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Accurate rainfall estimation is crucial to adequately assess the risk associated with extreme events capable of triggering floods and landslides. Data gathered from Rain Gauges (RGs), sensors devoted to measuring the intensity of the rain at individual points, are commonly used to feed interpolation methods (e.g., the Kriging geostatistical approach) and estimate the precipitation field over an area of interest. However, the information provided by RGs could be insufficient to model complex phenomena, and computationally expensive interpolation methods could not be used in real-time environments. Integrating additional data sources (e.g., radar and geostationary satellites) is an effective solution for improving the quality of the estimate, but it needs to cope with Big Data issues. To overcome all these issues, we propose a Rainfall Estimation Model (REM) based on an Ensemble of Deep Neural Networks (DeepEns-REM) that can automatically fuse heterogeneous data sources. The usage of Residual Blocks in the base models and the adoption of a Snapshot procedure to build the ensemble guarantees a fast convergence and scalability. Experimental results, conducted on a real dataset concerning a southern region in Italy, demonstrate the quality of the proposal in comparison with the Kriging interpolation technique and other machine learning techniques, especially in the case of exceptional rainfall events.

Providing an accurate rainfall estimate at individual points is a challenging problem in order to mitigate risks derived from severe rainfall events, such as floods and landslides. Dense networks of sensors, named rain gauges (RGs), are typically used to obtain direct measurements of precipitation intensity in these points. These measurements are usually interpolated by using spatial interpolation methods for estimating the precipitation field over the entire area of interest. However, these methods are computationally expensive, and to improve the estimation of the variable of interest in unknown points, it is necessary to integrate further information. To overcome these issues, this work proposes a machine learning-based methodology that exploits a classifier based on ensemble methods for rainfall estimation and is able to integrate information from different remote sensing measurements. The proposed approach supplies an accurate estimate of the rainfall where RGs are not available, permits the integration of heterogeneous data sources exploiting both the high quantitative precision of RGs and the spatial pattern recognition ensured by radars and satellites, and is computationally less expensive than the interpolation methods. Experimental results, conducted on real data concerning an Italian region, Calabria, show a significant improvement in comparison with Kriging with external drift (KED), a well-recognized method in the field of rainfall estimation, both in terms of the probability of detection (0.58 versus 0.48) and mean-square error (0.11 versus 0.15).

In the present work the spontaneous dynamics of a ring of N Chua's oscillators, mutually coupled through a resistor R in anearest-neighbor configuration, is investigated numerically for different strengths of the coupling. A transition from periodic to chaoticglobal dynamics is observed when the coupling decreases below a critical value and complex patterns in the spatiotemporal dynamics of thering emerge for a small coupling interval after the transition to chaos. The recovered behavior, as well asthe value of the criticalthreshold, appears to be independent of the size of the ring. We also propose aninterpretation of this property, which relates the regularsynchronized dynamics of the ring to the dynamics of the isolated oscillator. Finally,for the ring of the coupled oscillator, a theoretical wavedispersion relation is calculated and successfully compared with the results of thenumerical simulations, analyzed by classicaltechniques adopted for turbulent flows.

The work shows the results of a multi-analytical study performed on twenty-nine brick samples taken from Hagia Sophia in Istanbul (Turkey). Hagia Sophia, one of the most important historical buildings in the world, has a very complex construction history. This complexity is also reflected in the materials that were used for its construction. The main purpose of this work is to verify whether there are any compositional differences in the bricks used in different historical periods, but also to understand the reasons for any compositional differences between one period and another. The samples were studied by optical microscopy (OM), X-ray fluorescence (XRF), X-ray powder diffraction (XRPD) and thermogravimetric analysis (TGA), coupled with a new approach, based on the micro-chemical EDS analysis used to obtain information on the clay fraction of the matrix. The study showed that, most probably, the differences between the bricks belonging to the different construction phases are due to the composition of the clays used for their preparation.

