La conservazione viva dell'esistente. Storia, progetto e restauro nell'opera di Josef Wiedemann

La tesi analizza, nel quadro del secondo dopoguerra, quattro casi studio scelti tra le opere di ricostruzione dell’architetto Josef Wiedemann (1910-2001) nel centro di Monaco di Baviera: Odeon (1951-1952), Alte Akademie (1951-1955), Siegestor (1956-1958) e Glyptothek (1961-1972). L’architetto si occupa di opere simbolo della città di Monaco, affrontando la loro ricostruzione come un tema fondante per la storia e l’identità del popolo bavarese, ma soprattutto come un’occasione per definire un metodo d’intervento sulle rovine della guerra. Il suo lavoro è caratterizzato infatti per la ricerca costante di una sintesi tra interesse per la conservazione dell’antico e apertura al nuovo; ispirandosi all’insegnamento del maestro Hans Döllgast, Wiedemann traccia una nuova originale strada per l’intervento sull’antico, segnata da una profonda capacità tecnico-progettuale e dall'attenzione alle nuove esigenze a cui deve rispondere un’architettura contemporanea. Partendo dai suoi scritti e dalle sue opere, si può rilevare un percorso coerente che, partendo dalla conoscenza della storia dell'edificio, ripercorrendone l’evoluzione dallo stato che potremmo definire “originario” allo stato di rovina, giunge a produrre nel progetto realizzato una sintesi tra il passato e il futuro. L'architetto, nella visione di Wiedemann, è chiamato a un compito di grande responsabilità: conoscere per progettare (o ri-progettare) un edificio che porta impressi su di sé i segni della propria storia. Nel metodo che viene messo progressivamente a punto operando nel corpo vivo dei monumenti feriti dalla guerra, è percepibile fino a distinguerlo chiaramente l’interesse e l’influenza del dibattito italiano sul restauro. La conservazione “viva” dell'esistente, così come viene definita da Wiedemann stesso, si declina in modo diverso per ogni caso particolare, approdando a risultati differenti tra loro, ma che hanno in comune alcuni principi fondamentali: conoscere, ricordare, conservare e innovare.

Innovative techniques for sport turf managing: Agronomic and phyisiological implications

The growing substrate of the putting greens is considered a key factor for a healthy turf ecosystem. Actually detailed study on the effects of growth promoting bacteria and biostimulants on a professional sport turf are very limited. This thesis aimed to study the effectiveness of different microorganisms and biostimulants in order to improve the knowledge relative to the relationship between the beneficial microflora and root apparatus of sport turfs. The research project was divided in three principal steps: Initially, commercial products based on biostimulants and microorganisms were tested on a Lolium perenne L. essence grown in a controlled-environment. The principal evaluations were the study of the habitus of plants, biomass production and length of leaves and roots. Were studied the capacity of colonization of microorganisms within root tissues and rhizosphere. In the second step were developed two different biostimulant solutions based on effective microorganisms, mycorrhizae and humic acids. This test was conducted both on an Agrostis stolonifera putting green (Modena Golf & Country Club) in a semi-field condition and within a growth chamber on a Lolium perenne L. essence. Fungicide and chemicals applications were suspended in order to assess the effectiveness of the inoculants for nutrition and control of pests. In the last step, different microorganism mixes and biostimulants were tested on an experimental putting green in the Turf Research Center (TRC) (Virginia Tech, United States) in a real managing situation. The effects of different treatments were studied maintaining all chemicals and mechanicals managements scheduled during a sport season. Both growth-chamber and field results confirmed the capacity of microorganisms based biostimulants to promote the physiologic conditions of the plants, improve the growth of the roots and enhance the aesthetic performance of the turf. Molecular analysis confirmed the capacity of microorganisms to colonize the root tissues.

Hardware and software development of a multichannel readout board named CARLOSrx for the ALICE experiment

ALICE, that is an experiment held at CERN using the LHC, is specialized in analyzing lead-ion collisions. ALICE will study the properties of quarkgluon plasma, a state of matter where quarks and gluons, under conditions of very high temperatures and densities, are no longer confined inside hadrons. Such a state of matter probably existed just after the Big Bang, before particles such as protons and neutrons were formed. The SDD detector, one of the ALICE subdetectors, is part of the ITS that is composed by 6 cylindrical layers with the innermost one attached to the beam pipe. The ITS tracks and identifies particles near the interaction point, it also aligns the tracks of the articles detected by more external detectors. The two ITS middle layers contain the whole 260 SDD detectors. A multichannel readout board, called CARLOSrx, receives at the same time the data coming from 12 SDD detectors. In total there are 24 CARLOSrx boards needed to read data coming from all the SDD modules (detector plus front end electronics). CARLOSrx packs data coming from the front end electronics through optical link connections, it stores them in a large data FIFO and then it sends them to the DAQ system. Each CARLOSrx is composed by two boards. One is called CARLOSrx data, that reads data coming from the SDD detectors and configures the FEE; the other one is called CARLOSrx clock, that sends the clock signal to all the FEE. This thesis contains a description of the hardware design and firmware features of both CARLOSrx data and CARLOSrx clock boards, which deal with all the SDD readout chain. A description of the software tools necessary to test and configure the front end electronics will be presented at the end of the thesis.

