Etude de la durabilité des assemblages collés bois-béton soumis à des chargements hydriques

L'étude a pour objectif de déterminer les paramètres qui gouvernent les mécanismes d'endommagement d'un assemblage collé bois/béton soumis à des chargements hydriques. Des mesures des déformations globales par palmer et locales par la technique de corrélation d'image ont été réalisées lors du chargement hydrique. On analyse le comportement global de la structure en liaison avec des aspects plus locaux notamment les déformations du bois liées à sa teneur en eau (via des mesures par IRM) et les propriétés de la colle pour comprendre les couplages hygro-mécaniques

Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms

Importance: Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective: To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. Design, Setting, and Participants: In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. Main Outcomes and Measurements: Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. Results: Overall, 144 231 screening mammograms from 85 580 US women (952 cancer positive ≤12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166 578 examinations from 68 008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. Conclusions and Relevance: While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation

Sex- and age-related differences in the management and outcomes of chronic heart failure: an analysis of patients from the ESC HFA EORP Heart Failure Long-Term Registry

Aims: This study aimed to assess age- and sex-related differences in management and 1-year risk for all-cause mortality and hospitalization in chronic heart failure (HF) patients. Methods and results: Of 16 354 patients included in the European Society of Cardiology Heart Failure Long-Term Registry, 9428 chronic HF patients were analysed [median age: 66 years; 28.5% women; mean left ventricular ejection fraction (LVEF) 37%]. Rates of use of guideline-directed medical therapy (GDMT) were high (angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, beta-blockers and mineralocorticoid receptor antagonists: 85.7%, 88.7% and 58.8%, respectively). Crude GDMT utilization rates were lower in women than in men (all differences: P\ua0 64 0.001), and GDMT use became lower with ageing in both sexes, at baseline and at 1-year follow-up. Sex was not an independent predictor of GDMT prescription; however, age >75 years was a significant predictor of GDMT underutilization. Rates of all-cause mortality were lower in women than in men (7.1% vs. 8.7%; P\ua0=\ua00.015), as were rates of all-cause hospitalization (21.9% vs. 27.3%; P\ua075 years. Conclusions: There was a decline in GDMT use with advanced age in both sexes. Sex was not an independent predictor of GDMT or adverse outcomes. However, age >75 years independently predicted lower GDMT use and higher all-cause mortality in patients with LVEF 6445%

Formulation of a nonlinear porosity law for fully saturated porous media at finite strains

21 pagesInternational audienceIn this work, we develop a mathematical formulation of a nonlinear porosity law suitable for finite strain and high pore pressure conditions in porous media. The approach is built around the physical restriction that, by definition, the actual porosity is bounded in the interval [0, 1] for any admissible process. Specifically, the model is motivated by elementary considerations that have been extended to the nonlinear range and, at the limiting case of an infinitesimal approximation, it reaches the porosity law of the classical linear poromechanics. In a next step, the formulation is integrated within the unified framework of continuum thermodynamics of open media which is crucial in setting the convenient forms of the constitutive relations and evolution equations to fully characterize the behavior of porous materials. Finite strain poroelasticity as well as poroplasticity are considered in this work where, furthermore, a generalized constitutive law for the saturating fluid has been introduced such that both the incompressible fluid and ideal gas are embedded as particular cases. Parametric studies are conducted throughout the paper by means of simulated hydrostatic compression tests to highlight the effectiveness of the present modeling framework