The surroundings of Corigliano and Rossano (NE Calabria) were damaged by a short-lasting and localized rainstorm on 12 August 2015, when more than 230 mm of rain in sixteen hours caused damage exceeding 100 million euros. In the Map, shallow landslides triggered by the event are shown at 1:50,000 scale, together with areas affected by soil erosion and flooding, and most damaged sites; historical landslides are also mapped. The main slope-stability controlling factors are mapped at 1:200,000 scale, and an event-based geomorphological landslide inventory of the most damaged sector (Citrea basin) is provided at 1:10,000 scale. Isohyets (cumulative over 16 h), as obtained by interpolating rain-gauge and weather-radar data by Kriging techniques, are shown together with the density of event landslides. Estimated rain maxima (ca. 506 mm in 16 h) by far overcome measured ones, and ground effects well fit the sectors heavily stricken by the storm.

Accurate rainfall estimates are critical for areas presenting high hydrological risks. We have devised a general machine learning framework based on a deep learning architecture, which also integrates information derived from remote sensing measurements, such as weather radars and satellites. Experimental results conducted on real data from a southern region in Italy, provided by the Department of Civil Protection (DCP), show significant improvements compared to current state-of-the-art methods.

Defining criteria for correct distribution of water resource is a common engineering problem. Stringent regulations on environmental impacts underline the need for sustainable management and planning of this resource usage, which is sensitive to many parameters. Optimization models are often used to deal with these problems, identifying the optimal configuration of a Water Distribution Network (WDN) in terms of minimizing an appropriate function propotional to he construction cost of the WDN. Generally, this cost function increases as the distance between the source-user connection increases, therefore in minimum cost optimization models is important to identify minimum source-user paths compatible with the orography. In this direction, the methodology presented in the present work proposes a useful approach to find minimum-length paths on surfaces, which moreover respect suitable hydraulic constraints and are therefore representative of reliable gravity water pipelines. The application of the approach is presented in a real case in Calabria.

A severe rainfall event occurred in southern Calabria between 29 October and 2 November 2015, causing two deaths and serious damage to transport infrastructure. Widespread slope erosion and thousands of shallow landslides were triggered on the slopes, combined with flooding and transport of debris along the streams. Rains recorded by the regional gauge network and the national radar monitoring system were analysed by means of Kriging techniques. Ground effects were surveyed in the field, and mapped using post-event air photos taken along the coastal sector. Shallow landslides, soil erosion (including sheet, rill, and gully erosion), flooding, lateral erosion, and debris deposition along streambeds, overflow on lateral slopes, and fan deposition at the mouths of the streams were mapped at 1:60,000 scale. Isohyets of the rainfall event - in terms of cumulative maxima over 24 h - are also shown on the Main Map. In Annex A, a list of notices of processes and/or damage, arranged by municipalities, with coordinates, types of ground effect, and synthetic descriptions, is also provided.

The proper allocation of water resources is a very important practical problem in the field of water network planning. Optimization models that are expeditious and easy to use for all stakeholders of the sector play an important role for water resource management. The present work resumes and reviews a least-cost optimization model proposed by our group (Maiolo and Pantusa in Water Sci Tech-W Sup. https://doi.org/10.2166/ws.2015.114 , 2016), able to design a water distribution network with multiple supply sources and multiple users. This approach requires of solving an optimization problem based on a nonlinear objective function which is proportional to the cost of the water distribution network. The cost of pre-existing pipelines is considered null. A more realistic scenario, able to consider the maximum flow rate allowed for existing sources-users connections, is considered here. In order to illustrate the usefulness and flexibility of the proposed approach, an application of the model to the real case of the province of Croton, Southern Italy, is presented.

In this paper, a novel path planning and obstacles avoidance method for a mobile robot is proposed. This method makes use of a switching strategy between the attractive potential of the target and a new helicoidal potential field which allows to bypass an obstacle by driving the robot around it. The new technique aims at overcoming the local minima problems of the well-known artificial potentials method, caused by the summation of two (or more) potential fields. In fact, in the proposed approach, only a single potential is used at a time. The resulting proposed technique uses only local information and ensures high robustness, in terms of achieved performance and computational complexity, w.r.t. the number of obstacles. Numerical simulations, together with comparisons with existing methods, confirm a very robust behavior of the method, also in the case of a framework with multiple obstacles.

