dataesr/scientific-paragraphs-categorization huggingface dataset:
The clusters used are listed in supplementary table S1, Supplementary Material online. Each cluster was then multiply aligned with MUSCLE v3. 8. 31 and the resulting multiple alignment was used to build the HMM profile with hmmbuild from hmmer-3. 2. 1 (hmmer. org). Obtained profiles are used to scan the proteome database. Hits with an E-value <10 -2 covering >70% of at least one of the profiles were selected. Next, PFAM domains of interest were searched in the candidate sequences with hmmsearch (hmmer version 3. 2. 1), hits with an E-value <10 -2 were selected. Candidates containing all the PFAM motifs present in the A. thaliana peptidase were considered as homologs. The presence of additional conserved domains was determined with hmmsearch (hmmer version 3. 2. 1) (E-value <10 -5 ) on the entire PFAM database (3.
e main contributions of the paper, with respect to the existing works described above, are as follows: (i) e paper investigates the issue of high PV penetration effects with respect to the total installed power generation capacity of PV power plants in the MG and presents a coordinating voltage control solution to maintain the voltage limits of V min � 0. 95 and V max � 1. 05, which were chosen according to the ANSI standard. (ii) e current existing work with OpenDSS COM interface with MATLAB has only been small size of IEEE model less than 73 such as IEEE 7  [28] , 13  [21, 23, 27] , 17  [29] , 55  [24] , and 73  [20]  because of the required complex interface skills. However, we have proposed the bigger MG model-based IEEE 123 model compared to the existing work. (iii) In the existing similar works of our topic with OpenDSS-MATLAB  [21, 23, 24, [27] [28] [29] , there are three papers, which deal with voltage stability in terms of PU  [20, 21, 28].
e results were exported to MATLAB using the COM interface  [19]. (vi) Different scenarios are considered for testing the robustness of the proposed coordinated voltage control. (vii) A comprehensive approach is proposed, highlighting the steps to achieve a hybrid simulation between OpenDSS and MATLAB and validate the proposed coordinate voltage control. e rest of the paper is organized as follows. Section 2 presents the configuration of grid-tied MG operations, which include solar PV with different building loads for OpenDSS modeling. Section 3 describes the system framework of the OpenDSS tool COM interface with MATLAB. Section 4 provides the proposed coordinated voltage regulation control strategy of the PV smart inverter (SI) with capacitor banks (CBs). Finally, Section 5 evaluates the simulation results for the proposed system in various cases.
us, solar irradiance can be extracted using the PVGIS website  [35]. In total, 10 PV power plants were added to the IEEE 123node feeder, with a total of power generation capacity of 10 MVA, presented in Figure  1. In this case, the PV power plants supply and support the whole feeder, including the commercial buildings. e main objective is to study and analyze the effect of the PV energy penetration in the proposed MGs. All data from the Los Angeles area are considered part of the case study for this paper (Figure  2 ). e temperature inside the cell module is estimated based on the thermal model provided by  [36] T c � T m + Ir Ir 0 • ΔT. (1) e value of the back-surface module temperature T m can be calculated using  (2). e environmental conditions, namely, solar irradiance, wind speed, and ambient temperature measurements, are employed to calculate the backsurface temperature.
ese measurements are found in recognized databases, such as NASA and PVGIS  [35, 37] : T m � Ir. e a+b. WS 􏼐 􏼑 + T a. ( 2 ) Two coefficients, a and b, should be determined empirically, and the temperature difference between the cell and module back-surface must be calculated to determine the back-surface temperature. e values were chosen for polymer, thin-film, or steel PV technologies, according to Ref. [25]. erefore, the PV power output is calculated using P PV � P mpp. Ir. f. ( 3 ) Figure  3  displays the PV power generation under the influence of the weather conditions: temperature, wind speed, and irradiance. We analyzed the annual load consumption for each type of building used in the MG model. Table  3  lists different types of loads ordered from highest to lowest for the annual load consumption for each building, which is from the load consumption of the interior equipment, fans, cooling, and interior lights.
