COGNITIVE ASSESSMENT USING THE KINARM EXOSKELETON ROBOT IN PATIENTS WITH TRANSIENT ISCHEMIC ATTACK

Background: Transient ischemic attack (TIA) is a condition causing focal neurological deficits lasting less than 24hrs. TIA patients present similarly to other conditions with rapid onset of neurological symptoms such as migraine. The accurate diagnosis of TIA is critical because it serves as a warning for subsequent stroke. Furthermore, cognitive deficit associated with TIA may predict the development of dementia. Therefore, characterizing the cognitive symptoms of TIA patients and discriminating these patients from those with similar symptoms is important for proper diagnosis and treatment. Currently the diagnosis of TIA is made on clinical and radiographic evidence. Robotic assessment, with instruments such as the KINARM, may improve the identification of cognitive impairment in TIA patients. Methods: In this prospective cohort study, two KINARM tests, trail making task (TMT) and spatial span task (SST), were used to detect cognitive deficits. Two study groups were made. The TIA group was tested at 5 time points over the span of a year. The migraine active control group had one initial visit and another a year later. Both of these groups were compared to a normative database of approximately 400 healthy volunteers. From this database age and sex matched normative data was used to calculate Z-scores for the TMT. The Montreal Cognitive Assessment (MoCA) was also administered to both groups. Results: 31 participants were recruited, 20 TIA group and 11 active controls (mean ± SD age= 66 ± 11.3 and 62 ± 14.5). There was no significant difference in TIA and active control group MoCA scores. The TMT was able to detect cognitive impairment in TIA and migraine group. Also, both KINARM tasks could detect significant differences in performance between TIA and migraine patients while the MoCA could not. Changes in TIA and migraine performance on the MoCA, TMT, and SST were observed. Conclusions: The robotic KINARM exoskeleton can be used to assess cognitive deficits in TIA patients.

Computational Investigation of Inlet Distortion at High Angles of Attack

Lip separation is one of the primary sources of inlet distortion, which can result in a loss in fan stability. High angles of incidence are one of several critical causes of lip separation. There have been many studies into inlet performance at high incidence, including the resulting distortion levels when lip separation occurs. However, the vast majority of these investigations have been carried out experimentally, with little in the way of computational results for inlet performance at high incidence. The flow topology within an inlet when lip separation has occurred is also not well understood. This work aims to demonstrate a suitable model for the prediction of inlet flows at high incidence using ANSYS CFX, looking at both the performance of the inlet and the separated flow topology within the inlet. The attenuating effect of the fan is also investigated, with particular emphasis on the flow redistribution ahead of the fan. The results show that the model used is suitable for predicting inlet performance in adverse operating conditions, showing good agreement with experimental results. In addition, the attenuation of the distortion by the fan is also captured by the numerical model.

Impact of Metric Selection on Wireless DeAuthentication DoS Attack Performance

DeAuthentication Denial of Service attacks in Public Access WiFi operate by exploiting the lack of authentication of management frames in the 802.11 protocol. Detection of these attacks rely almost exclusively on the selection of appropriate thresholds. In this work the authors demonstrate that there are additional, previously unconsidered, metrics which also influence DoS detection performance. A method of systematically tuning these metrics to optimal values is proposed which ensures that parameter choices are repeatable and verifiable.