dataesr/scientific-paragraphs-categorization huggingface dataset:
We subsequently computed the vertical meridian asymmetry index as the difference in area between the lower and upper visual fields, divided by the mean of the two areas, and multiplied by 100: VMA ¼ lower visual field area À upper visual field area À Á meanðlower visual field area; upper visual field areaÞ 100 Indices close to 0 indicate no asymmetry between the areas of the two hemifields. Lower indices reflect a vertical asymmetry with a larger area dedicated to the upper than to the lower visual field. In contrast, higher indices reveal a vertical asymmetry with a larger area for the lower visual field than for the upper visual field. We excluded two older adults from the visual field analyses because their kinetic perimetry data were extracted in a wrong format. The comparison between their data and those from other participants was thus impossible.
Data analysis. We processed the behavioral and eye-tracking data using custom Python code (Python version 3. 8. 1 in Spyder IDE version 4. 1. 5) and we conducted all statistical analyses using R version 4. 0. 3 in RStudio version 1. 4. 1103 (R Core Team, 2020; RStudio Team, 2021). We disclose that there was no preregistration of the analysis plan for this research. To classify gaze data into fixations, we chose the velocity-based algorithm implemented in the saccades R library  46. Before analysis, we removed all fixations that lasted under 100 ms. We then used the 2/4 method and corresponding Fujii classification to assess fixation stability and identify participants who failed to maintain central fixation throughout the encoding phases  47, 48. The latter method considers participants to have unstable fixation if less than 75% of their fixation points are inside a 4°circle.
The normality of residuals of the two linear mixed models were carefully inspected using residual plots. Reaction time is positively skewed by nature. Following recent recommendations on how best to model such data  51  , we conducted two gamma generalized linear mixed models with a log link function to analyze reaction times for the item memory and spatial memory tasks. In both models, the random-effects structure included participant and block as random intercepts along with the random effect of trial in each age group. The fixed effects were age group, object position, and block number. Sex was removed because it did not improve model fit. We performed MPT modeling to distinguish item and spatial memory between objects presented in the upper part of the screen and objects presented in the lower part of the screen.
e. , answering that the item was old), and the parameter g estimates the probability of guessing that the item was presented in the upper part of the screen, given that item recognition has failed. We fit the behavioral data to the MPT model for each age group separately with the TreeBUGS package in R  58. This R package allows hierarchical models to be fit using Just Another Gibbs Sampler (JAGS) to estimate posterior distributions. Specifically, we used latent-trait hierarchical models because they take into account both interindividual variability and parameter correlation within participants  59. We kept the weakly informative default priors provided by the TreeBugs package and included sex as a covariate in the MPT models. The models estimate the latenttrait parameters using Markov chain Monte-Carlo simulations in JAGS.
We obtained posterior distributions of the age differences from which we extracted the mean and 95% credibility interval. An alternative approach to evaluating group differences consists in summing trial frequencies across young and older participants and fitting the aggregate data in a single extended MPT model. We conducted this analysis in order to further evaluate how the difference between I down and I up as well as between S down and S up changed across age groups (Supplementary Methods). Reporting summary. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Sdown Sup Mean group-level parameter estimates on probability scale Fig. 4  Behavioral performance on the spatial memory task evaluated with standard Pr and MPT analyses. Spatial memory is tested during the spatial memory task in which participants decide whether the object appeared in the upper or lower visual field. a Bean plots depicting the results obtained using standard Pr analyses (n = 25 young + 20 older participants). Source Pr corresponds to the percentage of correct answers weighted by the number of answered trials. We found evidence for a main effect of age (F(1, 43) = 16. 78, p < 0. 001, η p 2 = 0. 28, 95% CI [0. 08, 0. 47]) and a significant interaction between age and object position (F(1, 43) = 5. 37, p = 0. 025, η p 2 = 0. 11, 95% CI [0. 00, 0. 30]) on Source Pr. The bean plots provide the density curve for each group side-by-side, along with individual data points in a colored rug-plot.
