dataesr/scientific-paragraphs-categorization huggingface dataset:
The piezoelectric pressure signal has been referenced using low frequency measurement (piezoresistive sensor). A torque meter measured the torque delivered by the expander. In order to measure the expander speed with an ordinary brake, a pulley system was installed in order to increase the expander speed to a brake feasible speed. An exterior oil loop lubricates the swash-plate expander. The analysis of P-V diagrams in different conditions can be used to identify the irreversibilities of the expansion machine. All the signals were recorded with a sampling frequency of 50 kHz and processed with program Labview. The engine operating point was fixed until the expander variables were constant (after approximately 15 min). Then, the pressure inside the cylinder was measured during 2 s, which is approximately 50 cycles (depending on the expander speed).
Modelling The authors have developed a comprehensive model of the ORC using Amesim. Considering the swash-plate expander as the most critical element, one additional specific submodel was implemented to model the physical phenomena inside the piston of the swash-plate expander. The software package provides a 0D model suite to simulate and analyze multi-domain intelligent systems, and to predict their multi-disciplinary performances. This software consists of available object-oriented libraries, where the user should connect them properly and fix the parameters.
Global Organic Rankine Cycle Model A simple layout of the ORC consisting of a boiler, a positive displacement pump, a volumetric expander, a fluid receiver, a condenser and an expansion vessel is considered in this model. Figure  6  shows the Amesim model of the cycle based on the ORC installation. A detailed description for the main assumptions in this model are presented in the following subsections. The analysis of P-V diagrams in different conditions can be used to identify the irreversibilities of the expansion machine. All the signals were recorded with a sampling frequency of 50 kHz and processed with program Labview. The engine operating point was fixed until the expander variables were constant (after approximately 15 min). Then, the pressure inside the cylinder was measured during 2 s, which is approximately 50 cycles (depending on the expander speed).
Conclusions The presented work describes and analyzes some models based on an experimental ORC installation installed in a turbocharged 2. 0 L gasoline engine to recover waste heat in exhaust gases. These models correspond on the one hand to the global ORC cycle and on the other hand to a specific submodel of the swash-plate expander. The comparison of performance parameters have been made in three points by means of changing the inputs and obtaining the outputs of the model. The results are summarized in the following points: (1) An ORC model was developed using the software Amesim. This model allows to simulate the main parameters measured in the cycle. Comparing the three steady operating points, a maximum deviation of 4% regarding pressures and temperatures and a value of 5% regarding torque was attained.
Acknowledgments: This work is part of a research project called "Evaluation of bottoming cycles in ICEs to recover waste heat energies" funded by a National Project of the  Spanish Government  with reference TRA2013-46408-R. The authors thank also to  Raul Luján  and  Rafael Carrascosa  for their contribution in the testing process. Authors want to acknowledge the "Apoyo para la investigación y  Desarrollo (PAID)" grant for doctoral studies  ( FPI S2 2015 1067 ).
Focal Surface The focal surface (FS) of the updated KLYPVE detector utilizes a design similar to that proposed for the JEM-EUSO detector  [18]. It will be produced of almost 120 thousand Hamamatsu R11265-103-M64 multi-anode photomultiplier tubes (MAPMTs), grouped into photodetector modules (PDMs). The number of pixels in one MAPMT is 64, the number of MAPMTs in one PoS(ICRC2015)669  All four corners of the FS are free from PDMs and will be used for mechanical structures of the supporting frame and other systems of the detector (calibration, adjustment etc). In the METS version of KLYPVE, the number of PDMs in one detector equals 19, and their total number is 57.
In Section 3 we introduce the climate model used in this study, discuss the various numerical experiments and the climatic observables of our interest, and present the data processing methods used for predicting the climate response to forcings. In Section 4 we present the main results of our work. We focus on two observables of great relevance, namely the surface temperature and the yearly total precipitation, and investigate the skill of response theory in predicting the change in their statistical properties, exploring both changes in global quantities and spatial patterns of changes. We will also show how to flexibly use response theory for predicting when climate change becomes statistically significant in a variety of scenarios. In Section 5 we summarize and discuss the main findings of this work.
Acknowledgements The authors wish to thank  M. Chekroun ,  H. Dijkstra ,  M. Ghil ,  A. Gritsun ,  A. von der Heydt , and  A. Tantet  for sharing many ideas on climate sensitivity and climate response. Without such stimulations this work would have hardly been possible. The authors want to thank M. Ghil for providing a constructive and insightful review of this paper. VL wishes to thank D. Ruelle for sharing questions, answers, ideas, and doubts with such a special intellectual depth and generosity.
