dataesr/scientific-paragraphs-categorization huggingface dataset:
The obtained results confirmed the observation of Li et al. 8  in which the nanoparticles do not have a major influence on fluid frictional behavior. Lotfi et al. 35  reported the effect of different models of nanoparticle simulation on forced convection turbulent flow in a circular tube. They made comparisons among three different single-phase, two-phase mixture, and Eulerian models. Comparison of the experimental values showed that the mixture model is the most accurate one. Bianco et al. 36  examined the turbulent forced convection heat transfer to water/Al 2 O 3 nanofluids inside a 1-m-long tube of diameter 0. 01 m and used the twophase mixture model in FLUENT software. The aluminum oxide particles had 38 nm diameter. As expected, the highest heat transfer rate for a given concentration was achieved at the largest Reynolds number while the increase in particle volume fraction amplified the heat transfer.
,  69  Ede et al. ,  70  Seban et al. ,  71  Abbott and Kline,  72  Seban,  73  Filetti and Kays,  74  Goldstein et al. ,  75  Durst and Be Whitelaw,  76  and De Brederode and Bradshaw,  77  developed experimental and theoretical methods of studying separation flow that takes place due to changes in the cross section of the passage. With advances in measurement devices and CFD software, the researchers have identified detailed information regarding the structure of separation flow and recirculation zone. Backward-facing steps. Armaly et al. 78  employed a laser Doppler anemometer to measure the velocity distribution and reattachment length for air flow over a backward-facing step. They investigated the laminar, transition, and turbulent range domains, and the obtained results were in good agreement with the experimental and numerical findings.
This was corroborated by the results obtained from FIDAP software and also with the experimental data from plain fluids. Successively, research on free convection in a gradually heated cavity with water/Cu nanofluids at solid volume fraction of 0% j 20% was carried out. Consequently, substantial growth in nanofluids heat transfer and natural convection were obtained. It must be noted that the experimental observations of Putra et al. 115  contradicted with the results of this study which needs to be clarified in future studies. Jou and Tzeng 120 carried out similar study through a differentially heated cavity. The stream functionvorticity formulation was also used in this study, in exact manner to that used in a prior investigation by Khanafer et al. 119  The effects of Grashof number and cavity AR (width/height) on thermal characteristics of the cavity were studied.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article:  S. N. Kazi  gratefully acknowledges  High Impact Research Grant   UM. C/HIR/MOHE/ENG/45 ,  UMRG  Grant  RP012D-13AET , and  University of Malaya, Malaysia , for support in conducting this research work.
So, the decision was made to test soundscape predictive models directly through geo-referenced data already integrated into the GIS of most metropolises, without any physical model. To do so, the pleasantness dependent variable and the independent perceptual variables (overall loudness, and the three time ratios for traffic, voices and birds) which were collected in the Cart_ASUR project during the day period of the week days were used in this study (70 Parisian situations in the 13 th and 14 th districts). To increase the validity of the models, a new campaign was carried out on 19 new situations in the same districts during the GRAFIC project (Cartographic representation of urban sound quality for locations and for paths), collecting the same perceptual data than the Cart_ASUR project. For this study, a total of 89 urban situations were evaluated (Figure  1 ) by about 20 persons for each location.
There are different kernel functions in literature for the distribution of a punctual data such as Gaussian, quartic, uniform or triangular functions  [31]. In this paper, the QGIS software which proposes Gaussian and quartic functions only was used for calculations and visualizations. As a first approximation, the simplest fixed Gaussian kernel function has been chosen because it is proposed in most of the open source GIS which can be used by any city. The value is cancelled beyond the smoothing window (or the search radius R). For more than one point, the val-ues of density are simply the sum of the individual density for each point. Then, these values have no absolute meaning, but only a relative one, because they depend on several parameters, such as the radius parameter and the distance between points.
