dataesr/scientific-paragraphs-categorization huggingface dataset:
The likelihood of persistence and other data for the project were generated using expert elicitation and background information from threat and species databases (see subsection 'Data collection'). For a species group, our benefit metric (u in Eqs. (  1 ) and (  2 )) is the change in the probability of persistence of a 'typical' species within a species group as a result of the management action. For a KTP, the benefit metric is a weighted average of the species group benefits for all groups affected by the KTP as a result of the management action, where weights are determined according to the number of species in each species group. In this study, we defined the benefit metric as u(i) = (b i -b i0 ) , where b i is the expected probability of persistence of a typical species from the group (or KTP, depending on the level of aggregation) after taking management action to reduce threat i and b i0 is the expected persistence if no management action is taken.
Linear response functions assume that benefit is received in proportion to the effectiveness of the KTP. The response functions were generated from experts by eliciting the expected persistence with and without effective KTP management, as well as the minimum level of effectiveness required before persistence of the species group begins to increase and the level of effectiveness required to achieve maximum persistence of the species in the species group. Values of Pr b i jθ i ð Þcan be interpolated from the response function (Fig. 10 ). Data collection. Groups of listed threatened species affected by KTPs were collated by OEH staff, who referred to the NSW Threatened Species Profile Database and the species contained in each KTP listing to help assign species to KTPs. Species could be assigned to multiple KTPs.
For the questions about persistence of the species group with and without management, we used existing information to provide initial values. Species were assigned a preliminary probability of persistence under no management using expert-derived estimates from the SoS database. Persistence if the KTP is removed was estimated using the estimates of relative threat importance contained in an expert-derived OEH dataset which scores threats to each species as either high, medium or low. Where a threat was not included in the dataset, it was given a medium score. These scores were weighted using the threat impact scores contained in the IUCN Threat Classification Scheme (V3. 2) (http://www. iucnredlist. org/technical-documents/classification-schemes/threats-classificationscheme). The weighted scores were used to calculate the relative threat importance by dividing the weight of the threat by the sum of threat weights across all threats affecting the species.
a Yields are calculated based on the reacted/converted starting material and isolated pure material. b Full conversion was never observed even for increased excess of used reagents (RX, base and KI). c For shorter reaction times mono-alkylated side products have been observed and isolated beside the O,O-di-alkylated products. Changing the potassium carbonate for sodium hydride in DMF at 120 ˚C improved the yields from 11% to 19%, whereas an increase in temperature (up to 140 ˚C) together with changing the solvent gave essentially the same yield in NMP and a slight ly higher value of 25% in DMSO. Adding dropwise the alkyl reagent during the reaction gave lower results every time. For the same alkyl reagent in DMF/K2CO3 at high temperature, SciFinder database 3 gives eight publications with yields superior to 25%, the highest being of 56%.
To date, spatial networks have been derived from a variety of tracking technologies including radio tracking or satellite transmitters (e. g. Shimazaki et al. , 2004)  and most commonly acoustic telemetry (e. g. Jacoby, Brooks, Croft, & Sims, 2012; Lédée, Heupel, Tobin, Mapleston, & Simpfendorfer, 2016). Like most networks, movement networks evolve over time and are often derived from a subsample of individuals from a population for which tracking data were collected. Most spatial and movement ecology studies that employ network analyses have been restricted to monolayer networks, in other words, to a single, aggregated network layer. In fact, the traditional approach has been to build individual movement networks, but then to either compare network metrics between individuals using traditional statistics or null models or to aggregate each individual network into a single graph representing a subgroup of animals (e.
Empirical example from acoustic tracking of sharks Having outlined the rationale behind the integration of multilayer networks and multi-centrality approaches to movement ecology, we apply these methods to an empirical example of sharks tracked inside an acoustic array of monitoring stations. Movement data from grey reef sharks (Carcharhinus amblyrhynchos, Bleeker 1856) and blacktip reef sharks (Carcharhinus melanopterus, Quoy & Gaimard, 1824) at three offshore reefs (Heron, Sykes and One Tree) located in the southern Great Barrier Reef in Australia  (Heupel, Lédée, & Simpfendorfer, 2018)  were analysed using this methodology. The data were gathered as part of a long-term field study into the ecology of large predators along the coast of Australia. Open acoustic data were sourced from the Integrated Marine Observing System's Animal Tracking Facility (IMOS ATF, www.
