Alien Registration- Hayman, Elden B. (Augusta, Kennebec County)

https://digitalmaine.com/alien_docs/19094/thumbnail.jp

A Technique for Determining the Nozzle-Flow Properties of Air in an Equilibrium, Nonequilibrium, or Frozen State

One of the problems associated with the development of high-enthalpy hypersonic test facilities hos been the determination of nozzle flow properties. This note presents a technique for determining the flow properties of air in an equilibrium, non-equilibrium, or frozen state using two test section measurements. A knowledge of two stagnation properties is required before the technique can be applied. The stagnation pressure can be measured, and reference 1 offers a method for determining stagnation enthalpy. Results of the calculations using the method of reference 1 are presented in reference 2 in a chart (chart 21) which can be readily used to determine the stagnation enthalpy from measurements of stagnation pressure, mass flow of air through the tunnel, and throat diameter

Entire functions with Julia sets of positive measure

Let f be a transcendental entire function for which the set of critical and asymptotic values is bounded. The Denjoy-Carleman-Ahlfors theorem implies that if the set of all z for which |f(z)|>R has N components for some R>0, then the order of f is at least N/2. More precisely, we have log log M(r,f) > (N/2) log r - O(1), where M(r,f) denotes the maximum modulus of f. We show that if f does not grow much faster than this, then the escaping set and the Julia set of f have positive Lebesgue measure. However, as soon as the order of f exceeds N/2, this need not be true. The proof requires a sharpened form of an estimate of Tsuji related to the Denjoy-Carleman-Ahlfors theorem.Comment: 17 page

Fabrication and mechanical testing of a new sandwich structure with carbon fiber network core

The aim is the fabrication and mechanical testing of sandwich structures including a new core material known as fiber network sandwich materials. As fabrication norms for such a material do not exist as such, so the primary goal is to reproduce successfully fiber network sandwich specimens. Enhanced vibration testing diagnoses the quality of the fabrication process. These sandwich materials possess low structural strength as proved by the static tests (compression, bending), but the vibration test results give high damping values, making the material suitable for vibro-acoustic applications where structural strength is of secondary importance e.g., internal panelling of a helicopter

Epigenetic and Phenotypic Profile of Fibroblasts Derived from Induced Pluripotent Stem Cells

Human induced pluripotent stem (hiPS) cells offer a novel source of patient-specific cells for regenerative medicine. However, the biological potential of iPS-derived cells and their similarities to cells differentiated from human embryonic stem (hES) cells remain unclear. We derived fibroblast-like cells from two hiPS cell lines and show that their phenotypic properties and patterns of DNA methylation were similar to that of mature fibroblasts and to fibroblasts derived from hES cells. iPS-derived fibroblasts (iPDK) and their hES-derived counterparts (EDK) showed similar cell morphology throughout differentiation, and patterns of gene expression and cell surface markers were characteristic of mature fibroblasts. Array-based methylation analysis was performed for EDK, iPDK and their parental hES and iPS cell lines, and hierarchical clustering revealed that EDK and iPDK had closely-related methylation profiles. DNA methylation analysis of promoter regions associated with extracellular matrix (ECM)-production (COL1A1) by iPS- and hESC-derived fibroblasts and fibroblast lineage commitment (PDGFRβ), revealed promoter demethylation linked to their expression, and patterns of transcription and methylation of genes related to the functional properties of mature stromal cells were seen in both hiPS- and hES-derived fibroblasts. iPDK cells also showed functional properties analogous to those of hES-derived and mature fibroblasts, as seen by their capacity to direct the morphogenesis of engineered human skin equivalents. Characterization of the functional behavior of ES- and iPS-derived fibroblasts in engineered 3D tissues demonstrates the utility of this tissue platform to predict the capacity of iPS-derived cells before their therapeutic application

A General Bayesian Approach to Analyzing Diallel Crosses of Inbred Strains

The classic diallel takes a set of parents and produces offspring from all possible mating pairs. Phenotype values among the offspring can then be related back to their respective parentage. When the parents are diploid, sexed, and inbred, the diallel can characterize aggregate effects of genetic background on a phenotype, revealing effects of strain dosage, heterosis, parent of origin, epistasis, and sex-specific versions thereof. However, its analysis is traditionally intricate, unforgiving of unplanned missing information, and highly sensitive to imbalance, making the diallel unapproachable to many geneticists. Nonetheless, imbalanced and incomplete diallels arise frequently, albeit unintentionally, as by-products of larger-scale experiments that collect F1 data, for example, pilot studies or multiparent breeding efforts such as the Collaborative Cross or the Arabidopsis MAGIC lines. We present a general Bayesian model for analyzing diallel data on dioecious diploid inbred strains that cleanly decomposes the observed patterns of variation into biologically intuitive components, simultaneously models and accommodates outliers, and provides shrinkage estimates of effects that automatically incorporate uncertainty due to imbalance, missing data, and small sample size. We further present a model selection procedure for weighing evidence for or against the inclusion of those components in a predictive model. We evaluate our method through simulation and apply it to incomplete diallel data on the founders and F1's of the Collaborative Cross, robustly characterizing the genetic architecture of 48 phenotypes

Accuracy and Stability of Computing High-Order Derivatives of Analytic Functions by Cauchy Integrals

High-order derivatives of analytic functions are expressible as Cauchy integrals over circular contours, which can very effectively be approximated, e.g., by trapezoidal sums. Whereas analytically each radius r up to the radius of convergence is equal, numerical stability strongly depends on r. We give a comprehensive study of this effect; in particular we show that there is a unique radius that minimizes the loss of accuracy caused by round-off errors. For large classes of functions, though not for all, this radius actually gives about full accuracy; a remarkable fact that we explain by the theory of Hardy spaces, by the Wiman-Valiron and Levin-Pfluger theory of entire functions, and by the saddle-point method of asymptotic analysis. Many examples and non-trivial applications are discussed in detail.Comment: Version 4 has some references and a discussion of other quadrature rules added; 57 pages, 7 figures, 6 tables; to appear in Found. Comput. Mat

Hybrid networks: Improving deep learning networks via integrating two views of images

© 2018, Springer Nature Switzerland AG. The principal component analysis network (PCANet) is an unsupervised parsimonious deep network, utilizing principal components as filters in the layers. It creates an amalgamated view of the data by transforming it into column vectors which destroys its spatial structure while obtaining the principal components. In this research, we first propose a tensor-factorization based method referred as the Tensor Factorization Networks (TFNet). The TFNet retains the spatial structure of the data by preserving its individual modes. This presentation provides a minutiae view of the data while extracting matrix factors. However, the above methods are restricted to extract a single representation and thus incurs information loss. To alleviate this information loss with the above methods we propose Hybrid Network (HybridNet) to simultaneously learn filters from both the views of the data. Comprehensive results on multiple benchmark datasets validate the superiority of integrating both the views of the data in our proposed HybridNet