dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions In this paper, an advanced voltage-balancing control has been developed for a bipolar DC microgrid supplying unbalanced loads through a PV-fed three-level boost DC/DC converter. This converter topology was chosen in this study due to its merits compared to the conventional boost converter commonly used in DC microgrids, including higher reliability and efficiency. Moreover, the proposed voltage-balancing control strategy is based on a nonlinear backstepping approach, which ensures the robustness and the global asymptotic stability of the system proven throughout Lyapunov stability criteria To enhance the dynamic performance of the proposed controller, PSO has been used for optimal estimation of its design parameters. The theoretical analysis was validated through simulations under Matlab/Simulink.
Population structure Population structure was investigated using datasets allowing up to 20% of missing data per SNP. Thus, after filtering, the remaining number of SNP was 24,454 for the Principal Component Analysis (PCA) and the nonnegative matrix factorization (nmf) inference, and ranged from 8785 to 15,977 per population pair for the F ST computation. Pairwise F ST highlighted a moderate differentiation between Indo-Pacific (IP) and Atlantic Ocean (AO) sampling sites, with values ranging from 0. 117 to 0. 129 and systematically significant (P ≤ 0. 001, Fig. 2A  and Additional file 1: Table  S1 ). Conversely, the average F ST between IP sites was 0. 023 (ranging from 0. 018 to 0. 029) and not statistically significant for the majority of pairwise comparisons (Additional file 1: Table  S1 ).
To prevent this, we used a genotype free estimation of allele frequencies implemented in the software angsd v. 0. 923 (Analysis of Next Generation Sequencing Data;  [30] ), which has been proven to be a more efficient method for low to medium coverage next-generation sequencing (NGS) data than SNPs calling algorithms  [30]. We describe below the bioinformatics steps required to apply angsd to RAD-seq data from a non-model organism and the downstream population genetic analyses applied to the filtered datasets.
Assembly pipeline and filtering Angsd requires a reference sequence to work, which we were lacking. To circumvent this issue, we followed the approach described in  [54]  by creating an artificial reference sequence from loci previously assembled by stacks under the parameter m = 3, N = 3, M = 3 (based on the results of Mona, Bertorelle, Benazzo, in preparation). To this end, we concatenated the consensus sequences of each locus spaced by a stretch of Ns and then map reads back from individual fastq files using the bwa-mem algorithm with default parameters  [55]. Using custom bash scripts coupled with angsd, we then discarded: (i) sites with coverage < 3x (-minIndDepth = 3, corresponding to m in the first assembly performed by stacks) and/or of low quality (based on the per base alignment score, -baq = 1 flag); (ii) low quality bases and poorly aligned reads (-minQ and -minMapQ and -C flags with default values); (iii) SNPs present in the last 5 bp of each locus and SNPs genotyped as heterozygous in 80% or more of the individuals; (iv) loci with more than 5 SNPs that might be the result of paralog RAD loci alignment on the reference.
Population structure A single reference sequence was created for all populations and we retained sites shared by at least 80% of the samples. The PCA was computed with PCAngsd v. 0. 97 based on genotype likelihood  [56]. Admixture was then investigated by running the non-negative matrix factorization algorithm (nmf) implemented in PCAngsd which is based on the same covariance matrix inferred for the PCA. The number of ancestral populations (K) was automatically chosen by PCAngsd to be e + 1, where e is the optimal number of significant principal components depicting population structure, resulting from the Velicier's minimum average partial test run on the covariance matrix. The sparseness regularization parameter α (used to reduce the noise in low depth NGS data) that best fitted the data was tested between 0 and 100 and it was chosen by comparing the resulting likelihood following  [56].
