dataesr/scientific-paragraphs-categorization huggingface dataset:
Model Development and Curation Figure  5  outlines the work flow used for model development. Our previously developed maize model, iRS1563  (Saha et al. , 2011) , and biological databases such as MetaCrop (downloaded in December 2012;  Schreiber et al. , 2012)  and MaizeCyc (version 2. 0. 2; Monaco et al. , 2013)  provided information pertaining to the genes, proteins, reactions, and metabolites used to reconstruct the second-generation maize leaf genome-scale model. In addition, available proteomic and transcriptomic data, maize-specific biological databases, namely MetaCrop and MaizeCyc, and published literature were used to assign cellular (i. e. bundle sheath or mesophyll) and intracellular organelle specificity to the curated reactions. When the gene expression level was reported in reads per kilobase per million mapped reads (RPKM;  Li et al.
Since low counts are not statistically informative, a cutoff of 10 was used for adjSPC  (Zybailov et al. , 2008; Kim et al. , 2009). Similar to the threshold used for RPKM data, a 5-fold difference between the mesophyll and bundle sheath cell type normalized spectral abundance factor was used to determine the cellular specificity of any gene  (Friso et al. , 2010). The normalized spectral abundance factor is a weighted adjSPC based on the number of theoretical tryptic peptides with a relevant length  (Ehleringer et al. , 1997; Friso et al. , 2010). Additional intracellular compartmentalization was carried out based on the MetaCrop database  (Schreiber et al. , 2012) , the MaizeCyc database  (Monaco et al. , 2013) , and primary literature sources  (Chang et al. , 2012; Zhao et al. , 2013). The intracellular compartmentalization was determined based first on the MetaCrop database  (Schreiber et al.
, 2012) , literature sources  (Friso et al. , 2010; Chang et al. , 2012) , compartmentalization information in the MaizeCyc database, and finally the Plant Proteomics Database  (Sun et al. , 2009). An original set of intercellular and intracellular transporters was determined based on literature evidence  (Alberte and Thornber, 1977; Leegood, 1985; Stitt and Heldt, 1985; Furbank et al. , 1989; Weiner and Heldt, 1992; Doulis et al. , 1997; Burgener et al. , 1998; Taniguchi et al. , 2004; Sowi nski et al. , 2008; Friso et al. , 2010). In the subsequent standardization step, the MetRxn knowledgebase  (Kumar et al. , 2012)  as well as manual curation were used to standardize the description of metabolites and reactions such as fixing stoichiometric errors (i. e. elemental or charge imbalances) and incomplete atomistic details (e.
g. absence of stereospecificity and presence of unspecified side chains). Reactions and metabolites were given the Kyoto Encyclopedia of Genes and Genomes identifiers where available or were otherwise given new identifiers (in the form of MR or MC, respectively). Reaction directionality was adopted from the manually curated MetaCrop database, as available, and from the Maize-Cyc database for the remaining reactions. In the next step of model development, all reactions (including metabolic, intracellular, and extracellular transport reactions) were divided into two categories based on the evidence of their intercellular and intracellular compartmental specificity. The core set contains all metabolic reactions with experimental or literature-backed evidence of intracellular or intercellular compartmentalization as well as known intracellular and intercellular transporters.
In the final step, as shown in Figure  5 , the GapFind/GapFill  (Kumar et al. , 2007)  procedure was applied to identify blocked/dead-end metabolites and subsequently restore their connectivity. A gap-filling database of reactions was created by combining reactions from phylogenetically close/model plant species (i. e. rice [Oryza sativa ssp. japonica], Brachypodium distachyon, sorghum [Sorghum bicolor], and Arabidopsis thaliana), noncore reactions without compartmental specificity (not identified by our aforementioned algorithm), and all possible intracellular/intercellular transporters. The gap-filling procedure was modified by prioritizing the addition of reactions from closely related/ model plant species or noncore reactions over transporters to unblock the flow-through metabolites while ensuring that no new thermodynamically infeasible cycles are created.
