dataesr/scientific-paragraphs-categorization huggingface dataset:
The objective of this case study is to assess the failure and the recovery characteristic parameters, in view of building up some predictive capabilities. The predictive capabilities are intended to support decision making in the management process of the modernisation and the extension of the regional WSS. The assessment is carried out in two phases: the statistical analysis of the operational data trends and the development of a predictive global model. The paper describes, in Section 2, the general lines of the methodology of the study. In Section 3, the paper presents a general description of the case study. The general description of the WSS covers the pipe-lines network: pipe types, geometrical characteristics, material specifications, and lengths in km. In Section 3, the paper introduces the statistical assessment of both the failure database and the recovery database.
Methodology The background of the work is to assess the failure-recovery data of a regional WSS in Poland in view of building up first order predictive availability and reliability models. These preliminary predictive models were required to serve in predicting gross control indicators to assess the effectiveness of the decisions and the actions taken within the modernisation and the extension of a regional WSS. The developed models are preliminary as the available database is of a small size, heterogeneous, and tainted with high uncertainty level. The case study of this WSS covered two tasks: the statistical analysis of the failurerecovery data and the development of a preliminary predictive models exploiting the outputs of the statistical analysis. Our work methodology consisted of distinguishing three spheres of activities: 1.
3. Identification of the most adequate concepts to build up preliminary predictive models of availability and reliability, given the physical nature of the WSS and the nature of the outputs of the statistical analysis of the operational data. The WSS specifications are given in Section 3. 1. The specifications cover the extension of the WSS, the classification of the pipes, and the lengths and materials of each class of pipes. The specifications of the available failure and recovery data and time intervals covered by each dataset are determined. The differences in nature between the available failure and recovery datasets are put in evidence. Subsequently, the choice of the adequate statistical analysis methods is done for each dataset. Both datasets were processed differently, but the main output has the same nature: an overall failure rate and an overall recovery rate.
• Additional information (report date, environmental data, cost). The operational feedback failure-frequency and recovery-time data is presented in two independent databases with two different structures: the failure-frequency database and the recovery-time database. The structural differences between the two databases may be the result of the separation between the operational surveillance task and the maintenance task due to some local organisational conditions. The data type is different from one database to the other. In the failure database, one counts number of failures per given time-interval while one measures intervals of time-to-recover in the recovery database. Both measurables are random but different in nature. Regarding the scope of the paper, the final outcomes we are looking for are an "overall failure rate" and an "overall recovery rate" to globally characterise the WSS.
Failure Data Assessment Failures are generally classified by cause, mechanism, and/or criticality. Root causes can be of different origins such as: design, construction, and/or operational conditions  [8] [9] [10] [11] [12]. Failure analysis activities are very impacting on network everyday operational and maintenance decision making  [13, 14]. However, the failure database we have is limited. Its failure records contain only the number of failures per month in the network, over 18 years. The failures are classified by the pipe's position, geometry, material, and type (main, distribution, connection). No direct data were available regarding the "time to fail" per month per segment of pipe. Information about failure causes or criticality are almost absent, inconsistent, or incomplete. The assessment aims at the determination of an overall failure occurrences trend in the network considering the field operational reality and the continuous expansion of the network due to the expansion of the town itself and the modernisation of the WSS.
Given the dataset we have, different failure analysis models and methodologies are possible: the cumulative failure distributions  [15, 16] , the data-driven modelling techniques using neural network and neuro-fuzzy methods  [17, 18] , the Weibull-exponential failure model and the constant probability replacement model  [19] , the probability of main failure method  [20] , or the polynomial regression methods. The available failure database contains the number of failures per month. So, failure rates can be directly estimated using polynomial regression techniques, based on the number of failures of all pipes within a given sampling interval  [1, 2, 6]. The choice of the unit sampling interval is important to guarantee an acceptable statistical quality of the estimated failure rate. The notion of "Guarantee" in probabilistic analyses is generally replaced by a statistical meaningful term which is "the statistical quality".
We considered that a polynomial regression model with R 2 > 70% would be acceptable for a polynomial order n ≤ 6. The control coupled-criteria (R 2 , n) is arbitrary and justified by a good applied statistics expert judgement. Investigating more advanced analysis techniques such as Fourier decomposition or stepwise regression were not justified given the small size of the dataset, the strong heterogeneity of the data, and its low statistical quality. It can be noted that the average value of the annual failure rate in the mains (1. 06 no. o f f ailures•km -1 •a -1 ) significantly exceeds the recommended value for the mains, which should be less than 0. 3 no. o f f ailures•km -1 •a -1  [21]. The average values of the distribution-pipes and the connection-pipes failure rates do not exceed the recommended values given in the literature  [21, 22].
