dataesr/scientific-paragraphs-categorization huggingface dataset:
052. For attack duration, all pairwise comparisons: P < 0. 028, except for France vs. Germany-Austria (P = 0. 216), France vs. UK (P = 0. 800), Germany-Austria vs. Italy (P = 0. 335), Germany-Austria vs. Spain (P = 0. 231), Germany-Austria vs. UK (P = 0. 274) and Italy vs. Spain (P = 0. 738). Time to events were only calculated for attacks with complete data for time to administration, time to resolution and attack duration. consensus documents have been established in recent years for HAE to raise clinical awareness of the characteristics of C1-INH-HAE and the expanding range of treatment options available. 10, 13, 14  wide variation in baseline attack frequency was observed across all regions. The different rates of long-term prophylaxis use, potential regional variation in attack triggers or trigger avoidance behaviour could contribute to this result.
Similarly, self-reported severity of icatibant-treated attacks was highly variable. The difficulty of assigning attack severity as a surrogate measure of disease severity has been reported,  15  as with no validated measure of attack severity, such is open to patient interpretation of the limitation on activities of daily living. Overall, treatment outcome data confirm icatibant as an effective acute treatment option for C1-INH-HAE in a real-world setting across all countries assessed. Earlier administration of icatibant resulted in earlier attack resolution, supporting both previous outcome data derived from IOS  8, 9  and previous comparison of IOS data with clinical trial data. 16 mportantly, treatment outcomes revealed variation in practice, as there were significant differences in time to administration, time to resolution and attack duration between countries.
A notably consistent feature across all countries assessed was the majority use of icatibant self-administration; this has been previously reported. 9 These administration data are consistent with phase 3 open-label extension data that support self-administration of icatibant in clinical practice. 17 Furthermore, a high rate of self-administration is in keeping with the 2011 indication change to allow icatibant self-administration and would suggest IOS patient acceptance of icatibant self-administration. This outcome also be attributed to the effective adoption among the clinical community of the recent international guideline  14  and national consensus documents  13, 15, 18  that promote early treatment. The country-specific differences in self-administration vs. HCP administration may be due in part to physician advice, the aforementioned availability of icatibant or combinations thereof.
Spain (P = 0. 894) and Italy vs. Spain (P = 0. 053). For HCP administration: time to treatment all pairwise comparisons P ≥ 0. 051 except for France vs. Spain (P = 0. 043) and Italy vs. UK (P = 0. 028). For time to resolution, all pairwise comparisons: P ≥ 0. 052. For attack duration, all pairwise comparisons: P < 0. 028, except for France vs. Germany-Austria (P = 0. 216), France vs. UK (P = 0. 800), Germany-Austria vs. Italy (P = 0. 335), Germany-Austria vs. Spain (P = 0. 231), Germany-Austria vs. UK (P = 0. 274) and Italy vs. Spain (P = 0. 738). Time to events were only calculated for attacks with complete data for time to administration, time to resolution and attack duration.
Acknowledgements IOS is funded and supported by  Shire International GmbH, Zug, Switzerland. Medical writing support was provided by  David Lickorish, PhD , CMPP, of  Excel Scientific Solutions  and funded by  Shire Human Genetic Therapies. IOS investigators and their staff: Austria: W. Aberer; Italy:  F. Arcoleo ,  M. Bova ,  M. Cicardi ,  E. Cillari ,  V. Montinaro ,  G. Marone ,  A. Zanichelli ; France:  C. Blanchard Delauny ,  I. Boccon-Gibod ,  L. Bouillet ,  B. Coppere ,  C. Dzviga ,  O. Fain ,  B. Goichot ,  A. Gompel ,  S. Guez ,  P. Y. Jeandel ,  G. Kanny ,  D. Launay ,  H. Maillard ,  L. Martin ,  A. Masseau ,  Y. Ollivier ; Germany:  M. Maurer ,  M. Magerl ;  Spain: M. L. Baeza ,  T. Caballero ,  R. Cabañas ,  M. Guilarte ,  D. Hern andez ,  C. Hernando de Larramendi ,  R. Lleonart ,  T.
