No description available.

Social housing wait lists are indicative of the need for affordable housing in communities across Ontario. Growing wait lists also suggest that existing social housing supply and programs are not a solution to immediate or foreseeable housing problems for most low-income households. As a result, many households turn to shelters or make do with what they are able to find in the private market, often spending more than 30% of their income on rent. The focus of this study is one-person households under the age of 65 who make up approximately 40% of the applicants on Ontario social housing wait lists. This cohort has the longest wait times. What are the housing experiences of this demographic while they wait? How do municipalities respond and what do community advocates say about this response? This study addresses these questions through key informant interviews conducted with single non-senior social housing applicants, community advocates and policy-makers, doing so comparatively for two CMAs: Guelph and Kingston. Examining homelessness through a critical lens of neoliberalism, this study concludes with policy recommendations to address urban housing issues for low-income singles.

Thesis (M.S. in Mechanical Engineering)--Naval Postgraduate School, September 2008. / Thesis Advisor(s): Kwon, Young W. "September 2008." Description based on title screen as viewed on November 3, 2008. Includes bibliographical references (p. 51-52 ). Also available in print.

Thesis (D. Min)--Mid-America Baptist Theological Seminary, 1999. / Abstract. Includes bibliographical references (leaves 129-135).

Thesis--University of FLorida. / Description based on print version record. Typescript. Bibliography: leaves 140-142.

No description available.

In the realm of machine learning research and application, binary classification algorithms, i.e. algorithms that attempt to induce discriminant functions between two categories of data, reign supreme. Their fundamental property is the reliance on the availability of data from all known categories in order to induce functions that can offer acceptable levels of accuracy. Unfortunately, data from so-called ``real-world'' domains sometimes do not satisfy this property. In order to tackle this, researchers focus on methods such as sampling and cost-sensitive classification to make the data more conducive for binary classifiers. However, as this thesis shall argue, there are scenarios in which even such explicit methods to rectify distributions fail. In such cases, one-class classification algorithms become a practical alternative. Unfortunately, if the domain is inherently complex, the advantage that they offer over binary classifiers becomes diminished. The work in this thesis addresses this issue, and builds a framework that allows for one-class algorithms to build efficient classifiers. In particular, this thesis introduces the notion of learning along the lines sub-concepts in the domain; the complexity in domains arises due to the presence of sub-concepts, and by learning over them explicitly rather than on the entire domain as a whole, we can produce powerful one-class classification systems. The level of knowledge regarding these sub-concepts will naturally vary by domain, and thus we develop three distinct frameworks that take the amount of domain knowledge available into account. We demonstrate these frameworks over three real-world domains. The first domain we consider is that of biometric authentication via a users swipe on a smartphone. We identify sub-concepts based on a users motion, and given that modern smartphones employ sensors that can identify motion, during learning as well as application, sub-concepts can be identified explicitly, and novel instances can be processed by the appropriate one-class classifier. The second domain is that of invasive isotope detection via gamma-ray spectra. The sub-concepts are based on environmental factors; however, the hardware employed cannot detect such concepts, and quantifying the precise source that creates these sub-concepts is difficult to ascertain. To remedy this, we introduce a novel framework in which we employ a sub-concept detector by means of a multi-class classifier, which pre-processes novel instances in order to send them to the correct one-class classifier. The third domain is that of compliance verification of the Comprehensive Test Ban Treaty (CTBT) through Xenon isotope measurements. This domain presents the worst case where sub-concepts are not known. To this end, we employ a generic version of our framework in which we simply cluster the domain and build classifiers over each cluster. In all cases, we demonstrate that learning in the context of domain concepts greatly improves the performance of one-class classifiers.

