dataesr/scientific-paragraphs-categorization huggingface dataset:
While not shown in this essay, the specific cases represented by the specific dots in the XY plots are identified in the fsQCA. com program by clicking on each dot. Isaksson and Woodside (2016)  and  Woodside, Mir Bernal, and Coduras (2016)  provide examples of identifying cases with in XY plots. Models 4 and 8 are applicable to B-to-C firms only. Models 5, 7, and 9 are applicable to B-to-B firms only. The general customer industry category is irrelevant in models 1, 2, 3, and 6. Again, these findings illustrate the perspective that focusing exclusively or mostly on simple directional relationships (e. g. , correlations and regression analyses) of independent and dependent variables is embracing a shallow research stance.
The present study provides substantial support for the applicability of complexity theory tenets in strategic management. For achieving substantial results, applications of complexity theory in strategic management requires shifting away from MRA and NHST to algorithms and SPOT-an embrace of new theory and analytics (cf. Misangyi et al. , 2016; Woodside, 2014). The study of outcomesin-contexts such as the four-corner analyses in the present study, along with constructing and testing asymmetric models vis "computing with words" and fsQCA. com software, are means to achieve the richness necessary for achieving requisite variety. Thus, as  Weick (2007)  proclaims, "In other words, richness has power but we are not powerless to evoke it. " While harsh to read, but as  Ziliak and McCloskey (2008)  and others (e.
In line with the results in  [23] , we estimate a spatial Durbin error model (SDEM), which combines SEM and SXM models, using the R package spdep  [9, 10]. We use two spatial neighborhood matrices, W 1 and W 2. The former represents grid cell neighbors, the latter is built at the administrative region level. Both matrices are based on a Queen contiguity rule. Appendix C provides some results for the choice of spatial weight matrices. The explanatory variables are lagged with one of these two matrices depending on the geographical scale of the variable. In our model, spatial autocorrelation is essentially a data measurement problem related to explanatory variables such as rent values which are aggregated across space and are likely to be correlated. Spatial autocorrelation can also arise in our case as the result of omitted variables which are spatially correlated 10.
Physical data In our simulations, we use data on three types of physical parameters: climate, soils, and topography. Climate. As already mentioned, we simulate two CC scenarios from the  [43] , A2 and B1 (see figure  3  for the underlying hypothesis). The agricultural sector simulations exploit two sets of climate data were used. For calibration purposes (when we seek to adjust our results to a reference year, here 2002) we use reanalyzed ERA-Interim data on a 0. climate change simulations methodologies applied by  [36]  and described in  [5]. Another set of data are used for the baseline and counterfactual simulations of the forestry model FFSM++ which is computed using the ARPEGE model  [65]  and further downscaled 13     to an 8 km x 8 km grid (the same we use in our econometric model) by CERFACS (Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique).
Land use dataLand use data are from the Corine Land Cover (CLC) database for France at the scale of 100m x 100m (1ha) grids and for the year 2000. The land cover classes are agriculture, forest, urban, and other. Table6in appendix A summarizes the rules governing the aggregation of land use classes. The resulting map is depicted in figure2. We next calculate the share of each land use class for each (8km x 8km) grid cell; we know that each cell includes a maximum of 6,400ha. Land use shares are expressed as the sum of the same land use classes in hectares divided by the surface of the grid cell. Although these cells are generated to be homogeneous, they are changed by their intersection with the French borders. For instance, grid cells on the coast are restricted to their parts on dry land.
Acknowledgments: We thank  Pierre-Alain Jayet  for providing us with the results from the AROPAj model, and  Antonello Lobianco  and  Sylvain Caurla  for giving us access to the results of the FFSM++ model. We thank the two anonymous referees and the editor of  Ecological Economics  for their useful comments which have helped us to improve the paper significantly. The usual disclaimers apply. The research leading to these results received funding from the  European Union  within the  European Commission Seventh Framework Programme  in the frame of  RURAGRI ERA-NET under Grant Agreement   235175   TRUSTEE  ( ANR-13-RURA-0001-01 ), and from the  French Agence Nationale de la Recherche  through the  ModULand  project ( ANR-11-BSH1-005 ), the  ORACLE  project ( ANR-10-CEPL-011 ), and  STIMUL (Scenarios Towards integrating multi-scale land use tools) flagship project as part of the "Investments d'Avenir" Programme (LabEx BASC ;  ANR-11-LABX-0034 ).
