior of the RVE was removed (Fig. 1b ). It is important to note that once the 3D structures were generated on Matlab with Tait-Bryan angles (ψ,θ,φ), the latter were converted to the classical Euler angles (γ,β,α) to be integrated in Comsol for upcoming simulation.
COMSOL model and numerical simulations The 3D problem of diffusion was solved by using the numerical Finite Element Method (FEM) using COMSOL Multiphysics 5. 5 software. For mass diffusion simulation, the "Transport of Diluted Species" physics interface of the "Chemical Reaction Engineering" module was used, which already integrates the 2nd Fick's law equation. To support the complex geometry of tri-phasic structures and avoid mesh errors, the COMSOL CAD Import module was used. The simulations were performed using a DELL computer with Intel Xeon E-2176 M Processor (2. 7 GHz) and 32 Gb of Ram. The entire computational procedure (from structure generation to simulation) was driven within the MATLAB environment and was partially automated via the COMSOL LiveLink for MATLAB module (from the step of importing geometry data into COMSOL to simulation).
The French Guiana Hospital database on HIV After giving informed consent, adult HIV-infected persons with at least one consultation followed in Cayenne, Kourou, and Saint Laurent du Maroni hospitals between January 1, 1992 and December 31, 2021 were enrolled in the French Hospital Database for HIV (FHDH) a national database compiling data from all French regions. (  [17, 18]  This covers most adult in-and out-patients followed in French Guiana and nearly all AIDS cases. Recent estimates suggested that 85% of persons living with HIV were followed at the hospital centers  [19]. Trained clinical research technicians entered demographic data, diagnoses using the 10th international classification of diseases, treatments, medical events, CD4 and CD8 counts, and HIV viral loads  [18]. This data collection is focused on HIV not diabetes therefore the information on diabetes were scant.
Ethical and regulatory aspects All persons living with HIV included in the FHDH give informed consent for the use of their anonymized data and for the publication of anonymized results. This cohort has been approved by the Commission Nationale Informatique et Libertés (27th of November CNIL 1991, subsequently updated March 30th 2021. ) and published in the Journal Officiel (the official bulletin enacting all laws and decrees in France) on January 17th 1992 (JORF n° 0014 17/01/1992 ), and has led to over 400 international publications. By decree (JORF 0102, Décret n° 2017 -682 du 28 avril 2017), in all French regions the COREVIH are the structures mandated to collect and analyze data and transmit yearly epidemiological reports to the ministry of health.
Persons with diabetes were more likely to be virologically suppressed than persons without diabetes. Although this could partly reflect the role of antiretrovirals in triggering diabetes, we suspect that this reflects the greater adherence and follow up of diabetic persons because they often require more complex care for 2 chronic diseases. The present study has a number of limitations. The database is dedicated to HIV care, thus it lacked important aspects of the features of the person's diabetes. Body mass index was not available, the exact date of diabetes discovery was not available, some of the significant complications of diabetes, metabolic aspects, and cause of death were not available. Some patients may have died unbeknownst to us after right censoring and thus we may be missing some information in a territory where migration is intense.
Data analysis 2. 3. 1. Digital-Image correlation The local strain-rate field was evaluated, using the method of DIC, as described in detail in  [54]. For this process, a blackand-white speckle pattern was painted on one specimen surface, and its displacement was followed by a Charged-Coupled Device camera, which provided a spatial resolution of 25 μm/pixel. Local strains in the tensile direction were reconstructed from the difference between the current and the initial (reference) images with the aid of the standard image correlation software, VIC-2D  [56]  , using a subset size of 21 pixels and a spacing of 5 pixels. The spatial resolution of strain approximately corresponded to a window with a 150 μm side. The numerical derivation of the local strain field with regard to time allowed to calculate the strain-rate field and visualize the evolution of the heterogeneity of the plastic activity.
Besides the assessment of two-dimensional (2D) local strain-rate patterns at selected instants, the successive series of such images were used to construct spatiotemporal maps visualizing the evolution of the local strain rate along a selected cross-section of the sample as a function of time. The images were recorded at a rate of 1 frame per 65 ms in the fastest tests. When ˙ ε a was reduced, the acquisition frequency was reduced to gather data during the entire test duration. In this case, several intervals corresponding to various deformation stages were recorded with the highest time resolution.
The self-similarity is then assessed by investigating the scaling of the families of partition functions corresponding to different values of the order, q : Z q ( δt ) = N i μ q i ( δt ) , q = 1 Z 1 ( δt ) = N i μ i ( δt ) ln μ i ( δt ) , q = 1 (8) Herewith, the variation of q makes dominate different μ values in Z q ( δt ) and, therefore, allows to probe the scaling for distinct data subsets assembling boxes with similar values of the local measure. In the case of a MF signal, the partition functions obey a scaling law in the limit, δt → 0: Z q ( δt ) = δt ( q -1 ) D q , q = 1 Z 1 ( δt ) = D ( 1 ) ln ( δt ) , q = 1 ( 9 ) where D q denotes the generalized fractal dimensions. In the case of real signals, these relationships are satisfied in bounded ranges of δt because experimental data possess characteristic scales, e.
