dataesr/scientific-paragraphs-categorization huggingface dataset:
-The 1st Workshop on Scientific Knowledge Graphs (SKG 2020), organized by Andrea Mannocci (ISTI-CNR, Pisa, Italy), Francesco Osborne (The Open University, Milton Keynes, UK) and Angelo A. Salatino (The Open University, Milton Keynes, UK). -The 2nd Workshop of BI & Big Data Applications (BBIGAP 2020), organized by Fadila Bentayeb and Omar Boussaid (University of Lyon 2, France). -The Tenth IFIP 2. 6 -International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA 2020), organized by Paolo Ceravolo (Universitá degli Studi di Milano, Italy), Maurice van Keulen (University of Twente, The Netherlands) and Maria Teresa Gomez Lopez (University of Seville, Spain). -The 1st International Workshop on Assessing Impact and Merit in Science (AIMinScience 2020), organized by Paolo Manghi (Institute of Information Science and Technologies, CNR, Italy), Dimitris Sacharidis (TU Wien, Austria), and Thanasis Vergoulis (Athena Research Center, Greece).
The stability and robustness of the designed controller have been estimated by using Mathematica and Matlab/Simulink environments and successfully authenticated by performing experimental results in the laboratory. The results have been obtained using a 2. 5 kW prototype FC converter (by two-phase parallel interleaved boost converters) with a dSPACE MicroLabBox platform. The FC main source system is based on a fuel reformer engine that transforms fuel methanol and water into hydrogen gas H2 to a polymer electrolyte membrane FC stack (50 V, 2. 5 kW).
The FC Boost Converter Characteristics under CRL or CPL Simulation results for the FC boost converter (2-phase) in open-loop control (presented above in Section 2. 1) were obtained using the switching model realized in the Matlab/Simulink environment. The FC converter parameters are the real parameters. They can be seen in Table  A1. The simulation results under CRL are shown in Figures  4  and 5. In Figure  4   4 , at time = 2 ms, the FC boost converter is subjected to pure CRL step (the final power from 2500 W to 3200 W) to which a small oscillation and stability are observed at the DC bus output voltage. Moreover, given that the 2-phase FC boost converter is included in the plane, it is conceivable to achieve a comprehensive picture of the characteristic of the converter in open-loop by drawing their phase space (or phase plot): C v vs.
Obtained Simulation and Experimental Results for Static and Dynamic Operations Then, to validate the system modeling and the design of the controllers, simulations were carried out by using MATLAB/Simulink™ environment where the switching model of the interleaved parallel two-phase boost circuits were implemented. To corroborate the DC link voltage C v stabilization by the proposed Hamiltonian control law, the simulation and experimental results in Figure  10a ,b show the system response to a large load power step (disturbance) under CPL. 11a ,b display the system response to the large load power step (disturbance) under CRL: 48. 40 Ω to 16. 57 Ω (Figure  11a : positive load power transition ↑); and 16. 57 Ω to 48. 40 Ω (Figure  11b  negative load power transition ↓). The oscilloscope channels CH1-CH11 are the same measured signals as shown in Figure  10.
CH1-CH11 represent the DC link voltage C v (= 3 x ), Afterwards, Figure  12 ( ) ( ) 1 Pi L1d L1 Ii L1d L1 0 t d K i i K i i dτ = - + -  , (60) ( ) ( ) 2 Pi L2d L2 Ii L2d L2 0 t d K i i K i i dτ = - + -  , (61) ( ) ( ) FCd Pv Cd C Iv Cd C 0 t p K v v K v v d τ = - + -  , ( 62 ) with FCd L1d L2d FC 2 p i i v   = =       , (63) where Pi K , Ii K , Pv K , and Iv K are the tuning controller parameters. To assess the performance of the classic linear PI control law (60)-(  63 ) and the proposed adaptive Hamiltonian PI control law, simulations have been performed through MATLAB/Simulink. The choice of PI controllers for comparison purposes is motivated by the following reasons: 1. Since a classic interleaved boost converter (including only two phases) is used in this work to interface the fuel cell and the DC bus, the transfer function of the converter can be obtained easily by using Laplace Transform.
