dataesr/scientific-paragraphs-categorization huggingface dataset:
We have also included case studies and examples of successful applications of AM to illustrate the opportunities that the technology offers. This approach has been effective in engaging students and helping them to understand the potential of AM. In the section on the design process, we provide a detailed explanation of how the design process differs for additive manufacturing. We discuss how conventional design processes need to be adapted to incorporate AM principles for industrialization and how optimization software can be used to create complex geometries for specific objectives, such as light weighting (Generative Design software based on functional surfaces and external constraints). The teaching method described in this article is based on active pedagogy such as Problem and Databased learning.
Students identify the functional areas and discuss DfAM principles to minimize weight while AM maintaining manufacturability. Figure  5a  provides an example of the redesigned cylinder with compression bars and analyzed using the shell tool, and various tools are recommended for use in the DfAM process. Validation of orientation and feature recognition are also discussed using rules and previous tool steps. Once design ideas are finalized, students discuss the print orientation for the manifold based on functional surfaces and post-machining, as this can affect function and complexity of operation. If unsure, students are encouraged to save their work in STL format and present their idea with arguments to an expert. Using AM software, they can visualize indicators such as proximity or thickness for their current design (Figure  4b ) and supports (Figure  4c ), which teaches them the importance of quickly switching between different tool applications.
The survey was administered online, and participants were given a week to complete it. The sample size was 56 participants, who were recruited from a local university and had low degree levels of prior experience with additive manufacturing. The students followed and validated the DfAM courses. While the evaluation provides some insights into the participants' experience and feedback on the workshop, there are several limitations that need to be addressed. First, the survey questions were clear but not obvious (several orientations were possible), which makes it difficult to assess the validity and reliability of the data collected. Several justifications can be proposed and clear indicators need to be justified. It is important to ensure that the questions are clear, relevant, and unbiased to obtain meaningful results.
Additionally, they present a need for expertise in DfAM, and 90% of survey respondents believed academics would have the most significant impact on the future manufacturing workforce by teaching students to have a deeper knowledge of DfAM  (Chen et al. , 2017). Step C allowed students to work with DfAM thinking in order to better exploit the potential of AM, but the complexity of the problem was apparent due to the students' lack of experience with Laser Powder Bed Fusion technologyy. Additional data and expertise were needed to comprehensively develop successful industrial cases. 85% of students found features analysis tools to be necessary, and all students wanted to experiment with the processes. Part examples were insufficient and students mixed their design methods with conventional processes.
Also, active pedagogical strategy improved their skills over time and offer opportunities for collaboration and peer review foster a more supportive and engaging learning environment (75% of success was team work). Finally, three questions were asked in introduction. The students are learning about DfAM, which involves designing products in a way that considers the unique capabilities and limitations of additive manufacturing processes. They are also learning about the entire additive manufacturing process chain, including conception, design, data preparation, and post-processing. This approach enables connections between part geometry and processability. They gain an understanding of how their designs can be manufactured effectively and efficiently using AM processes. This study suggests that the students have gained critical engineering skills in the product design domain that can be applied to different industries.
In the current version of the HEPTopTagger package, all these methods are used together in a multi-variate classification  [53, 54]  which maximises the expected tagging performance. Access to this tool and all its embedded features within public frameworks like MadAnalysis 5 or Rivet is thus crucial for prototyping and reproducing collider-event data analyses, a key activity in collider phenomenology. In the rest of this section, we discuss technical details about the embedding of HEPTopTagger in these two software tools, and describe how they could practically be used. In practice, we rely on the latest public version of HEPTopTagger (i. e. its version 2 available from the webpage https://www. thphys. uni-heidelberg. de/~plehn/ index. php?show=heptoptagger). Moreover, we have validated our implementations by confronting the results of a few test calculations obtained by using the two interfaced versions of HEPTopTagger to those returned by HEPTop-Tagger when used in a standalone mode.
