One of the most controversial issues in the field of education is the determination of the educational placement of deaf K-12 students. Although school psychologists are involved in the determination process, little is known about their specific role in decision-making. School psychologists (n=357) with varying degrees of specialization in this area were surveyed with regard to their experience and perceptions. Results indicated that student audiological status predicts the types of assessment data that school psychologists collect and report in order to inform educational placement decisions. Participants who responded to the survey on the basis of their experience making placement decisions for deaf students (n=54) perceived themselves as less influential over the decisions than participants who responded on the basis of their experience making placement decisions for hearing students (n=303). Additionally, there was a relationship between school psychologists' specialization in the area of deafness and the interpretation of the phrase, the "least restrictive environment." Possible explanations and the associated implications of these findings are discussed. / School Psychology

Sustainability of the built environment is one of the most significant challenges facing the construction industry, and presents significant opportunities to affect change. The absence of quantifiable and holistic sustainability measures for the built environment has hindered their application. As a result, a sustainability performance metric (SPM) framework was conceptually formulated by employing sustainability objectives and function statements a-priori to identify the correlated sustainability indicators that need to be captured equally, with respect to the environment, the economy, and society. Projection to Latent Structures (PLS), a latent variable method, was adopted to mathematically formulate the metric. Detached single-family housing was used to demonstrate the application of SPM. Datasets were generated using Athena Impact Estimator, EnergyPlus, Building Information Modelling (BIM), Socioeconomic Input/Output models, among others. Results revealed that a holistic metric, such as the SPM is necessary to obtain a sustainable design, where qualitative or univariate considerations may result in the contrary. A building envelope coefficient of performance (BECOP) metric based on an idealized system was also developed to measure the energy efficiency of the building envelope. Results revealed the inefficiencies in the current building envelope construction technologies and the missed opportunities for saving energy. Furthermore, a decision-making tool, which was formulated using the PLS utilities, was shown to be effective and necessary for early stages of the design for energy efficiency. / Thesis / Doctor of Science (PhD) / Sustainability of the built environment is a significant challenge facing the industry, and presents opportunities to affect changes. The absence of holistic sustainability measures has hindered their application. As a result, a sustainability performance metric (SPM) framework was formulated by employing sustainability objectives and function statements a-priori to identify the indicators that need to be captured. Projection to Latent Structures was adopted to mathematically formulate the metric. A housing prototype was used to demonstrate the application of the SPM utilizing a bespoke dataset. Results revealed that holistic metric, such as the SPM is necessary for achieving sustainable designs. A building envelope coefficient of performance metric was also developed to measure the energy efficiency of the building envelope. Results revealed the inefficiencies in the current building envelope technologies and identified missed opportunities. Furthermore, a decision-making tool was formulated and shown to be effective and necessary for design for energy efficiency.

