Level set based eXtended finite element modelling of the response of fibrous networks under hygroscopic swelling

Materials like paper, consisting of a network of natural fibres, exposed to variations in moisture, undergo changes in geometrical and mechanical properties. This behaviour is particularly important for understanding the hygro-mechanical response of sheets of paper in applications like digital printing. A two-dimensional microstructural model of a fibrous network is therefore developed to upscale the hygro-expansion of individual fibres, through their interaction, to the resulting overall expansion of the network. The fibres are modelled with rectangular shapes and are assumed to be perfectly bonded where they overlap. For realistic networks the number of bonds is large and the network is geometrically so complex that discretizing it by conventional, geometry-conforming, finite elements is cumbersome. The combination of a level-set and XFEM formalism enables the use of regular, structured grids in order to model the complex microstructural geometry. In this approach, the fibres are described implicitly by a level-set function. In order to represent the fibre boundaries in the fibrous network, an XFEM discretization is used together with a Heaviside enrichment function. Numerical results demonstrate that the proposed approach successfully captures the hygro-expansive properties of the network with fewer degrees of freedom compared to classical FEM, preserving desired accuracy.Comment: 27 pages, 22 figures, 4 tables, J. Appl. Mech. June 19, 202

Strain gradient plasticity analysis of the strength and ductility of thin metallic films using an enriched interface model

The mechanical response of thin metallic films is simulated using a two-dimensional strain gradient plasticity finite-element model involving grain boundaries in order to investigate the effect of the thickness, grain shape and surface constraint on the strength, ductility and back-stress. The grain boundaries and surface layers are modeled as initially impenetrable to dislocations while allowing for relaxation at a critical stress level. The model captures the variation of the strength with grain size, film thickness, and with the presence or not of constraining surface layers, in agreement with experimental results on Al and Cu films. A decrease in the uniform elongation is predicted with decreasing film thickness due to a loss of strain-hardening capacity and the possible presence of imperfections. These two effects dominate over the stabilizing contribution of the plastic strain gradients. Accounting for the relaxation of the interface constraint affects the magnitude of the back-stress as well as the drop in ductility.Institute of Mechanics, Materials and Civil Engineering, Universite´ catholique de Louvain, 1348 Louvain-la-Neuve, Belgium b Universite´ Libre de Bruxelles, Building, Architecture & Town Planning Dept. (BATir) CP 194/02, Avenue F.D. Roosevelt 50, 1050 Bruxelles, Belgiu

Exponential and moment inequalities for U-statistics

A Bernstein-type exponential inequality for (generalized) canonical U-statistics of order 2 is obtained and the Rosenthal and Hoffmann-J{\o}rgensen inequalities for sums of independent random variables are extended to (generalized) U-statistics of any order whose kernels are either nonnegative or canonicalComment: 22 page

Interactions between Magnetic Nanowires and Living Cells : Uptake, Toxicity and Degradation

We report on the uptake, toxicity and degradation of magnetic nanowires by NIH/3T3 mouse fibroblasts. Magnetic nanowires of diameters 200 nm and lengths comprised between 1 {\mu}m and 40 {\mu}m are fabricated by controlled assembly of iron oxide ({\gamma}-Fe2O3) nanoparticles. Using optical and electron microscopy, we show that after 24 h incubation the wires are internalized by the cells and located either in membrane-bound compartments or dispersed in the cytosol. Using fluorescence microscopy, the membrane-bound compartments were identified as late endosomal/lysosomal endosomes labeled with lysosomal associated membrane protein (Lamp1). Toxicity assays evaluating the mitochondrial activity, cell proliferation and production of reactive oxygen species show that the wires do not display acute short-term (< 100 h) toxicity towards the cells. Interestingly, the cells are able to degrade the wires and to transform them into smaller aggregates, even in short time periods (days). This degradation is likely to occur as a consequence of the internal structure of the wires, which is that of a non-covalently bound aggregate. We anticipate that this degradation should prevent long-term asbestos-like toxicity effects related to high aspect ratio morphologies and that these wires represent a promising class of nanomaterials for cell manipulation and microrheology.Comment: 21 pages 12 figure

Iteratively regularized Newton-type methods for general data misfit functionals and applications to Poisson data

We study Newton type methods for inverse problems described by nonlinear operator equations $F(u)=g$ in Banach spaces where the Newton equations $F'(u_n;u_{n+1}-u_n) = g-F(u_n)$ are regularized variationally using a general data misfit functional and a convex regularization term. This generalizes the well-known iteratively regularized Gauss-Newton method (IRGNM). We prove convergence and convergence rates as the noise level tends to 0 both for an a priori stopping rule and for a Lepski{\u\i}-type a posteriori stopping rule. Our analysis includes previous order optimal convergence rate results for the IRGNM as special cases. The main focus of this paper is on inverse problems with Poisson data where the natural data misfit functional is given by the Kullback-Leibler divergence. Two examples of such problems are discussed in detail: an inverse obstacle scattering problem with amplitude data of the far-field pattern and a phase retrieval problem. The performence of the proposed method for these problems is illustrated in numerical examples

A Framework for the Evaluation of Biosecurity, Commercial, Regulatory, and Scientific Impacts of Plant Viruses and Viroids Identified by NGS Technologies

Recent advances in high-throughput sequencing technologies and bioinformatics have generated huge new opportunities for discovering and diagnosing plant viruses and viroids. Plant virology has undoubtedly benefited from these new methodologies, but at the same time, faces now substantial bottlenecks, namely the biological characterization of the newly discovered viruses and the analysis of their impact at the biosecurity, commercial, regulatory, and scientific levels. This paper proposes a scaled and progressive scientific framework for efficient biological characterization and risk assessment when a previously known or a new plant virus is detected by next generation sequencing (NGS) technologies. Four case studies are also presented to illustrate the need for such a framework, and to discuss the scenarios.Peer reviewe

Improving statistical inference on pathogen densities estimated by quantitative molecular methods: malaria gametocytaemia as a case study

BACKGROUND: Quantitative molecular methods (QMMs) such as quantitative real-time polymerase chain reaction (q-PCR), reverse-transcriptase PCR (qRT-PCR) and quantitative nucleic acid sequence-based amplification (QT-NASBA) are increasingly used to estimate pathogen density in a variety of clinical and epidemiological contexts. These methods are often classified as semi-quantitative, yet estimates of reliability or sensitivity are seldom reported. Here, a statistical framework is developed for assessing the reliability (uncertainty) of pathogen densities estimated using QMMs and the associated diagnostic sensitivity. The method is illustrated with quantification of Plasmodium falciparum gametocytaemia by QT-NASBA. RESULTS: The reliability of pathogen (e.g. gametocyte) densities, and the accompanying diagnostic sensitivity, estimated by two contrasting statistical calibration techniques, are compared; a traditional method and a mixed model Bayesian approach. The latter accounts for statistical dependence of QMM assays run under identical laboratory protocols and permits structural modelling of experimental measurements, allowing precision to vary with pathogen density. Traditional calibration cannot account for inter-assay variability arising from imperfect QMMs and generates estimates of pathogen density that have poor reliability, are variable among assays and inaccurately reflect diagnostic sensitivity. The Bayesian mixed model approach assimilates information from replica QMM assays, improving reliability and inter-assay homogeneity, providing an accurate appraisal of quantitative and diagnostic performance. CONCLUSIONS: Bayesian mixed model statistical calibration supersedes traditional techniques in the context of QMM-derived estimates of pathogen density, offering the potential to improve substantially the depth and quality of clinical and epidemiological inference for a wide variety of pathogens