Extreme rainfall events are a serious concern for regional hydrology and agriculture in the Ebro River Basin. Repeated anomalous rainfall in recent decades has had a devastating impact on this region, both socially and economically. Some studies developed in Italy and USA have shown that there is a change in seasonal patterns and an increasing frequency of extreme rainfall events, whereas other studies have pointed out that no global behaviour could be observed in monthly trends due to high climatic variability. The aim of this work is to test which of these scenarios is the case for the Ebro River Basin. For this purpose, 14 meteorological stations were selected based on the length of the rainfall series and the climatic classification to obtain a representative untreated dataset from the river basin. Daily rainfall series from 1957 to 2002 were obtained from each meteorological station. First, classical climatic indexes were analysed with an autoregressive test to study possible trends in rainfall. The results can be explained following the evolution of the NAO and WeMO indexes, which indicate that the initial period should be subdivided in two periods (1957–1979 and 1980–2002) to assume stationarity and to analyse the rainfall distribution functions. The general results obtained in this study for both sub-periods, through the generalised Pareto distribution (GPD) parameters and the maximum expected return values, do not support the results previously obtained by other authors that affirm a positive trend in extreme rainfall indexes and point to a slight reduction indicated by others. Three extreme precipitation indexes show negative statistical significant trends. GPD-scale parameters decrease except for only one rain gauge, although this decrease is only statistically significant for two rain gauges. Another two locations show statistical significance decreased for maximum expected return values.

One of the lightest metallic materials used in the aeronautics, aerospace, and automotive industries, among others, is magnesium, due to its excellent weight/strength ratio. Most parts used in these industries need to be made of materials that are rigid, strong, and lightweight, but sometimes the materials do not simultaneously satisfy all of the properties required. An alternative is to combine two or more materials, giving rise to a hybrid component that can satisfy a wider range of properties. The pieces machined in these industrial fields must satisfy stringent surface roughness requirements that conform to the design specifications. This work shows an experimental study to analyse the surface roughness reached in hybrid components made up of a base of magnesium alloy (UNS M11917) and two inserts of aluminium alloy (UNS A92024) obtained by turning. Its purpose is to determine the influence of the factors and their possible interactions on the response variable, the surface roughness Ra. The study is carried out using a design of experiments (DOE). A product of a full factorial 2³ and a block of two factors 3 × 2 was selected. The factors identified as possible sources of variation of the surface roughness are: depth of cut, feed rate, spindle speed, type of tool, location with respect to the specimen (LRS), and location with respect to the insert (LRI). Data were analysed by means of the analysis of variance (ANOVA) method. The main conclusion is the possibility to carry out the repair and maintenance of parts of magnesium–aluminum hybrid components by dry turning; that is, without cutting fluids and, therefore, in the most sustainable way that the process can be carried out. In addition, different combinations of cutting parameters have been identified that allow these operations to be carried out in an efficient manner, reducing mechanization times and, therefore, also the direct and indirect costs associated with them.

Drilling is one of the most common machining operations in the aeronautic and aerospace industries. For assembling parts, a large number of holes are usually drilled into the parts so that they can be joined later by rivets. As these holes are subjected to fatigue cycles, they have to be checked regularly for maintenance or repair, since small cracks or damage in its contour can quickly cause breakage of the part, which can have dangerous consequences. This paper focuses on finding the best combinations of cutting parameters to perform repair and maintenance operations of holes in stacked hybrid magnesium–titanium–magnesium components in an efficient, timely, and sustainable (without lubricants or coolants) manner, under dry drilling conditions. For the machining trials, experiments were designed and completed. A product of a full factorial 2³ and a block of two factors (3 × 2) was used with surface roughness as the response variable measured as the mean roughness average. Analysis of variance (ANOVA) was used to examine the results. A set of optimized tool and cutting conditions is presented for performing dry drilling repair operations.

Rainfall, one of the most important climate variables, is commonly studied due to its great heterogeneity, which occasionally causes negative economic, social, and environmental consequences. Modeling the spatial distributions of rainfall patterns over watersheds has become a major challenge for water resources management. Multifractal analysis can be used to reproduce the scale invariance and intermittency of rainfall processes. To identify which factors are the most influential on the variability of multifractal parameters and, consequently, on the spatial distribution of rainfall patterns for different time scales in this study, universal multifractal (UM) analysis—C₁, α, and γ_s UM parameters—was combined with non-parametric statistical techniques that allow spatial-temporal comparisons of distributions by gradients. The proposed combined approach was applied to a daily rainfall dataset of 132 time-series from 1931 to 2009, homogeneously spatially-distributed across a 25 km × 25 km grid covering the Ebro River Basin. A homogeneous increase in C₁ over the watershed and a decrease in α mainly in the western regions, were detected, suggesting an increase in the frequency of dry periods at different scales and an increase in the occurrence of rainfall process variability over the last decades.

We propose a fuzzy random survival forest (FRSF) to model lapse rates in a life insurance portfolio containing imprecise or incomplete data such as missing, outlier, or noisy values. Following the random forest methodology, the FRSF is proposed as a new machine learning technique for solving time-to-event data using an ensemble of multiple fuzzy survival trees. In the learning process, the combination of methods such as the c-index, fuzzy sets theory, and the ensemble of multiple trees enable the automatic handling of imprecise data. We analyse the results of several experiments and test them statistically; they show the FRSF’s robustness, verifying that its generalisation capacity is not reduced when modelling imprecise data. Furthermore, the results obtained using a real portfolio of a life insurance company demonstrate that the FRSF has a better performance in comparison with other state-of-the-art algorithms such as the traditional Cox model and other tree-based machine learning techniques such as the random survival forest.