This article presents a fuel consumption model, SEFUM (Semi Empirical Fuel Use Modeling), and its comparison with three models from the literature on a 600 km experimental database. This model is easy to calibrate with only a few required parameters that are provided by car manufacturers. The test database has been built from 21 drivers who drove in two conditions (normal and ecodriving) on a 15 km trip. For the model evaluation, three indicators have been selected: instantaneous fuel use root mean square error, cumulated error and computation time in order to evaluate the accuracy both in cumulated and instantaneous fuel use and to estimate computation time of each model. Results tend to prove that the model is able to compute rapidly (maximum of 1500 simulated kilometers under Matlab) in comparison to all other models while ensuring a high accuracy and precision for cumulated and instantaneous fuel use.
These algorithms, often relying on operational research methods, are time consuming, and each of its calculation step calls the fuel consumption model of the considered vehicle. Furthermore, the complexity of the computation increases with the predicted distance. Then, fast computing models are needed for database enrichment. Databases containing speed profile can be enriched with modeled fuel consumption data. These databases can be constituted of million of kilometers, at medium frequency (20Hz). Computing fuel use from these data can be time consuming and may generate server latencies. Furthermore, these models can be used for in-line fuel use estimation on nomadic devices. Smartphone, tablets, GPS and other nomadic devices are more and more used in vehicles for navigation or driving assistance purposes.
State of the art models A wide range of fuel consumption models is available in the literature. They vary mainly depending on the vehicle from motorcycle to heavy duty vehicles but also according to the engine type  (thermal, electric, hybrid. ). In this study the case of the Internal Combustion Engine (ICE) of lightweight vehicles will be analyzed. There exist different ways of classifying fuel consumption models. The first way defines two main categories. • The first being Forward facing, where the complete powertrain from the gas pedal to the wheel is modeled and a simulated driver regulates acceleration and deceleration to fit a provided speed profile. A well-known "forward-facing" model is ADVISOR developed under Matlab by the US Department of Energy  (Markel et al. , 2002). • The second category, called Backward facing uses the speed profile as input to compute the different forces from the wheel to the engine (gravitational, inertial, rolling resistance, aerodynamic drag, friction forces) and then estimates the fuel use with a transfer function depending on the vehicle type.
It is computed from equation 4. RMS E = 1 N N i=1 (dE meas -dE mod ) 2 , ( 4 ) where dE meas is the measured instantaneous fuel use, dE mod , the modeled instantaneous fuel use and N the number of elements in the dE vector data. • Computation time: Measured from the timeit method under Matlab, the execution time is the time required by the fuel use model to provide a result on one experimental trip. The timeit function runs the same simulation several time and computes an average of all simulations durations.
This model is the only compiled model tested in this study which might have an impact on the interpretation of the computation time. On the other hand, as the model is compiled, it requires a parser function to read the activity and configuration files which could slow down the computations. In the next sections, it has been assumed that the gain from the compiled code is lower than the parsing delays. CMEM has been used in several studies and commercial software to predict emissions. It requires a wide range of parameters that needs to be extracted from vehicle datasheet. For this study vehicle, 12 parameters needed to be calibrated. However, when using a precalibrated vehicle, the process only consists in defining a vehicle category.
Experimental dataset The experiment goal was to clearly identify two classes of driving behaviour on the same test track: "normal" and fuel efficient way to drive commonly known as "ecodriving". Twenty one drivers participated in this experiment that took place in June and July 2009 in Ponchartrain (Yvelines) in France. In order to minimize traffic influence, the chosen route is of inter-urban type and a length of 15 km. The trips were all performed under free flow conditions and with dry weather. First of all, the journey is discovered by the subjects while seeing the experimenter driving and giving safety and direction instructions. Then, the trip was driven twice by each driver: once while driving normally, and secondly while following the "Golden Rules" of ecodriving extracted from the Ecodrive project (Ecodrive, 2009): • Change Gear upwards as soon as possible: Shift up between 1.
Test site road description Experimental data have been acquired on road containing interurban and urban networks in the region of Paris in France. This road has been selected to represent a wide variety of travels on various road characteristics (road grade, curvature,. ). The trip is 15 km long. Road parameters have been measured using a dedicated instrumented vehicle, called VANI (Vehicle for ANalysis of Itineraries). This vehicle has been validated over a large range of roads in France. For instance, an important measurement is the road slope, as provided in figure  3. This figure presents how the slope varied along the trip and this information is taken into account in every fuel use modeling tested in this study. In the case of this route, the slope varies approximately from -5% to 5% which represents a hilly road in France.
