dataesr/scientific-paragraphs-categorization huggingface dataset:
This paper presents a new Fuel Cell Fuel Consumption Minimization Strategy (FCFCMS) for Hybrid Electric Vehicles (HEVs) powered by a fuel cell and an energy storage system, in order to minimize as much as possible the consumption of hydrogen while maintaining the State Of Charge (SOC) of the battery. Compared to existing Energy Management Strategies (EMSs) (such as the well-known State Machine Strategy (SMC), Fuzzy Logic Control (FLC), Frequency Decoupling and FLC (FDFLC), and the Equivalent Consumption Minimization Strategy (ECMS)), the proposed strategy increases the overall vehicle energy efficiency and, therefore, minimizes the total hydrogen consumption while respecting the constraints of each energy and power element. A model of a hybrid vehicle has been built using the TruckMaker/MATLAB software.
Similarly, the optimization methods based on Model Predictive Control (MPC) to solve the energy management problem online were presented in  [26]. An EMS based on the Pontryagin Minimum Principle (PMP) was introduced as an optimal control solution  [27, 28]. The global optimization requires the driving profile to be known a priori. Therefore, the results are only valid in the laboratory, but they can be used as a basis for comparison with other real-time strategies. A real-time optimization algorithm called the Equivalent Consumption Minimization Strategy (ECMS) was also developed in  [29] [30] [31] [32] [33] [34] [35]. It introduces the concept of equivalent consumption. A MATLAB model was implemented for the study of this algorithm, which was subsequently developed using Stateflow on the hybrid vehicle model.
In this paper, a new Fuel Cell Fuel Consumption Minimization Strategy (FCFCMS) is proposed and compared with existing EMSs (such as the well-known State Machine Strategy (SMC), Fuzzy Logic Control (FLC), Frequency Decoupling and FLC (FDFLC), and the Equivalent Consumption Minimization Strategy (ECMS)). The proposed strategy is to minimize the hydrogen consumption of the fuel cell during a vehicle run, while respecting the constraints of the power of the fuel cell and the SOC of the ESS. The model of a hybrid vehicle and the performance of the proposed strategy is compared with these different approaches (SMC, FLC, FDFLC, and ECMS) through TruckMaker/MATLAB software This paper is organized as follows: In Section 2, the problem formulation of multisource energy management is presented. In Section 3, the modeling of the hydrogen fuel cell/battery hybrid vehicle is presented.
The desired characteristic of the algorithms is the minimization of the hydrogen consumption during a given cycle. Moreover, the final SOC of the battery and supercapacitor should be equal to its initial SOC, which means that the balance of energy that the ES has gained or lost during the driving cycle must be zero at the end of the driving cycle. Thus, each of the algorithms to be performed will determine the reference current of the FC converter in the vehicle diagram shown in Figure  1 , which minimizes the fuel consumption while respecting the operating constraints. In the case that the algorithm calculates a reference power, this will be transformed into a reference current. The proposed FC/battery/supercapacitor hybrid vehicular system shown in Figure  1  was implemented in MATLAB and SimPowerSystems software packages.
Simulation and Validation Results To evaluate the developed strategy, a simulation using MATLAB software for the simulation of hybrid vehicles on standard cycles was implemented. For comparison purposes, all the EMSs were designed based on the same requirements given in Table  3  with the same initial conditions (SOC initial value is SOC(t o ) = [65%], battery, supercapacitor, and FC temperature are 30 • C, 25 • C and 40 • C, respectively). The supercapacitor and FC voltage are 270 V and 52 V, respectively, and were maintained for both cases. In this section, a precise comparative study of the performance of each EMS presented in Section 4 (namely the State Machine Control (SMC), EMS based on FLC, EMS based on Frequency Decoupling and Fuzzy Logic (FDFLC), EMS based on ECMS, and EMS based on FCFCMS), is made.
