dataesr/scientific-paragraphs-categorization huggingface dataset:
9 in men and ≥ 0. 8 in women was considered central obesity. 28 mong the sociodemographic data collected during the interviews, three variables were taken into account for this study: age, gender and educational level. Four age groups were defined: 20-29, 30-39, 40-49 and 50 years and over. Gender was coded as follows: 1 for women, 0 for men. In Dakar, five levels of education were defined based on the Senegalese school system: none, primary (one to five years of schooling), intermediate (six to eight years), secondary (nine to 12 years), and university (13 years and over). In the Tessekere municipality, given the large proportion of persons who have never attended school (76%), the educational level was dichotomised: no schooling/one or more years of schooling. Satisfaction with body weight was assessed in one question, with five possible responses: 'Do you think you are: too thin, a little too thin, average, a little too fat, too fat?' To determine ideal body size, we took the BMI at which the same percentage of individuals believed they were too heavy as those who felt they were too thin.
Statistical analysis To answer our research questions, we used the Student's t-test, ANOVA, chi-squared test and logistic regressions. Results are expressed as mean ± standard deviation for continuous variables or as percentages for categorical variables. Bivariate comparisons were performed using the Student's t-test, ANOVA for continuous variables, and chi-squared tests for categorical variables. Multivariate analyses were performed using binary logistic regression and results are expressed as odds ratios with 95% confidence intervals (CIs). The software used for the statistical analysis was SPSS Statistics 22 for Windows.
Lastly, "other" was included to give consumers an option to indicate an emotion that was not on the list. The assessors were instructed to provide their responses to the following: "How strongly does wine No __ make you feel each of the following emotions. " The list of emotions was randomised for each consumer in every sample to reduce bias from the emotion judgement order. A minute break was provided in between each sample and during the break, consumers were instructed to cleanse their palate with filtered water and crackers. Testing was held at the University of Adelaide, Waite campus. The questionnaire data (demographic details), plus wine liking and evoked emotions were recorded using the sensory analysis and consumer tests management software Fizz acquisition Ver. 2. 47b (Biosystèms, Courternon, France).
Consumer recruitment The survey was conducted in order to elucidate how consumers comprehend wine body as a sensory sensation and reveal the language they use to describe this attribute. Consumers for this survey were recruited from the consumer database of the University of Adelaide as well as social network sites. Consumers were initially screened for eligibility including residency status, age, and wine consumption (Australian citizen or resident, over 18 years old, had no formal wine education, not working in the wine industry, and had consumed wine at least once during the last month prior to the questionnaire) (initially n=286). A total of 195 responded to the survey. Some consumers held certifications related to wine education and/or currently worked in the wine industry (n=14), whilst others did not complete the entire survey and so their data was removed in order to focus on general and naïve wine consumers.
2. 3. Section five: Consumers' subjective wine knowledge was assessed using the Goldsmith scale with five items rated on a nine point scale ranging from "totally disagree" to "totally agree"  (Flynn & Goldsmith, 1999). This scale was added to confirm consumers' knowledge of wine and later the data used to segment consumers based on their subjective wine knowledge. The questionnaire consists of three positively and two negatively framed questions and as such, it also serves to reaffirm that the questions have been read and understood correctly by the consumers. Data analysis To determine the influence of mouthfeel agents on liking and emotions elicited from the wines, data was analysed with univariate GLM ANOVA using SPSS statistics ver. 24 (IBM Corporation, Chicago, IL, USA). The liking and emotion scores were taken as the dependent variables; the samples were taken as a fixed factor; and consumers as random factor.
The final categories were analysed with frequency analysis to determine the percentage of consumers who used the different terms to qualify wine body. Categories mentioned by a minimum of 5% of the consumers have been included for analysis  (Vidal et al. , 2015). Data obtained from the Goldsmith scale were calculated for their descriptive statistics and determined three groups of segments using interquartile differences. Three groups, lower 25 %, middle 50 %, and the upper 25 % percentile were extracted to give less knowledgeable, knowledgeable, and highly knowledgeable groups, respectively  (Johnson & Bastian, 2007).
Acknowledgements The authors wish to thank all the consumers who participated in this tasting trial and online survey. This research was funded by the  School of Agriculture, Food and Wine of the University of Adelaide. Authors also thank  Wine Australia  for funding. Wine Australia invests in and manages research, development and extension on behalf of Australia's grape growers and winemakers and the  Australian government  ( UA1203 ). The consumer studies were approved by the  Human Research Ethics Committee of the University of Adelaide  ( H-2013-048 ).
