Elementos Arquiteturais do Substrato da Lagoa dos Patos Revelados por Sísmica de Alta Resolução

A high-resolution seismic survey was performed on the Lagoa dos Patos, southern Brazil. The survey was conducted aboard the research vessel LARUS of the Fundação Universidade Federal do Rio Grande. The seismic profiles were obtained using a 3.5 kilohertz frequency, which provided a rather good penetration depth and resolution of the records. Results of the seismic records allowed the determination and mapping of seismic facies and seismic sequences, as well as related architectural elements, which were identified basically through the configuration patterns of the seismic reflectors. The analysis of the seismic records allowed the identification of the architectural elements that build up the sedimentary pile accumulated in the coastal prism of the State of Rio Grande do Sul, contributing to a better understanding of the geological evolution of the southern Brazilian coastal plain during the Quaternary period.Fil: Weschenfelder, Jair. Universidade Federal do Rio Grande do Sul; BrasilFil: Corrêa, Iran C. S.. Universidade Federal do Rio Grande do Sul; BrasilFil: Aliotta, Salvador. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentin

Segmentazione delle serie temporali nell’analisi dei dati: un esempio di applicazione a dati sismo-vulcanici.

Il presente report descrive quanto sviluppato dagli autori per l’analisi delle serie temporali utilizzate per il monitoraggio sismo-vulcanico del vulcano Etna. La necessità di ottenere una rappresentazione ridotta delle serie temporali ha portato alla ricerca ed alla implementazione degli algoritmi di segmentazione oggetto del presente lavoro. Le metodologie introdotte nel paragrafo 2, largamente applicate nella disciplina del data mining su serie temporali, costituiscono ad oggi lo stato dell’arte per quanto riguarda le tecniche di approssimazione di serie temporali. In particolare, l’applicazione dell’algoritmo bottom-up ha permesso una compressione elevata dei dati, consentendo quindi una rappresentazione con un numero di punti inferiore rispetto a quello delle serie temporali di partenza. In questo contesto la scelta delle soglie errore, legata indirettamente al numero di segmenti con cui si approssima la serie temporale, è stata scelta in modo empirico. Questa scelta è stata vincolata alla dimensione dei buffer di dati da impiegare per scopi di visualizzazione ed elaborazione. Future implementazioni riguarderanno l’ottimizzazione in linea degli algoritmi Sliding Window in modo da operare in real-time sugli streaming di dati ed ottimizzarne l’archiviazione e la visualizzazione

Solid-Solid Interfaces in Protonic Ceramic Devices: A Critical Review

The literature concerning protonic ceramic devices is critically reviewed focusing the reader's attention on the structure, composition, and phenomena taking place at solid-solid interfaces. These interfaces play a crucial role in the overall device performance, and the relevance of understanding the phenomena taking place at the interfaces for the further improvement of electrochemical protonic ceramic devices is therefore stressed. The grain boundaries and heterostructures in electrolytic membranes, the electrode-electrolyte contacts, and the interfaces within composite anode and cathode materials are all considered, with specific concern to advanced techniques of characterization and to computational modeling by ab initio approaches. An outlook about future developments and improvements highlights the necessity of a deeper insight into the advanced analysis of what happens at the solid-solid interfaces and of in situ/operando investigations that are presently sporadic in the literature on protonic ceramic devices

PYDBSCAN UN SOFTWARE PER IL CLUSTERING DI DATI

Con il termine clustering si indica il processo mediante il quale è possibile raggruppare oggetti in base a caratteristiche comuni (features). Questo approccio, alla base dei processi di estrazione di conoscenza da insiemi di dati (data mining), riveste notevole importanza nelle tecniche di analisi. Come verrà mostrato in questo lavoro, l’applicazione delle tecniche di clustering consente di analizzare dataset, con l’obiettivo di ricercare strutture che possano fornire informazioni utili circa i dati oggetto dello studio. Gli ambiti in cui tali algoritmi sono impiegati risultano essere eterogenei, a partire dalle analisi di dati biomedici, astrofisici, biologici, fino ad arrivare a quelli geofisici. La letteratura è ricca di vari casi di studio, dai quali il ricercatore può trarre spunto e adattare i differenti approcci alle proprie esigenze. Il software PyDBSCAN, oggetto del presente lavoro, permette di applicare tecniche di clustering basate sul concetto di densità, applicate ad oggetti (o punti) appartenenti ad insiemi definiti in uno spazio metrico. L’algoritmo di base è il DBSCAN (Density Based Spatial Clustering on Application with Noise) [Ester et al., 1996], di cui viene riportata una implementazione ottimizzata al fine di migliorare la qualità del processamento dei dati. Schematicamente, il sistema proposto può essere rappresentato come in Fig. 1. Il software, sviluppato in Python 2.6 [Python ref.], utilizza le librerie scientifiche Numpy [Numpy ref.], Matplotlib [matplotlib ref.] e la libreria grafica PyQt [PyQt ref.] impiegata nella realizzazione dell’interfaccia utente. Python è un linguaggio di programmazione che permette la realizzazione di applicazioni crossplatform in grado di funzionare su diversi sistemi operativi quali Windows, Unix, Linux e Mac OS. Nella prima parte del lavoro verranno brevemente descritte le tecniche oggetto del software presentato, mentre nella seconda parte verrà descritto un esempio di applicazione su dati reali

