dataesr/scientific-paragraphs-categorization huggingface dataset:
On this basis, the approach developed in this paper is also important for policy design. Future green payments within the CAP, and in particular the minimum thresholds to receive such payments, could be designed using the method proposed here in order to adequately compensate farmers for losses due to more extensive production practices. Our approach is flexible enough to incorporate 'in principle' differences between geographical units and between intensive and extensive technologies, respectively, and consequently could enable better targeted green payments. Specifically, this could be done by first re-estimating similar models with larger EU datasets across FADN farms. In the second step, the parameter estimates of the separating variables (in our case, livestock density, fodder share and rented land share) could be used for out-of-sample predictions of the most probable production technology (intensive or extensive) for all farms in IACS.
Finally, eligibility for green payments could be based on whether farms are found to operate under an extensive technology or not. The remainder of the paper is structured as follows. The next section provides some background information on dairy farming in Ireland, France and Austria and how the dairy farming systems differ between these three EU member states. The third section describes the methodology, starting with a description of the LCSFM, and then describes the conceptualisation and estimation of the novel nested metafrontiers. Next, the FADN dataset used for the analysis and the empirical specification of the model is described. The fourth section presents and discusses the results. First, an overview of the general LCSFM results is provided before focusing on results related to the nested metafrontiers.
Methodology and empirical specification The LCSFM is used to classify farms into extensive and intensive technologies. Technically, the LCSFM estimates a mixture of production functions by exploiting the dataset's information  (Parmeter and Kumbhakar, 2014). 1  Moreover, the probability of adopting a particular technology depends on farm characteristics. Explicitly, let us consider the following production function:  Latruffe et al. where 𝑦 𝑖𝑡 is the output and x it is a vector of inputs of an observation 𝑖 in period 𝑡. 𝜷 c is the vector of production function parameters when farms adopt tech-nology 𝑐. is ∈ 𝑖𝑡,𝑐 = 𝑣 𝑖𝑡,𝑐 -𝑢 𝑖𝑡,𝑐 and 𝑢 and 𝑣 are, respectively, the one-sided and two-sided error terms, with 𝑢 representing the inefficiency component. 𝜎 𝑣 𝜎 8 L. Assuming a half-normal distribution for 𝑢 and a normal distribution for 𝑣, the conditional probability associated with the production function in (1) can be easily derived (see  Aigner, Lovell and Schmid, 1977) : where 2 𝑣,𝑐 is the variance associated with the two-sided error term.
, 2021a, c)  provides information on possible separating variables. The final selection of separating variables for our model is based on data availability and technological characteristics relevant in all three countries studied. Three separating variables are used: livestock density (stocking rate), calculated as the number of LSU per hectare of forage area, the ratio of fodder area to UAA and the share of the rented area to UAA. The latter is included in the separating variables on the grounds that practices are very different between farmers who operate rented land and those who own their land. 3 In particular, rented land may be an obstacle to adopting sustainable management practices or conservation behaviour  (Ranjan et al. , 2022). As regards the inefficiency drivers, these are selected based on the rich existing literature, particularly in relation to articles on dairy farms in EU countries 2 2 𝑣,𝑐 = exp (𝑊𝑣,𝑐).
Data We use data from the FADN, a harmonised database of book-keeping information for commercial farms across the EU (European Commission, 2023b). The FADN farm survey is carried out annually in each EU Member State, collecting accountancy data from about 80,000 commercial farms in the EU  (European Commission, 2018). This paper focuses on specialist dairy farms, on which at least two-thirds of their standard output is obtained from dairy activity (Eurostat, 2023a). The sample used contains 2,527 farms. The distribution of farms per country and for the years 2015 to 2018 in our dataset is presented in Table  1. France has the highest number of observations, representing about 47 per cent of the total sample, followed by Austria with 37 per cent and Ireland with only 16 per cent. In terms of the evolution of dairy for all three countries, there is a slight decrease in the number of observations, following the general observation of the decrease in the number of European dairy farmers.