imos. org. au). Specifically, we analyse the movement patterns of 24 individual grey reef sharks and 20 blacktip reef sharks fitted with long-life V16 acoustic transmitters. Grey reef sharks were monitored over a period of 13 months between 13 th April 2012 and 14 th May 2013 (34 047 detections) within an array of 48 VR2W hydrophone receivers (VEMCO, Halifax, Nova Scotia). Blacktip reef sharks were monitored over a period of 10 months between 31 st August 2013 and 16 th June 2014 (3 888 detections) within an array of 50 VR2W hydrophone receivers. Receivers detect acoustic transmitters at an approximate range of 270 m; each time transmitters are detected, the identification number, date and time are recorded by the receiver (more details can be found in  Heupel et al. (2018) ). Applying network theory to acoustic telemetry data allowed the movement of sharks to be viewed as a system of connections, in which acoustic receivers are linked by shark movements (for further details on network theory and how it is applied to telemetry data, see  Jacoby et al.
ACKNOWLEDGMENTS We are grateful to  Dr. Michelle Heupel  for the use of the Blacktip Reef and Grey Reef sharks datasets. The Blacktip Reef and  Grey Reef  sharks datasets, and Heron Island acoustic receivers information were sourced as part of the  Integrated Marine Observing System (IMOS) Animal Tracking facility-IMOS  is supported by the  Australian Government  through the  National Collaborative Research Infrastructure Strategy  and the  Super Science Initiative. The research on Blacktip Reef and  Grey Reef  sharks movement was conducted under research permits from the  Great Barrier Reef Marine Park Authority  ( G10/33754. 1  and  G10/33758. 1 )and its funding was provided as part of a  Future Fellowship  (# FT100101004 ) to  Michelle R. Heupel  from the  Australian Research Council ; additional funding was provided by the  Australian Institute of Marine Science (AIMS).
However, these papers presented global parameters, mainly efficiencies but did not provide any aerodynamic maps of Mach number or velocities, and the experimental uncertainties were not included. Furthermore, all these experimental studies do not present the detailed internal flow structure and in particular the near-wall flow behaviour that would be extremely useful to validate numerical models. There is still a need for more detailed experimental work on ORC turbines to provide flow visualisations for validation of numerical models. This lack of experimental data for code validation is even more critical for high-density radialinflow turbines. As outlined by Congedo, et al. [16] , the optimised design of ORC turbines relies on the availability of accurate and efficient CFD tools due to lack of available experimental data.
Due to their importance in accurately predicting turbine performance, understanding the effect of EOS uncertainties becomes critical to better design turbines. Various Equations of State (EOS) for these high-density gases are available in the literature  [35]  as presented in Table  1. According to these studies, the EOS were considered the most accurate for their specific study. However, the research gap is that the reasons for choosing a suitable EOS for high-density fluid in ORC application have not been thoroughly investigated. This paper will fill this research gap by investigating the sensitivity of three EOS on the performance of an ORC turbine. Furthermore, most EOS derive from the classical mean-field theory, they not only suffer uncertainties in the mathematical model itself, but also in the errors in measurement of the EOS data around the fluid's critical point.
In order to better understand these results, the fluid properties of R143a are investigated and the pressure-temperature curve for R143a from REFPROP presented in Figure  16. As shown in Figure  16 , the fluids are classified in the following manner: the subcritical vapour, supercritical vapour, subcritical liquid, supercritical liquid, and mixed subcritical vapour/subcritical liquid. These different colour codes are divided by the critical point where the critical temperature and critical pressure and the liquid line are obtained using the REFPROP database  [65]. It is noticed that there is a region comprising mixed subcritical vapour and subcritical liquid, and this temperature range is between 300K and 345. 86K for the R143a fluid. While investigating the temperature field in the turbine obtained with the HHEOS for all the stochastic cases at   = 380K, it was observed that the temperature in the rotor was below the 345.