We generated pairwise site allele frequency likelihood files and then computed F ST with the realSFS program in angsd  [57]  using SNPs with a minor allele frequency ≥ 0. 05 (-minMaf flag). The significance of each pairwise F ST comparison was evaluated with 1000 permutations by randomly allocating individuals to one of the two populations. We finally tested isolation by distance (IBD) using a Mantel test  [58]  and plotted the relationship between genetic vs. geographic distances. We applied an approximate Bayesian computation (ABC) approach similar to previous studies  [7, 31, 32]  in all sampling sites to further investigate the presence of population structure. This approach is particularly helpful in the Atlantic Ocean (AO) where only one locality was sampled (Brazil; BRA population).
We performed 50,000 coalescent simulations under each model using fastsimcoal v. 2. 6. 0. 3  [59]  extracting parameters from the prior distributions displayed in Additional file 1: Table  S1. Model selection was evaluated by the random forest classification method implemented in the abcRF package in R  [60]. We used the SFS, θ π and TD as summary statistics and further added the first two axes of the Linear Discriminant Analysis in the dataset as suggested by  [60]  to increase the classification method accuracy. The number of trees was chosen by checking the evolution of the out-of-bag error.
Genetic diversity and effective population size variation We created one reference sequence per population in order to maximise the number of loci assembled. We filtered the sites with missing data by setting the -minInd flag in angsd to the total number of individuals in each population. The filtered dataset was then used to generate a site allele frequency likelihood (saf) file, where genotype likelihoods were computed using the SAMtools method (-GL = 1 flag). The folded site frequency spectrum (SFS) was directly computed from the filtered saf datasets through the realSFS program  [57]. Nucleotide diversity (θ π ), Watterson's theta based on segregating sites (θ w ;  [61] ) and Tajima's D (TD;  [62] ) were computed with custom script from the SFS. Significance of TD was evaluated after 1000 coalescent simulations of a constant population model with size θ w.
For all model parameters we used wide search ranges with uniform distributions (Table  2 ). We ran each model 100 times in order to determine the maximum likelihood parameters and to perform model selection using the Akaike's information criterion comparing the best run of each model  [59]. To check the robustness of the model selection procedure and to take into account the presence of linked sites in our dataset, we further examined the likelihood distribution obtained based on 100 expected 2D-SFS simulated under the parameters estimated in the best run of each model, each approximated with 10 6 coalescent simulations. This procedure is needed to take into account the variance in the likelihood estimation given our dataset: if the distributions obtained by the various models do overlap, the difference in the estimated likelihoods of our models is not significant  [63].
Finally, we determined the confidence interval of the parameter estimated under the best run of our best model by parametric bootstrapping. The 2D-SFS was bootstrapped 100 times using fastsimcoal and each of these datasets was analysed under the same conditions as the original data (one hundred independent runs for each dataset). Calibrating the molecular clock is crucial to obtain accurate estimates of demographic parameters and historical events, but it is challenging when fossil records and/or orthologous loci from an outgroup are lacking. Here, all demographic inferences were performed using the RAD-seq mutation rate of μ = 1. 93 × 10 -8 per site and per generation previously used for the tiger shark  [7] , and the generation time was set to 10 years  [24, 64]. exchange migrants with any other.
Acknowledgements We thank  Serge Planes  for providing DNA samples of all specimens but  Reunion Island ,  Gavin Naylor  for critical reading of the manuscript, and  Thibaut Caley  for discussion above the Agulhas current. We thank  Andrea Benazzo  for help with Angsd. We are grateful to the  Genotoul bioinformatics platform Toulouse Midi-Pyrenees (Bioinfo Genotoul ; http:// bioin fo. genot oul. fr) for providing computing resources. All data and analyses from  Reunion Island  were produced with the help of  A. Pirog  and financially supported by  DEAL Réunion  under the project  ECoReCo-Run.
