dataesr/scientific-paragraphs-categorization huggingface dataset:
The accurate determination of these zones is essential since sensors can be placed to control artificial lighting and combine it with natural light to preserve the required lighting conditions while minimizing the building's consumption. DZs depend on the orientation, openings and dimensions of the building, and are calculated using a combination of engineering data and the CAD software itself. To calculate the DZ, four parameters are taken into account, the most important being the width of the opening (  ) and the height (  ). This is followed by the depth (  ) and width (  ) of the DZ. These two quantities are calculated using equations (  1 ) and  (2). 𝐿 𝐷𝑍 =. 5 × 𝑂 (1) 𝑊 𝐷𝑍 = 𝑊 𝑂 + 𝐿 𝐷𝑍 (2) By way of illustration, as shown in Figure  7 , the DZ of the teaching area is represented by a "wedge" shape calculated by a triangular profile; a profile determined by adding the width of the opening (i.
Currently, the most important loads come from lighting. The TEE-KENAK software offers an additional option, called Net-metering, to enter the photovoltaic mask (i. e. , the inclusion of a photovoltaic system in the building under study), referring, of course, to the regulations in force for its use. The net metering is then defined, which allows the additional energy produced by the photovoltaic system to be stored on the electrical grid. The net metering is associated with the notion of energy credit in kWh corresponding to the energy produced by the solar panels, but not consumed. When the solar panels produce more electricity than they consume during the day, the excess is fed back into the grid. When the electricity consumption is higher than the solar panels' production, either at night or on cloudy days, the energy is taken from the grid.
Finally, as explained by the authors of  [38, 39] , it is important to evaluate the size of the battery pack and the estimated gains compared to the initial investment cost. One of the most important findings of this research is that window replacement has an inordinate initial capital cost. This is due to the WWR. In terms of energy efficiency, 19% of windows face north, 16% face south, 26% face east and 38% face west. The highest percentage of openings should be south-facing if this were a passive building, but it is not. Additionally, this percentage could be changed to reduce building envelope losses, nevertheless, the building also has a daylighting percentage of 59%. Changing the WWR would affect the daylighting and therefore the artificial lighting required for the building. All data indicate that all improvements are interconnected, thus answering research question RQ2 (i.
As mentioned in Section 2. 2, the building studied was built at a time when there were few, if any, regulations and no architectural plans. Thus, the data acquired during the audit could not be cross-checked. In order to be precise about the -value of the walls, it is necessary to take a sample, using a core drill. In this way, one can be sure of the layers included in the wall, and thus of their exact properties. Moreover, some variables, such as the precise percentage of age of the reinforced concrete, were estimated by consulting the tables of national regulations. Finally, due to confidentiality issues, we were not able to access the actual electrical bills for the building. The recording of electricity and heating bills was not implemented by the TEE-KENAK software, which may lead to some limitations, such as for the PV sizing method which could not be as accurate as we had hoped.
The quality of life of the young population is also improved, as sustainable measures are put into practice at an early age. The results of this study can be generalized and considered for buildings in most Mediterranean countries with similar climate and sun exposure. In Greece, most of the school buildings were originally constructed in the same period as the studied building. The methodology applied in this study can help in decision making, based on a software procedure that includes inputs from the data available in the building permit file, coupled with a basic and non-detailed simulation process of a solid CAD model. As a result, sustainable interventions are proposed, leading to a manageable business plan where interventions are not considered individually, but as the result of their optimal combination.
Bioinformatics and ingenuity pathway analysis. Fold changes of the identified proteins were calculated by comparing the BCO conditions to healthy birds (control) and differentially expressed (DE) proteins (fold change and P-value < 0. 05) as well as their ID (UniProt  18  ) were submitted to QIAGEN Ingenuity Pathway Analysis (IPA, Ingenuity Systems, Redwood City, CA) for core analysis  19. These proteins were mapped to the most significant canonical pathways, functional annotation, upstream regulators, as well as molecular discovery using the Ingenuity Knowledge Base as a reference set and a cut-off of FDR adjusted P-value < 0. 05 and a foldchange between -1. 5 and 1. 5. Right-tailed Fisher's exact test was used to determine the probability that biological functions and/or diseases were over-represented in the protein dataset.
