Properties and algorithms of the hyper-star graph and its related graphs

The hyper-star interconnection network was proposed in 2002 to overcome the drawbacks of the hypercube and its variations concerning the network cost, which is defined by the product of the degree and the diameter. Some properties of the graph such as connectivity, symmetry properties, embedding properties have been studied by other researchers, routing and broadcasting algorithms have also been designed. This thesis studies the hyper-star graph from both the topological and algorithmic point of view. For the topological properties, we try to establish relationships between hyper-star graphs with other known graphs. We also give a formal equation for the surface area of the graph. Another topological property we are interested in is the Hamiltonicity problem of this graph. For the algorithms, we design an all-port broadcasting algorithm and a single-port neighbourhood broadcasting algorithm for the regular form of the hyper-star graphs. These algorithms are both optimal time-wise. Furthermore, we prove that the folded hyper-star, a variation of the hyper-star, to be maixmally fault-tolerant.

Flow regime prediction via froude number calculation in a rock-bedded stream

Mathematical predictions of flow conditions along a steep gradient rock bedded stream are examined. Stream gage discharge data and Manning's Equation are used to calculate alternative velocities, and subsequently Froude Numbers, assuming varying values of velocity coefficient, full depth or depth adjusted for vertical flow separation. Comparison of the results with photos show that Froude Numbers calculated from velocities derived from Manning's Equation, assuming a velocity coefficient of 1.30 and full depth, most accurately predict flow conditions, when supercritical flow is defined as Froude Number values above 0.84. Calculated Froude Number values between 0.8 and 1.1 correlate well with observed transitional flow, defined as the first appearance of small diagonal waves. Transitions from subcritical through transitional to clearly supercritical flow are predictable. Froude Number contour maps reveal a sinuous rise and fall of values reminiscent of pool riffle energy distribution.

Automatic Inference of Graph Models for Complex Networks with Genetic Programming

Complex networks can arise naturally and spontaneously from all things that act as a part of a larger system. From the patterns of socialization between people to the way biological systems organize themselves, complex networks are ubiquitous, but are currently poorly understood. A number of algorithms, designed by humans, have been proposed to describe the organizational behaviour of real-world networks. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. The algorithms, called graph models, represent significant human effort. Deriving accurate graph models is non-trivial, time-intensive, challenging and may only yield useful results for very specific phenomena. An automated approach can greatly reduce the human effort required and if effective, provide a valuable tool for understanding the large decentralized systems of interrelated things around us. To the best of the author's knowledge this thesis proposes the first method for the automatic inference of graph models for complex networks with varied properties, with and without community structure. Furthermore, to the best of the author's knowledge it is the first application of genetic programming for the automatic inference of graph models. The system and methodology was tested against benchmark data, and was shown to be capable of reproducing close approximations to well-known algorithms designed by humans. Furthermore, when used to infer a model for real biological data the resulting model was more representative than models currently used in the literature.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.

New conserved vorticity integrals for moving surfaces in multi-dimensional fluid flow

For inviscid fluid flow in any n-dimensional Riemannian manifold, new conserved vorticity integrals generalizing helicity, enstrophy, and entropy circulation are derived for lower-dimensional surfaces that move along fluid streamlines. Conditions are determined for which the integrals yield constants of motion for the fluid. In the case when an inviscid fluid is isentropic, these new constants of motion generalize Kelvin’s circulation theorem from closed loops to closed surfaces of any dimension.

Object-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

Complex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.

Examining the roles of core and local temperature on forearm skin blood flow

The interaction between local and reflexive control of skin blood flow (SkBF) is unclear. This thesis isolated the roles of rectal (Tre) and local (Tloc) temperature on forearm SkBF regulation at normal and elevated body temperatures, and to investigate the interaction between local and reflexive SkBF control. While either normothermic (Tre ~37.0°C) or hyperthermic (∆Tre +1.1°C), SkBF was assessed on the dorsal aspect of each forearm in 10 participants while Tloc was manipulated in an A-B-A-B fashion between neutral (33.0°C) and hot (38.5°C). Finally, local heating to 44°C was performed to elicit maximal SkBF. Data are presented as a percentage of maximal cutaneous vascular conductance (CVC), calculated as laser-Doppler flux divided by mean arterial pressure. Tloc manipulations performed during normothermia had significantly greater effects on CVC than during hyperthermia. The decreased modification to SkBF from the Tloc changes during hyperthermia suggests that strong reflexive vasodilation attenuates local SkBF control mechanisms.

Effects of Cerebral Blood Flow and PETCO2 on Cognitive Function During Passive Heat Stress

This thesis tested whether cognitive performance during passive heat stress may be affected by changes in cerebrovascular variables as opposed to strictly thermally-induced changes. A pharmacological reduction in cerebral blood flow (CBF) using indomethacin along with a hypocapnia-induced CBF reduction during passive heat stress (Tre ~1.5°C above baseline) were used to investigate any cerebrovascular-mediated changes in cognitive performance. Repeated measures analysis of variance indicated that One-Touch Stockings of Cambridge (OTS) performance was not affected by a significant reduction in CBF during passive heat stress. More specifically, OTS accuracy measures did not change as a result of either a reduction in CBF or increasing passive heat stress. However, it was found that OTS response time indices improved with increasing passive heat stress independent of CBF changes. In conclusion, a significant reduction in CBF does not cause additional changes in performance of an executive functioning task during severe passive heat stress.