Implementation of OpenDSS COM Interface with MATLAB In this paper, the simulation framework chosen for the MG model is OpenDSS, developed by the Electric Power Research Institute  [38]. It is widely used in the industry to simulate largeand small-scale balanced/unbalanced networks, yielding comprehensive support documentation  [39]. It is up-to-date software following the electric grid modeling and analysis needs, specifically those related to the vast amount of renewable energy generation and penetration, smart grid applications in the modern utility grids, and harmonic analyses. is software was chosen for several reasons. First, it is free and open source, making the model easily transferable, modifiable, and updatable as needed. Furthermore, its COM server has extensive cosimulation capabilities, allowing a driver program to run the simulation from almost any programming language/environment.
International Transactions on Electrical Energy Systems In this work, the OpenDSS tool is hosted under MATLAB as the COM interface. us, several commands should be used to ensure communication between OpenDSS and MATLAB. Most of these commands are described in Table  4. All commands can be found in Visual Basic for Applications-Distribution System Simulator (VBA-DSS) interface properties and methods under Excel software. e following program structure can be followed when implementing the COM interface between OpenDSS and MATLAB.
Command Description DSSObj Define the server DSSObj. Text "Text" interface ensures easy access to the text interface command and result properties. For example, the DSS Text. command � "OpenDSS command" allows implementing any command of OpenDSS under MATLAB DSS Circuit. Solution "Solution" interface allows the use of the common command "DSS solution. Solve" to solve the circuit DSS Obj. Active circuit "Active circuit" interface is vital for obtaining various quantities via the COM interface DSS Circuit. Active CktElement "Active CktElement" interface allows access to the properties of any circuit element set in the program DSS Circuit. Active Bus "Active Bus" interface provides the properties of the active bus DSS Circuit. Generators "Generators" interface allows access to the generator collection in the active circuit DSS Circuit.
Results and Discussion is paper investigated the performance analysis of the proposed SI-CB control strategy during several PV penetrations in 11 buildings in the MG test model for two days from January 1 to 2, 2015. e MG test model was designed using the OpenDSS software platform with MATLAB. Our proposed SI-CB control strategy was verified via the following five cases. 6. 1. Case 1. Compare three approaches per unit (V) for 100% PV penetration in 11 buildings in the MG test model (Figure  12 ). 13 ).
Conclusion In this paper, a coordinate strategy based on SIs and CB control was proposed to mitigate the high PV energy penetration in a modified IEEE 123-node test feeder. e main contribution is to provide a comprehensive approach enhancing the proposed MG configuration in terms of voltage stability and ANSI standards. e proposed approach was achieved using the parallel processing of MATLAB and OpenDSS. us, several actual commercial buildings were connected to different buses, and actual weather data were considered to conduct a real feeder operation. Different cases of MG operation were considered for testing the robustness of the proposed algorithm for voltage mitigation. When the grid is connected to PV, the proposed strategy (100% PV with SI-CB) has slightly (2 kW) less than only SI control (100% PV with SI).
Study populations InterConnect was an EU-FP7 funded project which aimed to optimise the use of existing data by enabling crosscohort analyses within consortia without pooling of data at a central location ("http:// www. inter conne ct-diabe tes. eu/")  [34]. For the current study, the InterConnect Data Discovery registry (http:// www. inter conne ct-diabe tes. eu/ data-disco very/) and literature was screened to identify cohorts with suitable data like study populations representing the general population without prevalent T2D, dietary intake information (amount, frequency), incident T2D as outcome (self-report, objective measures), and information on the covariates age, sex, smoking, body mass index (BMI), waist circumference or waist-hip ratio, physical activity, alcohol consumption, education or occupation, family history of diabetes, other health exposures (cardiovascular diseases, history of previous illness).
Of 103 identified cohorts, 25 collaborating cohorts (Table  S1 ) contributed data to this project  [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] 37]. The Zutphen Elderly study also contributed data, but was excluded due to a too low number of cases  [37]. Other reasons for non-participation (Fig. S1 ) were failed contact (n = 46), no interest in research question (n = 10), insufficient data (n = 15) or no study capacity (n = 6). The collaborating cohorts  [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] 38]  included 13 cohorts from Europe, eight from the Americas (North and South America), three from Western Pacific (Australia, Republic of Korea), and one from the Eastern Mediterranean (Iran). All cohorts obtained ethical review board approval at the host institution and informed consent from participants.