Image Acquisition and Analysis were performed by AlphaView software (Version 3. 4. 0, 1993-2011, Proteinsimple, Santa Clara, CA). Total RNA extraction and real-time quantitative PCR. Total RNA was extracted using Trizol reagent (Life Technologies, Carlsbad, CA) according to the manufacturer's recommendations, and concentration and Metabolic pathway and network analysis. To gain biologically related molecular networks, the above identified DE proteins (222) were submitted into IPA knowledge-base. From these 222 proteins, 153 IDs were successfully mapped, while 69 IDs were unmapped to molecules in the Ingenuity Knowledge Base. The 153 mapped proteins were analyzed to outline the most enriched biological functions. As shown in Table  4 , the top five canonical pathways associated with Huntington's disease, inhibition of AREmediated mRNA degradation, protein ubiquitination, hepatic fibrosis signaling, and glycolysis were enriched in BCO THN.
In the current study, which constitutes the first to the best of our knowledge, we identified 222 locally (tibial) differentially expressed (DE) proteins, with 158 up-and 64 down-regulated proteins, in tibia of BCO compared to healthy chickens  28. The DE proteins were mapped to the reference pathway in IPA knowledge database (QIAGEN Inc. , https:// digit alins ights. qiagen. com/ IPA)  19  to derive biological insights and identify significantly enriched metabolic and/ or signal transduction pathways. IPA identified significant top canonical pathways, including HD disease, AREmediated mRNA degradation, protein ubiquitination, hepatic fibrosis signaling, and glycolysis pathways, with several common and overlapping proteins. Based on the IPA core analysis, most of these proteins are involved in several disorders such as bone injury and abnormality, connective tissue disorders and inflammatory diseases.
a Accession number b Primer sequence (5′ → 3′) Orientation Product size (bp) ACTN1 Actinin alpha 1 ACTN1 1. 45 0. 000637 AK2 Adenylate kinase 2 F1NJ73 1. 429 0. 045 Akr1b7 Aldo-keto reductase family 1, member B7 E1C1I6 1. 104 0. 045 AKR7A2 Aldo-keto reductase family 7 member A2 F1P331 1. 061 0. 0195 ALB Albumin ALBU 0. 847 0. 0425 ANXA1 Annexin A1 Q6QAZ9 1. 122 0. 0119 ANXA2 Annexin A2 ANXA2 0. 787 0. 0193 ANXA5 Annexin A5 ANXA5 1. 572 0. 0202 AP3B1 Adaptor related protein complex 3 beta 1 subunit E1BW97 2. 142 0. 000802 APEX1 Apurinic/apyrimidinic endodeoxyribonuclease 1 B3Y932 1. 681 0. 0187 ARHGDIA Rho GDP dissociation inhibitor alpha Q5ZMT1 1. 382 0. 00796 ARPC1B Actin related protein 2/3complex subunit 1B Q5ZI99 1. 398 0. 00936 ATIC 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase/IMP cyclohydrolase PUR9 1.
Several software have been developed since the 90s for geometric morphometrics. These include, without being exhaustive, IMP  [14] , Morphologika  [15] , MorphoJ  [16] , the R package Geomorph  [17] , and the series of tps software  [18]. These software programs are all capable of performing geometric morphometric analyzes from maps but cannot read cartographic file formats such as the shapefile format used by the MapMorphy software presented in this article. This file format allows existing cartographic data (e. g. , illustrations) to be directly integrated into data processing and can be easily produced (e. g. , image landmarks) by GIS tools used by cartographers. Likewise, all vector files produced by MapMorphy can be formatted using GIS software. Geometric morphometrics has already been used in many fields of research (biology, archaeology, botany, image analysis, chemistry, medicine, bioinformatics, genetics, geology, etc.
