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Full-Text Articles in Physical Sciences and Mathematics
Photonic Monitoring Of Atmospheric Fauna, Adrien P. Genoud
Photonic Monitoring Of Atmospheric Fauna, Adrien P. Genoud
Dissertations
Insects play a quintessential role in the Earth’s ecosystems and their recent decline in abundance and diversity is alarming. Monitoring their population is paramount to understand the causes of their decline, as well as to guide and evaluate the efficiency of conservation policies. Monitoring populations of flying insects is generally done using physical traps, but this method requires long and expensive laboratory analysis where each insect must be identified by qualified personnel. Lack of reliable data on insect populations is now considered a significant issue in the field of entomology, often referred to as a “data crisis” in the field. …
Interactions Of Amyloid Peptides With Lipid Membranes, Yanxing Yang
Interactions Of Amyloid Peptides With Lipid Membranes, Yanxing Yang
Dissertations
The aggregation of amyloid proteins into fibrils is a hallmark of several diseases including Alzheimer’s (AD), Parkinson’s, and Type II diabetes. This aggregation process involves the formation of small size oligomers preceding the formation of insoluble fibrils. Recent studies have shown that these oligomers are more likely to be responsible for cell toxicity than fibrils. A possible mechanism of toxicity involves the interaction of oligomers with the cell membrane compromising its integrity. In particular, oligomers may form pore-like structures in the cell membrane affecting its permeability or they may induce lipid loss via a detergent-like effect. This dissertation aims to …
One-Stage Blind Source Separation Via A Sparse Autoencoder Framework, Jason Anthony Dabin
One-Stage Blind Source Separation Via A Sparse Autoencoder Framework, Jason Anthony Dabin
Dissertations
Blind source separation (BSS) is the process of recovering individual source transmissions from a received mixture of co-channel signals without a priori knowledge of the channel mixing matrix or transmitted source signals. The received co-channel composite signal is considered to be captured across an antenna array or sensor network and is assumed to contain sparse transmissions, as users are active and inactive aperiodically over time. An unsupervised machine learning approach using an artificial feedforward neural network sparse autoencoder with one hidden layer is formulated for blindly recovering the channel matrix and source activity of co-channel transmissions. The BSS sparse autoencoder …