dataesr/scientific-paragraphs-categorization huggingface dataset:
We attribute this behavior to our use of an excess variance term while fitting, which accommodates for differences between the data and fitted model. These differences are apparent in the residual velocity field (bottom left) where the systematic deviations from our model are on the order of 10-15 km s -1. The excess variance term in this case converges to a similar level. For NGC 4654, as for the majority of galaxies, nonzero residuals reflect the presence of non-circular motions in the gas not included in our model of pure circular motion. In the specific case of NGC 4654, residuals at radii dominated by the stellar bar (a B = 29 ) in detail show the monopolar red-blue pattern characteristic of bars (red along one side, blue along the other; Canzian 1993). At larger radii, the velocity field exhibits the 3armed pattern expected for an m = 2 pattern outside corotation  (Canzian 1993).
Impact of imperfect spatial sampling The spatial sampling of our data varies significantly across our sample (see Section 2. 2) and impacts the coverage of the mapped velocity field. This can adversely affect the quality of our MCMC fit, depending on how well the major axis information is sampled in the map. When lines-of-sight close to the minor axis are significantly over-presented in the PHANGS-ALMA CO velocity field, rotational velocities and orientations are weakly constrained. CO velocity fields with limited radial extents, in which velocities along the major axis never reach the turnover or flat part of the rotation curve, are also compromised. As an example of how velocity field coverage affects the quality of our MCMC fits, we show the results for NGC 5042 and NGC 4293 in Figure  B1.
An increase in beam smearing on the velocity field at lower angular resolution, though, may be expected to affect our ability to constrain the inclination, possibly leading to measurement biases. In practice, we find that the values we derive for φ and i from the 12m maps are mostly comparable to the values derived from the 7m data as shown in Figure  6  (and tabulated in Table  D2  in Appendix D). For both Figure  6. Comparison between the best-fit orientations derived from our 12m PHANGS-ALMA data set to the same measurements on lower-resolution 7m ALMA data. Uncertainties are adopted from our jackknife method based on the 12m data set. Solid, dotted and dashed lines represent the one-to-one relation, measured offset, and scatter in the relations, respectively. We split our sample in barred and unbarred systems, indicated by red and blue symbols, respectively.
There are furthermore currently ill-understood non-electrostatic interactions (steric, dielectric, hydration, …) that complicate quantitative modeling  [27]. It is therefore usually impossible to predict the NF performance of a given membrane and therefore judge its suitability for a given application from the limited performance characteristics communicated by the membrane manufacturers. The question that arises from the previous discussion is how to decide which kind of membrane technology (NF or RO) and membrane should be used for desalting the different types of Tunisian groundwaters. This requires having an efficient modeling tool to save time by limiting the number of experimental validations. In the case of RO, most of the manufacturers provide prediction software such as for example ROSA (Dow Filmtech)  [28].
For the reasons mentioned above, it is more difficult to have the equivalent for NF. It should be noted that the ROSA software, which is well-recognized as a suitable tool for estimating RO membrane performance in relation to the plant operating conditions, also has an NF database. In this paper, comparison of performance prediction with the Nanoflux ® software, which is well adapted for NF transport mechanism, was carried out in the case of the NF270 and NF90 membranes, each being associated with a different group (Table  1 ). To do so, we confronted the model predictions with the results of filtration experiments carried out using the two simulated major types of Tunisian groundwaters. The obtained data were discussed in terms of water quality and productivity efficiency in order to optimize the membrane and process choice vs.
i Φ is the steric partitioning coefficient of ion i defined as the ratio between the available section (i. e. taking into account the finite size of the ion) and the pore cross section and D φ ∆ is the dimensionless Donnan potential arising at each nanopore/external solution interface. Finally, at the one membrane element level theoretical ion rejection predictions for multielectrolyte solutions can be obtained from: 1 p i i f i C R C = - (9) NanoFlux ® is a commercial nanofiltration simulation program (CNRS) that can solve numerically the HET model for up to eleven ionic species and composite membrane systems with up to three distinct layers. Given the composition of the feed solution, the characteristics of the membrane, and the configuration of the NF installations, NanoFlux ® uses an internal single salt database to predict the NF performance for arbitrary ionic mixtures in terms of ionic rejection and volume flux density  [32] [33] [34] [35].