The MapMorphy Software The MapMorphy software was designed to implement the methods previously described and allow the user to compare the morphology of several surfaces structured by homologous landmarks, in particular geographical maps. The application uses the open-source MapWinGIS ActivX (https://www. mapwindow. org/), a software component that brings all the functionalities of a geographic information system to MapWindow GIS. MapMorphy was developed in the VB. net language and the Microsoft™ company Visual Studio Community integrated development environment. Supplementary materials are available at: https://mapmorphy. fr. They include the MapMorphy installer, documentation, and examples.
This difference can then be visually compared to the partial reconstruction by the model. The third step calculates the PCA and the morphospace. The PCA is constructed from the covariance matrix of superimposition residuals. The PCA of MapMorphy makes it possible to reconstruct, for any point and thanks to its eigenvectors, typical models, four of which are displayed on the PCA graph. An additional window allows to calculate a model for each point of this continuous morphospace. This window also makes it possible to evaluate, by a linear regression test, the relevance of the data for a morpho-geometric analysis (cf. 4. 4). Finally, the fourth step, associated with the calculation of the PCA, is an unsupervised classification by K-means witch allow to associate the close shapes on the PCA.
This project file redisplays the data layers, dispensing with reloading them and recalculating the graphical and statistical processing. MapMorphy makes it easy to view and interact with the map dataset. The selection of a geographical map in the morphospace entails, for example, the visualization of its total difference in shape with the reference. The Model can then be displayed and the corresponding variables can be consulted in an attribute table and a floating identification window. Additional analyzes and formatting can be carried out in other software from the produced results saved in the project folder. The individuals in the morphospace can be thus easily joined with a geographical location in a GIS thanks to the shapefile format. Figures  6  and 7  show an example based on ancient maps.
Construction of the Shape Space Centered on the Consensus The statistical adjustment method occupies a central place in the software for the construction of the shape space and therefore that of the morphospace. MapMorphy uses bidimensional regression, classically used in geography to compare two surfaces and is considered equivalent to the ordinary Procrustes method  [65]. It is therefore used as it is in type 1 projects which only compare two sets of landmarks. By consistency, bidimensional regression is also used to construct, with an adaptation, the shape space of type 2 projects which compare several images. We present here the iterative process of optimal adjustment, that is to say the one using consensus as a reference. At the end of the generalized fitting, each point in the shape space corresponds to a specific shape and each particular shape corresponds to a point in the shape space.
The points must be globally aligned for the results of the PCA and the modeling to be relevant. The blue line has a slope of 1. The red line corresponds to the regression model and should ideally overlap and hide the blue line as in the case presented here This is why MapMorphy includes a tool  5  to test the relevance of a statistical analysis of tangent space coordinates (Fig. 8b ). Using a linear regression through the origin, the software compares all the distances between pairs of individuals in curved space (geodesic Procrustes distance in radians) and in tangent space (Euclidean Procrustes distance). When shape variations are small between individuals, i. e. , when the points corresponding to the objects are close to the point of tangency in the shape space, these two distances evolve together and the regression data are aligned.
Conclusion Geometric morphometrics is a powerful method for comparing the morphology of several structures and therefore of several maps. But like any method, it must be applied within a framework and on specific data (homologous landmarks, related shapes, etc. ). We have developed the MapMorphy software to easily apply this method to cartographic data. MapMorphy relies on the functions of the Morphoses plugin for MapWindow GIS (https://www. morphoses. eu/). This extension was intended to calculate by bidimensional regression and in a single pass, several adjustments on the same reference. The objective was then to express the shape of the average cognitive space in a group of individuals with a specific profile (age, sex, location, etc. ). MapMorphy's morphogeometric method is significantly different from its predecessor since it is no longer a question of calculating an average shape from an a priori on the profile of individuals but of comparing shapes and exploring the profile of individuals with similar shapes  6.
