dataesr/scientific-paragraphs-categorization huggingface dataset:
To overcome this drawback, it was coupled to libraries of extremely localized molecular orbitals (ELMOs)  (Meyer, Guillot, Ruiz-Lopez & Genoni, 2016; Meyer, Guillot, Ruiz-Lopez, Jelsch & Genoni, 2016; Meyer & Genoni, 2018) , giving rise to the HAR-ELMO method  (Malaspina et al. , 2019). H-atom treatment and comparison of H-atom parameters for HAR-ELMO have been described by  Malaspina et al. (2019). In HAR, HAR-ELMO and multipole database techniques, also called the transferable aspherical atom model, the electron density is calculated theoretically or transferred from a databank and then fixed during the refinement of atomic positions and displacement parameters. In contrast, in multipole modelling, the electron density is refined together with positions and displacement parameters. Therefore, the way in which H atoms are treated impacts directly on the distribution and topology of the refined multipolar electron density  (Hoser et al.
For HAR, the recently introduced software lamaGOET (  Malaspina et al. , 2021)  allows users to interface the quantumcrystallographic program Tonto  (Jayatilaka & Grimwood, 2003)  with quantum-chemical software such as Gaussian  (Frisch et al. , 2016). In this study, such 'Gaussian-HARs' were performed on compounds containing the hydrogen maleate anion, where an H atom bridges two O atoms in a strong and short intramolecular hydrogen bond. Depending on the counter-cation, the position of the H atom can shift from being perfectly symmetric to being asymmetric  (Malaspina et al. , 2017). Such a bridging H-atom position is an especially difficult situation to model using X-ray diffraction data. In HAR-ELMO  (Malaspina et al. , 2019) , the basic assumption of the model, namely the strict localization of the ELMOs, may impact on such regions where electronic delocalization plays a role, e.
Experimental details The four different data sets used in this study were taken from previously published and deposited high-resolution low-temperature single-crystal X-ray diffraction experiments. For the two compounds 8-hydroxyquinolinium hydrogen maleate (8HQ HMal) and magnesium bis(hydrogen maleate) hexahydrate (Mg HMal) we used the same synchrotron data, measured at beamline BL02B1 of SPring-8, that were used before by  Malaspina et al. (2020). The crystallographic structure factors of AGA were taken from Fo ¨rster et al. (2007). They were measured at the Swiss Light Source synchrotron, beamline X10SA. The data of xylitol are laboratory Mo K data, reported by  Madsen et al. (2004). Pertinent crystallographic and measurement details are reiterated in Tables  1  and 2. The hydrogen maleate compounds were subjected to a HAR using Tonto only ('normal HAR'), and to a HAR using lamaGOET as an interface to Gaussian for the quantumchemical calculations and to Tonto for the partitioning and least-squares refinement.
From now on we will refer to the latter kind of HAR as Gaussian-HAR. In all refinements, the crystal field was simulated with cluster charges within a radius of 8 A ˚around the central asymmetric unit. More refinement details (such as the different levels of theory used) and the refinement results are discussed in Section 3. 1. CIFs are deposited with the Cambridge Structural Database (CSD) and can be obtained via CCDC deposition numbers 1987762 (8HQ HMal) and 1987825 (Mg HMal). They are also included as supporting information for this article. More details on the software lamaGOET and the realization of Gaussian-HARs are given by  Malaspina et al. (2021). The AGA refinements serve as an example for a HAR-ELMO application on a peptide. The recently introduced HAR-ELMO procedure  (Malaspina et al.
, 2019)  uses the software lamaGOET to pass the ELMO-derived wavefunction from the ELMOdb software to the refinement software Tonto. lamaGOET is compatible with the ELMO nomenclature, also for tailor-made residues. Details of the software behind HAR-ELMO are discussed by  Malaspina et al. (2019 Malaspina et al. ( , 2021)). The HAR-ELMO treatment performed for AGA used transferred ELMOs expanded on the 6-311G(d, p) basis set, while the traditional HAR was based on repeated HF/6-311G(d, p) wavefunction calculations. No cluster charges were used in either HAR or HAR-ELMO. Refinement results are discussed in Section 3. 2. CIFs are deposited in the CSD and can be obtained via 1987828 or from the supporting information. For xylitol, XWR was performed as a sequence of HAR and XCW fitting in Tonto, mediated, facilitated and controlled by the software lamaGOET  (Malaspina et al.