Costing analysis The cost evaluation was based on retrospective data and limited to Comé HZ. The cost perspective was that of the national immunization program (NIP). The investigated period was 2013 and 2014. The demonstration project procured a number of solar powered CCE in health facilities without CCE or with non performance, quality and safety (PQS), outdated equipment, while other equipment used in the HZ are powered by electricity or kerosene. The economic evaluation aimed to represent these different contexts. Therefore, purposive sampling methods were used to draw a sample from 37 HPs, based on the following criteria: location (i. e. , commune), and CCE energy source (i. e. , grid, kerosene, and solar). Once categorized, facilities were randomly selected. All four CHCs were included in the sample.
In all, 19 health facilities were selected for the economic evaluation. There are multiple probabilities of being selected through the sampling method. This probability was accounted for when calculating the average cost for all health facilities in Comé. The inverse of the probability of each health facility being selected was used as the weight to calculate the weighted average of logistics cost per health facility. As the data collection was conducted at the end of 2014, the whole year data for 2014 were not available when the data collection began. In order to maintain consistency between the two years, we collected the cost data for the first six months of each year. We then multiplied the cost for the first six months by two to get the estimation for a 12-month period. The assumption is then that the cost flow is stable over the year.
Purposive sampling was used to ensure inclusion of the most informed and concerned individuals (Table  1 ). Of the 37 HPs in Comé, 15 were selected for interview. The four CHCs were automatically included. In total, between April and July 2015, two local researchers interviewed 16 individuals from CHCs and 28 from HPs, making a total of 44 interviews. Interviews were recorded, fully transcribed, and responses were coded with NVivo software based on preselected themes identified in the protocol. Quotations from different interviews regarding the same topic were compiled and compared to identify all existing viewpoints. Quantitative analysis was used to specify the rank of themes emerging from the interviews.
In addition, no performance indicators, such as stock-out rate and availability of vaccines were collected. Consequently, we could not compare the performance of the new system with the former one and measure the value for money of the redesigned system. Secondly, the actual standard ULY for vehicles might be shorter than that used in the study, given the poor road conditions and travel frequency. Some costs were not available or missing during data collection, which could potentially underestimate total costs. For instance, data on the proportion of energy costs used for the cold chain was incomplete or unreliable. As a consequence, no energy source costs were included in the results. The building costs at HZ level were also not available. Thirdly, we should also be cautious when directly interpreting and comparing the two years.
Contributors In relation to the economic study, XXH developed the database, performed data processing, cost calculation, analysis, and interpretation of the data, and provided a major contribution to the study report and manuscript writing. In relation to the anthropological study, EG developed the study protocol, supervised data collection, conducted the analysis, and provided a major contribution to the anthropological results and discussion in the manuscript. JBLG developed the economic study protocol and tools, conducted the sampling, provided technical guidance on data analysis and interpretation, contributed to the writing of study report and participated in manuscript writing. DA and RG revised the anthropological study protocol and tools, conducted anthropological interviews, and contributed to data analyses and report writing; JS supervised economic data collection, and provided input on economic protocol development, sampling, data interpretation, report writing, and manuscript writing.
Acknowledgements We would like to acknowledge  Benin's MoH  for facilitating study implementation; the  HERMES Logistics Modeling team  for discussions on data analyses and results interpretation;  Antoine Vollet ,  Sarah Chatanay , and  Mélanie Avella  for program operations management;  Caroline Lebrun  for her contributions to the protocol development;  Bradford Gessner  and  Emmanuelle Assy  for article review and valuable comments;  Jean Kouassi  for study coordination in the Comé HZ; and  Sabrina Gaber  for English editing. Appendix A. Supplementary material.
Bibliographic sources and analysis method The literature review aims at exploring the past contributions of TRIZ-based methods to GSC problem resolution. The review is based on crossed research for articles published in the databases « Science Direct » and « Scopus » using following keywords: "green, environmental, supply chain, logistics, TRIZ, innovation". In total, twenty one articles addressed the application of TRIZ methods to GSC problems and were analyzed in Table  1. According to a first overview of the uses of the TRIZ in GSC problems, the existing attempts primarily focus on green product & packaging design and green production & remanufacturing. The application of the TRIZ in the GSC domains concerns subjects that are similar to the traditional application of TRIZ-based methods, i.
Description of the system and inventive problem This case involves a simple manufacturing system managed by Kanban cards simulated with the aid of the flow simulation software Witness 14. The system is composed of one manufacturing machine producing two different items stored in two independent inventories, stock_1 and stock_2, and delivered to customers. The interval time of customers' arrival time follows a uniform random distribution, i. e. , the interval of customers' arrival time at inventory_1 follows a uniform distribution (0. 18, 0. 22), and the interval of customers' arrival time at stock_2 follows a uniform distribution (0. 9; 1. 1). It is a simple pull system, in which Kanban cards are sent back to the manufacturing machine when the container is empty. The Kanban card provides the quantity to be placed in a container.