This is the first order ARIMA model. Using higher orders of ARIMA models or more complex Fourier decomposition analyses were justified neither by the statistical quality of the data nor by the low seasonality effect. We recall that we have only failure occurrences per month in our database, with no additional information neither about failure modes nor about failure criticality. The basis of the implemented approach is presented among others in  [24, 25]. The analysis of the average failure rate (per month) indicates that the failure culmination occurred is in December (at p = 0. 00251, with current effect F (11. 33) = 3. 5081). Figure  4  presents main-pipes failure rate changeability with combinations of the last observations and previous predictions adjusted for seasonal fluctuations. For the connections, the series is more stationary regarding the ARIMA model.
Recovery Data Assessment The recovery database records contain the component-repair time and the organisationalrepair time for each recovery event. Being the sum of the component-repair time and the organisational repair time, the recovery times are classified by failure mode and pipe's geometry, material, and type (main, distribution, connection). The assessment aims at the determination of the global recovery trends of the network considering the field operational reality, the continuous expansion of the network, and the modernisation of the WSS. The recovery data cover ten years of operation. Recovery can generally be classified according to: the failure mode  [22] , the operational conditions  [29, 30] , the location  [31] , the logistic conditions  [32, 33] , and/or the managerial context  [34, 35].
The recovery times and the effective repair times analysis methodologies are well developed and published as well  [36] [37] [38] [39]. Regarding the recovery database we have, it contains only time intervals in two classes: physical component-repair times and associated organisational times, over 10 years. There is no additional information in the dataset. The recovery data are measured in timeintervals, not in repair frequencies, as in the case of failure data above. Then, the data is grouped by subclasses: annual, failure mode, pipe material, pipe diameter, and pipe type (connection, distribution, main). We will precisely be interested in analysing the component-repair time (T c ) and the organisation-repair time (T o ) within the intention to estimate the WSS overall recovery-time, (T r ).
These global indicators do not distinguish between failures severity, type, or location. However, they are very indicative regarding the qualification of the modernisation actions and the shared efforts between equipment technology (λ) and maintenance (µ) logistic organisation. The comparison between the operational estimated and the model expected values will allow us to validate and to better adjust the models. Subsequently, these predictive models present increasingly valuable support to the decision making in managing the modernization and the expansion the WSS and the water supply service performance. As our models have been conceived to predict the indicators mentioned above based on the operational database, the validation of the model will be carried on after updating the operational databases including failure-recovery post-modernisation data.
Conclusions Within the frame of the modernisation of a regional water supply system (WSS) in Poland, some R&D activities were conducted to develop predictive models to support decision making in managing the modernisation and the urban extension of the WSS. The modelling activities should be based on and validated by the operational experience feedback. The operational experience feedback is generally available in the form of failure and recovery databases. The paper presents a case study to assess the failure and the recovery characteristic parameters of a given regional WSS in Poland. The assessment is carried out in two phases: the statistical analysis of the available operational data and the development of a predictive global modelling capability. The concerned databases are failure and recovery databases.
In the failure database, one counts number of failures while one measures intervals of time-to-recover in the recovery database. Both measurables are random but different in nature. Regarding the scope of the paper, the final outcomes we looked for are an "overall failure rate" and an "overall recovery rate" to globally characterise the WSS. These characteristic parameters are necessary to develop global predictive models of availability and reliability, to assess the quality of the water supply service and to support the decision making regarding the modernisation of the WSS. Regarding the recovery database we have, it contains time intervals in two classes: physical component-repair times and associated organisational times, covering 10 years. The analyses considered the component-repair time (T c ) and the organisation-repair time (T o ) within the intention to estimate the WSS overall recovery-time, (T r ).
Search strategy and study selection The search was conducted in MEDLINE (Pubmed). The following search strings and Boolean operators were entered to identify relevant studies: Onchocerciasis AND Epilepsy, Onchocerciasis AND Nodding syndrome, Onchocerciasis AND Nakalanga syndrome, Onchocerciasis AND Encephalitis. Systematic reviews and meta-analyses were also searched. Rayyan QCRI (Qatar Computing Research Institute, Data Analytics Medical)  [9]  was used for managing and screening articles. Four authors (JL, SM, FB, DA) have independently performed an initial screening of title and abstract to select relevant studies. Full-length articles were examined to assess eligibility. We included publications evaluating the association between onchocerciasis and the previously mentioned neurological pathologies (epilepsy, nodding syndrome (NS), Nakalanga syndrome, and encephalitis) in tropical countries.