MapMorphy is a hybrid software combining bidimensional regression and the main principles of geometric morphometrics. In fact, the software no longer limits its objectives to spatial cognition but extends them to all surfaces structured by homologous landmarks, including biological structures. The specificity of the software remains however to process geographical data like ancient maps for example. In this case, it will be necessary to verify that the level of variation of the data is sufficiently low to be able to consider that the coordinates of the tangent space constitute a representative approximation of the data of the shape space. This condition is most often fulfilled in the natural sciences but not necessarily for geographical data. In addition, the software compares the variations of the shapes from a global point of view, because all the homologous landmarks are moved during the adjustment.
The method is therefore not suitable for analyzing the variation of a particular place in the sample. The current limits of the software concern the absence of statistical methods allowing to cross the shape variables (the scores on the PCA) with other variables. However, this can be done by other software from the saved results. In addition, the page layouts and vector formatting must also be produced by other software, for example, GIS software, from files in shapefile format produced during the automatic processing. The objective is now to be able to take semilandmarks into account, which would make it possible to extend the field of action of the software to structures delimited by curves and no longer only by landmarks. In the end, MapMorphy is presented as a software calculating the basic operations of geometric morphometrics using an easy-to-use user interface, not requiring the knowledge of programming language.
One example is given by a CFD study 38 of a human manikin wearing a surgical mask, in which the air and droplet leakage through and around the mask were evaluated for a five-second cyclical cough (with 1,008 droplets per cycle and a maximum expectoration velocity of 5 m. s -1 ). The numerical simulations were carried out with the Open FOAM software using an unsteady RANS approach for turbulence and a Lagrangian approach for particle tracking. The particle diameters were between 1 and 300 µm, and the filtration efficiency of the modelled surgical mask was assumed to be 91%. The authors used these simulations to identify the main locations of the leaks, evaluating the airflow velocities through these leaks to be approximately 0. 2-0. 4 m. s -1. The results also made it possible to estimate the relative proportions of droplets that were blocked by the mask, that passed through the mask and that escaped through leaks.
Next, manikins wearing a mask more or less correctly, especially the manikin assumed to be infected by the pathogenic agent, were introduced into the digital twin of the railway coach, and flow and dispersion computations were performed. The number and distribution of the droplets produced by the disseminator manikin in the inner volume of the coach were compared depending on whether the disseminator manikin was wearing a mask or not. The main achievements of our numerical study are summarised hereafter: • We have carried out an up-to-date review of scientific data on the modes of transmission of pathogenic biological agents, the production of particles by humans (especially through breathing and coughing, and also, while not presented in the article, speaking, singing or sneezing), and the influence of masks, in particular surgical masks, on the spread of the expectorated particles.
The data have been used to infer a model of the filtration efficiency of a surgical mask and the airflow around it. It is worth noting that the hypotheses formulated with regard to breathing with or without a mask and with regard to the filtration efficiency of the mask upon the exhalation or inhalation of drops and droplets may be easily adapted in the future should new scientific publications suggest an update is required. • We have illustrated the capability and value of CFD, notably through the use of Code_SATURNE software, for simulating the 3D turbulent flow and dispersion of particles carrying infectious agents in ventilated inner spaces with complex geometries. Dispersion computations performed using either an Eulerian model or a Lagrangian model led to similar results. Associated with the validation of Code_SATURNE, this consistency between results gives us confidence in our use of the CFD tool.
The velocities of the supplied and extracted air are less than 0. 2 m. s -1 , which corresponds to the "gentle" ventilation prescribed by railway operators to ensure comfortable conditions for passengers. The renewal rate of the air in each compartment is computed using the flow rate supplied to the compartment. It is equal to 8. 7 or, in other words, the air is refreshed every 7 minutes, which is better than the minimum recommended for ventilated public spaces. The average residence time is obtained by dividing the volume of each compartment by the air flow rate supplied to the compartment. Ventilation data used as inlet or outlet boundary conditions of the aerodynamic simulations are gathered in Table  3.