The formulation was run in an iterative manner for each metabolite with experimental measurements. The formulation was also repeated for each individual condition, ensuring that the proper nutrients and simulated knockouts were considered. By linearizing the objective function, the resulting formulation is a mixed-integer linear programming problem similar to the description by  Chung and Lee (2009). Therefore, the basic idea is to determine the range of the flux sum of a metabolite (for which metabolomic data are available) under a given condition by switching off reaction fluxes corresponding to gene transcripts and/or proteins with lower expression levels (i. e. constraint 9). The flux-sum ranges were determined at the maximum biomass for the condition as displayed in constraint 10. Predictions were made only when the flux-sum ranges did not overlap between the N background condition and the N + WT condition and when the direction of change in all compartments was consistent.
These are reactions that must change in response to the limited amount of N or the mutant conditions. Finally, we determined for each condition the minimum number of reactions that, when not regulated, will restore the biomass to the yield obtained when no omics-based regulation is applied. This was done by identifying the minimal set of reactions, included in the omics-based regulation, that when active would allow for a biomass yield equivalent to the yield under no omics-based regulation. This set of reactions represent the reactions whose restriction affects the biomass yield. The CPLEX solver (version 12. 3 IBM ILOG) was used in the GAMS environment  (version 23. 3. 3; GAMS Development)  to solve the optimization problems. The Python programming language was also used during model development (mainly for scripting and data analysis).
Purpose: The paper describes the methodology of contact problem solving that is used for simulation of aircraft assembly process. Methods: The dimension of the problem is reduced after eliminating the unknowns outside the contact zone and the initial problem transforms into the problem of quadratic programming with linear constraints. Results: The special computer code ASRP (Assembly Simulation of Riveting Process) was developed on the base of presented methodology. Now this code is utilized by Airbus. Some results of application of developed code are given in this paper.
It also contributes to addressing the problem of the presence of outliers in data sets. The frequency models presented in the present paper have been quite successful in the field of hydrometeorology and river flooding but they have not been applied to sea level data sets to prevent marine flooding. In this work, we suggest two methods of incorporating the HI: the peaks-over-threshold method with HI (POTH) and the block maxima method with HI (BMH). Two kinds of historical data can be used in the POTH method: classical historical maxima (HMax) data, and over-a-threshold supplementary (OTS) data. In both cases, the data are structured in historical periods and can be used only as complement to the main systematic data. On the other hand, in the BMH method, the basic hypothesis in statistical modeling of HI is that at least one threshold of perception exists for the whole period (historical and systematic) and that during a giving historical period preceding the period of tide gauging, only information about surges above this threshold have been recorded or archived.
More recently,  Bulteau et al. (2014)  have also used a Bayesian framework to estimate extreme sea levels for the design of coastal defences. The authors applied the Bayesian method to the site of La Rochelle in France. The probabilistic and statistical treatment of surge data containing outliers is also limited in the literature especially in a local frequency analysis context. A basic hypothesis in statistical modeling of HI is that at least one threshold of perception (S 0 ) (below which the magnitude of the surge is unknown) exists and that, during a giving historical period preceding the period of tide gauging, only surges above the threshold (large enough to be remembered and/or to leave a mark somewhere on the coastal region) have been recorded or archived. Typically S 0 is the surge at which significant economic damage occurs.
We named this method the block maxima method with HI (BMH). Plotting position rules, to calculate observed probabilities based on both historical and systematic information, have been proposed in the literature  (Hirsch, 1987; Hirsch and Stedinger, 1987; Guo, 1990). The development proposed by  Hirsch (1987)  is considered herein. The reader is referred to  Hirsch (1987)  and to Sect. 3. 1 of the present paper for more details on the BMH model. Another method of using HI in the frequency estimation of extreme events, which has not been frequently explored in the literature, is based on the use of two kinds of historical data: classical historical maxima (HMax) data, and over-a-threshold-supplementary (OTS) data. In both cases, the data are structured in historical periods and can be used only as complement to the main systematic data.
