2,441 research outputs found
TOYSTER
No full textSatya LUBATTI is the winner of "Premio Tesi di Laurea LEONARDO 2011" supported by CONFINDUSTRIA and assigned by the President of Italian Republic, Mr. Giorgio Napolitano at Palazzo del Quirinale in Rome. This thesis work, titled TOYSTER, was been recognized for its innovation and originality in the project of a tender-craft for a huge Sail-yacht of Perini Navi Yacht Yards.
Supervisor of the thesis work was been prof. Massimo Musio-Sale, President of the master degree DESIGN NAVALE e NAUTICO.
Review "BARCHE", May/2012 ISSN 1124-3732, pp. 68-6
La catena umana di Seamus Heaney
No full textDiscussion of "Human Chain" volume by Seamus Heaney, his 1995 sojourn in Liguria, Italy, and his translation of Giovanni Pascoli's "L'aquilone"
Appunti per una riflessione sull'autorità di cosa giudicata delle decisioni del Conseil constitutionnel francese
No full text
A Solid Nitrogen Cooled Linear Levitating System Based on MgB2 Bulks
No full textAn innovative linear levitating system based on MgB2 bulks produced by the Reactive Liquid Infiltration (RLI) method is designed and built. Details of the system are presented. The levitation performance is also evaluated by means of previous experimental data.
The linear levitating system consists of four MgB2 arc-shaped tiles lodged inside a double shells dewar sliding on a guide made of Nd-Fe-B permanent magnets. The magnets on the guide are properly arranged in a flux shaper configuration in order to pro-duce the maximum stiffness. Each MgB2 bulk have curvature radius of 165 mm (arc length 200 mm), thickness 10 mm and width 70 mm The system is cooled by means of a Solid Nitrogen heat sink. Helium gas supply to a heat exchanger is used in order to cool down the MgB2 slabs and the nitrogen at a temperature in the range 20-30 K. When the flow of helium gas is interrupted the temperature of the solid nitrogen remains below 39 K for half an hour. During this time interval levitation measurements can be performed. Experimental verification of the cooling concept has been carried out successfully
Recuerdos de Massimo.
No full textEl presente ensayo recuerda la rica relación personal e intelectual entre Massimo Pavarini e Iñaki Rivera Beiras desde que el segundo conoció siendo muy joven a Pavarini. En ese sentido, el trabajo recuerda los inicios de su fecunda relación en la ciudad de Bologna cuando Iñaki Rivera acudía a presentar a Massimo Pavarini los desarrollos de lo que sería su tesis doctoral. Las contribuciones de Pavarini al proceso de aprendizaje de Rivera, en el terreno de una epistemología crítica en la penología contemporánea, son analizados como un homenaje a la memoria y a la obra del autor italiano, tras su fallecimiento en septiembre de 2015.This paper recalls the rich personal and intellectual relationship between Massimo Pavarini and Iñaki Rivera Beiras. In that sense , the work recalls the beginning of his fruitful relationship in the city of Bologna when Iñaki Rivera came to present Massimo Pavarini’ developments in what would be his PhD thesis. Pavarini contributions to the learning process of Rivera, on the ground of a critical epistemology in contemporary penology , are analyzed as a tribute to the memory and to the work of Italian author, after his death in September 2015 .El presente ensayo recuerda la rica relación personal e intelectual entre Massimo Pavarini e Iñaki Rivera Beiras desde que el segundo conoció siendo muy joven a Pavarini. En ese sentido, el trabajo recuerda los inicios de su fecunda relación en la ciudad de Bologna cuando Iñaki Rivera acudía a presentar a Massimo Pavarini los desarrollos de lo que sería su tesis doctoral. Las contribuciones de Pavarini al proceso de aprendizaje de Rivera, en el terreno de una epistemología crítica en la penología contemporánea, son analizados como un homenaje a la memoria y a la obra del autor italiano, tras su fallecimiento en septiembre de 2015
Cartografia di rischio da mareggiata della fascia costiera della Regione Emilia-Romagna
No full textNegli ultimi dieci anni si sono verificate numerose catastrofi lungo le aree costiere di tutto il mondo (Uragano Katrina a New Orleans, gli tsunami nel 2004 e 2011 in India e Giappone, rispettivamente) e anche l’Europa ha dovuto affrontare delle emergenze provocate dall’effetto negativo di mareggiate intense (ad esempio, la tempesta Xinthia in Francia in febbraio-marzo 2010). Le coste dell’Emilia-Romagna sono soggette al danneggiamento causato da eventi energetici e, in particolare, sono vulnerabili all’erosione e all’inondazione provocate dalle acque alte e delle onde (Perini et al., 2011; Armaroli et al., 2012). L’acqua alta (surge) in alto Adriatico è un fenomeno originato dall’innalzamento temporaneo del livello del mare causato, principalmente, dall’accumulo di acqua lungo la fascia costiera spinta dai venti che soffiano da sud-est, oltre che dall’effetto barometrico, cioè dalla bassa pressione, associato a perturbazioni atmosferiche, che crea un sovralzo del mare al di sopra del livello di marea astronomica. Una valutazione della vulnerabilità delle spiagge della regione alle mareggiate è, pertanto, di grande importanza per conoscere quali sono le zone più soggette a inondazione. Il Servizio Geologico, Sismico e dei Suoli della Regione Emilia-Romagna ha iniziato da qualche tempo una valutazione del possibile effetto di diversi scenari meteo-marini sulle coste (Perini et al., 2010). In particolare, sono state prodotte due rappresentazioni cartografiche in GIS (Geographical Information System) che descrivono la vulnerabilità delle aree costiere: una di tipo puntuale (lungo dei profili topografici equispaziati) e una di tipo areale (limite massimo di ingressione del mare durante eventi estremi). Un ulteriore dato cartografico rilevante è rappresentato dalla cartografia delle aree storicamente colpite da eventi estremi che è stata prodotta nell’ambito del catalogo delle mareggiate realizzato per il progetto Micore (Morphological Impact and COastal Risk induced by Extreme storm events, [1]; Perini et al., 2011).
