dataesr/scientific-paragraphs-categorization huggingface dataset:
g. Fig  3b ). This is not surprising since the surface albedo is sensitive to climate forcing data (e. g. snowfall amount  and its timing determine snow age and albedo) and the simulated climate from CTRL-COU is different from CRUNCEP used in CTRL (especially for total precipitation; data not shown). Furthermore, the radiative forcing is calculated to quantify the instantaneous perturbation to Earth's top-of-atmosphere energy balance induced by snow albedo assimilation in the coupled model. We define the radiative forcing as the difference of net radiation flux at the top of the atmosphere between ASSALB-COU and CTRL-COU over the region north of 40 degrees. The radiative forcing over the snow season (October-May) is estimated to be -1. 82 W m -2 , with a peak in spring of -2. 50 W m -2. The magnitude of the spring value is much larger than that (0.
A potential criticism of this study is that we do not take into account any potential errors in the satellite dataset and albedo from GlobAlbedo product is assumed to be accurate with respect to model simulations. In reality, significant artifacts exist at high latitudes (north of 70°) in the GlobAlbedo product because of problems with snow detection, although a preliminary validation of the GlobAlbedo product displayed a generally good agreement with the MODIS albedo product on the global scale  [47]. But this issue is not specific to the GlobAlbedo product. For example, an inter-comparison of nine satellite-derived datasets has shown that albedo differences are large for early spring (February-April) in the northern hemisphere and also for the winter season particularly at high latitudes  [30].
Acknowledgments The authors express sincere gratitude to the  SNOWMIP1  provider for the Weissfluhjochsnow data. Data (and images) obtained from the  ESA  GlobAlbedo project was downloaded from http://www. GlobAlbedo. org. Data (and images) obtained from GlobSnow project was downloaded from http://www. globsnow. info/. This study has been supported by the  PAGE21  project, funded by the  European Commission   FP7-ENV-2011  (grant agreement  NO. 282700 ).
Introduction So far, Linked Data principles and practices are being adopted by an increasing number of data providers, getting as result a global data space on the Web containing hundreds of LOD datasets  [4]. There are already several guidelines for generating, publishing, interlinking, and consuming Linked Data  [4]. An important task, within the generation process, is to build the vocabulary to be used for modelling the domain of the data sources, and the common recommendation is to reuse as much as possible available vocabularies  [4, 5]. This reuse approach speeds up the vocabulary development, and therefore, publishers will save time, efforts, and resources. There are research efforts, like the NeOn Methodology  [7] , the Best Practices for Publishing Linked Data -W3C Working Group Note  [5] , and the work proposed by Lonsdale et al.
[6]. However, at the time of writing we have not found specific and detailed guidelines that describe how to reuse available vocabularies at fine granularity level, i. e. , reusing specific classes and properties. Our claim is that this difficulty in how to reuse vocabularies at low fine grained level is one of major barriers to the vocabulary development and in consequence to deployment of Linked Data. Moreover, the recent success of Linked Open Vocabularies (LOV 1  ) as a central point for curated catalog of ontologies is helping to convey on best practices to publish vocabularies on the Web, as well as to help in the Data publication activity on the Web. LOV comes with many features, such as an API, a search function and a SPARQL endpoint. In this paper we propose an initial set of guidelines for this task, and provide technological support by means of a plugin for Protégé, which is one of the popular frameworks for developing ontologies in a variety of formats including OWL, RDF(S), and XML Schema.
Linked Open Vocabulaires (LOV) The intended purpose of the LOV  [8]  is to help users to find and reuse terms of vocabularies in Linked Open Data. For achieving that purpose, the LOV gives access to vocabularies metadata and terms using programmatic access with APIs. LOV 7  catalogue is a hub of curated vocabularies used in the Linked Open Data Cloud, as well as other vocabularies suggested by users for their reuse. Some of the three main features of the LOV are for:  (1)  searching ontologies according to their scope, (2) assessing ontologies by providing a score for each term retrieved by a keyword search and (3) interconnecting ontologies using VOAF vocabulary  8. The search function uses an algorithm based on the term popularity in LOD and in the LOV ecosystem. The matched resource set for each term should be the value of the (a) rdfs:label (b) rdfs:comment, or (c) rdfs:description property.