Background: The significant risk of adverse events following medical procedures supports a clinical epidemiological approach based on the analyses of collections of electronic medical records. Data analytical tools might help clinical epidemiologists develop more appropriate case-crossover designs for monitoring patient safety. Objective: To develop and assess the methodological quality of an interactive tool for use by clinical epidemiologists to systematically design case-crossover analyses of large electronic medical records databases. Material and Methods: We developed IT-CARES, an analytical tool implementing case-crossover design, to explore the association between exposures and outcomes. The exposures and outcomes are defined by clinical epidemiologists via lists of codes entered via a user interface screen.
We tested IT-CARES on data from the French national inpatient stay database, which documents diagnoses and medical procedures for 170 million inpatient stays between 2007 and 2013. We compared the results of our analysis with reference data from the literature on thromboembolic risk after delivery and bleeding risk after total hip replacement. Results: IT-CARES provides a user interface with 3 columns: (i) the outcome criteria in the left-hand column, (ii) the exposure criteria in the right-hand column, and (iii) the estimated risk (odds ratios, presented in both graphical and tabular formats) in the middle column. The estimated odds ratios were consistent with the reference literature data. Discussion: IT-CARES may enhance patient safety by facilitating clinical epidemiological studies of adverse events following medical procedures.
INTRODUCTION The risks of adverse events following medical procedures can be assessed (at least in part) in randomized controlled trials (RCTs). However, the external validity of RCTs is limited by the strict eligibility criteria and the short follow-up period. 1  Therefore, a robust assessment of patient safety requires large population-based studies. 2  A clinical epidemiological approach based on electronic medical record (EMR) databases would enable a more systematic analysis of adverse events in routine clinical practice. Since many different designs are available, the Observational Medical Outcome Partnership recently carried out an empirically-based comparison of several designs for observational studies. 3 The project concluded that case-crossover and cohort-crossover designs were the most suitable for pharmacoepidemiological population-based studies.
4 n the case-crossover design, the case and the control are one and the same person (albeit at different times); this contrasts with the case-control design. The case-crossover design allows the investigator to control for time-constant confounding factors such as gender, age, weight, and lifestyle patterns. Each patient's likelihood of exposure during the period preceding the onset of a given outcome/event (the case period) can be compared with the likelihood of exposure during a similar period at another time (the control period). This design is notably appropriate for transient events or exposures, such as surgical procedures. Given the large size of today's administrative databases (including EMRs) and the many potential associations to be studied, there is a need to summarize and represent the information more effectively.
5, 6 ther epidemiological fields already use web-based strategies and data visualization methods to seek or predict epidemiological events. 7 ] [10]  The integration of visual analytics with advanced statistical methods can help policy makers make more reliable decisions. 11, 12 linical epidemiologists are involved in the validation of the data associated with suspected adverse events. 13 For instance, the US Food and Drug Administration reporting program MedWatch receives hundreds of thousands of reports each year. 14 These suspected associations between an exposure (e. g. , a medical procedure) and an adverse event should be explored in clinical epidemiological studies. However, this task requires advanced statistical knowledge and skill in effectively locating and exploiting information within large databases.
6] [17]  Indeed, almost half of all cases of VTE can be attributed to current or recent hospitalization,  18  and risk factors for VTE are very common among hospitalized patients. 19 ] [26] [27]  The present study had 2 objectives: (i) to develop an interactive tool for use by clinical epidemiologists to systematically design casecrossover analyses of large EMR databases and (ii) to assess the tool's applicability and methodological quality by using it to estimate thromboembolic and bleeding risks after medical procedures recorded in a very large nationwide administrative inpatient database.
A case-crossover design in IT-CARES A case was defined as a patient's first experience of the primary outcome (e. g. , VTE or bleeding). After including the case, we searched the EMR database for exposure in 2 periods: (i) immediately before the primary outcome and (ii) during a control period 1 year earlier (Figure  1 ). The case period and thus the control period were split into several intervals. A paired-matched interval approach was used, as described by Mittleman et al. 28  Conditional logistic regression was used to compare the likelihood of exposure during each interval of the case period with that of the control period. An odds ratio (OR) and its 95% confidence interval (CI) was computed for each interval. This OR reflects the risk of onset of the primary outcome compared with the baseline risk.
The age had to be within a defined range. (b) Criteria for case exclusion: patients were excluded by the clinical epidemiologist if they had a medical history defined by a list of 1 or more diagnoses. Exclusion criteria were applied to the case stay itself, if required. (c) Criteria for exposure: the clinical epidemiologist defined the exposure as a combination of diagnoses and medical procedures. The database was searched for this combination during the case period and control period. The clinical epidemiologist also defined the maximum possible length for an episode with exposure. The case stay was excluded from the exposure screening period, if required. Lastly, the epidemiologist could exclude a patient presenting several episodes of the exposure.