Bacteria identification plays an essential role in preventing health complications and saving patients' lives. The most widely used method to identify bacteria, the bacterial cultural method, suffers from long processing times. Hence, an effective, rapid, and non-invasive method is needed as an alternative. Raman spectroscopy is a potential candidate for bacteria identifi cation due to its effective and rapid results and the fact that, similar to the uniqueness of a human fingerprint, the Raman spectrum is unique for every material. In my lab at the University of Ottawa, we focus on the use of Raman scattering for biosensing in order to achieve high identifi cation accuracy for different types of bacteria. Based on the unique Raman fingerprint for each bacteria type, different types of bacteria can be identifi ed successfully. However, using the Raman spectrum to identify bacteria poses a few challenges. First, the Raman signal is a weak signal, and so enhancement of the signal intensity is essential, e.g., by using surface-enhanced Raman scattering (SERS). Moreover, the Raman signal can be contaminated by different noise sources. Also, the signal consists of a large number of features, and is non-linear due to the correlation between the Raman features. Using machine learning (ML) along with SERS, we can overcome such challenges in the identifi cation process and achieve high accuracy for the system identifying bacteria. In this thesis, I present a method to improve the identifi cation of different bacteria types using a support vector machine (SVM) ML algorithm based on SERS. I also present dimension reduction techniques to reduce the complexity and processing time while maintaining high identifi cation accuracy in the classifi cation process. I consider four bacteria types: Escherichia coli (EC), Cutibacterium acnes (CA, it was formerly known as Propi-onibacterium acnes), methicillin-resistant Staphylococcus aureus (MRSA), and methicillin-sensitive Staphylococcus aureus (MSSA). Both the MRSA and MSSA are combined in a single class named MS in the classifi cation. We are focusing on using these types of bacteria as they are the most common types in the joint infection disease. Using binary classi fication, I present the simulation results for three binary models: EC vs CA, EC vs MS, and MS vs CA. Using the full data set, binary classi fication achieved a classi fication accuracy of more than 95% for the three models. When the samples data set was reduced, to decrease the complexity based on the samples' signal-to-noise ratio (SNR), a classi fication accuracy of more than 95% for the three models was achieved using less than 60% of the original data set. The recursive feature elimination (RFE) algorithm was then used to reduce the complexity in the feature dimension. Given that a small number of features were more heavily weighted than the rest of the features, the number of features used in the classifi cation could be signi ficantly reduced while maintaining high classi fication accuracy. I also present the classifi cation accuracy of using the multiclass one-versus-all (OVA) method, i.e., EC vs all, MS vs all, and CA vs all. Using the complete data set, the OVA method achieved classi cation accuracy of more than 90%. Similar to the binary classifi cation, the dimension reduction was applied to the input samples. Using the SNR reduction, the input samples were reduced by more than 60% while maintaining classifi cation accuracy higher than 80%. Furthermore, when the RFE algorithm was used to reduce the complexity on the features, and only the 5% top-weighted features of the full data set were used, a classi fication accuracy of more than 90% was achieved. Finally, by combining both reduction dimensions, the classi fication accuracy for the reduced data set was above 92% for a signifi cantly reduced data set. Both the dimension reduction and the improvement in the classi fication accuracy between different types of bacteria using the ML algorithm and SERS could have a signi ficant impact in ful lfiling the demand for accurate, fast, and non-destructive identi fication of bacteria samples in the medical fi eld, in turn potentially reducing health complications and saving patient lives.