ROSA prediction As above mentioned, ROSA is popular software and well-established prediction tool used successfully for the performance assessment and design of RO systems  [28]. This is illustrated by the modeling of ion rejections obtained in the case of Feed 3 treated with the BWRO membrane BW30. Although comparison with the experimental data in Figure  6  shows only fairly good overall agreement for all the ions studied, in the domain close to the rejection plateau (applied pressure beyond 10 bar) the agreement is excellent with deviations of less than 1 %. The ROSA database contains also NF membranes produced by Dow Filmtec including the NF270 and NF90. The performance of this prediction tool was then checked for the NF treatment of the studied Tunisian groundwaters. It was demonstrated previously that NF270 and NF90 should be the best choice for desalting Feed 1 and Feed 2, respectively.
Closer agreement between the experimental monovalent ion rejection data and the ROSA simulation curves (about 10 % at plateau) were obtained in this case. Actually, group 2 NF membranes exhibit performance similar to those of BWRO membranes. Consequently, although it is thought that the transport mechanism is different between these two kinds of membranes (tight NF and RO), the ROSA software can lead to a good estimation of the NF90 performance. In contrast, this is not the case for group 1 NF membranes. With a higher selectivity between monovalent and divalent ions (a signature of NF membranes) the difference in transport mechanism is brought to the forefront and experimental data for monovalent ion rejection could not be properly predicted by the ROSA software. Another point to mention is the discrepancy between the permeate flux simulation and experimental data.
As seen in Figure  7 , the maximum flux at 15 bar is about 50 % and 25% lower than the values obtained with NF270 and NF90, respectively. The increase in ionic strength leads also to a decline of the experimental permeate flow rate particularly noticeable in the concentration range between 10 -2 and 10 -1 M. This observation is due to the rise in osmotic pressure (Figure  8 , left). Although this qualitative effect on membrane performance is well taken into account by the ROSA modeling, the predicted amplitude of the flow rate drop is much greater than the experimentally observed one. Apart the large overestimation of ion rejection seen in Figure  8  (left) in the case of 10 -1 M NaCl feed, the pure water permeability data input in the ROSA database (about 11 L/h. m 2. bar) that is one and half times lower than the experimental one (16.
3 ± 1. 0 L/h. m 2. bar) could likely be responsible of the observed discrepancy for the 10 -3 and 10 -2 M NaCl feeds. The membrane surface charge originates from the ionization of functional groups and/or the adsorption of charged species, especially in the case of polyvalent ions  [43]. As seen in Interestingly, ROSA can predict more or less the rejection at pH values corresponding to the plateau (pH = 10) but fails at lower pH when the membrane surface charge shows a large variation. We do not know how the ROSA software is designed for predicting the rejection performance of NF membranes, but it appears clearly that the electrical interactions between the charged solutes and the membrane are not considered in their full complexity and totality. To further investigate this issue, the following section is devoted to the validation of a prediction tool, Nanoflux ® , specifically designed to address the particularities of NF membrane transport.
Nanoflux ® validation The Nanoflux ® prediction tool is based on an internal database containing the specific membrane parameters (pore radius % , normalized membrane charge and effective pore length   6  gives the different membrane parameters used by Nanoflux ® for establishing the simulation curves presented below. From these data, it can be seen clearly that the feed composition impacts the normalized membrane charge and thereby the electrical interactions with the solutes. As indicated before, the membrane charge density decreases with the increase of the feed ionic strength by charge screening. This accounts for the reduction of from Feed 1 to Feed 2. However, an increase of this parameter is observed for Feed 3 in spite of a further increase of the salt content. As can be noted from the composition of Feed 3 in Table  2 , the pH of this solution is 8 compared to about 6 for the previous ones, explaining the increase membrane charge.
By contrast, the group 2 NF membranes with ion rejections close to those of RO (like NF90) provided an optimal compromise for groundwaters from the sedimentary coastal aquifers containing a high proportion of chloride ions. -Validation of available software was undertaken to have a decision-making tool for selecting the right membrane as a function of the feed to be treated and the water quality to be produced. It was shown that Nanoflux ® , a prediction tool specifically designed for taking into account the complexity of NF membrane transport, can be a useful tool for this technology. On the other hand, the ROSA software designed for RO was not found to be generally useful for the prediction of NF membrane performance because it does not appear to account for the electric interactions between membrane and solutes.