Density of gardens The density map of gardens was created in order to represent the more or less significant presence of birds (variable D_gardens) at any point of the map. It is noteworthy that these birds are better perceived in the center of the garden than at its periphery  [32]. The data layer "gardens" of the IGN's¹ BD TOPO French database was used. This is a polygonal vectorial layer. A particular transformation was proposed to show that the density is low on the perimeter, increasingly significant inside the garden, but with a degree of stability when getting closer to its center. Figure  3  shows three parameters. When we progress inward from the garden: 1 http://professionnels. ign. fr/sites/default/files/DC_BDTOPO_2-1. pdf • The distances of successive buffers (A1, A2, etc. ) are becoming greater; • The equidistance between the points on the buffer lines (B1, B2, etc.
Acknowledgement: This study was carried out as part of the  Cart_ASUR  project (Convention n ∘ 1217C0035) and GRAFIC (Convention n ∘  1317C0028 ), both being financed by  ADEME. The authors would like to thank the city of Paris for the free use of the geo-referenced data. They want also to thank  Jean-François Gleyze  for the design of the proposed maps.
Wikipedia case study To answer our research questions and to verify the validity of our hypotheses, we chose Wikipedia as data source. Wikipedia, is one of the most heralded success stories of peer collaboration and has become a notable example of the online epistemic community. The first important characteristic of Wikipedia data availability. Many useful data records publicly available online, includes useful information about Wikipedia  [24]. Furthermore, the case study will be Wikipedia, because of its connection with firms' knowledge management and production challenges. Another important aspect of Wikipedia is that MediaWiki site is maintained for every different language in Wikipedia.
Data collection The best way to retrieve large portions or the whole set of activity data from any Wikipedia language is using the database dump files. These dump files contain precise information about all actions performed in any Wikipedia language. Dump files can be retrieved from the Wikimedia Downloads center. For our purposes, we are interested in French wikipedia. We first retrieve French Wikipedia XML database dump le "pagesmetahistory. xml. 7z" from the set of available dump files. We use data extracted on December 12, 2015. This data contains the complete meta data of every version of all French articles from the beginning of the online encyclopedia (January 2001) to December 2015. Once dump file is loaded, we use WikiDAT and Media wiki API for data extraction. WikiDAT is a tool for Wikipedia data analytics, based on Python and R and using MySQL database.
It is aim to create an extensible toolkit for Wikipedia using Python and R to automate the extraction of Wikipedia data into 5 different tables of MySQL database (page, people, revision, revision hash, logging). The MediaWiki action API is a web service that allows the collection of data and metadata from the latest Wikipedia's dump and it's available for several languages, in particular, French, the one we're concerned with. It is a project maintained by the Mediawiki and contains a well structured documentation to be able to query data and can be returned as JSON. In our project, we used it to retrieve the articles that are part in the categories "Featured Articles" and "Good Articles".
Data selection We preprocessed the XML records in the raw data using WikiDAT into a tabular data set representing 7833289 articles and 114907858 edits. We used MediaWiki API to retrieve article quality; a qualified article needs to belongs to one of two classes: "Featured Articles" and "Good Articles". There are 2920 qualified article (1% of total number) and we randomly sampled 2500 non qualified articles. We finally analyzed these articles based on their revision and the historic of their members' revision on other articles. As a first contribution we start by analyzing 100 articles from 114907878 editors' revision made.
Dependent Variables Article Quality Wikipedia's internal quality categorization schema which assigns articles to a set of 7 distinct categories ( Stub> FA). We parse variables on article quality using the MediaWiki API. We used it to retrieve which articles are part of the categories "Featured Articles" and "Good Articles"  [28]. For the quality variable, we use Wikipedia's own quality grading as our metric and we classify article on «Qualify Articles»(FA and GA) and «Non Qualify Article» (the rest). This algorithm is a method for classification that operates by constructing a multitude of decision trees during training time, and outputting ordered attributes by importance  [29]. For our predictive model, we separated the dataset in train and test sets. The train set consisted in a random 70% of all the articles and the test set contained the remaining 30%.
