dataesr/scientific-paragraphs-categorization huggingface dataset:
We also regularly made textural analysis based on thin-section pictures. We ended up with a large dataset covering a complete cycle from growth to the start of melt (i. e. 28 November 2007 to 18 June 2008). Measurements of sea-ice temperature and bulk salinity extending over such a long time period are very scarce. Field observations are usually limited to the spring and summer seasons due to technical difficulties inherent to winter sampling. Hence, our dataset represented a unique opportunity to investigate the halo-thermodynamic evolution of sea ice over a long period. The annual cycle of sea-ice temperature has been well described by  Perovich and Elder (2001). Sea-ice temperatures in first-year Arctic sea ice are generally driven by the surface heat balance and buffered by the snow cover, the oceanic heat flux typically being small in most regions of the Arctic  (Steele and Flato, 2000).
In growing cold ice, gravity drainage is limited to the permeable ice layer at the interface with the ocean, which gives rise to a quasi-steady-state salinity. In warming permeable ice, fulldepth gravity drainage can develop, which results in a net desalination of the ice cover. The Rayleigh number has had some success as a proxy of sea-ice desalination in laboratory experiments. However, it has rarely been applied to field measurements  (Wettlaufer and others, 2000; Notz and Worster, 2008; Gough and others, 2012)  and certainly not over a complete cycle from growth to start of melt. The large dataset presented in this study provided an opportunity to achieve this. We therefore apply the evolution of the measured mean bulk ice salinity from November 2007 until June 2008 to the evolution of both calculated brine volume fraction and Rayleigh number.
It should be acknowledged here that this study was not initially designed as a real time-series study. Hence our dataset also integrates spatial variability, which we also discuss. Finally, we provide a regional analysis of the sea-ice formation and deformation processes based on textural analysis. The Amundsen Gulf is an oceanic area of particular interest since it is characterized by the development of polynya and flaw leads  (Barber and Massom, 2007). Expected to increase in number and to expand in size and duration, such areas could also represent a glimpse of the future sea-ice conditions in the Arctic.
Ice-core sections were stored in sealed plastic containers and melted at room temperature aboard the ship. Sea-ice bulk salinity, S i , of each melted core section was determined through conductivity using a Thermo-Orion 1 WP-84TPS meter (accuracy of AE0. 1%). Salinity data are reported using the practical salinity scale. Water aliquots (10 mL) were also collected in no-headspace vials from the melted core sections for water stableisotope (d 18 O) measurements. Snow was always carefully scraped off the surface of the ice cores collected, to avoid snow contamination of the samples. Aliquots were analyzed at the G. G. Hatch Isotope Laboratories, University of Ottawa, using a Gasbench + DeltaPlus XP isotope ratio mass spectrometer (ThermoFinnigan 1 , Germany) and the conventional water-CO 2 equilibration method (accuracy with respect to Vienna Standard Mean Ocean Water (VSMOW) was AE0.
Data have been previously presented by Chierici and others (2011). Values ranged between -4. 57% and -2. 11% (mean = -3. 18%, SD AE0. 56%). Snow d 18 O values were measured at stations D37 and D41 (-14. 92% and -12. 80%). One melt pond analyzed at station F6-1 had an isotopic composition of -9. 87%.
Conditions favourable to flooding did not develop during the IPY-CFL study since at the maximum the snow cover represented 15% of the ice thickness and the freeboard was always positive (Table  1 ). The d 18 O data indicate that only one value (-7. 95% at station F6-1) was compatible with the formation of snow ice. However, this value was recorded during surface melting and therefore more likely resulted from snow meltwater infiltration. A brine skim could have been present at some stations where frost flowers were observed (D4, D5, D33-3NF and D36NF) since brine skims are known  to favour the formation of frost flowers  (Perovich and Richter-Menge, 1994). If so, it only represented a very thin layer most likely scraped off the ice surface prior to coring. Superimposed ice forms when snow meltwater percolates and refreezes at depth in the snow cover or at the snow-ice interface  (Jeffries and others, 1994; Haas and others, 2001).