Assessing poverty with survey data. Uni-dimensional, multidimensional and resilience poverty analysis in Kenya

Traditionally Poverty has been measured by a unique indicator, income, assuming this was the most relevant dimension of poverty. Sen’s approach has dramatically changed this idea shedding light over the existence of many more dimensions and over the multifaceted nature of poverty; poverty cannot be represented by a unique indicator that only can evaluate a specific aspect of poverty. This thesis tracks an ideal path along with the evolution of the poverty analysis. Starting from the unidimensional analysis based on income and consumptions, this research enter the world of multidimensional analysis. After reviewing the principal approaches, the Foster and Alkire method is critically analyzed and implemented over data from Kenya. A step further is moved in the third part of the thesis, introducing a new approach to multidimensional poverty assessment: the resilience analysis.

Thermochronological evolution of the Eastern Pontides and the Eastern Anatolian Plateau and NW Lesser Caucasus (Turkey, Georgia, Armenia)

The analysis of apatite fission tracks is applied to the study of the syn- and post-collisional thermochronological evolution of a vast area that includes the Eastern Pontides, their continuation in the Lesser Caucasus of Georgia (Adjara-Trialeti zone) and northern Armenia, and the eastern Anatolian Plateau. The resulting database is then integrated with the data presented by Okay et al. (2010) for the Bitlis Pütürge Massif, i.e. the western portion of the Bitlis-Zagros collision zone between Arabia and Eurasia. The mid-Miocene exhumation episode along the Black Sea coast and Lesser Caucasus of Armenia documented in this dissertation mirrors the age of collision between the Eurasian and Arabian plates along the Bitlis suture zone. We argue that tectonic stresses generated along the Bitlis collision zone were transmitted northward across eastern Anatolia and focused (i) at the rheological boundary between the Anatolian continental lithosphere and the (quasi)oceanic lithosphere of the Black Sea, and (ii) along major pre-existing discontinuities like the Sevan-Akera suture zone.The integration of both present-day crustal dynamics (GPS-derived kinematics and distribution of seismicity) and thermochronological data presented in this paper provides a comparison between short- and long-term deformation patterns for the entire eastern Anatolia-Transcaucasian region. Two successive stages of Neogene deformation of the northern foreland of the Arabia-Eurasia collision zone can be inferred. (i) Early and Middle Miocene: continental deformation was concentrated along the Arabia-Eurasia (Bitlis) collision zone but tectonic stress was also transferred northward across eastern Anatolia, focusing along the eastern Black Sea continent-ocean rheological transition and along major pre-existing structural discontinuities. (ii) Since Late-Middle Miocene time the westward translation of Anatolia and the activation of the North and Eastern Anatolian Fault systems have reduced efficient northward stress transfer.

Matrix Designs and Methods for Secure and Efficient Compressed Sensing

The idea of balancing the resources spent in the acquisition and encoding of natural signals strictly to their intrinsic information content has interested nearly a decade of research under the name of compressed sensing. In this doctoral dissertation we develop some extensions and improvements upon this technique's foundations, by modifying the random sensing matrices on which the signals of interest are projected to achieve different objectives. Firstly, we propose two methods for the adaptation of sensing matrix ensembles to the second-order moments of natural signals. These techniques leverage the maximisation of different proxies for the quantity of information acquired by compressed sensing, and are efficiently applied in the encoding of electrocardiographic tracks with minimum-complexity digital hardware. Secondly, we focus on the possibility of using compressed sensing as a method to provide a partial, yet cryptanalysis-resistant form of encryption; in this context, we show how a random matrix generation strategy with a controlled amount of perturbations can be used to distinguish between multiple user classes with different quality of access to the encrypted information content. Finally, we explore the application of compressed sensing in the design of a multispectral imager, by implementing an optical scheme that entails a coded aperture array and Fabry-Pérot spectral filters. The signal recoveries obtained by processing real-world measurements show promising results, that leave room for an improvement of the sensing matrix calibration problem in the devised imager.

Network analysis and machine learning assist drug repurposing and safety assessment in neurological diseases

In recent decades, two prominent trends have influenced the data modeling field, namely network analysis and machine learning. This thesis explores the practical applications of these techniques within the domain of drug research, unveiling their multifaceted potential for advancing our comprehension of complex biological systems. The research undertaken during this PhD program is situated at the intersection of network theory, computational methods, and drug research. Across six projects presented herein, there is a gradual increase in model complexity. These projects traverse a diverse range of topics, with a specific emphasis on drug repurposing and safety in the context of neurological diseases. The aim of these projects is to leverage existing biomedical knowledge to develop innovative approaches that bolster drug research. The investigations have produced practical solutions, not only providing insights into the intricacies of biological systems, but also allowing the creation of valuable tools for their analysis. In short, the achievements are: • A novel computational algorithm to identify adverse events specific to fixed-dose drug combinations. • A web application that tracks the clinical drug research response to SARS-CoV-2. • A Python package for differential gene expression analysis and the identification of key regulatory "switch genes". • The identification of pivotal events causing drug-induced impulse control disorders linked to specific medications. • An automated pipeline for discovering potential drug repurposing opportunities. • The creation of a comprehensive knowledge graph and development of a graph machine learning model for predictions. Collectively, these projects illustrate diverse applications of data science and network-based methodologies, highlighting the profound impact they can have in supporting drug research activities.