Current Search: Analysis (x)
Pages
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Title
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Biofilm Detection through the use of Factor Analysis and Principal Component Analysis.
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Creator
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Gallant, Richard, Bloetscher, Frederick, Florida Atlantic University, College of Engineering and Computer Science, Department of Civil, Environmental and Geomatics Engineering
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Abstract/Description
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Safe drinking water is paramount to a healthy society. Close to a hundred contaminants are regulated by the government. Utilities are using chloramines to disinfect water to reduce harmful byproducts that may present themselves with the use of chlorine alone. Using chlorine and ammonia to disinfect, ammonia oxidizing bacteria can present themselves in an unsuspecting utilities distribution network.
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Date Issued
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2019
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PURL
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http://purl.flvc.org/fau/fd/FA00013309
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Subject Headings
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Biofilms, Factor analysis, Principal components analysis, Drinking water--Analysis, Nitrification
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Format
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Document (PDF)
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Title
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Light intensity influences on algal pigments, proteins and carbohydrates: implications for pigment-based chemotaxonomy.
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Creator
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Grant, Cidya S., Charles E. Schmidt College of Science, Department of Chemistry and Biochemistry
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Abstract/Description
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Phytoplankton Chlorophyll a (CHLa), total protein, colloidal carbohydrates, storage carbohydrates and taxonomic pigment relationships were studied in two cyanophytes (Microcystis aeruginosa and Synnechococcus elongatus), two chlorophytes (Dunaliella tertiolecta and Scenedesmus quadricauda), one cryptophyte (Rhodomonas salina), two diatoms (Cyclotella meneghiniana and Thalassiosira weissflogii) and one dinophyte (Amphidinium carterae) to assess if algal biomass could be expressed in other...
Show morePhytoplankton Chlorophyll a (CHLa), total protein, colloidal carbohydrates, storage carbohydrates and taxonomic pigment relationships were studied in two cyanophytes (Microcystis aeruginosa and Synnechococcus elongatus), two chlorophytes (Dunaliella tertiolecta and Scenedesmus quadricauda), one cryptophyte (Rhodomonas salina), two diatoms (Cyclotella meneghiniana and Thalassiosira weissflogii) and one dinophyte (Amphidinium carterae) to assess if algal biomass could be expressed in other indices than just chlorophyll a alone. Protein and carbohydrates are more useful currencies for expressing algal biomass, with respect to energy flow amongst trophic levels. These phytoplankton were grown at low light (LL = 37 (So(Bmol photons m-2 s-1), medium light (ML = 70-75 (So(Bmol photons m-2 s-1), and high light (HL= 200 (So(Bmol photons m-2 s-1)., Even though pigment per cell increased with increasing light intensity, statistically light had very little effect on the CHL a : taxonomic marker pigment ratios, as they covaried in the same way. Protein, colloidal carbohydrates and storage carbohydrates per cell all increased with increasing light intensity, but they did not covary with CHLa. Statistical data showed that light intensity had a more noticeable effect on protein: CHL a, colloidal carbohydrate: CHLa, storage CHO: CHLa, therefore a general mathematical expression for these relationships cannot be generated. This study showed that light intensity does have an influence on these biomass indices, therefore, seasonal and latitudinal formulas may be required for meaningful algal biomass estimation. However, more studies are needed if that goal is to be realized., While studying the effects of light intensity on algal pigment content and concentration, a new pigment was isolated from a cyanophyte (Scytonema hofmanii) growing between 300-1800 (So(Bmol photons¨m-2¨s-1 and from samples collected in areas of the Florida Everglades. This pigment was characterized and structurally determined to possess indolic and phenolic subunits that are characteristic of scytonemin and its derivatives. In addition, the pigment has a ketamine functionality which gives it its unique polarity and spectral properties. Based on the ultra violet/visible absorbance data, this pigment was postulated to be protecting the chlorophyll a and cytochrome Soret bands as well as a and (Sb (Bbands of the cytochromes (e.g. cytc-562) in the photosynthetic unit.