CONCLUSION We have reported sea-ice temperature and bulk salinity measurements from 33 stations sampled in the southern Beaufort Sea-Amundsen Gulf from November 2007 until June 2008 during the IPY-CFL study. This represents a significant dataset covering a complete cycle from growth to the start of melt. We provide this dataset in a spreadsheet in the supplementary material (http://www. igsoc. org/hyperlink/12j148/ ). It represents a useful tool for the IPY-CFL community but also for future experiments in the southern Beaufort Sea-Amundsen Gulf and is a good basis for model validation. We used this large dataset to investigate the halothermodynamic evolution of sea ice. The following trends were observed. There was a strong linear relationship (R 2 = 0. 87) between the mean sea-ice temperature and the mean air temperature and no correlation (R 2 = 0.
ACKNOWLEDGEMENTS We thank the crew members of  CCGS Amundsen and scientists of the IPY-CFL study  for their support. We especially thank  Rodd Laing ,  Nes Sutherland ,  Brent Else ,  Benoı ˆt Philippe ,  C. J. Mundy ,  N. Fergus  and  Jens Ehn  for assistance with fieldwork. Mapping was performed by.
Résumé de l'article Si le concept de résilience, avec ses notions d'absorption, d'adaptation et d'apprentissage à l'occasion d'une perturbation, semble prometteur pour la gestion des villes exposées aux risques, il manque aujourd'hui d'opérationnalité. Notre recherche vise alors, non pas à définir ou évaluer les caractéristiques de la résilience urbaine, mais à construire une démarche collaborative visant à développer une connaissance partagée et améliorer la coordination entre gestionnaires de la ville. En particulier, notre travail porte sur les services urbains qui ont montré leur importance dans la propagation des perturbations et dans le maintien et le rétablissement des fonctions urbaines pendant et après une crise. Sur la base de données collectée lors d'entretiens avec les gestionnaires des services urbains parisiens, des ateliers collaboratifs ont été organisés avec l'appui de la Ville de Paris.
La base de données constituée (uniquement sur la zone d'étude 12e, 13e et Ivry) permet ainsi d'identifier les décalages, temporels et spatiaux, entre les besoins (ou dépendances) des différents composants des services urbains et la continuité des services. Par exemple, il est possible d'identifier les composants situés dans une zone de fragilité électrique (modélisée à partir de la zone inondable et non de la structure réelle du réseau), pour lesquels le gestionnaire interrogé a prévu d'utiliser de l'électricité. Ainsi, le déménagement de l'atelier de propreté 12/2 n'a pas été prévu et son besoin en électricité reste indispensable alors qu'il se situe dans la zone de fragilité électrique dès le jour 7 (Figure  6 ). Cela ne veut pas dire que l'atelier sera privé d'électricité, mais il est nécessaire d'effectuer une analyse plus fine de la fiabilité de la ligne électrique ou de prévoir une stratégie de réponse.
Résumés Si le concept de résilience, avec ses notions d'absorption, d'adaptation et d'apprentissage à l'occasion d'une perturbation, semble prometteur pour la gestion des villes exposées aux risques, il manque aujourd'hui d'opérationnalité. Notre recherche vise alors, non pas à définir ou évaluer les caractéristiques de la résilience urbaine, mais à construire une démarche collaborative visant à développer une connaissance partagée et améliorer la coordination entre gestionnaires de la ville. En particulier, notre travail porte sur les services urbains qui ont montré leur importance dans la propagation des perturbations et dans le maintien et le rétablissement des fonctions urbaines pendant et après une crise. Sur la base de données collectée lors d'entretiens avec les gestionnaires des services urbains parisiens, des ateliers collaboratifs ont été organisés avec l'appui de la Ville de Paris.
Thus, our research is much more concerned in building the conditions for a resilient city rather than characterizing and assessing urban resilience. To do so, we develop a collaborative approach with Paris' urban services managers in order to increase their knowledge and their communication given that urban services are important both in risk propagation and in business continuity. We have collected data concerning urban services interdependencies and gathered urban managers in three collaborative workshops. The workshops have contributed to improve the global knowledge concerning interdependencies management and assess their impacts on urban systems in case of floods. It demonstrates the complexity of interactions and the criticality of urban services continuity in the resilience of Paris to the 100-year flood risk.