, 2017) , we investigated other ways of estimating the hydrogen ADPs or we refined the atom isotropically  (Malaspina et al. , 2020). Here, we want to test the influence of changing the method and the basis set on the H-atom position and the refined ADPs -a question left open by  Malaspina et al. (2020)  because a quantumcrystallographic interface such as the new lamaGOET software was needed for such a study  (Malaspina et al. , 2021). For the 8HQ HMal structure [Fig. 1 (a)], a normal HAR with the program Tonto using a recommended level of theory (HF/def2-TZVP; see  Fugel et al. , 2018)  produces an NPD hydrogen ADP matrix. Nevertheless, the O-H bond distances are accurate and agree with the neutron-diffractionderived bond distances within two standard uncertainties (see caption of Fig. 1 ).
Graphics were produced with the program VMD  (Humphrey et al. , 1996). XCW fitting in Section 3. 3. Here, this shift of electron density is responsible for the improvements in the refinements found according to Table  3. The difference between the DFT methods B3LYP and B3PW91 depicted in Figs. 2(b ) and 3(b) is also large. In contrast to the difference between HF and DFT, electron density is not accumulated in the core regions, only redistributed in the valence region. The effect on the refined atom positions, ADPs and refinement statistics is clearly less significant than in the case of HF versus DFT. Figs. 2(c) and 3(c) show the redistribution of electron density due to the change of the basis set. The effect is much smaller than the effect of changing the method discussed in the previous paragraphs.
In this study, the technico-economic viability is studied, on the basis of different scenarios of electric micro-grids with renewable energy production sources, autonomous and supplied by the interconnected grid. HOMER Pro 3. 13. 3 Pro simulation and optimization software, Xlstat and Energy sentinel software are used for the evaluation of different technical and economic scenarios of system components, in order to obtain the most cost-effective configuration. To do this, the case study of the village of ELOKATO identifies the optimal options for the use of renewable energies for rural electrification in order to increase the electricity coverage rate. In our case study, after the various simulations, HOMER identified 13 scenarios and configurations deemed to be the most profitable, from the parameters and configurations without photovoltaic components have high COEs and very high initial investment costs in the order of several million US $.
Homer Pro The HOMER Pro This tool is used in all sectors, from electricity supply in isolated villages and island utilities to sites connected to the grid. Homer Pro, or HOMER (Hybrid Optimization of Multiple Electric Renewable Energies), provides outstanding assistance by simplifying the design assessment tasks of off-grid and grid-connected power systems. It allows you to select the best system components and identify for each component the effective sizes and characteristics to meet the energy needs of the site. In its operation, HOMER requires input data on daily needs (kWh/day), parameters of available energy resources of the site studied, an architectural proposal for the components of the micro-grid, economic indicators and environmental data. The software uses these inputs to simulate different system configurations or combinations of components and generates displayable results as a list of achievable configurations ordered by current net cost.
Xlstat XLSTAT is statistical management software that integrates and relies on Microsoft Excel for data entry and publication of results. The calculations are carried out entirely in autonomous programs and algorithms. Using Excel as an interface makes the application very user-friendly, intuitive and robust. The quality, precision and relevance of the calculations are identical to those of renowned scientific software. XLSTAT is compatible with all versions of Excel since version 97 under Windows and Mac environments. The 2018 versions integrate a lot of useful features such as the R language and machine learning, which give it great computational robustness. The many features offered by XLSTAT make Excel an excellent tool for statistical analysis and modeling, making it possible to cover all data processing needs.