For the purpose of inference, we hence collect data from all simulations at a fixed snapshot (t = 100). To compare the model predictions to the synthetic observations, we focus on the emergent macroscopic properties of the ABM. As our summary statistics, we use five numbers describing the system as a whole: the number of GSCs, GPPs, GDSs, and dead tumour cells, as well as the total number of alive cells in the proliferating rim (cf. Brain tumour, CA model in S1 Appendix). Epidemic, SIRDS model. In our model for the spread of infectious diseases, the agents are people. These individuals fall into four different groups as signified in the acronym of the model: SIRDS. The first group comprises people who are susceptible (S) to the disease. The second group includes people who are infected (I).
As further discussed in the section Synthetic data, these scenarios are of no biological interest since they would not yield conclusive real-world data and hence not be studied. They are, at least for our purpose, effectively noise for the ML algorithms. By excluding them from the training sets, we boost the performance of the various inference techniques. More specifically, in connection with the cancer CA model of brain tumours, we remove all simulations for which the number of GSCs or GPPs is zero. As regards our epidemiological SIRDS model, we remove all cases in which the disease did not spread beyond the originally infected individuals, or the disease was not eradicated after 1825 days (5 years). The latter exclusion criterion stems from the simulation set-up (cf. S1 Appendix). For both ABMs, these criteria reduce the original grid sizes by roughly 10 per cent.
Synthetic data. We construct a synthetic dataset to discuss the performance of the different inference techniques. For each ABM, we generate 250 new input parameter sets and repeat each simulation 250 times to sample the resulting distributions of the output variables. These distributions can be compared to the predictions of the emulators discussed in the section Emulators while the original input parameters (Θ o ) of the simulations can be compared to the posterior distributions obtained by inference. We present the metrics for these comparisons in the section Metrics below. We pick the model parameter values for our synthetic data using tighter priors than those presented in Table  1. The benefits of this approach are twofold. First, we avoid drawing simulations at the edges of the regions covered by the grids.
One such scenario would be the case where the parameter values ensure that all tumour cells consistently die off before t = 100. Another example is the case where the disease is never passed on. To mitigate this problem, one might discard all synthetic data that produce delta functions as the marginals for one or more output variables of the ABMs-in the real world, these scenarios would not be studied, anyway. While these scenarios are certainly of mathematical interest, they are not of biological interest, at least for dealing with our synthetic data. Here, we apply a more conservative criterion, discarding all synthetic data, for which the width of the 68 per cent confidence interval is zero. With this selection criterion, we are left with 219 and 227 points in the parameter space for the cancer CA and epidemiological SIRDS models, respectively.
Just as in the case of the MDN, the stochasticity of the ABM can be emulated during inference by sampling from the obtained GP. Here, we set the mean squared error in the prediction to be the loss function, which amounts to the simplifying assumption that the output variables of the ABM are uncorrelated. We use the python implementation of Gaussian process regression from scikit-learn  [43]. We note that the employed implementation produces a uni-modal distribution for a unique set of input parameter values. The same holds for the NN. In contrast, the MDN can produce multi-modal outputs when given a single input vector. Rejection ABC. Having constructed an emulator, we can resort to standard sampling techniques to map the posterior probability distributions of the model parameters (Θ) of the ABM given a set of observations (x).
The basic observational time step is fixed to 1 h, considering the common time step of weather files. The second step is to simulate different BEMs comprising the DEM. To account for the potentially substantial computational costs induced by the consideration of building details and the large number of buildings within a district, the DESs are distributed. To this end, the global problem is converted using Dymola into functional mockup units (FMUs), each of which encompasses both a BEM and a numerical solver according to the FMIs  (Tardif, Diaz-Gonzalez, Kegel, Sola, & Salom, 2019) , an open-source community-driven standard developed for coupled simulation and model exchanges. The distributed co-simulations of the FMUs are managed via a specific script (*. dng) using Distributed Architecture for Controlled CO-SIMulation (DACCOSIM), an FMI compatible master algorithm generator  (Galtier et al.
