dataesr/scientific-paragraphs-categorization huggingface dataset:
Architecture and general principles The fbpmn tool suite is centered around a command, fbpmn, that is available for Linux, OSX, and Windows (binaries are available for the first two, the later requiring, for now, a compilation process). This command is used to transform a BPMN model into a TLA + representation of its BPMN Graph (Definition 2. 1). fbpmn is also in charge of the computation of the Pre N and Pre E sets that are used in the semantics of the OR gateways, since these two sets can be structurally computed from the BPMN graph structure. This generated TLA + module is then passed, together with modules for TLA + implementation of our well-formedness.
Desktop modeling and verification Since for a given model one may have different properties of interest (e. g. , safety, soundness, and message-relaxed soundness), and since several communication models are possible (e. g. , the seven ones presented in Section 2. 2. 4), it would be tedious to run fbpmn for each of the combinations. Hence, we provide the process designer with scripts (only under Linux or OSX) that ease verification. When the designer launches the fbpmn-check script (upper part of Fig. 34 ), it reads a configuration directory and runs fbpmn based on the designer preferences. Let us suppose the configuration directory is as follows. This will yield four different properties to be checked for seven different network models, generating at most 28 counter-example traces. Running the fbpmn-logs2html script on a working directory generated by fbpmn-check, finds out these counter-examples and generates an interactive animator for each of them.
Related work The formalization of the BPMN execution semantics, and on a wider scale the formal study of business processes, is an active field of research. In this section, we discuss the most significant propositions in formalizing BPMN. We then compare our approach to them. In our selection, we focus on two criteria: (1) the use of BPMN as a modeling language for describing the business process; (2) the existence of tooling support for the developed approach. In Table  5 , column Transformation artifact shows the formalism in which the BPMN models are expressed, column Tool for transformation presents the supporting tool for applying the approach, and column Tool for verification presents the tool that is used for the verification. The upper part of the table lists approaches dealing only with process diagrams, while the lower part describes approaches dealing with collaboration diagrams.
Besides, they tackle the detection and management of constraint violations at run-time. The authors propose this formal model to analyze the soundness of the collaboration and of its Discussion. Unlike the mentioned works, we have defined the execution semantics of BPMN models in terms of First-Order Logic, with the objective of defining a generic formal semantics that can be implemented into different languages and tools. Nevertheless, the above mentioned approaches have mainly focused on verifying the control flow of business process models against a correctness criterion. To the best of our knowledge, these approaches do not support verification of BPMN models under a specific, and parametric, communication model. Table  6  complements Table  5  by looking more into detail the BPMN features and properties of interest that are supported by recent works that provide tool support for the verification of collaboration diagrams and communication features in BPMN.
Acknowledgments The work of  Josu Doncel  has been supported by the  Department of Education of the Basque Government, Spain  through the  Consolidated Research Group MATHMODE  ( IT1294-19 ), by the  Marie Sklodowska-Curie, Spain  grant agreement No  777778  and by the  Spanish Ministry of Science and Innovation, Spain  with reference  PID2019-108111RB-I00  ( FEDER/AEI ). This work was also funded by the  French National Research Agency, France  grant  ANR-16-CE05-0008.
As reminded in territories, the clash of individual and collective interests is increasingly frequent  (Ryschawy et al. , 2019)  and has to be dealt with in a preventive mode whenever possible. Mediation assisted by digital technologies, e. g. computer simulation and visualization  (Becu et al. , 2008) , can help stakeholders find trade-offs  (Jackson et al. , 2013). Ecosystem services assessment is expected to be improved in the future for more accurate payments to agriculture for delivery of ecosystem services (ES). Remote sensing could be used to directly account ecosystem services (e. g. Carbon content of soil), and data collected from on-farm digital technologies could be used for assessing ecosystem services. However, a prerequisite for using agricultural data as intervening variables is to have better models of agriculture-ES interactions  (Green et al.
Data availability No data was used for the research described in the article. This research has been funded by INRAE (DigiGrAL focus group) and by the #DigitAg Convergence Institute, supported by the French National Research Agency under the Investments for the Future Program, referred as ANR-16-CONV-0004. The authors are grateful to Muriel Mambrini, INRAE, for her valuable advices on creative thinking.
Food lists One list containing 120 foods (hereafter, marketed foods) that were accompanied by detailed ingredient information. These marketed foods came from an official database of commercially available packaged foods in France  [21]. We focused on three categories of foods, namely fresh dairy products, bread products, and mixed dishes, because they contain marketed foods commonly consumed in France  [22]  and are thought to display diversity in recipes and formulations. Forty food products were randomly selected from each category, using a weighted approach to ensure product representativeness within categories (e. g. , the number of sandwich breads in the sample reflected the proportion of sandwich breads within the bread product market as a whole). The products were identified using generic descriptors; no brand names were employed for reasons of confidentiality.
