Nucleation and crystallization of Ba2Si3O8 spherulites in a barium aluminum silicate glass,and mechanical properties of the obtained glass-ceramics

The effect of spherulitic crystallization on the elastic moduli and fracture toughness of a barium aluminum silicate glass was investigated. The crystallization process results in Ba₂Si₃O₈ phase and is initiated from Ba rich nuclei. Nucleation is optimal in the 690-720 °C interval. Young’s modulus is increased by 12.5% when the glass-ceramic conversion is nearly complete. Nevertheless, as the size and the volume fraction of crystals are increased, some microcracking shows up upon cooling from the crystallization temperature. An optimal improvement of the fracture toughness (SEPB method) by 27 % is observed for a 49 % volume fraction of 5 to 10 μm large spherulites.     

Multivariate Analysis of Multiple Datasets: a Practical Guide for Chemical Ecology

Chemical ecology has strong links with metabolomics, the large-scale study of all metabolites detectable in a biological sample. Consequently, chemical ecologists are often challenged by the statistical analyses of such large datasets. This holds especially true when the purpose is to integrate multiple datasets to obtain a holistic view and a better understanding of a biological system under study. The present article provides a comprehensive resource to analyze such complex datasets using multivariate methods. It starts from the necessary pre-treatment of data including data transformations and distance calculations, to the application of both gold standard and novel multivariate methods for the integration of different omics data. We illustrate the process of analysis along with detailed results interpretations for six issues representative of the different types of biological questions encountered by chemical ecologists. We provide the necessary knowledge and tools with reproducible R codes and chemical-ecological datasets to practice and teach multivariate methods.     

Convergence of iterative methods based on Neumann series for composite materials: Theory and practice

Iterative fast Fourier transform methods are useful for calculating the fields in composite materials and their macroscopic response. By iterating back and forth until convergence, the differential constraints are satisfied in Fourier space and the constitutive law in real space. The methods correspond to series expansions of appropriate operators and to series expansions for the effective tensor as a function of the component moduli. It is shown that the singularity structure of this function can shed much light on the convergence properties of the iterative fast Fourier transform methods. We look at a model example of a square array of conducting square inclusions for which there is an exact formula for the effective conductivity (Obnosov). Theoretically, some of the methods converge when the inclusions have zero or even negative conductivity. However, the numerics do not always confirm this extended range of convergence and show that accuracy is lost after relatively few iterations. There is little point in iterating beyond this. Accuracy improves when the grid size is reduced, showing that the discrepancy is linked to the discretization. Finally, it is shown that none of the 3 iterative schemes investigated overperforms the others for all possible microstructures and all contrasts.     

Effective viscoelastic behavior of short fibers composites using virtual DMA experiments

Mechanics of Time-Dependent Materials - May it be for environmental or economic reasons, mass reduction has become one of the main goals in mechanics. The short fiber thermoplastics composite is an...     

A closed culture system for the ex vivo transduction and expansion of human T lymphocytes

The purpose of this qualitative study was to explore traditional beliefs and practices related to pregnancy and childbirth among Native American women and examine the relationship of these beliefs and practices to current use of prenatal care. Focus groups of elders and young women were held and the data analyzed with the Ethnograph software. The central theme was the breakdown in transmission of cultural wisdom among Native American women. The major causes described by women were federal assimilation policies and deaths of elders. Major factors influencing young women's nonutilization of prenatal care were this breakdown, the "Western model" of prenatal care, substance abuse, and domestic violence. Recommendations for improving the use of prenatal care are grounded in the reality that Native American women live in two different worlds. Prenatal care should be reconceptualized as traditional cultural wisdom, with the majority of care provided by natural helpers in the Native American community, including tribal elders, grandmothers, and aunts in collaboration with licensed providers.     

Diagnosability of repairable faults

The diagnosis problem for discrete event systems consists in deciding whether some fault event occurred or not in the system, given partial observations on the run of that system. Diagnosability checks whether a correct diagnosis can be issued in bounded time after a fault, for all faulty runs of that system. This problem appeared two decades ago and numerous facets of it have been explored, mostly for permanent faults. It is known for example that diagnosability of a system can be checked in polynomial time, while the construction of a diagnoser is exponential. The present paper examines the case of transient faults, that can appear and be repaired. Diagnosability in this setting means that the occurrence of a fault should always be detected in bounded time, but also before the fault is repaired, in order to prepare for the detection of the next fault or to take corrective measures while they are needed. Checking this notion of diagnosability is proved to be PSPACE-complete. It is also shown that faults can be reliably counted provided the system is diagnosable for faults and for repairs.     

Sensitivity versus accuracy in ensemble models of Artificial Neural Networks from Multi-objective Evolutionary Algorithms

Neural Computing and Applications - This paper proposes a framework to obtain ensembles of classifiers from a Multi-objective Evolutionary Algorithm (MOEA), improving the restrictions imposed by...