dataesr/scientific-paragraphs-categorization huggingface dataset:
g. ,  Zanchettin et al. , 2016; Piecuch et al. , 2019). For instance, both characteristics are evident in the wavelet coherence spectrum of the autumn series at the interannual timescales, posing caveats for the truthfulness of the connection. Similarly, in winter coherent interannual variations in antiphase are only observed intermittently. We therefore complement the statistical connection between NAO and Venetian sea-level time series with maps of seasonal anomalies of near-surface wind and sea-level pressure over the Euro-Mediterranean region obtained by compositing the data around years characterized by positive and negative values of the NAO index (Fig. 8c, d ). As for compositing around Venetian sea level, for each season, the years were identified using the 10th and 90th percentiles of the seasonal time series of the NAO as thresholds.
, 2014). There are several satellite radar altimetry operational products (alongtrack and gridded) dedicated to the monitoring of open-ocean sea level whose quality is constantly improving. Various experimental coastal altimetry products are also now available and validated in some regions, allowing sea-level research in the coastal zone  (Gómez-Enri et al. , 2019). An updated table is accessible at https://www. coastalt. eu/community#datasets (last access: 5 August 2021). Importantly, merging altimeter data from different missions requires homogenous reprocessing and minimization of drifts and systematic biases between missions. Overall, progress is expected in terms of improving the altimetry estimates closer to the coast, perhaps with different instruments, and understanding the relationship between the coastal and close-to-the coast sea-level changes in a complex environment  (De Biasio et al.
, 2006; Amitai et al. , 2017). Such behavior could have important implications for future sea-level variability in the Venice Lagoon. Overall, even under accurate representation of global steric and mass addition from the Atlantic, projections of Mediterranean sea-level change from current regional ocean models would be reliable only in the basin mean tendencies. Further, comparison of the regional simulations with satellite-derived data highlights local biases in the historical sea-surface height patterns and trends as well as large inter-model heterogeneity in projected changes at the local scale driven by differences in simulated circulation changes. Simulation-data comparison using updated hydrographic datasets, such as EN4  (Good et al. , 2013)  or  Ishii et al. (2017) , could be extremely valuable.
Conclusions The city of Venice and the surrounding lagoonal ecosystem are critically affected by variations in RSL height driven by a host of diverse processes. These encompass oceanic processes driving sea-level variations from diurnal astronomical oscillations to climatic interannual to multi-centennial fluctuations and vertical land movements causing RSL variations on timescales ranging from a few decades -due to, for example, anthropic activities -to multi-millennial trends due to tectonic activity. This review summarizes and reassesses recent progress in the estimation, understanding and prediction of the individual contributions to RSL by exploiting new observational datasets, improved statistical methods and more realistic numerical simulations of ocean and Earth system components achieved in the past decade.
Hence local trend estimates are not expected to be representative of the city or lagoon as a whole. Due to the nonlinear variations caused by ground subsidence, a linear detrending of Venetian RSL time series is unsuitable unless data are preliminarily corrected for the effect of vertical land motion. Remote climate forcing from the Atlantic sector via atmospheric and oceanic processes critically contributes to interannual to multidecadal Venetian RSL variability. Atlantic hydrographic boundary conditions are a major source of uncertainty for future projection of Mediterranean sea levels: uncertainty in water mass flows at the Strait of Gibraltar yields an ensemble spread between simulations comparable to that determined by uncertainty in greenhouse gas emissions. We confirm the existence of a strong link between interannual and interdecadal variability observed in Venetian sea levels and in the large-scale atmospheric circulation over the North Atlantic during the cold semester, particularly with the North Atlantic Oscillation (about 46 % of shared variability for the detrended winter average time series in the period 1872-2019).