Test vehicle The vehicle used was a petrol-driven Renault Clio III with a 5 gears manual gearbox from 2008 which is representative of the french private passenger car fleet. The vehicle has been equipped with a VECTOR box CAN datalogger direclty connected to a Windows PC. Data is collected through RTMaps software at a 20 Hz frequency. Fuel measurement reference is the CAN signal at a 80 mm 3 resolution. Other logged signals are the vehicle longitudinal speed, acceleration, RPM and gas pedal position. The vehicle has been also equipped with a GPS, a camera in front of the vehicle and a fuel flow meter DFL1x-5bar used to validate the fuel consumption logged with the CAN. Four of these drivers were ecodriving instructors while others were recruited among one thousand persons working in two different research institutes.
Akçelik's Akcelik's model (figure  5 ) showed medium performances in instantaneous error with an average RMS E of 4. 9 • 10 -5. Also, the standard deviation of the RMSE is 1. 9 • 10 -5. On the other 10 hand, the cumulated fuel use error is of 0. 15 L that is acceptable considering the standard deviation of 0. 06 L. Also, the cumulated error is positive, indicating that the modeled fuel use is overestimating real consumption. Computation time is low with average of 8. 5 ms and a standard deviation over the 42 trips of 1. 1 ms. This very low computation time enabled this model to be implemented in commercial traffic simulation softwares such as Aimsun (TSS).
Synthesis The computation time varies from a few milliseconds to 0. 16 seconds according to the model. CMEM model is the slowest but it is the only one to be a compiled model writing and reading text files to run. It is probable that the computation time would be lower if the model was implemented under Matlab as the other ones. On the other hand, SEFUM and Akcelik models have a very low computation time with SEFUM being the fastest in all situations. Detailed comparisons on all 21 trips can be seen on figure  8  RMSE for instantaneous fuel use represents how much the model represents the experimental data of fuel use (figure  9 ). This RMSE is higher for Akcelik model showing that this is less able to model instantaneous fuel use. On the other side, SEFUM and CMEM models have RMSE about 3.
Discussions In this study, a Semi Empirical Fuel Use Modeling (SEFUM) has been presented and evaluated in terms of computation velocity and results accuracy (both instantaneous and cumulated fuel consumption). Results showed that SEFUM has the capacity to estimate cumulated and instantaneous fuel consumption as accurately as Qi's model that was purely designed for the test vehicle. SEFUM is also the quickest of all tested models knowing that CMEM has the disadvantage, in our methodology, to be a compiled model that needs to be called externally by Matlab. In instantaneous estimation, the model is the second less accurate proving that strong assumption taken had an influence on results. However, SEFUM is more accurate than Akcelik's modeling that was used for decades in traffic simulation. These results still need to be discussed as several drawbacks arise from this study.
Conclusions This article has presented a a fuel consumption model, SEFUM, and its comparison with three models from the literature on a 600 km experimental database. Three indicators have been selected in order to evaluate the accuracy both in cumulated and instantaneous fuel use and to estimate computation time of each models. Results show that SEFUM is accurate and precise in cumulated fuel use and is efficient enough for instantaneous estimations although there were no significant differences with other models appart from Akcelik model. SEFUM has also shown the lowest computation time enabling this model to be implemented for in-line computations in vehicles, in traffic or driving simulators as well as in big data analysis algorithms. Future works will concentrate on testing this model on several vehicles categories.
, 2013)  and, in consequence, make the base of branches a likely ice nucleation site. Additional freezing experiments in the laboratory, where the direction of ice propagation was determined in excised branch segments, could not confirm this pattern consistently (data not shown). However, these laboratory experiments could not simulate the microclimatic effect of dense needle cover in the distal portion of the branch, which might have buffered the temperature decrease. Once nucleated, the ice front can propagate after the exothermal heat has dissipated through the bark  (Shibkov et al. , 2003) , whereby propagation rates within the water column are proportional to the degree of supercooling  (Langer et al. , 1978; Rauschenberger et al. , 2013). In excised branches of P. abies, propagation rates similar to those found in this field study were observed (2 and 4 mm s 21 for 1.
