dataesr/scientific-paragraphs-categorization huggingface dataset:
4) HYBRIDIZATION WITH GREY WOLF OPTIMIZER A hybrid method (GWGHA) based on hybridizing GWO with GOA was proposed by Teng et al. [127]  for the Cycle Traffic Light Timing Optimization Problem (CTLTOP). The performance of GWGHA was validated based on data taken from Kaohsiung, Taiwan, Bahıa Blanca, Argentina, and Malaga, Spain in comparison with PSO, SPSO2011, GWO, and GOA. In  [144]  a hybrid algorithm (GWO-GOA) was proposed by hybridizing GOA with GWO for tackling the text feature selection problem. GWO-GOA was assessed using eight datasets taking into account five metrics (i. e accuracy, sensitivity, specificity, precision, recall, and F-measure). According to the experiments, GWO-GOA showed better quality when comparing with GOA, GWO, and other techniques.
7) HYBRID GOA AND CAT SWARM OPTIMIZATION ALGORITHM Bansal et al. [130]  hybridized the binary variant of GOA known as Simple Matching Binary GOA (SM-BGOA) with a variant of Cat Swarm Optimization Algorithm known as NCSOA for feature selection and optimal design of multi-layer perceptron. The effectiveness of SM-GNCSOA was evaluated based on 10 data sets selected from the UCI repository and results showed the capability of SM-GNCSOA for solving the optimal design of multi-layer perceptron problem in terms of classification accuracy.
12) GRASSHOPPER OPTIMIZATION ALGORITHM WITH SUPPORT VECTOR MACHINE Aljarah et al. [72]  proposed a combined method (GOA-SVM) based on the hybridization of GOA with SVM for optimizing the parameters of SVM model and the location of the best features subset simultaneously. The efficiency of GOA-SVM was evaluated based on 18 low and high-dimensional benchmark datasets in comparison with 7 well-regarded algorithms. Another similar work was done by Barman et al. [73]  and Ibrahim et al. [135]. Shi et al. [136]  suggested an improved long short-term memory GOA (GOA-ImLSTM) by hybridizing GOA with SVM and LSTM for decision-making for Self-Driving Vehicles. GOA-ImLSTM was assessed using Vehicle trajectory data collected from Roads of Los Angeles and California. According to the experiments, GOA-ImLSTM enhanced the accuracy of solutions when compared with other decision-making techniques.
13) GRASSHOPPER OPTIMIZATION ALGORITHM WITH SUPPORT VECTOR REGRESSION Barman and Choudhury  [134]  hybridized GOA and Support Vector Regression (SVR) for Short Term Load Forecasting (STLF) during periods with substantial weather changes in North-East India. The performance of GOA-SVR was evaluated based on the data of the load dispatch center of Assam state in 3 case studies under 3 typical weather conditions. Experimental results demonstrated that GOA-SVR outperforms the conventional methods in all cases.
14) GRASSHOPPER OPTIMIZATION ALGORITHM WITH NEURAL NETWORK Talaat et al. [140]  proposed a hybrid approach(MFFNN-GOA) based on the combination of multi-layer feed-forward neural network (MFFNN) and GOA for load forecasting. The effectiveness of MFFNN-GOA was tested based on different training functions taking into account three parameters: RMSE, MAE, and Mean Absolute Percentage Error (MAPE). The effectiveness of MFFNN-GOA method was demonstrated compared to MFFNN, MFFNN-GA, and MFFNN-GWO optimization methods. Moghanian et al. [141]  suggested a novel model (GOAMLP) by combining GOA with multi-layer perceptron neural network for tackling the intrusion detection problem. KDD and UNSW datasets were used to validate the robustness of GOAMLP. Experimental results demonstrated that GOAMLP is more accurate and outperforms other existing techniques such as RF, XGBooston, HHO, BWO, and ANN with BOA.
A hybrid model (GORNN) by combining GOA with RNN was proposed by Jape et al. [142]  for boosting the speed of the induction motor. The viability and effectiveness of GORNN was validated using two scenarios: step demand torque with constant demand speed and ramp demand torque with constant demand speed. According to the experiments, GORNN provided good solutions when compared with other existing techniques. Renukadevi and Karunakaran  [143]  proposed hybridized GOA with Deep Belief Network(DBN) for liver disease classification. The performance of DBN-GOA was evaluated based on real-time and open-source computed tomography (CT) image datasets. It was demonstrated that DBN-GOA provide 98%, 95. 82%, 97. 52%, 98. 53% and 96. 8% for the accuracy, sensitivity, specificity, precision, and F-1 score, respectively.