The review highlights several major gaps of knowledge as well. Among these, altimetry data are recorded rather far from Venice and may not represent the lagoon RSL variability; uncertainties in geologic trends remain difficult to assess; a reliable framework is lacking to combine uncertain future estimates of RSL change due to individual contributions, which provides for a major opportunity for progress to better constrain ranges of future projections; and historical evidence demonstrates that subsidence can be temporarily dominated by the anthropic component. This last point shows the importance of sustaining the management regime that brought this anthropogenic component under control across the lagoon and possibly of strengthening it to bring the ongoing small-scale anthropic subsidence under control.
Finally, whereas several studies explored scenarios of RSL changes in Venice at the end of the 21st century under global climate change, near-term and transient predictions have not been attempted yet, and high-end scenarios have not been a subject of explicit focus. Code and data availability. Relevant data used in this study can be obtained at https://doi. org/10. 5281/zenodo. 5139890 (Zanchettin et al. , 2021). Author contributions. DZ and SB coordinated the paper with help from FR. Specific contributions to the sections are as follows (LA = leading author, CA = contributing author). Section 1 was written by DZ (LA) with help from SB (CA). Section 2 was written by FR and SZ (LA) with help from MT, SV, GW and SZ (CA). Section 3 was written by SB (LA) with help from FA, EC, GS, GW and SZ (CA).
It is not associated with a conference. Acknowledgements. The authors want to thank the European Space Agency (ESA), which funded the Climate Change Initiative to produce a climate quality record of sea level from satellite altimetry. This work is partially funded by the ESA under Phase 2 Bridging Phase to CCI (contract no. 4000109872/13/I-NB -Contract Change Notice 6). The Centro Previsioni e Segnalazioni Maree of the Venice Municipality is acknowledged for providing the tidegauge data. Thanks are due to Francesco De Biasio (CNR-ISP) for the joint work during the CCI project. Wavelet coherence spectra are calculated using the cross-wavelet and wavelet coherence package by Aslak Grinsted, John Moore and Svetlana Jevrejeva. Linear regressions are performed with the "regress" function of MATLAB.
The code to compute regional sea-level projections was formerly developed by Gonéri Le Cozannet. Twentieth Century Reanalysis V3 data were provided by the NOAA/OAR/ESRL PSL, Boulder, Colorado, USA, from their website at https://psl. noaa. gov/ (last access: 5 August 2021). The authors thank the two anonymous reviewers for their comments and insight on the manuscript, which greatly helped to improve the paper. Review statement. This paper was edited by Uwe Ulbrich and reviewed by two anonymous referees.
Financial support. support Scientific activity was performed as part of the research program Venezia2021, coordinated by CORILA, with the contribution of the Provveditorato for the Public Works of Veneto, Trentino Alto Adige and Friuli Venezia Giulia. Giogio Spada is funded by a FFABR (Finanziamento delle Attività Base di Ricerca) grant of MIUR (Ministero dell'Istruzione, dell'Università e della Ricerca).
6. ) is carried out to achieve an agreement on the decision makers' profiles. Note that, Consensual GAHPSort has been incorporated into AFRYCA, as there is currently no commercial software tool to carry out the processes that make up model. Finally, all decision makers use the same consensual limiting profiles represented by the collective optimal value Z * (see Table  3 ). Remark 9. Note that the value of the threshold of acceptable consensus level 8 is a key aspect in our approach. Depending on the needs of consensus of the problem, this value can be adjusted, the smaller the value, the higher the consensus to be reached and viceversa. To show the influence of this parameter, this case study is carried out by using two different values of 8, one more restrictive 0. 15, and another more moderate, 0.
4. 2. Evaluations 5) The criteria have been pairwise compared in a questionnaire (Fig. 7 ) by the decision makers. The evaluations are then entered in the software application AFRYCA  (Labella, Estrella et al. 2017)  for calculating the criteria weights by using Eq. (  13 ) to achieve an agreement among the different decision makers regarding the priorities of the criteria by obtaining the adjusted collective opinion ̅ *. 4 ). Example 2: The first three rows in Table  4  show the non-consensual weights of the decision makers, computed from the criteria pairwise comparisons provided by them in the questionnaire by using Eq. (1). To compute the consensual weighs, first we extract one by one the comparisons between pair of criteria and use Eq. (  13 ) to obtain a consensual pairwise comparison for such pair.