, 2015) , which enables direct integration of different levels of building interactions. For this purpose, data exchanges can be performed between FMUs by Daccosim every 900 s. All the different parts of the platform are interlinked using specific Python scripts. In terms of computational performance, a typical BEM lasts for 2 min on an 8-core computer with 2. 5 GHz and 8 GB RAM, and a DEM including 23 embedded BEMs lasts for 30 min on the same computer. A specific Python application called DacRun enables cluster computations.
Geometrical data The generation of a DEM obviously requires various information about the district, which is often an issue. Nevertheless, the management of district scale data is currently growing at a rapid pace, particularly with the use of GIS. GIS data are based on geometrical data that represent a specific territory, which are completed with various structured semantic information, such as type description, use, and construction period. Various cities around the world are developing GISs, often providing open access to databases. 4 However, these data often exhibit topologically unclosed geometries, multiple facets in collinear surfaces, or incorrect building splitting (see  Pedrinis (2017)   for instance). In addition, GIS data often do not provide information regarding the construction materials of buildings.
Building energy model The BEMs are created using the BuildSysPro Modelica library. Modelica is basically an object-oriented, equation-based, and acausal programming language that was developed to support all engineering domains. Hence, it is particularly suited for developing a flexible and techno-explicit DEM, such as MoDEM. The structure and parametrization of the Modelica model resulting from the automatic generation of the BEMs (Section 2) depend on the adaptations considered. This modularity of the model implies different sub-models and different connections between them, which need to be compatible regardless of the adaptation combination. From a modeling point of view, the MoDEM platform includes the different options listed below for the BEMs: Table  1  summarizes and illustrates most of these different model options.
District energy model Similarly to BEM, which is an assembly of sub-models (walls, air zones. ), DEM is an assembly of building energy models. However, the DEM is not written in the Modelica language as the resulting model may be too computationally intensive to simulate. From a computational point of view, it is clearly more beneficial to distribute the BEM simulations, notably because they consist of numerous problems of very similar computational weight that are relatively weakly linked. This option is a really new methodology developed to overcome weaknesses of most existing approaches. From a physical modeling point of view, the DEM also includes different options:   Table  2  Building thermal properties. Building component 𝑈 -𝑣𝑎𝑙𝑢𝑒 [W m -2 K -1 ] Wall 1. 7 Roof 3. 2 Windows 2. 8 Ventilation [vol -1 ] Natural 0.
Model description To illustrate the possibilities of MoDEM, especially when used as a support tool to relevantly choose a level of detail for a DEM with respect to the targeted application, a case study was developed. Fig. 3  depicts the selected use case for 23 apartment buildings with the same typology in a district in Paris. This district comprises approximately 900 flat units built in the 1930s. Table  2  presents the main thermal properties of the buildings according to the TABULA database, which correspond to relatively thermally inefficient buildings. For the modeling, geometric data were taken from the IGN 6  database called BD TOPO. The 23 buildings were connected to each other with adjoining walls (indicated in blue in Fig. 3 ). The weather file of Paris Montsouris was selected for the study.
Discussion The MoDEM platform aims to take advantage of existing tools and software that are currently known to have important developments (Modelica, FMU), particularly for building energy simulations at the district scale  (Wetter & van Treeck, 2017) , to efficiently and automatically create modular DEMs and ease the interoperability of the  platform. This developed tool chain enables the simulation of heating and cooling energy loads of a district considering the main interactions and couplings between buildings. Moreover, the distribution of the simulation facilitates the mitigation of computational time. In contrast to the common use of simplified models at the district scale, this platform favors the use of detailed models at the district scale and limits the need for simplifications because of up-scaling.
e. a common district case study, as introduced in the IEA EBC Annex 60 to support DEM development. Although validation is generally mandatory to support any numerical model development, such a DESTEST would overcome several major difficulties of addressing district-scale reliable and high-resolution energy simulations, such as: • access to detailed and accurate input data, such as the geometry, system efficiency, materials, and composition of housings and households. Efforts on the development of GIS and more particularly of standardized CityGML databases coupled with building information models would certainly help energy modelers 7  ,8 ; • access to the quantity of interest, currently the load curve, • as well as the integration of several complex and diverse aspects that the model needs to handle to reproduce conditions as realistically as possible, i.