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Date Issued
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2011
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PURL
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http://purl.flvc.org/FAU/3332257
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Subject Headings
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Plant pigments, Analysis, Photosynthetic pgiments, Analysis, Plant allometry, Enviornmental geochemistry, Marine algae, Analysis
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Format
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Document (PDF)
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Title
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The evolutionary relationships ofCephalaspidea S.L. (Gastropoda: Opisthobranchia): a phylogenetic analysis.
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Creator
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Mikkelsen, Paula M., Harbor Branch Oceanographic Institute
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Date Issued
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1996
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PURL
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http://purl.flvc.org/fau/fd/FA00007416
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Subject Headings
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Cephalaspidea, Gastropoda, Opisthobranchia, Phylogeny, Cladistic analysis
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Format
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Document (PDF)
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Title
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Monophyly versus theCephalaspidea (Gastropoda, Opisthobranchia) with an analysis of traditional Cephalaspid characters.
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Creator
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Mikkelsen, Paula M., Harbor Branch Oceanographic Institute
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Date Issued
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1993
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PURL
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http://purl.flvc.org/fau/fd/FA00007259
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Subject Headings
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Phylogeny, Cladistic analysis, Homoplasy, Cephalaspidea, Opisthobranchia
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Format
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Document (PDF)
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Title
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Phylogenetic classification of the halichondrids (Porifera, Demospongiae).
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Creator
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Van Soest, Rob W. M., Diaz, Maria Cristina, Pomponi, Shirley A.
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Date Issued
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1990
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PURL
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http://purl.flvc.org/fau/fd/FA00007404
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Subject Headings
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Cladistic analysis, Halichondrida, Demospongiae, Chemotaxonomy, Phylogeny
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Format
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Document (PDF)
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Title
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The effect of face-voice synchrony on infant allocation of visual attention.
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Creator
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Minar, Nicholas J., Hansen, Amy, Lewkowicz, David J., Graduate College
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Date Issued
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2011-04-08
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PURL
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http://purl.flvc.org/fcla/dt/3165808
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Subject Headings
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Speech perception, Language acquisition, Prosodic analysis (Linguistics)
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Format
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Document (PDF)
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Title
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Geometric properties of non-differentiable contours: concurrent spatial harmonic and fractal analyses.
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Creator
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Caimi, F. M., Schmalz, Mark S., Harbor Branch Oceanographic Institute
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Date Issued
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1985
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PURL
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http://purl.flvc.org/FCLA/DT/3180376
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Subject Headings
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Fractals, Pattern recognition, Spatial analysis, Fractal geometry
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Format
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Document (PDF)
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Title
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Determination of volatile halocarbons in water by purge-closed loop gas chromatography.
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Creator
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Wang, Tsen C., Lenahan, Robert A., Harbor Branch Oceanographic Institute
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Date Issued
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1984
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PURL
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http://purl.flvc.org/FCLA/DT/3333074
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Subject Headings
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Halocarbons, Gas chromatography, Halocarbons--Analysis
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Format
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Document (PDF)
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Title
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A rapid colorimetric microassay to detect agonists/antagonists of protein kinase C based on adherence of EL-4.IL-2 cells.
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Creator
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Longley, Ross E., Harmody, Dedra K., Harbor Branch Oceanographic Institute
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Date Issued
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1991
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PURL
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http://purl.flvc.org/FCLA/DT/3351944
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Subject Headings
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Protein kinase C, Colorimetric analysis, Diglycerides
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Format
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Document (PDF)
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Title
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Sterols of the marine sponge Petrosia weinbergi: implications for the absolute configurations of the antiviral orthoesterols and weinbersterols.
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Creator
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Giner, José-Luis, Gunasekera, Sarath P., Pomponi, Shirley A.
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Date Issued
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1999
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PURL
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http://purl.flvc.org/FCLA/DT/3158771
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Subject Headings
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Sponges, Sterols --Analysis, Steroids, Biosynthesis, Marine metabolites
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Format
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Document (PDF)
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Title
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Development of a six degree of freedom buoy design and analysis program with validating data.