Sample sites were randomly selected within each stratum. Five of the samples on the lower floodplain were under irrigated agriculture, a land use not represented on L'lara. A supplementary dataset of 30 existing sites on the dryland cropping areas of L'lara which are described in  Filippi et al. (2019) , and 20 sites on the pasture areas were also used as a test set for model predictions. At each of the 158 sites a topsoil (0 to 10 cm) sample was obtained by excavation using a shovel at a discrete location.
Spatial covariates for modelling and mapping slaking index A range of publicly available spatial datasets were used as input variables to model SI across the study area (Table  1 ). This included satellite imagery, a digital elevation model, terrain attributes, air-borne γ-radiometric maps, and a lithology indicator. Landsat 7 tier 1 surface reflectance satellite imagery from 2000 to 2018 was accessed through Google Earth Engine  (Gorelick et al. , 2017). To remove pixels that were affected by cloud cover or shading, a cloud-masking filter was applied to all images. The Normalized Difference Vegetation Index (NDVI) was then calculated for each pixel in each image. The 5 th , 50 th and 95 th percentile of the time-series of NDVI values were then determined for each pixel. The reason for using different NDVI percentiles was to characterise spatial variability in vegetation cover and vigour over the nineteen year period.
For example, the median (50 th percentile) gives a value of typical greenness and the 95 th percentile gives peak plant greenness. The 5 th percentile would likely be low and represent soil variability for areas that are tilled or heavily grazed, and remain higher for areas of perennial cover such as forests. The specific terrain attributes obtained included aspect, multi-resolution ridge-top flatness (MrRTF), multi-resolution valley bottom flatness (MrVBF), and slope. Gridded gamma radiometric data at 100 m spatial resolution derived from an air-borne gamma ray spectrometer was obtained through the Geophysical Archive Data Delivery System (GADDS) (Geoscience Australia, 2019). The individual datasets used included dose rate, and potassium concentration data, which were processed with low-pass filtering  (Minty et al.
Modelling and mapping procedure A regression-kriging approach was utilised to map SI across the study area. All data handling and processing was performed in the open software R (R Core Team, 2019). The data set was split into a training set (n = 108) and a test set (n = 50). At each of the 108 sampling sites in the training set, the spatial covariates described in Table  1  were extracted using the nearest neighbour method. A Cubist model was then used to build a relationship between SI and the spatial covariates at each observation point  (Kuhn and Quinlan, 2020). A 20 m grid of the study area was created and the spatial covariates were then extracted using the nearest neighbour method at each grid point. The developed Cubist model was then used to predict SI on this grid of the study area.
Mapping the simulated effect of increased soil organic carbon on slaking index Options to reduce soil slaking were investigated as a means to inform management practices. To achieve this the relationship between SI and measured soil properties was explored to identify potential causal factors of slaking and allocate observation points into classes with similar behaviour. Class-based regression was then used to construct individual predictive models between SI and these other measured soil attributes using either multiple linear regression or segmented, non-linear regression for more complex relationships  (Baty et al. , 2015). The effect of increasing soil OC levels on SI investigated by simulating a 1% increase in OC and applying the relevant class-based regression equation using the laboratory data at each point.
Mapping accuracy The quality of the predictions of SI from the regression-kriging approach was assessed using two validation techniques. The first technique involved using LOOCV on the training dataset (n = 108). This method showed that SI could be predicted to a relatively high degree of accuracy, with an LCCC of 0. 85, R 2 of 0. 75, RMSE of 1. 1 and a bias of 0. 0 (Fig. 5 ). The second approach involved comparing SI values observed for an independent test set (n = 50) with SI values extracted from the final map product. The second approach demonstrated the robustness of the model, as SI was predicted with similar accuracy to that of the training set, with an LCCC of 0. 82, R 2 of 0. 78, RMSE of 1. 1 and a bias of 0. 6 (Fig. 5 ). This demonstrates that SI can be accurately spatially predicted when using DSM techniques and ancillary spatial information.