Many different algorithms  [5, [7] [8] [9] [10]  for predicting pK a values have been developed, and a few have been packaged into commercial computer software applications. The main objective of the present study is to look for one (or more) reliable pK a prediction method(s) that can enable to determine the dissociation constants of some components of interest in food sciences. For this purpose, we used a training set of molecules (amino-acids, dipeptides and tripeptides) having known experimental pK a values. Then, we used 3 different predictions methods namely ChemAxon  [3, 4]  (Marvin version 5. 4. 1. 1), ACD/Labs [11] (version 10. 01, Release 10. 00) and COSMO-RS  [12, 13]  (COSMOtherm  [8, 9] , version C2. 1, Release 01. 11) to predict the pK a values of these molecules, and compare each predicted value to the corresponding experimental value.
The ACD/Labs Method The ACD/Labs pK a prediction method  [11]  enables to calculate single pK a values for all possible dissociation centers when the rest of the molecule is considered neutral, using an internal database containing chemical structures and experimental data. The algorithm of this calculation mimics the experimental order of protonation of the drawn molecule and determines the pK a values which can be experimentally measured in aqueous solution. This model is based on Linear Free Energy Relationships (LFER), applying the Hammett equation  [2, 7] : pK a ¼ pK 0 a þ D pK a ð Þ ð7Þ where pK a 0 is the ionization constant for the parent molecule, and D pK a ð Þ ¼ P D pK a ð Þ i is the sum over the influence of all other functional groups on the respective pK a of the D(pK a ) i values of each reaction center; D pK a ð Þ i ¼ À X q j r j ð8Þ where q j is the constant for a particular class j of molecules, and r j is the electronic effect of the jth substituent on the ionization constant of the parent molecule.
For this purpose, every ionizable group is characterized by several Hammet-type equations that have been parameterized to cover the most popular ionizable functional groups. The ACD/Labs internal training set contains more than 2 000 derived experimental electronic constants (r j ). When the required substituent constant is not available from the experimental database; the ACD/Labs method uses another algorithm to describe electronic effect transmissions through the molecular system. The flaw in this method is that the parent molecules inherently carry the majority of the chemical information and without training on a particular parent, predictions for such compounds are impossible. That's why an Internal Reaction Centers Database is used for pK a prediction using this method. The ACD/Labs internal training set contains more than 31 000 experimental values for 15 932 structures.
The COSMO-RS Method COSMO-RS  [13, 19, 20]  is a predictive method for thermodynamic equilibrium of fluids and liquid mixtures that is a combination of the quantum chemical dielectric continuum solvation model COSMO  [12]  (acronym of Conductor-like Screening Model) with a statistical thermodynamics treatment for more Realistic Solvation (RS) simulations. The equilibrium thermodynamic properties derived from the COSMO-RS theory are computed in COSMOtherm, a command line/file driven program which can be run directly from a UNIX or DOS shell. In the present study, we used the C21_0111 version of COSMOthermX  [8] , a Graphical User Interface to the COSMOtherm  [9]  command line program. In this software, the pK a of a solute j can be estimated from the Linear Free Energy Relationship (LFER)  [21, 22] : pK a ¼ A DG j neutral À DG j ion RT ln 10 ! þ B ð9Þ where DG j neutral and DG j ion are respectively the free energies of the neutral and ionic compounds, in the solvent (water in our case) at infinite dilution; A and B denote LFER parameters that were determined for example by correlating calculated free energies of dissociation with the experimental aqueous pK a for a set of 64 organic and inorganic acids (not including any peptide)  [21].
86 and 14. 03 respectively) while COSMO-RS predicts a pK a = 11. 44 which is significantly closer to the experimental value. One justification of these differences on predicted pK a should be the fact that the pK a calculated for this structure (on a dissociation center which is a cyclic nitrogen atom) that was probably not used in the internal database of ACD/Labs and ChemAxon, while COSMO-RS overcome this problem when performing a geometrical optimization during the QC calculations preceding its pK a prediction.
Multiple Experimental Data Points Influence on pK a Results Furthermore, in the experimental pK a values used, there were some cases where we got several different experimental values listed for the same dissociation center. By averaging these "multiple" experimental data to compare the prediction methods, we got 91 different data points (instead of 107) and the RMSE were slightly different than those got in Table  1. The combination of a conformations set treatment for 2 molecules (histidine and histidylglutamic acid) and the averaging of multiple pK a values (for the same dissociation center) decreased the RMSE of the COSMO-RS pK a values by 0. 02 while the ChemAxon and ACD/Labs RMSE increased by approximately 0. 03 for each method (Tab. 8).