Jet substructure tools in MadAnalysis 5 Since 2021, MadAnalysis 5 and its SFS framework for fast simulation of detector effects  [56]  have been equipped with jet substructure tools and methods. 1  In particular, the smearing functionality implemented in the SFS framework allows for modifications of the properties of the jets' constituents, so that the SFS is suitable for the embedding of HEPTop-Tagger in a way similar to what was achieved for Rivet in Sect. 2. 2. As the substructure branch is so far largely undocumented, we take benefit from the present work to provide some details on its functioning and how to make use of the code to embed top tagging in a generic analysis. When a jet reconstruction algorithm is turned on in Mad-Analysis 5, a so-called 'primary' jet collection is built from a hadronised event.
This primary jet collection is equivalent to the sole jet collection that used to be built in versions 1. X. Y of the code, which was documented in  [31, 56]. In practice, the code makes use of its interface with FastJet  [57] , that can be turned on from the MadAnalysis 5 command line interface by typing set main. fastsim. package = fastjet A specific jet algorithm is then activated through the commands set main. fastsim. algorithm = \ < algorithm > set main. fastsim. < property > = <value > The list of supported algorithms, together with the available properties, is provided in  [56]. By default, the anti-k T jet algorithm  [58]  is considered, with a radius parameter R = 0. 4 (radius) and a minimum p T value of 5 GeV (ptmin). The primary jet collection is identified by its jet identifier (or JetID), that is fixed to Ma5Jet by default.
radius = 0. 8 set CA08. ptmin = 200 Once multiple jet collections are defined, constituent-based smearing is always applied to the properties of all final-state hadrons before the different reconstructions are performed. This contrasts with the setup in which a single collection is defined, as here users can decide to smear reconstructed objects instead of their constituents. Reconstruction efficiencies can also be provided from the command line interface (see  [56] ), but they will only be applied to the primary jet collection. This limiting behaviour can however be bypassed by employing the expert mode of the code, in which users implement their analysis directly in C++ (and are thus free to do whatever they want). We therefore focus only on this expert mode from now on. 2 t the level of the C++ code generated by MadAnalysis 5 (or implemented from scratch by expert users), the primary jet collection can be accessed through the standard accessor event.
rec()->jets() (as described in  [30, 31] ), and all jet collections (including the primary one) can be accessed through the accessor event. rec()->jets ( <JetID>) (with <JetID> being the identifier referring to the collection). These accessors return a vector of pointers to constant RecJetFormat objects (or RecJet objects for short), the entire vector being also of the shorthand type RecJets. In the version 2. 0. X of MadAnalysis 5, a Substructure namespace has been implemented and includes wrappers to a large set of FastJet and FastJet Contrib functionalities. This substructure module allows for three standard infrared and collinear safe jet-clustering algorithms, that can be initialised as for instance through This initialises a Cluster object named cluster in which jet reconstruction relies on the anti-k T algorithm with parameter R = 0.
We refer to Ref. [59]  for more information. Reclustering is then proceeded as above, 3 See the webpage https://phab. hepforge. org/source/fastjetsvn/browse/ contrib/contribs/VariableR/tags/1. 2. 1/ for more information. In order to enable the usage of HEPTopTagger within MadAnalysis 5, the package must first be downloaded and linked to the code. This is achieved by typing in the Mad-Analysis 5 command line interface install HEPTopTagger once FastJet and FastJet Contrib are installed and available (which is achieved by typing in the interpreter the command install fastjet). When implementing an analysis in C++, the execution of HEPTopTagger is controlled from a dedicated structure called Substructure::HTT:: InputParameters. The latter is defined in the file "tools/ SampleAnalyzer/Interfaces/HEPTopTagger/HTT.
In other words, we enforce that the pseudo-data corresponding to the SM scenario (i. e. the origin of the Wilson coefficient parameter space) corresponds to the background expectation with suppressed statistical and systematical fluctuations, which consists, therefore, of an Asimov dataset. The above χ 2 test is thus asymptotically equivalent to a profile likelihood ratio χ 2 = χ 2 SMEFT -χ 2 best for a given SMEFT scenario with an implicit best-fit reference model evaluated in the case of the SM (therefore with χ 2 best = 0). Without explicitly performing any profiling, we thus estimate the sensitivity of a profile-likelihood fit by comparing the obtained χ 2 values with that expected from a χ 2 distribution with one degree of freedom. In practice, however, profiled constraints could be slightly weaker due to a less perfect fit of observed data to the background model.
