Changes in precipitation extremes for the Basilicata region, southern Italy, have been analyzed using data from 55 precipitation stations with complete daily time series during the period 1951–2010. All the series were submitted to quality control assessment and homogenization. To detect possible trends the time series analysis was performed with the Mann–Kendall non‐parametric test. The annual and seasonal total precipitation underwent a general downward trend over the period 1951–2010 mainly due to the autumn‐winter decrease of precipitation, although the tendency for the last decade is clearly positive. The precipitation intensity shows a general positive trend, mainly due to the upward trend of spring. The dry spell mean has increased throughout the region over 1951–2010, even if a really important opposite trend characterizes the last decade. The wet spell mean has decreased throughout the region from 1951 to 2010, although a strong inversion of tendency has been recorded in the last 10 years. Trends in the extreme daily precipitation have indicated a general downward tendency, mainly during the summer season. The analysis of multi‐day sequences of moderate to heavy rainfall has indicated a corresponding increase in their frequency and intensity, especially in the last decade. The overall results indicate a present hydroclimatic regime characterized by an increase in total rainfall and precipitation intensity and a small decrease in dry spell lengths. The positive change in precipitation magnitude is due to multi‐day extreme precipitation rather than to single‐day precipitation. This last observation is very important for its huge hydrological impact on the environment. In Basilicata, the increase in intensity/frequency of multi‐days extreme events has led to the growth of severe flooding and landsliding events, not only in autumn and winter, but even in the early spring.

The artificial neural networks (ANNs) are statistical models where the mathematical structure reproduces the biological organisation of neural cells simulating the learning dynamics of the brain. Although definitions of the term ANN could vary, the term usually refers to a neural network used for non-linear statistical data modelling. The neural models applied today in various fields of medicine, such as oncology, do not aim to be biologically realistic in detail but just efficient models for nonlinear regression or classification. ANN inference has applications in tasks that require attention focusing. ANNs also have a niche to carve in clinical decision support, but their success depends crucially on better integration with clinical protocols, together with an awareness of the need to combine different paradigms to produce the simplest and most transparent overall reasoning structure, and the will to evaluate this in a real clinical environment. We have performed an assessment of the evidence for improvements in the use of ANN in lung cancer research. Our analysis showed that often the use of ANN in the medical literature had not been performed in an accurate manner. A strict cooperation between physician and biostatisticians could be helpful in determine and resolve these errors.

The topic of attribution of recent global warming is usually faced by studies performed through global climate models (GCMs). Even simpler econometric models have been applied to this problem, but they led to contrasting results. In this article, we show that a genuine predictive approach of Granger analysis leads to overcome problems shown by these models and to obtain a clear signal of linear Granger causality from greenhouse gases (GHGs) to the global temperature of the second half of the 20th century. In contrast, Granger causality is not evident using time series of natural forcing. Copyright © 2011 Royal Meteorological Society