2010) Generating process Laws of evolution in correspondence to CP Strategies of cleaner production Suggested when limited improvement options strategies professional experience Ideal final result gives optimization strategy A2, A3 Speaks about functional analysis, ARIZ (D'Anna & Cascini Preliminary analysis for Laws of evolution Sustainability map Adaptation of TRIZ tools 2011) sustainable systems System operator A1, A2, A3 (Chulvi & Vidal 2011) Eco design of product Evolution trends = 7 LiDS principles Eco-design strategy wheel LiDS (7 One of three basic axiom used, principles) remake of solution principles by creating database; A2 (Kubota & da Rosa Identification and conception of IFR Developed methodology : Adaptation of TRIZ tools in 2013) CP opportunities Resources functional analysis, trimming, IFR, developed methodology; A3 Functional analysis resources, inventive solutions.
Contemporary public statistical datasets are ill suited for this purpose and for the measurement of OMRT dynamics in interaction with social stratification in the organizational society. Geographers and sociologists can design joint research projects on residents switching neighborhoods (e. g. ,  Lévy, 1998) , people migrating from countryside to the city and back  (Lemercier & Rosental, 2008) , entire populations making an exodus from one continent to the other, and the changes occurring at the meso-and macrolevels because of such movements. Much of what happens in social life at meso and macro levels combined is OMRT-related phenomena characterized by the dynamics of multilevel structures where relational infrastructures necessarily function as gears driving evolution. They can be reconstituted in all areas of social life provided that a longitudinal perspective articulates coevolutionary changes across places (mobility) and in relationships (networks).
Les résultats Les hypothèses ont été testées par des analyses de régressions effectuées avec le logiciel SPSS 22. 0, selon les préconisations  de Preacher et Hayes (2004 , 2008)  avec le modèle 1 de la procédure de  Hayes (2012) , et une procédure Bootstrap de 5000 réplications. Cette approche, préconisée par  Cadario et Parguel (2014) , permet l'analyse des effets modérateurs sans discrétiser a priori les variables modératrices étudiées. L'effet de chaque dimension de la légitimité des associationscognitive, pragmatique ou morale -, de la vulnérabilité perçue des individus et de leur interaction sur l'intention de confiance a été analysé par un modèle de régression distinct. Au total, trois modèles ont été conçus (Modèle 1 : effet de la légitimité cognitive ; Modèle 2 : effet de la légitimité pragmatique ; Modèle 3 : effet de la légitimité morale) (Tableaux 3, 4 et 5).
The goal of C2NET Project is the creation of cloudenabled tools for supporting the SMEs supply network optimization of manufacturing and logistic assets based on collaborative demand, production and delivery plans. C2NET Project will provide a scalable real-time architecture, platform and software to allow the supply network partners: -to master complexity and data security of the supply network, -to store and share product, process and logistic data, -to optimize the manufacturing assets by the collaborative computation of production plans, -to optimize the logistics assets through efficient delivery plans and -to render the complete set of supply chain management information on the any digital mobile device (PC, tablets, smartphones) of decision makers enabling them to monitor, visualize, control, share and collaborate.
C2NET will provide specific tools for optimisation and collaboration in the cloud, allowing enterprises to be responsive and flexible against changes in both the demand and unexpected events that take place during products production and distribution. Moreover, C2NET will provide a technological infrastructure that allows collecting real-world data from processes and products within the supply chain. Finally C2NET project provides collaboration tools with a set of services able to facilitate the decisionmaking processes, increasing the enterprises agility. Accordingly, the C2NET Project will generate a Cloud Architecture composed by the Cloud Platform (C2NET CPL), the Data Collection Framework (C2NET DCF), the Optimizer (C2NET OPT) and the Collaboration Tools (C2NET COT) (see Figure  1 ). The validation of C2NET outputs (collaborative platform) will be possible with the four industrial pilots participating in the project.
The study's context and data collection The results of this study are based on data which are collected from a well-recognized retail store in London. The high-end retail stores in London have well-known brands and are destinations for tourists and non-tourist visitors. According to  Dennis et al. (2014) , the highend store can influence consumers' perception associated with its brand identity. An exploratory study was conducted in this study to gain an in-depth understanding of the research questions in the retail industry. To develop an appropriate scale, we follow  Churchill's (1979)  procedures in order (i) to gain an in-depth understanding of the research area  (Dacin and Brown, 2002) , (ii) to understand the actual practice in the field in order to gauge whether the proposed research study is relevant, and (iii) to obtain insightful information and understand the proposed research questions, generate hypotheses and refine the measures for a questionnaire  (Churchill, 1979).