Introduction: Carbapenems and colistin are the last-line antibiotics for treating Gram-negative bacterial infections. However, increasing reports of resistance to these antibiotics is being reported in clinical settings, the environment and in animals. In this paper, we describe the molecular epidemiology and resistance mechanisms of colistin and carbapenem resistance in clinical, veterinary, and environmental Enterobacterales isolates in Pretoria, South Africa. Method: One hundred VITEK®-2-confirmed colistin and carbapenem-resistant clinical isolates were collected from the departmental isolate bank at the National Health Laboratory Service. A total of 88 porcine (stool) and 11 environmental (effluents) samples were collected in November 2018 and again in March 2019 from a farm in Pretoria. Both the porcine and environmental samples were screened using Eosin methylene blue agar with colistin and ertapenem disks. All isolates were identified and a minimum inhibitory concentration of colistin and carbapenems was determined using the MicroScan® WalkAway system. Isolates resistant to colistin were confirmed by the broth microdilution method. Isolates phenotypically resistant to colistin and carbapenems were selected for whole genome sequencing to determine the resistome and phylogenetic trees were drawn to determine the relatedness of isolates. Results: A total of 275 Gram-negative isolates were identified from the clinical (100), environmental (57) and veterinary (118) samples using the MicroScan® WalkAway system. The MicroScan® WalkAway system’s minimum inhibitory concentration results for clinical isolates revealed 88% and 93% resistance to colistin and carbapenems, respectively. BMD was found to be more reliable in all isolates, and it recorded higher MICs (increased resistance) than the MicroScan® WalkAway system. Overall, colistin susceptibility was higher among animal isolates compared to the clinical and environmental samples. Genomic analysis identified several resistance genes associated with resistance among the isolates and the CTX-M family were the dominant resistance genes. Phylogenomic analysis demonstrated closer evolutionary relationship between EB008 (environment), SW10B (animals), and C080 and C084 (both humans) strains as well as with strains from the United States of America, Canada, China, Russia and Durban (South Africa). Conclusion: The study established multiple resistance genes from different antibiotics to mediate resistance in Enterobacterales isolates from humans, animals and the environment. The presence of carbapenemases in animals is alarming and poses a public health concern. Strains EB008 (environment), SW10B (animals) and C080 and C084 (both human) were phylogenetically related with strains from the United States of America, China and Durban (South Africa) more commonly. Therefore, One Health approach studies are significant to ascertain colistin and carbapenem transmission from human to animals/the environment and vice versa to combat increasing resistance in Enterobacterales. / Dissertation (MSc)--University of Pretoria, 2020. / National Research Foundation (NRF) / National Health Laboratory Service research grant / Medical Microbiology / MSc / Unrestricted

The Ensemble Kalman Filter (EnKF) is a popular data assimilation method for state-parameter estimation. Following a sequential assimilation strategy, it breaks the problem into alternating cycles of forecast and analysis steps. In the forecast step, the dynamical model is used to integrate a stochastic sample approximating the state analysis distribution (called analysis ensemble) to obtain a forecast ensemble. In the analysis step, the forecast ensemble is updated with the incoming observation using a Kalman-like correction, which is then used for the next forecast step. In realistic large-scale applications, EnKFs are implemented with limited ensembles, and often poorly known model errors statistics, leading to a crude approximation of the forecast covariance. This strongly limits the filter performance. Recently, a new EnKF was proposed in [1] following a one-step-ahead smoothing strategy (EnKF-OSA), which involves an OSA smoothing of the state between two successive analysis. At each time step, EnKF-OSA exploits the observation twice. The incoming observation is first used to smooth the ensemble at the previous time step. The resulting smoothed ensemble is then integrated forward to compute a "pseudo forecast" ensemble, which is again updated with the same observation. The idea of constraining the state with future observations is to add more information in the estimation process in order to mitigate for the sub-optimal character of EnKF-like methods. The second EnKF-OSA "forecast" is computed from the smoothed ensemble and should therefore provide an improved background. In this work, we propose a deterministic variant of the EnKF-OSA, based on the Singular Evolutive Interpolated Ensemble Kalman (SEIK) filter. The motivation behind this is to avoid the observations perturbations of the EnKF in order to improve the scheme's behavior when assimilating big data sets with small ensembles. The new SEIK-OSA scheme is implemented and its efficiency is demonstrated by performing assimilation experiments with the highly nonlinear Lorenz model and a realistic setting of the Advanced Circulation (ADCIRC) model configured for storm surge forecasting in the Gulf of Mexico during Hurricane Ike.

One-parent families in the East-Kazakhstan region Abstract This paper addresses single-parent families in the East-Kazakhstan region and their role in the development of population, as well as the analysis of extra-marital births and the factors of family dissolution, such as divorce and widowhood. The data used were taken from censuses in 1989 and 1999, vital statistics, results of surveys, adjusted data. The aim of the thesis is investigation of one-parent families' contribution to population development in the East-Kazakhstan region through analysis of their structure, size, historical and modern conditions of origin and socio-economical situation. Keywords: one-parent family, single-parent household, traditional and modern family, extra-marital birth, family dissolution, divorce, East-Kazakhstan region. Неполные семьи в Восточно-Казахстанском регионе