5_AM, and Ag/TNT-2. 5_IN. Electroless and NaBH 4 reduction methods led to the creation of Ag NPs in the case of both lower and higher AgNO 3 solution concentration. The particle size distribution data in the insets in Figure  1d,f-l  indicate the presence of NPs with sizes between 1-10 nm on TNTs. The sizes ranging from 2 to 10 nm, 1 to 8 nm, 1 to 9 nm and 2 to 4 nm corresponded to Ag NPs present in Ag/TNT-100_AM, Ag/TNT-100_IN, Ag/TNT-2. 5_NB, Ag/TNT-100_NB, respectively. In the case of Ag/TNT-2. 5_EL (0. 1), Ag/TNT-100_EL (0. 1), Ag/TNT-2. 5_EL (1), and Ag/TNT-100_EL (1), the determined particle size (2 to 10 nm, 2 to 7 nm, 2 to 8 nm, and 2 to 10 nm, respectively) refers to both Ag and SnO 2 identified on the surface of the samples processed by the electroless method. The TEM images also demonstrate the difference between physical and chemical Ag deposition processes (Supplementary Materials, Figure  S1 ).
Considering that the surface concentration of silver w presented in atomic percent, the direct correlation of these data with the silver concentrations measured by ICP-OES method is not possible. However, a general relation of these concentrations between the samples is kept (Figure  S4 ). The high-resolution XPS spectra were analyzed to elucidate the chemical state of silver atoms formed by different types of preparation methods. The XPS Ag 3d spectra (Figure  S5 ) have virtually identical positions of the maximum and a very symmetric spectrum envelope. They contained two spin-orbit components: 3d 5/2 and 3d 3/2 located at the binding energy of 368. 3 eV and 374. 3 eV, respectively. The full-width at half maximum (FWHM) of these components was also identical for all samples and amounted to 1.
Acknowledgments This work has been supported in part by the  US National Science Foundation (NSF)  under Grant Nos. ECCS-1509361 , -1808976, and -1809099 and by the  United States-Israel Binational Science Foundation (BSF)  Grant No. 2017694. The authors declare no competing financial interests. Yaron Bromberg  is a senior lecturer at the  Physics Department, Hebrew University of Jerusalem. He received his PhD in 2010 from  Weizmann Institute of Science  working on quantum walks in correlated photons. His research interests are in the fields of quantum optics and complex photonic media. Brandon Redding is currently a research physicist at the  US Naval Research Laboratory , where he works on fiber optic sensors, computational sensing, and coherent LIDAR. He received his PhD from the  University of Delaware , working on integrated silicon photonics before working as a postdoc at Yale University on light generation and sensing in complex and disordered systems.
Methods We performed a retrospective cohort study using a convenient sample of consecutive patients who underwent EVAR surgery at two tertiary reference centres in Canada and Poland. Patients aged 18 yr or older undergoing EVAR were considered eligible for the study. Study personnel extracted data from hospital charts and entered these data in case report forms. Baseline characteristics, medications, duration of surgery, preoperative and postoperative hemoglobin and creatinine levels, as well as postoperative outcomes were collected up until patient's hospital discharge following the index EVAR intervention. Screening for myocardial injury was performed routinely in every patient after the procedure as per standard of care at participating centres; troponin level was measured at least once in the first three postoperative days.
Local Research Ethics Board (REB) approvals were obtained (October 5, 2012 at the Canadian centre and December 27, 2016 at the Polish centre) before data were extracted. Individual patient consent was not obtained as it was not required by local REBs. The primary outcome was myocardial injury occurring after an EVAR procedure and up to hospital discharge. Myocardial injury was defined as a troponin elevation above the 99th percentile upper reference limit for troponin I, and where troponin T was measured, we used the thresholds established in the VISION studies. 12, 19 efinitions and cutoff used for each type of troponin are reported in the eAppendix (available as Electronic Supplementary Material [ESM]). A high and ultrasensitivity troponin assay was used in 140 (52. 5%) of eligible patients.
