Academic literature on the topic 'Flaky dataset'

Abstract. Water bodies influence local weather and climate, especially in lake-rich areas. The FLake (Fresh-water Lake model) parameterisation is employed in the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF) model which is used operationally to produce global weather predictions. Lake depth and lake fraction are the main driving parameters in the FLake parameterisation. The lake parameter fields for the IFS should be global and realistic, because FLake runs over all the grid boxes, and then only lake-related results are used further. In this study new datasets and methods for generating lake fraction and lake depth fields for the IFS are proposed. The data include the new version of the Global Lake Database (GLDBv3) which contains depth estimates for unstudied lakes based on a geological approach, the General Bathymetric Chart of the Oceans and the Global Surface Water Explorer dataset which contains information on the spatial and temporal variability of surface water. The first new method suggested is a two-step lake fraction calculation; the first step is at 1 km grid resolution and the second is at the resolution of other grids in the IFS system. The second new method involves the use of a novel algorithm for ocean and inland water separation. This new algorithm may be used by anyone in the environmental modelling community. To assess the impact of using these innovations, in situ measurements of lake depth, lake water surface temperature and ice formation/disappearance dates for 27 lakes collected by the Finnish Environment Institute were used. A set of offline experiments driven by atmospheric forcing from the ECMWF ERA5 Reanalysis were carried out using the IFS HTESSEL land surface model. In terms of lake depth, the new dataset shows a much lower mean absolute error, bias and error standard deviation compared to the reference set-up. In terms of lake water surface temperature, the mean absolute error is reduced by 13.4 %, the bias by 12.5 % and the error standard deviation by 20.3 %. Seasonal verification of the mixed layer depth temperature and ice formation/disappearance dates revealed a cold bias in the meteorological forcing from ERA5. Spring, summer and autumn verification scores confirm an overall reduction in the surface water temperature errors. For winter, no statistically significant change in the ice formation/disappearance date errors was detected.

Fcc-Ti1−xAlxN-based coatings obtained by Physical Vapor Deposition (PVD) or Chemical Vapor Deposition (CVD) are widely used as wear-resistant coatings. However, there exists no JCPDF card of fcc-Ti1−xAlxN for the XRD analysis of such coatings based on experimental data. In this work, an aluminum-rich fcc-Ti1−xAlxN powder was prepared and, for the first time, a powder diffraction file of fcc-Ti1−xAlxN was determined experimentally. In the first step, a 10 µm thick Ti1−xAlxN coating was deposited on steel foil and on cemented carbide inserts by CVD. The steel foil was etched and flakes of a free-standing Ti1−xAlxN layer were obtained of which a part consisted of a pure fcc phase. A powder was produced using the major part of the flakes of the free-standing Ti1−xAlxN layer. Following the Ti1−xAlxN coating, a flake of the free-standing layer and the powder were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), selected area electron diffraction and high-resolution transmission electron microscopy (SAED–HRTEM), wavelength dispersive X-ray spectroscopy (WDS) and energy dispersive X-ray spectroscopy (EDS). The powder consisted of 88% fcc-Ti1−xAlxN. The stoichiometric coefficient of fcc-Ti1−xAlxN was measured on a flake containing only the fcc phase. A value of x = 0.87 was obtained. Based on the powder sample, the XRD data of the pure fcc-Ti1−xAlxN phase were measured and the lattice constant of the fcc-Ti1−xAlxN phase in the powder was determined to be a = 0.407168 nm. Finally, a complete dataset comprising relative XRD intensities and lattice parameters for an fcc-Ti0.13Al0.87N phase was provided.