Conclusions The paper presented a new Fuel Cell Fuel Consumption Minimization Strategy (FCFCMS) for Hybrid Electric Vehicles (HEVs). This strategy depends on the driving path and takes into account several performance criteria such as the slow dynamics of the FC, the reduction of fuel consumption, and good control of the storage element. In order to properly carry out this task, the work was divided into the following steps: (i) the definition of the main formulas that govern the operation of the system components, namely: the FC, the ES (the supercapacitor and the battery), the vehicle and its powertrain; (ii) the modeling of the hybrid vehicle; (iii) the implementation of the control strategies. A model of a hybrid vehicle was built using the TruckMaker/MATLAB software. Using the Urban Dynamometer Driving Schedule (UDDS), which includes several stops and accelerations, the performance of the proposed strategy was compared with these different approaches (SMC, FLC, FDFLC, and ECMS) through simulations.
Main text Advances in biological imaging over the past 20 years have been accompanied by a multitude of computerized approaches for indexation, reconstruction, processing, analysis, and exploitation of image data. As they address a variety of biological models at diverse scales of life, they are often dedicated to target case-studies (single cell scale, tissue-scale, embryos…). Consequently, imagining and developing a unified solution able to tackle any scenario in biological imaging very challenging. For more than ten years, several architectures and tools have been proposed to surmount many technological difficulties. The existing software platforms (e. g. , Fiji 1 , Icy 2 , Knime 3 ), developed in various programming languages (e. g. , java, c++, python) so far, are unfortunately not interoperable.
Additional efforts in scripting and codes development are generally required to gather some heterogeneous image processing components in ad-hoc workflows. Meanwhile, data is hosted in storage servers associated with a database system of their own (e. g. , OMERO 4 ), without much possible interaction with processing and analysis tools. A more integrative approach of organizing, viewing, and analyzing data, is more and more required in data science to avoid manual management or script writing which can be tedious for non-expert scientists. Also, the management of often-massive datasets requires dedicated expertise in computer science to scale up the storage and computing resources. Finally, with the emergence artificial intelligence (AI) such as deep learning in bioimaging (e. g. DeepImageJ 5 , ImJoy 6 ), the automation of processes and the implementation of processing pipelines must take all stages of the data life cycle and new human-machine interactions into account.
As such, data management and processing/analysis must meet quality criteria ensuring identification, accessibility, and interoperability of the data with their processing, storage, and analysis, and finally, anticipation of future reuse. These "FAIR" principles (findable, accessible, interoperable, and reusable) 7 impose new procedures and ethical issues to scientists whose research relies heavily on biological imaging, leading to a change in paradigm for image-based production of knowledge. To address all these requirements, we developed BioImageIT, a unique open-source system integrating image data management and analysis, and an operational solution for handling large image data sets to the.
requirements of open science (Fig. 1a ). In BioImageIT, data is automatically annotated and processed in a single framework, and once processed. Unlike previous solutions, BioImageIT integrates any existing data management and image processing software. For instance, data can be hosted in OMERO  4  database using Bio-Formats  8  and processed by applying machine learning algorithms embedded in the TensorFlow 9 framework. b, Due to the geometry of LLS scanning, 3D raw images are skewed. c, g, First, realignment (deskew) of raw stacks was performed using Pycudadecon (https://github. com/tlambert03/pycudadecon). d, h, Richardson Lucy deconvolution  12  is performed by using Pycudadecon. e, CD-M6PR-eGFP vesicles are tracked by using Trackmate  13. f, i, Deconvolved stacks and tracks are rendered by using Napari  14.
Full workflow, including Pycudadecon, Trackmate  13  and Napari  14  , was gathered in BioImageIT. Unlike previous frameworks (e. g. , Galaxy Project 15 , Snakemake  16  , BIAFLOWS  17  ), BioImageIT enables to address the following end-user's issues: BioImageIT reconciles data management and data analysis in a common and interactive framework. Most open-source bio-image software are developed separately and specialized in either data management or data analysis. Therefore, users need to write ad-hoc scripts or to apply manual operations to process the data. BioImageIT allows importing and tagging data for each experiment. Each operation automatically generates metadata that allows to keep track of any processing or analysis and then complies with the recommended FAIR principles 7 in data science.