Rain gauges are sensors providing direct measurement of precipitation intensity at individual point sites, and, usually, spatial interpolation methods are used to obtain an estimate of the precipitation field over the entire area of interest. Among them, Kriging with External Drift (KED) is a largely used and well-recognized method in this field. However, interpolation methods need to work with real-time data, and therefore can be hardly used in real-time scenarios. To overcome this issue, we propose a general machine learning framework, which can be trained offline, based on a deep learning architecture, also integrating information derived from remote sensing measurements such as weather radars and satellites. The framework allows to provide accurate estimations of the rainfall in the areas where no rain gauge data is available. Experimental results, conducted on real data concerning a southern region in Italy, provided by the Department of Civil Protection (DCP), show significant improvement in comparison with KED and other machine learning techniques.

In this note, the exponential synchronization of a class of chaotic uncertain systems is addressed. A linear state feedback control is used which guarantees the synchronization when uncertainties are present both in linear and nonlinear parts of the slave system dynamic. It is shown that, with some a-priori rough bounds on the slave uncertainties, the closed loop stability can be guaranteed. Numerical simulations are presented to show the effectiveness of the proposed method.

The accurate evaluation of the precipitation's time–spatial structure is a critical step for rainfall–runoff modelling. Particularly for small catchments, the variability of rainfall can lead to mismatched results. Large errors in flow evaluation may occur during convective storms, responsible for most of the flash floods in small catchments in the Mediterranean area. During such events, we may expect large spatial and temporal variability. Therefore, using rain-gauge measurements only can be insufficient in order to adequately depict extreme rainfall events. In this work, a double-level information approach, based on rain gauges and weather radar measurements, is used to improve areal rainfall estimations for hydrological applications. In order to highlight the effect that precipitation fields with different level of spatial details have on hydrological modelling, two kinds of spatial rainfall fields were computed for precipitation data collected during 2015, considering both rain gauges only and their merging with radar information. The differences produced by these two precipitation fields in the computation of the areal mean rainfall accumulation were evaluated considering 999 basins of the region Calabria, southern Italy. Moreover, both of the two precipitation fields were used to carry out rainfall–runoff simulations at catchment scale for main precipitation events that occurred during 2015 and the differences between the scenarios obtained in the two cases were analysed. A representative case study is presented in detail.

The assessment of precipitation over land is extremely important for a number of scientific purposes related to the mitigation of natural hazards, climate modelling and prediction, famine and disease monitoring, to cite a few. Due to the difficulties and the cost to maintain ground monitoring networks, i.e., raingauges and meteorological radars, remote sensing is receiving more and more attention in the recent decade(s). However, the accuracy of satellite observations of rainfall should be assessed with ground information as it is affected by a number of factors (topography, vegetation density, land-sea interface). Calabria is a peninsular region in southern Italy characterized by complex topography, dense vegetation and a narrow North-South elongated shape, thus being a very challenging place for rainfall retrieval from remote sensing. In this study, we built a high-quality rainfall datasets from raingauges and meteorological radars for testing three remotely sensed rainfall products: 1) the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement product (IMERG), 2) the SM2RASC product obtained from the application of SM2RAIN (Soil Moisture TO RAIN) algorithm to the Advanced SCATterometer (ASCAT) derived satellite soil moisture data, and 3) a product derived from a simple combination of IMERG and SM2RASC. The assessment of the products is carried out at different rainfall time accumulation (e.g., from 0.5 to 24 h) for a ~2-year period from 10th March 2015, to 31st December 2016. Results show that IMERG has good performance at time resolutions higher than 6 h. At daily time scale, IMERG and SM2RASC show similar results with median correlations, R, ~0.60, and root mean square error, RMSE, ~7.6 mm/day (BIAS is -0.85 and +0.51 mm/day, respectively). The combined product outperforms the parent products (median R > 0.70, RMSE

This paper considers an ensemble of Chua oscillators bidirectionally coupled in a ring geometry where locally coupled circuits form a closed loop of signal transmission. The spontaneous dynamics of this system is studied numerically for different coupling strength. A transition from periodic to chaotic regimes is observed when the coupling decreases. In the former situation, characterized by high coupling, all the circuits oscillate with pseudo-sinusoidal dynamics on periodic attractors; in the latter they evolve on the same-type of chaotic attractor with a progression of the dynamics from the Chua's spiral to the double scroll as the coupling decreases. The emerging global dynamics is markedly different in the two cases and a phase transition between highly ordered and highly disordered global dynamics is observed. Synchronization and traveling waves moving along the ring are identified in the non-chaotic regime, while spatio-temporal chaos results for very low coupling. Complex patterns formation appears at the “edge of chaos”, for a small couplings interval after the transition between these two regimes.