From the database, we also acquired information on the foods' ingredients (including food additives) and nutrient content (i. e. , as presented on food packaging). One list containing 111 foods (hereafter, generic foods) that came from a dietary survey that was performed as part of the Three-City Study (Bordeaux cohort) and that combined a food frequency questionnaire with a 24-h dietary recall approach  [23, 24]. We identified the most frequently consumed food based on the 24-h recall findings for each of the 54 FFQ food categories (e. g. , apples were the most frequently consumed fruit); this information was used to create a list of 54 generic foods. So that both lists were of similar size and structure, the list of generic foods was expanded by adding foods from the dairy (n = 22), bread (n = 13), and mixed dish (n = 22) categories using the 24-hour recall data.
Evaluators Since NOVA is mostly used by specialists, we specifically attempted to invite evaluators with at least a basic background in nutrition and/or the food sciences. We targeted four main groups: researchers with expertise in human nutrition, researchers with expertise in food technology, health professionals who provide nutritional guidance (i. e. , medical doctors and dieticians), and skilled research and development professionals working in the food industry. We invited people to take part in the study by directly contacting scientific and/or clinical societies, research institutes, and professional associations; we requested that our invitation be restricted to their professional networks. Anyone wishing to participate could immediately log into the online survey interface; the names and affiliations of evaluators were kept fully anonymous, as per the European General Data Protection Regulation and French regulatory requirements.
Nutrient profiling At present, several systems are used to assess food nutritional quality. Here, we employed the Nutri-Score system  [25] , the SAIN,LIM system  [26] , and the Nutrient Rich Food (NRF) Index (version 9. 3)  [27, 28]  to generate profiles for each food in the two lists; we also estimated their energy density levels (kcal/100 g). We obtained the nutritional information for these calculations from the OQALI database  [21]  and the CIQUAL database;  [29]  when a food was absent from the databases, we used the information for the most similar food that was present. Briefly, the Nutri-Score system considers a food's levels (per 100 g) of more beneficial nutrients (i. e. , protein, fiber, and percentages of fruits, nuts, vegetables, olive oil, canola oil, and walnut oil) and less beneficial nutrients (i.
Data analysis Identical but separate analyses were performed for each list. Quality control. To ensure the evaluators displayed caution and honesty when completing the survey, we performed a quality control test using five foods per list for which the NOVA group should have been obvious. From the list of marketed foods, we selected beef bourguignon and potatoes, fruit dairy dessert, Chinese fried rice, toasted bread with fruit chips (expected assignment of NOVA4 for all four foods), and plain yogurt (expected assignment of NOVA1) (see Supplementary Table  1 ). From the list of generic foods, we selected apple, lettuce, egg (expected assignment of NOVA1 for all three), butter (expected assignment of NOVA2), and soda (expected assignment of NOVA4). When evaluators arrived at an erroneous assignment for more than one test food, their data were excluded from the analysis.
We also excluded any data from evaluators who failed to assess all the foods on the list(s), which allowed us to better ensure that evaluators were committed to their task, and to limit confusion that may arise from statistical analyses based on different sample sizes of foods and/or evaluators. Description of the data. The NOVA system does not provide "goldstandard references" to which the evaluators' assignments could be compared. To describe the raw data obtained (i. e. , the assignments), we calculated, for each food, the percentage of assignments in each of the four NOVA groups and, for each list, the number of foods assigned to one, two, three, or four different NOVA groups. NOVA assignment patterns. Each evaluator assessed each food, assigning it to a NOVA group. Using these data, we obtained a frequency table for each food list (Supplementary Tables  1  and 2 ) on which a correspondence analysis (CA) was performed.
Confusing messages may arise from front-of pack labeling, such as when products with a NOVA4 label, signifying their ultraprocessed nature, would also bear a label conveying their good nutritional quality (e. g. , Nutri-Score "A"). This study has limitations. First, regarding the choice of the food lists, there were only three food categories for the marketed foods and different results might have been obtained if other categories had been used. Our goal was to balance survey duration with reasonable food representation, allowing us to explore potentially conflicting NOVA assignments. Representativeness was greater for the generic foods, which were taken from the results of a population-level dietary survey. Second, while all the evaluators were French, they were specialists in human nutrition and/or food technology; their expertise should thus have served to overcome potential cultural bias and ensure the validity of our data set.
All evaluators were given the exact same information about NOVA, taken from the original publications. The survey set-up provided easy access to the description of the NOVA system, accessible in full detail or as a summary. The food lists and blocks appeared in a randomized order to avoid habituation bias. Third, the vast majority of evaluators appeared to take the assignment process seriously. Just 7 evaluators (less than 2% of the total) failed to do so. The sensitivity analyses confirmed the robustness of our findings. Consequently, we feel confident that we obtained data from individuals who assessed the foods as best they could, given current NOVA criteria. All the evaluators were specialists in human nutrition and/or food technology and thus represent the body of individuals who may have to use NOVA in their professional lives.