1% was observed, below which slaking behaviour was not correlated with any of the measured soil properties and the most extreme SI values were observed. A regression-kriging approach utilising a Cubist model and diverse spatial covariates proved to be successful in spatially modelling SI. The model had high predictive power, with an LCCC of 0. 85 and RMSE of 1. 1, when using a LOOCV approach on the training dataset (n = 108). The results were also of high quality when assessed using an independent test set (n = 50), with an LCCC of 0. 82 and RMSE of 1. 1. The decrease in SI from a 1% increase in OC was also simulated and mapped across the study area. The results of this simulation suggested that considerable improvements in SI and soil aggregate stability could be achieved if practices that promote the sequestration of OC were implemented, particularly on cultivated areas.
The arch and strut sections are also clearly indicated in Figure  1(b)  along with the strut length, L. The distance along the stent-coating interface, x, originating at the top of the arch (x⁄(R=0)) is also indicated. A custom python code was developed to automatically generate stent meshes for parametric analysis. Both stent and coating are meshed using generalised plane strain elements. A mesh sensitivity study reveals that ten elements through the thickness of the coating provides a converged solution in terms of peak interface tangential traction at   =  2 ⁄ ⁄. Coatings are modelled as Neo-Hookean hyperelastic materials. In section 3. 1 the stent material is modelled as neo-Hookean elastic materials, representative of next-generation polymer stents. In Section 3. 2 the stent material is modelled using J2 plasticity materials with isotropic hardening, representative of metallic balloon expandable stents.
Furthermore, vaTIRF provides cellsubstrate binding energy, thus revealing a correlation between cell contractility and cell-substrate binding energy. Here, we highlight the quantitative measurements achieved by vaTIRF on U87MG glioma cells expressing different amounts of a 5 integrins and distinct motility on fibronectin. Regarding integrin expression level, data extracted from vaTIRF measurements, such as the number and size of high adhesive contacts per cell, corroborate the adhesiveness of U87MG cells as intended. Interestingly enough, we found that cells overexpressing a 5 integrins present a higher contractility and lower adhesion energy.
High and low adhesive contact hypothesis A typical vaTIRF measurement of a U87MG living cell adhered on glass coated with fibronectin is shown in Fig. 1. vaTIRF data acquisition and data processing were described in detail in a previous publication  (18). Briefly, by observing only the cell membrane in contact with the substrate (through a simple plasma membrane fluorescent labeling with DiO, Fig. 1  A), we have demonstrated that a stack of 10 different TIRF images, recorded by gradually increasing the incident angle of the laser beam on the sample at the acquisition rate of $1 s, allows us to reconstruct the cell membrane topography of adhesive cells with a nanometric axial precision (Fig. 1 B ). vaTIRF also permits us to map the effective refractive index, n eff , of the cell within the evanescent field.
S2 A ). Because we are able to control precisely the MY-133-MC layer thickness by dip coating  (26)  and the refractive index value of the water/glycerol mixture  (29) , we can measure the n eff variation for different MY-133-MC gaps and various n 3. It appears that for a given n 3 , the n eff -values obtained with vaTIRF are similar regardless of the thickness range (50-150 nm) of the MY-133-MC layer. Thus, for each water/glycerol mixture on the top layer, we decided to take the average of n eff -values measured for three different MY-133-MC gaps (50, 100, and 150 nm). As intended, Fig. S2  B shows that n eff increases along with n 3 , even though the measurement accuracy is debatable. These data can be fitted with a saturation-like function, thereby providing an analytical expression to transform n eff into n cortex (Fig.
They are supposed to include several membrane components that support weak binding energy, such as proteoglycans, or pure nonspecific repulsive force, such as glycocalyx. Compared to other techniques used to quantify cell adhesion, vaTIRF, like traction force microscopy, appears to be a nonperturbative method. For instance, techniques enabling direct force measurements related to cell adhesion strength, such as AFM or flow chamber, can drastically disturb cell properties. A shear stress can induce important modification of the cell speed and contractility  (47). Compared to other large field-of-view imaging techniques suited to probe cell spreading and adhesiveness, typically RICM, vaTIRFM appears to be a valuable alternative. Despite the impressive recent progress of multiwavelength RICM  (22) , data processing of such interferometric techniques is still complex.