RAMSES (RAilway Meteorological SEcurity System) is a pilot project co-funded by the Italian Railway Company - RFI S.p.A. and conceived for the mitigation of the hydrological risk along the Calabria railways. RAMSES aims at improving the forecast of very short life-cycle convection systems, responsible of intense and localized rainfalls affecting small catchment areas, which are often underestimated by the numerical weather models and even non-adequately detected by the network of sparse raingauges. The RAMSES operational design is based on a synergistic and integrated architecture, providing a series of information able to identify the most active convective cells and monitoring their evolution in terms of vertical structure, rain intensity and geo-hydrological effects at ground (debris flow, landslides, collapses of bridges, erosion of the ballast). The RAMSES meteorological component is designed to identify and track the short-term evolution (15-60 min) of convective cells, by means of imaging techniques based on dual-polarization weather radar and Meteosat data. In support of this quasi-real time analysis, the numerical model WRF provides the weather forecast at 3-6 hours range by ingesting, through the assimilation system LAPS, the observational data (rain gauges, ground weather stations, radar, satellites) in order to improve the initial condition. Finally, the hydraulic flow modeling is used to assess the ground effects in terms of landslide susceptibility, rainfallrunoff intensity, debris impact on the drainage network and evaluate of risk along the railway track.

In the present paper we report numerical observations of the spontaneous dynamics of N identical memristor-based Chua's circuits bidirectionally coupled in a ring geometry. Two different initial configurations are studied by varying N and the coupling strength: in the first configuration we consider only one circuit with non-zero initial conditions, in the second one all circuits have uniform random initial conditions. We observed both chaotic and non-chaotic synchronization. In the chaotic synchronized regime we identified emerging chaotic steady waves, characterized by an almost constant frequency and chaotic amplitude. Depending on the initial conditions, in the pseudo-sinusoidal oscillations regime both macroscopic quasi-periodic steady and traveling waves were observed.

The formulation of a standard computerized procedure for the indexing of powder X-ray diffraction (PXRD) patterns of columnar liquid crystals, with the determination of all structural information extracted from a properly indexed PXRD spectrum and the attribution of the columnar mesophase symmetry, is presented. In particular, the proposed program notably accelerates the identification of columnar mesophases together with thein situdetermination of their structural parameters such as mesophase type, space group, cell parameters, cross-section area, intermolecular stacking distance between consecutive discoids and, in the case of ordered mesophases, the estimation of the number of molecules constituting each discoid.

The present study aims to show how the problem of reproducing, as closely as possible, binders of historic mortars by mixing raw materials which are commercially available, can be formulated as a linear optimization problem. The study points out that by mixing five standard raw materials (end-members) it is possible to obtain mortar binders with the almost same chemical compositions of those determined on the historic and archaeological mortar samples studied in some recent scientific papers. An advanced function of the Microsoft Excel spreadsheet, the Solver add-in, was used for the calculation of the right amount of each raw material to be mixed for producing the new binders. This approach could be useful to provide an optimal solution in the process of restoration of ancient monuments, where it is necessary to replace the historic mortars with new highly compatible repair mortars.