A. GOA FOR CONTINUOUS OPTIMIZATION 1) CLUSTERING Łukasik et al. [147]  used GOA for generating accurate data clusterings. GOA was validated using benchmark datasets based on real-world examples taken from the UCI machine learning repository. Test results revealed that GOA outperforms the standard K-means algorithm by obtaining high accuracy. 2) OPTIMAL LINEAR ANTENNA ARRAY Amaireh et al. [149]  employed GOA and Antlion optimization (ALO) for optimal design of Linear Antenna Arrays (LAA). GOA was tested on three different case studies: 10, 16, and 24 elements of linear antenna arrays in comparison with Taguchi Algorithm (TA), PSO, and BBO.
In this paper, the development of an environmentally-friendly lithium-terephtalate/polylactic acid (Li 2 TP/PLA) composite filament, for its use, once 3D-printed via Fused Deposition Modeling (FDM), as negative electrode of a lithium-ion battery is reported. Solvent-free formulation of the 3D-printable filament is achieved through the direct introduction of synthesized Li 2 TP particles and PLA polymer powder within an extruder. Printability is improved through the incorporation of poly(ethylene glycol) dimethyl ether average M n ∼500 (PEGDME500) as plasticizer, while electrical performances are enhanced through the introduction of carbon black (CB). Thermal, electrical, morphological, electrochemical and printability characteristics are discussed thoroughly. By taking advantage of the 3D-printing slicer software capabilities, an innovative route is proposed to improve the liquid electrolyte impregnation within the 3D-printed electrodes.
In this context, herein for the first time, the development of an environmentally-friendly Li 2 TP/PLA composite 3D-printable filament for its use, once 3D-printed via FDM, as a negative electrode of a LIB, is reported. As depicted hereafter, a solvent-free filament formulation protocol is established and an original route based on the 3D-printing slicer software capability is proposed to improve the liquid electrolyte impregnation and electrochemical performances of the 3D-printed electrodes. Samples thermal, electrical, morphological, electrochemical and printability characteristics are discussed thoroughly.
Fused deposition modeling 3D-printing. -Electrodes cylinders (12. 7 mm diameter, 200 μm or 600 μm thick) were designed with Autodesk Fusion 360 software after which they were sliced into 100 μm thick 2D-slices using PrusaSlicer software. Innovative porous (30% infill) rectilinear, gyroid and archimedean chords patterns (Fig. 7a ) were generated. Electrodes were obtained with a Prusa MK3S 3D-printer (Prusa Research, Czech Republic). Thinner 3D-printed electrodes were employed to perform classical galvanostatic cycling while thicker ones (100% infill only) were used for electrochemical impedance spectroscopy. Standard nozzle input and output diameters of 1. 75 mm and 0. 4 mm were used. As reported in our previous study,  31  the highest resolution in the Z direction is 200 μm for the first layer and 50 μm for the following.
The nozzle temperature was set 30 °C higher than the melting temperature of the filament obtained from differential scanning calorimetry. Bed temperature was set to 60 °C to favor the adherence of the first printed layer. Prior to printing, the nozzle was methodically purged by extruding a 2 cm 3 purge cube with the corresponding filament composition. Thermal characterization. -Differential scanning calorimetry (DSC) of the filament and 3D-printed disc was carried out by means of a DSC204F1 supplied by NETZSCH-Gerätebau GmbH, Germany. Sample were characterized between -60 to 300 °C at a heating rate of 10 °C min -1 under argon atmosphere (50 mL min -1 ), using about 10 mg of sample of each composition. Heat flow data from the first heating operation was recorded. Thermogravimetric analysis (TGA) of the synthesized Li 2 TP powder was performed between 20 °C and 600 °C on a Netzsch thermal analyzer STA 449 C Jupiter equipped with a differential analysis microbalance coupled with a mass spectrometer QMS 403 Aeolos (heating rate: 5 C min -1 under argon flow).