This method is called the peaks-over-threshold method with HI (POTH) in this paper. More details about the OTS and HMax data are provided in Sect. 3. 2 of the present paper. Whatever the approach used, parametric methods based on the method of the maximum likelihood (ML) for estimating the distribution parameters have been developed and used  (Leese, 1973; Hosking and Wallis, 1986; Guo and Cunnane, 1991). The selection of the ML method was based on its statistical features for large samples and for its ability to easily incorporate any additional data in the estimation process. HI are often imprecise, and we should consider their inaccuracy in the analysis. However, even with important uncertainty, the use of HI is a viable mean to decrease the influence of outliers by increasing their representativeness in the sample  (Hosking and Wallis, 1986a; Wang, 1990; Salas et al.
Basic definitions and concepts A frequency estimation procedure with HI must include the following steps: (i) hypothesis testing (verification of stationarity, randomness and homogeneity) and detection of outliers; (ii) selection of S 0 (for the BMH method) and the historic threshold (for the POTH-OTS); (iii) compute empirical probabilities corresponding to systematic observations (for the BMH method, only observations below S 0 are taken into account); (iv) the computation of empirical probabilities of historical data (using the Weibull formula for the POTH approach and the exceeding formula for the BMH one). Because some surge events exceeding the perception-threshold S 0 occur among the systematic data, these events are virtually removed from the systematic period and are treated as historical data  (Bayliss and Reed, 2001)  in the BMH frequency model; (v) the selection of a parameters-estimation method (ML in our case) and estimating a theoretical distribution function (the GEV (generalized extreme value) for the BMH model and the GP for the POTH one) to fit the observations; (vi) calculating from the theoretical distribution the T -year values of interest (say 100 or 1000 years).
We analyze systematic observed skew surges that are available in the period from 1941 to 2010. Data sets were extracted from the predicted tide levels and corrected observations  (Bardet and Duluc, 2012). The selected extremes for a particular year were obtained from statistically independent surges. It should be noted that the same raw data set analyzed by  Bardet and Duluc (2012)  is used in this paper. A common problem in statistical modeling is the existence of gaps within the observation period (due to damage or failure of the measurement system, human errors, strikes, wars, etc. ). Whatever the HI approach (BMH, POTH) to be used, we must take into account these missing periods in the analysis. Worth noting is that it happens quite often that failure in the measuring stations occurs because of the storm, thus creating a non-independent gap.
Then we should ensure that the gaps occur independently from a measured variable. By means of classical extreme value distributions we are often able to analyze the statistical behavior of the maximum of a sequence of independent identically distributed (iid) random variables. Such a sequence usually represents values measured on a regular time scale. In our application the extremesurge data used for the BMH frequency model are the annual maximum (AM) surges. As it is the maximum of a block of values, it is often denoted as block maximum (Fig. 1 ). Figure  1. A sketch of a systematic record. To the left: the block maxima data set (for the BMH method). To the right: the POT data set (for the POTH method). In POT only surges X i > u s are modeled through exceedances Y i = X iu s. The distribution F (X) is known only for the upper part x > u s.
On the other hand and in the context of a point process model, surge events occur at successive random times T i when a random variable is observed. We consider that only large surge values are of interest (values exceeding a sufficiently high threshold u s ). We can assume that the times T i corresponding to large enough surges X i should be described by a homogenous Poisson process for which the number N of surges on a time interval of length w s follows a Poisson distribution with mean λw s. The λ parameter is the rate, and it is generally given in number of events per year. Surges X i are assumed to be iid random variables with continuous distribution F X (x) and density f X (x). The return period in such a case is given by T = 1/λ (1 -F X (x)). The annual maximum surges (for the BMH model) and the peaks-over-threshold (POT) surges (for the POTH model) are used in this paper as systematic data sets.