Conclusions This article has proposed an improved control law based on the Hamiltonian Lyapunov function to balance the output DC bus voltage of multi-phase DC/DC boost converters supplying a CRL or CPL for PEMFC applications compared to the previous works. The control scheme considers that the load power is known (by using a load current sensor) and is realized through the development of an IDA-PBC that balances the output DC bus voltage. An integral action (to guarantee the DC bus voltage error = 0) has been combined to enhance the performance of the IDA-PBC approach. The system modeling and design of the controllers have been thoroughly investigated through simulations performed in the MATLAB/Simulink environment and experiments through a suitable experimental platform, demonstrating the practical feasibility of the approach.
MATERIALS AND METHODS Regarding ethics, this post hoc substudy comprises the 6 patients mentioned above, from the PACIFIC trial (NCT01521468). The study complied with the Declaration of Helsinki, the study protocol was approved by the VUmc Medical Ethics Review Committee, and all patients provided written informed consent. This section further consists of five parts: generation of the coronary arterial network from the aorta down to the arteriole level, the one-dimensional blood flow model, the myocardium perfusion model, the coupling of the one-dimensional blood flow model and the myocardium model, and the definition of post-processed quantities. In modeling the coronary arterial circulation, the aorta, epicardial coronary arteries and left ventricle myocardium are first segmented from patient cCTA image data using custom methods (HeartFlow Inc.
Patient with Obstructive CAD Here we present preliminary results for an extension of the model to a patient with a lesion in the left anterior descending coronary artery (close to 80% reduction in diameter)-apparent from the FFR CT analysis (Fig. 1c ). The [  15  O]H 2 O PET exam perfusion map for hyperemic conditions revealed a significant perfusion deficit in the myocardial region corresponding to the LAD tree. For this patient, estimating total coronary flow using the power-law relation between flow and myocardial mass,  8  led to significantly increased flow compared to PET data (24% and 56% excess flow in resting and hyperemic conditions respectively). For this reason, blood flow in the system was instead estimated with the MBF myo value provided by the PET exam in order to determine if the relative effect of the stenosis could be modeled.
These biological characteristics will be simplified and formulated as agents' rules. The different parameters were retrieved by first looking for human studies, but regarding the lack of several information, we extended our search to include general characteristics of the components, animal models of BJI, and mathematical models of the bone remodeling process. Search was conducted in PubMed and ScienceDirect databases exploring the relevant journal papers from their inception to date, July 2017. Literature search strategy, i. e. , search queries and inclusion/exclusion criteria, are described in detail in Supplementary Material and Supplementary Table  S1.
The Agent-Based Model of Early Stages Bone and Joint Infections The Environment NetLogo was chosen for implementing the BJI model for its several features such as supporting investigating the agent behaviors over time and space and emerging the agents' heterogeneity within its framework  (Wilensky, 1999). Among ABM toolkits, NetLogo introduces an academic and communitysupported tool to simulate the compound phenomena using a high-level multi-agent programming language and a robust, simple modeling platform. The general 2D space of our model was divided into two rectangles of overall dimension 151 × 101 patches (patches represent the grids in the landscape in NetLogo). The first rectangle (101 × 101 patches) was a bone tissue represented as a two-dimensional layer; we assumed this dimension according to 2 mm 2 of bone tissue.
We ran the simulations for n = 100 iterations for each of the infected inoculum state, while all other changeable parameters were set to the median of their range (Table  3 ). For each iteration, the dynamics of the population for bacteria, osteocytes, and neutrophils agents for a time duration of 300 h (t = 300 ticks) was tracked. The generated data at each time click (1 h) were saved to an output data (. csv) file for analysis. Subsequently, the mean and standard deviation for the population at each time step for each type of these agents was quantified. Further, the relation between two agents over time under the same initial conditions was analyzed. We benefitted from the 3D surface graphs and the generated data from the previous step to analyze the relationships between each of bacteria and neutrophils populations over time, bacteria and osteocytes populations over time, and neutrophils and osteocytes populations over time.