We propose the individual-level microfoundations of subsidiary CEOs in emerging markets as antecedents of the strategic agility of multinational enterprises, and subsidiary embeddedness as a key organizational-level moderator of these relationships. Combining quantitative data on subsidiary CEOs operating in India with qualitative interviews with Italian HQ counterparts, our results suggest that subsidiary CEOs' tenure in emerging markets, along with their overall experience, affects MNE strategic agility positively. Similarly, CEOs' cognitive characteristics -problem solving and reasoning, and language and communication skills (individual-level microfoundations) -affected strategic agility positively, while subsidiary embeddedness moderated these relationships in different ways, leaving space for fresh managerial and theoretical considerations.
Data collection and measures -quantitative analysis This research is part of a large project that investigates the key success factors of Italian MNEs in India. We use novel primary data from Indian subsidiaries of Italian MNEs that are included in the India Brand Equity Foundation (IBEF). 5 The IBEF is a trust established by the Department of Commerce in India and works closely with different stakeholders across the government and industry. Within this relevant list of Italian MNEs operating in India, we contacted the 50 largest MNEs (based on total revenues in India) and received positive acceptance from 28. Italian HQs assisted us in the selection of subsidiaries in India (253) that were representative of the HQ's business activities to improve the chance of reaching broad-spectrum conclusions  (Perri et al.
Measures used for the quantitative analysis appear in Table  4. The data (except from the years of experience used for tenures) was collected and rated on a seven-point Likert scale (1 = poor to 7 = excellent). The two sets of independent variables (microfoundations) are supported by prior literature. First, the tenure of CEOs used as experience acts generally as a conduit of learning and is associated with capability development  (Zollo & Winter, 2002; Mäkelä et al. , 2012) , which is critical for strategic agility. Second, we draw on the concept developed by  Helfat and Peteraf (2015)  that aimed at explicitly linking managerial capabilities and mental activities that may influence the development of dynamic capabilities. Indeed,  Gavetti (2005)  drew on cognitive psychology and called for more attention to managerial cognition as a possible microfoundation of capability thinking.
, 2003). In fact, this is less likely the more complicated the model  (Chang et al. , 2010) ; additionally, all questions were mixed and spread across the questionnaire  (Bresciani & Ferraris, 2016) , and we employed a Harman's One-factor test  (Harman, 1967). We found five factors with an eigenvalue greater than 1 explaining a total variance of 67%, which limited the problems in our dataset. Further, when we designed and administered the questionnaire, we explicitly chose to mix the items of each variable with the aim of not allowing the respondents to understand the underlining construct and answer in a socially desirable manner  (Podsakoff et al. , 2003). We also ran a post hoc analysis. We estimated the paths between all the individual-level antecedents and the three dependent variables of strategic agility.
To eliminate potential problems of CMV, we collected new quantitative data from a different source (e. g. HQ managers) on strategic agility with all HQ managers interviewed (contacted for the qualitative portion of the study). The analysis confirmed almost all the quantitative findings of Models 1, 3, and 5, in which the direct effects of experience and managerial cognitive capabilities on strategic agility were positive and significant. The most interesting results (and most different from the main analysis) were with regard to "Experience (tenure) in whole working life" and "Language, communication, and social cognition. " In fact, we found that subsidiary CEO tenure in their whole working life was positively associated (β= 0. 186, t= 1. 814, p<10%) with resource fluidity and not with strategic sensitivity (as in Table  3 ).
This helped them significantly in making fast strategic decisions with the consensus of both top managers at HQ and the board of directors of the subsidiary. Moreover, "He is very good at using all available data to identify coherent and multiple growth patterns". Most respondents explained that their subsidiary managers in India had a tendency to go beyond applying known rules, a winning approach in emerging markets. "After three months from his engagement (and many analyses), he [our subsidiary manager] smartly proposed that we change strategically through a combination of outsourcing strategy and flexible contracts", showing how the subsidiary became more agile in the emerging market. Another stated that "Our subsidiary manager is a problem solver who is able to guide the team achieving strong consensus in host-country employees (both local and expatriates), making fast strategic decisions.