Input dataset We set up a simple data model consistent with the case-crossover design; the input dataset had to comply with a denormalized format containing at least the following 8 columns: (1) patient ID, (2) episode ID, (3) diagnoses, (4) procedures, (5) age, (6) admission day, (7) year of the episode, and (8) length of stay (for details, see Supple mentary Appendices). In order to be analyzed with IT-CARES, data from our test case had to be formatted as described; these data are presented below in the Data sources section.
Technical specifications The IT-CARES software and a set of simulated data are made available via an R package to allow any clinical epidemiologist to analyze his/her own collection of EMRs using a systematic case-crossover design. IT-CARES was implemented using R 3. 2. 0 29 and RShiny, a framework for creating interactive web applications based on R. 30  Support for processing large databases (i. e. , with tens of millions of rows) was optimized with the data. table  31 and dplyr 32 packages. Final rendering was performed with ggplot2,  33  xtable,  34  and markdown. 35 Statistical analysis used the survival package. 36 All analyses were performed on a server with an Intel V R Xeon V R Processor E5-2620 v2 @ 2. 10 GHz chip and 32 Go of DDR3 1600 MHz memory. Given the large number of records typically used to implement case-crossover studies, computational performance is a challenging issue.
Data sources The French national inpatient stay database contains an exhaustive structured description of all inpatient stays in French public-and private-sector hospitals. It was first designed for health insurance payment purposes. We reused the "acute hospital admissions" part of the database, which contains 171 556 421 inpatient stays for the 7-year period from January 1, 2007, to December 31, 2013. Each record contained data on the diagnoses (according to the International Statistical Classification of Diseases and Related Health Problems, 10th Revision, ICD-10), medical procedures (according to the French Classification Commune des Actes Medicaux classification), age, year of the discharge, length of stay, time between 2 admissions, and a unique patient identifier. Although the database is pseudoanonymized, the unique identifier allowed us to track individual patients in the database and compile each inpatient stay within the study period.
Comparison of IT-CARES outputs with relevant published studies The results generated by IT-CARES were compared with relevant published studies in a 3-step process. We first replicated Kamel et al. 's findings on the risk of thrombotic events after delivery. 17 In Kamel et al. 's study, the risk was computed using a retrospective crossover-cohort analysis on claims data for discharges from acute care hospitals and emergency departments in California. A composite primary outcome of ischemic stroke, acute myocardial infarction, or VTE was used, although the researchers also provided ORs for VTE alone. We thus compared the ORs computed with IT-CARES with the ORs for the composite outcome and for VTE computed by Kamel et al. Cases of thromboembolism were screened for using pulmonary embolism (PE) as the primary outcome.
We included patients aged between 15 and 45 years. Patients with a history of VTE were excluded. We screened the cases for codes for delivery as the exposure. We did not screen for exposure in the case stay and allowed only 1 exposure. We computed seven 42-day intervals. Second, we assessed the bleeding risk after total hip replacement (THR) and compared it with the value from Lalmohamed et al. 's  15  study of Danish national registry data. The latter database included (but was not limited to) information on hospital stays, outpatient visits, drugs, and death. Using a Cox proportional hazards model, Lalmohamed et al. computed the hazard ratio (HR) for gastrointestinal bleeding in THR patients, relative to age-and gender-matched controls. The researchers used time interaction terms to estimate HRs for different periods.
Availability of IT-CARES IT-CARES will be available from the Comprehensive R Archive Network (https://cran. r-project. org/) as an R package. The source code is freely available on GitHub (https://github. com/jomuller/ ITCARES). IT-CARES was also deployed as an interactive web application through RShiny. Thus, IT-CARES outputs can be generated through the web application or by calling its methods in the R console.
Main findings IT-CARES was developed as an interactive analytical tool that enables clinical epidemiologists to design and perform a case-crossover analysis exploiting a large collection of EMRs. We demonstrated the tool's capabilities and accuracy in 2 test assessments of the population-based thromboembolic risk after delivery and the bleeding risk after THR, respectively. IT-CARES makes it possible for clinical epidemiologists to design and rapidly execute a complex casecrossover analysis in a very large database. Clinical epidemiologists are likely to want to explore a wide range of medical procedures and potential outcomes. These associations can be addressed by redefining the primary outcome and appropriate design settings in IT-CARES. Moreover, IT-CARES makes it easy to assess the trade-off between different risks of adverse events (such as bleeding and thrombosis in the test case presented in our study), which can be studied over different periods of time after medical procedures.