Data collection We sourced true and habitat island bird data sets from the literature. For most data sets, we used previous synthetic ISAR analyses (e. g. Matthews et al. , 2021; Triantis et al. , 2012)  to locate potential data sets and returned to the source papers (and subsequent papers by the source paper authors) to obtain the species lists for each island. True island data sets were also supplemented using  Baiser et al. (2017)  and  Sin et al. (2022) , and habitat islands using  Chase et al. (2019). For the former, we updated some of the data sets using a range of literature sources (see Appendix S1). For a number of true island cases (the Ryukyus Islands, the Azores, Canaries, New Zealand) we created new data sets through comprehensive literature and database searches (Appendix S1).
, 2022). The four kiwi species (Apteryx) represent extreme outliers in terms of the wing length, secondary and tail length traits (e. g. for wing length, the kiwis had values 267 times smaller than the species with the next smallest wing length). To avoid these species affecting the functional space to an extreme degree (which occurred even when logtransforming the traits), for these three traits, we replaced the trait values for the four kiwi species with the mean values across all extant species excluding the kiwis. This approach was preferred to the option of simply removing the kiwis, as one of our analysed data sets comprised islands in New Zealand. Four of the extinct species in our data sets were also in BirdTree and AVONET. For the remaining 154 extinct species in our data sets, we sourced data for the same set of traits (described below).
10. 4, Drummond & Rambaut, 2007). The resultant consensus tree had a small number of negative branch lengths which we resolved by converting negative branch lengths to zero, while shortening only the two branches immediately below by the same absolute amount to ensure the tree remained ultrametric and there were no polytomies (we have added this functionality to the 'BAT' R package; tree. zero function). The PD values generated using the original consensus tree and the consensus tree with the negative branches removed were highly correlated (Pearson's r = 0. 999). The 154 extinct species not in BirdTree were grafted on to this consensus tree (detailed below). We used this global maximum clade credibility tree to calculate Faith's PD metric (including the tree root;  Faith, 1992)  for all islands in a data set as outlined for FD.
As the denominator in AIC c must not be negative, for the Hawaii data set (with only six islands) it was necessary to exclude the two four-parameter models from the model set. For each data set and diversity metric, we stored the model ranks, and a multi-model curve was constructed using the AIC c weights from all converged model fits (see Matthews,   Triantis, et al. , 2019). In each case, we also extracted the z-value and c-value from the non-linear power model fit; this model converged in all cases. To ensure the same models were fitted in all cases, we did not remove model fits based on residual assumption checks (e. g. normality). However, given that the least square parameter estimates equal the maximum likelihood estimates only under the assumption of normal errors with constant variance (see discussion in the 'sars' package vignette), and given we are using AIC c , we re-ran the model selection including checks for both these properties.
Exploratory modelling of IDAR slope variation First, we tested whether there were significant differences between the z-values of the ISAR, IFDAR and IPDAR. As the IDAR z-values within a data set were not independent, we compared the z-values between data sets using a generalized linear mixed effects model (beta family and logit link; fit using restricted maximum likelihood) and the 'glmmTMB' R package  (Brooks et al. , 2017) , with diversity type (i. e. richness, FD, PD) as a categorical fixed effect, and the data set as a random effect. We used the same approach to compare the slopes from the FD. ES and PD. ES-area relationships, except here we used the Gaussian family as many of these slopes were negative. Second, we assessed what archipelago characteristics drove variation between data sets in the (i) z-value of the ISAR, IFDAR and IPDAR, and (ii) slope of the ES-area relationships.
Following  Marx et al. (2017) , we undertook an exploratory modelling approach using Pearson's correlations, which were preferred over regression analyses due to the relatively small size of our data set. The z-values of one of the ISAR, IFDAR or IPDAR were correlated against each of our archipelago-level predictor variables in turn, using log-transformations when necessary to meet assumptions. As the IFDAR and IPDAR z-values were correlated with the ISAR z-value, we also ran a series of partial correlations with these two variables, allowing us to control for the ISAR z-value. We ran the modelling using all data sets (i. e. true and habitat islands) and using as predictor variables: Ni, Gamma (or FD / PD Gamma), AreaScale, ArchArea, (maximum) elevation, temperature and the power model c-value.