Phonon spectra and thermal properties of some fcc metals using embedded-atom potentials /

By employing the embedded-atom potentials of Mei et ai.[l], we have calculated the dynamical matrices and phonon dispersion curves for six fee metals (Cu,Ag,Au,Ni,Pd and Pt). We have also investigated, within the quasiharmonic approximation, some other thermal properties of these metals which depend on the phonon density of states, such as the temperature dependence of lattice constant, coefficient of linear thermal expansion, isothermal and adiabatic bulk moduli, heat capacities at constant volume and constant pressure, Griineisen parameter and Debye temperature. The computed results are compared with the experimental findings wherever possible. The comparison shows a generally good agreement between the theoretical values and experimental data for all properties except the discrepancies of phonon frequencies and Debye temperature for Pd, Pt and Au. Further, we modify the parameters of this model for Pd and Pt and obtain the phonon dispersion curves which is in good agreement with experimental data.

A framework for understanding the factors that influence spectators' recall and recognition of embedded sponsorship stimuli

Now, more than ever, sponsors of athletic events demand to see evidence of a commercial return, such as enhanced brand awareness, for their investment of cash or non-cash resources (Lough et aI., 2000). The most common way to measure the impact of perimeter signage (Le., any billboard or sign that displays a company's brand name and/or logo and which surrounds the playing area) on spectators' awareness of event sponsors has been through the use of brand name recall and recognition tests (Shilbury & Berriman, 1996). Recall testing requires spectators to list all of the sponsors they can remember seeing at, for example, an athletic event, strictly from memory and without any help (Cuneen & Hannan, 1993). With recognition testing, spectators are required to identify sponsors from a prepared list which include "dummy" brand names (i.e., sponsors that are present in the list but which do not actually sponsor the event). In order to determine whether sponsors' brand awareness objectives are being met, it is important for sport and recreation marketers to understand what influences a spectator's ability to remember (Le., recall and/or recognize) the brand names of companies who advertise on perimeter signage. The purpose this study was to examine the factors that influence spectators' recall and recognition of embedded sponsorship stimuli (i.e., company brand names on perimeter signage surrounding the play area) at a Canadian University's men's basketball game and football game. These factors included the number of games spectators attended over the course of the season (i.e., repeated exposure to sponsorship stimuli), spectators' level of involvement with the event, and spectators' level of involvement with the advertisements (i.e., perimeter signage). This study also examined the differences between recall and recognition as a means of measuring spectators' awareness of sponsors, and attempted to determine if there are sport differences in spectators' recall and recognition of perimeter signage. Upon leaving the football stadium or gymnasium, spectators were approached, at random, by trained research assistants located at each exit and asked to complete a brief survey questionnaire. Respondents completed the survey on-site. A total of 358 completed surveys were collected from spectators who attended the football (N = 277) and basketball (N = 81) games. The data suggest that football and basketball respondents recognized more sponsors' brand names than they recalled. In addition, football respondents who were highly involved with the event (i.e., those individuals who viewed attending the events as fun, interesting and exciting) attended more games over the course of the season and had significantly higher brand name recognition of sponsors who advertised on perimeter signage than those individuals with low involvement with the athletic event. Football respondents who were highly involved with the sponsors' advertisements (i.e., those individuals who viewed sponsors' perimeter signage as appealing, valuable and important) had significantly higher brand name recall of event sponsors than those individuals with low involvement with these sponsors' advertisements. Repeated exposure to perimeter signage did not have a significant influence on football or basketball respondents' recall or recognition of sponsors. Finally, the data revealed that football respondents had significantly higher recall of sponsors' brand names than basketball respondents. Conversely, basketball respondents had significantly higher recognition of sponsors' brand names than did football respondents.

Evolutionary synthesis of stochastic gene network models using feature-based search spaces

A feature-based fitness function is applied in a genetic programming system to synthesize stochastic gene regulatory network models whose behaviour is defined by a time course of protein expression levels. Typically, when targeting time series data, the fitness function is based on a sum-of-errors involving the values of the fluctuating signal. While this approach is successful in many instances, its performance can deteriorate in the presence of noise. This thesis explores a fitness measure determined from a set of statistical features characterizing the time series' sequence of values, rather than the actual values themselves. Through a series of experiments involving symbolic regression with added noise and gene regulatory network models based on the stochastic 'if-calculus, it is shown to successfully target oscillating and non-oscillating signals. This practical and versatile fitness function offers an alternate approach, worthy of consideration for use in algorithms that evaluate noisy or stochastic behaviour.

