No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

Two studies are reported which investigated the effect of individual demographics on training performance in air traffic controllers. The first study investigated the relationship between the demographics of age, sex and education level, and pass/fail rates at an air traffic control training centre. The data for this study was an historic data set provided by Airways Corporation, which oversees air traffic management in New Zealand. This includes training all controllers and providing all air traffic control in the country. The primary result of the first study was that trainees with post-secondary education achieved better during training than trainees with a high school education. Additionally, the level of education attained by the trainee appeared to be the best predictor (of the three demographic characteristics) for a trainee’s success. The data was limited, due to a significant amount of incomplete trainee records. This impaired the ability to conclusively resolve the role of these demographic characteristics for trainee success. The second study, investigated the impact these same demographics may have on trainee success in a much smaller (N=16), but complete (i.e., no missing records) and current cohort. In addition, the feedback given and received in a training centre to the 16 trainees was examined. The trainees under consideration in this study were attending the Airways training centre. The trainees’ debriefs after their air traffic control training sessions in Airways’ immersive tower simulator were recorded. The trends in the data validate the need for further research. The primary result of the second study however, was that there was a significant difference in the pass rates of those trainees with only a high school education (66.7% failed) and those with post-secondary education (0% failed). The other two demographic characteristics of interest, age and sex, did not significantly differ for those trainees who passed and failed. The combined results of these studies indicate that the air-traffic control community in New Zealand may benefit from further investigating these differences and potentially raising education requirements for air traffic control trainees.

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In this study, 22 homes in Milton, Ontario had their electricity consumption monitored for between seven and 15 months, and they were provided access to their data via an online webportal. The webportal provided appliance-level and house-level data, allowed them to set consumption goals, and schedule when their appliances would be used. The households were chosen to participate because they had previously expressed interest in advanced smart meter grid technologies, and when contacted again by Milton Hydro, they agreed to participate in the study. The main question being asked in this research is: what effect, if any, does having access to one???s consumption data have on consumption? To investigate this question, consumption data from the monitoring period, and the previous year (the base year) were obtained from Milton Hydro and were used to determine how consumption changed between these two periods. The consumption data for the cooling months were weather normalized to account for increases in consumption that result from cooling the dwelling. Data regarding users??? engagement with the webportal, including how often they would login, for how long and what pages they were visiting, were collected from the webportal. An engagement index was adapted and refined from Peterson & Carrabis (2008), and along with the engagement data from the webportal, was used to calculate the engagement index. Data from two surveys were used to profile the households and to investigate their attitudes and behaviours towards electricity consumption. There were several key findings. First, engagement with the webportal was quite low; the engagement index (a value between zero and one) for the first three months the hub was open averaged 0.285 and ranged from 0 to 0.523. These numbers dropped by the end of the seventh month to an average engagement index of 0.163, and ranged from 0 to 0.341. The second key finding was that the hubs were not consistently conserving electricity; for the first three months, 10 of the 22 households had conserved electricity between the base year and monitoring period; at the end of the seventh month, this dropped to nine households. At the end of the third month, the change in consumption was an increase of 8.22%, and at the end of month seven it was an increase of 7.71%. The third finding was that there did not appear to be a connection between energy conserving attitudes and energy conserving behaviours. In the surveys, 12 households stated that their goal was to conserve electricity, however, of these 12, only four actually conserved electricity at the end of month seven. Finally, when comparing the engagement index with the change in consumption, there appeared to be only a weak, negative correlation between the variables. This weak correlation may be a result of two things: (1) a lack of engagement, which limits the ability to find correlation between engagement and change in consumption; (2) there is actually a weak relation between the two variables. Based on these findings, some recommendations are put forth, specifically about how to engage householders with the webportal. Suggestions include getting applications for mobile devices, and delivering electricity saving tips to households via e-mail, text message, and/or on the homepage of the portal. These tips could be given based on the season, or based on the goals that were set, and would encourage and explain to householders how to decrease consumption.