All responses were measured using a seven-point Likert-type scale (from 1=strongly disagree to 7=strongly agree). At this stage, reliability as an essential condition of validity and exploratory factor analysis (EFA) were tested to identify any patterns in the data  (De Vaus, 2002). The scales illustrated a high degree of reliability, with a Cronbach's alpha of 0. 95; this is greater than 0. 70, which is appropriate for most research purposes  (De Vaus, 2002; Hair et al. , 2006; Nunnally, 1978). After the validation process (pre-test), the main data set was collected from a self-structured administered questionnaire. Based on the study by  Dennis et al. (2014)  to represent sample as practicable of shoppers in the store, a usable sample of 347 internal and external stakeholders (employees and visitors to the store) collected on various days and hours including weekdays and weekends over a two month period.
Yet, since the early 2000s, technologies have increased and the collection of data has deeply changed. For example, "Wal-Mart handles more than a million customer transactions each hour and imports those into databases estimated to contain more than 2. 5 petabytes of data. " (Ittmann, 2015). Today, as companies are drowned in information which doubles every two to three years, they have to find the best way to understand it and gain a competitive advantage. It is more important to know what information the company needs, how and when to match the customer needs. Therefore, software has been created in order to answer these questions. One of the most used practice is business intelligence (BI) which integrates and analyses various software. BI provides a set of technologies and products for supplying users with the information they need to answer business questions, and make tactical and strategic business decisions  (Stefanovic et al.
, 2006). The BI field is growing over the years because as said by  Gartner (2012) , "the global spending on BI systems, including analytics and performance management applications, has risen from $10. 5 to $12. 2 billion in 2011". Given the increase in data, competition and customers requirement, it is vital for a company to have rapid access to its information in order to take the best decision and reduce its cost. BI, consequently, is also appropriate for supply chain management (SCM) which needs to be functional. Thus, it can provide real-time data of this supply chain. Before using BI in the supply chain it is important to understand and to know how to use it. In this respect, some questions must be asked:.
It is difficult to give a clear definition of BI when many of them can be used. Business intelligence is seen as a concept of conscious, organized, continuous, legal and legitimate gathering, analysing and using data and information for strategic and tactical marketing decisions according to  Šerić et al. (2014). Adelman et al. (2002)  describe BI as a term that "encompasses a broad range of analytical software and solutions for gathering, consolidating, analysing and providing access to information in a way that is supposed to let an enterprise's users make better business decisions". Malhotra (2000)  points out that BI benefits facilitate the connections in the newform organization, bringing real-time information to centralized repositories and support analytics that can be exploited at every horizontal and vertical level within and outside the firm.
According to Partrige (2013), BI is the use of computing technologies for the identification, discovery, and analysis of business data such as sales revenue, products, costs, and incomes. However, BI can also be viewed as more technical and integrate several software for extraction, transformation and loading (ETL), such as data warehouse (database where data is collected for the purpose of being analysed; it collects, organizes, and makes data available for the purpose of analysis), database query and reporting  (Berson et al. , 2002). We also find multidimensional/online analytical processing (OLAP) and data mining (used to solve different kinds of analytical problems, OLAP summarizes data and only makes forecasts, data mining discovers hidden patterns in data and operates at a detailed level instead of a summary level).
BI is a system designed to support decision making, it finds information from many other systems (Figure  1 ). Some of these terms have briefly been explained in order to understand the BI dimension. BI helps to create knowledge from a world of information, get the right data, discover its power, and share the value. BI transforms information into knowledge. (Reinschmidt Consequently, BI is the application of putting the right information into the hands of the right user at the right time to support the decision-making process  (Reinschmidt & Francoise, 2000). The business success factor for any enterprise is finding ways to bring the vast amounts of data that are flowing within and across the business processes together and making sense out of them  (Sahay & Ranjan, 2008). For those reasons, BI is not business as usual.
ht m). The second function is the SCE (supply chain execution) and this function integrates the data related to the operational activities management of the supply chain. Software like TMS (transport management systems) and WMS (warehouse management systems) are associated with SCE. The last function is the SCEM: the supply chain event management. Another type of software to take into consideration is ERP (enterprise resource planning). It is a software that integrates all the functions of a company. It is constituted of several units named business objects (BO) (for example: supply, sell, production, finance, HR, or stock). These units share the same database, so it facilitates the control of the company (http://www. logistiqueconseil. org/Articles/New -tech/SCM. htm) even if "traditional ERP players are now facing competition from cloud providers"  (Trebilcock, 2016) , the leader in the ERP software market remains SAP with €2.
There are many ways to extract data in order to create business intelligence, for example "statistical and quantitative analysis, explanatory and predictive models"  (Ittmann, 2015). Therefore, big data and analytics can directly be related to BI because it can help firms to make decisions and improve their businesses. As mentioned by  Partridge (2013) , "being able to find, understand, and use that data to make strategic decisions that improve supply chain effectiveness is crucial. ".