dataesr/scientific-paragraphs-categorization huggingface dataset:
The goal of this optimization study is to improve the performance of the wind turbines, then, the expected output of a wind turbine built with the optimized airfoils is thereafter computed. These investigations are applied to the NREL phase VI wind turbine. First, the S809 airfoil is used as a reference profile and, using the CFD code ANSYS Fluent, numerical optimizations are carried out at an angle of attack equal to 6. 1°. Three Reynolds numbers are considered, Re = 3 10 5 , 4. 8 10 5 and 10 6. Then, the lift and drag coefficients of the optimized airfoils are evaluated for angles of attack ranging from 0° to 20°. Subsequently, the original S809 airfoil of the NREL Phase VI blade is replaced by an optimized airfoil, and the output power of the new wind turbine is evaluated using the open-source code QBlade.
Performance enhancement of the wind turbine The results obtained suggest that improvements in the power output of wind turbines made with these optimized airfoils are expected. Therefore, a study on wind turbine performance improvements using the optimized airfoils is applied to the NREL Phase VI wind turbine whose main operating parameters are summarized in table 5. The reference blade is designed with the S809 airfoil, and three new blades are designed with the optimized airfoils (Optimized A, Optimized B, and Optimized C). Distributions  along the span of the twist angle and chord are those of the NREL phase VI wind turbine blade  (Simms et al. 1999) , the only difference is the airfoil shape (Fig. 17 ). The wind turbine performances are computed using the software Qblade, an open-source framework dedicated to the design and performance assessment of wind turbines, based on the Blade Element Momentum (BEM) method.
CONCLUSION In the present study, airfoil shape optimization is performed using CFD and the discrete adjoint approach to maximize the lift to drag ratio of wind turbine airfoils and to improve wind turbine performance. The simulations are carried out with the industrial code ANSYS Fluent. In the flow solver, the turbulence is modeled by the SST k- model and in the adjoint solver the frozen turbulence assumption is applied. The S809 airfoil and NREL Phase VI wind turbine are used as the reference airfoil and turbine. The main findings of this study are summarized as follows: • Optimization performed using the Adjoint Solver resulted in significant improvements in the aerodynamic characteristics of the optimized airfoils compared to the reference airfoil. • The optimal airfoil shape obtained for the same airfoil at various wind speeds is different.
Current software packages for the analysis and the simulations of rare variants are only available for binary and continuous traits. Ravages provides solutions in a single R package to perform rare variant association tests for multicategory, binary and continuous phenotypes, to simulate datasets under different scenarios and to compute statistical power. Association tests can be run in the whole genome thanks to C++ implementation of most of the functions, using either RAVA-FIRST, a recently developed strategy to filter and analyse genome-wide rare variants, or user-defined candidate regions. Ravages also includes a simulation module that generates genetic data for cases who can be stratified into several subgroups and for controls. Through comparisons with existing programmes, we show that Ravages complements existing tools and will be useful to study the genetic architecture of complex diseases.
, 2018; Lee et al. , 2013) , making it difficult to simulate and directly estimate power levels in different scenarios, especially for the development of new methods. Even when these two features are implemented as in SEQPower  (Wang et al. , 2014) , these programmes are only available for binary and/or continuous phenotypes. Here, we present Ravages, an R package available on the CRAN (R Core Team, 2019), which answers the previous reported issues. Ravages makes possible to perform RVAT for multicategory, binary and continuous phenotypes and offers the possibility to perform genome-wide analysis through the RAVA-FIRST procedure. In addition, Ravages provides a whole simulation module to simulate data in a wide range of scenarios using three methods of simulations. In this work, we focus on the simulation component of Ravages and show that resulting patterns of allele frequency and linkage disequilibrium are more similar to the template data than when using other simulation programmes.
| Implementation Figure  1  shows an overview of the functions available in Ravages, which depend on the R package gaston  (Dandine-Roulland & Perdry, 2015)  specifically developed to handle genetic data. Using Ravages, it is possible to analyse and simulate rare variants in the whole genome and for multiple groups of individuals. In our previous works, we described the extensions of RVAT to multicategory phenotypes  (Bocher et al. , 2019 (Bocher et al. , , 2021) )  and the analysis in the noncoding genome  (Bocher et al. , 2022) , all implemented in Ravages. The reader is advised to look at the specific publications and documentation of the package for extensive details about the methods. It is yet important to note that all extended RVAT can take covariates into account: the burden tests are based on a multinomial regression model and SKAT includes a bootstrap procedure to compute p-values in such situations.