Background:Patients with rheumatoid arthritis (RA) are at increased risk for cardiovascular disease (CVD)[1]. Quantifying the effect of inflammation on CVD risk is important because rheumatologists can reduce inflammation with effective RA medications. A new score has been developed for predicting the risk for a CVD event (MI, stroke or CV death) in RA patients. It combines serological measures of inflammation (the multi-biomarker disease activity [MBDA] score, a measure of RA disease activity; and three individual biomarkers [TNF-RI, MMP-3 and leptin]), with age and four conventional CVD risk factors (smoking, hypertension, diabetes and history of a high- risk CVD condition)[2]. To gain insight into the potential effect that treating inflammation may have on the CVD risk score, it would be useful to know how the score is affected by the level of inflammation.Objectives:Explore the quantitative contribution of inflammation to CVD risk score in individual RA patients.Methods:To quantify the effect of inflammation on the CVD risk score across a range of MBDA scores, a commercial dataset of 177,486 RA patients with ≥2 MBDA tests between October 2010 and June 2019 was split 2:1 into training and validation datasets. Curves showing variation in the CVD risk score across the spectrum of all possible MBDA scores (1-100) were generated for canonical patient types differing in the number of conventional risk factors (0 to 4) and age (45, 55, 65, 75, 85 years). To generate these curves, the contributions of TNF-RI, MMP-3 and leptin to the CVD risk score were treated in aggregate (denoted the molecular score) and estimated using a linear regression model of the difference in molecular scores vs. the difference in MBDA scores. This model for the molecular score was fit in the training dataset, then in the full dataset, with dataset (training or validation) and the interaction between dataset and change in MBDA score included as additional predictor variables. The method was considered validated if the F-test for the interaction variable was not significant at the 0.05 level.Results:The model for estimating the molecular score from the MBDA scores was validated and shown to fit the data well (Figure 1). The estimated molecular score was applied to the CVD risk score algorithm to generate curves that show how CVD risk score varies with MBDA score for several distinct patient types. These curves demonstrate that the predicted 3-year CVD risk increases continuously and markedly with increasing level of inflammation, as represented by the MBDA score (Figure 2). Age and the number of conventional risk factors also affected the predicted CVD risk, with older patients (Figure 2a) and those with more conventional risk factors (Figure 2b) being at higher risk for a CVD event.Conclusion:The level of CVD risk predicted by a new prognostic test for RA patients depends not only on conventional risk factors, which are relatively time invariant, but also varies greatly due to inflammation, which can potentially be reduced with RA treatment.References:[1]Agca et al (2017).Ann Rheum Dis.76(1):17-28. doi: 10.1136/annrheumdis-2016-209775.[2]Curtis JR, Xie F, Crowson CS et al. (2019) ACR meeting abstract #446Disclosure of Interests:Rotem Ben-Shachar Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Darl Flake Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Richard Bamford Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Brent Mabey Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Eric Sasso Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Jeffrey Curtis Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB

Abstract. Lakes are sensitive indicators of climate change. There are thousands of lakes on the Tibetan Plateau (TP), and more than 1200 of them have an area larger than 1 km2; they respond quickly to climate change, but few observation data of lakes are available. Therefore, the thermal condition of the plateau lakes under the background of climate warming remains poorly understood. In this study, the China regional surface meteorological feature dataset developed by the Institute of Tibetan Plateau Research, Chinese Academy of Sciences (ITPCAS), MODIS lake surface temperature (LST) data and buoy observation data were used to evaluate the performance of lake model FLake, extended by simple parameterizations of the salinity effect, for brackish lake and to reveal the response of thermal conditions, radiation and heat balance of Qinghai Lake to the recent climate change. The results demonstrated that the FLake has good ability in capturing the seasonal variations in the lake surface temperature and the internal thermal structure of Qinghai Lake. The simulated lake surface temperature showed an increasing trend from 1979 to 2012, positively correlated with the air temperature and the downward longwave radiation while negatively correlated with the wind speed and downward shortwave radiation. The simulated internal thermodynamic structure revealed that Qinghai Lake is a dimictic lake with two overturn periods occurring in late spring and late autumn. The surface and mean water temperatures of the lake significantly increased from 1979 to 2012, while the bottom temperatures showed no significant trend, even decreasing slightly from 1989 to 2012. The warming was the strongest in winter for both the lake surface and air temperature. With the warming of the climate, the later ice-on and earlier ice-off trend was simulated in the lake, significantly influencing the interannual and seasonal variability in radiation and heat flux. The annual average net shortwave radiation and latent heat flux (LH) both increase obviously while the net longwave radiation and sensible heat flux (SH) decrease slightly. Earlier ice-off leads to more energy absorption mainly in the form of shortwave radiation during the thawing period, and later ice-on leads to more energy release in the form of longwave radiation, SH and LH during the ice formation period. Meanwhile, the lake–air temperature difference increased in both periods due to shortening ice duration.

Boulders provide ecologically important hard grounds in shelf seas, and form protected habitats under the European Habitats Directive. Boulders on the seafloor can usually be recognized in backscatter mosaics due to a characteristic pattern of high backscatter intensity followed by an acoustic shadow. The manual identification of boulders on mosaics is tedious and subjective, and thus could benefit from automation. In this study, we train an object detection framework, RetinaNet, based on a neural network backbone, ResNet, to detect boulders in backscatter mosaics derived from a sidescan-sonar operating at 384 kHz. A training dataset comprising 4617 boulders and 2005 negative examples similar to boulders was used to train RetinaNet. The trained model was applied to a test area located in the Kriegers Flak area (Baltic Sea), and the results compared to mosaic interpretation by expert analysis. Some misclassification of water column noise and boundaries of artificial plough marks occurs, but the results of the trained model are comparable to the human interpretation. While the trained model correctly identified a higher number of boulders, the human interpreter had an advantage at recognizing smaller objects comprising a bounding box of less than 7 × 7 pixels. Almost identical performance between the best model and expert analysis was found when classifying boulder density into three classes (0, 1–5, more than 5) over 10,000 m² areas, with the best performing model reaching an agreement with the human interpretation of 90%.