In this paper, we present a 3D numerical model of Wire Arc Additive Manufacturing to simulate the material deposition and the temperature field from the operating parameters. This predictive model, applied to a Cold Metal Transfer (CMT) process, takes into account electromagnetism, fluid flow and heat transfer in all domains (wire, arc, melt pool, substrate). This model, developed using COMSOL Multiphysics® software, calculates the creation of the molten metal drop at the filler wire tip, the detachment of the drop when the filler material retracts during the short circuiting phase and the growth of the deposit. This innovative work describes for the first time the wire behaviour dynamically and its interaction with the melt pool in 3D in a fully coupled approach. The dynamics of the free surface are treated with the level set method, particularly efficient for strong topological changes.
This work represents a straight continuation of our previous 2D axisymmetric model  [18]. This 3D model, developed with Comsol Multiphysics® software version 5. 4, describes the four steps to metal transfer during CMT. The model simulates the forward and backward motion of the filler wire. It also predicts its impingement into the melt pool, the growth of the melt pool and the formation of the deposited bead. It is based on the computation of magnetothermal-hydrodynamic equations in all domains (wire, arc, melt pool, substrate) with the level set method for the interface tracking. The CMT model is used to simulate the build-up of a wall in a 3D configuration. This multiphysics model is limited to the scale of the melt pool but allows to study the arc and melt-pool dynamics. This model requires only the knowledge of the operating parameters without any assumption on the arc distribution, to predict the temperature and fluid velocity fields in the melt pool during material deposition as well as its geometry.
Level set approach In order to simulate the metal transfer, the level set method  [25]  is chosen to track the moving interface between the shielding gas and the metal phase. The wire, the substrate and the molten metal are treated as a unique metal phase. The level set method enables to simulate the dynamic shape of the wire tip, the detachment of the droplet during wire retraction, and the dynamic shape of the deposit during material addition. Although the VOF method is more often used for this kind of simulations, the finite element software COMSOL Multiphysics was chosen here. This software does not include the VOF method, which is proposed rather by finite volume softwares, dedicated to CFD simulations. In addition, the level set method has been used with success in previous works  [18, 26].
Φ = 0 in domains 3 to 6. The boundary conditions used for the thermal problem are the following: -A temperature of 300 K is prescribed at the surfaces normal to theaxis, front side, of the domains 1, 2, 3, 4, and 5. -For the open boundary of the plasma domains (domains 3, 4, 5 and 6 (Fig. 1 )), the outflow condition of Comsol Multiphysics software is chosen, which is equivalent to an insulation condition: •. I $ = 0 (22) -Radiation and convection losses are applied to the surfaces of the substrate (domains 1 and 2 (Fig. 1 )). The convection losses are calculated from: • B = -ˆB( - ' ) (23) whereB is a convective heat transfer coefficient and ' is ambient temperature. The radiation losses are determined from: • > = -(-• -- ' -  (24)  where ( is the metal emissivity, -• is the Stefan-Boltzmann constant and is the temperature at the metal surface.
Experimental data The present simulations have been based on experimental data carried out at the Institut des Matériaux Jean Rouxel (IMN) in Nantes (France). The experiment consists in the buildup of a wall of five layers. The deposition direction alternates between layers, as shown in Fig. 2. The substrate is made of 304 stainless steel and is 130 mm long by 12 mm wide and 50 mm high. The built part was manufactured using a filler wire also in stainless steel 304 of 1 mm in diameter. The operating parameters chosen for the multiplayer deposits are as follows: the travel speed of the torch is 30 cm / min, -the flow rate of argon gas is 15 L / min, -the average speed of the filler wire is 3. 5 m / min. Since the CMT system used does not provide an accurate measurement of the instantaneous wire speed during its forward and backward movements, an average speed was determined from a mass balance of deposited material.
