Surveillance Studies and the Surveilled Subject

Surveillance studies has been somewhat inattentive to the perspective of the surveilled subject. It is the functioning of the surveillance apparatus, not the relatively inconsequential subject, which has tended to frame the focus of surveillance inquiries; leaving understandings of surveilled subjects’ experiences relatively limited. This research addresses this gap in the literature, exploring ways in which surveillance studies might understand the surveilled subject with greater consistency. Participants (N=47) shared their encounters with and perceptions of surveillance in a specific (Pearson International Airport) and general (everyday life) context through semi-structured interviews. The findings suggest that surveilled subjects’ encounters can be understood with some consistency – characterized by consistent criteria across subjects and contexts, and through a consistent theoretical framework across subjects in a specific context. However, consistency should not be confused with uniformity; encounters with surveillance must also be recognized for the extent to which they are nuanced and situated. For example, as this study also highlights, participants’ perceptions of encounters with surveillance at Pearson International Airport were differentially distributed in relation to identity characteristics (particularly minority status).

Robotic Outcome Assessment in a Non-Human Primate Model of Middle Cerebral Artery Stroke

Stroke is a prevalent disorder with immense socioeconomic impact. A variety of chronic neurological deficits result from stroke. In particular, sensorimotor deficits are a significant barrier to achieving post-stroke independence. Unfortunately, the majority of pre-clinical studies that show improved outcomes in animal stroke models have failed in clinical trials. Pre-clinical studies using non-human primate (NHP) stroke models prior to initiating human trials are a potential step to improving translation from animal studies to clinical trials. Robotic assessment tools represent a quantitative, reliable, and reproducible means to assess reaching behaviour following stroke in both humans and NHPs. We investigated the use of robotic technology to assess sensorimotor impairments in NHPs following middle cerebral artery occlusion (MCAO). Two cynomolgus macaques underwent transient MCAO for 90 minutes. Approximately 1.5 years following the procedure these NHPs and two non-stroke control monkeys were trained in a reaching task with both arms in the KINARM exoskeleton. This robot permits elbow and shoulder movements in the horizontal plane. The task required NHPs to make reaching movements from a centrally positioned start target to 1 of 8 peripheral targets uniformly distributed around the first target. We analyzed four movement parameters: reaction time, movement time (MT), initial direction error (IDE), and number of speed maxima to characterize sensorimotor deficiencies. We hypothesized reduced performance in these attributes during a neurobehavioural task with the paretic limb of NHPs following MCAO compared to controls. Reaching movements in the non-affected limbs of control and experimental NHPs showed bell-shaped velocity profiles. In contrast, the reaching movements with the affected limbs were highly variable. We found distinctive patterns in MT, IDE, and number of speed peaks between control and experimental monkeys and between limbs of NHPs with MCAO. NHPs with MCAO demonstrated more speed peaks, longer MTs, and greater IDE in their paretic limb compared to controls. These initial results qualitatively match human stroke subjects’ performance, suggesting that robotic neurobehavioural assessment in NHPs with stroke is feasible and could have translational relevance in subsequent human studies. Further studies will be necessary to replicate and expand on these preliminary findings.

Role of Dopamine D2 Receptors in the Consolidation of Amphetamine-Cue Memory in Conditioned Activity in Rats

