Browsing by Subject "Mathematical models"

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    Average Consensus in Wireless Sensor Networks with Probabilistic Network Links
    (2010) Saed, Steve; Li, Lingxi; Kim, Dongsoo S.; King, Brian
    This study proposes and evaluates an average consensus scheme for wireless sensor networks. For this purpose, two communication error models, the fading signal error model and approximated fading signal error model, are introduced and incorporated into the proposed decentralized average consensus scheme. Also, a mathematical analysis is introduced to derive the approximated fading signal model from the fading signal model. Finally, differnt simulation scenarios are introduced and their results analyzed to evaluate the performance of the proposed scheme and its effectiveness in meeting the needs of wireless sensor networks.
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    COMPUTATIONAL STUDY OF SURFACE-SEGREGATED PT ALLOY CATALYSTS FOR OXYGEN REDUCTION REACTION
    (2010-07-27T19:21:50Z) Xiao, Chan; Chen, Rongrong; EI-Mounayri, Hazim; Wang, Guofeng
    In this thesis two research objectives have been accomplished using computational simulation techniques. (1) The surface segregation phenomena in the surfaces of (111), unreconstructed (110) and reconstructed (1x2) missing row (110) surfaces of Pt-Ni and Pt-Co disordered alloys have been accurately predicted using Monte Carlo (MC) simulation method, and (2) the configuration and energy of the adsorption of O, O2, OH, and H2O molecules which are presented in oxygen reduction reaction (ORR) on the surface of pure Pt and surface-segregated Pt-binary alloys (i.e., Pt-Ni, Pt-Co and Pt-Fe) have been determined using density functional theory (DFT) calculations. This thesis yields some guiding principles for designing novel catalysts for proton exchange membrane fuel cells. The Pt concentration profiles of the surfaces of Pt-Ni and Pt-Co alloys were attained from the MC simulations in which the system energy was evaluated through the developed modified embedded atom method (MEAM) for Pt-Ni and Pt-Co alloys. It was found from our simulations that the Pt atoms strongly segregate to the outermost layer and the Ni atoms segregate to the second sub-layer in the (111) surface of both Pt-Ni and Pt-Co alloys. When Pt concentration is higher than 75 at.%, pure Pt top layer could be formed in the outermost layer (111) surface of both alloys. Moreover, segregation reversal phenomenon (Ni atoms segregating to the outermost layer while Pt atoms to the second sub-layer) was observed in our MC simulations of unreconstructed (110) surface of Pt-Ni alloys. In contrast, a Pt enriched outermost surface layer was found in a Pt-Ni reconstructed (1x2) missing row (110) surface. Our MC simulation results agree well with published experimental observations. In addition, adsorption of atomic and molecular oxygen, water and hydroxyl on the (111) and (100) surfaces of pure Pt and Pt-based alloys (Pt-Ni, Pt-Co and Pt-Fe) were studied using spin DFT method and assuming a coverage of 0.25 monolayer. Both the optimized configurations and the corresponding adsorption energies for each species were obtained in this study. In particular, we elucidated the influence of the adsorption energies of atomic oxygen and OH on the activity for ORR on Pt binary alloy catalysts in acidic environment. The calculated adsorption energies of atomic oxygen on the (111) surfaces of pure Pt, Pt-Ni, Pt-Co and Pt-Fe are -3.967 eV, -3.502 eV, -3.378 eV and -3.191 eV, respectively. The calculated adsorption energies of hydroxyl on the (111) surfaces of pure Pt, Pt-Ni, Pt-Co and Pt-Fe are -2.384 eV, -2.153 eV, -2.217 eV and -2.098 eV, respectively. The interaction between the adsorbed atomic and hydroxyl and the corresponding (111) surface becomes weaker for the surface-segregated alloys compared to pure Pt catalyst. The same results were obtained for the (100) surfaces.
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    Ocular blood flow as a clinical observation: Value, limitations and data analysis
    (Elsevier, 2020) Harris, Alon; Guidoboni, Giovanna; Siesky, Brent; Mathew, Sunu; Verticchio Vercellin, Alice C.; Rowe, Lucas; Arciero, Julia; Radiology and Imaging Sciences, School of Medicine
    Alterations in ocular blood flow have been identified as important risk factors for the onset and progression of numerous diseases of the eye. In particular, several population-based and longitudinal-based studies have provided compelling evidence of hemodynamic biomarkers as independent risk factors for ocular disease throughout several different geographic regions. Despite this evidence, the relative contribution of blood flow to ocular physiology and pathology in synergy with other risk factors and comorbidities (e.g., age, gender, race, diabetes and hypertension) remains uncertain. There is currently no gold standard for assessing all relevant vascular beds in the eye, and the heterogeneous vascular biomarkers derived from multiple ocular imaging technologies are non-interchangeable and difficult to interpret as a whole. As a result of these disease complexities and imaging limitations, standard statistical methods often yield inconsistent results across studies and are unable to quantify or explain a patient's overall risk for ocular disease. Combining mathematical modeling with artificial intelligence holds great promise for advancing data analysis in ophthalmology and enabling individualized risk assessment from diverse, multi-input clinical and demographic biomarkers. Mechanism-driven mathematical modeling makes virtual laboratories available to investigate pathogenic mechanisms, advance diagnostic ability and improve disease management. Artificial intelligence provides a novel method for utilizing a vast amount of data from a wide range of patient types to diagnose and monitor ocular disease. This article reviews the state of the art and major unanswered questions related to ocular vascular anatomy and physiology, ocular imaging techniques, clinical findings in glaucoma and other eye diseases, and mechanistic modeling predictions, while laying a path for integrating clinical observations with mathematical models and artificial intelligence. Viable alternatives for integrated data analysis are proposed that aim to overcome the limitations of standard statistical approaches and enable individually tailored precision medicine in ophthalmology.