| Simulations An important feature of Ravages is the simulation of rare genetic variants for any number of groups of individuals, giving the possibility to consider multi-categorical traits, a feature that is not available in existing simulation programmes for rare variants in our knowledge and that has not been extensively described. This can be performed in three different ways: using allele frequencies and genetic relative risks (GRR), using haplotypes and precomputed haplotype frequencies, or using haplotypes and a liability model. A schematic representation of the three methods is given in Figure  2. Simulated data are stored in bed matrices, a data type mirroring plink file formats, which is part of the R package gaston  (Dandine-Roulland & Perdry, 2015) , and accessible to the user.
| Characteristics of the simulated datasets To validate the simulation procedures, we compared Ravages to three existing programmes: RAREsim  (Null et al. , 2022) , sim1000G  (Dimitromanolakis et al. , 2019)  and SEQPower  (Wang et al. , 2014). These programmes were selected for the following reasons. Sim1000G was shown to be the best in mimicking allele frequency spectrum and linkage disequilibrium patterns among multiple simulation programmes for rare variants  (Dimitromanolakis et al. , 2019). SEQPower is one of the tools the most commonly used in RVAT allowing the simulation of data under various scenarios and the direct computation of RVAT power. Finally, RAREsim is one of the most recently developed simulation tool that presents the interesting feature of matching the number of singletons and doubletons between the original and the simulated datasets.
We compared the allelic frequency spectrum and the linkage disequilibrium (LD, estimated using the R package gaston) observed in simulated data to those from the data used as a template for the simulations. It is important to maintain those two parameters close to the original data set as they have an impact on the power of RVAT  (Jadhav et al. , 2016). To do so, we simulated a group of 2000 individuals according to the allelic frequencies or haplotypes observed in the 1000Genome European populations for the 500 first kb of chromosome 22 (1886 variants including 1273 rare variants defined as having a MAF lower than 1%, corresponding to our template for the simulations). We chose to simulate 2000 individuals not to be too divergent from the number of individuals present in the 1000Genome European populations (503 individuals).
| Assessment of the power estimations To assess the quality of the power estimations in Ravages, we compared them with two programmes that integrate power computations: SKAT  (Lee et al. , 2013)  and SEQPower  (Wang et al. , 2014). SKAT provides analytical power while SEQPower computes power based on simulations. We did not compute power of RVAT using Sim1000G and RAREsim as they do not directly implement functions to do so and propose to use external packages such as SKAT. As variant-level and not regionlevel effect sizes are modelled in SEQPower and SKAT packages, we used only the GRR simulation module in Ravages to estimate power using varying parameters similar to the ones proposed in these programmes: the proportion of causal and protective variants, their effect (GRR), the number of individuals and the proportion of cases.
We used the first 5 kb of the example haplotypes from the SKAT package with the following default parameters: 1000 individuals with 50% of cases, 50% of causal variants without protective variants and GRRs of causal variants set to 4. Finally, we used two different filtering strategies to compute the power: we either did not apply any frequency filtering after the simulations, or we filtered variants with the same frequency threshold as the one used to define causal variants (MAF lower than 1%, the default strategy in Ravages). Note that when using the SKAT package, the power can be computed only for the variance-component test SKAT and there is no possibility to filter the data before the power estimation.
| Validation of the simulated datasets For Ravages and the three concurrent simulation programmes Sim1000G, RAREsim and SEQPower, we compared the allelic frequencies and the LD pattern in the simulated datasets and in the original data that served as template for the simulations. The correlation of allelic frequencies is excellent for all programmes (Figure  3a ). The inverted V-shape observed for SEQ-Power can be explained by the fact that we are looking at the frequency of variant alleles (i. e. , nonreference alleles) that can vary between 0 and 1 whereas in SEQPower the reference and variant alleles will be swapped if needed to ensure that the variant alleles have a frequency below 50%. The correlations presented in Figure  3  were obtained on variants present in both the original and the simulated datasets.
Nevertheless, RAREsim aims to match the number of variants in different Minor Allele Count (MAC) categories between the original and the simulated datasets with variants being pruned when necessary. Therefore, we also investigated the allele frequency spectrum obtained after the simulations (Figure  3b ). For all methods, simulated allele frequency spectra are very similar to real ones, with RAREsim presenting the most differences from the original spectrum. These differences are certainly due to the pruning of rare variants performed in RAREsim, even though expected numbers in the MAC bins were estimated in a target sample of the same sample size as the simulated data set (2000 individuals, Supporting Information: Methods). In concordance with these results, we also observed a lower correlation between original and simulated allelic frequencies for RAREsim than for the other methods when focussing on rare and low-frequency variants (MAF < 5%, Supporting Information: Figure  S1 ).