Lean Management and Lean Six Sigma (LM/LSS) are improvement methodologies that have been utilized to achieve better performance outcomes at organizational and operational levels. Although there has been evidence of breakthrough improvement across diverse organizational settings, LM/LSS remains an early-stage improvement methodology in public sector organizations, specifically within local government organizations (LGOs). Some LGOs have benefited from LM/LSS and reported significant improvements, such as reducing process time by up to 90% and increasing financial savings by up to 57%. While the success of LM/LSS can lead to satisfactory outcomes, the risk of failure can also result in a tremendous waste of financial and non-financial resources. Evidence from the literature indicates that the failure to achieve the expected outcomes is likely due to the lack of attention paid to critical success factors (CSFs) that are crucial for LM/LSS success. Furthermore, research in this research area regarding characterizing and statistically examining the CSFs associated with LM/LSS in such organizational settings has been limited. Hence, the aim of this research is to provide a comprehensive investigation of the success factors for LM/LSS in LGOs. The initial stage of this dissertation involved analyzing the scientific literature to identify and characterize the CSFs associated with LM/LSS in LGOs through a systematic literature review (SLR). This effort identified a total of 47 unique factors, which were grouped into 5 categories, including organization, process, workforce knowledge, communications, task design, and team design. The next stage of this investigation focused on identifying a more focused set of CSFs. This involved evaluating the strength of the effect (or importance) of the factors using two integrated approaches: meta-synthesis and expert assessment. This process concluded with a total of 29 factors being selected for the empirical field study. The final stage included designing and implementing an online survey questionnaire to solicit LGOs' experience on the presence of factors during the development and/or implementation of LM/LSS and their impact on social-technical system outcomes. Once the survey was concluded, an exploratory factor analysis (EFA) was conducted to identify the underlying latent variables, followed by using a partial least square-structural equation model (PLS-SEM) to determine the significance of the factors on outcomes. The EFA identified three endogenous and five exogenous latent variables. The results of the PLS-SEM model identified four significant positive relationships. Based on the results from the structural paths, the antecedent Improvement Readiness (IR) and Change Awareness (CA) were significant and had a positive influence on Transformation Success (TS). For the outcome Deployment Success (DS), Sustainable Improvement Infrastructure (SII) was the only significant exogenous variable and had the highest positive impact among all significant predictor constructs. Furthermore, Measurement-Based Improvement (MBI) was significant and positively influenced Improvement Project Success (IPS). Findings from this dissertation could serve as a foundation for researchers looking to further advance the maturity of this research area based on the evidence presented in this work. Additionally, this work could be used as guidelines for practitioners in developing implementation processes by considering the essential factors to maximize the success of LM/LSS implementation. Given the diversity of functional areas and processes within LGO contexts, it is also possible that other public sector organizations could benefit from these findings. / Doctor of Philosophy / Lean Management and Lean Six Sigma (LM/LSS) is an improvement methodology that is used by businesses and organizations to improve how they work and achieve better results. LM/LSS has been especially helpful in various organizations; however, the implementation of this improvement methodology has been limited by many challenges for public sector organizations, especially local government organizations (LGOs). The overall aim of this dissertation is to improve the success of LM/LSS implementation within the context of LGOs. More specifically, this dissertation systematically studied the critical success factors associated with LM/LSS success. Different research approaches, including research formulation, development, and testing techniques, were conducted to achieve the aim of this dissertation. Publications related to LM/LSS in LGOs have been rigorously analyzed to identify a comprehensive list of CSFs. To identify the most important factors, a meta-synthesis evaluation and expert survey assessment have been conducted. Following the refinement of the factors, a large-scale field study using a survey questionnaire has been designed and distributed to LGOs. Once the survey concluded, statistical methods that included Exploratory Factor Analysis (EFA) and Partial Least Squares-Structural Equation Modeling (PLS-SEM) were conducted. The former was used to identify the underlying latent variables, while the latter was conducted to examine the influence of the factors on social and technical outcomes. This dissertation could be used as a reference guideline helping practitioners to increase the success of LM/LSS implementation in LGOs. This dissertation can also guide scholars to potential research avenues that could advance this research area.

Yes / Automatic facial age progression (AFAP) has been an active area of research in recent years. This is due to its numerous applications which include searching for missing. This study presents a new method of AFAP. Here, we use an Active Appearance Model (AAM) to extract facial features from available images. An ageing function is then modelled using Sparse Partial Least Squares Regression (sPLS). Thereafter, the ageing function is used to render new faces at different ages. To test the accuracy of our algorithm, extensive evaluation is conducted using a database of 500 face images with known ages. Furthermore, the algorithm is used to progress Ben Needham’s facial image that was taken when he was 21 months old to the ages of 6, 14 and 22 years. The algorithm presented in this paper could potentially be used to enhance the search for missing people worldwide.

