Study abroad and complexity, accuracy and fluency (CAF) development: a longitudinal investigation of French and Chinese Learners of L2 English

This longitudinal study tracked third-level French (n=10) and Chinese (n=7) learners of English as a second language (L2) during an eight-month study abroad (SA) period at an Irish university. The investigation sought to determine whether there was a significant relationship between length of stay (LoS) abroad and gains in the learners' oral complexity, accuracy and fluency (CAF), what the relationship was between these three language constructs and whether the two learner groups would experience similar paths to development. Additionally, the study also investigated whether specific reported out-of-class contact with the L2 was implicated in oral CAF gains. Oral data were collected at three equidistant time points; at the beginning of SA (T1), midway through the SA sojourn (T2) and at the end (T3), allowing for a comparison of CAF gains arising during one semester abroad to those arising during a subsequent semester. Data were collected using Sociolinguistic Interviews (Labov, 1984) and adapted versions of the Language Contact Profile (Freed et al., 2004). Overall, the results point to LoS abroad as a highly influential variable in gains to be expected in oral CAF during SA. While one semester in the TL country was not enough to foster statistically significant improvement in any of the CAF measures employed, significant improvement was found during the second semester of SA. Significant differences were also revealed between the two learner groups. Finally, significant correlations, some positive, some negative, were found between gains in CAF and specific usage of the L2. All in all, the disaggregation of the group data clearly illustrates, in line with other recent enquiries (e.g. Wright and Cong, 2014) that each individual learner's path to CAF development was unique and highly individualised, thus providing strong evidence for the recent claim that SLA is "an individualized nonlinear endeavor" (Polat and Kim, 2014: 186).

A facilities maintenance management process based on degradation prediction using sensed data

Energy efficiency and user comfort have recently become priorities in the Facility Management (FM) sector. This has resulted in the use of innovative building components, such as thermal solar panels, heat pumps, etc., as they have potential to provide better performance, energy savings and increased user comfort. However, as the complexity of components increases, the requirement for maintenance management also increases. The standard routine for building maintenance is inspection which results in repairs or replacement when a fault is found. This routine leads to unnecessary inspections which have a cost with respect to downtime of a component and work hours. This research proposes an alternative routine: performing building maintenance at the point in time when the component is degrading and requires maintenance, thus reducing the frequency of unnecessary inspections. This thesis demonstrates that statistical techniques can be used as part of a maintenance management methodology to invoke maintenance before failure occurs. The proposed FM process is presented through a scenario utilising current Building Information Modelling (BIM) technology and innovative contractual and organisational models. This FM scenario supports a Degradation based Maintenance (DbM) scheduling methodology, implemented using two statistical techniques, Particle Filters (PFs) and Gaussian Processes (GPs). DbM consists of extracting and tracking a degradation metric for a component. Limits for the degradation metric are identified based on one of a number of proposed processes. These processes determine the limits based on the maturity of the historical information available. DbM is implemented for three case study components: a heat exchanger; a heat pump; and a set of bearings. The identified degradation points for each case study, from a PF, a GP and a hybrid (PF and GP combined) DbM implementation are assessed against known degradation points. The GP implementations are successful for all components. For the PF implementations, the results presented in this thesis find that the extracted metrics and limits identify degradation occurrences accurately for components which are in continuous operation. For components which have seasonal operational periods, the PF may wrongly identify degradation. The GP performs more robustly than the PF, but the PF, on average, results in fewer false positives. The hybrid implementations, which are a combination of GP and PF results, are successful for 2 of 3 case studies and are not affected by seasonal data. Overall, DbM is effectively applied for the three case study components. The accuracy of the implementations is dependant on the relationships modelled by the PF and GP, and on the type and quantity of data available. This novel maintenance process can improve equipment performance and reduce energy wastage from BSCs operation.