We then compared pairwise LD between variants on real and simulated data (Figure  4 ). For this comparison, we did not include the Ravages GRR simulation module, nor SEQPower, as simulated variants are in linkage equilibrium. All methods show lower LD in the simulated data compared to the original ones. Correlations of LD between real and simulated data are similar when using the two Ravages haplotypes simulation modules. RAREsim shows slightly lower correlations, probably due to the pruning performed in this method which affect LD patterns in the simulated data. For Sim1000G, there is a clear shrinkage of LD in the simulated data set, resulting in lower correlations. As before, correlations are computed only for variants present in the original and in the simulated datasets. By further looking at the mean LD computed in different bins of genetic distances (ranging from 0 to 500 kb by increments of 2 kb), we retrieve previous results with all methods showing lower LD compared to what is observed in the original data, especially Sim1000G (Supporting Information: Figure  S2 ).
Both Ravages and RAREsim show LD patterns closer to the one observed in the original data, with a slight advantage of RAREsim for variants in close proximity in the genome and a slight advantage of Ravages for variants further away in the genome. The two haplotype simulation modules proposed in Ravages show very similar LD pattern which is totally expected as they both simulate data based on the same set of haplotypes. Overall, the two haplotype simulation modules proposed in Ravages are the best in maintaining frequency and LD pattern distributions.
| Power study There exists various software that provide power estimations for RVAT under multiple scenarios defined based on parameters that differ from one software to another, challenging the comparison between them. In this study, we chose to compare power estimations obtained using the Ravages GRR module against estimations obtained using SEQPower and the SKAT package (only for the SKAT test) that both allow simulations based on GRR. Figure  5  compares power estimations between the different programmes for WSS (top) and SKAT (bottom) by varying five parameters: odds ratio (OR) of causal variants, the proportion of causal and protective variants, the number of individuals and the proportion of cases. Causal variants were restricted to rare variants (MAF < 1%). Power was computed by keeping after the simulations either all variants or only variants with a MAF lower than 1% to mimic real data analysis (not possible for the SKAT package).
Power estimations obtained with the three software are similar in all scenarios. The SKAT package shows similar power levels with Ravages and SEQPower when no filtering of rare variants is applied. Moreover, Ravages and SEQPower show similar power when applying the same filtering approach. Differences between the approaches with and without filtering of variants are mostly observed when there is a large effect of the simulated genomic region. This is due to the fact that when very large effects are associated with causal variants, they largely increase in frequency in the cases group and therefore in the overall sample. Accordingly, these variants are usually not included in RVAT. Figure  5  was restricted to the GRR module of Ravages as, to our knowledge, there is no available programme that allow RVAT power estimation based on a percentage of variance explained and haplotypes to compare with the haplotypes and liability module.
We still provide F I G U R E 5 Power comparison of the programmes Ravages (GRR module), SKAT and SEQPower for the WSS (top panel) and SKAT (bottom panel) using the following parameters unless otherwise stated: 1000 individuals with 50% of cases, 50% of causal variants without protective variants and GRRs of causal variants set to 4. The power was evaluated at three significance thresholds by using 1000 replicates for each set of parameters. Solid and dashed lines correspond to the power computed with or without a frequency filtering after the simulations. The SKAT package can only be used to compute power for the SKAT test and no frequency filtering can be applied before the power estimation. GRR, genetic relative risks. some power estimations obtained under the liability model to compare with those from the GRR module of Ravages (Supporting Information: Figure  S3 ).
| Computation time to run the simulations For Ravages, simulation computation time was measured for the haplotypic liability model as this is the model that is the most computationally intensive. The computation time was studied as a function of the number of individuals, the number of groups (of equal sizes) and the number of variants in the simulated genomic region (Figure  6 ). Haplotypes data from the European populations from 1000Genome project  (Auton et al. , 2015)  in the LCT gene were used as this is the example data set provided in the R package Ravages. As expected, the computation time increases with the number of variants and the number of individuals, but is insensitive to the number of groups. Even for data sets of 1000 variants and 8000 individuals, it takes less than 2 min to simulate genetic data for 1000 genomic regions.
For all analyses, only variants with a frequency lower than 1% were kept, and for the first three analyses, only variants having a functional consequence at least as severe as missense on the genes were considered as qualifying variants. Association tests using WSS or SKAT using the three methods were applied on genomic regions containing at least five variants. For the bootstrap procedure, a normally distributed covariate was generated. Table  1  gives the computation times to run RVAT with Ravages under the different scenarios and for both the burden and SKAT tests implemented in Ravages. Burden WSS tests have a sublinear complexity with the number of variants, regions and individuals, enabling the analysis of large datasets in an efficient way. The SKAT permutation procedure is also very efficient, especially on small datasets, with a computation time that depends on the number of individuals, variants and regions.