The neurotransmitter dopamine (DA) plays an essential role in reward-related incentive learning, whereby neutral stimuli gain the ability to elicit approach and other responses. In an incentive learning paradigm called conditioned activity, animals receive a stimulant drug in a specific environment over the course of several days. When then placed in that environment drug-free, they generally display a conditioned hyperactive response. Modulating DA transmission at different time points during the paradigm has been shown to disrupt or enhance conditioning effects. For instance, blocking DA D2 receptors before sessions generally impedes the acquisition of conditioned activity. To date, no studies have examined the role of D2 receptors in the consolidation phase of conditioned activity; this phase occurs immediately after acquisition and involves the stabilization of memories for long-term storage. To investigate this possible role, I trained Wistar rats (N = 108) in the conditioned activity paradigm produced by amphetamine (2.0 mg/kg, intraperitoneally) to examine the effects of the D2 antagonist haloperidol (doses 0.10, 0.25, 0.50, 0.75, 1.0, & 2.0 mg/kg, intraperitoneally) administered 5 min after conditioning sessions. Two positive control groups received haloperidol 1 h before conditioning sessions (doses 1.0 mg/kg and 2.0 mg/kg). The results revealed that post-session haloperidol at all doses tested did not disrupt the consolidation of conditioned activity, while pre-session haloperidol at 2.0 mg/kg prevented acquisition, with the 1.0 mg/kg group trending toward a block. Additionally, post-session haloperidol did not diminish activity during conditioning days, unlike pre-session haloperidol. One possible reason for these findings is that the consolidation phase may have begun earlier than when haloperidol was administered, since the conditioned activity paradigm uses longer learning sessions than those generally used in consolidation studies. Future studies may test if conditioned activity can be achieved with shorter sessions; if so, haloperidol would then be re-tested at an earlier time point. D2 receptor second messenger systems may also be investigated in consolidation. Since drug-related incentive stimuli can evoke cravings in those with drug addiction, a better understanding of the mechanisms of incentive learning may lead to the development of solutions for these individuals.

An Islanding Detection Method based on Impedance Estimation and Grid Identification for Grid-Tie Distributed Power Generation Systems

Power system policies are broadly on track to escalate the use of renewable energy resources in electric power generation. Integration of dispersed generation to the utility network not only intensifies the benefits of renewable generation but also introduces further advantages such as power quality enhancement and freedom of power generation for the consumers. However, issues arise from the integration of distributed generators to the existing utility grid are as significant as its benefits. The issues are aggravated as the number of grid-connected distributed generators increases. Therefore, power quality demands become stricter to ensure a safe and proper advancement towards the emerging smart grid. In this regard, system protection is the area that is highly affected as the grid-connected distributed generation share in electricity generation increases. Islanding detection, amongst all protection issues, is the most important concern for a power system with high penetration of distributed sources. Islanding occurs when a portion of the distribution network which includes one or more distributed generation units and local loads is disconnected from the remaining portion of the grid. Upon formation of a power island, it remains energized due to the presence of one or more distributed sources. This thesis introduces a new islanding detection technique based on an enhanced multi-layer scheme that shows superior performance over the existing techniques. It provides improved solutions for safety and protection of power systems and distributed sources that are capable of operating in grid-connected mode. The proposed active method offers negligible non-detection zone. It is applicable to micro-grids with a number of distributed generation sources without sacrificing the dynamic response of the system. In addition, the information obtained from the proposed scheme allows for smooth transition to stand-alone operation if required. The proposed technique paves the path towards a comprehensive protection solution for future power networks. The proposed method is converter-resident and all power conversion systems that are operating based on power electronics converters can benefit from this method. The theoretical analysis is presented, and extensive simulation results confirm the validity of the analytical work.

Analysis of Non-Stationary Flows Using Eulerian and Lagrangian Frameworks

Recent developments have made researchers to reconsider Lagrangian measurement techniques as an alternative to their Eulerian counterpart when investigating non-stationary flows. This thesis advances the state-of-the-art of Lagrangian measurement techniques by pursuing three different objectives: (i) developing new Lagrangian measurement techniques for difficult-to-measure, in situ flow environments; (ii) developing new post-processing strategies designed for unstructured Lagrangian data, as well as providing guidelines towards their use; and (iii) presenting the advantages that the Lagrangian framework has over their Eulerian counterpart in various non-stationary flow problems. Towards the first objective, a large-scale particle tracking velocimetry apparatus is designed for atmospheric surface layer measurements. Towards the second objective, two techniques, one for identifying Lagrangian Coherent Structures (LCS) and the other for characterizing entrainment directly from unstructured Lagrangian data, are developed. Finally, towards the third objective, the advantages of Lagrangian-based measurements are showcased in two unsteady flow problems: the atmospheric surface layer, and entrainment in a non-stationary turbulent flow. Through developing new experimental and post-processing strategies for Lagrangian data, and through showcasing the advantages of Lagrangian data in various non-stationary flows, the thesis works to help investigators to more easily adopt Lagrangian-based measurement techniques.