dataesr/scientific-paragraphs-categorization huggingface dataset:
Data overview and underlying hypotheses The model is illustrated with an example inspired by a real case study in the furniture sector. The product family is a kitchen cabinet, which is a cornerstone of all kitchen variants regardless of the specific customers' requirements. The data about the variants are fully gathered from the case company. Because data about available monthly production and storage capacities allocated to the considered product family were not available, estimates are used (H1, H2, H3). Similarly, an estimate of the stock holding ratio is used (H4). The aim is to analyze the production shares among variants proposed by the model to meet a given demand profile. Consequently, preliminary computations were run to eliminate the production capacity limitation constraints. More specifically, the production capacity was adjusted to cover the total demand of each period (i.
These impacts will be reflected by the unit sustainability indicators that will be derived from the system modelling and are associated with the functional unit-namely, the kitchen cabinet. The tools used for the modelling are developed within the framework of the S-MC-S project  [30]. These modelling tools are connected to the Ecoinvent  [28]  database, which simplifies the calculation of both environmental and economic indicators using the same modelling instance. At the product level, the basic structure of the kitchen cabinet variants was modelled along with customization attributes (i. e. , dimensions, handle type) and their occurrence probabilities. As mentioned before, the current case study considers 6 variants of the kitchen cabinet, whose main differentiating features are the dimensions of the panels and material and shape of the handle.
At the process level, the list of activities contributing to the realization of the cabinet was identified and modelled (e. g. , wood cutting, edging, assembly) including unitary costs and respective standard processes identified in the Ecoinvent database  [28]. Finally, the supply chain of the kitchen cabinet was modelled including the suppliers and markets to consider (i) the transportation data and (ii) the unit purchasing costs when calculating the indicators during the subsequent step. The UVC, GWP, and NRD indicators were calculated out of the above modelling, and the subsequent values are shown in Table  3 , where the last column of the table includes the selling prices of the variants. Table  4  shows the weights of the indicators derived from interview outputs. The weights reflect the concerns of the firm, which are dominated by the economic perspective (UVC weight).
Data variables and analysis The data variables collected in this study included the number of patients with hypertension, DM, asthma and epilepsy newly registered in each quarter and cumulatively registered at the end of each quarter; the demographic characteristics such as sex and age; risk factors for NCDs such as current cigarette smoking (1 cigarettes per week), alcohol consumption (drinking any alcohol at any time) and obesity (body mass index 30 kg/m 2 ); existing disease-related complications; cumulative programmatic outcomes (see Table  1 ) and management and treatment outcomes of each NCD (see Table  1  for the quality of NCD control determined by specific indicators). The data were sourced from integrated cohort quarterly reports generated by the electronic medical record (EMR) system and the paper-based database at the Malawi Epidemiology and Research Unit, Lilongwe.
Treatment is based on what is stipulated in the Malawi Standard Treatment Guidelines. 13 The clinic monitors the care of DM patients through FBG testing and of other patients through use of a 'vital signs kiosk' that is directly interfaced with an EMR. There is no paper-based record system EMR In the clinic, an EMR has been set up in a way similar to those being used in the antiretroviral therapy clinics throughout the country and the diabetic clinics in Blantyre, Zomba and Lilongwe, as previously described. 14, 15 These use a touch screen, point-of-care electronic system that has data fields for NCDs and their management. Demographic, clinical and treatment information for the patient is collected during registration and entered in real time into the EMR. Clinical and treatment information is also entered in real time at each follow-up visit.
Public Health Action Chronic disease in primary care in Malawi 63 lence of these lifestyle habits was more common than we have reported. We were able to track programmatic outcomes through the EMR, although this required help from the Wellcome Trust database in the Malawi Epidemiology and Research Unit, Lilongwe. Worryingly, a large proportion of patients who did not attend either in the quarter or after 1 year were recorded as lost to follow-up, meaning that they had not been seen in the clinic for 1 year. We do not know whether they were truly not seen or whether this was a feature of missing data. Furthermore, we do not know whether these patients who were lost to follow-up or recorded as non-attending had died or transferred out, and this requires further investigation. Of patients who were seen, it was encouraging that most were prescribed medication, that most of those with hypertension and/or DM had their blood pressure and/ or blood glucose measured and that a substantial proportion with epilepsy had their seizures controlled according to our pre-set parameters.
Acknowledgements: We thank  Laure Dossus  for the coordination of the International Cancer Research Funders Nutrition working group and for her support in the development of this work. The International Cancer Research Funders Nutrition working group was created following the 3rd International Cancer Research Funders meeting convened at the  National Cancer Institute (INCA ) headquarters in Paris co-organised by  INCa  and  IARC  in conjunction with  Cancer Research UK  and the  NCI, USA. Leaders of cancer research organisations from fifteen countries across the world met and discussed areas of great importance for global coordination to reduce cancer incidence and mortality, improve cancer care, and enlarge understanding of the many forms of the disease. Among the challenging issues of cancer worldwide, nutrition was recognized as playing a major role in need of an international focus.