The SKAT bootstrap procedure is sensitive to the same parameters and requires more computation time than the permutation procedure due to the inclusion of covariates. Finally, the computation time of the SKAT theoretical approach is slightly lower than the permutation procedure for small datasets but rapidly increases with the number of groups, individuals and variants. When looking at whole-genome data, the RAVA-FIRST procedure enables a genome-wide analysis in a reasonable time by retaining only the most promising variants within CADD regions: WSS took less than 8 h to analyse more than 28 million variants in 2504 individuals and both SKAT permutation procedures run in less than 30 h, and even less than 1 day when no covariates are present. Consistent with the other simulated datasets, the theoretical approach takes more computation time to run.
| DISCUSSION In this work, we presented the R package Ravages that was developed to run simulations and association tests on rare variants for binary and categorical phenotypes. We showed in previous studies the advantage of taking into account subphenotypes of a disease when there is a genetic heterogeneity  (Bocher et al. , 2019 (Bocher et al. , , 2021)). We focused here on the simulation procedures available in Ravages that allow the simulation of rare variant genetic data in a wide range of scenarios. We first validated the simulated datasets by showing that they respect the allele frequency spectrum observed in the original data when using any of the simulation modules of Ravages. We further highlighted that the two simulation modules based on haplotypes better maintain linkage disequilibrium patterns from real data than other existing programmes.
We also showed through a power study that the GRR module gives similar power estimations to the classically used software SEQPower and SKAT when the same set of parameters and the same filtering strategies were used. It is important to note that the GRR simulations in Ravages and the aforementioned external programmes are based on variant-level effects and that the global effect of the entire simulated region can rapidly increase and become incompatible with realistic scenarios in complex diseases. For example, the genetic variance estimated by GCTA  (Yang et al. , 2011)  for the GRR-based simulation of a genomic region composed of 195 variants and 30% of causal variants with GRRs of 2 is on average 5% (respectively 9% when GRRs are increased to 4). This is much higher than the percentage of phenotypic variance explained by rare variants associated with complex diseases  (Weiner et al.
A first idea of the magnitude of the expected power could however be obtained by computing the analytical power expected for the burden test CAST with Ravages  (Morgenthaler & Thilly, 2007). This burden test was indeed shown to have the lowest power in different scenarios  (Wu et al. , 2011)  and could therefore provide the lower bound of the expected power under the studied scenario. A future work in Ravages would be to speed up the computation time of RVAT to handle very large datasets and enable analyses at biobank scale as proposed by the STAAR programme  (Li et al. , 2020). As previously mentioned, Ravages enables the analysis of whole-genome sequence data through the use of the RAVA-FIRST approach that is integrated in the package and easy to run. Unlike sliding windows procedures, the RAVA-FIRST approach takes benefit of biological information for the grouping and filtering of rare variants with a qualifying strategy based on biological information which could help to detect new association signals  (Bocher et al.
, 2022). In conclusion, Ravages is an R package offering a complete set of tools for the analysis of rare variants. Using Ravages, RVAT can be performed on any type of phenotype (continuous, binary or categorical) with an easy implementation for a genome-wide analysis using candidate regions or the RAVA-FIRST approach. Simulations can be performed on more than two groups using: (1) similar approaches than in existing programmes using the GRR module and (2) more realistic approaches based on haplotypes that allow the simulation of rare variants datasets with a constraint on the region-level effect. Coupled with the association tests, power levels can be easily estimated on these simulations. Ravages is therefore a useful and complete tool that will help to design more efficient association studies to unravel the role of rare variants in complex diseases.
I G U R E 3 Distribution of allelic frequencies in the original and the simulated datasets using Sim1000G, RARESim, SEQPower and Ravages (three methods). (a) Correlation of allelic frequencies. (b) Distribution of allelic frequencies. F I G U R E 4 Correlation of pairwise LD between the original and the simulated datasets using Sim1000G, RAREsim and Ravages (modules based on haplotypes). LD, linkage disequilibrium.
F I G U R E 3 Distribution of allelic frequencies in the original and the simulated datasets using Sim1000G, RARESim, SEQPower and Ravages (three methods). (a) Correlation of allelic frequencies. (b) Distribution of allelic frequencies. F I G U R E 4 Correlation of pairwise LD between the original and the simulated datasets using Sim1000G, RAREsim and Ravages (modules based on haplotypes). LD, linkage disequilibrium.
Soil erosion is a major type of land degradation in Bangladesh, especially in the tertiary hilly regions that are located at the northeastern and southeastern region of the country. The Revised Universal Soil Loss Equation, combined with Geographic Information System, is a reliable methodology to estimate the potential soil loss in an area. The aim of this research was to use the RUSLE model to estimate the soil erosion in the tertiary hill tracts of Bangladesh from 2017 to 2021. The erosivity factor was determined from the annual average precipitation of the region, and erodibility factor was estimated from the soil database. The elevation model was used to analyze slope length steepness factors, while land use land cover was used to compute cover management factor. Lastly, land use and elevation was integrated to estimate the support practice factor.