ProtégéLOV ProtégéLOV is an open source tool that provides support to the methodological guidelines described in Sect. 2. It is written in Java programming language as a Currently, ProtégéLOV 11 provides the following five functionalities: 1. Search for a particular term (class or property) in LOV repository. The user selects a particular term from the Class, Object property or Data property navigator. Next, the user switches to the Linked Open Vocabularies View and performs a search on the LOV repository. The system takes as input the selected term, and calls the LOV REST API to get the LOV terms that match the criteria. The plugin provides the following information (if it is available) for each term (1) URI, (2) score, (3) label, (4) local name, and (5) usage note. 2. Reuse directly a particular term from LOV repository.
We use the quantitative HTS (qHTS) method of Inglese et al. 19  to establish "gold-standard" activity levels and to show that while randomization and better normalization individually improve detection of rare biological events, their combination provides maximum benefit. We also report results of an online survey that indicate that most HTS studies do not randomize feature placement across replicate plates or use the most effective normalization methods available. The Statistics and dIagnostic Graphs for HTS (SIGHTS) Microsoft Excel Add-In software is available to conduct most analyses reported here (http://nadonmugqic. mcgill. ca/Welcome. html).
Cells. HeLa cells were purchased from American Type Culture Collection (Rockville, MD) and were grown at 37 °C, 5% CO 2 in RPMI 1640 medium with Glutamax, supplemented with 10% fetal bovine serum and 1% penicillinstreptomycin (complete medium). Instrumentation. Biological assays on 96-well microplates were performed using a fully automated platform, which is described in Vassal et al. 21 They were completed with a TECAN Te-MO 3/3-96 96-channel pipetting module (TECAN, Lyon, France). The platform was controlled using the TECAN Gemini 4. 00 and FACTS 4. 81 software. Fluorescence was measured using the FLUOstar Optima plate reader (BMG Labtechnologies, Offenburg, Germany). For Cy3 and Hoechst measurements, 544-ex and 355-ex filters as excitation filters and 580-12 and 460-em filters as emission filters were used, respectively.
An alternative analysis approach is possible for assays that show one-tailed effects as with the CMBA data (low scores indicate activity). For these analyses, 2 (Compound Activity Status) × 5 (Normalization Method) within-subjects ANOVAs were performed on the medians of the compound replicates of the normalized data at each concentration of the CMBA data. These analyses, which were not possible for the two-tailed effects in the Inglese et al. 19  data, are potentially more sensitive because they are based on the measurements rather than on their ranks as with the pAUC index. Indeed, results for these analyses were more consistent across concentrations than were the pAUC results. Figure  1c  shows these results at the lowest concentration that showed a statistically significant overall Activity × Method interaction and a significant interaction between at least one of the normalization methods and Z scores.
The effects of randomizing compound placement in replicate plates, although statistically significant, were considerably smaller than the effects of the normalization method. This is encouraging to the extent that good results can be achieved even in confounded designs when a powerful normalization is used. This is doubling encouraging because applying normalization algorithms requires substantially less effort than the randomization procedures used in this study. The SIGHTS software we provide in particular should facilitate the use of normalization methods that can provide superior screening results. It is important to note, however, that our results also indicate that randomizing compound placement in replicate plates is nonetheless a recommended strategy when attempting to detect small effects.
g. , two of three). The false-positive and false-negative rates, however, are unknowable with these strategies. Our results show that the RVM t-test, available in the SIGHTS software, is a good alternative that provides all of the advantages of formal statistical testing with as few as two replicates, although a minimum of three is generally recommended. The p-values generated from the statistical tests can then be used to balance false positives and false negatives based on objective statistical benchmarks. We conclude that potent, reliable, and accurate HTS requires replication, randomization design strategies, and more extensive normalization than is typically done and that formal statistical testing is desirable.