"People-centric sensing will help drive this trend by enabling a different way to sense, learn, visualize, and share information about ourselves, friends, communities, the way we live, and the world we live in. " (Campbell et al. , 2008)  Thus, it is worthy to explore a way to collect and aggregate public opinions to enhance assessment of service quality  (Qiu et al. , 2015). This paper uses the US higher education as a service example to show how big data-mediated public opinion aggregation can be well applied to augmenting the assessment of service quality. Please keep in mind, a system of computing rather than a service quality modeling approach is presented in the remaining paper. The remaining paper is organized as follows. Section 2 briefly reviews a framework for capturing and visualizing public options that has been used to develop a ranking module, part of the Leveraging Innovative Online Networks to Learn Education Networks and Systems (LIONLENS) research project by the authors.
, 2010; Alper et al. , 2011; Marcus et al. , 2012). To test out the concept of retrieving public opinions from a variety of data sources, tweets thus are used in this study. In this study, we focus on a list of targeted colleges in the US. Tweets from Twitter. com are retrieved using predefined queries (Figure  7 ) or streamed using a list of keywords in real time (Figure  8 ). Clean texts are extracted from received raw tweets and then further analyzed using our developed sentiment analyzer.
Big Data Computing Platforms Data and information from the Internet are generally unstructured and mostly stored as images and texts. Our endeavor in enhancing service quality in this study substantially relies on the successful design, development, and deployment of big data computing platforms. Our deployed platforms use a scalable Lambda architecture to deal with big data volume and velocity simultaneously, supporting a hybrid computation model as both batch and real-time data processing can be combined transparently. The distribution layer consists of an Apache Kafka messaging broker. The batch layer includes HDFS, MapReduce, Hive, Pig, and Spark batch. The Apache Spark streaming layer includes Spark core and resilient distributed datasets, HBase, Cassandra, and MongoDB to perform lightning-fast cluster computing transformations and actions.
Analysis of pyrosequencing data Bioinformatics analyses of reads obtained by pyrosequencing were performed using the GnS-PIPE pipeline developed by the GenoSol platform (INRA, Dijon, France)  (Terrat et al. , 2012) , or the Qiime pipeline developed by scikit-bio  (Caporaso et al. , 2010). The parameters chosen for each step were the same for the two pipelines and can be found in supplementary material (Table  S2 ). First, all the 18S raw reads were sorted according to the multiplex identifier sequences. The raw reads were then filtered and deleted based on: (i) their length, (ii) their number of ambiguities (Ns) and (iii) their primer(s) sequence(s). A PERL program was then applied for rigorous dereplication (i. e. clustering of strictly identical sequences). The dereplicated reads were then aligned using Infernal alignment  (Cole et al.
, 2009) , and clustered into operational taxonomic units (OTU) using a PERL program that groups rare reads with abundant ones without counting differences in homopolymer lengths. A filtering step was then carried out to check all single-singletons (reads detected only once and not clustered, which might be artefacts such as PCR chimeras) based on the quality of their taxonomic assignments. The high-quality reads retained were then taxonomically assigned using similarity approaches against dedicated reference databases from SILVA  (Quast et al. , 2013)  (see supplementary material) (Table  S2 ). The raw data sets are available on the EBI database system under project accession number PRJEB12990 (awaiting attribution). Linear discriminant analysis effect size was used to determine significant taxonomic differences between the grape must sample of each phytosanitary protection  (Segata et al.
Mass spectra were obtained with an FT-ICR-MS Solarix (Bruker Daltonics, Bremen, Germany) equipped with a 12 Tesla superconducting magnet (Magnex, UK). The instrument was equipped with an electrospray ionization source Apolo II. The samples were injected directly into a micro electrospray source at a rate of 120 ll h À1. The MS was externally calibrated using a 5 ppm solution of arginine (0. 2 ppm tolerance). Spectra were recorded in negative ionization mode and for a mass range between m/z 100 and 1000. For each sample, 300 scans per sample were accumulated with a time domain of a 4 MW (megaword) per second. Spectra were then internally calibrated using a mass list of ubiquitous wine compounds with a mass error below 50 ppb. Peaks with a signal to noise ratio (S/ N) of 4 and higher were used for further data processing.