'(( ) required to solve the set of equations describing the HET model. This database is built from the rejection data of neutral solutes (% ) and of representative single salts including NaCl, Na 2 SO 4 , MgSO 4 , CaCl 2 in different conditions of pH and concentration. By decomposing the feed solution in a combination of single salts, the software defines a new set of membrane parameters by mathematical interpolation according to the feed composition and pH. Calculation using the HET model then enables Nanoflux ® to predict membrane performance in terms of flux and ion rejection. For instance, Table.
& '(( ) required to solve the set of equations describing the HET model. This database is built from the rejection data of neutral solutes (% ) and of representative single salts including NaCl, Na 2 SO 4 , MgSO 4 , CaCl 2 in different conditions of pH and concentration. By decomposing the feed solution in a combination of single salts, the software defines a new set of membrane parameters by mathematical interpolation according to the feed composition and pH. Calculation using the HET model then enables Nanoflux ® to predict membrane performance in terms of flux and ion rejection. For instance, Table.
Finite difference time-domain simulations The simulations were performed using LUMERICAL 8. 18 (Lumerical Solutions Inc. , Vancouver, BC, Canada), a commercial software based on the FDTD method. We used a fixed number of scatterers for a simulation volume of 5 × 5 μm with a filling fraction (ff ) of ff = 0. 6. The value of ff royalsocietypublishing. org/journal/rsif J. R. Soc. Interface 17: 20200196 was decided to guarantee the same number of particles both in the disordered and in the perfect hexagonal packing. Periodic boundaries conditions in the lateral direction, i. e. perpendicular to the incoming beam, Y and perfect matching layer (PML) boundaries in the X direction were used in all the calculations. A plane incident wave is sent into the sample and the angular integrated reflection (-90°to 90°) is measured.
A total of ~50 mg of as-prepared α-Li 1 IrO 3 powder was sealed in the Swagelok cell in the Ar-glovebox. The Swagelok cell was then connected to the HIDEN mass spectrometer through a flexible 1 m capillary inlet and two porous stainless steel membrane filters (~2 μm pore diameter, Valco Instruments Co. Inc. ) for online gas analysis. A total of 2 ml of Ar-saturated KOH solution was injected using a syringe through the silicon stopper into the Swagelok cell. Blank test was conducted by injecting KOH solution into a sealed Swagelok cell without the α-Li 1 IrO 3 powder. Density functional theory calculations. Spin-polarized DFT calculations were performed using the Vienna ab initio simulation package  67  , projector augmentedwave pseudopotentials and an energy cutoff of 585 eV. The atomic simulation environment  68  was used to set up and analyze the structures.
Acknowledgements We would like to thank  S. Saha  for help with high-temperature XRD measurements,  M. Yassine , and  C. Robert-Laberty  for assistance with BET measurements,  M. Courty  for TGA-MS measurements,  T. Picard  for electrochemical measurements as well as  D. Giaume  for ICP measurements. We thank the  Diamond Light Source  of the award of beam time as part of the  Energy Materials Block Allocation Group SP14239. C. Y. , G. R. , J. -M. T. , and A. G. would like to acknowledge funding from the  European Research Council (ERC)  (FP:2014)/ ERC  Grant-Project  670116-ARPEMA. C. Y. would also like to acknowledge financial support from  National Natural Science Foundation of China  (Projects No. 21972172 ). A. G. acknowledge financial support from the  ANR MIDWAY  (Project  ID :  ANR-17-CE05-0008 ).
K. L. S, H. A. H. , and T. V. are supported by the  Velux Foundations  through the research center  V-Sustain  (grant no. 9455 ). A. G. , J. -M. T. , M. D. and D. A. D. C. thank the  French National Research Agency  for its support through the  Labex STORE- EX  project ( ANR-10LABX-76-01 ).