The numerical results show that the maximum flow velocities are concentrated at the location of the impact of the droplet and are approximately 0. 07 m. s - 1. In the front part of the melt pool, the fluid velocities are significantly lower, and are of the order of 0. 01 m. s -1. From the velocity vector field in the wire, it can be noticed the direction of the wire movement. In Fig. 6 , the dimensions of the melt pool are analysed and then compared with the experimental data for the first layer in order to validate the consistency of the model (Fig. 7 , Fig. 8 ). The numerical melt pool length is estimated at 13. 7 mm, which is in agreement with the experimental value of 13. 2 mm. In addition, the height of the melt pool estimated by the model is 3. 1 mm, which is close to the height obtained during the experiment, which was 2.
Stimulus selection and materials One hundred and twenty word pairs were selected from the Manulex lexical database  (Lété, Sprenger-Charolles, & Colé, 2004). All words were four letters long, were classified as Grade 1 in the Manulex corpus (meaning that these words are regularly encountered by beginning readers), and contained no diacritics. The mean frequency of these words was 2. 55 log parts per million (ppm) (range 0. 78-3. 94 log ppm). The word set included verbs, adjectives, participles, pronouns, adverbs, and auxiliaries. None of the words in each pair were orthographically or semantically related, and these pairs of words were used to generate the target words and the unrelated flanker words. A set of 120 pseudoword pairs were generated with Wuggy  (Keuleers & Brysbaert, 2010). These were used to induce the lexical decision task, and were thus not included in our analyses.
With a Latin-square design we ensured that all items (i. e. , both words and pseudowords) were shown in all conditions, but only once per participant. The total of 240 trials was presented in randomized order. The total of 2,260 measurements per experimental condition (pseudoword trials excluded) meets the recommendation of  Brysbaert and Stevens (2018)  for having abundant statistical power (their recommendation being 1,600 measurements per condition). We additionally estimated statistical power ad hoc based on data from the study of  Snell and Grainger (2018). In our previous study, in which we also employed repetition flankers and unrelated flankers, we observed a mean 36-ms difference in response time based on 2,000 measurements per condition, with a Cohen's d = 0. 30. Statistical power was estimated using simulations with the simR package  (Green & MacLeod, 2016)  in the R computing environment.
Apparatus and software The stimuli and experimental design were implemented with OpenSesame  (Mathôt, Schreii, & Theeuwes, 2012). Stimuli were presented on an HP ProBook 640 G2 monitor calibrated in 18-in. (1,366 × 768 px, 80 Hz). Participants were seated at a 60-cm distance from the display. Stimuli were displayed in lower case, Courier New font, black color on light grey background. Each character space subtended 0. 35°of visual angle.
Acknowledgements This research was supported by the  European Research Council  (grant  ERC742141  awarded to  Jonathan Grainger ), the  French national research agency  ( ANR , grant  15-CE33-0002-01 ), and the  European Commission  (grant  H2020-MSCA-IF-2018 833223  awarded to  Joshua Snell ). The authors thank the schools, children and caregivers for their participation in this study. Open Practices Statement All data are available at https://osf. io/9jkt6/.
Second, libraries that ask us for authorship statistics are conceiving of the model as though it were one of APCs. This is a theoretical misunderstanding of the economic model. This misconception may be higher in the case of institutions who join after we point out that their authors are publishing in OLH titles. Such institutions appear to conceive of OLH as a retrospective waiver model, in which the default is to waive all APCs unless an institution is able to pay. This is not what we intended, but it does seem a powerful driver of new signups. Liber Quarterly Volume 30 2020 Nonetheless, across both readership and authorship metrics demands from libraries, we face a substantial challenge in delivering such statistics. This is because we are, in some senses, a meta-organisation. We have publications that span multiple platforms: at Ubiquity Press, at Liverpool University Press, and on our own Janeway software.
Extracting such information, in a timely fashion, from third-party organisations can be challenging as, even with COUNTER-compliant metrics, the platforms are not interoperable. It is often hard, also, accurately to determine authorship information from university sources. Bad metadata provided by authors, including outdated institutional affiliations, mean that we must perform loose queries. A good example of this are the three entries: 'Cambridge University', 'The University of Cambridge', and 'The Massachusetts Institute of Technology, Cambridge, MA'. Even with a loose query for 'Cambridge' -again, spread between multiple organisations who must perform the lookup -we can end up with entities that are radically different from one another and this must all be checked manually. Given that we distrust author-provided metadata, requests for authorship affiliation are usually fulfilled by using Google's 'site' operator to search and then by manually verifying that the affiliation is correct.