2. 4. 2). As mentioned earlier, each of the studied methods includes optionally parameterization tools to incorporate "local" data in order to bias predictions. This will increase for sure the prediction results; however these optional facilities were not studied in this paper since we are looking for a predictive tool that is able to predict pK a of complex structures (that are ubiquitous in foods and biological systems). The COSMO-RS method seems very promising to determine the pK a of a given molecule in a fully predictive way (especially when the conformations are well treated in calculations) with no available experimental data. Ideally, a benchmarking of pK a prediction models would require a universal training set to train all models, and a universal disjoint and similarly the use of diverse test set to compare their prediction.
However it is very difficult to perform such task on all of the commercial pK a prediction utilities used in the present study. With no true benchmarks for pK a prediction utilities, the only way to identify a superior model is by trusting statistics. The statistics for both training and test data should be separate; unfortunately this is not the case in the present study since we are not able to distinguish the molecules that were used to parameterized ChemAxon and ACD/ Labs methods. All empirically based models should have r 2 closed to 1. 0 and RMSE as close to 0. 0 as possible over a wide range of compounds. Regarding the r 2 values, each of the studied models should be considered as accurate (r 2 = 0. 98 for ChemAxon and ACD/Labs and r 2 = 0. 96 for COSMO-RS). The averaged RMSE of the predicted pK a values for each method compared to experimental results were respectively 0.
Likewise, it is also reported that the best performance that might be expected from a kinetic model encompassing the effect of temperature, pH and aw on growth rate, is $ 30%, or an accuracy factor A f of 1. 3. This value is greater than all the A f values determined within this study, confirming again that all the studied models are reliable. However, it is difficult to determine the best model because we were not able to distinguish the data used to train each model (especially ChemAxon and ACD/ Labs). As suggested by Lee and Crippen  [10] , it should be interesting to study the performance of a consensus model based on these 3 methods. Since the statistics obtained from a consensus model may not reflect its performance on new data, this kind of study is out of the scope of this paper in which we are looking for a fully predictive model able to treat other products of interest in foods and biological systems.
This effect was studied for the case of 2 molecules of our training set and reduced the RMSE of the COSMO-RS method by about 0. 02. All these packages include the ability to bias predictions using "local" data but these facilities were not evaluated. ChemAxon's Marvin and ACD/Labs are the fastest tools in term of computer-time. Due to the time-consuming QC calculations preceding its thermodynamics calculations, the COSMO-RS is less fast. But when this calculation is done once for each molecule and ion of interest, the COSMO-RS thermodynamics algorithm takes the same amount of time as ChemAxon and ACD/Labs to perform pK a predictions. Regarding these results, COSMO-RS appears as a promising method to predict the pK a values of molecules of interest in food science with scarce available pK a values such as peptides.
It is essentially based on the concepts formulated by Van Laar  (Scatchard and Hildebrand, 1934) : (8) where v i is the molar volume of component i, δ i is its solubility parameter and δ is the solubility parameter for the solution, which is calculated as follows  (Walas, 1985) : (9) The quantity φ i is the volume fraction, i. e. the ratio of the molar volume of component i to the weighted molar volume of the mixture. The solubility parameter depends on the temperature but only its value at 25°C is usually taken as the difference between solubility parameters is almost independent of temperature  (de Hemptinne et al. , 2012). The quantities δ i and v i are available in most databases for pure components at 25°C. The parameters used in this work are shown in Table  2. The solubility parameter can also be calculated from following relation  (Riazi, 2005) : (10) where Δh i vap is the molar heat of vaporization of component i.
Some Results The Grayson Streed model is evaluated using experimental data on the solubility of hydrogen in pure hydrocarbons. The physical properties of each pure component are summarized in Table  2. The data sources for hydrogen solubility in various pure hydrocarbons are given in Table  3. These data are taken from:  Grayson and Streed (1963)  for hydrogen, nheptane, n-decane and n-hexadecane. Yao et al. (1977 Yao et al. ( -1978) )  reported the physical properties for 1-methyl naphthalene,  Park et al. (1995)  for eicosane, octacosane and hexatriacontane. Park et al. (1996)  for phenanthrene and pyrene. The normal boiling temperatures (T b ) are taken from  Riazi (2005) , it must be observed at this point that even though the trend of these properties with increasing molecular weight and within a given family, is monotonous, this is not the case for solubility parameters.