Historical information HI refers to events which have been experienced before the systematic gauging period. Although the frequency analysis using HI often lacks precision in the data used, it has been shown to improve the frequency estimation of extreme events. As mentioned earlier in this paper, there are two methods (BMH and POTH) to integrate HI with systematic data in a frequency model. Whatever the method used and comparing with the traditional frequency models, there are two fundamental differences when using HI. i. A different class of plotting position formulas can be used for the empirical probabilities. For instance, the exceedance formula is used when applying the BMH frequency model. The exceedance formulas are based on the fact that the frequency of the whole surges ex-Y. Hamdi et al.
: Use of historical information in extreme-surge frequency estimation ceeding S 0 (since it subdivides the range of probabilities between groups of below and above thresholds) is a key descriptor of the data set. For the POTH model, the same plotting position formula  (Weibull, 1939)  is used, but the probabilities of HI and of the systematic data are separately calculated. ii. A second fundamental difference when using HI in a frequency model is that an adapted MLE methodology should be used. To take into consideration available information on historical events, this methodology leads to use additional terms (depending on the method and the information type) in the likelihood expression. The development of the plotting position formula for the BMH model and derivations of the likelihood equations for the frequency models with historical information are addressed hereafter.
On the basis of these HI types, a joint likelihood function of the historical and systematic data (G|θ ) will be expressed and discussed in more detail in the rest of the paper. With the tools exploited in this study, both BMH and POTH models can use exact values as HI. However, the second and the last HI types can only be used with the BMH frequency model whereas the third type can only be used with the POTH model (particular case of OTS historical information without events above the threshold).
The block maxima method with HI (BMH) In applying the BMH method one may assume that HI are available in relation to one or more fixed thresholds of perception. Typically these thresholds of perception (S t ) are surges at which important economic damage occurs. A first difficulty with this method is to quantify the magnitude of historical surge events (as it is often subject to important uncertainty) and to estimate the empirical frequencies from a sample of both systematic data and HI. A second difficulty lies in the hypothesis of the HI completeness. Indeed, historical surge data are supposed to be exhaustive and all the events exceeding S t are reported. In other words, for the entire historical period, we assume that the thresholds have not been exceeded except for the available historical data.
The selected statistical distribution for the BMH model is the generalized extreme value (GEV). Indeed, the annual maxima data converge asymptotically to this distribution function. Mathematical derivations to calculate plotting positions of systematic and historical data for the BMH model using more than a threshold of perception is detailed in Appendix A and illustrated by Fig. 2. The ML estimators are obtained by maximizing the likelihood function over the parameter space θ (or usually simpler, by maximizing the logarithm of this function). Under the assumption that the surges are iid, the global likelihood function of the whole data sample is any function L(G| θ ↔ ) which is the product of the likelihood functions of the particular types of events and information. It can then be expressed as L G| θ ↔ = sys.
The peaks-over-threshold method with HI (POTH) The historical data (HMax and OTS data) are structured in historical periods and can be used only as complement to the main POT systematic data (Fig. 3 ). The OTS data are overa-threshold historical data recorded on periods with known durations and known exceedances. The periods are assumed to be potentially disjoint from the systematic period and other historical periods. For each period with known duration w OTS , we must have a threshold u h and all observations exceeding this threshold. The historical threshold u h cannot be smaller than the main threshold u s (used for the POT systematic data). A HMax data period corresponds to a time interval of known duration w HMax during which historical n k -largest values are available. Periods are also assumed to be potentially disjoint from the systematic period and other historical periods.
The selected statistical distribution for the POTH model is the generalized pareto (GP) distribution. Indeed, the exceedances law converges to this distribution function. Calculating empirical probabilities of historical data and constructing an empirical distribution function (in a POTH context) is the same as that of systematic data. A classic empirical formula can be used. Otherwise, as mentioned earlier in this paper, it was shown that the Weibull plotting position formula p = i/( n + 1) is more adequate than the other commonly used rules. In this formula, n is a prediction of the number of events on the historical period for HMax (since this number is unknown). A natural choice is n = λw HMax (λ is the events rate on the systematic period). For an OTS period with duration w h and with no surge observations, the  never exceeded threshold u h is shown as a horizontal segment with return period up to w h.