Conversely, for TeV-scale new physics, couplings of O(0. 1) can be reached. The bounds are found to be mildly more constraining with the increase in luminosity as well as with a harder cut on m rec t t , as expected, and the impact of offshell top-antitop production is additionally found to be subleading. Such a sensitivity is of comparable size with that estimated on the basis of global fits (see e. g. predictions from Ref. [42] ), which demonstrates the potential of including dedicated analyses of boosted top quark pair production and decay in SMEFT global fits. Global fits of LHC Run 2 data indeed indicate that |C/ | has to be smaller than about 0. 1-1 TeV -1 too. Our results should however additionally be compared with individual limits extracted from fits of a large set of observables when one SMEFT operator is considered at a time (for a fairer comparison).
Conclusion and outlook Jet substructure methods are known to be among the key players in the search for new phenomena beyond the Standard Model of particle physics. Among these, a set of dedicated techniques are related to the identification of jets originating from the hadronic decay of a boosted top quark. In this paper, we have reported the development of an interface between the HEPTopTagger package and two software tools widely used in the high-energy physics community, namely the MadAnalysis 5 and Rivet frameworks. Thanks to this development, the many users of these platforms now have the possibility to exploit boosted hadronically-decaying top quarks and their properties in analyses of high-energy physics events for the Large Hadron Collider and beyond. We have briefly described these two implementations and how to use them.
Our developments equip the Rivet toolkit from version 3. 1. 7, which is available from HepForge (see https://rivet. hepforge. org/), as well as the MadAnalysis 5 framework from version 2. 0. 4, available from GitHub (see https://github. com/MadAnalysis/madanalysis5/releases). Moreover, detailed tutorials exploiting all the possibilities can be found in the ''analyses/examples/EXAMPLE_HTT. cc'' analysis file shipped with Rivet, as well as in the MadAnalysis 5 tutorial available from https://github. com/ MadAnalysis/tutorial_osu. To illustrate the power of these developments, we have considered the SMEFT framework in which new physics manifests through non-renormalisable operators in the Standard Model fields. We have focused on eight dimension-six, four-fermion operators relevant to the top quark sector, chosen as they are not stringently constrained by current SMEFT global fits.
Acknowledgements This work has been partly supported by the  French ANR  (Grant  ANR-21-CE31-0013 , ' DMwithLLPatLHC '), by the  UK Royal Society  (Grant  UF160548 ) and  STFC  (Grant  ST/S000887/1 ), and by a short-term studentship funded by the  European Union 's  Horizon 2020 research and innovation programme.
The areas of meaning in life listed by people with Parkinson's disease in our cohort were each assigned to one of the 15 categories, according to the current version of the SMiLE manual (https:// www. psych other apie-muenc hen. de/ downl oads/ SMiLE_ Manual. pdf). Other assessments of patients and carers within the CLaSP cohort study include standardized questionnaires to evaluate disease severity, comorbidities, depression, cognition, non-motor symptoms, quality of life in patients and carers as well as caregiver burden. Data from the CLaSP cohort were compared to a representative sample of 856 healthy German participants from all age groups (12. 9% were age 70 and above)  (Fegg et al. , 2007).
Statistical tests were performed using the Statistical Package for Social Sciences (SPSS Version 25) and Excel. In the comparative study in healthy participants  (Fegg et al. , 2007) , the original version of the SMiLE was used where the importance of each area was rated with a five-point adjectival scale, ranging from 1 'somewhat important' to 5 'extremely important'. To adjust this to the newer version using the eight-point scale from 0 'not important' to 7 'extremely important', the original data were adjusted (W/5 * 7).