Acknowledgements The authors would like to express their most sincere gratitude to the volunteers who took part in this study. We also thank  Elisa Tartaglia  and  Valérie Parmentier  ( Essilor International ) for their helpful inputs. This research was supported by the  Fondation pour la Recherche sur Alzheimer , the  French National Research Agency  ( ANR-18-CHIN-0002 ), the  LabEx LIFESENSES  ( ANR-10-LABX-65 ), and the  IHU FOReSIGHT  ( ANR-18-IAHU-01 ). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Velocity field Taking advantage of the 2D-nature of the granular film, we placed a camera underneath the cell to capture, through a glass window, the movement of the particles in the sheared film (Fig. 1(a) ). The images are then processed with DIC software (DaVis, LaVision) to compute the instantaneous velocity field V(r,y,t) of the granular film (Fig. 1(b) ). Given the axisymmetry of the setup, the results are discussed in polar coordinates (r,y) with a reduced radial position defined as s = (r À R)/e. In the permanent regime, we consider the mean velocity as hV(s,y,t)i = V r (s,y)e r + V y (s,y)e q , with e r (resp. e q ) the unit vector in the radial (resp. orthoradial) direction and hÁi the temporal average. Finally, the velocities will be discussed through their normalized values v = hV(s,y,t)i/V w , with V w = RO the velocity of the moving wall.
Acknowledgements We are grateful to  L. Auffray ,  J. Amarni ,  A. Aubertin ,  C. Manquest  and  R. Pidoux  for the development of the experimental setup. The authors thank  N. Retailleau ,  Y. Khidas ,  X. Chateau ,  V. Langlois ,  O. Pitois  and  F. Rouyer  for fruitful discussions. This work is supported by the project  PhyGaMa ANR-19-CE30-0009-02  and '' Investissements d'Avenir ''  LabEx Physique: Atomes Lumiere Matiere  ( ANR-10-LABX-0039-PALM ).
5: 20 𝑙𝑜𝑔 D 𝐸 "#$%& E𝜃 *+,%-. F 𝐸 "#$%& (𝜃 /0&& ) 9 G = 17 𝑑𝐵 (5) We used the Matlab multi objective genetic algorithms from the Global Optimization Toolbox  [14]  to optimize this design. The multi objective genetic algorithm gives us 70 dominant solutions for the optimization problem. We restricted these solutions by selection only those which respect the inequalities in Eq. 6. 𝑐𝑜𝑠𝑡 ' < 3𝑑𝐵, 𝑐𝑜𝑠𝑡 ( < -10𝑑𝐵, 𝑐𝑜𝑠𝑡 ) < 15° (6) We manually selected a solution with a good trade-off between the three cost functions from this reduced set of dominant solutions and the design currents obtained from this solution are presented in Table  1.
The manufacturing process of VDs is more complex than FDs or MDs, which takes up to five months. In order to demonstrate the effectiveness of the dampers, there are either the experimental  [18, 35, 45, 71, 78, 88, 98, 112, 121, 131, 139, 143, 150, 160, 167, 176]  or the numerical researches  [14, 42, 49, 73, 75, 80, 86, 127, 140, 142, 169]. FE models play a key role in the ordinary design process of new structures and in the assessment of existing ones. The Finite Element Method (FEM) has become the most popular method in both research and industrial numerical simulations, as it takes into consideration material laws, contact interface conditions, and other parameters, which lead to the exact response of the strengthen-ing solutions and reduce the cost of the experimental test. Without wanting to promote any software, some of the used software are Abaqus, Ansys, Comsol, Etabs, Sap2000 etc.
Elite children are the individuals in the current population with the best fitness values. These individuals automatically survive to the next population without any change and the number of elite children is ap 0. Crossover children are created by combining the vectors of a pair of parents and the number of crossover children is (1-a) bp 0. The rest are mutation children that are created by introducing random changes, or mutations, to a single parent. The process described above is for one optimization round and this optimization round is repeated until the average change in the fitness value is less than the specificized value. In this paper, the GA toolbox embedded in MATLAB is utilized to solve the optimization problem and, by default, a = 0. 05, b = 0. 8. The objective function in this paper is defined as the summation of the difference between optimized amplitudes and target amplitudes and the difference between optimized phases and target phases as minF(x) = α|r t -r o | + β|φ t -φ o | x = (h R1 , h R2 , h R3 , h R4 , h R5 , h R6 , h R7 , h R8 ) s.
