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<p>Humans and domesticated animals have lived alongside one another for thousands of years, yet scholars have only recently begun to examine how companion animals, or pets, influence human lives (Knight, 2005). Today, 67 percent of United States households contain companion animals (APPA, 2019-2020) and many people consider their pets as family (Irvine & Cilia, 2017; McConnell et al., 2019). Given this, the death and dying period for pets can bring about distinctive concerns and difficulties within the family system, yet little is known about how individuals navigate the pet end-of-life period within the family, and how family communication manifests in this context. This study aimed to understand the interactional challenges for families communicating about the end-of-life period for their pets. Guided by family systems theory (FST) as an analytic framework, the goal was to better understand these dynamics using qualitative methods to address two main research questions. First, what are the challenges families face during the companion animal death and dying period? And second, how do families respond communicatively to these challenges? This qualitative study used a grounded theory approach to analyze 27 in-depth interviews with individuals who had experienced a death of their pet in the prior year and centered on how families described the place of their pets within the system. Of those participants, 14 individuals consisted of family dyads (51%) in various subgroups of partners, parent-child and siblings. Analysis affirmed some of what is known from the literature (such as feelings of guilt and financial worries associated with pet end-of-life) but also extended what is known by documenting family decision-making dilemmas, such as the dialectic tension between holding on and letting go, as well as noting the reverberations through the family of avoiding talk about death and disruptions to family communication after death. This manifested in primarily a decrease in talk about the pet or talk with family members (both in-person and mediated channels) or an increase in talk following the death. These findings suggest that pet death has the potential to disrupt a family's regular communication patterns.</p> <p>These findings affirmed that supportive communication after a death loss is important to individuals and particularly helpful from others with prior pet loss experience. Along with social support, participants indicated that the artifacts left behind after the death of their pet provided comfort and were also elements of tension and cohesion within the system. As scholars suggest, companion animals are liminal creatures, existing as kin and "other" within the family (Irvine & Cilia, 2017; Sayers et al. 2022). Indeed, participants described their pets as existing in a space at the edge of true family member whose fate was exclusively in the hands of the family, particularly in the context of euthanasia, which contributed to some of the communicative challenges identified. </p> <p>Given limitations of a mostly regional and homogenous sample and the conceptual limitation of extrapolating from two individuals in a family to the whole system, this study was still able to take the next step needed for understanding the communicative challenges faced by families in confronting pet death. Findings extend the concept of pet-as-family, in that pets were found to be an element of the "family tapestry" serving the role of both emotional support and kin for some family members. In that manner, they were also discovered as integral to the family timeline, a catalyst for connection between family members, and thus worthy of further study from a communication and family system perspective.</p>

<p> Popular theories about the origin of life can be classified to two classes: metabolism first or genes first. As a metabolism first theory, the lipid world theory, in which non-covalent assemblies of lipids, such as micelles and vesicles store information in the form of a non-random molecular composition, has been proposed to investigate the possibility of inheritance without genes. Our models assume that interaction occurs between nearest neighbour molecules only, and account for spatial segregation of molecules of different types within the assembly. We also draw a distinction between a self-assembly model, in which the composition is determined by mutually favourable interaction energies between the molecules, and a catalytic model, in which the composition is determined by mutually favourable catalysis. We show that compositional inheritance occurs in both models, although the self-assembly case seems more relevant if the molecules are simple lipids. In the case where the assemblies are composed of just two types of molecules, there is a strong analogy with the classic two-allele Moran model from population genetics. This highlights the parallel between compositional inheritance and genetic inheritance. We also investigated the polymerization reactions which may bridge the gap between simple organic molecules and the beginning of the RNA world, which belongs to the class of genes first theories. We found that different from normal chemical systems, catalysts for the polymerization system can shift the equilibrium toward longer polymers. Together with RNA's potential as catalyst, the RNA polymerization system may form a feedback loop which makes the formation of functional RNA molecules easier, and come more close to the beginning of RNA world.</p> / Thesis / Master of Science (MSc)

<p> Potassium channels allow selective flow of K+ ions across impermeable membranes. They switch between closed and open states in response to stimulus. The closed state of KcsA, a potassium channel from the bacteria streptomyces lividans, has been crystallized and studied. However, attempts to obtain a structural description of gating transition of the channel have been hampered because the open state is transiently occupied. In this study, we investigated changes in gating functions of KcsA caused by variations in environmental parameters such as pH, voltage and also mutation in KcsA using planar bilayer and patch clamp channel recording techniques to improve our understanding on the functional aspect of gate activation of KcsA.</p> / Thesis / Master of Science (MSc)

Recirculating turbulent flow within a cavity with an inlet wall jet was examined. In steady water flow velocity profiles were constructed with measurements taken with a Laser Doppler Anemometer system mounted on a traversing mechanism. Two test cases were examined Re jet = 1167 and Re jet = 3231 as well as developing wall jet profiles. The results are presented with mean velocity plots and turbulent kinetic energy contours. Comparisons are then made with results obtained using a finite difference computational scheme based on the k - e turbulence model. Good agreement was obtained between the computer code predictions and the experimental data. / Thesis / Master of Engineering (ME)

