Doctor of Philosophy

dissertationSlips and falls during egress from heavy truck cabs are a major contributor to injury and disability for truck drivers. A large-scale laboratory study was conducted to quantify the dynamics of ingress/egress (IE) for Class 7 and 8 commercial truck cabs. A simulated truck cab was constructed in a laboratory allowing manipulation of many geometric variables affecting ingress and egress. Experienced commercial truck drivers were recruited to participate. Subjective responses and anthropometric information for all participants were obtained along with detailed biomechanical data, including whole-body kinematics and reaction forces on the ground, steps, and handholds. This study involves three-dimensional reconstruction of truck driver egress motions, detailed analysis of spatiotemporal parameters and driver behaviors (i.e., IE tactics), as well as a description of access system egress cycles and methods of analyses. In addition, the influence of cab design and driver anthropometric and behavioral factors on biomechanical parameters are investigated. This research also provides a detailed quantitative description of the driver interaction with the cab elements (steps and handholds) and presents valuable insight into the dynamics of cab egress that will allow for a more accurate definition of etiological risk factors for slipping during truck cab egress. In summary, driver biomechanics largely depends on their interaction with the cab, tactics, foot behaviors, and the quality of contact with the steps. In general, during egress, study participants used the right handhold most frequently, followed by the door handle and then the steering wheel. Findings from this research also indicated that a portion of drivers performed egress facing away from the cab and given the prevalence of high body mass index (BMI) among this population, handhold and step location and design should incorporate the base of support (BoS) and stability metric calculations to allow such population for proper "footing" and allow for their center of mass (CoM) to be as close to the truck as possible in the event the drivers utilized the facing away egress tactic. Finally, BMI is a factor that has been associated as an indicator of increased level of risk. Therefore, driver training should include opportunities to get the drivers' weight lowered and fitness level increased. Additionally, drivers may also benefit from stability and strength training as stair stepping is physically more demanding and requires more stability when compared to walking

DCU-TCD@LogCLEF 2010: re-ranking document collections and query performance estimation

This paper describes the collaborative participation of Dublin City University and Trinity College Dublin in LogCLEF 2010. Two sets of experiments were conducted. First, different aspects of the TEL query logs were analysed after extracting user sessions of consecutive queries on a topic. The relation between the queries and their length (number of terms) and position (first query or further reformulations) was examined in a session with respect to query performance estimators such as query scope, IDF-based measures, simplified query clarity score, and average inverse document collection frequency. Results of this analysis suggest that only some estimator values show a correlation with query length or position in the TEL logs (e.g. similarity score between collection and query). Second, the relation between three attributes was investigated: the user's country (detected from IP address), the query language, and the interface language. The investigation aimed to explore the influence of the three attributes on the user's collection selection. Moreover, the investigation involved assigning different weights to the three attributes in a scoring function that was used to re-rank the collections displayed to the user according to the language and country. The results of the collection re-ranking show a significant improvement in Mean Average Precision (MAP) over the original collection ranking of TEL. The results also indicate that the query language and interface language have more in uence than the user's country on the collections selected by the users

Identifying common user behaviour in multilingual search logs

The LADS (Log Analysis for Digital Societies) task at CLEF aims at investigating user actions in a multilingual setting. We carried out an analysis of search logs with the objectives of investigating how users from different linguistic or cultural backgrounds behave in search, and how the discovery of patterns in user actions could be used for community identification. The findings confirm that users from a different background behave differently, and that there are identifiable patterns in the user actions. The findings suggest that there is scope for further investigation of how search logs can be exploited to personalise and improve cross-language search as well as improve the TEL search system

Joint Geo-Spatial Preference and Pairwise Ranking for Point-of-Interest Recommendation

Recommending users with preferred point-of-interests (POIs) has become an important task for location-based social networks, which facilitates users' urban exploration by helping them filter out unattractive locations. Although the influence of geographical neighborhood has been studied in the rating prediction task (i.e. regression), few work have exploited it to develop a ranking-oriented objective function to improve top-N item recommendations. To solve this task, we conduct a manual inspection on real-world datasets, and find that each individual's traits are likely to cluster around multiple centers. Hence, we propose a co-pairwise ranking model based on the assumption that users prefer to assign higher ranks to the POIs near previously rated ones. The proposed method can learn preference ordering from non-observed rating pairs, and thus can alleviate the sparsity problem of matrix factorization. Evaluation on two publicly available datasets shows that our method performs significantly better than state-of-the-art techniques for the top-N item recommendation task

Localized Computation of Newton Updates in Fully-implicit Two-phase Flow Simulation

AbstractFully-Implicit (FI) Methods are often employed in the numerical simulation of large-scale subsurface flows in porous media. At each implicit time step, a Newton-like method is used to solve the FI discrete nonlinear algebraic system. The linear solution process for the Newton updates is the computational workhorse of FI simulations. Empirical observations suggest that the computed Newton updates during FI simulations of multiphase flow are often sparse. Moreover, the level of sparsity observed can vary dramatically from iteration to the next, and across time steps. In several large scale applications, it was reported that the level of sparsity in the Newton update can be as large as 99%. This work develops a localization algorithm that conservatively predetermines the sparsity pattern of the Newton update. Subsequently, only the flagged nonzero components of the system need be solved. The localization algorithm is developed for general FI models of two phase flow. Large scale simulation results of benchmark reservoir models show a 10 to 100 fold reduction in computational cost for homogeneous problems, and a 4 to 10 fold reduction for strongly heterogeneous problems