Data This study uses Automatic Dependent Surveillance-Broadcast (ADS-B) data retrieved from the OpenSky Network  [31] , which contains, among other parameters, time-stamped position and altitude observations of aircraft. Go-arounds that have occurred since the year 2017 were retrieved and a total of 2,522 eastbound departures from the year 2022 were collected for analysis using the traffic library  [32]. Out of approximately 800 GA collected, only 386 were retained for this study. Many of the retrieved GA did not adhere to the published procedure due to early left turns, which was the result of air traffic control interference. As the primary objective of this study is to assess the safety of the published procedure, only GA that followed the published procedure were considered for further analysis.
Collision probabilities With the GPD fitted to the tail, we employed Equation (  6 ) to express the probability of a mid-air collision. However, in the practical example presented in this study, a collision can only occur if landing aircraft is performing a GA. Thus, the probability of a collision is conditioned on a GA occurring simultaneously: P(MAC | GA) ≈ 𝑁 𝑛 ⋅ ( 𝜆 𝑢 ) -1∕ ξMLE ( 11 ) Using the chain rule, the probability of a collision is expressed as P(MAC) = P(MAC | GA) ⋅ P(GA) (12) with P(GA) being the probability of a GA. Note that, as stated in Section 4. 2, it is assumed that every time a take-off is released, an aircraft is on the approach of runway 14. P(GA) was estimated by using the dataset proposed by Monstein et al. [33] , which contains a large number of both landings and GAs at Zurich Airport (amongst many others).
This dataset has been expanded to include landings from 2020 to 2023, allowing for a more precise estimation of the GA rate on runway 14 at Zurich Airport. Out of 181,933 recorded landings on runway 14, 598 resulted in a go-around. Consequently, the estimated GA probability is P(GA) ≈ 598∕181, 933 ≈ 3. 3∕1, 000. Finally, we estimate the probability of a MAC as follows: • Probability of a collision, given a simultaneous GA: • Probability of a GA: P(MAC | GA) ≈ 𝑁 𝑛 ⋅ ( 𝜆 𝑢 ) -1∕ ξMLE ≈ 73 973, 492 ( 𝜆 𝑢 ) -1∕ ξMLE P(GA) ≈ 598 181, 933 ≈ 3. 3 1, 000 • Probability of a mid-air collision: P(MAC) = P(MAC | GA) ⋅ P(GA) ≈ 3. 68 × 10 -7 ⋅ 3. 3 1, 000 ≈ 1. 22 × 10 -9 The estimated probability for a mid-air collision is P(MAC) = 1. 22 × 10 -9. This implies that an eastbound departure from runway 16 has a probability of a mid-air collision of 1.
Experiments The experiments (Fig. 5b-e ) comprise a 12 × 12 × 12 cm 3 transparent box. The box is attached to a horizontal shaker (Tira® S51120) fed with a harmonic signal from a signal generator (Agilent® 33220A) through an amplifier (BAA500). The box's face perpendicular to the shaking direction is filmed by a high-speed camera (Photron® SA5) at a rate of 250 frames per second. The frames are analyzed by using MATLAB® image processing toolbox and PIVlab  70, 71  , an open-source MATLAB® toolbox, to identify changes in the layer's height and define instantaneous grain velocity. An array of three pressure transducers (Honeywell 24PC) is mounted vertically on the opposite parallel face of the box at depths of 1, 3. 6, and 6. 1 cm above the box base. Before the experiment starts, the experimental box is filled with tap water, and the pressure transducers are calibrated under hydrostatic conditions.
Acknowledgements S. B. -Z. and R. T. thank  Alain Steyer  and  Miloud Talib  for technical assistance with the experiments,  Martine Trautmann  for granulometry and the support of  Institut Français d'Israël and Campus France  via the  Chateaubriand Fellowship. S. B. -Z and R. T. wish to thank  Yossef Hatozr  and  Valérie Vidal  for fruitful discussions. S. B. -Z. and E. A. wish to thank  Assaf Klar  and  Eitan Cohen  for a fruitful discussion. R. T. wishes to thank  Eirik Flekkøy  for fruitful discussions. L. G. acknowledges support from  ISF  grant  562/19. E. A. thanks  ISF  grant # 910/17. R. T. acknowledges the support of the  CNRS   INSU ALÉAS and CESSUR programs , the  CNRS   MITI program , the  Universities of Strasbourg and Oslo  and the  Research Council of Norway  through its  Centre of Excellence  funding scheme, project number  262644.