The correct identification of homogeneous areas in regional rainfall frequency analysis is fundamental to ensure the best selection of the probability distribution and the regional model which produce low bias and low root mean square error of quantiles estimation. In an attempt at rainfall spatial homogeneity, the paper explores a new approach that is based on meteo-climatic information. The results are verified ex-post using standard homogeneity tests applied to the annual maximum daily rainfall series. The first step of the proposed procedure selects two different types of homogeneous large regions: convective macro-regions , which contain high values of the Convective Available Potential Energy index, normally associated with convective rainfall events, and stratiform macro-regions , which are characterized by low values of the Q vector Divergence index, associated with dynamic instability and stratiform precipitation. These macro-regions are identified using Hot Spot Analysis to emphasize clusters of extreme values of the indexes. In the second step, inside each identified macro-region, homogeneous sub-regions are found using kriging interpolation on the mean direction of the Vertically Integrated Moisture Flux. To check the proposed procedure, two detailed examples of homogeneous sub-regions are examined.

In order to characterize colloidal structures, T2-nuclear magnetic resonance (NMR) and rheological relaxation times are used. NMR Car–Purcell sequence and mechanical stress relaxation experiments have been performed on a bitumen system at different temperatures. The rheological relaxation times spectra are fingerprints of the aggregates that constitute the system. These typical relaxation times have been obtained from an exponential fitting of the experimental data, based on a Prony-like method. The unknown parameters are estimated on the basis of a linear regression equation that uses altered signals obtained directly from the NMR and rheological measurements. This approach uses the derivative method in the frequency domain, yielding exact formula in terms of multiple integrals of the signal, when placed in the time domain. These integrals are explicitly solved by projecting signal on some set of orthogonal basis functions or, more in general, by using a polynomial that fits data in the least-squares sen...

In this work, the unknown set of parameters of the Chua system is recovered under the hypothesys that the voltages of the capacitors are available. To this end, focusing on the differential equations, the Volterra kernel-based approach is used to perform an estimation without the uncertainty of the unmeasurable derivatives and the unknown initial conditions.

Among many weather phenomena, convective storms are one of the most dangerous since they are able to cause, in a relatively small time window, great damages. Convective precipitations are in fact characterized by relatively small spatial and temporal scales, and as a consequence, the task of forecasting such phenomena turns out to be an elusive one. Nonetheless, given their dangerousness, the identification and tracking of meteorological convective systems are of paramount importance and are the subject of several studies. In particular, the early detection of the areas where deep convection is about to appear, and the prediction of the development and path of existing convective thunderstorms represent two focal research topics. The aim of the present work is to outline a framework employing various techniques apt to the task of monitoring and characterization of convective clouds. We analyze meteorological satellite images and data in order to evaluate the potential occurring of strong precipitation. Techniques considered include numerical, machine learning, image processing. The techniques are tested on data coming from real convective events captured in the last years on the Italian peninsula by the Meteosat meteorological satellites and weather radar.Among many weather phenomena, convective storms are one of the most dangerous since they are able to cause, in a relatively small time window, great damages. Convective precipitations are in fact characterized by relatively small spatial and temporal scales, and as a consequence, the task of forecasting such phenomena turns out to be an elusive one. Nonetheless, given their dangerousness, the identification and tracking of meteorological convective systems are of paramount importance and are the subject of several studies. In particular, the early detection of the areas where deep convection is about to appear, and the prediction of the development and path of existing convective thunderstorms represent two focal research topics. The aim of the present work is to outline a framework employing various techniques apt to the task of monitoring and characterization of convective clouds. We analyze meteorological satellite images and data in order to evaluate the potential occurring of strong precipitation. Te...

The main objective of a water distribution network is to provide water to users in compliance with quality and service standards under different conditions. The ability to meet the water demand at the nodes, under the required pressures head, depends on many characteristic factors of the water network, such as various infrastructural components. A water distribution network is a complex system consisting of numerous structural elements and dependent by several factors. Resilience, robustness and vulnerability are of great interest, for these systems, in relation to the possible failure conditions which may compromise the network&rsquo, s ability to fulfill the project conditions. Vulnerability measures how much the network is fragile: a higher value of vulnerability means that the network is prone to fail in achieving the project conditions. In the present work, a new vulnerability measure based on a topological approach is proposed. A first application of the proposed vulnerability measure on two water networks known in the literature is described, and the obtained results are compared with other performance indices showing a significant correlation.