Conclusions This paper used a method that can easily and statistically classify farms as extensive or intensive, namely the LCSFM and combined this classification with a novel nested metafrontier approach. The method was applied to dairy farms in Ireland, France and Austria, with homogeneous book-keeping data from the EU-FADN from 2015 to 2018. Using three easy-to-measure indicators (share of fodder area in UAA, livestock density and share of rented land), the analysis identified intensive and extensive specialist dairy farms. The latter farms not only differ in terms of these indicators (extensive farms have a lower livestock density, a higher share of fodder area and a lower share of rented land), but also use fewer pesticides and chemical fertilizers. This method thus allows for the identification of thresholds of the three abovementioned indicators to define classes of farms depending on their degree of intensive technology.
The methodological approach developed here is flexible enough to incorporate 'in principle' differences between countries and intensive and extensive technologies, respectively, and could consequently facilitate better targeting of green payments. Specifically, this could be done by first re-estimating similar models to the one presented in this paper with larger EU datasets of FADN farms. In the second step, the parameter estimates of the separating variables (in our case, livestock density, fodder share and rented land share) could be used for out-of-sample predictions of the most probable production technology (intensive or extensive) for all farms in the IACS. This is, however, only possible if the selected separating variables are also available in IACS data. Finally, eligibility for green payments could be based on whether farms have adopted extensive production technology or not.
Further research could therefore analyse a more extended time period to investigate not only differences but also variability (e. g. vulnerability to price shocks) of performance for intensive and extensive dairy farms, as well as changes on farms over time between the two identified production technologies. In addition, unobserved heterogeneity could be controlled with the help of individual effects and panel data. Another limitation lies in the existing FADN data where identification of intensive or extensive production technologies can only be achieved using proxies. While our analysis shows that such proxies have the discriminatory capacity to identify intensive and extensive production technologies and can provide helpful insights in the medium to long run, FADN data should be complemented by additional indicators to more clearly identify extensive farming systems and their farming practices.
| Mediation analysis We next turn to mediation analysis. Table  6  contains the results of the estimation of the relationship for both subsamples between internal and external restructuring on the one hand and market restructuring on the other. From these results, it is clearly apparent that both internal and external restructuring positively and statistically significantly influence market restructuring, as argued in earlier sections of our paper. We relied on Hayes's PROCESS software to examine the presence of mediating effects and additionally employed a bootstrapping approach  (Preacher & Hayes, 2008)  with 50,000 bootstrap samples  (Hayes, 2013; Preacher & Hayes, 2004)  to explore the indirect effects of internal and external restructuring on competitive focus. Table  7  and Appendix A1 and A2 reveals the results of this analysis.
Computational experience The algorithms presented in this paper were coded in C programming language and run over Dell PowerEdge C6420 cluster nodes with Intel Xeon Gold 6152 CPUs at 2. 10GHz with 32Gb RAM each. All the convex NLPs are solved using the callable library of IPOPT  [50, 63] , using as a subroutine the linear solver Pardiso  [48, 49].
Acknowledgments The first author was supported by the  MINT research school (Future Investments Program   (PIA)  of the  French National Research Agency (ANR) , Grant No. ANR-18-EURE-0023 ) and by  ISAE-Supaero  during her stay at  McGill University (Montréal, Canada ) as a visiting researcher. This funding is gratefully acknowledged. The second author acknowledges continuing financial support from the  Canada Research Chairs Program , the  Natural Sciences and Engineering Research Council of Canada , and the  Trottier Institute for Science and Public Policy.
These steps are described as follows: (1) We identified a set of keywords relevant to our case study that would be used in the research procedure; (2) We conducted a search string using web search engines, such as Google Scholar and Scopus databases, taking into account synonyms and abbreviations to ensure a more reliable result; (3) We used specific operators to improve the search system, including adding date range, document types, using keywords with AND, OR, and ALINTITLE (all keywords mentioned in the article's title), and source types to acquire high-quality and up-to-date papers; (4) We eliminated duplicated documents;  (5)  We evaluated the remaining article and conference papers by reading their titles and abstracts to determine whether they addressed our research question and met our inclusion and exclusion criteria; and (6) We extracted useful information from the filtered documents required for our research topic  [4].