Acknowledgements This work was supported by  Integris Composites ; and  Direction Générale de l'Armement  [grant number  2020867 ]. The authors acknowledge the assistance of  Pauline Respaud ,  Jean Beugels  from  Integris Composites ,  Gregory Haugou ,  Hervé Morvan ,  Rémi Dellile  from  LAMIH Laboratory , and  Georg Gutter ,  Nicolas Montmasson ,  Josip Novak  from  ISL.
Intensive land use for bioenergy or for carbon sequestration by the Agriculture, Forestry, and Other Land Use (AFOLU) sector could also lead to land degradation, food insecurity, biodiversity loss, and water scarcity  28, 29, 31. The Working Group III (WGIII) contribution to the Intergovernmental Panel on Climate Change (IPCC)'s Sixth Assessment Report (AR6) compiled and assessed 3131 scenarios generated by almost 100 different model versions from more than 50 model families, with varying regional scope and temperature outcomes 21. This new scenarios database is likely to be extensively used to inform socio-political discourse on energy system transformations in the years ahead, including on identifying fossil fuel reduction pathways that are consistent with the Paris Agreement 32,33. However, there is a large variety of IAMs and associated modelling approaches and assumptions, resulting in important differences in the outputs.
The mitigation scenarios database of the Intergovernmental Panel on Climate Change's Sixth Assessment Report is an important resource for informing policymaking on energy transitions. However, there is a large variety of models, scenario designs, and resulting outputs. Here we analyse the scenarios consistent with limiting warming to 2 °C or below regarding the speed, trajectory, and feasibility of different fossil fuel reduction pathways. In scenarios limiting warming to 1. 5 °C with no or limited overshoot, global coal, oil, and natural gas supply (intended for all uses) decline on average by 95%, 62%, and 42%, respectively, from 2020 to 2050, but the long-term role of gas is highly variable. Higher-gas pathways are enabled by higher carbon capture and storage (CCS) and carbon dioxide removal (CDR), but are likely associated with inadequate model representation of regional CO 2 storage capacity and technology adoption, diffusion, and path-dependencies.
Sensitivity of global fossil fuel reduction pathways to different mitigation strategies and uncertainties in CDR potential To further assess and demonstrate how much the reduction pathways of coal, oil, and gas are influenced by different scenario storylines and assumptions, in this section, we first focus on a number of individual illustrative scenarios and then explore what happens when we take a conservative approach to future CDR and fossil-CCS potential. The AR6 scenarios database identified five different "illustrative mitigation pathways" (IMPs) that reflect different prominent mitigation strategies for reaching a given temperature outcome (see Fig. 5 legend ). We emphasize that no two scenarios are alike or more "correct" than others; the IMPs are not representative of the AR6 ensemble.
Methods The IPCC AR6 WGIII scenarios database The raw data and metadata from all models and scenarios were downloaded from the IPCC AR6 WGIII Scenarios Database (release 1. 1) hosted by the International Institute for Applied Systems Analysis  94. Although the "IMP-Neg" scenario ("EN_NPi2020_400f_lowBECC") from the COFFEE 1. 1 model is technically categorized as a C3 in the database, we categorize it as a C2 scenario here, following the IPCC AR6 WGIII Chapter 3, Table  3. 2 (i. e. , "The warming profile of Neg peaks around 2060 and declines to below 1. 5 °C (50% likelihood) shortly after 2100. Whilst technically classified as a C3, it strongly exhibits the characteristics of C2 high overshoot scenarios"). Model outputs are available at 5-or 10-year intervals; for the latter, linear interpolation is applied to derive values at 5-year intervals for all scenarios.
Classification and regression tree analysis (CART) CART is a commonly used algorithm for identifying groups of scenarios by a sequence of classification rules  38. CART can statistically demonstrate which factors are particularly important in a model or relationship in terms of explanatory power and variance while presenting the data in a way that is easily interpreted by those not wellversed in statistical analysis. We performed a CART analysis to identify the combinations of characteristics and features that are most predictive of the modelled levels of cumulative 2020-2100 supply of coal, oil, or gas in each temperature category (C1-C3 scenarios); the results are shown in Fig. 2  and Supplementary Figs. 6-11. We implemented our CART modelling using the R package rpart  95. CART analysis consists of finding splits of the independent continuous or categorial variables that yield the strongest possible predictions of a dependent variable.