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Creator
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Venezia, William A., Clark, A. M., Schmitt, K. F., Harbor Branch Oceanographic Institute
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Date Issued
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1993
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PURL
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http://purl.flvc.org/FCLA/DT/3351957
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Subject Headings
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Buoys, Buoys--Design and construction, Analysis
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Format
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Document (PDF)
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Title
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Characterization of the spinster gene ortholog C13C4.5 in Caenorhabditis elegans.
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Creator
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Zahornacky, Darrin., Harriet L. Wilkes Honors College
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Abstract/Description
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The dauer larva is an alternate larval stage which allows the nematode C. elegans to survive environmestress during development. Dauer formation requires autophagy, a cellular process responsible for degrading and recycling cytoplasmic components. I investigated the role of a spinster orthiolog, C13C4.5, by examining the effects of C13C4.5 loss-of-function and by generating a transgenic strain which expressed a C13C4.5::GFP fusion protein. Under normal conditions C13C4.5::GFP is expressed...
Show moreThe dauer larva is an alternate larval stage which allows the nematode C. elegans to survive environmestress during development. Dauer formation requires autophagy, a cellular process responsible for degrading and recycling cytoplasmic components. I investigated the role of a spinster orthiolog, C13C4.5, by examining the effects of C13C4.5 loss-of-function and by generating a transgenic strain which expressed a C13C4.5::GFP fusion protein. Under normal conditions C13C4.5::GFP is expressed diffusely in the intestine, but under autophagy-promoting conditions the expression pattern becomes more punctate. This is consistent with localization of C13C4.5 to autophagolysomoes during autophagy, as has been shown for spinster in D. melanogaster. Loss of C13C4.5 function in a dauer-constitutive mutant resulted in a reduction in the proportion of animals entering into the dauer stage. Together these data suggest that C13C4.5 is involved in dauer formation and the autophagy pathway.
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Date Issued
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2012
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PURL
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http://purl.flvc.org/FAU/3359320
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Subject Headings
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Gene expression, Apoptosis, Caenorhabditis elegans, Proteins, Analysis
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Format
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Document (PDF)
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Title
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CONNECTING THE NOSE AND THE BRAIN: DEEP LEARNING FOR CHEMICAL GAS SENSING.
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Creator
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Stark, Emily Nicole, Barenholtz, Elan, Florida Atlantic University, Department of Psychology, Charles E. Schmidt College of Science
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Abstract/Description
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The success of deep learning in applications including computer vision, natural language processing, and even the game of Go can only be a orded by powerful computational resources and vast data sets. Data sets coming from the medical application are often much smaller and harder to acquire. Here a novel data approach is explained and used to demonstrate how to use deep learning as a step in data discovery, classi cation, and ultimately support for further investigation. Data sets used to...
Show moreThe success of deep learning in applications including computer vision, natural language processing, and even the game of Go can only be a orded by powerful computational resources and vast data sets. Data sets coming from the medical application are often much smaller and harder to acquire. Here a novel data approach is explained and used to demonstrate how to use deep learning as a step in data discovery, classi cation, and ultimately support for further investigation. Data sets used to illustrate these successes come from common ion-separation techniques that allow for gas samples to be quantitatively analyzed. The success of this data approach allows for the deployment of deep learning to smaller data sets.
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Date Issued
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2019
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PURL
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http://purl.flvc.org/fau/fd/FA00013416
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Subject Headings
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Deep Learning, Data sets, Gases--Analysis
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Format
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Document (PDF)
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Title
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Generalized Feature Embedding Learning for Clustering and Classication.
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Creator
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Golinko, Eric David, Zhu, Xingquan, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
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Abstract/Description
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Data comes in many di erent shapes and sizes. In real life applications it is common that data we are studying has features that are of varied data types. This may include, numerical, categorical, and text. In order to be able to model this data with machine learning algorithms, it is required that the data is typically in numeric form. Therefore, for data that is not originally numerical, it must be transformed to be able to be used as input into these algorithms. Along with this...