This trend certainty follows, e. g. , from the observation of an approximate similitude between the evolution of the cumu-Fig. 11. Power-law statistics of the AE. The log-log plot of the PDF for the normalized intensity of the AE, A 2 / < A 2 > , for a sample tested at 10 -3 s -1. The analyzed dataset contained 392 hits. The dashed line corresponds to an exponent, β = 2. 1 ± 0. 1. lated amplitude [ Fig. 10 (c) ] and the deformation curve [ Fig. 10 (a) ]. It is also noteworthy that the AE is stronger at the beginning of deformation and decreases during the test presented in this figure. Such behavior is typical of many materials and is usually explained by the work hardening leading to a reduction in the dislocation free-flight distance. In the case of the studied alloy, it may also be suggested that there exists a relationship between the decrease in the AE and the weakening of the strain localization after some straining [cf.
Author contributions M. L. , J. B. , and P. K. L. conceived and designed the experiments, R. F. fabricated samples, R. F. , T. L. , and M. L. performed the mechanical, DIC, and AE tests, B. B. and H. J. performed the microstructure analysis, J. B. , H. J. , T. L. , and M. L. conducted the analyses of the experimental data, J. B. , R. F. , P. K. L. , and M. L. helped write the manuscript and carried out text editing. All the authors discussed the results and commented on the manuscript. show the repartition of the density of geometrically necessary dislocations (GND) for the regions sketched in the BSE images, as calculated from the EBSD data. To accomplish this task, the electron-microscopy data were acquired, using a FEG-SEM JEOL F100 equipped with an Oxford Symmetry camera. The sample to detector distance was 15 mm, and the accelerating voltage was 15 kV.
Acknowledgements M. L. and T. L gratefully acknowledge the support from the  French State  through the program "Investment in the future" operated by the  National Research Agency , in the framework of the  LabEx DAMAS  [ ANR-11-LABX-0 0 08-01 ]. P. K. L. greatly thanks the support from (1) the  National Science Foundation  ( DMR-1611180  and  1809640 ) and (2) the  Army Research Office  ( W911NF-13-1-0438  and  W911NF-19-2-0049 ).
Research in context Evidence before this study Before analysing the data and samples collected in the Liver Investigation: Testing Marker Utility in Steatohepatitis (LITMUS) metacohort, we did a systematic review of diagnostic accuracy studies of blood-based biomarkers to evaluate fibrosis and detect non-alcoholic steatohepatitis (NASH). A sensitive search strategy was developed in collaboration with an experienced search specialist. We searched MEDLINE, the Cochrane registry of studies, Embase, and Web of Science in Aug 10, 2018. Searches were updated on Feb 20, 2019, with the same search strategy. We included studies, reported in an English full-length study report, in which liver biopsy was used as the reference standard test. Potentially eligible studies were those with adult patients (aged ≥18 years) with biopsy-proven or suspected non-alcoholic fatty liver diseased (NAFLD) with non-invasive test and paired liver histology data.
Furthermore, our proposed strategy showed that some tests would substantially reduce the number of individuals who would need to have a biopsy in future drug trials, as only those testing positive for the marker would require further evaluation. Because of the high screen failure rate in current trials, such preselection could facilitate trial recruitment and accelerate drug development. This is one of the first studies to address this specific question and provide data to support a tractable solution. The LITMUS consortium will continue to collect and evaluate data on blood-based and imaging biomarkers in the prospective LITMUS study cohort. fibrosis [NAS≥4 and F≥2]), deterring their implementation by clinicians. 2  People with NAFLD and advanced fibrosis (F≥3) are at increased risk of adverse liver-related outcomes, liver transplantation, and death.
Study design and participants Data were collected in the LITMUS metacohort section of the European NAFLD Registry, a cohort of individuals with NAFLD prospectively recruited via standardised procedures and monitoring (details of which have been published previously), by multiple collaborators. 7 Briefly, the European NAFLD Registry recruited patients with suspected NAFLD from secondary and tertiary care centres in Austria, Belgium, Finland, France, Germany, Greece, Italy, the Netherlands, Portugal, Spain, Sweden, Switzerland, and the UK between Jan 6, 2010, and Dec 29, 2017. Participants were required to provide written informed consent before inclusion. Studies contributing to the metacohort were approved by the relevant ethical committees in the participating countries and conform to the guidelines of the Declaration of Helsinki.