Conclusion This work was focused on the development of an environmentally-friendly lithium-terephtalate/polylactic acid (Li 2 TP/PLA) composite filament, for its use, once 3D-printed via Fused Deposition Modeling (FDM), as negative electrode of a lithium-ion battery. A solvent-free formulation protocol to develop a composite electrode 3D-printable filament was established. The 3D-printable filament was here obtained through the introduction of synthesized Li 2 TP and PLA polymer powders within an extruder. Printability is improved through the incorporation of poly(ethylene glycol) dimethyl ether average M n ∼500 (PEGDME500) as plasticizer, while electrical performances are enhanced through the introduction of carbon black (CB). While electrochemical performances of the printed electrodes are relatively low due the poor electronic conductivity and poor liquid electrolyte impregnation, an innovative route involving the use of the slicer software to propose custom-shaped electrodes (infill pattern) to improve the liquid electrolyte impregnation within the 3D-printed electrodes was demonstrated.
Acknowledgments This work was supported by the  Fonds Européen de Développement Régional (FEDER) , the  Conseil Régional des Hauts-de-France  and the  Université de Picardie Jules Verne (UPJV). The authors would like to thank  M. Courty  for technical assistance (DSC),  Michel Armand  for discussions, and the UPJV microscopy platform for sharing their facilities.
The present work investigates different models of polymer electrolyte membrane fuel cell. More specifically, three models are studied: a nonlinear state-space model, a generic dynamic model integrated into MATLAB/Simulink, and an equivalent RC electrical circuit. A new equivalent electrical RL model is proposed, and the methodology for determining its parameters is also given. An experimental test bench, based on a 1200-W commercial PEMFC, is built to compare the static and dynamic behaviour of the existing models and the proposed RL model with the experimental data. The comparative analysis highlights the advantages and drawbacks of each of these models. The major advantages of the proposed RL model lie in both its simplicity and its ability to provide a similar transitory behaviour compared to the commercially manufactured PEMFC employed in this research.
It is for this reason that state-space models were introduced in some works  [14, 15]. However, state-space models are further complicated because they are highly nonlinear, and involve a large number of state variables and parameters. Then, some works have attempted to develop equivalent electric models, because they are still simple and easy to understand and to implement  [16] [17] [18] [19] [20] [21] [22] [23] [24] [25]. The present work investigates different classes of models proposed in the literature. More specifically, three models are presented: a nonlinear state-space model, a generic dynamic model integrated into MATLAB/Simulink, and an equivalent electric RC circuit. Using the dynamic behaviour of a 1200-W commercialized PEMFC, a new equivalent electric model is proposed. A comparative study between the proposed model and the previous models is conducted, showing the pros and cons of each model.
The rest of the paper is organized as follows: In Section 2, the electrochemical principle of a PEMFC is presented. Section 2. 1 is devoted to the presentation of the nonlinear statespace model. A generic dynamical model integrated into MATLAB/Simulink is illustrated in Section 2. 2. In Section 2. 3, an equivalent electrical RC circuit is presented. The proposed equivalent electrical RL circuit is shown in Section 2. 4. Section 3. is devoted to the experimental behaviour of a 1200-W commercialized PEMFC. The comparative study between different models is conducted in Section 4. A conclusion and a reference list end the paper.
More specifically, three models are presented: a nonlinear state-space model, a generic dynamic model integrated into MATLAB/Simulink, and an equivalent electric RC circuit. Using the dynamic behaviour of a 1200-W commercialized PEMFC, a new equivalent electric model is proposed. A comparative study between the proposed model and the previous models is conducted, showing the pros and cons of each model. The rest of the paper is organized as follows: In Section 2, the electrochemical principle of a PEMFC is presented. Section 2. 1 is devoted to the presentation of the nonlinear state-space model. A generic dynamical model integrated into MATLAB/Simulink is illustrated in Section 2. 2. In Section 2. 3, an equivalent electrical RC circuit is presented. The proposed equivalent electrical RL circuit is shown in Section 2.
Nonlinear State-Space Model of the PEMFC (NLM) It has been suggested in many studies  [12, 15, 16, 28 ] that a nonlinear state-space model of the PEM fuel cell could be represented by Equations (  3 )-  (8). In this model, the open-circuit output voltage of the PEM fuel cell, mass balance and thermodynamic energy balance, irreversible voltage losses, and the formation of the charged double layer in the PEM fuel cell is modelled: Nonlinear state-space representing the internal behaviour of the fuel cell and equivalent electrical circuits. ̇ = - + -( ) (3) ̇ = 2 -2 - (4) ̇ = 2 -2 - (5) In this paper a comparison between four models is investigated: a nonlinear statespace model, a generic model from MATLAB Toolbox, an equivalent electrical circuit RC, and a new proposed equivalent electrical circuit RL.
