Tip-over injuries in children under 5 years of age in Queensland 1999-2008

Furniture and appliance related injuries in children under 5 years of age accounts for an estimated 180 emergency presentations annually in Queensland. Injuries occur when children push or pull items over, climb and fall off furniture, or climb and tip the item over. Children under 2 years of age tend to injure themselves by pulling items over onto themselves Children over 2 years of age are more likely to be injured after climbing the item and either falling off or tipping the item over onto themselves. Tip over injuries (where the item falls over and injures the child) in children under 5 years of age account for an estimated 115 emergency presentations annually in Queensland. The item most commonly associated with a tip over injury is a television (with or without the cabinet) Prevention requires better design and selection of furniture with inherent stability coupled with mechanisms to install or fix less stable items

Pram and stroller related injury in Queensland children under 5 years of age

An estimated 200 Queensland children under 5 years of age are injured every year in incidents involving prams or strollers. The majority of injuries are due to falls from or falls with the pram or stroller Nineteen children were identified as having been caught in the pram or stroller mechanism (13 sustained finger injuries). Stairs and escalators were a factor in nearly 10 percent of pram or stroller fall injuries, with children being tipped out of the pram or stroller, or rolling down the stairs in the device. Roll away injuries accounted for eight percent of all pram or stroller fall injuries (some also involving stairs) Roll away injuries could be prevented by a default brake system similar to airport trolleys. Pram or stroller failure was identified in 2% of injuries

An effective approach to verbose queries using a limited dependencies language model

Intuitively, any `bag of words' approach in IR should benefit from taking term dependencies into account. Unfortunately, for years the results of exploiting such dependencies have been mixed or inconclusive. To improve the situation, this paper shows how the natural language properties of the target documents can be used to transform and enrich the term dependencies to more useful statistics. This is done in three steps. The term co-occurrence statistics of queries and documents are each represented by a Markov chain. The paper proves that such a chain is ergodic, and therefore its asymptotic behavior is unique, stationary, and independent of the initial state. Next, the stationary distribution is taken to model queries and documents, rather than their initial distri- butions. Finally, ranking is achieved following the customary language modeling paradigm. The main contribution of this paper is to argue why the asymptotic behavior of the document model is a better representation then just the document's initial distribution. A secondary contribution is to investigate the practical application of this representation in case the queries become increasingly verbose. In the experiments (based on Lemur's search engine substrate) the default query model was replaced by the stable distribution of the query. Just modeling the query this way already resulted in significant improvements over a standard language model baseline. The results were on a par or better than more sophisticated algorithms that use fine-tuned parameters or extensive training. Moreover, the more verbose the query, the more effective the approach seems to become.

The effect of language models on phonetic decoding for spoken term detection

Spoken term detection (STD) popularly involves performing word or sub-word level speech recognition and indexing the result. This work challenges the assumption that improved speech recognition accuracy implies better indexing for STD. Using an index derived from phone lattices, this paper examines the effect of language model selection on the relationship between phone recognition accuracy and STD accuracy. Results suggest that language models usually improve phone recognition accuracy but their inclusion does not always translate to improved STD accuracy. The findings suggest that using phone recognition accuracy to measure the quality of an STD index can be problematic, and highlight the need for an alternative that is more closely aligned with the goals of the specific detection task.