Dietary assessment and construction of dietary patterns Dietary intake was assessed by food frequency questionnaires (FFQ) in most cohorts, by dietary history interview and a 24-h recall in one cohort each (Table  S1 ). For the present study food intake encoded in g/day was used. Some cohorts provided only standard portion sizes and frequency of consumed food items, which were converted into g/day. For some US cohorts, where information on portion size was not available, variable-specific standard portion sizes sourced from the United States Department of Agriculture  [39]  were used. The dietary data of all cohorts were then harmonized to form a set of food groups. For this purpose, the FGs used in the published DPs associated with T2D risk were compared. Based on this, a set of FGs was defined to be used across all published DPs (Tables  1, S2  and S3 ).
Assessment of covariates We defined a set of potential confounders to be used in analyses based on: (1) frequent usage in the studies of the 13 published T2D-associated DPs and (  2 ) availability across all participating InterConnect cohorts (Table  S4 ). The final set of confounders included: age at baseline (years), sex, body mass index (BMI) (kg/m 2 ), physical activity (PA, cohort specific items were used), education (cohort specific items were used), smoking (never, former, current smoker), alcohol consumption (g/day), hypertension (yes/no), and energy intake (kcal/day). The recorded data of confounders of the respective InterConnect cohorts were used and harmonized across all cohorts, if possible (Table  S5 ). All cohorts provided age in years, BMI in kg/m 2 , hypertension as yes or no.
Results In the present analysis, data from 390,664 participants across 25 cohorts with a median follow-up time ranging from 3. 8 to 25. 0 years were included (Table  2 ). Four cohorts included only women (EPIC-InterAct-France, Mexican Teachers' Cohort (MTC), Swedish Mammography Cohort (SMC), Women's Health Initiative Observational Study (WHI-OS)) and two only men (Cohort of Swedish Men (COSM), Puerto Rico Heart Health Program (PRPHH)). Participants from Coronary Artery Risk Development in Young Adults (CARDIA) study, MTC and Seguimiento University of Navarra (SUN) cohort were of younger age (24. 9-41. 8 years), whereas participants from other cohorts were older (49. 5-63. 1 years). The mean BMI ranged from 23. 9 kg/m 2 in SUN to 29. 3 kg/m 2 in EPIC-InterAct-Spain. During follow-up, 29,386 clinically incident cases of T2D were recorded for the primary outcome and 36,527 incident cases for the secondary outcome.
Acknowledgements We would like to thank the participants, principal investigators, and study teams of the individual cohorts included in this collaboration. The work presented herein was made possible using the OBiBa suite (http:// www. obiba. org), a software suite developed by  Maelstrom Research  (www. maels trom-resea rch. org), and DataSHIELD (http:// www. datas hield. ac. uk), a software suite developed by the Data to Knowledge ( D2K ) Research Group. We thank EPIC-InterAct collaborators and  Nicola Kerrison  at the  MRC Epidemiology Unit  for assistance relating to the  EPIC-InterAct dataset. We also thank the  Aus-Diab Steering Committee  for providing data from the AusDiab study. Moreover, we thank the  NIH Biologic Specimen and Data Repository Information Coordinating Center  and the  ARIC, CARDIA, MESA, PRHHP  and  WHI OS study  for providing data for this study.
A French Ethics Committee approved the study (IDRCB 2014-A01362-45), and it was also registered on ClinicalTrials. gov with ID: NCT02413632. From the participants included in this study, clinical and biological data, history of genital, anal or oropharyngeal STIs were collected. Laboratory analysis was performed to investigate genital, anal or oropharyngeal ASTI including syphilis chlamydia and gonorrhea infections (screening by PCR). Participants last HCV-antibody and HBV serology were collected from the patient's les. Participants completed also self-administered questionnaire, which collected patients' sociodemographic characteristics and data on behavioral, sexual practices information and STIs' knowledge.