Results A total of 267 patients who underwent EVAR were enrolled (170 from Poland and 97 from Canada). Patients baseline characteristics including perioperative data are shown in Table  1. The participants were predominantly elderly males (86. 5%), and commonly had a history of smoking (77. 9%), coronary artery disease (60. 3%), and congestive heart failure (30. 0%). Overall, the most common cardiovascular complication was myocardial injury (78/267; 29. 2%; 95% CI, 24. 1 to 34. 9) (Table  2 ) of which 18/78 (23. 1%) met the Universal Definition of Myocardial Infarction. 23 Acute kidney injury occurred in 9. 4% (25/267; 95% CI, 6. 4 to 13. 5) of patients. No patient required new renal replacement therapy (i. e. , dialysis) after their EVAR procedure. Other postoperative cardiovascular events included new atrial fibrillation (4.
Acute kidney injury Characteristics of patients who developed AKI compared with characteristics of patients without AKI are presented in Table  1. The majority of patients with postoperative AKI (24/25, 96%) met the stage 1 AKIN criteria (i. e. , increase in serum creatinine C 27 lmolÁL-1 or C 1. 5-fold compared with baseline level) and 4% (1/25) met the stage 3 AKIN criterion for AKI (i. e. , increase in serum creatinine [ three-fold compared with baseline level). In general, patients who developed AKI had lower preoperative eGFR,   4 ). The model performance was good, as estimated by the Cstatistic corrected for optimism (0. 79). In 60 patients, postoperative creatinine was not measured up to discharge. We used the missing-indicator method to determine the impact of missing data on the association between the predictors and AKI.
A measurement of the energy density in proton-proton collisions at a centre-of-mass energy of √ s = 13 TeV is presented. The data have been recorded with the CMS experiment at the LHC during low luminosity operations in 2015. The energy density is studied as a function of pseudorapidity in the ranges -6. 6 < η < -5. 2 and 3. 15 < |η| < 5. 20. The results are compared with the predictions of several models. All the models considered suggest a different shape of the pseudorapidity dependence compared to that observed in the data. A comparison with LHC proton-proton collision data at √ s = 0. 9 and 7 TeV confirms the compatibility of the data with the hypothesis of limiting fragmentation.
By event we refer to the data of one single LHC bunch crossing. To investigate various aspects of MPIs in high-energy proton-proton collisions the measurement is performed for several different categories of collision, each category defined by a specific event selection. Moreover, the data collected at √ s = 13 TeV are analysed together with data collected at 0. 9 and 7 TeV  [6]. This is interesting since projectile fragmentation can then be studied in the regions close to the beam rapidity, y beam = acosh( √ s/2m p ), where m p is the mass of the projectile particle, i. e. a proton in the present case. At √ s = 13 TeV, y beam ≈ 9. 5, while at √ s = 0. 9 TeV it is just ≈6. 8. Thus, the detectors of CMS, although located at fixed η, cover a very wide range in η = ηy beam when data recorded at different centre-of-mass energies are combined.
The CMS detector At the heart of the CMS detector is a superconducting solenoid of 6 m internal diameter, providing a strong magnetic field of 3. 8 T. The data used for this paper were taken in June 2015 during a period without magnetic field. Within the CMS magnet volume are an inner silicon pixel and strip tracker that measure charged particles in the range |η| < 2. 5, a homogeneous lead tungstate crystal electromagnetic calorimeter, and a brass and scintillator hadron calorimeter. The corresponding endcap detectors instrument the pseudorapidity range up to |η| 3 with tracking and calorimetry. Forward Cherenkov calorimeters extend the coverage beyond |η| 3. Muons are measured in gas-ionization detectors embedded in the steel return yoke. The hadron forward (HF) calorimeters cover the region 2.
4 m away from the nominal interaction point. The overall longitudinal depth of both CASTOR and HF corresponds to 10 hadronic interaction lengths. The CASTOR calorimeter is only operated during periods of low LHC luminosity (L inst < 10 30 cm -2 s -1 ) since it cannot distinguish the secondaries from simultaneous pileup collisions. The present analysis is restricted to the range of pseudorapidity covered by the HF and CAS-TOR calorimeters, excluding the two lowest |η| segments of the HF calorimeters because they are partially located in the shadow of the endcap calorimeters. This corresponds to a combined pseudorapidity range of 3. 15 < |η| < 5. 2 and -6. 6 < η < -5. 2. The analysis is performed using a data sample corresponding to an integrated luminosity of 0. 06 nb -1 recorded with an average number of proton-proton interactions per bunch crossing of about 0.