Partial least square discriminative analysis (PLS-DA) models were used to provide enhanced representations of the sample category discriminations and extract the most discriminative metabolites, which were also checked manually within the spectra. Discriminative masses with a variable importance in projection (VIP) value > 2 and P values < 0. 05 were considered as relevant. PLS-DA was performed with the SIMCA 9 software (http://www. umetrics. com/). Two dimensional van Krevelen diagrams of discriminative metabolites were obtained using compositional networks (based on elemental compositions) and functional networks, based on selected functional group equivalents that enable improved assignment options of elemental compositions and better classification of organic complexities with tunable validation windows  (Tziotis et al.
, 2014). Amplicon Library Preparation, NGS Sequencing, and Sequence Analysis DNA was isolated from transfected cells (EZNA tissue kits; Omega Biotek) or zebrafish embryos, and 100-150 ng was added to a 50-ml Phusion polymerase (NEB) reaction. Each sample was assigned a primer set with a unique barcode to enable multiplex sequencing (Table  S5 ). PCR products were purified on a 2% agarose gel, quantified, and pooled into a single sample for sequencing. The single combined sample was treated by the MNHN Genomics Center and sequenced on Ion Torrent PGM. A custom python pipeline was used to count and characterize indels and insert types.
ACKNOWLEDGMENTS We thank  Loı ¨c Ponger  and  Nicolas Buisine  for advice in the development of the software for sequence analysis,  Jose Utge  and  Regis Debruyne  for PGM sequencing,  Erika Brunet  for helpful discussions, genOway's production lab, Wim van Schooten at Cergentis for TLA analysis, and  Alain Hilgers  for support. This work was supported by  ANR   Investissement d'Avenir   ANR-II-INSB-0014 ,  AFM  n  18566 ,  Region Pays de la Loire through Biogenouest, IBiSA program ,  Fondation Progreffe , and  ANR LabCom  ( project ''SOURIRAT' '). Sequencing experiments were performed by the integrative genomic facility of Nantes and the  SSM department at the Museum National d'Histoire Naturelle in Paris. S. F. , Y. C. , and A. F. are employees of genOway company.
On the basis of the data collected at the first time before activities taken and the data after activities taken, the variation of the concerned materials between two times can be calculated. By checking databases or measure on site, the emission factors of each material in each phase can be acquired. Multiple the value of variations with the value of emission factors and then add the original environmental emissions, the new environmental emissions of these materials can be calculated out. This paper is organized as follows: Section (2) describes the related works. Section (3) focuses on the main proposed model and approach. A case study is provided in Section (4) to illustrate the approach. Finally, some conclusions are presented. LCA is more accepted in industry because it can assess product's EIs associated with all the phases of the product's life from cradle-to-grave  (Rebitzer et al.
Step 5. Assign a likelihood rating to each failure mode of the rolling stock component The failure data are analysed using statistical techniques (e. g. Weibull analysis, regression models, data mining) to create models for estimation of the likelihood of rolling stock defects. The likelihood of occurrence of a failure is evaluated on the basis of failure rates (in year) estimated from historical data or expert knowledge. The failure rate of the failure mode i is estimated by k i ¼ Total number of failures resulting mode i since installation time Duration of time (in years) operation : Based on the failure rates obtained, a likelihood of occurrence rating based on a 10-point scale is assigned to each failure mode (see Table  1 ). As shown, the recommended likelihood rating scale varies from 1 to 10, where 1 represents ''remote'' and 10 indicates ''almost certain''.
An analysis of performance data indicates that a great number of failures are associated with door system (see Fig. 5 ), having a detrimental effect on the train reliability and consequentially passenger satisfaction. A door system consists of the following major components: -Door drive Gearbox, upper locking devices, synchronising cable and guides; -Control elements and switches Open/close limit switches and pushbuttons; -Door leaf Mounting of leaf, window and lead-mounted guides; -Safety and emergency devices Mechanical switches, finger protection and light barrier; -Other components Interior panelling, wiring, lighting and steps. The data required for this study were collected from the literature, the company's maintenance management software system called EQUINOX and the UK's railway performance management software DATASYS BUGLE [23].
The delay information was extracted from a database system called TRUST (TRain RUnning SysTem TOPS) that is used for monitoring the progress of trains and tracking delays on the UK's railway network. The total delay time of the train due to door defects was 518 min. The train operating company is penalised £50 per minute delay in service. Thus, the total penalty charges due to train delays will be 518 min 9 £50/ min = £25900. A Delphi technique was used to elicit the experts' estimates of the failure likelihood and damage severity. Three academics who have published several papers in the field of risk and reliability, three maintenance engineers from the operating company with over 15 years of experience, one designer from the design consultancy and one designer from the manufacturer company were involved in this FMECA study.