Background-Bacterial meningitis, which is caused mainly by Neisseria meningitidis, Haemophilus influenzae, and Streptococcus pneumoniae, inflicts a substantial burden of disease worldwide. Yet, the temporal dynamics of this disease are poorly characterised and many questions remain about the ecology of the disease. We aimed to comprehensively assess seasonal trends in bacterial meningitis on a global scale. Methods-We developed the first bacterial meningitis global database by compiling monthly incidence data as reported by country-level surveillance systems. Using country-level wavelet analysis, we identified whether a 12 month periodic component (annual seasonality) was detected in time-series that had at least 5 years of data with at least 40 cases reported per year. We estimated the mean timing of disease activity by computing the centre of gravity of the distribution of cases and investigated whether synchrony exists between the three pathogens responsible for most cases of bacterial meningitis.
Database compilation In our database, we included country-level time-series data from reports of laboratoryconfirmed cases of meningitis caused by H influenzae, N meningitidis, and S pneumoniae or reports of suspected or laboratory-confirmed cases of bacterial meningitis (when the pathogen responsible was not specified) that were collected by a national surveillance system serving the general population of an entire country (as opposed to sentinel surveillance or other methods for collecting data from select populations or subnational regions) with weekly or monthly temporal resolution. No age or patient-based restrictions were used. We obtained data using three strategies. First, we searched public databases using general search terms such as "bacterial meningitis incidence data", "national meningitis surveillance", and "meningitis database", and additional country-specific queries.
The databases searched were Google, Google Scholar, PubMed, EuroSurveillance, Global Infectious Diseases and Epidemiology Network (GIDEON), WHO Databases, and Centers for Disease Control and Prevention (CDC) Vital Statistics. Second, we wrote web-crawling Java data-scraping programs to automatically search for links to ministry of health websites, reports, and other databases. The Java scripts were designed to automate the process of locating and exporting data from these websites into Excel spreadsheets. Each result was manually reviewed by AC and evaluated using the inclusion criteria described above. Third, we actively corresponded with public health officials such as ministries of health and authors of papers in which relevant time-series were analysed. Only aggregate and anonymous data were obtained; permission to use the data was acquired from data owners whenever necessary.
We specified that confirmed cases were those confirmed with laboratory-based diagnostic tests, although the specific criteria differed between countries and methods included macroscopic or microscopic examination of cerebrospinal fluid, antigen detection, culture, or PCR. Suspected cases (ie, defined by clinical criteria only) were sometimes the only available data from countries in which laboratory confirmation is a challenge, and therefore we included these in the database under a separate category and analysed them separately. Most data sources only reported aggregated number of cases per week or month, without any other information about age or sex of the patients. Information about serogroups or serotypes was rarely reported. We included case data in the database without modification, except we aggregated weekly data by month.
Time-series analyses Our primary aim was to assess whether a significant seasonal signature was detected in bacterial meningitis time-series at country-level. To do this we used wavelet analyses to explore the periodicity in each time-series. 17 To ensure the highest quality data were included in the analysis and to avoid spurious results due to noise, we used only time-series that included at least 5 years of continuous data with at least 40 cases per year. A single country's surveillance system could contribute up to four time-series: reports of cases due to each of the three pathogens (laboratory confirmed) or due to suspected bacterial meningitis cases (undifferentiated aetiologies). For each time-series, we used the wavelet power spectrum to identify whether a 12 month periodic component (annual seasonality) was detected.
We used the R package WaveletComp for the analyses. Additional technical details are provided in the appendix. As a supplementary analysis, we also examined whether the seasonal trends differed between pre-vaccine and post-vaccine introduction in those countries where one or more meningitis vaccines were introduced during the years for which time-series data were available (appendix). Our secondary aim was to measure the mean timing of disease activity, for each time-series for which the wavelet analyses detected a significant seasonality that was persistent over at least 3 consecutive years. For this measure, we computed the centre of gravity of the monthly distribution of cases in a given country for each pathogen. We defined the centre of gravity as the mean month of the distribution, where each month is weighted by its number of cases.
