Growth of Al<SUB>2</SUB>O<SUB>3</SUB>/Al composites from Al-Zn alloys

Observations are presented here of the initiation and growth of an Al2O3composite by the directed oxidation of a molten binary Al-Zn alloy with and without preforms. The oxidation behaviour into free space begins with the formation of ZnO on the melt surface followed by a second stage of relatively high growth rate associated with the constant presence of ZnO and a final region of slow growth rate during which the surface consists of both ZnO as well as Al2O3. Composite formation is explained on the basis of a cyclic formation and reduction by molten aluminium of ZnO. Oxidation was carried out with ternary Mg additions into Al2O3 preforms of different particle sizes. The infiltration of an Al2O3 preform is governed by reaction induced wetting between alloy and ZnO. Nucleation of the alumina is epitaxial with respect to particles of the preform and growth rates are higher than that for composite growth into free space

In-situ Water quality monitoring in Oil and Gas operations

From agriculture to mining, to energy, surface water quality monitoring is an essential task. As oil and gas operators work to reduce the consumption of freshwater, it is increasingly important to actively manage fresh and non-fresh water resources over the long term. For large-scale monitoring, manual sampling at many sites has become too time-consuming and unsustainable, given the sheer number of dispersed ponds, small lakes, playas, and wetlands over a large area. Therefore, satellite-based environmental monitoring presents great potential. Many existing satellite-based monitoring studies utilize index-based methods to monitor large water bodies such as rivers and oceans. However, these existing methods fail when monitoring small ponds-the reflectance signal received from small water bodies is too weak to detect. To address this challenge, we propose a new Water Quality Enhanced Index (WQEI) Model, which is designed to enable users to determine contamination levels in water bodies with weak reflectance patterns. Our results show that 1) WQEI is a good indicator of water turbidity validated with 1200 water samples measured in the laboratory, and 2) by applying our method to commonly available satellite data (e.g. LandSat8), one can achieve high accuracy water quality monitoring efficiently in large regions. This provides a tool for operators to optimize the quality of water stored within surface storage ponds and increasing the readiness and availability of non-fresh water.Comment: 15 pages, 8 figures, SPIE Defense + Commercial: Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXI

Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundDisorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.MethodsWe estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.FindingsGlobally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.InterpretationAs the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed

Reduced dimensionality hyperspectral classification using finite mixture models

Classification of h yperspectral imaging (HSI) data is a challenging problem for two main reasons. First, with limited spatial resolution of HSI sensors and/or the distance of the observed scene, the images invariably contain pixels composed of several materials. It is desirable to resolve the contributions of the constituents from the observed image without relying on high spatial resolution images. Remote sensing cameras have been designed to capture a wide spectral range motivating the use of post-processing techniques to distinguish materials via their spectral signatures. Secondly, available training data for most pattern recognition problems in HSI processing is severely inadequate. Under the framework of statistical classifiers, Hughes was able to demonstrate the impact of this problem on a theoretical basis. Concerning the second problem, feature extraction and optimal band selection are the methods most commonly used for finding useful features in high-dimensional data. On the other hand, reduced dimensionality algorithms suffer from theoretical loss of performance. This performance loss occurs due to reduction of data to features, and further approximating the theoretical features to PDFs. Traditional Hyperspectral classification algorithms have used deterministic techniques such as the spectral angle mapper (SAM) and minimum Euclidean distances (MED). Although the supervised methods are easy to implement they do not quite model the inherent behavior of the HSI pixel vector for the reduced dimensional case. This is primarily due to the absence of a statistical treatment. Hyperspectral data represents a mixture of several component spectra from many classifiable sources. The knowledge of the contributions of the underlying sources to the recorded spectra is valuable in many remote sensing applications and thus demands further investigations. In this dissertation, we propose a hidden Markov model (HMM) based on a probability density function (PDF) classifier for the reduced dimensional feature space. The proposed classifier is derived from two major finite mixture models. We utilize the Gaussian mixture model (GMM) that uses dynamic component allocation to model mixture classes. This classification scheme incorporates the HMM which in turn uses the GMM to represent class-specific features. The HMM is a powerful stochastic model that could closely approximate many naturally occurring phenomena. While being a very powerful stochastic model, a single HMM cannot easily act as a good classifier between wide varieties of signal classes. Instead, it is best to design them specifically for each signal type and feature type. The Markovian principle assumes consecutive samples are statistically independent when conditioned on knowing the samples that preceded it. This leads to an elegant solution of HMM which employs a set of M PDFs of dimension P. The HMM regards each of the K samples as having originated from one of the M possible states and there is a distinct probability that the underlying model “jumps” from one state to another. Our approach uses an unsupervised learning scheme for maximum-likelihood (ML) parameter estimation that combines both model selection and estimation in a single algorithm. This technique could be applied to any type of parameter mixture model that utilizes the EM algorithm. Our experiments exemplify that the proposed method models and well synthesizes the observations of the HSI data in a reduced dimensional feature space. The likelihood measurements obtained from HMM trained classes are then used to derive the classifier rules. We then provide comparative results of the proposed methodology to ML classifier and popular deterministic classifier such as the minimum Euclidean distance (MED) and the parallelepiped. The classification results show that the proposed classifier model outperforms the other classifiers used in our study on basis of overall classification accuracy. The derived classification technique, unlike classical classifiers, can circumvent the curse of dimensionality if each class can be represented (statistically described) using a separate low-dimensional feature set. The outcome of this dissertation presents a seamless integration of advanced data analysis and modeling tools to scientists, advancing the state-of-the-practice in the utilization of satellite image data to various types of Earth System Science studies. With the ever increasing volume of Earth Science data and computational requirements of Earth system models, the proposed methodology could put together a new paradigm of methods that will increase the productivity of researchers in the Science Mission Directorate as well as the science return from NASA data. (Abstract shortened by UMI.