Participation in the Study 692 people with Parkinson's disease fulfilled the inclusion criteria and were included in the CLaSP study. At Baseline (T1), SMiLE questionnaires were distributed to people in 8 centres and we received the completed SMiLE from 509 participants (74% of all CLaSP study participants). Table  1  shows the number of responses from each centre. Participants' characteristics of the CLaSP study are described in detail elsewhere  (Schrag et al. , 2020). Average disease duration in the CLaSP cohort was 15. 4 (SD 7. 7) years, and mean total UPDRS score was 82. 7 (SD 22. 4). Some demographic data of participants of the SMiLE study within CLaSP are presented in Table  2.
Funding Open access funding provided by  Paracelsus Medical University. The study was funded through the  Joint Programme-Neurodegenerative Disease Research ; through national funding bodies in all six countries- United Kingdom: Economic and Social Research Council   ES/L009250/1 ; Sweden: the  Swedish Research Council  and  CLaSP/JPND HC-559-002 ;  Germany :  Federal Ministry of Education and Research (BMBF) ,  Marburg ,  01ED1403A ,  Munich ,  01ED1403B ;  France :  ANR-13-JPHC-0001-07 ; Portugal:  HC/0002/2012 ; Holland:  733051003. In addition, the study was funded by  MultiPark , the strategic.
| Study area and bottom trawl surveys The study area covers most of the European continental shelf seas, including the Baltic Sea, the North Sea, the English Channel, the Scottish west coast, the Celtic Sea, Rockall and Porcupine Banks, the Irish Sea, Bay of Biscay and the Portuguese coast and is divided into 1° longitude by 0. 5° latitude grid cells (hereafter 'rectangles') as defined by the International Council for the Exploration of the Sea (ICES) (Figure  1 ). Abundance data were collated from 20 scientific bottom trawl surveys (see Supporting Information Appendix S1-Table  S1 ) during the last four decades, from 1984 to 2020. Sampling was carried out during daylight at an average speed of 4 knots for at least 20 min. Although gears and sampling schemes differ between surveys (see Supporting Information Appendix S1-Table  S1 ), all captured individuals were identified at the finest taxonomic level possible and reported in a standardized number of individuals per species caught per hour of trawling, including potadromous species that may be caught in brackish water  (Kawamura, 2008).
The abundance dataset we used was obtained from the ICES Database on Trawl Surveys (DATRAS), 2023, ICES, Copenhagen, Denmark. (https://datras. ices. dk) and includes 425 species across 536 standard ICES rectangles. Due to the absence of reporting on non-commercial species from Spanish surveys, we did not include these surveys. Since the total number of hauls per rectangle is spatially heterogeneous (from 3 to 1070; see Supporting Information Appendix S2-Figure  S1a ), we standardized sampling effort by means of species accumulation curves (SACs)  (Chao, 1987)  for each rectangle, considering each haul as a sampling unit, using the 'vegan' R package  (Oksanen et al. , 2008). Nonlinear Michaelis-Menten curves were fitted to each of the SACs. When the total number of hauls was lower than the estimated number of hauls necessary to achieve 65% of the asymptotic species richness, we discarded the rectangle (59 rectangles out of 536) following the approach of Maureaud et al.
| Species traits and gap filling We selected traits that reflect habitat use, life-history and trophic ecology of species expected to be implicated in the response of species to environmental changes and ecosystem functioning (see Supporting Information Appendix S1-Table  S2  for reasoning;  Beukhof, Dencker, Pecuchet, & Lindegren, 2019; McLean et al. , 2019 , Murgier et al. , 2021). Among the selected traits, eight are continuous (trophic level, age at sexual maturity, length at sexual maturity, fecundity, offspring size, Von Bertalanffy growth coefficient K, maximum length and length infinity) and three are categorical (position in the water column, feeding mode and spawning type). Trait data for the 425 species were initially collected by extracting trait values from the North Atlantic and Northeast Pacific shelves dataset collated by  Beukhof, Dencker, Palomares, and Maureaud (2019).