In EPOS-LHC a hydrodynamic, or collective, component is included in a parametrised form  [27]. The collisions simulated with the MONASH and MBR tunes of PYTHIA8, and the EPOS-LHC and QGSJETII. 04 event generators, have been processed with a detailed simulation of the full CMS detector based on GEANT4  [30]  and reconstructed using the same software sequence that is used for recorded collision events. These four models are used to correct for detector effects.
The SD-enhanced selection is defined by requiring activity in one of the calorimeters on exactly one side, with a veto condition being applied to the other E HF+ > 4 GeV and E HF-> 4 GeV one stable particle in -4. 4 < η < -3. 9 and 3. 9 < η < 4. 4 side. Energy deposition in the HF calorimeters is characterised by the calorimeter tower with the highest energy in the negative (positive) pseudorapidity region, E HF-(E HF+ ), considering all towers, except those belonging to the two rings closest to the endcap (i. e. at smallest |η|). The energy thresholds for event selection are determined from a study of events without beam and are optimised to effectively reduce the contribution from detector noise, while still allowing a high selection efficiency. In Fig. 1 , the measured distributions for E HF-and E HF+ from collision data are shown together with the noise distributions obtained from data without the presence of LHC beams.
This is achieved at the trigger level by requiring prescaled triggers where the two BPTX detectors are silent. In Fig. 1  simulated events are also shown. Events are selected for the INEL class if max(E HF-, E HF+ ) > E threshold , and for the NSD-enhanced class if min(E HF-, E HF+ ) > E threshold. An energy threshold of E threshold = 5 GeV is found to be optimal to suppress the noise contribution in both event classes for simulated and measured events. For the NSDenhanced category, the threshold could in principle be lowered down to about 3 GeV without increasing the noise contribution, but for consistency a unified threshold of 5 GeV is used for all event classes. The data were recorded at low luminosity with an interaction probability of about 5%. Most non-empty events contain a single proton-proton collision.
A small fraction also has two or more interactions. In contrast, the simulation was done without pileup, i. e. , each simulated event contains exactly one proton-proton collision. The detector noise distribution as measured from empty-beam data are also overlaid as shaded areas. In simulated collisions particle four-momenta are used to build sums of energies. At the stableparticle level (i. e. for particles with proper decay length cτ > 1 cm), simulated collisions are selected to be in the inclusive inelastic category if ξ = max(ξ X , ξ Y ) > 10 -6 , where ξ X = M 2 X s , ξ Y = M 2 Y s , (2) and M X and M Y are the invariant masses of the particle systems on the negative and positive side of the largest rapidity gap in the collision, respectively. This particular criterion for stableparticle level is identical within a few percent with the INEL detector level selection  [32].
The NSD-enhanced collisions are selected at the stable-particle level with a requirement of at The SD-enhanced collision at the stable-particle level are defined by the presence of at least one stable particle with energy E > 5 GeV within the pseudorapidity range 3. 15 < |η| < 5. 2 on one side, whereas the other side must be devoid of particles with energy E > 5 GeV. The phase space definitions for the NSD-enhanced, INEL and SD-enhanced categories at the detector and stable-particle level are summarised in Table  1. The last row of the table indicates the event selection needed for the limiting fragmentation study. This is chosen to be identical to that used in previously published data  [6]  to allow a direct comparison of the results. The energy density is measured with the HF and CASTOR calorimeters by summing up all the energy deposits in the calorimeter towers above noise threshold.
Results The measured energy density, dE/dη, in the range -6. 6 < η < -5. 2 and 3. 15 < |η| < 5. 20, corrected to the stable-particle level, is presented in Figs. 2  and 3. A comparison of the measured average energy density to model predictions for the INEL selection is shown in Figs. 2 (upper) and 3 (upper). The gray band represents the total systematic uncertainty correlated across |η| bins. The statistical uncertainties are <1% and are not shown. In the left panel the comparison of the distribution in data and simulation is shown, while in the right panel the ratio quantifies the agreement between them. While the cosmic ray models (EPOS-LHC and QGSJETII. 04) and the PYTHIA8 MONASH tune describe the data well at |η| < 4 and in the CASTOR region, they overshoot the data around |η| ≈ 4. 5.