Egg-laying mammals (monotremes) are the only extant mammalian outgroup to therians (marsupial and eutherian animals) and provide key insights into mammalian evolution1,2. Here we generate and analyse reference genomes of the platypus (Ornithorhynchus anatinus) and echidna (Tachyglossus aculeatus), which represent the only two extant monotreme lineages. The nearly complete platypus genome assembly has anchored almost the entire genome onto chromosomes, markedly improving the genome continuity and gene annotation. Together with our echidna sequence, the genomes of the two species allow us to detect the ancestral and lineage-specific genomic changes that shape both monotreme and mammalian evolution. We provide evidence that the monotreme sex chromosome complex originated from an ancestral chromosome ring configuration. The formation of such a unique chromosome complex may have been facilitated by the unusually extensive interactions between the multi-X and multi-Y chromosomes that are shared by the autosomal homologues in humans. Further comparative genomic analyses unravel marked differences between monotremes and therians in haptoglobin genes, lactation genes and chemosensory receptor genes for smell and taste that underlie the ecological adaptation of monotremes.
Acoustic emission signals are information rich and can be used to estimate the size and location of damage in structures. However, many existing algorithms may be deceived by indirectly propagated acoustic emission waves which are modulated by reflection boundaries within the structures. We propose two deep learning models to identify such waves such that existing algorithms for damage detection and localization may be used. The first approach uses long short-term memory recurrent neural networks to learn distinct patterns directly from the time-series data. In the second approach, we transform the time-series data into spectrograms and utilize convolutional neural networks to perform binary classification by leveraging spectro-temporal features. We achieved 80% classification accuracy using long short-term memory and near-perfect accuracy using convolutional neural networks on a dataset of acoustic emission signals generated by the Hsu-Nielsen sources. Both long short-term memory and convolutional neural network models were able to learn general and context-specific features of the direct and reflected acoustic emission waves. Once accurately identified, the indirectly propagating waves are filtered out while the directly propagating waves are used for source location using existing methods.
Constant stress amplitude fatigue tests were conducted on the notch pre-cracked Aluminum 7075-T6 rivet hole dog-bone coupons. Monitoring of visible surface crack length by special surface engraving using digital microscope images and by ultrasonic sensors signals was carried out to yield fatigue crack length measurements in relation to number of fatigue cycles applied. The experimental results provide ultrasonic sensor validation for fatigue crack length measurements. Fracto-graphic examination of failed fatigue surfaces has provided further confirmation of notch pre-crack length, crack initiation process, and crack growth marker bands. These experimental inputs were used in NASGRO and AFGROW software fatigue crack growth simulations. The simulation results did not match the crack initiation fatigue life measured by experiments. However, there was good agreement with crack growth simulations of larger cracks. Hence, we plan to develop a machine learning application that will learn the fatigue crack initiation and crack growth processes from data obtained from our own experiments and other fatigue data available from AFGROW databases. Nonlinear AutoRegressive models with eXogenous input (NARX) artificial neural network were used to predict crack growth longer than 5.0-mm. Particle filtering modeling with Bayesian updating was applied to these experimental data for prognostics of fatigue crack growth. A concept design and preliminary implementation results will be presented.
Abstract:The integrity of composite structures gradually degrades due to the onset of damage such as matrix cracking, fiber/matrix debonding, and delamination. Over the last two decades, great strides have been made in structural health monitoring (SHM) community using various sensing techniques such as acoustic emission, eddy current, strain gages, etc., to diagnose damage in aerospace, mechanical and civil infrastructures. Embedded sensing offers the prospects of providing for real-time, in-service monitoring of damage were weight savings is a major factor in Aerospace Industry. In this present work, magnetostrictive particles such as Terfenol-D were embedded in a composite structure, along with multiple SHM techniques, to capture the damage in an IM7-carbon fiber reinforced polymer composite system undergoing fatigue loading. As the internal stress state increases, the change in the magnetization flux intensity was captured using a non-contact magnetic field sensor. A damage diagnosis system was established along with an acoustic emissions technique to further validate the damage captured by the embedded system. The goal of this project is to identify the change in the mechanical and magnetic property within a composite material during the evolution of damage. Several characterization techniques were used to determine interfacial fiber-matrix interactions which will provide for a more comprehensive understanding of the composite interfaces.
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