Acknowledgements We thank all the participants included in this study. ANRS DRIVER Study Group  :  Dr Virgine Masse ,  Dr Genet  ( CH Victor Dupouy, d'Argenteuil) ,  Pr Laurence Weiss  ,  Dr Marina Karmochkine ,  Dr Jade Ghosn  ( CHU Hôtel Dieu, Paris ),  Pr Elisabeth Rouveix  ( CHU Ambroise Paré, Boulogne-Billancourt ),  Dr Antoine Chéret  ( CHU de Bicètre, Le Kremlin-Bicètre ),  Dr Julie Timsit ,  Dr Sébastien Fouéré  ( CHU Saint Louis, Paris ),  Dr Emmanuel Mortier ,  Dr Anne-Marie Simonpoli  ( CHU Louis Mourier, Colombes ),  Dr Loïc Bodard  ( Institut Mutualiste Montsouris, Paris ),  Dr Claudine Duvivier  ( CHU Necker, Paris ),  Dr Eric Froguel  ( Grand Hôpital de l'Est Francilien, Marne La Vallée ),  Dr Gilles Force  ( Institut Hospitalier Franco-Britannique, Levallois-Perret ),  Dr Pierre De Truchis  ( CHU Raymond Poincaré, Garches ),  Henintsoa Seheno Harijaona ,  Nicholas Renaud ,  Dr Svetlane Dimi ,  Dr David Zucman ,  Dr Eric Farfour  ( Hôpital Foch, Suresnes ),  Dr Nadia Voilin ,  Pr Pierre-Marie Girard  ( CHU St Antoine, Paris ),  Dr Greder-Belan  ( CH de Versailles, Le Chesnay ),  Dr Olivier Patey  ( CHI Lucie et Raymond Aubrac, Villeneuve St Georges ),  Dr Yves Welker  ( CHI Poissy St Germain, Saint-Germain en Laye ),  Dr Martin Duracinsky ,  Pr Olivier Chassany ,  Pr Isabelle Durand-Zaleski ,  Dr Cathérine Majerholc ,  Emilie Fleury ,  Christophe Lalanne ,  Maria-Patrizia Carrieri ,  Virginie Villes ,  Issifou Yaya ,  Cécile Goujard ,  Yann Quertainmont ,  Françoise Churaqui.
e emergence of intelligent connected vehicles (ICVs) is expected to contribute to resolving traffic congestion and safety problems; however, it is inevitable that ICV safety issues in mixed traffic (involving ICVs and human driven vehicles) will be a critical challenge. e numerical simulation of scenarios involving a mix of different driving profiles is expected to be an important safety assessment tool in the process of testing and validating ICVs, especially regarding extreme scenarios, including car collisions, which are rarely captured in real-world datasets. In this study, we propose a novel approach for car collision generation in numerical simulations based on the assumption that car collision occurrences are mostly associated with certain specific driver profiles. Using a dataset provided by the Next Generation Simulation (NGSIM) project, NGSIM 101 dataset, we identify three different driver profiles: aggressive, inattentive, and normal drivers.
In our work, we focus on driver profiles only in terms of how they react when directly behind another car (the car-following behavior). Several open datasets on vehicular trajectories are currently available and can be applied to studying and analyzing human driving behavior. Next Generation Simulation (NGSIM) has published four vehicle trajectory datasets (NGSIM  [9] ), which have been widely used in transportation research  [10]. More recently, datasets on motion trajectory of traffic objects detected by autonomous vehicle sensors have been published (e. g. , Waymo data  [11] , Argoverse  [12] , and nuScenes  [13] ). However, in these datasets, vehicle collisions are rarely observed. Nevertheless, the Strategic Highway Research Program 2 Naturalistic Driving Study (SHRP2-NDS)  [14]  provides a dataset where several accident situations are captured, yet seem unsatisfactory in terms of quantification of vehicle collision causes and impacts.
erefore, there is a lack of data on real-world vehicle collisions connected to human driving behavior, including a lack of both video data and vehicle trajectories in collision situations. On the other hand, traditional road safety analyses are based on statistical methods which aim to understand the importance of geometric (road shape) and track (road type) characteristics for safety or to locate collision black-spots, based on crash datasets  [15]. Crash datasets as mentioned in  [16]  provide only vehicle collision statistics, without involving car trajectories and driver profiles. A large body of the literature on road traffic safety explores the link between ambient traffic conditions and collision occurrences  [17]  and/or vehicle collision injury severity  [18]. On highways specifically, the authors of  [19]  provide evidence that vehicle collisions have a specific relationship with traffic flow characteristics (volume, density, and speed).
