Audit report on the Muscatine Agricultural Learning Center for the year ended December 31, 2013

Audit report on the Muscatine Agricultural Learning Center for the year ended December 31, 2013

Data-driven topo-climatic mapping with machine learning methods

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.

Learning form Nature to improve the heat generation of iron-oxide nanoparticles for magnetic hyperthermia applications.

The performance of magnetic nanoparticles is intimately entwined with their structure, mean size and magnetic anisotropy. Besides, ensembles offer a unique way of engineering the magnetic response by modifying the strength of the dipolar interactions between particles. Here we report on an experimental and theoretical analysis of magnetic hyperthermia, a rapidly developing technique in medical research and oncology. Experimentally, we demonstrate that single-domain cubic iron oxide particles resembling bacterial magnetosomes have superior magnetic heating efficiency compared to spherical particles of similar sizes. Monte Carlo simulations at the atomic level corroborate the larger anisotropy of the cubic particles in comparison with the spherical ones, thus evidencing the beneficial role of surface anisotropy in the improved heating power. Moreover we establish a quantitative link between the particle assembling, the interactions and the heating properties. This knowledge opens new perspectives for improved hyperthermia, an alternative to conventional cancer therapies.

Satisfaction survey of Directed Experiential Learning (DEL)

The aim of this questionnaire is to assess your experience of the DEL learning process as developed by your university. The questionnaire is anonymous and it should not take more than fifteen minutes to complete.

Sharing and Learning= Changing. A real story at the Institute of Public Security of Catalonia

The Institute has professionals with extensive experience in training, specifically with experience in the field of police and emergencies training. Moreover, it also has very talented people. But above all, our institution has public professionals with a desire to serve, who love security and emergency responders and want to provide them with the best knowledge to make them every day better professionals. In the quest for continuous training improvement, its during 2009 when e-learning begins to have a presence at the Institute. Virtual training methodology becomes a facilitator for the training of various professionals, avoiding geographical displacement and easing the class schedule.