Hydrogen permeation through a palladium membrane has been measured in the presence of several gases, such as CO, N 2 , CO 2 , and Ar, both in the feed side and in the shell side of the (membrane) module. It has been found that CO molecules, remarkably inhibit hydrogen permeation. In particular, in the presence of carbon monoxide the permeation decreases with two different slopes: (I) for low CO concentrations, the hydrogen permeation decreases quickly (surface effects), whereas (II) for higher ones it decreases smoothly (dilute effect). Permeation of hydrogen, in the presence of the other gases, i.e. N 2 , CO 2 and Ar, always decreases with the same slope (dilute effect). In order to describe the CO inhibition, a theoretical investigation has been proposed. In particular, the framework of the Density Functional Theory has been used. CO and N 2 Density Functional full optimisations on palladium clusters show that CO and N 2 molecules present two minima on the cluster surfaces with bond lengths of 2.0 and 3.8 A, respectively. The CO minima are much stable than N 2 minima, resulting in a surface effect on the hydrogen permeation through the membrane.

We present the results of a numerical investigation of charged-particle transport across a synthesized magnetic configuration composed of a constant homogeneous background field and a multiscale perturbation component simulating an effect of turbulence on the microscopic particle dynamics. Our main goal is to analyze the dispersion of ideal test particles faced to diverse conditions in the turbulent domain. Depending on the amplitude of the background field and the input test particle velocity, we observe distinct transport regimes ranging from subdiffusion of guiding centers in the limit of Hamiltonian dynamics to random walks on a percolating fractal array and further to nearly diffusive behavior of the mean-square particle displacement versus time. In all cases, we find complex microscopic structure of the particle motion revealing long-time rests and trapping phenomena, sporadically interrupted by the phases of active cross-field propagation reminiscent of Levy-walk statistics. These complex features persist even when the particle dispersion is diffusive. An interpretation of the results obtained is proposed in connection with the fractional kinetics paradigm extending the microscopic properties of transport far beyond the conventional picture of a Brownian random motion. A calculation of the transport exponent for random walks on a fractal lattice is advocated from topological arguments. An intriguing indication of the topological approach is a gap in the transport exponent separating Hamiltonian-like and fractal random walk-like dynamics, supported through the simulation., Comment: 10 pages (including cover page), 7 figures, improved content, accepted for publication in Physica Scripta

Thin wall permeator tubes have been produced by diffusion welding of Pd-Ag foils. These dense metal membranes are proposed for separating and producing ultra-pure hydrogen as a result of their complete hydrogen selectivity. Furthermore, they present high hydrogen permeation fluxes as well as reduced costs on account of their reduced metal thickness. The reliability of these membrane tubes has been continuously tested during over 12 months of experimental campaign in presence of thermal and hydrogenation cycles. The permeability tests have been carried out measuring the hydrogen fluxes under controlled conditions of temperature and trans-membrane differential pressure. In particular, the behaviour of the membrane tube has been investigated in a wide range of temperatures (150-400 °C) and pressures (100-200 kPa) in order to simulate the use within permeators and membrane reactors where processes for producing hydrogen from hydrocarbons takes place. At temperature of 350-400 °C hydrogen permeability values around 1.0 × 10-8 mol m-1 s-1 Pa-0.5 have been measured and they are comparable with the literature data. A new mechanical design (finger-like configuration) of the membrane module has ensured the long life of the Pd-Ag permeator. The complete hydrogen selectivity of the membrane has been demonstrated by the tests. © 2006 Elsevier B.V. All rights reserved. 10.1016/j.memsci.2006.08.006

Chaotic dynamics of numerous memristor-based circuits is widely reported in literature. Recently, some works have appeared which study the problem of synchronization control of these systems in a master-slave configuration. In the present paper, the spontaneous dynamic behavior of two chaotic memristor-based Chua’s circuits, mutually interacting through a coupling resistance, was studied via computer simulations in order to study possible self-organized synchronization phenomena. The used memristor is a flux controlled memristor with a cubic nonlinearity, and it can be regarded as a time-varying memductance. The memristor, in effect, retains memory of its past dynamic and any difference in the initial conditions of the two circuits results in different values of the corresponding memductances. In this sense, due to the memory effect of the memristor, even if coupled circuits have the same parameters they do not constitute two completely identical chaotic oscillators. As is known, for nonidentical chaotic systems, in addition to complete synchronizations (CS) other weaker forms of synchronization which provide correlations between the signals of the two systems can also occur. Depending on initial conditions and coupling strength, both chaotic and nonchaotic synchronization are observed for the system considered in this work.