The Almost-Exact Scheme (AES), as proposed by Oosterlee and Grzelak, has been applied to the Heston stochastic volatility model to show improved error convergence for small time-steps, as opposed to the classical Euler-Maruyama (EM) scheme, in European option pricing. This idea has been extended to the double Heston stochastic volatility model, to show similar improved results for Bermudan options. In this thesis, we extend this idea even further and develop an Almost-Exact Scheme to the Gatheral double mean reverting (DMR) model, to show improved error convergence for American put options. We illustrate that, because of the complexity of the dynamics of our model, a direct application of the AES is not possible, and therefore derive a diffusion trick, so we can instead use a partial implementation of the AES. Our partial implementation has two variants. In the first variant, we implement the AES on the long-run mean process combined with the Milstein scheme on the variance process. In the second variant, the Milstein scheme is replaced by a second order refinement. We name these two schemes AEMS and AEMS-SOR respectively. We conduct extensive simulation studies to evaluate the proposed schemes. The results indicate improved error convergence of the proposed scheme for small time-steps when time-to-maturity is equal to half a year, but does not seem to differ much from the EM scheme for a shorter time-to-maturity.

Utility sponsored conservation programs encourage participants to consume less energy. One of the most popular methods used to achieve this is to offer monetary rebates to purchasers of high-efficiency appliances. The costs of these conservation programs are then passed-on to all customers as increased energy prices. Economic theory predicts that the income and substitution affects should decrease the consumption of non-participants in the programs and may increase the consumption of participants. Recent claims in the literature argue that the standard net benefit tests used to evaluate these programs fail to incorporate the full impact of the income and substitution affects. Relying on these theoretical arguments, new evaluation techniques, referred to as Net Economic Benefits (NEB) tests, are being introduced as solutions to this problem. Using the actual experience of a natural gas utility, this thesis analyzed the need for NEB evaluations. The results show that the price of gas is not a significant factor in determining household gas consumption. Therefore, empirical evidence cannot support the NEB claims. The evidence does show that, on an average annual basis, participants are consuming less than non-participants. / Master of Arts

This thesis complements current studies by focusing on developed OECD countries as they are the major sources and recipients of world FDI and current studies relating to developed countries using aggregate country FDI data are limited. This study empirically tests the determinants of FDI inflows and outflows and their relationship with economic growth using 2SLS simultaneous equations model between 1981 and 2008 for a sample of 20 developed OECD countries. The empirical findings suggest that FDI inflows do not contribute to economic growth in the host country and economic growth positively affects FDI inflows. In addition, trade openness and flexible employment protection legislation in the host country attract FDI inflows. In terms of FDI outflows, the results show that FDI outflows reduce economic growth in the home country, while economic growth in the home country increases FDI outflows. Moreover, high past level of outward FDI stock, trade openness, low labour cost and currency depreciation in the home country provide incentives for domestic firms to invest abroad. Therefore, this study does not support offering special incentives to foreign investors to attract FDI inflows or offering promotional policies to domestic firms to encourage FDI outflows. Instead, government should provide incentives for domestic investment and other sound policies to increase economic growth, which in itself provides a good environment to attract FDI inflows and to encourage FDI outflows. Keywords: FDI inflows, FDI outflows, two stage least squares simultaneous equations, economic growth, labour market flexibility.

Soil fertility is an important component of forest ecosystem, yet evaluating soil fertility remains one of the least understood aspects of forest science. Phytocentric and geocenctric approaches were used to assess soil fertility in loblolly pine plantations throughout their geographic range in the United States. The model to assess soil fertility using a phytocentric approach was constructed using the relationship between site index and aboveground productivity. Geocentric models used physical and chemical properties of the A-horizon. Soil geocentric models were constructed using two modeling approaches. In the first approach, ordinary least squares methods of multiple regression were used to derive soil fertility estimated from site index using soil physical and chemical properties from the A-horizon. Ordinary least squares methods were found unsuitable due to multicollinearity among the soil variables. In the second approach, a multivariate modeling approach, partial least squares regression, was used to mitigate multicollinearity effects. The best model to quantify soil fertility using soil physical and chemical properties included N, Ca, Mg, C, and sand percentage as the significant predictors. The 3-PG process-based model was evaluated for simulating the response of loblolly pine to changes in soil fertility. Fertility rating (FR) is a parameter in 3-PG that scales soil fertility in the range of 0 to 1. FR values estimated from phytocentric and geocentric approaches were tested against observed production. The 3-PG model prediction of aboveground productivity described 89% percent of the variation in observed aboveground productivity using FR derived from site index and 84% percent of the vari- ation in observed aboveground productivity using FR derived from physical and chemical properties of the A-horizon. A response function to model dynamics of FR (∆FR) due to one time midrotatoin fertilization of N and P was developed using the Weibull function. The magnitude of ∆FR varied with intensity of N and time since application of fertilizer. The hypothesis that repeated fertilization with N and P eliminate major nutrient deficiency in the southeastern US was tested and a relationship between baseline fertility rating and fertilizer response was developed. An inverse relationship was observed between fertilizer response and baseline FR. / Ph. D.