La costa dell’Emilia-Romagna è una zona micro-tidale con maree semidiurne (80-90 cm in sizigie, 30-40 cm in quadrature). Il clima meteo-marino è caratterizzato da condizioni di bassa energia (91% delle onde è inferiore a 1.25 m) e la durata media delle mareggiate è inferiore a 24 ore. Le mareggiate più estreme che si possono verificare annualmente sono caratterizzate da Hs = 3.3 m e Ts = 7.7 s (Idroser, 1996), con elevazioni di surge che possono raggiungere 85 cm (Yu et al., 1998). Le direzioni dei venti che generano le tempeste più significative sono: Scirocco (SE) e Levante - Bora (E-NE). Circa il 60% della costa è protetto da opere rigide (barriere longitudinali emerse e soffolte, pennelli, scogliere radenti, ecc)
Towards analytics over dirty databases
No full textIn today's data-driven world, organizations increasingly rely on large and complex datasets to drive decision-making, build predictive models, and optimize operations. From e-commerce companies leveraging customer behavior data to improve marketing strategies, to financial institutions analyzing transactions for fraud detection, the demand for efficient, scalable, and seamless data processing is critical. However, real-world data is often incomplete, inconsistent, or erroneous, which undermines the accuracy and reliability of the insights drawn from it. Traditional workflows require moving data out of database systems into external analytical tools for machine learning, data cleaning, and query answering tasks, introducing significant inefficiencies and creating bottlenecks. This thesis presents integrated solutions that enable these operations to be performed directly within the database, addressing three key problems in modern database systems: (1) inefficiency in executing machine learning tasks, (2) inability to handle missing data effectively, and (3) limitations in querying inconsistent databases.
First, we address the challenge of efficiently training machine learning models over relational data. Most data resides in databases, yet current machine learning systems typically require exporting data into external tools, leading to excessive data transfer and redundant computations. To overcome these inefficiencies, we propose an in-database machine learning library implemented on PostgreSQL and DuckDB. Our approach rewrites popular machine learning algorithms to run directly within the database, leveraging the relational data structure. By training models over aggregate values computed from normalized tables, we eliminate the need for expensive joins and preprocessing, achieving 10 to 100-fold faster model training compared to state-of-the-art solutions like MADLib. Additionally, our library allows multiple models to be constructed using the same set of aggregate computations, further optimizing the learning process.
Second, missing data is a pervasive issue in real-world datasets, often necessitating the use of imputation techniques to fill in gaps before analysis or model training can proceed. External tools for imputation, such as those implementing the Multiple Imputation by Chained Equations (MICE) method, typically require data export and preprocessing, adding complexity to the workflow. We introduce an in-database imputation framework that integrates MICE directly into database systems, allowing it to operate over normalized data. By re-engineering the MICE algorithm to share computations across iterations and optimize for fast access to frequently used data, we significantly reduce runtime. Our solution, implemented in both PostgreSQL and DuckDB, outperforms traditional methods, providing a more efficient and scalable way to handle missing data without leaving the database environment.
Third, we tackle the problem of query answering over inconsistent databases -- a critical challenge for organizations that rely on accurate query results despite data inconsistencies. Conventional methods often involve data cleaning to restore consistency, but this can be impractical in real-time environments or where altering the original data is not feasible. To address this, we develop a method based on Consistent Query Answering (CQA), which allows queries to be evaluated directly over inconsistent data. We model the concept of ``minimal repairs'' -- smallest changes that restore consistency -- as a logical formula and use model counting techniques to determine the number of possible repairs. Furthermore, by optimizing the size of the logical formula, we achieve up to a 1000-fold reduction in computational complexity. To efficiently compute the number of repairs supporting each query answer, we introduce two Monte Carlo approximation algorithms that leverage the compiled logical formula. These algorithms provide theoretical guarantees for approximation accuracy while maintaining practical efficiency, enabling the execution of CQA over large datasets with multiple functional dependency violations.
In conclusion, this thesis presents a comprehensive set of in-database solutions designed to overcome inefficiencies in machine learning, data imputation, and query evaluation processes, particularly in the presence of incomplete or inconsistent data. By integrating these tasks directly into modern relational databases, our approach not only streamlines workflows but also significantly improves performance. Our contributions include a high-performance machine learning library, a scalable imputation technique for handling missing data, and a robust framework for consistent query answering over erroneous databases. Collectively, these innovations represent a significant advancement toward more efficient, reliable, and scalable data management solutions
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