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Background: Vulnerable populations in Canada shoulder a disproportionately high burden of disease. Transmission of sexually transmitted infections is behaviourally mediated. Previous research indicates an association between depression and sexual risk-taking. Evidence also suggests that social support is an effect modifier. Methods: Data were collected from a population of injection drug users, between 2003 and 2004 in Winnipeg, using respondent driven sampling. Demographic and social behaviors were analyzed to characterize the population, as well as social networks and ego networks. Logistic regression was used to examine the association between depressive symptoms and sexual risk interactions. Social support was examined as an effect modifier. Results: The majority of the study participants and network members were aged between 35 and 44, and a high percentage identified as Native Canadians. The highest percentage of people reported welfare as their primary source of income, and injecting stimulants, as their most frequently injected drug. Logistic regression models indicated an increase in the odds of individuals engaging in high-risk sexual interactions, if they had also self-reported elevated depressive symptoms. It was not possible to conclude that social support was an effect modifier. Conclusion: This research supports a positive association between elevated depressive symptoms, and higher levels of sexual risk interactions. Further research is needed to understand the role of social support.

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In the software engineering process some tasks of software engineers are to design software documents, analyze the documents, and comprehend component relationships within software diagrams. Those diagrams represent the software architecture which models the structure, behavior, relationships, and constraints among system components while ignoring implementation detail. In the software lifecycle, the system is implemented from the software architecture and errors and mistakes caused from a lack of comprehension or incorrect comprehension could cause engineers to incorrectly design the system. These errors can be defined as lapses, slips, or lack of understanding and fall into three categories: skill, rule, and knowledge errors. The Gestalt principles of organization, from the cognitive science domain, deal with how humans perceive the world around them. This dissertation seeks to identify whether the Gestalt principles of continuity, similarity of size, proximity, and similarity of name affect comprehension of the Unified Modeling Language (UML) class diagrams. Diagram comprehension is measured by response time and subject accuracy on questions and the mental workload perceived by subjects while answering questions related to the diagrams. The research hypotheses are diagrams that utilize the Gestalt principles of continuity, similarity of size, proximity, and similarity of name will have faster response times, higher accuracy, and lower mental workload scores than diagrams that do not use the Gestalt principles. The results of the research indicate that the Gestalt principle of proximity helped ease diagram comprehension. Through the use of this design principle, the Gestalt principle of continuity is applied because line crossings, line bends, and line length are minimized. Subjects were prone to make more errors on knowledge based questions that dealt with system understanding and UML semantics than skill and rule questions that dealt with system connections and UML syntax. These results provide software designers heuristics that can lead to better diagram design and identifies software engineering tasks that can lead to more errors.

To effectively operate and manage the complex wastewater treatment system, simplified representations, known as wastewater modeling, are critical. Wastewater modeling allows for the understanding, monitoring, and prediction of wastewater treatment processes by capturing intricate relationships within the system. Process-driven models (PDMs), which rely on a set of interconnected hypotheses and assumptions, are commonly used to capture the physical, chemical, and biological mechanisms of wastewater treatment. More recently, with the development of advanced algorithms and sensor techniques, data-driven models (DDMs) that are based on analyzing the data about a system, specifically finding relationships between the system state variables without relying on explicit knowledge of the system, have emerged as a complementary alternative. However, both PDMs and DDMs suffer from their limitations. For example, uncertainties of PDMs can arise from imprecise calibration of empirical parameters and natural process variability. Applications of DDMs are limited to certain objectives because of a lack of high-quality dataset and struggling to capture changing relationship. Therefore, this dissertation aims to enhance the stable operation and effective management of WWTPs by addressing these limitations through the pursuit of three objectives: (1) investigating an efficient data-driven approach for uncertainty analysis of process-driven secondary settling tank models; (2) developing data-driven models that can leverage sparse and imbalanced data for the prediction of emerging contaminant removal; (3) exploring an advanced data-driven model for influent flow rate predictions during the COVID-19 emergency. / Thesis / Doctor of Philosophy (PhD) / Ensuring appropriate treatment and recycling of wastewater is vital to sustain life. Wastewater treatment plants (WWTPs), which have complicated processes that include several intricate physical, chemical, and biological procedures, play a significant role in the water recycling. Due to stricter regulations and complex wastewater composition, the wastewater treatment system has become increasingly complex. Therefore, it is crucial to use simplified versions of the system, known as wastewater modeling, to effectively operate and manage the complex system. The aim of this thesis is to develop data-driven approaches for wastewater modeling.