Abstract. Natural lakes and big artificial reservoirs can affect the weather regime of surrounding areas but, usually, consideration of all aspects of this impact and their quantification is a difficult task. The Alqueva reservoir, the largest artificial lake in western Europe, located on the south-east of Portugal, was filled in 2004. It is a large natural laboratory that allows the study of changes in surface and in landscape and how they affect the weather in the region. This paper is focused on a 3-day case study, 22–24 July 2014, during which an intensive observation campaign was carried out. In order to quantify the breeze effects induced by the Alqueva reservoir, two simulations with the mesoscale atmospheric model Meso-NH coupled to the FLake freshwater lake model has been performed. The difference between the two simulations lies in the presence or absence of the reservoir on the model surface. Comparing the two simulation datasets, with and without the reservoir, net results of the lake impact were obtained. Magnitude of the impact on air temperature, relative humidity, and other atmospheric variables are shown. The clear effect of a lake breeze (5–7 m s−1) can be observed during daytime on distances up to 6 km away from the shores and up to 300 m above the surface. The lake breeze system starts to form at 09:00 UTC and dissipates at 18:00–19:00 UTC with the arrival of a larger-scale Atlantic breeze. The descending branch of the lake breeze circulation brings dry air from higher atmospheric layers (2–2.5 km) and redistributes it over the lake. It is also shown that despite its significant intensity the effect is limited to a couple of kilometres away from the lake borders.

Background:Rheumatoid arthritis (RA) patients are at elevated risk for cardiovascular (CV) events, but risk stratification based on CV prediction models is not part of routine rheumatology practice.Objectives:To develop and validate a biomarker-based CV risk prediction model and compare it to alternative risk prediction models.Methods:We constructed a cohort of RA patients - age ≥40 with ≥1 RA diagnosis from a rheumatologist, excluding patients with malignancy, past myocardial infarction (MI) or stroke - by linking Medicare administrative data from 2006-2016 to multi-biomarker disease activity (MBDA) test results obtained as part of routine care. The cohort was split 2:1 to create training and internal validation datasets. The composite CV outcome was MI, stroke or CV death occurring within 3 years. Clinical predictors examined were: age, sex, race, traditional CV risk factors (e.g. diabetes, hypertension, hyperlipidemia, high-risk CV conditions [e.g. angina]), RA-related factors (e.g. glucocorticoid use, MTX, number of prior biologics), adjusted MBDA score1and its 12 biomarkers, log-transformed. Backward elimination was used to remove predictors with p ≥0.05. The resulting CV risk score was compared to four prediction models (age+sex; age+sex+CRP; age+sex+diabetes+hypertension+ smoking+high risk CV [±CRP]) in the validation dataset. We evaluated: 1) incremental improvement in the likelihood ratio test (LRT) statistic, 2) discrimination (AUROC), and 3) goodness-of-fit (predicted vs. observed, based on Kaplan-Meier estimates). Validation analyses were prespecified.Results:30,751 RA patients with 904 CV events were linked to MBDA test results and eligible for analysis. Patient characteristics were mean (SD) age 68.7 (9.5) years; 23.4% age <65; 82% women. Comorbidities included diabetes (39%), hypertension (78%), smoking (24%) and history of high-risk CV condition (37%). RA-related features included use of glucocorticoids (58%), MTX (60%), TNFi (33%) and other biologics (16%). Mean (SD) MBDA score was 41 (14). The final covariates included in the MBDA-based CV risk score were age, diabetes, hypertension, smoking, history of high-risk CV conditions, adjusted MBDA score, leptin, TNFRI and MMP-3. Median (IQR) of the predicted 3-year CV risk was 3.4% (2.1%, 5.6%). Based on extrapolation to 10-year risk, 9.4% of patients would be considered low, 10.2% borderline, 52.2% intermediate, and 28.2% high risk per 2019 ACC/AHA guidelines.Compared to four simpler CV prediction models, significant improvement in the LRT statistic was observed with the addition of the biomarker-based CV risk score (Figure 1). Model fit was good across deciles (Figure 2). The AUROC was 0.70. The MBDA-based model reclassified 28.5% of patients vs. the model based on age+sex+diabetes+hypertension +smoking+high risk CV+CRP.Figure 1.Incremental Improvement of MBDA-based CV Risk Score Compared to Other CV Risk Prediction ModelsFigure 2.MBDA-Based CV Risk Score Calibration for Composite CV Outcome at 3 YearsConclusion:A biomarker-based prediction score incorporating a few clinical risk factors appears to have good accuracy to predict CV risk in RA. Additional validation in independent cohorts will help verify its performance characteristics.References:[1] Curtis et al.,Rheumatology2018;58:874.Disclosure of Interests:Jeffrey Curtis Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Fenglong Xie: None declared, Cynthia S. Crowson Grant/research support from: Pfizer research grant, Brent Mabey Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Darl Flake Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Richard Bamford Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Cheryl Chin Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Eric Sasso Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Elena Hitraya Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Rotem Ben-Shachar Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Alexander Gutin Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Jerry Lanchbury Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc.