Generic Model from MATLAB Toolbox (GMM) A fuel cell stack block integrated into MATLAB/Simulink implements a generic model parameterized to represent the most popular types of fuel cell stacks fed with hydrogen and air. The block represents two versions of the stack model: a simplified model, and a detailed model. The user can switch between the two models by selecting the level in the mask under the model detail level in the block dialogue box. In this paper, we consider the detailed model represented by Figure  3. The notations used are the same as those from  [29, 30]. The fuel cell voltage is related to the fuel cell current as follows: V f c = E -R i × I f c (9) where R i is the internal resistance, and the controlled voltage source E is described by the following equation: E = E oc -N Aln I f c i 0 × 1 s T d 3 + 1 ( 10 ) where s is the Laplace operator and E oc is an open circuit voltage (V); N is the number of cells; A is a Tafel slope (V); i 0 is the exchange current (A); and T d is the response time (at 95% of the final value) (s).
Experimental Model (EXM) In this section, we will determine the static and the dynamic behaviour of the Ballard Nexa 1200 fuel cell module, which has a rated power of 1. 2 kW. To this end, an experimental bench was built, as shown in Figure  7 ; it consists-in addition to the fuel cell, with its monitoring software-of three metal hydride canisters from Heliocentris with storage capacities of 800 NL hydrogen, an H2 connection Kit 15 bar for connecting the metal canisters, a Nexa 1200 DC/DC converter, a power supply from BK Precision used for the fuel cell starter, Hall effect sensors to measure the voltage and current variables, a programmable DC electronic load from BK Precision and power resistors to make load changes, and a MicroLabBox-dSPACE with Control Desk software plugged into a personal computer for signal acquisition.
Comparison between Different Models In this section, we will evaluate the static and the dynamic behaviour of the studied models, and compare them to the experimental data of the used Nexa 1200 fuel cell module. All models are simulated using MATLAB/Simulink software. As a load of these models, we used a controlled current source whose variations were programmed similarly to those used for the experiment. Figure  11  illustrates the simulated models, while the experiments were carried out according to Figure  7. All of the parameters used for the simulation models are listed in Table  3.
Type of Model Parameters Nonlinear model (NLM) Parameters used in  [1]  Generic MATLAB mode (GMM) In this section, we will evaluate the static and the dynamic behaviour of the studied models, and compare them to the experimental data of the used Nexa 1200 fuel cell module. All models are simulated using MATLAB/Simulink software. As a load of these models, we used a controlled current source whose variations were programmed similarly to those used for the experiment. Figure  11  illustrates the simulated models, while the experiments were carried out according to Figure  7. All of the parameters used for the simulation models are listed in Table  3.
Dynamic Behaviour In this section, a comparison between the dynamic behaviour of four models and the experimental results in the presence of fuel cell current changes was studied. Figure  13  illustrates the dynamic behaviour of each model. As is clearly shown, the proposed RL equivalent electrical model presents the best dynamic behaviour compared to the conventional RC model used in the literature. The main advantages of this model lie in its simplicity and stability to produce the same behaviour as the fuel cell. Unlike the proposed RL equivalent electrical model, the mathematical model, the conventional generic MATLAB model and the RL model all provide the same static behaviour of the fuel cell, but do not produce a smooth transitory fuel cell behaviour. In addition, the disadvantages of the mathematical model are that it is complicated and requires a longer computational time for calculation.
The classic RC model and the proposed RL Unlike the proposed RL equivalent electrical model, the mathematical model, the conventional generic MATLAB model and the RL model all provide the same static behaviour of the fuel cell, but do not produce a smooth transitory fuel cell behaviour. In addition, the disadvantages of the mathematical model are that it is complicated and requires a longer computational time for calculation. Consequently, this is a handicap for real-time control and implementation. Therefore, the present modelling approach permits the researchers and students who do not own a real fuel cell to approximate its static and dynamic behaviour in a simple, fast, and affordable way.