In this study, we propose a novel approach for car collision (collision between cars) generation in numerical simulation by varying the percentages of different driver profiles in the traffic, aiming at establishing a relationship between driving profiles and car collision occurrences. e profiles are extracted from the NGSIM database and integrated in the traffic simulator SUMO  [4, 27, 28]  using the calibrated intelligent driver model (IDM), which is one of the most human-like car-following models  [29, 30]. For the simulation, we used the IDM with an extension of driver reaction times. erefore, our main goals in this study are to extract driver profiles from real driving data and replicate them using a microscopic traffic model, establish a method of reproducing realistic traffic simulation based on microscopic road traffic modeling in the SUMO simulator, generate car collisions by appropriately varying the percentages of the driver profiles, characterize the relationship between the car collision occurrences with the driver profiles, and observe the severity of the generated car collisions.
Materials and Methods In this section, we present the relevant materials and the proposed method. e approach structure is shown in Figure  1. In this work, we study the driver profiles from car trajectories and propose the definition of an aggressive driver profile as a driver who always leaves short time headways with respect to the leading vehicle and an inattentive driver profile as a driver with particularly long reaction times compared to other drivers. We classify all drivers with intermediate values of reaction time and time headway as the normal profile. e selection and thresholds for the three driver profiles were based on the NGSIM 101 dataset  [9]. After the classification of the driver profiles, the specific driver profiles (aggressive and inattentive) are represented using the calibrated IDM model (the existing IDM model with an extension of reaction time), and the road traffic is then simulated using SUMO ("Simulation of Urban MObility")  [28].
e IDM model calibration is performed using a genetic algorithm to find the optimal set of IDM parameters with an objective of minimizing the predefined error between real driver trajectory and the output of the IDM model. In essence, we artificially increase the percentage of drivers with extreme profiles (aggressive or inattentive) and then count and analyze the car collisions generated by traffic simulation. In the numerical simulation experiments, we propose 4 different experiments using different combinations of driver groups. e remaining sections of this study are structured as follows: in the rest of this section, from Subsection 2. 1 to 2. 4, we present the necessary elements we predefined for our method of vehicular collision generation, including (1) the presentation of the NGSIM 101 dataset, (2) the proposition of different driver profiles, (3) SUMO traffic simulator and the choice of car-following model (IDM model), and (4) the method of IDM model calibration to present different driver profiles.
In Sections 3-5, we present the specific driver profiles in the NGSIM 101 dataset, the calibration of the IDM model for the specific drivers, and the simulation experiments description. Section 6 presents the results obtained from numerical simulations and the investigation of the relationship between generated car collisions and driver profiles. Section 7 presents the validation of the approach with a different part of data in the NGSIM 101 dataset. Section 8 deals with the severity of simulated car collisions with respect to different driving profiles. Finally, Section 9 summarizes major findings and provides recommendations for further research. [9]  is an open dataset released by the US Federal Highway Administration (FHWA). On a highway section in Los Angeles, California, covering 640 meters of length, all vehicle trajectories are provided with a rate of 10 Hz, and all data were collected during the rush hour from 7 : 50 a.
m. to 8 : 35 a. m. is section has 5 normal lanes and one auxiliary lane connecting an on-ramp and an off-ramp. In our study, we focused on the car trajectories only in the 5 main lanes. In addition, the whole NGSIM 101 dataset is divided into three subsets of 15 minutes, which cover the traffic from 7 : 50 a. m. to 8 : 05 a. m. , from 8 : 05 a. m. to 8 : 15 a. m. , and from 8 : 20 a. m. to 8 : 35 a. m. In Figure  2 , we present traffic volumes and speeds during each of the three time periods. It can be observed that in the beginning of the first 15 minutes, the mean speed is between 10 and 15 m/s. After that, traffic becomes denser resulting in a sudden fall of the mean speed. In the second and third 15minute time periods, traffic is more congested and the mean traffic speed varies between 5 and 10 m/s.
NGSIM 101 Dataset Description. NGSIM 101 In this work, we applied the proposed approach to the first 15-minute dataset. e second 15-minute dataset was then used for method validation. In our approach, we are only interested in understanding driving profiles in terms of car-following behavior, without considering lane change or other behaviors. After processing the data of the first 15minute time frame, we selected approximately 1500 car trajectories (drivers) from a total population of 1993 vehicle trajectories to extract specific driver profiles. e selection was based on the following condition: each car must always have a leading car whose trajectory is continuous for at least 40s. e same data processing was made for the second 15minute of data and resulted in the selection of 1300 car trajectories out of a total of 1495.