Abstract Keyword extraction is one of the most important aspects of text mining. Keywords help in identifying the document context. Many researchers have contributed their work to keyword extraction. They proposed approaches based on the frequency of occurrence, the position of words or the similarity between two terms. However, these approaches have shown shortcomings. In this paper, we propose a method that tries to overcome some of these shortcomings and present a new algorithm whose efficiency has been evaluated against widely used benchmarks. It is found from the analysis of standard datasets that the position of word in the document plays an important role in the identification of keywords. In this paper, a fuzzy logic-based automatic keyword extraction (FLAKE) method is proposed. FLAKE assigns weights to the keywords by considering the relative position of each word in the entire document as well as in the sentence coupled with the total occurrences of that word in the document. Based on the above data, candidate keywords are selected. Using WordNet, a fuzzy graph is constructed whose nodes represent candidate keywords. At this point, the most important nodes (based on fuzzy graph centrality measures) are identified. Those important nodes are selected as final keywords. The experiments conducted on various datasets show that proposed approach outperforms other keyword extraction methodologies by enhancing precision and recall.

AbstractLakes are an integral part of the geosphere, but they are challenging to represent in Earth system models which either exclude lakes or prescribe properties without simulating lake dynamics. In ECMWF Interim reanalysis (ERA-Interim), lakes are represented by prescribing lake surface water temperatures (LSWT) from external data sources, while the newer generation ERA5 introduces the FLake parameterization scheme to the modelling system with different LSWT assimilation data sources. This study assesses the performance of these two reanalyses over three regions with the largest lakes in the world (Laurentian Great Lakes, African Great Lakes, and Lake Baikal) to understand the effects of their simulation differences on hydrometeorological variables. We find that differences in lake representation alter the associated hydrological and atmospheric processes and can affect regional hydroclimatic assessments. There are prominent differences in LSWT between the two datasets which influence the simulation of lake-effect snowstorms in the Laurentian winters and lake-land breeze circulation patterns in the African region. Generally, ERA5 has warmer LSWT in all three regions for most months (by 2-12 K) and its evaporation rates are up to twice the magnitudes in ERA-Interim. In the Laurentian lakes, ERA5 has strong biases in LSWT and evaporation magnitudes. Over Lake Baikal and the African Great Lakes, ERA5 LSWT magnitudes are closer to satellite-based datasets, albeit with warm bias (1-4 K), while ERA-Interim underestimates the magnitudes. ERA5 also simulates intense precipitation hotspots in lake proximity that are not visible in ERA-Interim and other observation-based datasets. Despite these limitations, ERA5 improves the simulation of lake-land circulation patterns across the African Great Lakes.

With today’s focus on Continuous Integration, test cases are used to ensure the software’s reliability when integrating and developing code. Test cases that behave in an undeterministic manner are known as flaky tests, which threatens the software’s reliability. Because of flaky test’s undeterministic nature, they can be troublesome to detect and correct. This is causing companies to spend great amount of resources on flaky tests since they can reduce the quality of their products and services. The aim of this thesis was to develop a usable tool that can automatically detect flakiness in the Randomness category. This was done by initially locating and rerunning flaky tests found in public Git repositories. By scanning the resulting pytest logs from the tests that manifested flaky behaviour, noting indicators of how flakiness manifests in the Randomness category. From these findings we determined tracing to be a viable option of detecting Randomness as a cause of flakiness. The findings were implemented into our proposed tool FlakyReporter, which reruns flaky tests to determine if they pertain to the Randomness category. Our FlakyReporter tool was found to accurately categorise flaky tests into the Randomness category when tested against 25 different flaky tests. This indicates the viability of utilizing tracing as a method of categorizing flakiness.