Detection from hyperspectral images compressed using rate distortion and optimization techniques under JPEG2000 part 2

This research studies the effect of two different bit rate allocation strategies in JPEG2000 part 2 compression of Hyperspectral data on the results of background classification. Hyperspectral imagery (HSI) brings a whole new set of capabilities in the field of remote sensing. The major disadvantage being its analysis and processing that leads to high computation and memory costs. This thesis proposes lossy compression to HSI with very high target hit rate. We compare traditional bit rate allocation approach based on the high bit rate quantizer model with the Rate Distortion Optimal (RDO) approach that produces a bit rate allocation optimal in the mean squared error (MSE) sense. ^ The experiments show that for relatively low bit rates both rate allocation strategies perform with excellent and almost similar accuracy (96% at 0.125 bits per pixel per band (bpppb)). However at a very low bit rates RDO outperforms (90% at 0.0375 bpppb) the high bit rate quantizer approach in terms of background classification results. The experiments also confirm that RDO bit rate allocation achieves a lower MSE than the high bit rate quantizer model approach. (Abstract shortened by UMI.)

Fracture and R-curves in high-volume fraction Al<SUB>2</SUB>O<SUB>3</SUB>/Al composites

Fracture toughness and fracture mechanisms in Al2O3/Al composites are described. The unique flexibility offered by pressureless infiltration of molten Al alloys into porous alumina preforms was utilized to investigate the effect of microstructural scale and matrix properties on the fracture toughness and the shape of the crack resistance curves (R-curves). The results indicate that the observed increment in toughness is due to crack bridging by intact matrix ligaments behind the crack tip. The deformation behavior of the matrix, which is shown to be dependent on the microstructural constraints, is the key parameter that influences both the steady-state toughness and the shape of the R-curves. Previously proposed models based on crack bridging by intact ductile particles in a ceramic matrix have been modified by the inclusion of an experimentally determined plastic constraint factor (P) that determines the deformation of the ductile phase and are shown to be adequate in predicting the toughness increment in the composites. Micromechanical models to predict the crack tip profile and the bridge lengths (L) correlate well with the observed behavior and indicate that the composites can be classified as (i) short-range toughened and (ii) long-range toughened on the basis of their microstructural characteristics

Kinetics of pressureless infiltration of Al-Mg melts into porous alumina preforms

Effective "hydrodynamic" radii governing infiltration kinetics of reactive Al-Mg melts into alumina preforms were found to be three orders of magnitude smaller than the average pore size of the packed bed and also smaller compared with the kinetics for a nonreactive system. A sinusoidal capillary model was developed to predict flow kinetics within the packed bed. For the reactive system, two factors were ascribed for additional melt retardation: (1) different intrinsic wettabilities of the two liquids on alumina, thereby leading to significantly different "effective" local contact angles; and (2) local solute depletion from the meniscus, which was incorporated as a time-dependent contact angle

Detection of thin intergranular cobalt layers in WC-Co composites by lattice imaging

Much discussion has taken place concerning the contiguity of the carbide phase in sintered WC-Co composites. In the present study, the lattice fringe imaging technique was used to demonstrate that WC grains which appear macroscopically contiguous are sometimes truly so at the atomic level. In other instances, they are separated by films of cobalt ~1 nm thick

Role of interface curvature on stress distribution under indentation for ZrN/Zr multilayer coating

Contact damage in curved interface nano-layeredmetal/nitride (150 (ZrN)/10 (Zr) nm) multilayer is investigated in order to understand the role of interface morphology on contact damage under indentation. A finite element method (FEM) model was formulated with different wavelengths of 1000 nm, 500 nm, 250 nm and common height of 50 nm, which gives insight on the effect of different curvature on stress field generated under indentation. Elastic-plastic properties were assigned to the metal layer and substrate while the nitride layer was assigned perfectly elastic properties. Curved interface multilayers show delamination along the metal/nitride interface and vertical cracks emanating from the ends of the delamination. FEM revealed the presence of tensile stress normal to the interface even under the contact, along with tensile radial stresses, both present at the valley part of the curve, which leads to vertical cracks associated with interfacial delamination. Stress enhancement was seen to be relatively insensitive to curvature. (C) 2014 Elsevier B.V. All rights reserved