Publications by authors named "Ryan A McCarthy"

Mineral-rich hardgrounds, such as ferromanganese (FeMn) crusts and phosphorites, occur on seamounts and continental margins, gaining attention for their resource potential due to their enrichment in valuable metals in some regions. This study focuses on the Southern California Borderland (SCB), an area characterized by uneven and heterogeneous topography featuring FeMn crusts, phosphorites, basalt, and sedimentary rocks that occur at varying depths and are exposed to a range of oxygen concentrations. Due to its heterogeneity, this region serves as an optimal setting for investigating the relationship between mineral-rich hardgrounds and benthic fauna.

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Disposal of industrial and hazardous waste in the ocean was a pervasive global practice in the 20th century. Uncertainty in the quantity, location, and contents of dumped materials underscores ongoing risks to marine ecosystems and human health. This study presents an analysis of a wide-area side-scan sonar survey conducted with autonomous underwater vehicles (AUVs) at a dump site in the San Pedro Basin, California.

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In this work, we explore machine learning through a model-agnostic feature representation known as braiding, that employs braid manifolds to interpret multipath ray bundles. We generate training and testing data using the well-known BELLHOP model to simulate shallow water acoustic channels across a wide range of multipath scattering activity. We examine three different machine learning techniques-k-nearest neighbors, random forest tree ensemble, and a fully connected neural network-as well as two machine learning applications.

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The aim of this interdisciplinary work is a robust signal processing and autonomous machine learning framework to associate well-known (target) as well as any potentially unknown (non-target) peaks present within gas chromatography-mass spectrometry (GC/MS/MS) raw instrument signal. Particularly, this work evaluates three machine learning algorithms abilities to autonomously associate raw signal peaks based on accuracy in training and testing. A target is a known congener that is expected to be present within the raw instrument signal and a non-target is an unknown or unexpected compound.

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The main contribution of this interdisciplinary work is a robust computational framework to autonomously discover and quantify previously unknown associations between well-known (target) and potentially unknown (non-target) toxic industrial air pollutants. In this work, the variability of polychlorinated biphenyl (PCB) data is evaluated using a combination of statistical, signal processing, and graph-based informatics techniques to interpret the raw instrument signal from gas chromatography-mass spectrometry (GC/MS/MS) data sets. Specifically, minimum mean-squared techniques from the adaptive signal processing literature are extended to detect and separate coeluted (overlapped) peaks in the raw instrument signal.

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