•Allintitle: ("Building Information Modelling" OR "Building Information Modeling" OR "BIM") AND ("IPD" OR "Integrated Project Delivery") AND "LEAN". After conducting the literature search through web search engines (Google Scholar and Scopus databases), 131 publications were identified. Initial relevance was assessed by examining the titles of each paper, and those deemed potentially relevant were further evaluated through several iterations. To ensure the review included recent literature, only publications between 2012 and 2022 were selected  [29] , which allowed us to retrieve and synthesize information from the past 10 years. Inclusion and exclusion criteria were then applied to the remaining publications by carefully reading the abstracts to determine their relevance to our research goal.
Tools Applied in the Design Phase Based on the documents obtained in our research on Lean/BIM synergy, several tool applications were used to facilitate the integration of the Lean method with BIM during the design phase. Table  4  presents these tools and their impact on one or more parts of this phase. Most studies focused on using tools to improve the planning and control zone, particularly for the information flow part. Among the tools that were found to be useful in improving information flow are dRofus ® , Solibri ® , Level of Development (LOD), and Workplan software [4,  63, 64]. Team Performance Improvement Project Organization D-Rofus X Solibri X SNA X X VE X BDA X X X Work Plan software X X LOD X CSM X X dRofus ® and Solibri ® are BIM-based tools that make it possible for different parts and requirements of a product model to be communicated using the Industry Foundation Classes (IFC) Open standard, to ensure that the design solution meets stakeholder requirements, depending on construction project types  [65].
dRofus ® focuses on modeling requirements that can be used to improve safety and indoor thermal comfort. On the other hand, Solibri ® can encode requirements into logical rules by using checking software, such as Solibri Model Checker ®  [63]. Moreover, LOD is a tool that can be used to enhance BIM from the perspective of Transformation, Flow, and Value (TFV), in order to manage the design phase based on lean design management and TFV theory. It also facilitates the integration of design workflows into BIM projects  [64]. Workplan software improves the LPS methodology by instructing the design team to check constraints, determine work packages, release weekly work plans, and allocate resources. Additionally, it improves communication among project stakeholders  [4, 66]. Social Network Analysis (SNA) is a tool that can enhance team performance and communication within a team, particularly in team management.
Numerical experiments In this section, we consider three geometric problems to evaluate the performance of our neurodynamic approach. All the computations were performed using Python on an Intel Core i7-10610U CPU. The random data was generated using numpy. random and the solution of ODE systems was obtained using the function solve_ivp of scipy. integrate. The gradients and partial derivatives were computed using autograd. grad and autograd. jacobian. We only account for the quality of the solution and do not record the CPU time as current ODE solvers are time consuming.
ACKNOWLEDGMENTS This research is part of the collaborative project  D-TechnoSS , funded by  French National Research Agency  ( ANR-20-CE10-0006 ). We would also like to thank the students that participated in our experiments and supported our activities. Special thanks are extended to  Romain Guillerm, PhD  candidate in low technology for territorial resilience and  Charlène Bouchot , research engineer in circular economy. This research contributes to the Sustainable Development Goal (SDG) 12: Responsible consumption and production.
Preamble This is an addendum rather than an erratum. To overcome the challenge raised by multibiomarker approaches in ecotoxicology (analyze and interpret the datasets in a synthetic and integrative manner), we proposed in 2014 an integrative index, the Integrated Biomarker Response -IBR  (Devin et al. 2014). This index was quite successful (129 citations in Web of Sciences, 169 in Google Scholar), but its usage remains limited by the need to use a quite complex R code, and lack of solution to produce a robust statistical analysis of the data produced. We thus developed a web interface to facilitate the appropriation of the index by researchers and stakeholders. It was built for up to 9 biomarkers, as described in the original paper, and for up to 16 conditions.