The analysis model was applied to the passenger door unit of a fleet of 38 Class 380 trains operating on Scotland's railway network. The data required for the analysis were collected from the literature, the company's maintenance management software system called EQUI-NOX, the UK's railway performance management software DATASYS BUGLE and the UK's train movements monitoring system called TRUST. The five why's technique was used to identify the potential failure modes of door unit components and their root causes, including the defects in relation to pushbuttons, door control unit (DCU), mechanical failures, light barrier, door drive, guard operating panel (GOP), limit switches, loose plugs, obstructions, door roller and lubrication. The results of the risk evaluation showed that the nine failure modes (12 % of the total number of failure modes identified) are ''high critical'' to door system functionality.
Acknowledgments The authors gratefully acknowledge the support provided by the Scottish train operating company during field visits and data collection. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creative commons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Donn ées et m éthodologie Il a été utilisé les données sur les parcelles en vignes nobles, dans les départements de la région. Ces données ont été extraites et fournies du Registre des Plantations Viticoles (R. P. V. ) qui est sous la gestion de l'Office  Pour les départements où il y a eu de la reconversion de cépages, il a été distingué deux groupes de parcelles  pour chaque département : les parcelles existantes en terrain au niveau de l'année 2006 et les parcelles qui ont été reconverties pendant la période 2007-2015. Dans un premier temps la surface totale par cépages des deux groupes de parcelles a été classée en fonction de la couleur de la bais du raisin. Ensuite, pour chacune des classes il a été mis en évidence les cépages. Pour vérifier la couleur de la bais du raisin, la base de données de l'Organisation Internationale de la Vigne et du Vin (O.
Data analysis All responses were entered to the statistical package for social sciences package for social sciences (SPSS) version 23 program -Statistical Product and Service Solutions, an IBM product acquired by IBM in 2009  (Hejase and Hejase, 2013). The study was performed using exploratory statistics; data tables including frequency and percentage distributions were used and supported by their respective figures. Moreover, cross tabs and regression analysis were performed to study relationships between variables that may add value to the findings of the research.
The Adaptive Monitoring (M) is able to detect events according to the QoS specifications of the CS. The Cognitive and Predictive Models (AP) search for the causes and the effects correlating data incoming from the Adaptive Monitoring. The Reaction Strategies (E) perform the proper recovery action. The considered SoS can be integrated in a more complex SoS; Fig. 8  gives a representation of the recursive foreseen architecture. In this section, we report on the broad topic of monitoring in SoSs. In particular, focusing on observation and data analysis (i. e. , the Monitoring and Analysis components of the MAPE building block). We investigate basics on monitoring and detection (Sect. 3. 1) and main monitoring approaches (Sect. 3. 2).
Although the framework hides the complexities of parallelism from the users, deploying an efficient MapReduce implementation poses multiple challenges. MapReduce's ad-hoc configuration and provisioning require a high level of expertise to tune  [9]. Ensuring performance and dependability of MapReduce systems still poses several challenges. One of the most popular open source implementations of the MapReduce programming model is Hadoop. It is composed of the Hadoop kernel, the Hadoop Distributed Filesystem (HDFS) and the MapReduce engine. Hadoop's HDFS and MapReduce components originally derived from Google's MapReduce and Google's File System initial papers. HDFS provides the reliable distributed data storage and the MapReduce engine provides the framework to efficiently analyse this data.
25. Client suggestions that change an original project are more acceptable when these involve software that has the potential to generate gains for society. 26. People from our staff support each other so as to incorporate software changes suggested by clients. Willingness to Change in Service scale has been applied on a survey carried out in 2012 on a random sampling of 351 software development companies in Brazil. This sample is composed of companies that provide software development services with high interaction between client and service providers (mainly software development or customization). 18 regional agents from SOFTEX have collaborated to mobilize companies to answer to our research. They have provided us a database of 897 companies linked to SOFTEX across the country. We chose to extend this number with the incorporation of a second database of software companies associated to Brazilian Association of Software Companies (Associação Brasileira das Empresas de Software [ABES]), comprised of 1216 companies, and the base of Association of Brazilian Companies for Information Technology (Federação das Associações das Empresas Brasileiras de Tecnologia da Informação [ASSESPRO]), consisting of approximately 200 companies.
By aggregating the tree mentioned databases, we have composed our own national database formed by 2,300 companies. The original scale has been written and applied to respondents in Brazilian Portuguese. Translation into English has been provided for the publication of the present work. We selected companies that provided highly interactive services on demand to order or customized software solutions to make sure the phenomenon of willing to change could be present. In each company, an information technology expert has been selected to participate on the survey. Companies have been contacted via telephone and questionnaires have been filled on real time follow-up. A Likert scale of ten points (where 1 means strongly disagree and 10 means strongly agree) has been applied to measuring WCS items.