The Influence of Cerebral Blood Flow and Carbon Dioxide on Neuromuscular Responses During Environmental Stress

Although reductions in cerebral blood flow (CBF) may be implicated in the development of central fatigue during environmental stress, the contribution from hypocapnia-induced reductions in CBF versus reductions in CBF per se has yet to be isolated. The current research program examined the influence of CBF, with and without consequent hypocapnia, on neuromuscular responses during hypoxia and passive heat stress. To this end, neuromuscular responses, as indicated by motor evoked potentials (MEP), maximal M-wave (Mmax) and cortical voluntary activation (cVA) of the flexor carpi radialis muscle during isometric wrist flexion, was assessed in three separate projects: 1) hypocapnia, independent of concomitant reductions in CBF; 2) altered CBF during severe hypoxia and; 3) thermal hyperpnea-mediated reductions in CBF, independent of hypocapnia. All projects employed a custom-built dynamic end-tidal forcing system to control end-tidal PCO2 (PETCO2), independent of the prevailing environmental conditions, and cyclooxygenase inhibition using indomethacin (Indomethacin, 1.2 mg·Kg-1) to selectively reduce CBF (estimated using transcranial Doppler ultrasound) without changes in PETCO2. A primary finding of the present research program is that the excitability of the corticospinal tract is inherently sensitive to changes in PaCO2, as demonstrated by a 12% increase in MEP amplitude in response to moderate hypocapnia. Conversely, CBF mediated reductions in cerebral O2 delivery appear to decrease corticospinal excitability, as indicated by a 51-64% and 4% decrease in MEP amplitude in response to hypoxia and passive heat stress, respectively. The collective evidence from this research program suggests that impaired voluntary activation is associated with reductions in CBF; however, it must be noted that changes in cVA were not linearly correlated with changes in CBF. Therefore, other factors independent of CBF, such as increased perception of effort, distress or discomfort, may have contributed to the reductions in cVA. Despite the functional association between reductions in CBF and hypocapnia, both variables have distinct and independent influence on the neuromuscular system. Therefore, future studies should control or acknowledge the separate mechanistic influence of these two factors.

Relación de estado del flow y las estrategias de enseñanza significativa del docente.

Tesis (Maestría en Docencia con orientación en Educación Media Superior) UANL, 2014.

Standardization of cytokine flow cytometry assays

Affiliation: Faculté de médecine, Université de Montréal & CANVAC

Production Linkages Between Informal and Formal Activities Considering Domestic and Imported Inputs: an Application of the Minimal-Flow-Analysis Method to Senegal.

We apply to the Senegalese input-output matrix of 1990, disagregated into formal and informal activities, a recently designed structural analytical method (Minimal-Flow-Analysis) which permits to depict the direct and indirect production likanges existing between activities.

Approche efficace pour la conception des architectures multiprocesseurs sur puce électronique

Les systèmes multiprocesseurs sur puce électronique (On-Chip Multiprocessor [OCM]) sont considérés comme les meilleures structures pour occuper l'espace disponible sur les circuits intégrés actuels. Dans nos travaux, nous nous intéressons à un modèle architectural, appelé architecture isométrique de systèmes multiprocesseurs sur puce, qui permet d'évaluer, de prédire et d'optimiser les systèmes OCM en misant sur une organisation efficace des nœuds (processeurs et mémoires), et à des méthodologies qui permettent d'utiliser efficacement ces architectures. Dans la première partie de la thèse, nous nous intéressons à la topologie du modèle et nous proposons une architecture qui permet d'utiliser efficacement et massivement les mémoires sur la puce. Les processeurs et les mémoires sont organisés selon une approche isométrique qui consiste à rapprocher les données des processus plutôt que d'optimiser les transferts entre les processeurs et les mémoires disposés de manière conventionnelle. L'architecture est un modèle maillé en trois dimensions. La disposition des unités sur ce modèle est inspirée de la structure cristalline du chlorure de sodium (NaCl), où chaque processeur peut accéder à six mémoires à la fois et où chaque mémoire peut communiquer avec autant de processeurs à la fois. Dans la deuxième partie de notre travail, nous nous intéressons à une méthodologie de décomposition où le nombre de nœuds du modèle est idéal et peut être déterminé à partir d'une spécification matricielle de l'application qui est traitée par le modèle proposé. Sachant que la performance d'un modèle dépend de la quantité de flot de données échangées entre ses unités, en l'occurrence leur nombre, et notre but étant de garantir une bonne performance de calcul en fonction de l'application traitée, nous proposons de trouver le nombre idéal de processeurs et de mémoires du système à construire. Aussi, considérons-nous la décomposition de la spécification du modèle à construire ou de l'application à traiter en fonction de l'équilibre de charge des unités. Nous proposons ainsi une approche de décomposition sur trois points : la transformation de la spécification ou de l'application en une matrice d'incidence dont les éléments sont les flots de données entre les processus et les données, une nouvelle méthodologie basée sur le problème de la formation des cellules (Cell Formation Problem [CFP]), et un équilibre de charge de processus dans les processeurs et de données dans les mémoires. Dans la troisième partie, toujours dans le souci de concevoir un système efficace et performant, nous nous intéressons à l'affectation des processeurs et des mémoires par une méthodologie en deux étapes. Dans un premier temps, nous affectons des unités aux nœuds du système, considéré ici comme un graphe non orienté, et dans un deuxième temps, nous affectons des valeurs aux arcs de ce graphe. Pour l'affectation, nous proposons une modélisation des applications décomposées en utilisant une approche matricielle et l'utilisation du problème d'affectation quadratique (Quadratic Assignment Problem [QAP]). Pour l'affectation de valeurs aux arcs, nous proposons une approche de perturbation graduelle, afin de chercher la meilleure combinaison du coût de l'affectation, ceci en respectant certains paramètres comme la température, la dissipation de chaleur, la consommation d'énergie et la surface occupée par la puce. Le but ultime de ce travail est de proposer aux architectes de systèmes multiprocesseurs sur puce une méthodologie non traditionnelle et un outil systématique et efficace d'aide à la conception dès la phase de la spécification fonctionnelle du système.