Graph measures and network robustness

Network robustness research aims at finding a measure to quantify network robustness. Once such a measure has been established, we will be able to compare networks, to improve existing networks and to design new networks that are able to continue to perform well when it is subject to failures or attacks. In this paper we survey a large amount of robustness measures on simple, undirected and unweighted graphs, in order to offer a tool for network administrators to evaluate and improve the robustness of their network. The measures discussed in this paper are based on the concepts of connectivity (including reliability polynomials), distance, betweenness and clustering. Some other measures are notions from spectral graph theory, more precisely, they are functions of the Laplacian eigenvalues. In addition to surveying these graph measures, the paper also contains a discussion of their functionality as a measure for topological network robustness

Biomass to Biochar and Fast Pyrolysis Oil Fractions

Cody Ellens - Avello Bioenergy, Inc.Ope

Stochastic methods for measurement-based network control

The main task of network administrators is to ensure that their network functions properly. Whether they manage a telecommunication or a road network, they generally base their decisions on the analysis of measurement data. Inspired by such network control applications, this dissertation investigates several stochastic modelling techniques for data analysis. The focus is on two areas within the field of stochastic processes: change point detection and queueing theory. Part I deals with statistical methods for the automatic detection of change points, being changes in the probability distribution underlying a data sequence. This part starts with a review of existing change point detection methods for data sequences consisting of independent observations. The main contribution of this part is the generalisation of the classic cusum method to account for dependence within data sequences. We analyse the false alarm probability of the resulting methods using a large deviations approach. The part also discusses numerical tests of the new methods and a cyber attack detection application, in which we investigate how to detect dns tunnels. The main contribution of Part II is the application of queueing models (probabilistic models for waiting lines) to situations in which the system to be controlled can only be observed partially. We consider two types of partial information. Firstly, we develop a procedure to get insight into the performance of queueing systems between consecutive system-state measurements and apply it in a numerical study, which was motivated by capacity management in cable access networks. Secondly, inspired by dynamic road control applications, we study routing policies in a queueing system for which just part of the jobs are observable and controllable

Preferential adsorption of high density lipoprotein (HDL) in blood plasma/polymer interaction

A few studies on the adsorption of plasma proteins to polymeric surfaces show that major plasma proteins: albumin (Alb), fibrinogen (Fb) and immunoglobulin (IgG) are adsorbed in much smaller quantities from plasma than from protein solutions (1,2). Present results show that this difference in adsorption is due to the preferential adsorption of high density lipoprotein from plasma onto the material surfaces studied (PVC and PS)

Effect on Opioid Use following the Implementation of Evidence-Based Pain Management

Pain is the most common reason people seek healthcare. Initiatives to prevent the undertreatment of pain have resulted in overreliance on opioids to treat pain. Despite significant increases in opioid use, pain is more prevalent than ever. Additionally, devastating consequences from opioid use have resulted, such as dependence, addiction, and increasing opioid-related deaths. Research demonstrates multimodal analgesic therapy is an effective alternative to the overreliance on opioids to treat pain. Multimodal analgesia is the synergistic use of two or more analgesics with different mechanisms of action. Multimodal analgesia produces significantly more effective and efficient pain management than opioid-only therapy. This project will focus on implementing multimodal analgesia pain management practices and determine its effect on opioid use on an Observation unit

Development and Localization of Spike-Wave Seizures in Animal Models

Animal models allow for detailed investigation of neuronal function, particularly invasive localization and developmental studies not possible in humans. This thesis will review the technical challenges of simultaneous EEG-fMRI, and epileptogenesis studies in animal models, including issues related to anesthesia, movement, signal artifact, physiology, electrode compatibility, data acquisition, and data analysis, and review recent findings from simultaneous EEG-fMRI studies in epilepsy and other fields. Original research will be presented on the localization of neuronal networks involved during spike-and-wave seizures in the WAG/Rij rat, a model of human absence epilepsy. Simultaneous EEG-fMRI at 9 Tesla, complimented by parallel electrophysiology, including Multiple Unit Activity (MUA), Local Field Potential (LFP), and Cerebral Blood Flow (CBF) measurements were employed to investigate the functioning of neuronal networks. This work indicates that while BOLD signal increases in the Somaotsensory Cortex and Thalamus during SWD are associated with MUA, LFP, and CBF increases, BOLD signal decreases in the Caudate are associated with CBF decreases and relatively larger increase in LFP and smaller increase in MUA. Complimenting the localization studies, original research will also be presented on the development of spike-and-wave epilepsy in the C3H/Hej mouse, a model which will allow for more advanced genetic and molecular investigation. This work shows seizure development progressing though immature, transitional, and mature stages

Design, optimization and evaluation of a free-fall biomass fast pyrolysis reactor and its products

The focus of this work is a radiatively heated, free-fall, fast pyrolysis reactor. The reactor was designed and constructed for the production of bio-oil from the fast pyrolysis of biomass. A central composite design of experiments was performed to evaluate the novel reactor by varying four operating conditions: reactor temperature, biomass particle size, carrier gas flow rate and biomass feed rate. Maximum bio-oil yields of 72 wt % were achieved at a heater set point temperature of 600 yC, using particle sizes of 300 micron, carrier gas flow rates of 4 sL/min and Red oak biomass feed rates of 1.75 kg/hr. Optimal operating conditions were identified for maximum bio-oil yields at a heater set point temperature of 572 ˚C, feeding 240 micron sized Red oak biomass particles at 2 kg/hr. Carrier gas flow rates were not found to be significant over the 1 - 5 sL/min range tested