Comparison of classification ability of hyperball algorithms to neural network and k-nearest neighbour algorithms

The main focus of this thesis is to evaluate and compare Hyperbalilearning algorithm (HBL) to other learning algorithms. In this work HBL is compared to feed forward artificial neural networks using back propagation learning, K-nearest neighbor and 103 algorithms. In order to evaluate the similarity of these algorithms, we carried out three experiments using nine benchmark data sets from UCI machine learning repository. The first experiment compares HBL to other algorithms when sample size of dataset is changing. The second experiment compares HBL to other algorithms when dimensionality of data changes. The last experiment compares HBL to other algorithms according to the level of agreement to data target values. Our observations in general showed, considering classification accuracy as a measure, HBL is performing as good as most ANn variants. Additionally, we also deduced that HBL.:s classification accuracy outperforms 103's and K-nearest neighbour's for the selected data sets.

Riffing on a Theme: Faculty Experiences With Service-Learning in a Food Security Research Network in Ontario

While service-learning is often said to be beneficial for all those involved—students, community members, higher education institutions, and faculty members—there are relatively few studies of the attraction to, and effect of, service-learning on faculty members. Existing studies have tended to use a survey design, and to be based in the United States. There is a lack of information on faculty experiences with service-learning in Ontario or Canada. This qualitative case study of faculty experiences with service-learning was framed through an Appreciative Inquiry social constructionist approach. The data were drawn from interviews with 18 faculty members who belong to a Food Security Research Network (FSRN) at a university in northern Ontario, reports submitted by the network, and personal observation of a selection of network-related events. This dissertation study revealed how involvement with service-learning created opportunities for faculty learning and growth. The focus on food security and a commitment to the sustainability of local food production was found to be an ongoing attraction to service-learning and a means to engage in and integrate research and teaching on matters of personal and professional importance to these faculty members. The dissertation concludes with a discussion of the FSRN’s model and the perceived value of a themed, transdisciplinary approach to service-learning. This study highlights promising practices for involving faculty in service-learning and, in keeping with an Appreciative Inquiry approach, depicts a view of faculty work at its best.

Using Network Analysis to Understand and Advance Falls Prevention Services and Programs

The purpose of this study was to understand referral linkages that exist among falls prevention agencies in a southern Ontario region using network analysis theory. This was a single case study which included fifteen individual interviews. The data was analyzed through the constant comparative approach. Ten themes emerged and are classified into internal and external factors. Themes associated with internal factors are: 1) health professionals initiating services; 2) communication strategies; 3) formal partnerships; 4) trust; 5) program awareness; and 6) referral policies. Themes associated with external factors are: 1) client characteristics; 2) primary and community care collaboration; 3) networking; and 4) funding. Recommendations to improve the referral pathway are: 1) electronic database; 2) electronic referral forms; 3) educating office staff; and 4) education days. This study outlined the benefit of using network analysis to understand referral pathways and the importance of implementing strategies that will improve falls prevention referral pathways.

A game-theoretic network formation model

We study the dynamics of a game-theoretic network formation model that yields large-scale small-world networks. So far, mostly stochastic frameworks have been utilized to explain the emergence of these networks. On the other hand, it is natural to seek for game-theoretic network formation models in which links are formed due to strategic behaviors of individuals, rather than based on probabilities. Inspired by Even-Dar and Kearns (2007), we consider a more realistic model in which the cost of establishing each link is dynamically determined during the course of the game. Moreover, players are allowed to put transfer payments on the formation of links. Also, they must pay a maintenance cost to sustain their direct links during the game. We show that there is a small diameter of at most 4 in the general set of equilibrium networks in our model. Unlike earlier model, not only the existence of equilibrium networks is guaranteed in our model, but also these networks coincide with the outcomes of pairwise Nash equilibrium in network formation. Furthermore, we provide a network formation simulation that generates small-world networks. We also analyze the impact of locating players in a hierarchical structure by constructing a strategic model, where a complete b-ary tree is the seed network.

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.

Untangling the Organizational Ambiguities in Supply Chain Risk Recognition, Assessment, and Response: The Role of Network Embeddedness

In this thesis, I focus on supply chain risk related ambiguity, which represents the ambiguities firms exhibit in recognizing, assessing, and responding to supply chain disruptions. I, primarily, argue that ambiguities associated with recognizing and responding to supply chain risk are information gathering and processing problems. Guided by the theoretical perspective of bounded rationality, I propose a typology of supply chain risk related ambiguity with four distinct dimensions. I, also, argue that the major contributor to risk related ambiguity is often the environment, specifically the web of suppliers. Hence, I focus on the characteristics of these supplier networks to examine the sources of ambiguity. I define three distinct elements of network embeddedness – relational, structural, and positional embeddedness – and argue that the ambiguity faced by a firm in appropriately identifying the nature or impacts of major disruptions is a function of these network properties. Based on a survey of large North American manufacturing firms, I found that the extent of the relational ties a firm has and its position in the network are significantly related to supply chain risk related ambiguity. However, this study did not provide any significant support for the hypothesized relationship between structural embeddedness and ambiguity. My research contributes towards the study of supply chain disruptions by using the idea of bounded rationality to understand supply chain risk related ambiguity and by providing evidence that the structure of supply chain networks influences the organizational understanding of and responses to supply chain disruptions.