Show moreData comes in many di erent shapes and sizes. In real life applications it is common that data we are studying has features that are of varied data types. This may include, numerical, categorical, and text. In order to be able to model this data with machine learning algorithms, it is required that the data is typically in numeric form. Therefore, for data that is not originally numerical, it must be transformed to be able to be used as input into these algorithms. Along with this transformation it is common that data we study has many features relative to the number of samples in the data. It is often desirable to reduce the number of features that are being trained in a model to eliminate noise and reduce time in training. This problem of high dimensionality can be approached through feature selection, feature extraction, or feature embedding. Feature selection seeks to identify the most essential variables in a dataset that will lead to a parsimonious model and high performing results, while feature extraction and embedding are techniques that utilize a mathematical transformation of the data into a represented space. As a byproduct of using a new representation, we are able to reduce the dimension greatly without sacri cing performance. Oftentimes, by using embedded features we observe a gain in performance. Though extraction and embedding methods may be powerful for isolated machine learning problems, they do not always generalize well. Therefore, we are motivated to illustrate a methodology that can be applied to any data type with little pre-processing. The methods we develop can be applied in unsupervised, supervised, incremental, and deep learning contexts. Using 28 benchmark datasets as examples which include di erent data types, we construct a framework that can be applied for general machine learning tasks. The techniques we develop contribute to the eld of dimension reduction and feature embedding. Using this framework, we make additional contributions to eigendecomposition by creating an objective matrix that includes three main vital components. The rst being a class partitioned row and feature product representation of one-hot encoded data. Secondarily, the derivation of a weighted adjacency matrix based on class label relationships. Finally, by the inner product of these aforementioned values, we are able to condition the one-hot encoded data generated from the original data prior to eigenvector decomposition. The use of class partitioning and adjacency enable subsequent projections of the data to be trained more e ectively when compared side-to-side to baseline algorithm performance. Along with this improved performance, we can adjust the dimension of the subsequent data arbitrarily. In addition, we also show how these dense vectors may be used in applications to order the features of generic data for deep learning. In this dissertation, we examine a general approach to dimension reduction and feature embedding that utilizes a class partitioned row and feature representation, a weighted approach to instance similarity, and an adjacency representation. This general approach has application to unsupervised, supervised, online, and deep learning. In our experiments of 28 benchmark datasets, we show signi cant performance gains in clustering, classi cation, and training time.
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Date Issued
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2018
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PURL
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http://purl.flvc.org/fau/fd/FA00013063
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Subject Headings
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Eigenvectors--Data processing., Algorithms., Cluster analysis.
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Format
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Document (PDF)
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Title
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ANALYSIS OF COMPUTATIONAL METHODS FOR THE ELECTRONIC TRANSITIONS OF ISOBUTENE.
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Creator
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Garbaran, Avinash, Snyder, Patricia, Florida Atlantic University, Department of Chemistry and Biochemistry, Charles E. Schmidt College of Science
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Abstract/Description
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To help determine the ideal computational method for the analysis of the π → π* region in ethylene, analysis of isobutene’s computational results versus previously obtained experimental results was performed. Several time-dependent DFT calculations were performed, with eleven functionals being paired with twelve basis sets. The LSDA functional with the cc-pVQZ basis set came the closest to experimental results, with calculated transitions at 50502 cm-1, 56484 cm-1, and 59594 cm-1 compared to...
Show moreTo help determine the ideal computational method for the analysis of the π → π* region in ethylene, analysis of isobutene’s computational results versus previously obtained experimental results was performed. Several time-dependent DFT calculations were performed, with eleven functionals being paired with twelve basis sets. The LSDA functional with the cc-pVQZ basis set came the closest to experimental results, with calculated transitions at 50502 cm-1, 56484 cm-1, and 59594 cm-1 compared to experimental transition at 49875 cm-1, 55277 cm-1, and 59944 cm-1 (difference of 1.258%, 2.184%, and 0.583%, respectively). With BVP86 cc-pVQZ, calculated transitions were at 51195 cm-1, 56541 cm-1, and 59984 cm-1 (difference of 2.647%, 2.288%, and 0.067%, respectively). While LSDA cc-pVQZ was the best, it was notable how close second place came, thus the inclusion of BVP86 cc-pVQZ.
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Date Issued
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2019
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PURL
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http://purl.flvc.org/fau/fd/FA00013373
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Subject Headings
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Density functionals, Computational Chemistry, Ethylene--Analysis
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Format
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Document (PDF)
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Title
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Governance and earnings management surrounding dividend initiation.