Data extraction and synthesis of results Data were extracted from included studies in an ad hoc document. The following standard information was collected: author, year of publication, country, study objective, study design, study population, number of cases, definition and confirmation methods (onchocerciasis and neurological disorders), main results, and the measure of association together with the corresponding 95% confidence interval (CI). The data were summarized in tabular form. We presented a descriptive and critical summary of the results to provide an overview of the epidemiological evidence.
Evidence from meta-analyses The first meta-analysis was published in 2004  [33]. It included nine primary case-control studies using a cross-sectional approach. O. volvulus infection or exposure was determined using scarification, skin biopsies, or serological examination. A neurologist or a physician confirmed epilepsy diagnosis. An increased risk was reported with a common relative risk (RR) in the limit of significance. When the odds ratio (OR) was calculated using a random-effect analysis based on the method described by DerSimonian and Laird  [45] , the pooled OR was 1. 70 (95% CI: 0. 97-2. 98, p = 0. 066). The test of heterogeneity was significant (p < 0. 001) and the I 2 was 76. 7% (unpublished data). The data was then re-analyzed after excluding two publications: a study in a hypo-endemic area possibly resulting in a low prevalence of onchocerciasis-associated epilepsy  [46]  and another study where participants with growth retardation (Nakalanga like symptoms) were considered as non-epileptic controls  [47].
The major strength of this work relies on the critical review of key studies focusing on methodological aspects to establish a link with the findings for their interpretation. The major limitation of this study is the use of only one major database (Medline/Pubmed), which could lead to a lack of inclusion of certain studies, mainly in the grey literature. We only included studies providing evidence from confirmed cases and we have placed particular emphasis on the measure of association to quantify the relationship. However, relevant data from studies that did not meet the inclusion criteria were also described and discussed. Another limitation is the lack of formal assessment of the risk of bias. Scoping reviews aim to provide an overview of the evidence. Due to this, an assessment of the risk of bias of the evidence included within a scoping review is generally not performed (unless there is a specific requirement due to the nature of the scoping review aim)  [6, 7].
Based on the knowledge of common mutations in Caucasian and Japanese patients or individual families, DNAbased targeted mutation testing can be offered. If only one (or no) pathogenic variant is detected, Sanger sequencing of PYGM , followed by deletion/duplication analysis may be indicated. In cases in which variants of unknown significance (VUS) are detected, a functional exercise test such as a cycle test may be helpful, as may a non-ischaemic forearm test and muscle biopsy for muscle phosphorylase enzyme activity. In the case of GSD VII, 27 pathogenic variants are described in the Human Gene Mutation Database. GSD VII has marked phenotypic and genotypic heterogeneity, and the majority of mutations result in markedly reduced activity of the enzyme  [33] [34] [35] [36]. Reported variants include deletions, duplications, intronic deletions, insertions, and single nucleotide changes.
However, as allo-HCT continues to improve, with better donor choices and supportive care, the predictive value of HCT-CI needs to be frequently re-evaluated in the light of new practice patterns. To address this knowledge gap, the EBMT collects data on pre-existing comorbidities, transplant characteristics, and outcome. Here we present the first comprehensive EBMT database analysis on the association of comorbidities with outcome. We hypothesized that the association of pre-existing comorbidities with non-relapse mortality (NRM) after allo-HCT would be lower now as compared with the original HCT-CI population  [3, 4]. The basis for our hypothesis was the improved overall NRM after alloSCT over time  [5] , as well as our assumption that the better awareness of comorbidities on the mortality risk had beneficial effects.
PATIENTS AND METHODS Study design and data collection This is a retrospective multicenter analysis using the data set of the EBMT registry. The EBMT is a voluntary working group of more than 600 transplant centers that are required to report regular follow-up on all consecutive stem cell transplantations. Audits are routinely performed to determine the accuracy of the data. The study was planned and approved by the Transplant Complications Working Party of the EBMT. All patients gave their written informed consent to use their personal information for research purposes. The study was conducted in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines. Eligibility criteria for this analysis included patients older than 18 years of age at allo-HCT with hematologic malignancies including acute leukemia, chronic leukemia, lymphoma, myelodysplastic syndrome, or myeloproliferative neoplasms, who underwent a first allo-HCT (previous autologous transplantation(s) allowed) from a matched sibling donor or unrelated donor, using either bone marrow or peripheral blood, between 2010 and 2018.
We only included patients with a full available data set on pre-existing comorbidities. Further exclusion criteria were lack of information on disease progression (status or date), conditioning (intensity, or use of total body irradiation), status at transplant. Data collected included recipient and donor characteristics (age, sex, cytomegalovirus (CMV) serostatus, and Karnofsky performance status (KPS) score), diagnosis and status at transplant, and transplant-related factors, including conditioning regimen, use of anti-thymocyte globulin or alemtuzumab for pre-transplant in vivo T-cell depletion, stem cell source, and post-transplant GVHD prophylaxis. Grading of acute GVHD was performed using established criteria  [6]. Chronic GVHD was classified as limited or extensive, according to published criteria  [7].