Concluding remarks The first objective of our work was to develop the MoDEM platform with the specific requirements detailed along the manuscript. But, beside the modularity of MoDEM and its capability to handle various models via the co-simulation layer, different findings have been highlighted. In particular, the energy consumption few depends on the model choice, among those implemented in the platform. The diversity also few depends on the modeling assumption among those implemented in the platform. On the contrary, the correct estimation of heating and cooling loads requires an appropriate discretization of the walls as well as an appropriate modeling of longwave radiative heat transfer between external building walls. In addition, the developed platform aims to take advantage of existing tools and software to efficiently and automatically create modular district energy models.
In this paper, we investigate the power degradation in a novel photovoltaic (PV) cell reconfiguration named KenDoKu (KDK) topology under different shading patterns. We analyze how a modification in the linkage between the PV cells within a shaded PV module can affect its effectiveness. The proposed approach relocates the physical position of the PV cells within the PV module without any change in electrical connections and redistributes the shading effects over the PV module for the improvement of the power generation. To achieve this purpose, modeling and simulation are performed for a set of various shading patterns such as homogeneous, sectional, and scattered shadings. The simulation model used is a combination of twodiode model and Bishop's model. This model is applied to a PV module and is implemented in LTSpice software to quantify the impact of shading on P-V characteristics.
To extract the global maximum power, advanced MPP methodologies are employed  [6, 7]. Due to an eventual malfunction of PV modules, a change in physical conditions or partial shading conditions may result in a low performance of PV modules  [8]. To resolve these issues, Chao et al. [9]  and Chen et al. [10]  have proposed alternative approaches to minimize the power losses, whether dividing a PV array into a fixed and adaptive bank with an array switch or connecting bypass diodes across each cell. However, the addition of bypass diodes can exhibit multiple peaks in the output characteristics of the PV module  [11]. To overcome these issues, a reconfiguration technique was used. The main goal of reconfiguration is to disperse the shading effects for uniform row current. Belhachat and Larbes  [12]  have proposed a numerical approach based on the MATLAB program to analyze and compare various topologies of PV arrays for different shading patterns to find the best configurations that minimize power losses under partial shading conditions.
We used, for the simulation, LTSpice software to implement the relationships described in the previous Equation  (4). LTSpice is a high-performance simulation program with integrated circuit emphasis (SPICE) simulator. Different scenarios are simulated for various PV cell sizes. The current-voltage (I-V) curve for each component cell in the PV module is characterized by PV cell specific parameters' values. The parameters of a typical solar cell named KX0B22-12X1F are used for our modeling and simulation  [30]. It is a complementary study of a previous work in which the parameters of the PV cell extracted under dark/ 4 International Journal of Photoenergy light conditions by using a nonlinear least squares method  [30, 31]. For Bishop Parameter's extraction, a fit of the I-V characteristic is carried out under dark condition by using the measurement data as shown in Figure  4.
Thus, we performed our experiments for the simulation test with homogeneous and heterogeneous impacts on light I-V curves. We compared the proposed KDK topology with traditional topologies such as TCT, BL-TCT, HC-BL, HC-TCT, and SP-TCT. Their efficiencies are analyzed on the P-V characteristics for different solar irradiance levels, as presented in Section 3. The electrical LTSpice model of the PV cell and the circuit arrangement of KDK topology of the PV module used for the simulation are presented in Figure  10. Case 1. Homogeneous shading conditions. Figure  11  shows the P-V characteristics obtained by simulating the different topologies under the DS condition. We can see that most of the topologies exhibit some maxima on the P-V characteristics. Among all topologies, KDK topology generates a better profile.