On the contrary, vaTIRF data analysis is easy to implement. Moreover, membrane height h reconstruction with usual RICM (or multi-l RICM) needs a perfect knowledge of the cytoplasmic refractive index. The latter is very inhomogeneous and changes drastically over time and space. Thus, h-measurement with RICM is limited to cell membrane side protrusions, such as large and flat lamellipodia  (22). However, the key advantage of vaTIRFM is that we do not need to actually know the cytoplasmic refractive index, seeing as we measure it in addition to the membrane height. On top of that, we are currently working on a new, to our knowledge, strategy based on objective back-focal plane observation to improve n cortex precision  (57) (58) (59). Such observation can be easily implemented and provides a fine quantification of the supercritical angle that is directly given by the refractive index of the sample.
Undeniably, a large range of data is available with vaT-IRF. One can determine for both strong and weak adhesion the height of the plasma membrane, the size of the cell-substrate contact area, and the binding energy. Regarding HACs associated with strong adhesion, their individual size and their number per cell can be assessed. We also revealed how cellular contractility can influence the binding energy. As a consequence, adhesion studies conducted on U87MG cells revealed that as expected, the number of HACs per cell increases along with integrin expression level. Moreover, as expected, the higher migration speed of U87MG cells overexpressing a 5 integrins is associated with a low binding energy because of a greater contractile force. This finding was also previously observed with MDA-MB-231 cells overexpressing a 5 b 1 integrins  (41).
Moreover, our study surprisingly reveals that low adhesive contacts are also affected by integrin level and play a crucial role during adhesion. This can be explained by a cross talk between integrins and Rho-GTPases, which controls cell's contractility through myosin motors and the Arp2/3 complex (60). Actin cortex is then disturbed by integrin overexpression, as well as the plasma membrane  (61). This work opens new, to our knowledge, promising opportunities to probe integrin-or actin-mediated adhesion and migration. Further studies can be proposed when cell migration is guided along one direction according to the surface gradient of extracellular matrix proteins. This surface gradient can be obtained by electropolymerization and microfluidics  (62)  or by using micropatterning. Capitalizing on all the data achievable with vaTIRF, various drugs acting on focal adhesion and actin network can be tested during migration, such as integrin antagonists or contractile forces inhibitors.
Presentation of the DACCOSIM-2017 platform DACCOSIM  [15]  is a physical system platform based on the FMI for co-simulation standard (FMI-CS) used in its 2017 version. It has been developed by CentraleSupélec and EDF R&D as part of the RISEGrid institute. DACCOSIM allows running many heterogeneous gray boxes, called FMU-CS, on multicore PC clusters that interact with each other. This platform integrates wrappers that encapsulate FMI API, in order to manipulate these FMUs. So, it uses the JavaFMI 6  package in Windows and Linux versions. Moreover, DACCOSIM 2017 includes a user-friendly Graphical User Interface (GUI) for designing and performing a complex system. Note that DACCOSIM 2017 allows designing the physical system either through a ''DSL'' (Daccosim Script Language) code or its GUI.
Global architecture DACCOSIM 2017 achieves parallel and distributed co-simulations, dedicated to physical systems, with a hierarchical co-simulation master  [24]. In our attempts to design, distribute and co-simulate on cluster nodes very wide systems composed of thousands of FMUs, we needed both to synchronize all the communication points (for accuracy purpose) and decentralize the usual control function of the Master Algorithm (for performance purpose). As shown in Fig. 1 , the overall architecture of DACCOSIM consists of a set of local masters supervised by a global master. The leaves of this architecture represent blocks of co-simulation, containing a wrapper code connected to the FMUs following the FMI standard. Each local master controls its simulation blocks located in the same compute node.