The samples are eventually chemically etched with an aqua regia solution. This last step leads to selective etching of the γ phase. The γ microstructure images are obtained with a Field Emission Gun JEOL 7000F Scanning Electron Microscope (FEG-SEM, JEOL, Ltd. , Akishima, Tokyo, Japan. Observations are conducted with a secondary electron detector at a constant acceleration voltage of 25 kV and a ~6 mm working distance. The smallest γ populations are analyzed using 15 images taken in randomly selected zones with a 50,000× magnification. The corresponding area covers 60 µm 2 and at least 5,000 particles per sample are analyzed in order to ensure a sufficient statistics. The Visilog ® 6. 9 software (Thermo Fisher Scientific, Waltham, MA, USA) is used for the subsequent image analysis. The particle characteristics studied are the mean equivalent radius, the surface fraction and density, and the inter-particle distance.
Results In this section, we show two important facts. First, the NRG-X-Change incentive system and the one described by  (Lilliu et al. , 2019b)  do not guarantee the existence of a NE in the game described in Section 3. Second, we will see that a NE always exists with the functions we have defined in the end of Section 4, as expected by the known theoretical results. A script in Python language has been created in order to simulate this framework. For this, real data relative to a Cardiff grid with 184 users, 40 of which are prosumers, have been used. More detailed information about the grid can be found in  (Sisinni et al. , 2016)  3. This is how the game has been simulated: 1. A set of N users, U 1 ,. ,U N , is randomly chosen from the 184 Cardiff users: depending on the scenario that we want to simulate, N is predetermined, and it is the number of users that are prosumers.
The library was run on an Illumina MiSeq instrument (2×300 bp) yielding 2. 4 Gb of data. Single nucleotide variations (SNVs) were identified using a bioinformatic pipeline, paloma (Polymorphism Analyses in Light Of MAssive DNA sequencing), which allowed comparison of the variants and the CB2 reference sequences. Based on the ssaha2 package (https://www. sanger. ac. uk/ science/ tools), paloma takes into account raw sequence data and associated qualities to discriminate between true variations and sequencing errors. We used two parameters, a quality score and a minimum coverage threshold, to analyse sequences. The first score indicated the confidence in the prediction and was computed as follows: SNP score=0. 5×S bio +0. 5×S tech , where S bio =allele rates and S tech =f(quality, strand bias).
First, multiplexed overlapping paired-end libraries with a mean insert size of 560 bp were constructed and run on an Illumina MiSeq instrument (2×300 bp) generating 347 Mb (Bio17B3), 461 Mb (Sup16B3) and 377 Mb (OC7) of data. In parallel, mate-pair libraries with an insert size of approximately 8000 bp were constructed and loaded on the MiSeq (2×150 bp) generating 549 Mb (Bio17B3), 736 Mb (Sup16B3) and 578 Mb (OC7) of data. A subset (equivalent to approximately 120× coverage) of the paired-end and the mate-pair reads was assembled using Newbler version 2. 9. Manual expertise was used to reconstruct the genome sequences. Automatic functional annotation was performed using the MicroScope platform (http://www. genoscope. cns. fr/ agc/ microscope). The GenBank/EMBL/DDBJ accession numbers for the genome sequences are LR131947-LR131949, LR131950-LR131952, LR131953-LR131955 and LR131956-LR131958 for Bio17B3, Sup16B3, OC7 and the new sequence of CB2, respectively (BioProject number PRJEB29999).
For the comparative genomic analysis, high-throughput sequencing (HTS) data were analysed using the paloma bioinformatic pipeline implemented in the MicroScope platform  [39]. First, the HTS data were pre-processed to assess quality by completing the following steps: read trimming, merging and splitting of paired-end reads. Second, reads were mapped onto the Thiomonas sp. CB2 reference sequence  [28]  using the ssaha2 package  [40]. Only unique matches with an alignment score equal to at least half of their length were retained as seeds for full Smith-Waterman realignment  [41]  with a region extended on both sides by 5 nucleotides of the reference genome. All alignments generated then were screened for discrepancies between read and reference sequences, and finally, a score based on coverage, allele frequency, quality of bases and strand bias was computed for each detected event to assess relevance.
CB2, Bio17B3 and Sup16B3 were grown in liquid cultures with shaking as described above, in M126 with 5. 33 mM arsenite or without arsenite. After 24 h, growth was halted by incubating cultures at 4 °C, the cultures were centrifuged at 8000 g for 15 min at 4 °C and stored at -80 °C. Three replicates from three independent cell cultures were sequenced. RNA extraction and sequencing were performed as described previously  [29] , using an Illumina HiSeq instrument (Illumina). Reads were cleaned to remove adapter sequences and low-quality sequences using an in-house program (https:// github. com/ baj12/ clean_ ngs). Only sequences at least 25 nt in length were considered for further analysis. Bowtie (v 0. 12. 7) using default parameters was used to align to the Thiomonas sp. CB2, Bio17B3 and Sup16B3 genomes  [42].