3. 1. 1 Land use dataLand use data are from the Corine Land Cover (CLC) database for France at the scale of 100m x 100m (1ha) grids and for the year 2000. The land cover classes are agriculture, forest, urban, and other. Table6in appendix A summarizes the rules governing the aggregation of land use classes. The resulting map is depicted in figure2. We next calculate the share of each land use class for each (8km x 8km) grid cell; we know that each cell includes a maximum of 6,400ha. Land use shares are expressed as the sum of the same land use classes in hectares divided by the surface of the grid cell. Although these cells are generated to be homogeneous, they are changed by their intersection with the French borders. For instance, grid cells on the coast are restricted to their parts on dry land.
A broad literature search was conducted during the month of October 2017 looking for research articles having used the terms ERP or 'Enterprise Resource Planning' in their body. This search was restricted to the International Journal of Production Research because the authors considered it to be highly representative of the research conducted on ERPs in the field of production. Given the journal's fame and age, it is one of the oldest and most cited journals in the domain. At the end of the search, a total of 321 articles were selected, the abstracts and references of which were downloaded into EndNote reference management software while their full texts were downloaded into a computer for sorting, leading to the identification of 61 relevant articles for this study. Articles deemed irrelevant were discarded; and this was the case when ERP was not the main theme of the article or when it was cited superficially.
5% Improved decision  [56] ;  [35] ;  [45] ;  [47] ; 5 14% making and planning  [4]  Performance improvement  [20] ;  [39] ;  [22]   [44]  and  [46]. 23% of the articles identify business process reengineering (BPR), system integration, and minimum software customization as critical success factors for ERP implementation projects. System flexibility and expansibility, and cross-department and cross-region application, are also regarded as critical success factors in 10% and 8% of articles respectively. The following factors was identified by exactly 7% of the reviewed articles: change management program and culture; compatibility between application structure and database; the professional capacity of consulting companies; project management; service quality level of consulting companies; support of top management; and training quality improvement.
Furthermore, it is problematic to find unsuccessful examples of attempts to build informal institutions since this informality means that these examples, if not successful, fall by the wayside and their existence is often not recorded; and actors are often unwilling to discuss initiatives that have not enjoyed some measure of success. Nonetheless, the model of the informal institution (as opposed to the unsuccessful individual example) -be it a voluntary partnership, or a Tariff Union -persists more widely because the critical interfaces that they are intended to mediate also persist. Data for the cases were drawn from document reviews and semi-structured interviews with key actors in different public transport related organisations. Documentation for the cases was sourced from publicly available publications in the local languages, which explained the structure of the formal and informal institutions examined and the current system of public transport and land use planning.
Acknowledgements The work is part of a research project ( Collaborative Models for Better Public Transport ) funded by the  Swedish  research funding organisation Vinnova from 2013 to 2016 ( Dnr 2013-03020 ) and carried out by researchers who are part of the  Swedish National Public Transport Research Centre ,  K2  (www. k2centrum. se). The authors would like to thank all the interviewees who contributed their time and also our collaboration partners from the different regions.
This function cleans and transforms the collected data in the first function into the proper event logs which suit the process mining techniques in the following function Figure  1. The DIAG methodology modeling. The modeling function uses the process discovery algorithms and illustrates the execution of a processes based on the qualitative process analysis. Fourth, in analyzing function, different techniques are being used in order to provide a quantitative analysis for users. The performance level of processes will be evaluated in this function, and one could see and comprehend the way patients are circulating in the organization to receive their treatment. In the diagnosing function, multiple sets of techniques will be used to highlight the real cause behind the problems and weaknesses in the performance of processes.
ACKnOwLeDGMenTS AH is completing a PhD program at the University of Saint-Etienne and he wishes to acknowledge Association Recherche et Transfusion (Paris, France) for its support in the PETM/s (Program "Epidémiologie Transfusionnelle Méditerranéenne"/Sud) project, presented in part in this paper, and the association Les Amis de Rémi (Savigneux, France), together with the University of Lyon, Saint-Etienne.
This feedback loop enables a product to Fig. 5. Example: modular product architecture for "Aircraft" comprising modules for "Wings", "Fuselage", "Stabilizer", "Structure", "Cabin", "Cargo". be delivered in line with up to date requirements using remote access to the product in production. The paper proposes VCI and feedback gathering (Panel 3 of Fig. 8 ) for these validations without physical visits by the customer. The product (product modules) is connected virtually to the customer through an AR (Augmented Reality) system for customer inspection and validation. An inspection person (a person from the manufacturer company who assists a customer during VCI and feedback recording, also referred to as "VCI assistant"), who is wearing a Microsoft HoloLens, physically walks through the real product being produced.