Acknowledgements We are grateful to the anonymous reviewers for a careful reading of our manuscript and helpful remarks. The first and the fourth authors are partially supported by the  French ANR  project  ANR-18-CE40-0004-01  (FOCAL). The second and the fourth authors are partially supported by the  French ANR  project  ANR-17-CE40-0033  (SoS) and the  French ANR  project  ANR-19-CE40-0014  ( MIN-MAX ). The third author is supported by  ERC   Consolidator Grant SYSTEMATICGRAPH  (No. 725978 ). The fourth author is partially supported by the  ANR  project  ANR-16-CE40-0009-01  (GATO) and the  CNRS   PEPS  project  COMP3D. Parts of this work were realized when he was working at  GIPSA-lab in Grenoble.
Measures and data collection Measures were developed for the evaluation of the implementation and the mechanisms of impact, for each of the four intervention components. Table  1  provides the adaptation of the MRC taxonomy to the intervention components and for the context, and measures and sources of data collection. Primary data were collected in each country using structured and informal observations of eld visits by project management staff, interviews with implementers and participants, project documents including participant attendance lists, country reports (which in Sweden were based upon a REDCAP continuous intervention tracking tool) and monthly SMART2D consortium meetings. Structured process evaluation tools included: interaction checklists contextualized into a peer group checklist (Webannex 2a, Uganda), a quality assurance checklist (Webannex 2b, South Africa); a quality assurance/interaction tool (Webannex 2c, Sweden); and a care companion checklist (Webannex 3).
Acknowledgements We are grateful to the institutional support of the country site institutions to the SMART2D consortium. The  SMART2D  consortium includes the following six partner institutions:  Makerere University, School of Public Health, Uganda; the University of Western Cape, School of Public Health, South Africa ;  Karolinska Institutet and Uppsala University, Sweden ;  Institute of Tropical Medicine, Belgium ; and  Collaborative Care Systems Finland. We acknowledge the contribution of the other  SMART2D  Consortium members:  David Sanders  (RIP),  Barbara Kirunda ,  Anthony Muyingo ,  Ronald Kusolo , and  Edward Ikona. We also acknowledge the study participants for volunteering to participate in the trial, the contribution of the participating health centers and their staff in Uganda and South Africa including The Caring Network Community Health Workers and management,  Diabetes SA ,  Katherine Murphy  and  Buyelwa Majikela -.
Unsteady RANS Simulations The highest level of fidelity consists here of full-annulus URANS simulations, carried out with the elsA software. They feature two sliding mesh interfaces, between the inlet of the domain and the rotor and between the rotor and the stator. All the numerical settings are presented in  [33]. Except for the turbulence modeling, this approach requires no particular hypothesis. It enables oe to capture rotor-stator interactions and interblade passage phenomena and is therefore considered here as a numerical reference.
Data Availability Statement: Not applicable. Acknowledgments: This work was granted access to the HPC resources of CINES under the allocation 2019-A0072A06879 made by GENCI and to the HPC resources of CALMIP Supercomputing Center under the allocation 2019-P18021. The authors are grateful to Safran Ventilation Systems for the successful collaboration in the low-speed fan domain and to Safran Group for funding this study, which is part of the joint research initiative AEGIS engaged with ISAE-SUPAERO. Francesco Salvatori's contribution during his research project about parallel compressors at ISAE-SUPAERO is also acknowledged.
Acknowledgements This work is financially supported by  H2020 -Marie Skłodowska-Curie Individual Fellowships "MTOAC #   795754 " to BS;  Fundação para a Ciência e a Tecnologia (FCT, Portugal)  under the program CEEC Individual 2017 ( CEECIND/00352/2017 ) to DC; FCT under 2MATCH (num. 028070 ) to DC and SCK;  European Union Framework Programme for Research and Innovation Horizon 2020ERA Chair programme (FoReCaST)  under grant agreement no. 668983  to RLR. SCK holds a European Research Area Chair position of this programme at  3B's Research Group, University of Minho (Portugal).
ACKNOWLEDGMENTS The authors thank all responders to the survey for their valued time in participating. We also thank the coauthor association and affiliated patient advocacy group members for piloting, proofreading, and translations during preparation of the survey. We thank  Ms Elisa Giarola  ( Dravet Italia Onlus, Research Center for Pediatric Epilepsies Verona, Verona, Italy ) for her assistance with editing. We thank  David Macari  for English editing and  Alessandro Catini  for statistical analysis.