Energy Sentinel Software (Energy Team, Italy) As part of this study, for the collection of real-time data, a metering devicewas set up and connected to the power transformer that supplies the village of Elokato with electricity. Energy sentinel is the proprietary software of the Italian smart metering system supplier Energy Team (Figure  3 ). The counting system is a microcontroller called X-METER which allows data acquisition through sensors and probes. These data are stored on a remote data The energy resources available, easily mobilized on the Elokato site, to produce electricity are solar photovoltaic, hydraulic (small hydro) and wind sources. The following sessions provide more details on the resources and components to build an electricity production system from renewable sources (solar, wind, tidal).
Data Collection and Major Components of the System The electrical load to be covered The identification and analysis of demand remain the most important steps in the design and sizing of a micro-grid, it will determine the scope of the production system to be built. The village of ELOKATO is connected to the national electricity grid. To obtain real consumption values, a measurement system is installed at the level of the power transformer, of the H61-15kV/400V/100kVA type of apparent power, which supplies the village. An Energy team X-METER type metering device, installed on the site since 2018, has made it possible to take remote readings of 50 electrical quantities (current, voltage, power, energy, etc. ) quantitatively and qualitatively. The village's consumption was recorded on a computer in time steps (1 min, 10 min, 15 min, 30 min, 60 min) for more than a year.
Major components of the system The essential components of the hybrid system for the micro-grid within the framework of this project are: a photovoltaic system, a wind turbine, a chemical storage system (battery), a diesel generator, the national grid, a DC/AC converter (Figure  8 ). We choose to use the solar irradiation and light data for the village of Elokato come from the American national database of the National Solar Radiation Database (Figure  9 ). The global annual horizontal solar radiation is shown in Figure  9. The daily average over the year is 4. 76 kWh/m².
Wind turbine The wind generator used for the simulations is of the generic type Eolien Generic 3kW-G3 from the HOMER Energy database. It is a 3kW wind turbine connected to the AC bus (Figure  11 ). The values used in this study are taken from NASA metrological data available in the HOMER Pro The average annual speed was about 3. 28 m/s, measured at an anemometer height of 50 m (Figure  12 ). HOMER calculates the output power of the wind turbine at each time step using a three-step process: • Calculation of the wind speed at the height of the hub of the wind turbine. • Calculation of the amount of energy produced by the wind turbine at this wind speed with a standard air density. • Adjustment of the power value according to the real air density. • Minimum statement of charge (SOC): 30%; • The storage system is wired in 48 V; • Number of thongs: 24; • Manufacturer: BAE Secura PVV Solar Batteries (Figures  13 14 15 ).
Minayégnan et al. DOI: 10. 4236/sgre. 2021. 1211011 188 Smart Grid and Renewable Energy server connected to the Internet (cloud) and viewable by secure access (Figure 4). HOMER Pro 3. 13. 3 software is used for the simulation and optimization of the microgrid to be planned. It provides an overview of the different configurations and combinations of possible production sources in order to minimize the costs of micro-grid components.
T. Minayégnan et al. DOI: 10. 4236/sgre. 2021. 1211011 188 Smart Grid and Renewable Energy server connected to the Internet (cloud) and viewable by secure access (Figure 4). HOMER Pro 3. 13. 3 software is used for the simulation and optimization of the microgrid to be planned. It provides an overview of the different configurations and combinations of possible production sources in order to minimize the costs of micro-grid components.
Affordable solutions are few. Image processing algorithms for automatic measuring inhibition diameters have been published  [17] [18] [19] [20] [21]. Among these, only AntibiogramJ 21 presents a fully functional userfriendly software, but it operates on a desktop computer onto which the images need to be previously transferred. We believe that reducing hardware requirements to just a smartphone is key for the adoption and diffusion of such a tool. Moreover, smartphone applications are easy to adopt and use if they follow established design patterns, and they benefit from an ecosystem that facilitates setup and updates. This paper introduces a fully offline mobile application (the App hereafter) capable of analyzing disk diffusion ASTs and yielding interpreted results, operating entirely on a smartphone.