We henceforth referred to this as the seasonal timing or timing of disease activity. We used circular statistics to compute the centre of gravity and its 95% CI (based on 1000 bootstrap samples). For data where there is no true zero and the designation of high and low values is arbitrary (such as monthly data), circular statistics are better adapted than commonly used techniques. We used the R package circular to conduct these analyses. When significant seasonality was detected for at least two different pathogens within a single country, we compared the seasonal timing for these pathogens and determined whether they followed synchronous dynamics. We used Watson's non-parametric test to test the null hypothesis of a common centre of gravity for the two distributions of cases, with a 5% significance level.
Results Our database contained monthly bacterial meningitis incidence time-series from 66 countries: 27 in Europe, 20 in Africa (one outside the meningitis belt), six in east or southeast Asia, four in the Middle East, four in South America, three in North America, and two in Oceania (figure  1 , table  1 ). For each country, we report the data source, causative agents, time resolution, and time period of the time-series, and reported annual incidence ranges (appendix). About 700 000 cases of bacterial meningitis due to any cause were included in the database. The time-series had an average length of 9•7 years (range 0•3-32•5). Most time-series started in the late 1990s to the early 2000s. 51 time-series from 38 countries met the criteria to be included in the wavelet analyses (table  1 , figure  1 , appendix).
Discussion In this study, we compiled a database of country-level monthly time-series for bacterial meningitis incidence for 66 countries, and did the most detailed analysis of bacterial meningitis seasonality so far. Countries in the African meningitis belt were already known to feature a seasonal peak in incidence during the dry season. Here we show that bacterial meningitis also has significant seasonal patterns in the countries analysed outside the belt for the three primary causes of bacterial meningitis, N meningitidis, S pneumoniae, and H influenzae. We show that the timing of disease activity follows a latitudinal trend and that relatively higher incidences occur during winter in both the northern and southern hemispheres. A significant 12 month periodicity was detected in all regions where the wavelet analyses could be performed-ie, mainly North America, South America, Europe, Africa, and Oceania.
Although all data came from national surveillance systems, we had no information about the potential degree of under-reporting. However, as imperfect as the surveillance systems might be, there is no obvious reason why the seasonal peaks would be pure artifacts. Our results and conclusions are based on the relative seasonal increase in incidence and the peak timing, not on the absolute magnitude of the peak. Inherent to our analysis is the assumption that while the magnitude of incidence might be underestimated year round, the seasonal peaks are more likely due to the intrinsic dynamics of the disease than to systematic biases in the reporting systems. The consistency of the observed trends across a wide range of countries suggests that this assumption is reasonable. Both northern and southern hemisphere countries are well represented in our database, suggesting broad generalisability of our results.
Added value of this study We compiled the first global database of monthly bacterial meningitis incidence data as reported by country-level surveillance systems in 66 countries, including 47 countries outside the meningitis belt, and we performed the most comprehensive analysis of global bacterial meningitis seasonality thus far. Our study provides a unique and global perspective on bacterial meningitis seasonality with time-series data from all continents. Our findings confirm previous reports of seasonality in specific contexts and countries and present strong evidence that bacterial meningitis incidence exhibits persistent seasonal patterns across a wide geographic range. In addition, we have shown, for the first time, that the timing of the seasonal peak in bacterial meningitis incidence is correlated to the latitude of the country's most populous city.
Monthly incidence data were obtained for the 66 countries highlighted in pink or red. The 38 countries in red met our inclusion criteria for the wavelet analyses for at least one pathogen. (A) Raw time-series of reported cases. (B) Log-transformed, detrended, and standardised time-series used for wavelet analysis. (C) Wavelet power spectrum: wavelet power values increase from blue to red, and white contour lines indicate the 5% significance level. In this example, the time-series shows a significant 12 month periodicity over the entire time period. Shaded regions on either end delimit the cone of influence, where edge effects become important and spectral information is less robust. (D) Average wavelet power over time, with red dots indicating significant periods at the 5% level. Here the significant peak of power occurred at the 12 month period.