The correct identification of homogeneous areas in regional rainfall frequency analysis is fundamental to ensure the best selection of the probability distribution and the regional model which produce low bias and low root mean square error of quantiles estimation. In an attempt at rainfall spatial homogeneity, the paper explores a new approach that is based on meteo-climatic information. The results are verified ex-post using standard homogeneity tests applied to the annual maximum daily rainfall series. The first step of the proposed procedure selects two different types of homogeneous large regions: convective macro-regions, which contain high values of the Convective Available Potential Energy index, normally associated with convective rainfall events, and stratiform macro-regions, which are characterized by low values of the Q vector Divergence index, associated with dynamic instability and stratiform precipitation. These macro-regions are identified using Hot Spot Analysis to emphasize clusters of extreme values of the indexes. In the second step, inside each identified macro-region, homogeneous sub-regions are found using kriging interpolation on the mean direction of the Vertically Integrated Moisture Flux. To check the proposed procedure, two detailed examples of homogeneous sub-regions are examined.

Anisotropic fluids are a class of soft materials that offer wide possibilities for engineering a small scale laboratory; their physical properties can be manipulated on short length scale by appropriate confining conditions and external stimuli leading the systems across fascinating phenomena. In this work, two of these phenomena are combined to create a microfluidic platform for reconfigurable nanoparticles (NPs) patterning: the formation of topological defects and the electrically controlled convective flows. Here, the nanoscopic environments created by defects within liquid crystals have been used as linear nano-reservoirs of NPs. Afterwards, virtual channel flows that connect the linear reservoirs have been created by exploiting electro-convective rolls. The reported results reveal a strategy for managing nanometric objects based on anisotropic fluids and connected phenomena, proposing an unconventional microfluidic device characterized by switchable and contactless micro-channels.

In the Mediterranean area, flash-floods in small catchments are mainly due to deep convective rainfalls which show both large spatial and temporal variability. For this reason the reconstruction of the ground rainfall field is difficult and the use of rain gauges can prove poor to depict convective events from a hydrological point of view. The geostationary satellite MSG is a very important tool for monitoring the dynamic evolution of cloud structures above the European area. In the infrared window many algorithms have been proposed to relate the brightness temperature of the top of the convective cloud with rainfall. To explore more possible applications of Meteosat image analysis, in evaluating areal rainfall in small basins, a very deep convective event was analysed, which hit Vibo Valentia on 3 July 2006. Starting from this deep convective event, the paper explores new possible relations between some characteristics of infrared isotherm images and rainfall.

The Italian territory, as shown by the recent landslide inventory carried out after 267/98 National law, is affected by diffuse and high hydrogeological risks all over the mountainous areas. In most situations, in presence of high and extensive hydrogeological hazard, traditional "structural" engineering actions can't be done due their expensive costs. In such conditions the only viable way to reduce hazard may be pursued by real time territory monitoring. AMAMiR (Advanced Monitoring Action for Mitigation of Hydrogeological Risk) is a distributed real-time monitoring system created to control landslide activities in the city of San Martino di Finita, in Calabria (South of Italy). It can also be easily used for monitoring a wide range of natural phenomena. AMAMiR is capable of collecting data from heterogeneous sensors as GPS, inclinometers, ground raingauges, piezometers, extensometers, etc. All the data are collected and processed in real time and immediately made available on the web through a special user-friendly portal that permits also navigation through geocoded maps. AMAMiR system represents a very cost-effective powerful tool that can be used to support decision making for public administrations and risk management for civil protection purposes.

A novel electronic system is presented for 100% in-line testing of capsules in high-speed packaging machines for the pharmaceutical industry. The system exploits transmission analysis of near-infrared radiation and works at a speed compatible with production throughput of tens of parts per second. A prototype of the system has been realized and validated with pharmaceutical products. The presented solution, however, can be used in other contexts, such as, in particular, food production and small-part manufacturing.