Computational Mesoscale Modeling and Homogenization of Liquid-Phase Sintering of Particle Agglomerates

Liquid phase sintering of particle agglomerates is simulated as the viscous deformation due to particle-particle contact, whereby the single driving force is the surface tension on the particle/pore interface. Particles are modeled as purely viscous fluids (with no elasticity). Computational homogenization is adopted for the RVE with Dirichlet boundary conditions. A surface motion algorithm was developed that requires complete remeshing of the FE-mesh based on a maximum deformation criterion. Since the particles are intrinsically incompressible, the macroscopic compressibility is determined from shrinking porosity in the substructure. The numerical examples include free sintering of an RVE and a fully coupled FE2-simulation of a specimen with inhomogeneous initial distribution of porosity

Computational modeling of gradient hardening in polycrystals

A gradient hardening crystal plasticity model for polycrystals is introduced in Ekh et al. (2007). It is formulated in a thermodynamically consistent fashion and is capable of modeling a grain-size-dependent stress-strain response. In this contribution we extend that model to also include cross-hardening. A free energy is stated which includes contributions from the gradient of hardening along each slip direction. This leads to hardening stresses depending on the second derivative of the plastic slip. The governing equations for a nonlinear coupled system of equations is solved numerically with the help of a dual-mixed finite element method. The numerical results show that the macroscopic strength increases with decreasing grain size as a result of gradient hardening: Moreover, cross-hardening further enhances the strengthening gradient effect

Rapid Accumulation of Polymorphonuclear Neutrophils in the Corpus luteum during Prostaglandin F2α-Induced Luteolysis in the Cow

Prostaglandin F2α (PGF2α) induces luteolysis within a few days in cows, and immune cells increase in number in the regressing corpus luteum (CL), implying that luteolysis is an inflammatory-like immune response. We investigated the rapid change in polymorphonuclear neutrophil (PMN) numbers in response to PGF2α administration as the first cells recruited to inflammatory sites, together with mRNA of interleukin-8 (IL-8: neutrophil chemoattractant) and P-selectin (leukocyte adhesion molecule) in the bovine CL. CLs were collected by ovariectomy at various times after PGF2α injection. The number of PMNs was increased at 5 min after PGF2α administration, whereas IL-8 and P-selectin mRNA increased at 30 min and 2 h, respectively. PGF2α directly stimulated P-selectin protein expression at 5–30 min in luteal endothelial cells (LECs). Moreover, PGF2α enhanced PMN adhesion to LECs, and this enhancement by PGF2α was inhibited by anti-P-selectin antibody, suggesting that P-selectin expression by PGF2α is crucial in PMN migration. In conclusion, PGF2α rapidly induces the accumulation of PMNs into the bovine CL at 5 min and enhances PMN adhesion via P-selectin expression in LECs. It is suggested that luteolytic cascade by PGF2α may involve an acute inflammatory-like response due to rapidly infiltrated PMNs

Mixed-effects models for health care longitudinal data with an informative visiting process: A Monte Carlo simulation study.

Electronic health records are being increasingly used in medical research to answer more relevant and detailed clinical questions; however, they pose new and significant methodological challenges. For instance, observation times are likely correlated with the underlying disease severity: Patients with worse conditions utilise health care more and may have worse biomarker values recorded. Traditional methods for analysing longitudinal data assume independence between observation times and disease severity; yet, with health care data, such assumptions unlikely hold. Through Monte Carlo simulation, we compare different analytical approaches proposed to account for an informative visiting process to assess whether they lead to unbiased results. Furthermore, we formalise a joint model for the observation process and the longitudinal outcome within an extended joint modelling framework. We illustrate our results using data from a pragmatic trial on enhanced care for individuals with chronic kidney disease, and we introduce user-friendly software that can be used to fit the joint model for the observation process and a longitudinal outcome