Identification of a soft matrix-hard inclusion material by indentation

peer reviewedA new procedure for identifying the mechanical behavior of individual phases within a bi-material (matrix-particles) is presented. The case of AlSi10Mg (large globularized Si-rich particles surrounded by an α-Al phase) processed by additive manufacturing and post-treated is taken as a typical example. Grids of nano-indentation tests are performed at different locations on the nanocomposite using a Berkovich indenter and show an impact of the hard inclusions on the experimental curves. The elastoplastic properties of the matrix are identified based on the lowest load–indentation depth curves. Several representative finite element (FE) models demonstrate the influence of the particles on the nano-indentation response. The capacity of the FE model to predict the indentation curve of a cube corner indenter experiment and the Berkovich grid result scattering was checked. A representative volume element (RVE) based on a scanning electron microscope (SEM) image is defined. The identified material parameters of the α-Al phase and Si phase, it allows the prediction of the stress-strain curve of a macroscopic experimental tensile test.LongLifeA

Accurate numerical prediction of ductile fracture and micromechanical damage evolution for Ti6Al4V alloy

peer reviewedA CPB06-based Stewart-Cazacu micromechanical damage model is implemented and validated for Ti6Al4V material. It provides accurate numerical predictions in terms of macromechanical material response and damage accumulation. The Stewart & Cazacu–Tvergaard & Needleman–Thomason (SC11–TNT) based damage model presented here is developed and implemented in the finite element software Lagamine following a semi-implicit cutting plane algorithm and a well-chosen flow rule approach. The damage of the material is characterized by the porosity ratio contained within the material. It is modelled by void nucleation, growth and coalescence mechanisms. The onset of the coalescence is established by a criterion based on Thomason’s approach. The macroscopic results obtained by the implemented model demonstrate a strong ability to predict the experimental elastoplastic mechanical behaviour of the material across a full deformation range and different types of loadings. At the microscopic level, the predicted accumulated porosity ratio of the material matrix at fracture exhibits a good correlation with the experimental observations. The element deletion feature, activated when a certain damage threshold is reached, provides a physical description of the loss of load-carrying capacity of the material during fracture

Movimientos sociales, estado y democracia en Colombia

Este libro surge en el marco del tercer observatorio sociopolítico y cultural, en convergencia con diversos esfuerzos investigativos y con el fin de hacer un análisis acerca del papel de los actores sociales en la construcción de democracia. / Contenido; Preliminares; Capítulo 1 - Luchas laborales y cívicas; Capítulo 2 - Protestas agrarias; Capítulo 3 - Acción colectiva y etnicidad; Capítulo 4 - Movimientos de mujeres; Capítulo 5 - Movilizaciones por la Paz y Derechos Humanos; Capítulo 6 - Imaginarios, territorios y normatividad; Anexos

Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach

Introduction Previous reports in European populations demonstrated the existence of five data-driven adult-onset diabetes subgroups. Here, we use self-normalizing neural networks (SNNN) to improve reproducibility of these data-driven diabetes subgroups in Mexican cohorts to extend its application to more diverse settings.Research design and methods We trained SNNN and compared it with k-means clustering to classify diabetes subgroups in a multiethnic and representative population-based National Health and Nutrition Examination Survey (NHANES) datasets with all available measures (training sample: NHANES-III, n=1132; validation sample: NHANES 1999–2006, n=626). SNNN models were then applied to four Mexican cohorts (SIGMA-UIEM, n=1521; Metabolic Syndrome cohort, n=6144; ENSANUT 2016, n=614 and CAIPaDi, n=1608) to characterize diabetes subgroups in Mexicans according to treatment response, risk for chronic complications and risk factors for the incidence of each subgroup.Results SNNN yielded four reproducible clinical profiles (obesity related, insulin deficient, insulin resistant, age related) in NHANES and Mexican cohorts even without C-peptide measurements. We observed in a population-based survey a high prevalence of the insulin-deficient form (41.25%, 95% CI 41.02% to 41.48%), followed by obesity-related (33.60%, 95% CI 33.40% to 33.79%), age-related (14.72%, 95% CI 14.63% to 14.82%) and severe insulin-resistant groups. A significant association was found between the SLC16A11 diabetes risk variant and the obesity-related subgroup (OR 1.42, 95% CI 1.10 to 1.83, p=0.008). Among incident cases, we observed a greater incidence of mild obesity-related diabetes (n=149, 45.0%). In a diabetes outpatient clinic cohort, we observed increased 1-year risk (HR 1.59, 95% CI 1.01 to 2.51) and 2-year risk (HR 1.94, 95% CI 1.13 to 3.31) for incident retinopathy in the insulin-deficient group and decreased 2-year diabetic retinopathy risk for the obesity-related subgroup (HR 0.49, 95% CI 0.27 to 0.89).Conclusions Diabetes subgroup phenotypes are reproducible using SNNN; our algorithm is available as web-based tool. Application of these models allowed for better characterization of diabetes subgroups and risk factors in Mexicans that could have clinical applications