Our obtained trait dataset had 38% missing trait values (1289 out of 3386), explained by the presence of missing data in  Beukhof, Dencker, Palomares, and Maureaud (2019)  and because some species we studied were not in  Beukhof et al. (2019b). We filled missing values by following the procedure undertaken by  Beukhof, Dencker, Palomares, and Maureaud (2019)  and by using the predictive lifehistory model developed by  Thorson et al. (2017). Continuous traits were supplemented with information extracted from FishBase (Froese & Pauly, 2020) restricted to the Northeast Atlantic and completed by scientific literature as undertaken by  Beukhof, Dencker, Palomares, and Maureaud (2019). For 'trophic level', 'offspring size' and 'fecundity' traits not available at the species levels, we averaged the trait values at the genus level or family level if genus was not available, following  Beukhof, Dencker, Palomares, and Maureaud (2019).
, 2017). The R code is freely available at Figshare public data repository 10. 6084/m9. figshare. 22317643. v1 (see also Appendix 1-Data sources). We calculated the dissimilarity matrix (i. e. dissimilarities between species based on their traits) of each possible combination of traits among the 11 traits selected for this study, considering that a minimum of 4 is needed to characterize the difference between species  (Petchey & Gaston, 2002). This procedure therefore provides a total of 1816 dissimilarity matrices. We then calculated the IntDi value of each species from the integrated dissimilarity matrix itself computed as the average between the 1816 matrices (see Supporting Information Appendix S2-Figure  S2  for example). Additionally, to prevent any disproportionate contribution of categorical traits in dissimilarity matrices and thus on distinctiveness computation, we applied the approach developed by de Bello et al.
(  2021 ) using the 'gawdis' package. Functional distinctiveness was standardized so that it ranges between 0 and 1. We followed the same procedure for the resulting trait database, the database with no missing data, and the database with missing data predicted with the R package 'missForest'  (Stekhoven & Buehlmann, 2012). We developed two linear models (Supporting Information Appendix S2-Figure  S3 ; R   S4 ). The 425 species were then grouped on the basis of their IntDi values: the first group (D1, the most functionally common species) was defined according to the first decile of IntDi, while the last group (D10, the most functionally distinct species) was defined according to the last decile. This division into deciles was done a posteriori according to the number of groups allowing to best isolate the most functionally distinct species according to the distribution of distinctiveness values (Figure  2 ).
The functional dissimilarity between the species of a functional distinctiveness group and all other species was calculated using the R function 'dissim' of the package 'TPD'  (Carmona et al. , 2019). It reflects the degree of functional dissimilarity between the probabilistic distributions of species in the trait space between the two assemblages and ranges from 0 (complete overlap) to 1 (no overlap). We used a TPD (Trait probability density; 'TPD' package;  Carmona et al. , 2019)  approach-estimation of the probabilistic distribution of the species within the trait space-by performing multivariate kernel density estimations. We divided the 2-dimensional space into 40,000 cells, that is, 200 per dimension. For each species, the kernel was a multivariate normal distribution centred in the coordinates of the species in the trait space and bandwidth was chosen using unconstrained bandwidth selectors from the 'Hpi' R function in the 'ks' package  (Chacón & Duong, 2018).
For a given species, we compared its geographical range with the geographical extent of the entire study area. The spatial restrictedness R i is an index between 0 and 1 based on species occurrence  (Violle et al. , 2017)  (weakly impacted by gear selectivity) and is computed as follows: with K i the number of ICES rectangles where species i occurs and Ktot the total number of ICES rectangles in the dataset. R i nearly equals one when a species is present in a single site and 0 when the species is present in all sites  (Grenié et al. , 2017). Because there are differences in sampling effort between the 536 ICES rectangles, we computed the restrictedness to prevent under or overestimation. The restrictedness was computed for each grid cell by randomly selecting the minimum number of hauls to catch 65% of the asymptotic number of species with enough sampling effort (477 rectangles out of 536).
For both restrictedness and scarcity, a unilateral Welch's t-test was performed to compare the restrictedness/scarcity between the most functionally common and distinct species (D1 and D10, respectively). We also implemented two Generalized Linear Mixed Models (GLMM) using the 'lme4' R package  (Bates et al. , 2015)  using the restrictedness/scarcity index as a response, the deciles (D1 and D10) as predictors and the species family as a random factor.