Temperature Measurements Xylem and air temperature were monitored using copper-constantan thermocouples connected to a data logger (1000 Series Squirrel Meter/Logger; Eltek) at 30-to 60-s intervals. Thermocouples were installed in the xylem at 0. 5-to 1-cm depth at six positions, along the main trunk (apex [T1] to base [T5]) and on a side branch (B), and attached using Terostat putty (Henkel).
Infrared Imaging Surface temperature was recorded using a thermal infrared camera (Jenoptik VarioCam HR; InfraTec) at ;7 m distance from the trees. This camera is sensitive to radiation at 7. 5 to 14 mm wavelength of the electromagnetic spectrum with a temperature resolution of less than 0. 08 K. Thermal images (thermograms) were recorded at 0. 025 m focal length, resulting in 384-3 288-pixel images at ;0. 01 m 2 pixel 21 spatial resolution. Thermograms were recorded every 5 s and processed using IRBIS 3 plus software (InfraTec).
UE Analysis UEs were monitored on tree 1 with a four-channel USB-based system (1283 USB AEnode, 18-bit A/D, 20 MHz) and 150-kHz, 26-dB preamplified resonance sensors (PK15I; all components from Physical Acoustics) set to a 20-dB threshold. On tree 2, an autonomous AE system device was installed, also equipped with four 150-kHz, 26-dB preamplified resonance sensors (PK15I; all components from Mistras Holdings). Registration and analysis of ultrasonic events were performed with AEwin software version E. 4. 40 (Mistras Holdings). Acoustic sensors were attached using spring-loaded clamps at three positions along the trunk (T2, T3, and T5) and on a side branch (B; Fig. 1 ) after removing ;1 cm 2 of bark and covering the exposed xylem with silicone grease to ensure acoustic coupling and prevent dehydration.
Microdendrometers Diurnal and longer term variations in stem diameter were monitored using dendrometers on both trees during late winter 2015. A PépiPIAF system (Forest Future) was installed in the upper third of the main axis (between T2 and T3; Fig. 1 ) of each tree. Each unit contains a linear variable differential transformer sensor (sensitivity, 61 mm) connected to a data logger recording at 5-min intervals.
They used the same mitochondrial marker as that of Bounamous et al. [6] , called Cytb-CB3, in order to compare their sequences with those available in GenBank. They also sequenced a longer fragment of Cytb (called Cytb-CB) and the nuclear elongation factor-1alpha gene (EF-1a)  [30, 41]. Their molecular results did not match with the morphological identification, not only for the intermediate specimens, but also for the differentiation between Ph. chabaudi and Ph. riouxi: all specimens were clustered in the same clade. According to these results, based on specimens from the single locality of Ghomrassen, they proposed to consider Ph. riouxi as a junior synonym of Ph. chabaudi. In order to better understand the situation, we decided to broaden the approach by performing a comparative and combined sequence analysis of three loci on larger samples from different geographical populations we previously investigated.
For each processed specimen, the number of coxite lobe setae was counted and morphometrics analysis, using Stream Motion 1. 9. 1 software (Olympus, Japan), was also applied. In 1998, it was suggested that the number of setae, and the width and length of the coxite lobe were informative to differentiate the species  [10]. The width measure did not cause any difficulty: a transversal line was perpendicularly traced in the larger part of the lobe. Considering the difficulties in measuring the length of the basal lobe of the coxite, we substituted it by its perimeter and area, as indicated in Figure  2.
Morphological identification All of our specimens were correctly identified as Ph. chabaudi and Ph. riouxi, according to morphological criteria of initial descriptions for males  [9, 10]  and to the pharyngeal armature for females  [6]. All females were morphologically identified based on the presence or absence of anterolateral teeth in the pharynx. All measures related to the coxite lobe are listed in Table  2. Considering these data, we found significant interspecific differences between all measurements of the basal lobe of the coxite, without significant geographical variability (data not shown). We did not find any morphological intermediate specimen. All Ph. riouxi were found in the south of Tunisia and in the south of Algeria.