The modelling of Cherenkov based detectors is traditionally done using Geant4 toolkit. In this work, we present another method based on Python programming language and Numba high performance compiler to speed up the simulation. As an example we take one of the Forward Proton Detectors at the CERN LHC -ATLAS Forward Proton (AFP) Time-of-Flight, which is used to reduce the background from multiple proton-proton collisions in soft and hard diffractive events. We describe the technical details of the fast Cherenkov model of photon generation and transportation through the optical part of the ToF detector. The fast simulation is revealed to be about 200 times faster than the corresponding Geant4 simulation, and provides similar results concerning length and time distributions of photons. The study is meant as the first step in a construction of a building kit allowing creation of a fast simulation of an arbitrary shaped optical part of detectors.
Conclusion We presented the fast modeling of the AFP Time-of-Flight detector, that uses the geometrical method. The distributions of lengths obtained by the fast simulation and Geant4 have the same shape, with relative difference < 2%. An advantage of the fast simulation compared to Geant4 is the speed-up of a simulation; it is about 200 times faster, and allows one to generate a higher number of proton trajectories, thus improving statistics. The code of the fast simulation, written in Python 3, is available at: https://github. com/olivierrousselle/ Fast-simulation-AFP. Users can choose the train, the number and positions of proton trajectories they want to model, and other parameters as the material of bars. We suggest that such a fast simulation with Python and Numba could be used to model other types of ToF detectors; it seems more convenient and faster than Geant4, provided that physical processes aren't too complex.
, 2017)  therefore necessitates either generating individualized germline gene databases for each study subject or using a preexisting database sufficiently comprehensive that the allelic diversity within the species is adequately represented. Computational inference from high-throughput sequencing of expressed recombined VDJ (variability, diversity, and joining) repertoires (Rep-seq) has emerged as a powerful approach for the identification of functional germline Ig alleles in outbred populations. In macaques, two tools were used to identify germline Ig alleles, IgDiscover  (Corcoran et al. , 2016)  and IMPre  (Zhang et al. , 2019). IgDiscover includes modules that support the tracing of antigen-specific Ab lineages in Rep-seq data  (Kong et al. , 2019; Martinez-Murillo et al. , 2017; Phad et al.
, 2015; Sussman, 1986). Using IgDiscover, we previously reported a high level of Ig allelic diversity in macaques  (Corcoran et al. , 2016). This highlighted the need to study larger groups of animals for an improved understanding of macaque Ig genetics and to generate more comprehensive databases. A critical question was whether studies in different macaque sub-groups require population specific databases or whether the construction of a common database from many animals could provide a comprehensive resource for high-quality gene assignments. To address this, we used IgDiscover to generate Ig germline gene databases from 15 Indian-origin rhesus macaques, 12 Chinese-origin rhesus macaques, 6 Indonesian-origin cynomolgus macaques, and 12 Mauritian-origin cynomolgus macaques, amounting to 45 macaques in total.
All of the animals were different from those analyzed by  Corcoran et al. (2016). We generated 2 independent IgM libraries for each animal and identified germline alleles that passed the stringent quality control of the IgDiscover software. We used multiple approaches to validate the alleles, including (1) identification of the same allele with 2 independent primer sets used for next-generation sequencing (NGS) library construction, (2) identification of the same alleles in >1 animal, (3) confirmation by targeted genomic PCR, cloning, and Sanger sequencing, and (4) identification of the same allele in previously reported data. We compiled databases of IGHV, IGHD and IGHJ alleles. For rhesus macaques, the database comprised 762 IGHV alleles and for cynomolgus macaques 615 IGHV alleles. In addition, we identified 16 IGHJ alleles and 54 IGHD alleles in the 45 macaques.
Experimental design and sequencing The objective of this study was to determine the Ig germline gene content in groups of macaques to obtain a deeper understanding of VDJ allelic diversity in both rhesus and cynomolgus macaques and to construct improved genetic databases. To achieve this, we analyzed 45 macaques, comprising Indian-origin rhesus macaques, Chinese-origin rhesus macaques, Indonesian-origin cynomolgus macaques, and Mauritius-origin cynomolgus macaques, for which peripheral blood mononuclear cells (PBMCs) were available from previous studies. We extracted RNA for IgM repertoire sequencing and DNA for genomic validation studies. For human germline gene inference, we previously demonstrated that library preparation using 5 0 multiplex (5 0 MTPX) primers was superior to 5 0 Rapid Amplification of cDNA Ends (5 0 RACE) due to the shorter amplicon lengths obtained when using 5 0 MTPX primers  (Vazquez Bernat et al.