Data and sample We collect data on different proxies of waste, financial, and governance variables from two renowned databases, that is, (a) ASSET4 and (b) WorldScope, for the global sample of listed firms for 2002-2019. We use the waste data from ASSET4 because this database has been widely used in environmental studies  (Nadeem et al. , 2020a,b; Nadeem et al. , 2020) , and scholars argue that ASSET4 specializes in providing objective, relevant, auditable, and systematic ESG information  (Cheng et al. , 2014). Our sample period begins in 2002 and ends in 2019, as this is the time period in which maximum data on firm-level waste generation becomes accessible. Our initial sample consists of 19,408 firm-year observations. After screening data for missing observations, our final sample comprises 17,443 firm-year observations from 43 countries between 2002 and 2019.
Data-driven modeling of remanufacturing systems The simulation approach is severely limited by the considerable time and effort involved as well as the expertise of the modeler. To address these shortcomings,  (Hubl et al. , 2011)  presented a data-driven modeling approach for flowand job-shop production systems based on the Manufacturing Resource Planning (commonly known as MRP II) data. Such data-driven approaches enable users to generate simulation models of manufacturing systems using the data and provide valid production schedules within seconds. An overview of data-driven modeling approaches has been provided by  (Reinhardt et al. , 2019). For the generation of simulation models, data-driven approaches for modeling and simulation use data typically stored in enterprise resource planning, management execution, and production planning systems and provided via technical interfaces such as spreadsheets and databases.
The primary aim of the data preparation phase is the collection of simulation data for the database. The modeling phase is categorized into multiple subphases. The DES-Simulator pulls data from the database and generates a simulation model in the digital factory. During experimentation, the DES-Simulator parameterizes the digital factory for each experiment, simulates the scenario, and finally obtains the results. Following the modeling phase, the DES-Simulator records the results of all experiments in the database. The evaluation phase involves the evaluation of simulated schedules, determination of robustness, and preparation of key performance indicators (KPIs) for the decision-maker. This phase consists of extracting data from the database, evaluating the robustness, and pushing the evaluation results.
Data-driven-modeling phase In the modeling phase, the simulation data are initially extracted from the database. To set up the simulation model, referred to as the digital factory, the algorithm uses manually predefined and reusable standard elements for the remanufacturing processes and resources. The digital factory is created based on these elements and the static data pulled from the database. The elements of the process include the logic of disassembly, processing, and reassembly of products and parts, as well as the prioritization and material routing through the system. The elements of resources include the logic of the physical system and mirror the remanufacturing capacities by adhering to the shift plans and business rules of the shop floor. For each experiment, the simulation model was parameterized using dynamic data, and a simulation was subsequently performed.
Robustness evaluation phase In the robustness evaluation phase, the simulated schedules were first extracted from the database. Generally, robustness is defined as the capacity of a schedule to assimilate the impact of perturbations. A schedule is considered to be robust if it exhibits low sensitivity to uncertainty. In this definition of robustness, the notion of schedule sensitivity is subjective. To overcome this subjectivity,  (Himmiche et al. , 2021)  proposed that the robustness be measured as a probability measure. In this study, we used this measure and defined the robustness for the probability that the KPI measure of a schedule s i under uncertainties, U , is less than or equal to a KPI limit defined by the decision-maker (Equation  1 ).
RL(s i , U, KP I lim ) = P r(KP I(s i , U) ≤ KP I lim ) (1) In the second step of the robustness evaluation phase, a statistical analysis was performed for the simulation results to obtain the robustness level of each schedule and for each KPI. In the third step, the evaluation results are pushed to the database and made readily available to the decision-maker. The results of the evaluation are used by the decision-maker to obtain indications of the schedule performance.
Methodology design We performed the bibliometric analysis according to the PRISMA guidelines for reviews (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)  (Moher et al. , 2009 ; Figure  1 ). We used the Web of Science Core Collection Citation Index to identify the articles and build our database. First of all, we build the query to identify the articles. The keywords were identified through Thesaurus Agrovoc from FAO to standardize the terms and obtain greater accuracy in the search, resulting in the identification of relevant articles for our topic. Regarding "labor, " the similar terms identified were: "work, " "job, " "employment, " and "occupation. " Regarding "value chain, " we remained with the same term, as it encompasses global value chains. All time available was considered (from 1945 to 2020) in order to have an overview of the theme.
g. , fiber and fuel) were excluded. We finally selected 343 articles published between 2000 and 2020. Then, we performed a normalization of authors' keywords to retain only one keyword between singular and plural (e. g. , value chain/value chains), British and American English (e. g. , labor/labor), lowercase and uppercase letter (e. g. , employment/Employment). Finally, we obtained 934 keywords. The variables of our database included the metadata of the selected articles: authors, title, journal, keywords, publication year, citation times, and country. When authors' keywords are not indicated in the article, we used the words from the title to fulfill this information in order to maintain the article in our database, since keywords are central in our analysis. The descriptive analysis was performed using the Web of Science tool "Analyze results, " which is based on the frequency of authors, journals, and countries to identify the main publishers related to labor in agri-food value chains.