Description of the interface The interface is accessible through the following link: https://shiny. otelo. univlorraine. fr/calibri/R/. It is structured in 3 main different sheets and a help page, and provides sample datasets that can be used to (1) test the interface and (2) get an example of the data file organization. The first page is dedicated to file loading. Datasets are checked to fit with the format used by the interface. This first pages also allows for biomarker selection. This page proposes a visual checking of the first lines of the uploaded tables. Finally, a last page is dedicated to the statistical analysis of differences between experimental conditions. A detailed help page gathers all the necessary information to understand IBR calculation, permutation procedure, website utilization and statistical approach developed.
We further enhance computational efficiency by employing an active-set strategy, which significantly reduces the number of samples (or data points) that need to be considered in each iteration. S-BORM has rigorous convergence properties: for limit-state functions that are linear in the decision variables, the algorithm can only converge to critical points. We design three numerical examples with increasing complexity to demonstrate that the algorithm works practically well for both linear and nonlinear limit-state functions. In addition, we perform extensive parametric test, which shows the robustness of the algorithm. The S-BORM algorithm is developed as a Matlab-based function applicable for customized problems, which is available at https://github. com/ jieunbyun/sborm. The paper is organized as follows.
Examples include complex and large-scale systems such as a power network with 70 random variables and 10,062 cut sets and combined with reliability growth models. We show that the S-BORM algorithm provides a handy means to solve these complex problems. Moreover, extensive parametric investigations show that the algorithm remains insensitive to algorithm parameter values and the magnitude of target failure probability. Utilizing realizations of random variables greatly facilitates general applications as it eliminates the need for problem-specific derivations and enables non-parametric analysis. By leveraging such advantages, we develop a Matlab-based tool, which is available at https://github. com/jieunbyun/sborm. To run the algorithm, users only need to provide general information, i. e. , cost functions and limit-state functions (including their gradients) and realizations of random variables.
Acknowledgments The research of the first author is in part supported by the  Humboldt Research Fellowship for Postdoctoral Researchers  from  Alexander von Humboldt Foundation ,  Germany. The second author acknowledges financial support from the  Gaspard-Monge Program for Optimization and Operations Research (PGMO)  project '' SOLEM -Scalable Optimization for Learning and Energy Management ''. The research of the third author is supported in part by the  Air Force Office of Scientific Research, Mathematical Optimization ,  United States  ( 21RT0484 ).
An equivalent formulation is discussed in Section 3. 3 and used in Section 4 to develop methods to identify a Nash equilibrium, to enumerate all Nash equilibria, to find an upper bound to their utility which is compared to the methods in literature  [13]  and to identify the best and the worst Nash equilibrium for a player. Section 5 presents extensive computational experiments of such methods on a given dataset of games. Section 6 ends the paper with some insights and possible research directions.
Each instance of the dataset thus formed is referred to with a specific name encoding the three key features of the game. Such encoding is shown in Table  1. The games' structure is encoded as follows: Rn indicates that the number of actions at every child of a node h ′ ∈ H is chosen uniformly at random U({0,. , n • |A(h ′ )|}), given the constraint that they have on average n actions; Cn indicates that every node has the same number of actions, with n actions per node; and finally U n indicates that at every node all actions but one lead to an outcome, with n actions per node. The players' utility is encoded as follows: R if the utility of an outcome is chosen uniformly at random U([0, 1]) in the interval [0, 1]; D if the utility is chosen randomly from a discrete set U({1,. , 10}), namely a natural number between 1 and 10; Z if the game is zero-sum, i.
e. , at every node h ∈ H the winner is chosen at random i ∈ {1, 2} and gets u i (h) = 1, while the loser 0; A if the game is zero-sum and the winner has a utility chosen randomly from a discrete set U({1,. , 10}); F if the utility is chosen randomly from U([0, 1]) at every outcome and it is the same for every player; E if every utility of every outcome is a constant. The latest value of a label is the size of the game. An instance of game labeled C4E100 has 100 outcomes, every node has 4 actions (C) and utility is a constant function (E). The library is publicly accessible  [29]  and composed of three distinct datasets. Dataset 1 contains 21 extensive-form games which vary in their structure, within the range {R, C, U }, and in their size {100, 216, 324, 400, 512, 625, 729}, but not in their utility, which is always Random R.