Implementation of the proposed framework for customer feedback during aircraft production The overall process of the VCI, feedback gathering, evaluation and management has three sub-processes based on seven steps (Fig. 10 ). Fig. 6. Example: modular product architecture for the "Cabin" module comprising the modules for "Seats", "Galley 1", "Galley 2", "Lavatory 1", "Lavatory 2". Fig. 7. Example: modular product architecture for the "Galley" module comprising the modules for "Storage Area", "Coat Hook", "Coffee Maker", "Oven", "Opener". The sub-processes are: 1. Creating the inspection data, including AR-based visualization information; 2. Selecting relevant inspection modules and items and performing the actual inspection; and 3. Evaluating feedback and providing results to the production planning and design departments.
The preparation of the inspection data is mandatory and is the base for the list of inspection items. Although the AR visualization data is optional, it helps to find these items quickly and highlights the details that are important for the inspection tasks. All three processes share data stored in and retrieved from a PLM repository (database). The complete process is shown in Fig. 10. The three black-framed boxes contain the sub-processes: the AR design tool, the AR VCI tool and the Feedback Evaluation tool. Data is exchanged in XML format, either as a proprietary format for defining the inspection data including the AR visualization data, or in Microsoft Excel format to enable easy editing of feedback data for evaluation. The sub-process of "creating inspection data" has four steps: to extract the modules, features and attributes of interest from the related product architecture in the repository, including the AR visualization data (link 1 in Fig.
VCI system architecture The proposed VCI system has a mobile or wearable computing device, i. e. a computer that is worn on or close to the human body, which is connected to the PLM and can transmit video and audio to other locations (i. e. other mobile or stationary devices). Microsoft HoloLens (Microsoft HoloLens, 2017) (Fig. 11 ) is selected for VCI and feedback recording and is connected to the PLM via WiFi and the Internet. It is essential to have an open system and to develop a demonstrator that can connect to the PLM system and show the processes of customer feedback recording. Other additional requirements are that the VCI device is light-weight and easy to handle, and data during production and inspection, and data exchanges to and from the remotely connected customers. In the proposed setup, the input data for an inspection is retrieved from an FTP server, which has a connector to the PLM.
Fig. 14  shows screenshots of the demonstrator. Fig. 14  shows screenshots of the case study. The screenshot in Fig. 14(a)  shows kitchen equipment (i. e. Coffee Maker) in a galley to the user, a display based on recognized AR markers. The buttons for importance and satisfaction levels are placed as fixed elements around the actual view and are implemented as clickable buttons. In the HoloLens setup, the buttons are placed on the objects to be rated as the user needs to be able to link the module to the buttons. Fig. 14(b ) shows a mock-up of the HoloLens view. The recorded feedback on the Coffee Maker is shown in Fig. 15  as instantiated to the product architecture. The first column of the table (Fig. 15 ) shows the attributes and the second column contains the values of these attributes.
The first row has the module or attribute name used in the VCI and feedback. The second row contains the customer description and inspection setup, for example, and the customer reference number and timestamp. The third and forth rows have the recorded satisfaction and importance levels respectively as integer values. The last row contains the customer's comments and preferences, if any, as text. The product architecture with the recorded feedback is navigated and used as per the requirements of feedback evaluation and product development. For the implementation, the recorded feedback is saved as an Excel file and sent for evaluation. The evaluation process and use of the recorded feedback are presented in the following section.
Customer feedback evaluation and management from a product development perspective A Satisfaction Importance Evaluation (SIE) model is proposed to identify whether a module in a product is produced according to targeted customers and to compute corrections needed to make the module frugal. The SIE model is based on the modular approach (Section 3. 1) for the evaluation of customer feedback from a product development perspective. A module is a frugal module if customer satisfaction and importance levels for all the frugal attributes of this module are placed in the FrugalDesign zone (see Fig. 16 ). Thus a product consisting of frugal modules is defined as a frugal product. Frugal attributes are Function, Robust, User-friendly, Growing, Affordable, and Local  (Berger, 2013). Even though safety and security are of prime importance for an aircraft, companies design the product to satisfy customer needs which are dynamic.
It is important to retain customers and attract new customers in today's competitive markets. To provide a product that respects all the frugal attributes is extremely difficult, so companies look for a goodenough and affordable product that meets the needs of competitive markets for frugal design  (Zeschky et al. , 2011). The proposed modular organization is used for product development in a company. The company or customer selects attribute(s) from a list of frugal attributes against each module in the product for feedback. The SIE model evaluates the product and suggests the corrections required to make it frugal. For example, if customer feedback for frugal attributes (for example: Robust, Affordable) on the Galley Module is in the FrugalDesign zone, then the Galley is a frugal product.