The Forest Vegetation Simulator (FVS) is a growth and yield modeling system widely-used for predicting stand and tree-level attributes for management and planning applications in North American forests. The accuracy of FVS predictions for a range of tree and stand level attributes depends a great deal on the performance of the diameter increment model and its predictions of change in diameter at breast height (DBH) over time. To address the challenge of predicting growth in highly variable and geographically expansive forest systems, FVS was designed to include an internal calibration algorithm that makes use of growth observations, when available, from permanent inventory plots. The basic idea is that observed growth rates on a collection of remeasured trees are used to adjust or "calibrate" FVS diameter growth predictions. Therefore, DBH modeling was the focus of this investigation. Five methods were proposed for local calibration of individual tree DBH growth predictions and compared to two sets of results generated without calibration. Data from the US Forest Service's Forest Inventory and Analysis (FIA) program were used to test the methods for eleven widely-distributed forest tree species in Virginia. Two calibration approaches were based on median prediction errors from locally-observed DBH increments spanning a five year average time interval. Two were based on simple linear regression models fitted to the locally-observed prediction errors, and one method employed a mixed effects regression model with a random intercept term estimated from locally-observed DBH increments. Data witholding, specifically a leave-one-out cross-validation was used to compare results of the methods tested. Results showed that any of the calibration approaches tested in general led to improved accuracy of DBH growth predictions, with either of the median-based methods or regression based methods performing better than the random-effects-based approach. Equivalence testing showed that median or regression-based local calibration methods met error tolerances within ± 12% of observed DBH increments for all species with the random effects approach meeting a larger tolerance of ± 17%. These results showed improvement over uncalibrated models, which failed to meet tolerances as high as ± 30% for some species in a newly-fitted DBH growth model for Virginia, and as high as ± 170% for an existing model fitted to data from a much larger region of the Southeastern United States. Local calibration of regional DBH increment models provides an effective means of substantially reducing prediction errors when a relatively small set of observations are available from local sources such as permanent forest inventory plots, or the FIA database. / MS / The Forest Vegetation Simulator (FVS) is a growth and yield model widely-used for predicting stand dynamics, management and decision support in North American forests. Diameter increment is a major component in modeling tree growth. The system of integrated analytical tools in FVS is primarily based on the performance of the diameter increment model and the subsequent use of predicted in diameter at breast height (DBH) over time in forecasting tree attributes. To address the challenge of predicting growth in highly variable and geographically expansive forest systems, FVS was designed to include an internal calibration algorithm that makes use of growth observations, when available, from permanent inventory plots. The basic idea was that observed growth rates on a small set of remeasured trees are used to adjust or “calibrate” FVS growth predictions. The FVS internal calibration was the subject being investigated here. Five alternative methods were proposed attributed to a specific site or stand of interest and compared to two sets of results, which were based on median prediction errors, generated without calibration. Results illustrated that median-based methods or regression based methods performed better than the random-effects-based approach using independently observed growth data from Forest Service FIA re-measurements in Virginia. Local calibration of regional DBH increment models provides an effective means of substantially reducing prediction errors. The results of this study should also provide information to evaluate the efficiency of FVS calibration alternatives and a possible method for future implementation.