Background and purpose: Musculoskeletal system deterioration among the aging is a major reason for loss of autonomy and directly affects the quality of life of the elderly. Articular evaluation is part of physiotherapeutic assessment and helps in establishing a precise diagnosis and deciding appropriate therapy. Reference instruments are valid but not easy to use for some joints. The main goal of our study was to determine reliability and intertester reproducibility of the MP-BV, an inertial sensor (the MotionPod ® [MP]) combined with specific software (BioVal [BV]), for elbow passive range-of-motion measurements in geriatrics. Methods: This open, monocentric, randomized study compared inertial sensor to inclinometer in patients hospitalized in an acute, post-acute, and long-term-care gerontology unit.
Methods location and subjects This open, monocentric, randomized study (NCT01462760) took place from October 1, 2011 to May 1, 2012. Patients were recruited within the acute, post-acute, and long-termcare gerontology unit of University Hospital of Nice (Nice, France). The study was approved by the South Mediterranean V Ethical Review Board (Comité de Protection des Personnes Sud Méditérrannée V), as well as by the French Agency for the Safety of Health Products (ANSM). Patients who agreed to participate gave their written informed consent. Included patients were 65 years or older and had received a physiotherapy prescription from the physiotherapists of Department of Geriatrics mentioning the need for a passive joint assessment. Patients who agreed to participate had to be covered by social insurance.
The wireless band ranged from 10 m (30 feet) to 30 m (100 feet), depending on the environment, with a frequency band of 2. 4 Ghz. The measurement range was a full 360°. The fusion of data from accelerometers, magnetometers, and gyroscopes was done with an application programming interface developed by Movea. Then, data were delivered by the BioVal software to the practitioner as yaw, pitch, roll, quaternion, or rotation matrix. The resolution was 0. 5° for static accuracy and 1° for dynamic accuracy. The inclinometer used as reference was the Baseline ® Bubble inclinometer (Fabrication Enterprises, Withe Plains, NY, USA), the dimensions of which were 101. 6×5. 08×101. 6 mm (4×0. 2×4″) for 90. 72 g (0. 2 lbs). For the measure, the inclinometer was placed near the elbow. The physiotherapist turned the dial until the scale read 0, then took the elbow through its range of motion.
An ICC value ,0. 40 was considered poor, 0. 40-0. 50 moderate, 0. 50-0. 70 good, and 0. 70-1. 0 excellent. 31 Examination duration was compared between the two techniques using the Student's t-test for paired data. For this analysis, only data recorded in room A were used, as examinations were performed there with both techniques and by the same physiotherapist. Finally, for acceptability analyses, VAS scores (recorded in room A) were compared using the Student's t-test for paired data. All the tests were two-sided and the significance level was set to 5%. Statistical analyses were performed using SAS Enterprise Guide (v 4. 1; SAS Institute Inc. , Cary, NC, USA).
Results Three hundred and fifteen patients were screened during weekly physiotherapy sessions. Of these, 84 patients were deemed eligible for inclusion in the study, and 77 of these 84 (91. 7%) were eventually included in the validation study (Figure  3 ). Characteristics of the patients with missing data for analyses did not differ significantly from analyzable patients. For these 77 patients, the mean age was 83. 5±6. 4 (range 68-101) years; 40 (52. 0%) were women and 37 (48. 0%) men, resulting in a sex ratio of 1. 08.
Acknowledgments The authors wish to thank  Eric Fontas  for his methodological support and  Mathilde Demory  for reviewing the English. We thank  Marie Geronimi  from  RM Ingénieurie  and  Yanis Caritus  from  MOVEA  for the technical informations. We also wish to thank the physiotherapists from the  Gerontology Department  ( Damian Argini ,  Urko Larranaga , and  Jérôme Danfali ) and the rehabilitation health care manager Valérie Victor, as well as  Professor Alain Franco  and  Mr Patrick Malléa.
In addition, Guan et al. [20]  built a geometrical tricrystal model with one cavity at the point where three crystal boundaries intersect and analyzed the stress distribution of the polycrystalline material by using the finite element method (FEM) software ABAQUS. Vedula et al. [21]  predicted the residual stresses in polycrystalline alumina samples using experimentally determined grain orientations and object-oriented finite element analysis. A micro-macro method based on MC and FEM was proposed by Mori et al. [22] tosimulatea sintering process of ceramic powder compacts. However, the underlying microstructure morphology is often neglected in simulation of layered composites. In our previous work  [23] , the numerical simulation was successfully applied by combining MC and FEM to perform the stress analysis of the polycrystalline materials during the microstructure evolution, and we found that the average stress and the grain size agree well with the Hall-Petch relation when the properties of the grain boundaries are taken into account.