Strengths and limitations of IT-CARES IT-CARES relies on the exploitation of EMR data. The use of EMR databases has already revealed opportunities for improving patient safety and the quality of care. 37, 38 However, many concerns about the reliability of the information in EMRs have been expressed. 39 These potential limitations are closely related to the design of a system that generally focuses on a specific aspect such as cost, efficiency, quality, or patient safety. 40 Many different types of EMR databases are found across the world, which leads to poor interoperability and contrasting data models. 41 This is one of the reasons we chose to publish the code for IT-CARES and selected a simple, denormalized format for the data model. Reusing administrative data is advantageous, insofar as this approach provides a very large sample size and is relatively inexpensive (with a moderate marginal cost).
42 Furthermore, subgroups of patients with particular characteristics can be targeted in order to evaluate a specific risk and its persistence over time. For a given medical risk, IT-CARES could be effectively used to estimate changes over time or to compare these risks from one hospital to another. This would be of great interest to health authorities or hospitals per se. Furthermore, the integration of an administrative database with drug prescription data would enable IT-CARES to assess drug safety-related risk. Given the inherent complexity of implementing case-crossover study designs, IT-CARES should not be considered the sole means of conducting these studies. In fact, it is a tool that can help an investigator conduct case-crossover analyses in a more systematic way. Thus, clinical epidemiologists will need to be trained in the routine use of IT-CARES.
5 This also implies that IT-CARES's usability needs further testing. One of IT-CARES's key features for the clinical epidemiologist is the ability to adjust the observation window and case/control periods. Nonetheless, users have to be cautious when comparing the case period with the usual length of stay associated with the medical procedure. A case period shorter than the length of stay would lead to overestimation of the first OR by erroneously including late cases. This problem could be solved by replacing the admission day with the exact start day for the primary outcome onset-if the latter is available in the EMR dataset. The case-crossover design also requires the onset of the case to be acute;   the time between exposure and onset of the primary outcome must therefore be accurately computed.
Lessons learned from our test case Our test case emphasized a number of issues that have to be taken into account by the clinical epidemiologist. 43 We did not know the precise date of event occurrence within a stay. Consequently, if the exposure and the primary outcome are present in the same record, we did not know which occurred first. However, we assumed that most medical procedures would be postponed if the primary outcome occurred first-making it highly probable that exposure preceded the outcome in the cases we analyzed. This assumption is not valid for delivery, which by definition is rarely deferrable; hence, we excluded the case stay when screening for exposure in the test case. Another point mentioned above is the reliability of coding medical data. Our database allowed us to use only PE and a limited number of bleeding codes, since this event has a sudden onset and is almost always serious enough to require hospitalization.
44, 45 Lastly, our database did not record deaths outside the hospital, which might have led to the underestimation of associations. Moreover, a difference in the way the primary outcome is measured in the case period vs the control period would introduce a classification bias. For instance, diagnosis of PE can be overestimated after surgical exposure because the clinicians are aware of the high risk and are more likely to look for signs of this condition. Likewise, better outpatient follow-up might lead to overestimation of the association.
CONCLUSION IT-CARES was developed as an interactive, freely-available, opensource tool enabling the clinical epidemiologist to implement the case-crossover design systematically in EMR databases. This tool may enhance patient safety by facilitating adverse event assessment studies following medical procedures. Although IT-CARES provided reliable results in a test case, further research must be carried out in order to evaluate it in additional patient safety studies and elaborate on its usability for advancing the end-user experience.
Feeds contribute highly to environmental impacts of livestock products. Therefore, formulating low-impact feeds requires data on environmental impacts of feed ingredients with consistent perimeters and methodology for life cycle assessment (LCA). We created the ECOALIM dataset of life cycle inventories (LCIs) and associated impacts of feed ingredients used in animal production in France. It provides several perimeters for LCIs (field gate, storage agency gate, plant gate and harbour gate) with homogeneously collected data from French R&D institutes covering the 2005-2012 period. The dataset of environmental impacts is available as a Microsoft ® Excel spreadsheet on the ECOALIM website and provides climate change, acidification, eutrophication, non-renewable and total cumulative energy demand, phosphorus demand, and land occupation.
LCIs in the ECOALIM dataset are available in the AGRIBALYSE ® database in SimaPro ® software. The typology performed on the dataset classified the 149 average feed ingredients into categories of low impact (co-products of plant origin and minerals), high impact (feed-use amino acids, fats and vitamins) and intermediate impact (cereals, oilseeds, oil meals and protein crops). Therefore, the ECOALIM dataset can be used by feed manufacturers and LCA practitioners to investigate formulation of low-impact feeds. It also provides data for environmental evaluation of feeds and animal production systems. Included in AGRIBALYSE ® database and SimaPro ® , the ECOALIM dataset will benefit from their procedures for maintenance and regular updating. Future use can also include environmental labelling of commercial products from livestock production.