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El proceso para realizar una buena obtención de requisitos depende ampliamente de las habilidades de las personas que lo llevan a cabo y lamentablemente, hoy día sigue siendo una de las principales causas de fallas en el software. Indepen-dientementedel conocimiento y experiencia de los ingenieros de software, surgen problemas de comunicación ya que los dominios de aplicación en los que se desarrolla el software generalmente son demasiado complejos como para entenderse por completo. La forma en la que los participantes perciben el contexto y consecuentemente la información que deriva en requisitos de software, afecta sus apreciaciones tanto en aspectos de modelado como de priorización. Al priorizar requi-sitos, interpretamos una realidad que es modelada durante el proceso de elicitación, entrando así en el ámbito de los as-pectos comunicacionalesde la ingeniería de requisitos. En este ámbito, la realidad percibida sugiere que los aspectos físicos no son suficientes para describir un estímulo, sino que debe-mos examinar el comportamiento de las personas relacionado al entorno y sus percepciones. La percepción se relaciona estrechamente con la comunicación y con la forma en que las personas expresan sus pensamientos. Es decir, depende de su capacidad de interpretar, abstraer y modelar la realidad de acuerdo a los estímulos que normalmente percibe. Asignar prioridades se transforma entonces en una actividad con un alto margen de incertidumbre, ya que no podemos estar seguros de entender realmente el objeto priorizado - por ejemplo, su percepción podría haberse distorsionado si un individuo es más propenso a identificar un tipo de estímulo que otro. Este trabajo de Tesis está orientado a la forma de establecer prioridades sobre una selección de requisitos de software considerando la manera en que las personas perci-ben y abstraen el entorno. En particular, utilizamos conceptos de la psicología cognitiva (concerniente a la manera en que la gente obtiene información y cómo estos mecanismos de infor-mación afectan el comportamiento humano), para mejorar el proceso de elicitación de requisitos. La contribución principal de esta Tesis es un marco conceptual para priorización de requisitos de software que tiene en cuenta el perfil cognitivo de los participantes. La temática encarada profundiza no sólo cuestiones teóricas, sino que lleva al campo de la aplicación un espacio aún no resuelto denitivamente y que es necesa-rio abordar científicamente: la definición de requisitos de software relacionada en gran medida a las preferencias de los participantes como aspectos fundamentales de la negocia-ción. La realidad de los desarrollos de software es que existe una brecha importante entre lo que realmente considera el participante y lo que verdaderamente será el producto final, por lo cual tratando de resolver este problema, nuestra estra-tegia considera la influencia que ejercen las características personales en los procesos de priorización de requisitos. En particular, el marco conceptual propuesto en esta Tesis se instancia en un caso específico de modelado utilizando orien-tación por objetivos y se valida utilizando modelos visuales y no visuales. Esa diferenciación permite destacar la incidencia de los perfiles cognitivos en la conceptualización y priorización de requisitos. / The degree of success of a process for eliciting and gathering software requirements strongly depends on stakeholders abilities and, unfortunately, this aspect is still one of the main causes of software failure. In spite of knowledge and experience of software engineers, there are many communication problems because of the inherent complexity of the application domains. The way stake-holders perceive a domain, and consequently information that derives into software requirements, affects their appreciations about modeling as well as prioritization. Prioritizing requirements involves interpreting reality during the elicitation process, highlighting communica-tional aspects of the requirements engineering process. In this context, perceived reality suggests that physical aspects are not enough to describe a stimuli and that we should examine peoples behavior related to their percep-tions. They are related to the way people communicate and express their thoughts. In other words, it depends on peoples capability to interpret, abstract and model reali-ty from perceived stimulus. Therefore, assigning require-ments priorities becomes an uncertain activity since we cannot be certain of understanding really what the priori-tized object is for instance, its perception might have been distorted if an individual is more likely to identify one kind of stimulus than another (such as visual effect against verbal ones). This Thesis is focused on the prio-ritizing process during software requirement elicitation by considering the way people perceive and abstract their environment. Particularly, we use concepts from the cogni-tive psychology field (related to the way people obtain information and how these mechanisms affect human behavior) to improve the software elicitation process. The main con-tribution of this Thesis relies on a conceptual framework for requirements prioritization, which takes into account the cognitive profiles of stakeholders. The study is approached from a not traditional perspective and based on scientific assumptions: the definition of software requi-rements related to stakeholders preferences as a fundamen-tal matter for negotiation. So far, there is an important gap between what a stakeholder really wants and what the final product does; then, trying to contribute to solving this problem, our strategy considers the influence of personal characteristics on the requirements elicitation process. Particularly, the conceptual framework we are proposing is instantiated in a specific case of modeling by using goal-oriented approaches; and it is validated by using visual and non-visual models. These differences allow us to highlight the incidence of cognitive profiles on requirements conceptualization and prioritization.

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In order to introduce learning cases with real-world contexts to the Computer Science students in their early stage of learning, a set of Web applications that utilize Web services are simplified and customized to demonstrate the core concept of high-level test-driven learning methodology. Four e-commerce Web applications were implemented for this project. These applications show how real-world Web services work and interact with each other. By systematically planting a number of errors into the services, we created a learning environment for the students to understand the system structure and basic programming through their critical thinking. A goal is to keep students' interest in computer science. In doing so, a set of features that help students observe the systems' behavior are designed, and collectively formed a pattern of user interface "the Learner's Corner."