First, to ensure that resistance to arsenite was not due to slower cell growth, the growth rates of the eight variants and original CB2 strain were measured. We determined that the same growth kinetics were observed for variants as well as CB2, all displaying exponential phrases between 24 and 40 h, reaching a final OD 600 between 0. 2 and 0. 3 (see Fig. S4  for four of the growth curves obtained, additional data not shown). Variant Sup16B3 had a similar growth curve compared to CB2, whereas Bio17A3, Bio17B3 and Sup16A1 had longer lag phases. Growth rates were similar among all strains except for Bio17B3, which grew more rapidly after the lag phase. Second, the ability to form biofilms was assessed with the eight selected variants at different concentrations of arsenite by crystal violet staining and compared to the original strain Thiomonas sp.
Funding information This work was supported by the  Université de Strasbourg , the  Centre National de la Recherche Scientifique (CNRS)  and the  Region Alsace  (J. F. ). This study was also financed by  THIOFILM  ( ANR-12-ADAP-0013 ) projects. K. C. F. , O. C. and J. F. were supported by the  Agence Nationale de la Recherche ,  ANR THIOFILM  ( ANR-12-ADAP-0013 ). The Transcriptome and  EpiGenome Platform  is a member of the  France Génomique consortium  ( ANR10-NBS-09-08 ).
A variety of LCA approaches is available in the literature. Guinée et al. (2018)  provide a non-exhaustive review of the currently used LCA approaches. According to the authors, the most known and discussed in the literature are attributional and consequential LCA. Consequential LCA (CLCA) is defined by UNEP's Global Guidance Principles for LCA Databases as 'a modeling approach that provides information on the environmental burdens that occur, directly or indirectly, as a consequence of a decision, generally represented by changes in demand'  ( UNEP, 2011 ). Because of this direct relationship between CLCA and decisionmaking, this approach can play an important role in helping decision-makers of buildings and construction sector while providing a wider comprehension of the environmental impacts caused by their decisions, in particular when assessing large-scale policies.
Construction sector This review found 25 studies that adopted a CLCA approach to evaluate environmental impacts in the construction sector. This is a small sample, however, some observations may be pointed out. The first study dates from 2007 and, from 2013 onward, it is observed an increase in the interest for CLCA by the sector, with a peak of publications in 2016. This increase in interest is associated with the launch of the Ecoinvent version 3 in 2013, which included a consequential database based on the step-wise method provided in  ( Weidema et al. , 2009 ). Among the CLCA's works, 15 publications compare the ALCA and CLCA methodologies, most of them date from 2007 to 2014, showing that the dichotomy ALCA-CLCA has become less important in recent years. Table  1  presents the number of publications for each scale of change evaluated in the studies (small, medium and large).
The majority of works assesses small-scale changes (22 out of 25) and, of these, 8 explicitly specify the use of the Ecoinvent consequential database, corroborating the importance of an available database for conducting CLCA studies. The only publication found by this research that can be considered as a large-scale change assesses the increase in demand of 1 million apartments by year in Europe using wood-based construction materials  ( Eriksson et al. , 2012 ). The authors use an integrated approach by coupling CLCA with a partial equilibrium model. The time horizon of the studied change is explicitly mentioned in 15 publications. The great majority considers long-term changes, which is one of the assumptions from Ecoinvent consequential database as it is the situation "by default"  ( Weidema et al.
Partially supported by  Serbian Ministry of Education, Science and Technological Development  project  174033. ‡ CNRS, LIP,  ENS de Lyon. Partially  supported by  ANR  project  Stint  under reference  ANR-13-BS02-0007  and by the  LABEX MILYON  ( ANR-10-LABX-0070 ) of  Université de Lyon , within the  program Investissements d'Avenir  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). Also Partially supported by  EPSRC  grant  EP/K016423/1 , and  Serbian Ministry of Education and Science projects 174033  and  III44006.