Over design "Over design" feedback on a module places it in the blue zone in the SIE model (Fig. 16 ). The "OverDesign" indicator along with the blue flag is used for such kinds of modules. The zone on the upper-left side of the upper bound profile curve corresponds to over design. A module in this zone has the possibility to reduce design constraints in order to save resources and time spent in the production of this module. An over design module can be replaced with a less expensive module to reduce cost, time and resources. The percentage of corrections required in and over a design module to make it frugal is computed by Eq. (  2 ). The company may replace this module by a low-cost (or economical) module according to the percentage of OverDesign. = * * Do L OverDesign (2 / ) 100; (2) where L = maximum space available for customer feedback; = * L (10 2); Do = shortest distance from location of customer feedback to Upper bound profile curve; X Y ( , ) j j = feedback location on the SIE model for a module.
Feedback categorization from a product development perspective After evaluation, the feedback is categorized as either being a product design issue or one relating to production quality. The feedback is then sent to the appropriate department and people. As this categorization is a task for internal staff without customer involvement, an additional application is able to read all inspected modules and add any missing information. Hence, after feedback evaluation, a technician uses this application to enter the categorization data. The application (Fig. 17 ) enters the values of the "Feedback Category", i. e. whether feedback on the modules concerns the product design or the production. After the categorization of all inspection items, the results are stored in Excel file format. Feedback evaluation data contains prioritization (or ranking) as (i) PoorDesign modules, (ii) OverDesign modules, (iii) FrugalDesign  modules, along with the percentage of corrections required in these modules and categorization as per the relevance in the product development i.
e. design, production. The evaluation data is structured across the product architecture by associating the indicators, flags, percentage of corrections required and feedback category of the modules. The recorded feedback during VCI is used for the construction of an SIE model. The feedback and feedback evaluation data are presented in Figs. 18 and 19 using modules "Galley", "Seats", "Oven", "Coat Hook" and "Coffee Maker". The indicators and percentage of corrections are required to identify the relative uses of the resources for the development of the next version of the product modules in comparison with the resources used to develop the original product modules. The evaluation results along with the customer preferences are saved in the modular product architecture (Fig. 18 ). The results include (i) evaluation data for each module as a graphical representation (see Fig.
The optimal parameters used in TEAM were determined after a prototype lifecycle analysis based on lifecycle assessment (LCA) principles  (ISO, 2006). This LCA evaluated environmental performance by considering the potential impacts from all stages of manufacture, prototype use and end-of-life. The relevant inputs and outputs measurement useful for the inventory analysis, have been measured after an experimental campaign achieved on three AM technologies commonly used for prototyping (FDM, material jetting and binder jetting) and four different machines (professional and consumer). Results were then analyzed with OpenLCA databases. Then the impact assessment was calculated considering midterms impacts such as climate change, ozone depletion, … Thus, indicators were available for manufacturing strategies comparisons and AM machine selection regarding consumption.
Tool development Tool Platform. To study, through users' tests, the relevance of assisting designers for an efficient use of AM during early design stages, we aimed at developing a tool capable to be intuitive and with the lowest possible constraints. Two different platforms commonly used for software development were selected: POP and Axure RP. On the one hand, POP is an open source software available for all kinds of devices. It allows to create basic applications, is very easy to handle and user friendly but gives limited options concerning the interface ergonomics. the other hand, Axure is a professional software mainly used for dynamically model a web interface. It gives more possibilities for developing the environment to the programmer (due to several libraries). For the development of the tool a tablet and a mobile phone were chosen to be the support of the tool interface, respectively for Axure and POP.
Methods: Data from the European survey on Income and Living Conditions and Slovenian national census for the year 2011 were used in the SI-EDI construction. The concept of relative deprivation was used where deprivation refers to unmet need(s), which is caused by lack of all kinds of resources, not only material. The SI-EDI was constructed for 210 Slovenian municipalities. Its geographical distribution was compared to the distribution of two existing deprivation scores previously applied in health inequality research in Slovenia. Results: There were 36% of adults recognized as deprived in Slovenia in 2011. SI-EDI was calculated using 10 census variables that were associated with individual deprivation. A clear east-to-west gradient was detected with the most deprived municipalities in the eastern part of the country.
Socioeconomic problems are now seen as health problems that must be addressed to ensure that everyone has an equal chance for a healthy life  (5, 6). Tackling social inequalities in health is an ongoing priority for international health authorities and for many national governments in Europe  (6). In Slovenia, the Ministry of Health coordinates all intersectoral action aiming to reduce health inequalities by improving the accessibility and use of health care services, including preventive and other public health-care programmes. It specifically focuses on vulnerable target groups  (7). Evidence-based health policies require reliable and accurate measures of the socioeconomic environment of populations. Several approaches exist for measuring the socioeconomic status. Since individual socioeconomic data are often absent or poorly collected in large routine health databases, ecological measures based on aggregated census data are typically applied in such studies.