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Creator
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Smith, Deborah Drummond., College of Business, Department of Finance
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Abstract/Description
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Essay I: Governance surrounding dividend initiation. According to the free cash flow hypothesis, managers prefer to invest surplus cash, even in value reducing projects, rather than release it to shareholders. Yet, previous studies of dividend payout conclude that managers pay more in dividends when they are entrenched, supporting the substitute model... The results indicate that initiating firms have stronger shareholder rights, in contrast with much of the prior research on continuous...
Show moreEssay I: Governance surrounding dividend initiation. According to the free cash flow hypothesis, managers prefer to invest surplus cash, even in value reducing projects, rather than release it to shareholders. Yet, previous studies of dividend payout conclude that managers pay more in dividends when they are entrenched, supporting the substitute model... The results indicate that initiating firms have stronger shareholder rights, in contrast with much of the prior research on continuous divident payout. Firms with lower entrenchment index are more likely to initiate dividends... Essay II: Earnings management surrounding dividend initiation. Prior research tests earnings management surrounding changes in dividend payout and researchers conclude that the earnings management is a means of amplifying the dividend signal to the market. However, dividend initiation is a unique event. If initiation represents signaling, similar to a dividend increase, then management will manage earnings upward. If, on the other hand, divident initiation is better explained by the free cash flow hypothesis, then initiation may be entered into with caution or reluctance by management.
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Date Issued
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2012
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PURL
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http://purl.flvc.org/fcla/dt/3362041
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Subject Headings
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Investment analysis, Portfolio management, Dividends, Econometric models
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Format
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Document (PDF)
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Title
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In the Land of Lakes and Volcanoes: A Ceramic Analysis of the Santa Cristina Site, Chinandega Nicaragua.
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Creator
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Testa, Taylor C., Brown, Clifford, Florida Atlantic University, Department of Anthropology, Dorothy F. Schmidt College of Arts and Letters
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Abstract/Description
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Nicaragua falls on the edge of what is often referred to as Mesoamerica’s “southern periphery.” Only a small amount of archaeological research has been conducted in Nicaragua, and there has been little of it in the northwestern portion of the country. Because of this, there are no local ceramic typologies or sequences which can make the identification and classification of artifacts difficult. The proposed research focuses on investigating the ceramic assemblage from the Santa Cristina...
Show moreNicaragua falls on the edge of what is often referred to as Mesoamerica’s “southern periphery.” Only a small amount of archaeological research has been conducted in Nicaragua, and there has been little of it in the northwestern portion of the country. Because of this, there are no local ceramic typologies or sequences which can make the identification and classification of artifacts difficult. The proposed research focuses on investigating the ceramic assemblage from the Santa Cristina archaeological site located in the Department of Chinandega, in northwest Nicaragua. The goal of this research will be to create a ceramic typology for the site, taking into consideration ceramic wares, groups, types, and varieties that have already been identified in other parts of Central America and defining taxa that have not been previously identified. Establishing the ceramic typology and defining taxa will help establish cultural affiliations as well as chronological markers.
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Date Issued
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2020
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PURL
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http://purl.flvc.org/fau/fd/FA00013496
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Subject Headings
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Chinandega (Nicaragua), Ceramics--Analysis, Archaeology
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Format
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Document (PDF)
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Title
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The Classical Dilemma and Probation Officer Training in Florida: An Ethnographic Content Analysis of Rules, Routines, Roles, Rituals, and Relationships.
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Creator
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Irizarry, Jose Luis, Leip, Leslie A., Florida Atlantic University, School of Public Administration, Dorothy F. Schmidt College of Arts and Letters
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Abstract/Description
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American bureaucracies are often assigned inconsistent goals, expectations, roles, and functions (Goodsell, 2004; Lipsky, 2010), exemplified in probation by Klockars’ (1972) classical dilemma of corrections that describes a punitive-rehabilitative dichotomy. A failure to prepare bureaucrats in corrections to address the classical dilemma this results in probation officers (POs) making decisions between and among competing options that consequently generally emphasize only one of the primary...