BioImageIT is interoperable and reusable. The processing tools are packaged in Docker  18   BioImageIT is user-friendly (Supplementary Note 2). BioImageIT consists of 3 layers (backend plugins, python API, Graphical User Interface (GUI)). Users can choose the most appropriate level of interaction with BioImageIT. Biologists may prefer to be assisted step-by-step; GUI is then appropriate for data management and analysis. Data analysts are more familiar with scripts and Jupyter 10 notebooks using the python API. Finally, data scientists may adopt the packing backend system to provide a standalone demonstration of the new processing tool. In summary, BioImageIT is a generic framework for data management, analysis, and traceability. Unlike previous software platforms, it meets the needs of end-users and is a very flexible solution to link annotated data and processing tools.
Furthermore, it facilitates interactions between experimental and data scientists. As BioImageIT is built upon existing technologies, it may be considered as a computer overlay allowing a user-friendly use of existing software by end users, without hindering the addition of new analytical methods by more experienced developers. Finally, the ability of BioImageIT to plug in data interaction tools allows for the deployment of highly specialized instances of BioImageIT for a specific application domain, as illustrated in Figure  1  depicting Lattice Light Sheet Microscopy  11  Data processing (Fig. 1b-i ), reconstruction of Multi-Angle TIRF Azimuthal  20  (Fig. 2 ), as well as advanced image processing using conventional techniques (e. g. ND-SAFIR  21  ) and deep learning (e. g. N2V  22  ) (Supplementary Fig.
1  et Supplementary Video 4). These examples show the ability of BioImageIT to exploit advanced imaging analysis tools and workflows applied to datasets from various advanced microscopies. BioImageIT is being deployed on several imaging platforms, members of the France BioImaging infrastructure. On this basis, our next step is to create a BioImageIT community. Documentation, source code and video tutorials are available at https://bioimageit. github. io, and help is available via GitHub https://github. com/bioimageit. passage number were used and grown in DMEM-F12 medium without phenol red (Life Technologies, Carlsbad, CA) supplemented with 10% (vol/vol) fetal bovine serum (FBS) at 37°C in humidified atmosphere with 5% CO2. RPE1 cells were transiently transfected with plasmid coding for TfnR-pHluorin  24  , using Lipofectamin 3000, following basically the manufacturer recommendations (Invitrogen, Waltham, MA).
493. Composite volumetric datasets were obtained using ~5-ms exposure/slice/channel at a time resolution of 0. 833 s per total cell volume (about 40 slices). One hundred time points were acquired within 1 to 2 minutes. Acquired data were deskewed, a necessary step to realigned image frames, using pycudadecon, a python wrapper for cudaDecon, which is a CUDA/C++ implementation of an accelerated Richardson Lucy Deconvolution algorithm  12  (copyright to T. Lambert, Harvard Medical School, Boston, USA; https://github. com/tlambert03/pycudadecon). Then deskewed images were deconvolved also using pycudadecon. Later, tracking was performed using Trackmate  13. Napari 14 , a multi-dimensional image viewer for Python (https://github. com/napari/napari), was used for 3D rendering. Full workflow, including Pycudadecon, Trackmate  13  and Napari 14 , was implemented in BioImageIT.
Rab5 stably expressing Hela cell was scanned incrementally through a 20 μm long light sheet in 500 nm steps using a fast piezoelectric flexure stage equivalent to ∼270 nm with respect to the detection objective and were imaged using a sCMOS camera (Orca-Flash 4. 0; Hamamatsu, Bridgewater, NJ). Excitation was achieved with a 488-diode laser (Coherent, Santa Clara, CA) at 15 % acousto-optic tunable filter transmittance with 300 mW. Volumetric datasets were obtained using ~10-ms exposure/slice at a time resolution of 2s per total cell volume (about 50 slices). One single volume was used for the analysis. Deskew was performed using pycudadecon prior denoising. LLSM Deskewed 3D noisy data could also be directly treated using Noise to Void (N2V)  22  as a training-free denoising methods embedded in BioImageIT and as such could be applied as another preprocessing step in a workflow when other methods cannot.