Cytochrome b analyses For the 56 specimens successfully amplified, the length of the cytochrome b fragment Cytb-CB used for analysis was 628 bp. The phylogenetic tree obtained by adding 17 haplotypes of Tabbabi's data  [41]  highlighted two distinct clades (Figure  3 ). The first one was only composed of Ph. chabaudi as supported by a bootstrap value of 88. 6%, with a geographical subdivision between Algeria (bootstrap value < 50%) and Tunisia (bootstrap value = 97. 4%). The second clade was composed of a mix of our own Ph. riouxi and the 17 haplotypes of Tabbabi from the Ghomrassen area, identified as Ph. chabaudi, Ph. riouxi and intermediates. For this clade, the bootstrap value was lower (59. 6%). Within this clade, the specimens were also subdivided into two clusters according to the geographic area, i.
This low divergence is confirmed by the low bootstrap values observed in Cytb phylogeny for the Ph. riouxi branch (Figure  3 ), in EF-1a phylogeny for the Ph. chabaudi branch (Figure  4 ), and in D1D2 phylogeny for the Ph. chabaudi branch (Figure  5 ). The phylogeny of the concatenated genes revealed bootstrap values above 70% for the two branches, suggesting that added data can only increase the differentiation between the two species. When we consider the two phylogenies including the sequences published by Tabbabi et al. [41] , i. e. Cytb and EF-1a phylogenies (Figures  3  and 4 ), all Tabbabi's sequences are included in the Ph. riouxi branch, without clear distinction between our specimens and Tabbabi's specimens. From these data, it is difficult to explain the disagreement between morphological characters and molecular data observed by Tabbabi et al.
At this stage, the stack will be "floating" on the PPC film, which can be completely evaporated in a vacuum annealer at 350 o C for around 20 min. Followed by standard lithography and metallization. MoS 2 flake is half covered by 1∼3 layer h-BN, and Au electrode with thickness of 20 nm is deposited onto the stack, forming conventional direct contacts and tunnel contacts, respectively. Electronic transport was measured on a Cascade probe station at room temperature, and in a Quantum Design PPMS system with a home-made sample probe interfaced with external measurement setup at low temperatures, respectively. The device simulations in this work are carried out by using the first-principles software package Atomistix ToolKit, which is based on density-functional theory in combination with the nonequilibrium Greens function  [40].
This work is supported by the National Natural Science Foundation of China (NSFC) with Grant 11504385 and 51627801, and is supported by the National Basic Research Program of China (973 Grant Nos. 2013CB921900, 2014CB920900). X. W. Jiang acknowledges supports by the NSFC Grant 11574304, Chinese Academy of Sciences-Peking University Pioneer Cooperation Team (CAS-PKU Pioneer Cooperation Team), and the Youth Innovation Promotion Association CAS (grand 2016109). D. M. Sun thanks the NSFC grant 51272256, 61422406, 61574143, and MSTC grant 2016YFB04001100. J. H. Chen thanks the NSFC Grant 11374021. Z. D. Zhang acknowledges supports from the NSFC with grant 51331006 and the CAS under the project KJZD-EW-M05-3. V. Bouchiat acknowledges support from the EU FP7 Graphene Flagship (project no. 604391), J2D project grant (ANR-15-CE24-0017) from Agence Nationale de la Recherche (ANR), and the Hsun Lee Award program of the Institute of Metal Research, CAS.
ACKNOWLEDGEMENT This work is supported by the National Natural Science Foundation of China (NSFC) with Grant 11504385 and 51627801, and is supported by the National Basic Research Program of China (973 Grant Nos. 2013CB921900, 2014CB920900). X. W. Jiang acknowledges supports by the NSFC Grant 11574304, Chinese Academy of Sciences-Peking University Pioneer Cooperation Team (CAS-PKU Pioneer Cooperation Team), and the Youth Innovation Promotion Association CAS (grand 2016109). D. M. Sun thanks the NSFC grant 51272256, 61422406, 61574143, and MSTC grant 2016YFB04001100. J. H. Chen thanks the NSFC Grant 11374021. Z. D. Zhang acknowledges supports from the NSFC with grant 51331006 and the CAS under the project KJZD-EW-M05-3. V. Bouchiat acknowledges support from the EU FP7 Graphene Flagship (project no.