Results Image processing steps. The App presented in this paper is an automatic AST reading system capable of running the whole analysis of a disk-diffusion antibiogram offline on mobile devices, from image acquisition to interpreted results. It helps laboratory technicians throughout the whole analysis process, suggesting measurements, results, and interpretations. The App can be summarized in three major components: first, a dedicated image processing module (IP) that reads and analyzes the AST image; second, an expert system (ES) responsible for the interpretation of the data extracted by IP; third, a Graphical User Interface (GUI) that allows the execution of IP and ES on a smartphone, and user interaction. The application's image processing library  22  implements an original algorithm for the measurement of the inhibition diameters (described in Methods) and uses ML for the identification of the antibiotic disks, which is unprecedented in this kind of application.
The IP module consists in a C++ library developed on OpenCV  23  and Tensorflow  24. The choice of C++ makes our library exploitable in various contexts, including desktop computers and Android and iOS mobile devices. Moreover, the library has a Python wrapper, useful for application prototyping, image batch processing, and benchmarking. The App includes an expert system capable of performing coherence checks on the raw susceptibility and providing interpreted results, with extrapolation on non-tested antibiotics and clinical commentaries. The expert system's knowledge base is provided and regularly updated by i2a (Montpellier, France)  13  and based on up-to-date EUCAST expert rules  11. The expert system's engine was completely developed in TypeScript and works completely offline within the App.
Since smartphone cameras are not designed for quantitative measurements, we provide a simple method to assess the camera's optical distortions with a numerically generated AST image. Moreover, while taking the picture, the application uses the device's gyroscopes and accelerometer, if available, to force the device orientation (parallel to ground, to avoid perspective distortions) and stability (to avoid motion blur). The application also displays a visual frame that helps center the Petri dish in the picture. Although Petri dishes have standard shapes (square or disk), we do not rely on this assumption for the analysis. The IP module analyzes an AST picture in three different sequential stages: plate cropping, detection of antibiotic disks, and Fig. 1  Analysis of an AST plate with the App.
Such strains are among the main pathogens and have known inhibition diameters to various antibiotics. Species distribution, preparation information, and other details are reported in the Methods for all three sets. Table  1  Description of the AST sets used for the performance evaluation of the automatic reading. For each dataset, the columns indicate: the number of single Petri dishes in the dataset, the corresponding total number of antibiotic disks, the type of growth medium, the shape of the plates, the number of independent raters measuring the diameters, the reference used as control diameter. AST  Data acquisition. All plates in AST groups A1 and A2 were imaged with a smartphone camera (Honor 6x with a resolution of 12 megapixels). Since the App was still under development at the time these images were taken, we used the default Android camera application for acquisition.
Then the images were analyzed with the App's full automatic procedure (without manual intervention). As control inhibition diameters for sets A1 and A2, we collected the measurements effectuated by the laboratory technician using a commercial automatic reading system (SIRscan  13  , i2a, Montpellier, France). The control diameters measured by the technicians with the SIRscan system were extracted retrospectively from the hospital database, since these antibiograms were performed during routine analysis in the hospital. The SIRscan system allows for correction on automatic measurements. Nevertheless, for productivity reasons, the technician did not always adjust the diameters if the adjustment did not yield a different categorization result, therefore diameters can be unadjusted even if they give the right susceptibility categorization.
Following the same terminology proposed by  17, 20, 21  , we calculated the agreement as the rate of identical categorization; disagreement is classified as very-major, major and minor. Very-major disagreement occurs when an antibiotic is categorized S (Susceptible) while the control is R (Resistant), major error corresponds to a categorization of R with control S and minor disagreement is any other categorization error involving the Intermediate value I (Intermediate). As a measure of agreement between the App and control, we calculated the unweighted Cohen's kappa index  25. Image processing performance. The App's IP procedure proved its reliability at each step. For each dataset, the photos were automatically cropped to isolate the Petri dish. The automatic crop procedure never failed if the image respected the acquisition protocol (see Supplementary Note 1).