Dataset 1 has games of smaller size, i. e. , small enough to manage methods already known in the literature, that provide bounds to the   i (h) ∼ U(0, 1), ∀i ∈ {1, 2}, ∀h ∈ H D Discrete u i (h) ∼ U({1, 2, 3,. , 10}), ∀i ∈ {1, 2}, ∀h ∈ H Z Zero-sum u i (h) = 1, u j (h) = 0, i ∼ U({1, 2}), i ̸ = j, ∀h ∈ H A Asymmetric u i (h) ∼ U({1, 2, 3,. , 10}), u j (h) = 0, i ∼ U({1, 2}), i ̸ = j, ∀h ∈ H F Indifferent u 1 (h) = u 2 (h) ∼ U(0, 1), ∀h ∈ H E Equal u i (h) = 1, ∀i ∈ {1, 2}, ∀h ∈ H.
Enumeration of realisations of Nash equilibria In the next experiment, we tested the methods introduced in Section 4. 1 on Dataset 2 and on Dataset 3. We have measured the performance of Algorithm [EA] while enumerating the Nash equilibria of every instance. Analysis on Dataset 2. For the space's sake, in Table  3  we display only the results for all the games' instances with size 729. More precisely, for each game, we show how many outcomes are the realisations of Nash equilibria N E, the average size X avg of all sets X 1 and X 2 , the total time required to run the algorithm t tot , the average time t avg and the maximal time t max for an outcome of the game. In addition, Figure  6  displays the average time and the maximal time required to execute the algorithm on a game outcome, and the percentage of Nash equilibria identified among all the game's outcomes.
We observe that the computation time is lower for the case Z, as the size of X 1 and X 2 is smaller. We cannot infer significant correlations on the other cases (R, D, A, F ) and we thus defer such analysis to Dataset 3. Impact of structure on performance. The structure of the game influences the efficiency of the average time necessary to verify if an outcome is the realisation of a Nash equilibrium. Indeed, we observe in Figure  6  that in games with the same size those whose nodes have the same number of children (structure coding C) require more time on average to compute an equilibrium. This is due to the fact that the neighborhoods of an outcome V 1 ∪ X 2 and V 2 ∪ X 1 in the graph and its complementary have always the same size in both parts of system [N E]. On the other hand, in games with more asymmetrical structure (structure coding R and U ) one of the two graphs is often smaller and thus much easier to be solved.
Analysis of Dataset 3. Table  4  shows the average of performances on 5 sample games for each one of the 15 patterns. Every curve of Figure  7  shows the computation time distribution of [N E] for an outcome of a game. The 15 curves provide such result for the 15 games having the same utility coding. Namely, every line appearing in the plots shows for every game type Γ = ⟨I, H, u⟩ the function f (t) = P(t(h) ≤ t|h ∈ H), i. e. , the probability that the algorithm solving system [N E] takes less than t seconds to determine if h is the realisation of a Nash Equilibrium. Impact of the structure on performance. These numerical results show empirically that the structure is the main factor influencing the performance of the algorithm solving system [N E]. Indeed, on a game with totally unbalanced (U ) structure it takes a negligible time for the vast majority (around 99%) of the outcomes of a game instance (cf.
Conclusions In this paper, we introduce a new representation for a two-player extensive-form game as a graph of its outcomes. We prove that identifying a Nash equilibrium of an extensive-form game corresponds to identifying two cliques on such graph and its complementary. Thanks to this result, we introduce the first method of the literature to determine if an outcome of an extensive-form game is a realisation of a Nash equilibrium. Such method allows to define the first algorithm to enumerate the realisations of all Nash equilibria of an extensive-form game. The algorithm performs very well on a sample dataset of games of different shapes and sizes. Moreover, it is possible to reframe the algorithm so that it provides any given bound to the utility of all Nash equilibria. Such algorithm performs significantly better than the most known method in literature, which provides a (not always the tightest) upper bound to the utility of Nash equilibria.