Numerical Results and Discussion The algorithms mentioned previously have been implemented into an in-house software platform OMT (Objected and Methodological Technology), and a software named PCLab/GrainStress (Particle Cloud Laboratory/Grain Stress) is especially developed to simulate the grain growth process using MC method and to perform stress analysis of the microstructure with FEM (Fig. 4 ). This software is written using the object-oriented programming language C++, and the flow diagram is shown in Fig. 5. There are mainly two procedures, namely the Monte Carlo simulation module and the finite element stress analysis module. After each MC step, the microstructure and the analysis data including grid orientations, mean grain size, number of grains, and simulation time (MCS) are recorded in files.
The microstructure distributions are different at each MC step, and the grains grow larger as the simulation time as a whole. The structures with different microstructure distributions will exhibit different mechanical performance. So we investigated the stress distribution after finishing all the MC steps simulation. Several numerical tests are carried out to investigate the effects of the microstructure and the geometry parameters on the mechanical performance of layered materials. Part of the results is displayed by the postprocessing software FERView  [27].
The average stress increases almost linearly as modulus ratios for the homogeneous material, whereas it is nonlinear to the modulus ratios for the materials with heterogeneous microstructures. It is therefore possible to tune the stress distribution by varying the grain size, the layer thickness ratio, the number of layers, and the modulus ratios. A software package, PCLab/GrainStress, for the grain growth process and stress analysis will provide an efficient tool for faster material design and application.
Acknowledgments This work is supported by the  National Natural Science Foundation of China  (Nos. 51372203  and  11372260 ), the  National Basic Research Program of China  ( 973 Program ) (2011CB605806), the  Basic Research Foundation of NWPU  (Nos. JCY20130114  and  JCY20110248 ), and the  Foreign Talents Introduction and Academic Exchange Program  (No. B08040 ).
The GOSIA code The unknown matrix elements coupling the low-lying states in the polonium isotopes affect the Coulomb-excitation cross section in a complex way. In order to determine these matrix elements, the coupled-channels code gosia  [70, 71]  is used. gosia is a least-squares fitting code that calculates the de-excitation γ-ray yields based on a set of matrix elements coupling the relevant states and compares them to the experimental γ-ray yields. Semi-classical perturbation theory is used to calculate the Coulomb-excitation probabilities based on the kinematics of the reaction. From that the γ-ray yields are determined by taking into account the position and geometry of the germanium detectors and the relative detection efficiencies at the relevant energies. The total yields result finally from integration over the particle scattering range and the energy of the incoming projectile, taking into account the energy loss in the target.
3 for the 2009 data of 200 Po on 104 Pd), has two consequences. The first is that at small values of θ LAB , a contribution of the projectile peak is included in the gate. This does not cause a problem however, as the Doppler correction for these events will be wrong. Indeed, it is wrong to assume for these events that the recoil is detected in the DSSSD. A faulty Doppler correction removes the γ-ray energies from the central peak and only contributes to the background. A second consequence of the applied gates is that at small θ LAB , not all recoils are included in the gate. As the Coulombexcitation data of the projectile is analyzed relatively to the Coulomb-excitation data of the target, this also does not harm the analysis. Indeed, the projectile Coulex cross section is normalized to the target Coulex cross section.
In the "normal" situation of the 2009 data, none of the events in the prompt peak are downscaled. However the mistake in the settings for the p-γ coincidence window in 2012 gave rise to the downscaling of the prompt p-γ Table  3. 3: Definition of prompt and random windows used for the different experiments. Q i denotes the ith quadrant of the DSSSD. The problem with the downscaling technique occurred in 2012 in quadrants 1 and 2. p/r is the scaling factor that was used to scale the random events before subtracting them from the prompt events. Year Q 1 and Q 2 Q 3 and Q 4 p/r Prompt [ns] Random [ns] Prompt [ns] Random [ns] 2012 [-850, -500] [0, 350] [-900, -550] [-1250, -900] 1 2009 [-915, -615] [-1315, -915] [-915, -615] [-1315, -915] 0. 75 events. This leads to a significant loss of useful data for these two quadrants of the DSSSD.
