Climate changes and mycotoxins: two risk factors associated!

Many species of fungi produce bioactive compounds called mycotoxins. These compounds are produced by filamentous fungi and can contaminate food, feeds and specific indoor environments resulting in high economic losses. Severe health problems and death have been related with mycotoxins exposure through the consumption of several food commodities. There are many factors involved in mycotoxin production by fungi but climate is the most important. Thus, when changes in the weather occur, mycotoxins production will be affected. We looked for articles that were available in scientific databases, written in English and that mention in the title and/or abstract the combined terms fungi and climate change and also mycotoxins and climate change.

The daily and hourly energy consumption and load forecasting using artifitial neural network method: a case study using a set of 93 households in Portugal

It is important to understand and forecast a typical or a particularly household daily consumption in order to design and size suitable renewable energy systems and energy storage. In this research for Short Term Load Forecasting (STLF) it has been used Artificial Neural Networks (ANN) and, despite the consumption unpredictability, it has been shown the possibility to forecast the electricity consumption of a household with certainty. The ANNs are recognized to be a potential methodology for modeling hourly and daily energy consumption and load forecasting. Input variables such as apartment area, numbers of occupants, electrical appliance consumption and Boolean inputs as hourly meter system were considered. Furthermore, the investigation carried out aims to define an ANN architecture and a training algorithm in order to achieve a robust model to be used in forecasting energy consumption in a typical household. It was observed that a feed-forward ANN and the Levenberg-Marquardt algorithm provided a good performance. For this research it was used a database with consumption records, logged in 93 real households, in Lisbon, Portugal, between February 2000 and July 2001, including both weekdays and weekend. The results show that the ANN approach provides a reliable model for forecasting household electric energy consumption and load profile. © 2014 The Author.