Understanding Network Governance: A Case Study Exploration of Active Canada 20/20

Consistent with the governance shift towards network forms of governance, a number of new social movements have formed in response to the declining levels of physical activity in the Western world. One such movement is Active Canada 20/20: A Physical Activity Strategy and Change Agenda for Canada. Network governance is employed as the theoretical framework for this case study exploration of Active Canada 20/20 and the political landscape surrounding its development and implementation. Semi-structured interviews were conducted in addition to document/policy analysis and direct observations. Analysis of the data resulted in three overarching themes – the defining characteristics of network governance, the political landscape, and intersectoral linkages – that interconnect multifariously based the nature of the Canadian federal government and its relationship with the voluntary sector for physical activity. Despite progress in driving Active Canada 20/20 forward, entrenched dynamics of power need to be navigated within the political landscape surrounding network governance.

Identification of Network-Based Risk Factors Associated with Gonorrhea

The evolving antimicrobial resistance coupled with a recent increase in incidence highlights the importance of reducing gonococcal transmission. Establishing novel risk factors associated with gonorrhea facilitates the development of appropriate prevention and disease control strategies. Sexual Network Analysis (NA), a novel research technique used to further understand sexually transmitted infections, was used to identify network-based risk factors in a defined region in Ontario, Canada experiencing an increase in the incidence of gonorrhea. Linear network structures were identified as important reservoirs of gonococcal transmission. Additionally, a significant association between a central network position and gonorrhea was observed. The central participants were more likely to be younger, report a greater number of risk factors, engage in anonymous sex, have multiple sex partners in the past six months and have sex with the same sex. The network-based risk factors identified through sexual NA, serving as a method of analyzing local surveillance data, support the development of strategies aimed at reducing gonococcal spread.

Community Detection in Multi-Layer Networks

In the scope of the current thesis we review and analyse networks that are formed by nodes with several attributes. We suppose that different layers of communities are embedded in such networks, besides each of the layers is connected with nodes' attributes. For example, examine one of a variety of online social networks: an user participates in a plurality of different groups/communities – schoolfellows, colleagues, clients, etc. We introduce a detection algorithm for the above-mentioned communities. Normally the result of the detection is the community supplemented just by the most dominant attribute, disregarding others. We propose an algorithm that bypasses dominant communities and detects communities which are formed by other nodes' attributes. We also review formation models of the attributed networks and present a Human Communication Network (HCN) model. We introduce a High School Texting Network (HSTN) and examine our methods for that network.

The Lived Experience of Nursing Students With Formative Assessment Formally Embedded in Clinical Courses: A Transcendental Phenomenological Study

The potential of formative assessment (FA) for informing learning in classroom-based nursing courses is clearly established in the literature; however, research on FA in clinical courses remains scarce. This inquiry explored the lived experience of nursing students using transcendental phenomenology and described the phenomenon of being assessed in clinical courses. The research question guiding the study was: How is the phenomenon of assessment experienced by nursing students when FA is formally embedded in clinical courses? Inherent in this question were the following issues: (a) the meaning of clinical experiences for nursing students, (b) the meaning of being assessed through FA, and (c) what it is like to be assessed when FA is formally embedded within clinical experiences. The noematic themes that illuminated the whatness of the participants’ experience were (a) enabled cognitive activity, (b) useful feedback, (c) freedom to be, (d) enhanced focus, (e) stress moderator, and (f) respectful mentorship. The noetic themes associated with how the phenomenon was experienced were related to bodyhood, temporality, spatiality, and relationship to others. The results suggest a fundamental paradigm shift from traditional nursing education to a more pervasive integration of FA in clinical courses so that students have time to learn before being graded on their practice. Furthermore, this inquiry and the literature consulted provide evidence that using cognitive science theory to inform and reform clinical nursing education is a timely option to address the repeated calls from nursing leaders to modernize nursing education. This inquiry contributes to reduce our reliance on assumptions derived from research on FA in nursing classrooms and provides evidence based on the reality of using formative assessment in clinical courses. Recommendations for future research are presented.