To guarantee a high indoor air quality is an increasingly important task. Sensors measure pollutants in the air and allow for monitoring and controlling air quality. However, all sensors are susceptible to failures, either permanent or transitory, that can yield incorrect readings. Automatically detecting such faulty readings is therefore crucial to guarantee sensors' reliability. In this paper we evaluate three Machine Learning algorithms applied to the task of classifying a single reading from a sensor as faulty or not, comparing them to standard statistical approaches. We show that all tested machine learning methods -- Multi-layer Perceptron, K-Nearest Neighbor and Random Forest -- outperform their statistical counterparts, both by allowing better separation boundaries and by allowing for the use of contextual information. We further show that this result does not depend on the amount of data, but ML methods are able to continue to improve as more data is made available.

The establishment of the Gaelic Athletic Association in November 1884 ushered in a sporting revolution in Ireland. Within four years it was by far the largest sports body in the country. Yet despite this remarkable initial success, the 1890s would witness its almost total disintegration. The accepted historiography has tended to concentrate disproportionately on the impact of idiosyncratic factors such as the Parnell split on the GAA's fortunes at this time. Instead, this article argues that the effects of political dissension represented only the Association's death knell. It contends that by 1890, the GAA was already in a virtual state of collapse due to the effects of economic recession and mass emigration, and the impact of factors affecting other contemporary sports bodies such as poor administration, ill-defined playing rules and alcohol fuelled hooliganism.

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