Acknowledgments This study was financially supported by:  Princeton University Health Grand Challenge  (supporting NEB, JP, and AC); the  US National Institutes of Health (NIH)   Early Independence Award  from the  Office of the Director  ( 1DP5OD009162 , to NEB); the  Research and Policy for Infectious Disease Dynamics (RAPIDD)  programme of the  Science & Technology Directorate, Department of Homeland Security , and the  Fogarty International Center, NIH  (to NEB and BG);  Bill & Melinda Gates Foundation (Vaccine Modelling Initiative , to BG). We thank  Argentina's National Institute of Infectious Diseases , the  European Centre for Disease Prevention and Control , the  US Centers for Disease Control and Prevention , the  Robert Koch Institute , the  Public Health Agency of Canada, Public Health England, Ireland's Health Protection Surveillance Centre , the  Australia Government Department of Health and Ageing , and  PAHO/WHO  for actively corresponding with and/or providing data to the authors for this study.
Monitoring Environment and Experimental Conditions Due to, at least, the growth of the MCC collection of benchmarks, the number of required executions to evaluate tools is increasing every year. From 54 293 in 2013, it grew to 83 308 in 2014, and to 169 078 in 2015. Such a number of executions thus requires a dedicated software environment that can take benefits from recent multi-core machines and powerful clusters. Moreover, we need to measure key aspects of computation, such as CPU or peak memory consumption, in the least intrusive way. Since the second edition of the Model Checking Contest in 2012, we have been confident that relying on virtual machines to operate tools is a suitable solution. To achieve this in an automated way, we developed BenchKit  [20] , a software technology (based on QEMU) for measuring time and memory during tool execution.
e. provides at least an answer for one instance of a model). Then, the reliability is trivially the ratio C/S. Some outcomes need an explanation. There are tools with a lower reliability rate such as GreatSPN-Meddly, ITS-Tools, LTSmin, or one of the TAPAAL variants. In fact, these tools exhibited bugs introduced in recent development (TAPAAL), or translation mistakes (in formulas for GreatSPN-Meddly and ITS-Tools, in the PNML import for LTSmin). Such bugs could not be identified by their developers before the full benchmark was passed and compared to other tools outputs. We believe this is a valuable information for tool developers, helping them to increase the confidence when implementing new algorithms. In some cases, we also have tools for which the reliability rate was computed on a very small dataset.
Acknowledgments. The MCC team heartily thanks all scientists who proposed new models in 2015 and the tool developers for their participation effort. Acknowledgments are also due to the  Universities of Rostock, Geneva, Paris Ouest Nanterre La Défense , and  Pierre & Marie Curie  for giving access to the powerful computers needed to operate the contest. We are grateful to  Hugues Evrard  for proof-reading the present paper.
2-mm pore-size polycarbonate filters (Millipore, GTTP, 25 mm diameter). For nucleic acid extractions and sequencing, surface seawater was collected and subsequently separated into four size fractions (0. 2-3, 0. 8-5, 5-20 and 40. 8-mm pore-size filters)  34, 32. After filtration, filters were kept for B4 weeks at À 20 °C on the schooner and then at À 80 °C in the laboratory until processed for hybridization or nucleic acid extraction. Design of CARD-FISH probes. For the design of specific oligonucleotide probes targeting B. bigelowii and the closely related prymnesiophyte partner, a total of 580 sequences, 18S rRNA gene sequences, belonging to the class Prymnesiophyceae were retrieved from the PR2 database  35  , aligned using MAFFT  36  and the alignment was verified manually to remove chimeras and sequences with ambiguities (466 sequences were kept).