A maximum is visible for eicosane. This trend is also visible in the data originating from the DIPPR (Design Institute for Physical PRoperties) database. Since the gas solubility is essentially proportional to the pressure, it is often convenient to use a quantity that considers the ratio of pressure and solubility. The Henry constant of a gas is defined rigorously as follows: (18) where H i is the Henry constant for component i (solute). It has the unit of pressure. Therefore, H i is in fact the slope of f i L versus x i at x i = 0. Note that the pressure is necessarily the H f x i x i L i i = ⎛ ⎝ ⎜ ⎞ ⎠ ⎟ → lim 0 P c T c ω v δ (g/mol) (°C) (Pa) (K) (m 3 /kmol) (J/m 3 ) 0. 5 Hydrogen (1) Source:  Grayson and Streed (1963) , p. 175. (2) Source:  Park et al. (1995) , p. 243. (3) Source:  Yao et al.
57). L'anglais britannique semble néanmoins offrir plus de possibilités pour cette étude. Recherchant une certaine spontanéité dans l'usage de la langue, il est préférable de se tourner vers un corpus oral avec les pistes audio disponibles. Pour étudier l'acquisition, nous avons choisi la base de données du système Child Language Data Exchange System 2. 1. Choix du corpus13 L'étude de la littérature permet de faire plusieurs remarques qui influent sur le choix du corpus. Selon les études psycholinguistiques il semble plus intéressant d'étudier des L'acquisition du Present Perfect chez un enfant anglophone : méthodologie, co. Corela, HS-13 | 2013 enfants âgés (http://childes. psy. cmu. edu, MacWhinney, B. , 2000. The CHILDES Project: Tools for Analyzing Talk. Mahwah, NJ: Lawrence Erlbaum Associates).
Acknowledgements I would like to thank  Florent HIVERT  and  Nicolas M. THI ÉRY  for their patience and advice during the preparation of this manuscript. Several examples in this paper were computed using the open-source mathematical software Sage [S + 12] and its extension:  Sage-Combinat  [SCc12]. The implementation of several Hopf algebras is available at http://code. google. com/p/sage-hopf-algebra/and will be available on Sage-Combinat soon and later in Sage. That code is due to  Rémi MAURICE  and I.
Revue de littérature 1. Relation ambiguë entre monitoring et contrat bancaire RECHERCHE autres termes du contrat et il a un effet sur réalisons une régression logistique où le leurs propriétés incitatives. De ce fait, notre monitoring est la variable expliquée et les recherche s'articule en deux temps. Nous termes du contrat les variables explicatives mettons d'abord en évidence le rôle des pour caractériser le rôle des termes du termes du contrat bancaire sur la réalisation contrat bancaire dans le monitoring. Dans ou non du monitoring. Ensuite, nous étu- la quatrième partie, nous nous intéressons dions leurs propriétés incitatives en fonc- aux propriétés incitatives des termes des tion du monitoring réalisé par le banquier. contrats et plus particulièrement à l'ef- Notre travail empirique s'appuie sur une base de données originale construite en partenariat avec huit caisses régionales du Crédit Agricole comprenant les princi-pales régions viticoles (hormis Bourgogne fet du monitoring sur ces propriétés par l'identification de leurs interactions.
Du point de vue fonctionnel, ces centres traitent des informations électroniques nécessaires à l'activité d'une entreprise ou bien de diverses entreprises clientes. Les hébergeurs proposent aussi à leurs clients une présence sur Internet, et des services dont ils assurent la maintenance et la sécurité (Web, Mail, Bases de données, PHP et MySQL, d'e-mails, FTP, programmes nécessaires, raccordement aux réseaux des opérateurs de télécommunication, sauvegarde, etc. ). Ils fournissent pour l'essentiel un service d'hébergement Internet et peuvent être chargés de l'enregistrement des noms de domaines auprès des organismes internationaux régissant cette ressource. En résumé, les entreprises ne possèdent généralement pas l'expertise nécessaire et les compétences techniques indispensables pour assurer elles-mêmes la réalisation et la gestion quotidienne d'un datacentre dédié à leurs activités  18.