Browsing by Subject "Graph theory"

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    Disturbed resting state EEG synchronization in bipolar disorder: A graph-theoretic analysis
    (Elsevier, 2013-03-22) Kim, Dae-Jin; Bolbecker, Amanda R.; Howell, Josselyn; Rass, Olga; Sporns, Olaf; Hetrick, William P.; Breier, Alan; O'Donnell, Brian F.; Psychiatry, School of Medicine
    Disruption of functional connectivity may be a key feature of bipolar disorder (BD) which reflects disturbances of synchronization and oscillations within brain networks. We investigated whether the resting electroencephalogram (EEG) in patients with BD showed altered synchronization or network properties. Resting-state EEG was recorded in 57 BD type-I patients and 87 healthy control subjects. Functional connectivity between pairs of EEG channels was measured using synchronization likelihood (SL) for 5 frequency bands (δ, θ, α, β, and γ). Graph-theoretic analysis was applied to SL over the electrode array to assess network properties. BD patients showed a decrease of mean synchronization in the alpha band, and the decreases were greatest in fronto-central and centro-parietal connections. In addition, the clustering coefficient and global efficiency were decreased in BD patients, whereas the characteristic path length increased. We also found that the normalized characteristic path length and small-worldness were significantly correlated with depression scores in BD patients. These results suggest that BD patients show impaired neural synchronization at rest and a disruption of resting-state functional connectivity.
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    De novo genome assembly of the blow fly Phormia regina (Diptera: Calliphoridae)
    (2014) Andere, Anne A.; Randall, Stephen Karl, 1953-; Liu, Yunlong; Atkinson, Simon; Picard, Christine
    Phormia regina (Meigen), commonly known as the black blow fly is a dipteran that belongs to the family Calliphoridae. Calliphorids play an important role in various research fields including ecology, medical studies, veterinary and forensic sciences. P. regina, a non-model organism, is one of the most common forensically relevant insects in North America and is typically used to assist in estimating postmortem intervals (PMI). To better understand the roles P. regina plays in the numerous research fields, we re-constructed its genome using next generation sequencing technologies. The focus was on generating a reference genome through de novo assembly of high-throughput short read sequences. Following assembly, genetic markers were identified in the form of microsatellites and single nucleotide polymorphisms (SNPs) to aid in future population genetic surveys of P. regina. A total 530 million 100 bp paired-end reads were obtained from five pooled male and female P. regina flies using the Illumina HiSeq2000 sequencing platform. A 524 Mbp draft genome was assembled using both sexes with 11,037 predicted genes. The draft reference genome assembled from this study provides an important resource for investigating the genetic diversity that exists between and among blow fly species; and empowers the understanding of their genetic basis in terms of adaptations, population structure and evolution. The genomic tools will facilitate the analysis of genome-wide studies using modern genomic techniques to boost a refined understanding of the evolutionary processes underlying genomic evolution between blow flies and other insect species.
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    Real-time Optimization of Printing Sequence to Mitigate Residual Stress and Thermal Distortion in Metal Powder-bed Fusion Process
    (2023-12) Malekipour, Ehsan; El-Mounayri, Hazim; Zhang, Jing; Qattawi, Ala; Al Hasan, Mohammad
    The Powder Bed Fusion (PBF) process is increasingly employed by industry to fabricate complex parts with stringent standard criteria. However, fabricating parts free of defects using this process is still a major challenge. As reported in the literature, thermally induced abnormalities form the majority of generated defects and are largely attributed to thermal evolution. Various methodologies have been introduced in the literature to eliminate or mitigate such abnormalities. However, most of these methodologies are post-process in nature, lacking adaptability and customization to accommodate different geometries or materials. Consequently, they fall short of adequately addressing these challenges. Monitoring and controlling temperature, along with its distribution throughout each layer during fabrication, is an effective and efficient proxy to control the thermal evolution of the process. This, in turn, provides a real-time solution to effectively overcome such challenges. The objective of this dissertation is to introduce a novel online thermography and closed-loop hybrid-control (NOTCH)©, an ultra-fast and practical control approach, to modify the scan strategy in metal PBF in real-time. This methodology employs different mathematical thermophysical concept-based or thermophysical-based models combined with optimization algorithms designed to optimize the printing sequence of islands/stripes/zones in order to avoid or mitigate residual stress and distortion. This methodology is adaptable to different geometries, dimensions, and materials, and is capable of being used with machines having varying ranges of specifications. NOTCH’s objective is to achieve a uniform temperature distribution throughout an entire layer and through the printed part (between layers) to mitigate residual stress and thermally related distortion. To attain this objective, this study explores modifying or optimizing the printing sequence of islands/stripes in an island or the strip scanning strategy. This dissertation presents three key contributions: First, this work introduces two potential models: the Genetic Algorithm Maximum Path (GAMP) strategy and Generalized Advanced Graph Theory. Preliminary results for a printed/simulated prototype are presented. These models, along with the Tessellation algorithm (developed in my M.Sc. thesis), were employed within NOTCH. Second, I developed two optimization algorithms based on the greedy and evolutionary approaches. Both algorithms are direct-derivative-free methods. The greedy optimization provides a definitive solution at each printing step, selecting the island/stripe that ensures the highest temperature uniformity. Conversely, the evolutionary algorithm seeks to obtain the final optimal solution at the end of the printing process, i.e., the printing sequence with the highest uniformity in the last printing step. This approach is inspired by the concept of Random Search algorithms, offering a non-definitive solution to find an optimal solution. Last, this work presents the NOTCH methodology, enabling real-time modification of printing sequences through the integration of a novel thermography methodology (developed in my M.Sc. thesis), developed models, and optimization algorithms.