Clinical assessment Detailed clinical data were collected from all participants by a trained investigator and entered directly into a central registry. Sex data were self-reported, with the options "Female" or "Male" provided. BMI was calculated by dividing weight (kg) by height (m) squared. Clinical laboratory blood assays were done in laboratories of the recruitment centres. Lipid (ie, LDL, HDL, cholesterol, and triglyceride) and liver (ie, platelet count, ALT, aspartate amino transferase [AST], and GGT) profiles were collected. Comorbidities, such as dyslipidaemia (fasting triglyceride concentration ≥150 mg/dL [1•7 mmol/L] or fasting HDL <40 mg/dL [1•03 mmol/L] in men and <50 mg/dL [1•29 mmol/L] in women, or those on treatment for dyslipidaemia), hypertension (systolic blood pressure ≥130 mm Hg or diastolic pressure ≥85 mm Hg, or those on treatment for hypertension), and diabetes (fasting glucose >7•0 mmol/L, or those on treatment for diabetes) were also recorded.
Biomarker measurements All serum samples were collected with standard collection kits and processed locally before storage at -80°C, according to prespecified biobanking standard operating procedures. These were prospectively collected samples that were stored and subsequently analysed. Samples were shipped in batches on dry ice from recruitment sites to the LITMUS Central Biobank, where serum samples were catalogued and subsequently sent for central analysis at Nordic Biosciences (Herlev, Denmark), a laboratory accredited by the College of American Pathologists. Only serum samples collected within 6 months of liver biopsy were eligible for this analysis. All measurements were done by investigators masked to all clinical data associated with the samples. Because of differences in available sample volumes, not all biomarkers could be measured in every participant.
Multimarker scores We used available clinical laboratory data to calculate Fibrosis-4 index for liver fibrosis (FIB-4) using its originally published formula. 13 The following previously reported multi marker scores were also calculated: MACK-3 (HOMA-IR, AST, and CK-18 M30); 14 a scoring system proposed by Cao and colleagues in 2013 (ALT, platelet count, CK-18 M30, and triglycerides), hereafter referred to as Cao 2013; 15 ADAPT (age, platelet count, diabetes, and PRO-C3); 16 FIBC3 (age, BMI, diabetes, platelet count, and PRO-C3); 17 ABC3D (age, BMI, diabetes, platelet count, and PRO-C3);  17  NAFLD Fibrosis Score (NFS; age, BMI, impaired fasting glycaemia or diabetes, ratio of AST to ALT, platelet count, and albumin);  18  and APRI (AST to platelet count ratio). 19 nhanced Liver Fibrosis (ELF) test scores, based on hyaluronic acid, tissue inhibitor of matrix metalloproteinase-1, and aminoterminal propeptide of procollagen type III,  20  were measured in the central laboratory with the ADVIA Centaur CP system (Siemens, Munich, Germany).
Because of the absence of any existing validated non-invasive test for NASH and the high collinearity between NASH and fibrosis stage, performance of FIB-4, a widely used simple fibrosis test, was used as a comparator in all target conditions. To account for differences in the subgroups of patients for which marker results were available, we recalculated the AUC for FIB-4 in each of the corresponding marker subgroups. In an additional head-to-head direct comparison, we evaluated the performance of the most clinically available biomarkers and scores (ie, PRO-C3, CK-18 M30 and M65, ELF, NFS, APRI, ADAPT, FIBC3, ABC3D, and FIB-4) in a subgroup in which results for all ten biomarkers and scores were available. We also evaluated the subgroupspecific performance of all markers according to diabetes status (R-package ROCnReg).
Participants with missing data were not included in the analysis. 23  The performance of each biomarker for which results were available in its subgroup is shown in table 2 and in the appendix (p 10). In evaluating the biomarkers' diagnostic performance in detecting both NASH and clinically significant fibrosis, we observed that no single marker reached the prespecified 0•80 AUC threshold. Performance similar to that of FIB-4 was observed for most biomarkers. The multimarker score with the best performance was the SomaSignal test, with an AUC of 0•81 (0•75-0•86) for NASH and clinically significant fibrosis (table  2 ). However, the 95% CI still included 0•80. FIB-4 had an We also evaluated each biomarker as a screening test for recruiting trial participants with NASH and clinically significant fibrosis (ie, at-risk NASH).
34 he limited performance of biomarkers in detecting NASH and clinically significant fibrosis provides a mandate for further study of novel biomarker algorithms, implementing both hypothesis-driven approaches from pathophysiology and machine learning approaches. Such approaches should be adapted to a specific target condition and context of use. The ultimate utility of these or any other biomarkers would be their ability to predict clinical outcomes. The longitudinal outcome data currently being generated by LITMUS within the Europe NAFLD Registry will be an important asset for evaluating their prognostic value. We have done one of the largest comparative diagnostic accuracy studies, with 17 different non-invasive markers for NAFLD. The results from this study showed that none of the single biomarkers achieved the desired performance to replace liver histology in detecting NASH and clinically significant fibrosis.