The automatic pellet detection correctly found all antibiotic pellets in A1 and A2. In A3 0. 5% of pellets were missed (false negatives). Half of the missed pellet show visible flaws (see Supplementary Fig. 4 ) and should not be considered in the analysis, according to experts' advice. False positives never occurred (other objects wrongly identified as pellets). In case a pellet was missed, the users can add it with the help of the graphical interface. The antibiotic labels were always correctly interpreted in A3, even if the image was not perfectly focused or the text was damaged (by bad printing or by positioning it with tweezers). In datasets A1 and A2, the accuracy was 98%. Misclassification happened in cases of very poorly printed labels or for pellets from non-supported providers, on which the ML model was not trained.
However, due to user experience considerations in combination with concerns about model transferability, we ultimately determined not to incorporate these resistance mechanism-detecting models into the App. We do not exclude reconsidering this approach in a future version of the application if we can generalize it to a larger and diverse dataset. Instead, every time a resistance mechanism can appear in a culture (given the bacteria species and the tested antibiotics) the application will systematically ask the user to verify the presence of the associated shape, showing illustrated examples (see Fig. 2 ). Susceptibility categorization. Our new diameter measurement approach yielded good classification results over most of the available images, as shown in Table  2. Only a small fraction of the pictures, classified as problematic in Table  2  (1.
The counterpart of this choice is a certain difficulty in obtaining a constant image quality (notably because of the intrinsic variability of smartphone hardware and software). The acquisition setup and GUI assistance are there to limit this variability, but can not standardize the image acquisition at the level of commercial systems with dedicated hardware. Nevertheless, the data in this study display a certain variability (especially in contrast, as shown in Fig. 3 ) with which the application's IP could easily cope. Strong light reflections and other important issues will result in evident wrong readings, which are easily detectable by eye with the App's user interface. We remarked that, even without training, users can easily notice such problems and adapt the acquisition setup in order to eliminate them in future acquisitions.
Methods Detail of the AST sets. In this section, we give a detailed description of the AST datasets used for the benchmark. The specific characteristics of each data set are summarized in Table  1. The bacterial species appearing in this study are reported in Fig. 5. The plates were inoculated with 0. 5 McFarland of a pure culture of the studied organism. Then antibiotic disks were positioned onto the plates with a dispenser gun (A1 and A2) or by hand (A3) and the plates were incubated from 16 to 24 h under aerobic or 5% CO 2 conditions depending on the species. • Datasets A1 and A2: More than 91% of the plates in these datasets were square; the remaining 9% were circular. The antibiotic disks were bought from i2a (Montpellier, France) and positioned onto the plate with a dispenser gun. Antibiograms were performed according to the EUCAST 10 recommendations.
Standard Mueller-Hinton agar was used in A1, whereas in A2, we used MH-F agar (blood-agar) for fastidious organisms (Biorad, Marnes-la-Coquette, France). The bacteriology laboratory of Creteil University Hospital is accredited under ISO15189 (Accreditation certificate N8-3372 rev. 9) therefore antibiotic disks and culture media are routinely quality checked. Image processing procedure. The IP module of the App consists of a custom C+ + library and it is endowed with a Python wrapper module and a quick-start documentation. We have deliberately developed the IP as a standalone module in order to facilitate its use in other projects involving, for example, batch processing of many images, or the integration in a Desktop application with the development of dedicated image acquisition hardware.
In the context of AI, an Expert System is a program capable of taking reasoned conclusions from a given input, thereby simulating a human expert. Expert Systems have long been successfully used in microbiology  30  and most commercial systems use them today. An Expert System consists of an inference engine that takes reasoned conclusions on the input information, based on a set of rules written by human experts. The Expert System integrated in the App takes as input the diameter of the inhibition zones of the observed plate. It categorizes the susceptibility of the bacteria to the tested antibiotics and runs a coherence check and a final interpretation. The coherence check examines the input information and alerts the user if incoherent data are found (for example, if an antibiotic is not coherent with the entered species, or if a natural resistance is not observed).