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This paper seeks to examine the development of a pilot program for using podcasting as a tool in the provision of information literacy in an academic library. It aims to discuss the implementation process and the issues encountered in developing a podcasting series at the Curtin University Library.

The possibilities for using podcasts to deliver library information literacy in an academic library are discussed in reference to current literature and trends. The method for creating a podcasting series, including the equipment, software, RSS feed, legal issues and cost and staffing implications, is outlined along with the parameters used by the Curtin University Library in the development of a pilot series.

The paper finds that podcasts offer libraries a new method of delivering information literacy to their clients. It is possible to create a podcasting series with minimal expense and the simple production method enables many libraries to take advantage of this new technology. The podcasting series at Curtin has proven to be popular with downloads increasing steadily over the course of the semester. There have been over 9,000 downloads of the audio files to the end of November 2006. By taking advantage of this ubiquitous technology libraries can communicate with their clientele in a new and exciting way.

The paper outlines how to create a podcasting series for information literacy in an academic library environment, and provides recommendations for other libraries wishing to create their own podcasting series.

The assessment of the Real Estate (RE) prices depends on multiple factors that traditional evaluation methods often struggle to fully understand. Housing prices, in particular, are the foundations for a better knowledge of the Built Environment and its characteristics. Recently, Machine Learning (ML) techniques, which are a subset of Artificial Intelligence, are gaining momentum in solving complex, non-linear problems like house price forecasting. Hence, this study deployed three popular ML techniques to predict dwelling prices in two cities in Italy.

An extensive dataset about house prices is collected through API protocol in two cities in North Italy, namely Brescia and Varese. This data is used to train and test three most popular ML models, i.e. ElasticNet, XGBoost and Artificial Neural Network, in order to predict house prices with six different features.

The models' performance was evaluated using the Mean Absolute Error (MAE) score. The results showed that the artificial neural network performed better than the others in predicting house prices, with a MAE 5% lower than the second-best model (which was the XGBoost).

All the models had an accuracy drop in forecasting the most expensive cases, probably due to a lack of data.

The accessibility and easiness of the proposed model will allow future users to predict house prices with different datasets. Alternatively, further research may implement a different model using neural networks, knowing that they work better for this kind of task.

To date, this is the first comparison of the three most popular ML models that are usually employed when predicting house prices.