To estimate the distribution parameters by using the maximum likelihood technique in the POTH model, let us assume a set of POT systematic observations X sys, i with two sets of historical OTS and HMax surges X OTS,i and X HMax,i. The global log-likelihood can be expressed as (G|θ ) = systematic data X sys,i |θ + OTS data X OTS,i |θ + HMax data X HMax,i |θ. (2) Details about each term of this expression are presented in Appendix C.
Data and models settings The frequency analysis is performed at the La Rochelle site, which is located on the French Atlantic coast. Independent surges time series were obtained from the predicted tide levels and corrected observations  (Bardet et al. , 2012) , provided by the French Oceanographic Service SHOM (Service Hydrographique et Océanographique de la Marine) for the tide gauge named "La Pallice". One of the most important features of the station La Rochelle is the fact that the region, in which this station is located, has experienced important storms during the last 2 decades (the storm Martin in 1999 and Xynthia in 2010). Figure  4  displays the geographic location of the La Rochelle site.
Systematic data and statistical tests In the case of the BMH model, the annual maximum surges available in the period from 1941 to 2010 constitute the systematic record and all events occurred before 1941 are considered to be HI. The choice of systematic data for the POTH approach is different. Indeed, to use all available data separated by periods of missing data, we concatenated all the surges gauged since 1941 to form one systematic record. It was shown in previous researches (e. g. ,  Bardet et al. , 2011; Hamdi et al. , 2014 ) that a threshold u s equal to 41 cm is an adequate choice that gives a rate λ equal to 4 events year -1. BMH and POTH systematic data from 1941 to 2010 are represented by bar plots in Figs. 5  and 6  respectively. Stationarity, homogeneity and randomness of time series are required conditions in a frequency analysis  (Rao and Hamed, 2001).
But these events have not been measured by tide gauges or the estimated levels are not clearly associated with La Rochelle site. However, this is worrying for us since ex-  haustiveness in HI has been assumed in the developed BMH model and in the POTH model with OTS data. A possible solution is to choose a threshold of perception S t (in the case of the BMH method) or a historical threshold u h (in the case of the POTH method) as high as possible. This would allow us to get closer to the completeness of the HI. Care must be taken when using the available HI (Table  1 ), it is not necessary to use all the available historical data and depending on the selected threshold, one or more historical data can be excluded from the analysis. This is indeed the case here, only the historical surge of 111 cm induced by the event that took place in 1866 was used.
Settings of the POTH frequency model The parameters characterizing a POTH model data set are u h and w h. In order to be consistent with the BMH model assumptions and to compare the two models results, the HI is used in this work as OTS data. As shown in Fig. 6 , HI complements then the systematic record with these OTS data recorded on one historical period (1866-1940) with known duration (w OTS = 75 years) and one exceedance (n k = 1). As was the case for the BMH frequency model and to ensure exhaustiveness of HI, a threshold as high as possible should be chosen. A value of u h = 110 cm is then utilized. Unlike the BMH frequency model, systematic events that exceeded the historical threshold are not considered to be historical in the POTH model. Consequently their empirical probabilities will be different (compared to their equivalents in the BMH model) but theoretically the likelihood remains insensitive to the fact that these observations are considered to be systematic or historic.
Worth noting is that theoretically, with the proposed BMH and POTH models settings, the portion of the likelihood related to the addition of the HI must converge to the same value and the impact of the use of the HI must be the same for the two concepts. The POTH frequency model is applied with the Renext tool developed by the French Institute for Radiological Protection and Nuclear Safety (IRSN). The Renext software proposes fits and diagnostics for the POT method. The software is programmed in the R environment (open-source software for statistical computing: http://www. r-project. org/).