Show moreAmerican bureaucracies are often assigned inconsistent goals, expectations, roles, and functions (Goodsell, 2004; Lipsky, 2010), exemplified in probation by Klockars’ (1972) classical dilemma of corrections that describes a punitive-rehabilitative dichotomy. A failure to prepare bureaucrats in corrections to address the classical dilemma this results in probation officers (POs) making decisions between and among competing options that consequently generally emphasize only one of the primary goals of probation (Ellsworth, 1990). This dissertation offers insight into and prompts rethinking of how corrections agencies prepare POs to address the classical dilemma. Few studies focus on how organizations educate POs to address the classical dilemma. This dissertation applies ethnographic content analysis to examine the messages communicated to correctional probation officers in the 95 lessons of the curriculum used by Florida Department of Corrections (FDC) to train new officers. To analyze the data and the meaning conveyed by the FDC I applied Saldana’s (2016) 5Rs framework of rules, routines, roles, rituals, and relationships.
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Date Issued
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2020
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PURL
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http://purl.flvc.org/fau/fd/FA00013577
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Subject Headings
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Probation officers--Training of, Content analysis
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Format
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Document (PDF)
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Title
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Unearthing.
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Creator
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Hobbie, Erin., Dorothy F. Schmidt College of Arts and Letters, Department of English
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Abstract/Description
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Unearthing is a hybrid of nonfiction genres, and follows a narrator as she attempts to piece together past and present memories and meditations about family history, travel, and the idea of home. Using an orchid as a metaphor for someone who is searching for home, Unearthing attempts to expose in the author what might also be found in the reader, an exploration of what is meant by home. By following a trail of biography, personal narrative, and memoir, the reader is given every opportunity to...
Show moreUnearthing is a hybrid of nonfiction genres, and follows a narrator as she attempts to piece together past and present memories and meditations about family history, travel, and the idea of home. Using an orchid as a metaphor for someone who is searching for home, Unearthing attempts to expose in the author what might also be found in the reader, an exploration of what is meant by home. By following a trail of biography, personal narrative, and memoir, the reader is given every opportunity to identify with the narrator's struggle with the idea of rootlessness and rootedness, travel and home.
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Date Issued
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2012
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PURL
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http://purl.flvc.org/FAU/3342109
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Subject Headings
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Symbolism in literature, Metonyms, Discourse analysis
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Format
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Document (PDF)
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Title
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SEAWALL DETECTION IN FLORIDA COASTAL AREA FROM HIGH RESOLUTION IMAGERY USING MACHINE LEARNING AND OBIA.
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Creator
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Paudel, Sanjaya, Su, Hongbo, Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
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Abstract/Description
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In this thesis, a methodology and framework were created to detect the seawalls accurately and efficiently in low coastal areas and was evaluated in the study area of Hallandale Beach City, Broward County, Florida. Aerial images collected from the Florida Department of Transportation (FDOT) were processed using eCognition Developer software for Multi-Resolution Segmentation and Classification of objects. Two classification approaches, pixel-based image analysis, and the object-based image...
Show moreIn this thesis, a methodology and framework were created to detect the seawalls accurately and efficiently in low coastal areas and was evaluated in the study area of Hallandale Beach City, Broward County, Florida. Aerial images collected from the Florida Department of Transportation (FDOT) were processed using eCognition Developer software for Multi-Resolution Segmentation and Classification of objects. Two classification approaches, pixel-based image analysis, and the object-based image analysis (OBIA) method were applied for image classification. However, Pixel based classification was discarded for having less accuracy in output. Three techniques within object-based classification-machine learning technique, knowledge-based technique and machine learning followed by knowledge-based technique were used to compare the most efficient method of classification. While performing the machine learning technique, three algorithms: Random Forest, support vector machine and decision tree were applied to test the best algorithm. Of all the approaches used, the combination of machine learning and a knowledge-based method was able to map the sea wall effectively.
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Date Issued
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2021
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PURL
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http://purl.flvc.org/fau/fd/FA00013802
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Subject Headings
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Image analysis, Coasts--Florida, Machine learning
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Format
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Document (PDF)
Pages