A real-time framework for fast data retrieval in an image database of volcano activity scenarios

Explosive Activity at Stromboli Volcano (Aeolian Islands) is continuously monitored by INGV-OE in order to analyze its eruptive dynamics and specific scenarios. In particular, the images acquired from thermal cameras represent a big collection of data. In order to extract useful information from thermal image sequences, we need an efficient way to explore and retrieve information from a huge amount of data. In this work, a novel framework capable of fast data retrieval, using the "metric space" concept, is shown. In the light of it, we implemented an indexing algorithm related to similarity laws. The focal point is finding objects of a set that are “close” in relation to a given query, according to a similarity criterion. In order to perform this task, we performed morphological image processing techniques to each video frame, in order to map the shape area of each explosion into a closed curve, representing the explosion contour itself. In order to constitute a metric space, we chose a certain number of features obtained from parameters related to this closed curve and used them as objects of this metric space where similarity can be evaluated, using an appropriate “metric” function to calculate the distances. Unfortunately, this approach has to deal with an intrinsic issue involving the complexity and the number of distance functions to be calculated on a large amount of data. To overcome this drawback, we used a novel abstract data structure called "K-Pole Tree", having the property of minimizing the number of distances to be calculated among objects. Our method allows for fast retrieval of similar objects using an euclidean distance function among the features of the metric space. Thus, we can cluster explosions related to different kinds of volcanic activity, using "pivot" items. For example, given a known image sequence related to a particular type of explosion, it is possible to quickly and easily find all the image sequences that contain only similar explosions. Our framework is able to both classify each new explosion and dynamically insert the corresponding object into our tree data structure. This approach is able to cluster the entire data space, ensuring that objects with similar features are grouped and classified together

A Dynamic Bayesian Network for Mt. Etna Volcano State Assessment

Nowadays, the real-time monitoring of Mt. Etna volcano is mostly delegated to one or more human experts in volcanology, who interpret the data coming from different kind of monitoring networks. Among their duties, the evaluation of the volcano state is one of the most critical task for civil protection purposes. Unfortunately, the coupling of highly non-linear and complex volcanic dynamic processes leads to measurable effects that can show a large variety of different behaviors. Moreover, due to intrinsic uncertainties and possible failures in some recorded data the volcano state needs to be expressed in probabilistic terms, thus making the fast volcano state assessment sometimes impracticable for the personnel on duty at the 24h control room. With the aim of aiding the personnel on duty in volcano monitoring, here we present an expert system approach based on Bayesian networks to estimate automatically the ongoing volcano state from all the available different kind of measurements. A Bayesian network is a static probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph. We consider model variables both the measurements and the possible states of the volcano. In order to include the time in the model, we use a Dynamic Bayesian Network (DBN) which relates variables to each other over adjacent time steps. The model output consists of an estimation of the probability distribution of the feasible volcano states. We build the model by considering the long record of data from 2011 to 2014 and we cross-validate it by considering 3 years for parameter estimation and 1 year for testing in simulated real-time mode

Role of virtual break-up of projectile in astrophysical fusion reactions

We study the effect of virtual Coulomb break-up, commonly known as the dipole polarizability, of the deuteron projectile on the astrophysical fusion reaction 3He(d,p)4He. We use the adiabatic approximation to estimate the potential shift due to the E1 transition to the continuum states in the deuteron, and compute the barrier penetrability in the WKB approximation. We find that the enhancement of the penetrability due to the deuteron break-up is too small to resolve the longstanding puzzle observed in laboratory measurements that the electron screening effect is surprisingly larger than theoretical prediction based on an atomic physics model. The effect of the 3He break-up in the 3He(d,p)4He reaction, as well as the 7Li break-up in the 7Li(p,alpha)4He reaction is also discussed.Comment: 9 pages, 2 eps figure