However, some multimarker scores, such as the SomaSignal test and ADAPT, are promising tools for identifying advanced fibrosis. No biomarkers have been approved by the US FDA or EMA, which further highlights the urgency of the LITMUS consortium's aim to validate and advance towards regulatory qualification markers for NAFLD and NASH. The LITMUS project will continue to collect data in the prospective LITMUS study cohort and will analyse blood-based and imaging biomarkers to further facilitate the evaluation of new and existing interventions in trials and to improve the clinical care and outcomes of people with NAFLD.
Modelling of the Bus Transportation Network In this section, the modelling approach of the transportation network is presented to determine the charging powers and time delays of EBs. First, the technical terms used to characterize the bus network are defined, then the utilized input data are presented with the deployed models and the simulation approach. The modelling steps are presented in Figure  1. The inputs represent the bus network, the bus fleet operation, the solar irradiation, and the assumptions made on the bus technology and the charging infrastructures. General Transit Feed Specification (GTFS) data are used for modelling the bus network accurately. Models of bus consumption, charging process, and PV production are then integrated into a simulation process to analyse the operation of a bus fleet.
Speed Profile The speed of the bus must be determined based on the distances and travel times between bus stops taken from the GTFS data in order to calculate the power consumption. Inspired by SORT standard driving cycles  [48] [49] [50] , the present paper considers a speed profile with a trapezoid shape between two consecutive bus stops, as seen on Figure  3. The trapezoid is defined by the maximal speed v max and the acceleration a. Travel time and the distance between two bus stops t bs and d bs are determined using GTFS data. The spatial and temporal constraints of the bus travel can be determined from the speed profile. The distance between the consecutive stops is therefore determined as follows: d bs = 2 • d var + d cst (4) d var = T var 0 v(t) dt = T var 0 a • t dt = a 2 • T 2 var ( 5 ) d cst = T var +T cst T var v(t) dt = v max • T cst (6) where d var and d cst are the distances travelled during the acceleration/deceleration and constant speed phases, respectively.
GTFS Data The GTFS format was initially developed by Google and has since become a de facto standard. Open data in GTFS format are generated by urban transport organizing authorities. They are composed of several tables gathering information about bus schedules and line topography  [52]  via various text files. The major advantages of GTFS files are that they are open access and the data are provided in the same format for the transportation network of different cities. However, they must be combined with a description of bus services to simulate the operation of buses. The location information (i. e. , latitude and longitude) of bus stops can be found in GTFS data but the distances and slopes between stops must be computed with the post-treatment as in the following section.
Post-Treatment of GTFS Data The GTFS data are used to determine the list of all routes and trips. As GTFS data only consider trips to model buses' operations and do not use the concept of routes defined in Section 3. 1, all trips must be analysed to identify their corresponding route. After that, the distance between consecutive stops of each route is determined using Open Street Map, which is a free editable geographic database of the world  [53]. The elevation of each stop is retrieved from Open Elevation Software. GTFS data also allows analysing the time spent between two consecutive bus stops. This duration can vary during a day mainly due to the hourly/daily traffic conditions. Firstly, the distance between stops are determined. Based on the latitude and longitude information, a path between two bus stops is generated on Open Street Map using a "bus" transportation mode.
To generate the path, Open Street Map creates a sequence of intermediate points on the road between the two stops with a higher density in curved portions (see Figure  4 ), and defines the total distance as the sum of distances between each intermediate point. This matches quite accurately the real path of the bus; however, it requires manual verification that the path proposed by Open Street Map fits the one on the bus network map. Secondly, determination of the altitude is performed. The bus consumption also depends on the slope of the road. Therefore, the Open Elevation software has been used to compute the altitude of all stops based of their latitude and longitude  [54]. However, only the mean slope between consecutive stops is accessible in this way. The limit is the resolution of topographic map, around 200 m for open-source maps.
Case Study In this paper, the GTFS data are gathered for the city of Compiègne in France. The bus line "ARC EXPRESS", which is 20 km long and composed of 12 bus stops, is chosen as the case study (see Figure  6 ). According to GTFS data, there are 11 routes and 36 trips used for providing transportation services over a day. The services of two buses were defined manually, so they matched the timetable. In order to realize all the trips defined on the timetable, the first bus "bus n°1" begins its service at 06:30 and ends it at 19:15. It performs 11 trips, including 2 between the bus depot and the line terminals. The second bus "bus n°2" operates from 06:10 to 18:50 and performs 15 trips. Both buses have long idle period in the middle of the day. The cumulative distance travelled by bus n°1 and bus n°2 are 149 km and 219 km, respectively.