Acknowledgements P. A. thanks  Michael Lazarus  ( Stockholm Environment Institute ) for helpful feedback on manuscript drafts. P. A. and P. E. acknowledge funding support from the  Energy Transition Fund  (Grant number  G-21-2122456 ), a project of  Rockefeller Philanthropy Advisors (RPA). R. S. acknowledges funding support from  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ,  Brazil  (Grant number  310992/ 2020-6 ). C. G. acknowledges funding support from the  European Union 's  Horizon Europe research and innovation programme  under grant agreement No. 101081604  (PRISMA). Any errors are the sole responsibility of the authors.
Data availability All raw data and metadata of the scenarios analysed in this study were downloaded from the IPCC AR6 Scenarios Database (release 1. 1) hosted by the International Institute for Applied Systems Analysis, available at: https://data. ece. iiasa. ac. at/ar6/. Data on the feasibility assessments of the C1-C3 scenarios used in this study, and all data generated in this study, can be downloaded from https://github. com/ ploy-a/NCOMMS-AR6-FF.
Maintenance in nuclear facilities As seen in the introduction, 18857 articles were found in the Web of Science database considering "Maintenance" and "Nuclear" keywords. The process for relevant papers collections and selections consisted of three main phases. First, we have filtered 18857 articles by categories, considering only papers that are closed to our research area. Then, we keep papers written in English that are journals and proceedings and removed duplicated articles. At least, a total of 8882 research papers were short-listed for classification. In order to deepen our investigation of the short-listed papers, VOSviewer software  [22]  was used to carry out an analysis of the co-occurrence of authors keywords (Figure  1a ). Firstly, we can highlight a cover of keywords around maintenance, CBM, PdM, fault diagnosis in the NPP.
Also, the support by the management is very important. We identified four important factors for PdM implementation: Data integration (highly facilitated by Cloud-based mechanisms), failure detection (enabled by advanced analytics), PdM operations execution (supported by data-driven process automation) and the visualization  [19]. It is important to note that the failure modelling approaches for PdM are a bit common to the maintenance and fault computing. PdM also relies on two distinct approaches: -The first approach is the probabilistic approach. It is based on a statistical and reliability analysis. The input data is retrieved from the Computerized Maintenance Management Information System (CMMIS) software. The CMMIS centralizes maintenance information and facilitates maintenance operations processes.
A new method called Prognostic and Health Management (PHM) was introduced  [2, 24]. The PHM is an engineering process where algorithms are used to detect anomalies, diagnose faults and predict RUL. Moreover, the financial benefits such as operational and maintenance cost reductions and extended lifetime are achieved with this method  [21]. To implement a PHM, many steps are required. Author of  [2]  addresses the Open System Architecture for Condition-Based Maintenance (OSA-CBM), and draws up six steps for performing a PHM based on ISO 13374: Data acquisition (Access to digitized sensors), Data manipulation (perform single and/or multichannel signal transformations), State detection or Fault Detection, Diagnosis step, Prognostics assessment and Advisory generation  [2, 5, 7, 24]. The architecture system for the PHM includes a database which stores data at each step and a Human Machine Interface for visualization.
Predictive maintenance in a nuclear context The state of the art on maintenance has shown that sometimes, a maintenance intervention can be complex especially when a failure is not anticipated. That is why nuclear industry is today moving towards PdM to overcome this problem. The explosion of digital technologies has enabled the collection of sensor data to better model the failure of an equipment. However, is this industry enough mature in deploying such maintenance strategy? In this sense, we made a literature review based on Web Of Science. 90 articles have been found considering the combination of the following keywords: "predictive maintenance" and "nuclear". By using the same process as seen previously for relevant papers collection, we finally get a total of 44 research papers. VOSviewer software makes it possible to analyze the co-occurrence of authors keywords.
Critical analysis and conclusions The research literature conducted in this paper enhance various research aspect of maintenance in nuclear. To come with clear and constructive outcomes, we moved from exploring the maintenance in the nuclear industry with special focus on PdM as a whole and PdM in nuclear industry. The first finding of this literature study is the gap in the use of PdM technologies between industries. In fact, the maturity is not the same. All the papers that introduced PdM using AI in the nuclear industry are very recent in comparison with the airline industry. In order to maintain and optimize their facilities, nuclear operators use traditional methods based on failure probability analysis, failure modelling, most often computed using data retrieved from CM-MIS. These analyses contributed in computing some classic performance criteria such as MTBF, MTTR.