Multiangle Azimuthal Averaging TIRFM. Acquisition and high-resolution three-dimensional reconstruction of Multiangle Stacks were carried out as previously published and using the same setup  20  , except that the custom C++ code based on the CImg library (https://github. com/dtschump/CImg ) used for reconstruction, was integrated in BioImageIT. Our current setup is based on a Nikon Ti Eclipse equipped with the Ring-TIRF ILas2 module (GATACA systems, Massy, FR), with a PLAN APO TIRF 100X oil objective (1. 49 NA). The acquisition of multiangle TIRF image stacks is performed from the iLas2 software interacting with the Metamorph 8. 6 software (Molecular Devices, San Jose, CA). This system is interfaced with a hardware and software implementations allowing the full control of the illumination incident angle as a function of penetration depth for multiple wavelengths.
b, Due to the geometry of LLS scanning, 3D raw images are skewed. c, g, First, realignment (deskew) of raw stacks was performed using Pycudadecon (https://github. com/tlambert03/pycudadecon). d, h, Richardson Lucy deconvolution 12  is performed by using Pycudadecon. e, CD-M6PR-eGFP vesicles are tracked by using Trackmate 13. f, i, Deconvolved stacks and tracks are rendered by using Napari 14. Full workflow, including Pycudadecon, Trackmate 13  and Napari 14  , was gathered in BioImageIT.
Acknowledgements This work was supported by the  France-BioImaging Infrastructure (French National Research Agency  - ANR-10-INBS-04-07 , "Investments for the future"). We acknowledge the Cell and  Tissue Imaging  ( PICT IBiSA, Institut Curie ) platform, also member of the national infrastructure  France-BioImaging  ( ANR-10-INBS-04-01 ) for maintaining the spinning-disk confocal microscope.
01% penicillin-streptomycin, 1mM pyruvate, and 20mM HEPES) containing the Tubulin TrackerTM DR (Tub-   This file includes the developer manual. This developer manual was generated using Sphinx  4. Code source can be found in https://github. com/bioimageit/bioimageit_core/tree/master/docs Supplementary Note 2:.
High-throughput DNA sequencing is becoming an increasingly important tool to monitor and better understand biodiversity responses to environmental changes in a standardized and reproducible way. Environmental DNA (eDNA) from organisms can be captured in ecosystem samples and sequenced using metabarcoding, but processing large volumes of eDNA data and annotating sequences to recognized taxa remains computationally expensive. Speed and accuracy are two major bottlenecks in this critical step. Here, we evaluated the ability of convolutional neural networks (CNNs) to process short eDNA sequences and associate them with taxonomic labels. Using a unique eDNA data set collected in highly diverse Tropical South America, we compared the speed and accuracy of CNNs with that of a well-known bioinformatic pipeline (OBITools) in processing a small region (60 bp) of the 12S ribosomal DNA targeting freshwater fishes.
We found that the taxonomic labels from the CNNs were comparable to those from OBITools, with high correlation levels for the composition of the regional fish fauna. The CNNs enabled the processing of raw fastq files at a rate of approximately 1 million sequences per minute, which was about 150 times faster than with OBITools. Given the good performance of CNNs in the highly diverse ecosystem considered here, the development of more elaborate CNNs promises fast deployment for future biodiversity inventories using eDNA. Effective ecosystem governance and management require an increase in speed, accuracy and ease of collecting and processing of biodiversity data  31, 49. Biodiversity data collection requires a shift in focus from expert monitoring towards high-throughput data acquisition technology 24.
Validating the networks on the reference sequences yielded higher accuracies of 96% and 94% for the base and augmented CNNs, respectively. With a binarization threshold of 0. 9, the accuracy rose to 99% for both the base and augmented data sets, at the cost of rejecting 9-13% of all sequences evaluated (Supplementary Material Fig. 1 ). We used a binarization threshold of 0. 9 for all further evaluations. CNN application on the raw and cleaned eDNA data set. We found that there were limited differences in the output between the CNNs trained on the raw sequence data compared to those trained on cleaned data. To attenuate sequencing noise in the analysis, we considered a second threshold of the minimum number of reads required for a species to be retained. We compared the number of reads per species needed for each CNN with that needed for OBITools and observed that the median Kendall Tau-b correlation increased when a more stringent threshold on the minimum number of reads per species was applied to all levels of sample aggregation.