A 4 µs correlation window is opened by a particle and contains all the particles and γ rays that are detected within 4 µs after the detection of the first particle. At this stage, each event contains a list of coincident particles and γ rays. γγ coincidences have to be treated at this moment in the data-treatment process as the γ rays will be stored independently from the next step onwards. The rt_ana code treats every detected γ ray independently and pairs them with the respective coincident particles. Each particle is being labeled as prompt (particles in black in Figure  3. 5) or random (particles in red in Figure  3. 5) based on the selection rules shown in Table  3. 3. Notice that p-γγ events are split up into two entries, e. g. γ 1 and γ 3 in Figure  3. 5. In the last step, the g_clx code loops over all the detected γ rays and decides whether or not to include them in the final Coulex spectrum.
4) where r ON/OFF = I Tl I Po = σ Po σ Tl 1 N DSSSD ON N DSSSD OFF -1 (3. 5) where σ Po σ Tl = Z Po Z Tl 2 is the ratio of the Rutherford cross section of Po to Tl on the respective target and N DSSSD ON(OFF) is the number of scattered particles on the DSSSD when the lasers are switched ON(OFF). I Tl gives the beam intensity of the thallium contaminant which is incident on the secondary Miniball target while I Po represents the incident polonium beam intensity, both in the laser ON periods of the laser ON/OFF runs and expressed in particles per second. The beam purities for the experiments with 198,200,202 Po, listed in Table  3. 1, have been acquired this way. As the thallium contaminant was substantial at mass 196, the data on 196 Po is treated in a more detailed way. Data was taken in laser ON/OFF mode regularly during the 196 Po experiment.
7) where I T T otal is the total intensity of the target de-excitation γ ray, I T P o is the intensity of the target de-excitation caused by the polonium nuclei in the beam and P is the extracted beam purity. The target excitation yields for the 196 Po data are extracted separately for the laser ON/OFF data and the laser ON data, taking into account the respective beam purity. 196,198,200,202 Po The data analysis, analysis using gosia and results of the two Coulombexcitation campaigns performed at the REX-ISOLDE facility in CERN, were submitted for publication in Physical Review C. The full text of this paper is the main part of this chapter. Next to that, additional details related to the data and gosia analysis are provided.
200 Po Coulomb excitation of 200 Po was studied only with the 104 Pd target. The 4 + 1 → 2 + 1 and 0 + 2 → 2 + 1 γ rays were not observed above the level of 0. 9 % and 0. 7 % relative to the 200 Po 2 + 1 → 0 + 1 γ ray, respectively, so an exclusive population of the 2 + 1 state was assumed. Figure  4. 13 shows the total χ 2 surface that was constructed applying the χ 2 definition given in equation 4. 4 with N data P = 14 and N data T = 16. Significantly higher sensitivity to the second-order effect of the diagonal matrix element results from the large statistics that were Table  4. 7: Final results for the matrix elements extracted in 200,202 Po in this work, using the gosia2 code. Two values of the transitional matrix element 0 + 1 ||E2||2 + 1 are listed: the first results from the full χ 2 surface analysis, the second value 0 + 1 ||E2||2 + 1 (Q=0) is the value that results from projecting the surface at 2 + 1 ||E2||2 + 1 = 0 eb.
14, additional matrix elements were included in the gosia analysis. The experimental data set did not allow to extract values for these matrix elements, but they were included to make sure that the additional correlations were taken into account in the gosia analysis. This means that the extracted uncertainties mentioned in Table  4. 14 include possible variations for these additional matrix elements. The starting values for these extra matrix elements were determined as follows: • 4 + 1 ||E2||6 + 1 : estimated from the rigid rotor model, using 0 + 1 ||E2||2 + 1 • 2 + 2 ||E2||4 + 2 : estimated from the rigid rotor model, applied to the nonyrast band, using 0 + 2 ||E2||2 + 2 • 2 + 1 ||M 1||2 + 2 : related to 2 + 1 ||E2||2 + 2 through the E2/M 1 mixing ratio δ • 2 + 2 ||E2||2 + 2 : assumed to be zero.