We further showed that the information of molecules such as molecular types or molecular properties can be deduced by automatic reasoning using logical rules. The information necessary to populate BiPOm can be extracted from existing databases or existing bio-ontologies. Conclusion: BiPOm provides a formal rule-based knowledge representation to relate all cellular components together by considering the cellular system as a whole. It relies on a paradigm shift where the anchorage of knowledge is rerouted from the molecule to the biological process.
Moreover, to capture even more the constraints and the semantics of a given application domain, logical rules can be defined. These rules can be used by reasoners to infer new assertions from existing ones, thus enriching knowledge. For instance, the rule ∀x, y : Animal(x) ∧ Animal(y) ∧ eats(x, y) ⇒ Carnivore(x) means that every animal that eats another animal is a carnivore. Therefore, if we have Tiger(Tigger) ∧ Giraf (Ernestine) ∧ eats(Tigger, Ernestine), we can automatically deduce that Tigger is also a carnivore that is Carnivore(Tigger). In biology, various bio-ontologies have been developed and stored in public repositories  [8, 9]. Most of them correspond to lightweight ontologies that are suitable for specification and classification of data using annotations that link entities to their corresponding concepts in the ontology.
BioPAX was not designed for annotating data but for facilitating the collection and exchange of available pathway data in a controlled way. However, the low expressiveness of current bio-ontologies limits advanced automatic reasoning for complete query answering and new information generation. In addition, the molecule function is anchored to the cellular component as an annotation whatever the state of the cellular component is. Thus, they are not fully adapted to describe the cell complexity, in particular the interplay between cellular entities and the biological processes. Recently, the systemic description of the cell was shown to be very effective in tackling the complexity of the cell  [19]. In systems biology, the description of the cell is processcentered and not gene-or molecule-centered, and is able to intrinsically handle multiple states and functions of molecules.
The question remains if information anchored on processes could really be used to formally describe process' participants. Based on this previous work, we present in this article BiPOm (Biological interlocked Process Ontology for metabolism), a concise and expressive OWL-DL (Web Ontology Language)  [21]  ontology dedicated to the representation of metabolic processes. BiPOm can benefit from the numerous tools, such as ontology editors  [22] , reasoners  [23] , and ontology browsers  [24] , which are developed and widely used in the semantic Web community. BiPOm has two main original characteristics: 1) it represents biological knowledge through classes, properties, and instances and 2) it uses automatic reasoning through Semantic Web Rule Language (SWRL  [25] ) in order to automatically infer, formalize, and refine properties of molecules.
Data extraction and curation, instances definition We collected information on the metabolic reactions and on the different steps of protein complex formation and activation using Uniprot and literature to complement the description of Plant reactome. Information was structured in a table (Additional file 5: Table  2 ) that was imported to instantiate BiPOm within the Protégé editor. We fully described the RPPC pathway and the RuBisCO activation pathway. RPPC pathway is an aggregated process that starts with carboxylation of D-ribulose For our use case, we created in BiPOm ontology 289 instances among which 123 are typed either as a biological process, a participant or an activity. We illustrated in Additional file 6: Table  3  the progressive enrichment of BiPOm content. BiPOm instantiation with RPPC information was performed with 123 declared class assertions and 469 declared property assertions.
Discussion Here we introduced BiPOm, a concise and highly expressive ontology describing metabolic processes. We explicitly handled the different states of molecules as instances including the "active state" involved in a metabolic process, and more generally in a biological process, which are traditionally poorly described in bio-ontologies. Using SWRL rules to perform automatic reasoning on BiPOm instances, we enriched information on cellular entities such as molecular types or molecular properties from a few assertions used to describe biological processes. Enriched knowledge is encoded in a formal language. We thus made explicit and machine-readable some implicit biological knowledge. Few information is required to instantiate BiPOm and can be extracted from existing databases or bio-ontologies and completed (if necessary) by information from literature.