Hence, the results were slightly better than those from the Kendall Tau b values, as those take the relative abundance of the predictions into account. All approaches recovered similar gradients of composition, differentiating between coastal and upstream assemblages (Fig. 1 ). The composition difference between methods resulted from a slightly larger number of species predicted by the CNN (median species number 63) than by OBITools (median species number 56). Furthermore, the CNNs still lacked feature parity with OBITools with regard to ambiguous sequences, which can result in more pronounced differences in the OBITools output. Validation with the known species list of the region. We found a major overlap between historical records and the species composition recovered from the CNN. The data synthesis across historical fish surveys yielded a total of 351 species in the Maroni and Oyapock rivers, 293 of which were present in the reference database and thus potentially detectable with eDNA.
Computation time. Overall, the CNN processed approximately 1 million input sequences per minute, compared with 20,000 input sequences per minute for OBITools. For the CNN, we distinguished between two computational efforts, which were measured independently: (1) network training, which needed to be performed once per reference database, and (2) the application on field data. Training a network on the augmented and complete reference database currently took around 10 min on an Nvidia Titan RTX GPU. Training a network on the clean reference database was faster and takes 6 min on the same GPU. The training and application time is dependent on the size of the input data and the network size. A large part of the computational time for  the OBITools pipeline is dedicated to the alignment (up to 80%) and demultiplexing (up to 15%) steps.
Discussion The monitoring of biodiversity in highly species-rich ecosystems has generally been challenging, with gaps in biodiversity data existing in the tropics  23. eDNA metabarcoding is a revolutionary method that can enhance the monitoring of species in complex ecosystems  48  , but is associated with the challenge of rapidly processing large data sets. Our study demonstrates the application of a CNN to process short eDNA sequence reads directly from raw sequencing Illumina outputs. We show that the CNN approach delivers species compositions comparable to those from OBITools and historical records. Fish assemblages retrieved using OBITools and CNN were consistent with the current knowledge on Guianese fish fauna, with marked differences between coastal and inland sites  29. Fish homogeneity in coastal areas was explained by a historical connectivity between the coastal basins during the Miocene  21  , but also by the salt tolerance of a substantial number of the fishes inhabiting coastal streams  45.
Composition analysis further highlighted sites with a markedly different fauna, corresponding to the areas heavily disturbed by gold mining, forestry and agriculture  4. In only a few minutes, the software transformed a raw fastq sequence data set into a species list associated with each eDNA sample collected in the field, which can serve further biodiversity analyses. Overall, our findings indicate that machine learning offers new possibilities for the taxonomic labelling of short DNA sequences and can transform rapidly collected eDNA data samples into interpretable taxonomy-based biodiversity indicators  25, 30, 68. In classical bioinformatic pipelines, the processing from raw sequence reads to taxonomic identifications includes seven steps (paired-end read merging, demultiplexing, dereplication, quality filtering, removal and correction of PCR/sequencing errors, and taxonomic labelling) expected to be essential to generate high-quality results from metabarcoding studies, but which can be computationally demanding  8, 16  and challenging to articulate  50.
We show that a CNN can embed all these steps in a single process applied directly to the raw Illumina reads when the CNN is trained to handle noisy data. Moreover, for relatively short eDNA markers (e. g. 60 bp for the 'teleo' marker used here), merging paired-end reads is not necessary, which leads to a significant computational gain  52. While still offering results roughly comparable to those of OBITools, the CNN decreases the processing time of the whole data set analysis by a factor of around 150. In a recent comparison, Barque (https:// github. com/ enorm andeau/ barque) combined with a fast demultiplexing module was able to process over 15 million reads in 30 min, while it took 17 h for OBITools V1  52. Assuming the same rate as found for CNN, i. e. 1 million read per minute, to this data set, the application of the CNN would be two times faster than the fastest existing bioinformatic pipeline in a single model  52.
Our study represents a first successful adaption of CNN to the processing of eDNA metabarcoding data, but we foresee several avenues of optimization to gain speed and accuracy, making it a promising tool for scaling-up biodiversity inventories via eDNA  5, 64. The training of CNNs leads to an efficient adjustment to the reference database, avoiding the need to explore a large number of parameters and arbitrary thresholds, as required in classical bioinformatic pipelines. Existing bioinformatic pipelines contain a variety of modules (i. e. QIIME2, DADA2, Vsearch), each with its own set of parameters  7, 17, 62. Selecting the appropriate modules and parameters requires advanced knowledge of the functioning of the program, since changes in those parameters can considerably modify the outputs  8, 13, 36.