The purpose of this research was to explore traffic event severity relationships, evaluate the potentiality of a hazardous event, and develop a framework of observable event factors. Data was collected from three regions in Virginia, each assumed to exemplify a unique driving environment due to amount of traffic and infrastructure characteristics. In combination, a broad spectrum of site, traffic, and driver performance variables were accounted for. Observational techniques of surveillance, incident reporting, and inventorying were used to collect site, traffic, and driver data. This effort resulted in 368 observed traffic events that were evenly distributed among the three regions that represented metropolitan, mid-sized city, and town/rural driving environments. The 368 events were evaluated for severity and contributing variables where 1% of the events were non-injury crashes, 10% were serious, near-crashes, 24% were near-crashes, and the remaining 65% were serious errors with a hazard present. Exploratory analyses were performed to understand the general relationship between event severity levels. Binary logistic regression analyses (α = 0.05) were performed to further scope predictor variables to identify traffic event characteristics with respect to severity level, maneuver type, and conflict type. The results were that 69 of 162 observed predictor variables were valuable in characterizing traffic events based on severity. It was found that variables could be grouped to create event severity signatures for crashes, serious near-crashes, and near-crashes. Based on these signatures, it was found that there is a trend between severity levels that included a propensity for problems with straight path maneuvers, lateral and longitudinal vehicle control, and information density within the driving environment as contributing to driver error and hence crashes and near-crashes. There were also differences between the severity levels. These differences were evident in the degree of control the driver appeared to have of the vehicle, type of control regulating the driving environment, and type of road users present in the driving environment. Modifications to roadway evaluative techniques would increase awareness of additional variables that impact drivers to make more informed decisions for roadway enhancements. / Ph. D.

No / A reinvestigation into the unsolved murder of 25-year-old estate agent Suzy Lamplugh who went missing in 1986. Despite appeals on 'Crimewatch' and four separate investigations her killer was never found. Documentary profiles John Cannan, currently serving three life sentences for robbery, rape and murder, and who senior police officers believe to be the estate agents murderer.

The increasing volume and complexity of text data are challenging the cognitive capabilities of expert analysts. Machine learning and crowdsourcing present new opportunities for large-scale sensemaking, but it remains a challenge to model the overall process so that many distributed agents can contribute to suitable components asynchronously and meaningfully. In this work, I explore how to crowdsource sensemaking for intelligence analysis. Specifically, I focus on the complex processes that include developing hypotheses and theories from a raw dataset and iteratively refining the analysis. I first developed Connect the Dots, a web application that implements the concept of "context slices" and supports novice crowds in building relationship networks for exploratory analysis. Then I developed CrowdIA, a software platform that implements the entire crowd sensemaking pipeline and the context slicing for each step, to enable unsupervised crowd sensemaking. Using the pipeline as a testbed, I probed the errors and bottlenecks in crowdsourced sensemaking,and suggested design recommendations for integrated crowdsourcing systems. Building on these insights and to support iterative crowd sensemaking, I developed the concept of "crowd auditing" in which an auditor examines a pipeline of crowd analyses and diagnoses the problems to steer future refinement. I explored the design space to support crowd auditing and developed CrowdTrace, a crowd auditing tool that enables novice auditors to effectively identify the important problems with the crowd analysis and create microtasks for crowd workers to fix the problems.The core contributions of this work include a pipeline that enables distributed crowd collaboration to holistic sensemaking processes, two novel concepts of "context slices" and "crowd auditing", web applications that support crowd sensemaking and auditing, as well as design implications for crowd sensemaking systems. The hope is that the crowd sensemaking pipeline can serve to accelerate research on sensemaking, and contribute to helping people conduct in-depth investigations of large collections of information. / Doctor of Philosophy / In today's world, we have access to large amounts of data that provide opportunities to solve problems at unprecedented depths and scales. While machine learning offers powerful capabilities to support data analysis, to extract meaning from raw data is cognitively demanding and requires significant person-power. Crowdsourcing aggregates human intelligence, yet it remains a challenge for many distributed agents to collaborate asynchronously and meaningfully. The contribution of this work is to explore how to use crowdsourcing to make sense of the copious and complex data. I first implemented the concept of ``context slices'', which split up complex sensemaking tasks by context, to support meaningful division of work. I developed a web application, Connect the Dots, which generates relationship networks from text documents with crowdsourcing and context slices. Then I developed a crowd sensemaking pipeline based on the expert sensemaking process. I implemented the pipeline as a web platform, CrowdIA, which guides crowds to solve mysteries without expert intervention. Using the pipeline as a testbed, I probed the errors and bottlenecks in crowd sensemaking and provided design recommendations for crowd intelligence systems. Finally, I introduced the concept of ``crowd auditing'', in which an auditor examines a pipeline of crowd analyses and diagnoses the problems to steer a top-down path of the pipeline and refine the crowd analysis. The hope is that the crowd sensemaking pipeline can serve to accelerate research on sensemaking, and contribute to helping people conduct in-depth investigations of large collections of data.