Effect of V and N on the microstructure evolution during continuous casting of steel

Low Carbon (LC) steel is not expected to be sensitive to hot tearing and/or cracking while microalloyed steels are known for their high cracking sensitivity during continuous casting. Experience of the Direct Sheet Plant caster at Tata Steel in Ijmuiden (the Netherlands), seems to contradict this statement. It is observed that a LC steel grade has a high risk of cracking alias hot tearing, while a High Strength Low Alloyed (HSLA) steel has a very low cracking occurrence. Another HSLA steel grade, with a similar composition but less N and V is however very sensitive to hot tearing. An extreme crack results in a breakout. A previous statistical analysis of the breakout occurrence reveals a one and a half times higher possibility of a breakout for the HSLA grade compared to the LC grade. HSLA with extra N, V shows a four times smaller possibility of breakout than LC. This study assigns the unexpected effect of the chemical composition on the hot tearing sensitivity to the role of some alloying elements such as V and N as structure refiners.This research was carried out under project number M41.5.08320 within the framework of the Research Program of the Materials innovation institute M2i (www.m2i.nl)

Pig fecal and tonsil contamination of Yersinia enterocolita in one French slaughterhouse

Pig is considered to be the main animal reservoir of human pathogenic Yersinia enterocolitica strains which is frequently isolated from tonsils, but can also be found in the feces and onto carcasses. In France, while the main pathogenic biotypes are known for humans, few data are available regarding their prevalence in the pork chain production, and generally focus on tonsils contamination

Impact of the slaughter process on the pork carcasses contamination by Yersinia entrocolitica

The aim of the study was to evaluate the impact of the tongue handling practice on the contamination of the pork carcasses: the tongue removed with the pluck set (3 slaughterhouses) vs the intact tongue inside the head (3 slaughterhouses). A total of 1920 pigs from 120 different farms were sampled both on their tonsils and carcass surfaces over a one year period. The individual prevalence of Y. enterocolitica on tonsils and carcasses was unexpectedly low and estimated respectively to be 5.7% [4.7-6.9] and 0.6% [0.3-1.0] from the pooled samples. The presence of Y. enterocolitica on the carcasses was statistically linked to its presence on tonsils. It was nearly five times higher on pigs with positive tonsils, than on pigs with negative tonsils. Despite the experimental design, we were not able to confirm that the removal of the tongue on the slaughter line had a significant impact on the carcass contamination with Yersinia enterocolitica. These results confirm that cross contaminations occur during the slaughtering process and that good hygiene practices are necessary to limit the transfer of Y. enterocolitca from the tonsils, or the feces, to the carcasses

Validity and reliability of the Patient-Reported Arthralgia Inventory; validation of a newly-developed survey instrument to measure arthralgia

BACKGROUND: There is a need for a survey instrument to measure arthralgia (joint pain) that has been psychometrically validated in the context of existing reference instruments. We developed the 16-item Patient-Reported Arthralgia Inventory (PRAI) to measure arthralgia severity in 16 joints, in the context of a longitudinal cohort study to assess aromatase inhibitor-associated arthralgia in breast cancer survivors and arthralgia in postmenopausal women without breast cancer. We sought to evaluate the reliability and validity of the PRAI instrument in these populations, as well as to examine the relationship of patient-reported morning stiffness and arthralgia. METHODS: We administered the PRAI on paper in 294 women (94 initiating aromatase inhibitor therapy and 200 postmenopausal women without breast cancer) at weeks 0, 2, 4, 6, 8, 12, 16, and 52, as well as once in 36 women who had taken but were no longer taking aromatase inhibitor therapy. RESULTS: Cronbach’s alpha was 0.9 for internal consistency of the PRAI. Intraclass correlation coefficients of test-retest reliability were in the range of 0.87–0.96 over repeated PRAI administrations; arthralgia severity was higher in the non-cancer group at baseline than at subsequent assessments. Women with joint comorbidities tended to have higher PRAI scores than those without (estimated difference in mean scores: −0.3, 95% confidence interval [CI] −0.5, −0.2; P<0.001). The PRAI was highly correlated with the Functional Assessment of Cancer Therapy-Endocrine Subscale item “I have pain in my joints” (reference instrument; Spearman r range: 0.76–0.82). Greater arthralgia severity on the PRAI was also related to decreased physical function (r=−0.47, 95% CI −0.55, −0.37; P<0.001), higher pain interference (r=0.65, 95% CI 0.57–0.72; P<0.001), less active performance status (estimated difference in location (−0.6, 95% CI −0.9, −0.4; P<0.001), and increased morning stiffness duration (r=0.62, 95% CI 0.54–0.69; P<0.0001). CONCLUSION: We conclude that the psychometric properties of the PRAI are satisfactory for measuring arthralgia severity

BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees

The rising volume of datasets has made training machine learning (ML) models a major computational cost in the enterprise. Given the iterative nature of model and parameter tuning, many analysts use a small sample of their entire data during their initial stage of analysis to make quick decisions (e.g., what features or hyperparameters to use) and use the entire dataset only in later stages (i.e., when they have converged to a specific model). This sampling, however, is performed in an ad-hoc fashion. Most practitioners cannot precisely capture the effect of sampling on the quality of their model, and eventually on their decision-making process during the tuning phase. Moreover, without systematic support for sampling operators, many optimizations and reuse opportunities are lost. In this paper, we introduce BlinkML, a system for fast, quality-guaranteed ML training. BlinkML allows users to make error-computation tradeoffs: instead of training a model on their full data (i.e., full model), BlinkML can quickly train an approximate model with quality guarantees using a sample. The quality guarantees ensure that, with high probability, the approximate model makes the same predictions as the full model. BlinkML currently supports any ML model that relies on maximum likelihood estimation (MLE), which includes Generalized Linear Models (e.g., linear regression, logistic regression, max entropy classifier, Poisson regression) as well as PPCA (Probabilistic Principal Component Analysis). Our experiments show that BlinkML can speed up the training of large-scale ML tasks by 6.26x-629x while guaranteeing the same predictions, with 95% probability, as the full model.Comment: 22 pages, SIGMOD 201

Surface Oscillations in Overdense Plasmas Irradiated by Ultrashort Laser Pulses

The generation of electron surface oscillations in overdense plasmas irradiated at normal incidence by an intense laser pulse is investigated. Two-dimensional (2D) particle-in-cell simulations show a transition from a planar, electrostatic oscillation at $2\omega$, with $\omega$ the laser frequency, to a 2D electromagnetic oscillation at frequency $\omega$ and wavevector $k>\omega/c$. A new electron parametric instability, involving the decay of a 1D electrostatic oscillation into two surface waves, is introduced to explain the basic features of the 2D oscillations. This effect leads to the rippling of the plasma surface within a few laser cycles, and is likely to have a strong impact on laser interaction with solid targets.Comment: 9 pages (LaTeX, Revtex4), 4 GIF color figures, accepted for publication in Phys. Rev. Let

MRI plaque imaging reveals high-risk carotid plaques especially in diabetic patients irrespective of the degree of stenosis

<p>Abstract</p> <p>Background</p> <p>Plaque imaging based on magnetic resonance imaging (MRI) represents a new modality for risk assessment in atherosclerosis. It allows classification of carotid plaques in high-risk and low-risk lesion types (I-VIII). Type 2 diabetes mellitus (DM 2) represents a known risk factor for atherosclerosis, but its specific influence on plaque vulnerability is not fully understood. This study investigates whether MRI-plaque imaging can reveal differences in carotid plaque features of diabetic patients compared to nondiabetics.</p> <p>Methods</p> <p>191 patients with moderate to high-grade carotid artery stenosis were enrolled after written informed consent was obtained. Each patient underwent MRI-plaque imaging using a 1.5-T scanner with phased-array carotid coils. The carotid plaques were classified as lesion types I-VIII according to the MRI-modified AHA criteria. For 36 patients histology data was available.</p> <p>Results</p> <p>Eleven patients were excluded because of insufficient MR-image quality. DM 2 was diagnosed in 51 patients (28.3%). Concordance between histology and MRI-classification was 91.7% (33/36) and showed a Cohen's kappa value of 0.81 with a 95% CI of 0.98-1.15. MRI-defined high-risk lesion types were overrepresented in diabetic patients (n = 29; 56.8%). Multiple logistic regression analysis revealed association between DM 2 and MRI-defined high-risk lesion types (OR 2.59; 95% CI [1.15-5.81]), independent of the degree of stenosis.</p> <p>Conclusion</p> <p>DM 2 seems to represent a predictor for the development of vulnerable carotid plaques irrespective of the degree of stenosis and other risk factors. MRI-plaque imaging represents a new tool for risk stratification of diabetic patients.</p> <p>See Commentary: <url>http://www.biomedcentral.com/1741-7015/8/78/abstract</url></p

Auto-Classifier: A Robust Defect Detector Based on an AutoML Head

The dominant approach for surface defect detection is the use of hand-crafted feature-based methods. However, this falls short when conditions vary that affect extracted images. So, in this paper, we sought to determine how well several state-of-the-art Convolutional Neural Networks perform in the task of surface defect detection. Moreover, we propose two methods: CNN-Fusion, that fuses the prediction of all the networks into a final one, and Auto-Classifier, which is a novel proposal that improves a Convolutional Neural Network by modifying its classification component using AutoML. We carried out experiments to evaluate the proposed methods in the task of surface defect detection using different datasets from DAGM2007. We show that the use of Convolutional Neural Networks achieves better results than traditional methods, and also, that Auto-Classifier out-performs all other methods, by achieving 100% accuracy and 100% AUC results throughout all the datasets.Comment: 12 pages, 2 figures. Published in ICONIP2020, proceedings published in the Springer's series of Lecture Notes in Computer Scienc