1,722,151 research outputs found
La città del futuro. Il vicesindaco "Apriamo le mura delle Molinette"
No full textL'articolo (a cura di Diego Longhin) recensisce il primo incontro del ciclo di incontri "Torino. Argomenti per un futuro possibile" organizzato dal DAD al Castello del Valentino. Nell'articolo è citato espressamente il nome e il ruolo di Paolo Mellan
A new design for the CERN-Fréjus neutrino Super Beam
No full textWe present an optimization of the hadron focusing system for a low-energy high-intensity conventional neutrino beam (Super-Beam) proposed on the basis of the HP-SPL at CERN with a beam power of 4 MW and an energy of 4.5 GeV. The far detector would be a 440 kton Water Cherenkov detector (MEMPHYS) located at a baseline of 130 km in the Fr\'ejus site. The neutrino fluxes simulation relies on a new GEANT4 based simulation coupled with an optimization algorithm based on the maximization of the sensitivity limit on the mixing angle. A new configuration adopting a multiple horn system with solid targets is proposed which improves the sensitivity to and the CP violating phase
The Machine in the Mountain. Territories of hydro power in the Piave basin
Get PDFAs an examination of the energy spatial project deployed along the most engineered hydro basin in Europe, the Piave River, this work explores the interplay of modernity and environmental transformation in the Italian north-east hydroelectric landscapes of the Veneto region. Focusing on the territorial, urban and social implications of the politics of exploitation of water, it explores the rationalities through which water is abstracted, appropriated, accumulated and used across the region. It seeks an understanding of the centrality of water management, hydro-politics and engineering in the Italian process of modernization and development, exploring their capacity to transform the territory.
The research explores the role of water apparatus within the urbanisation processes, and how its embedded dynamics of production —would they be energetic, of agriculture or redistribution— are reciprocally entangled, and consequentially dependent upon, the ecologies of specific spaces, often seemingly disconnected or remote. To do so, it unpacks the stratification of nineteenth century projects which re-structured territories of northern Italy through water flows, incrementally constructing a paradoxical interdependent machinic landscape. Deconstructing systems of capital production sedimented across the modern aspiration of the Fascist regime to cultivate and construct an idealistic idea of productive nature, the thesis aims at dissecting techno-natures legacy across the basin to declare the multiplicity of processes of rationalization of the territory and the interplay of socio-environmental conflicts with dams’ economy of power. By bringing upfront the socio-ecological rationalities of the Piave consequential landscape the thesis uncovers fragmented understandings of the basin which prevent an encompassing understanding of the machine ability in forging the territorial palimpsest.
In doing so, transcending binaries of society/nature and urban/rural environments, the thesis builds on interdisciplinary insights from the fields of urbanism, geography, environmental history and political ecology, to frame water infrastructure as a fundamental territorial support. Raising a series of questions stemming from the materiality of water and its political implications in the frame of the current climate regime, the research attempts to understand and question the social, political, institutional and ecological dynamics that the machine in the mountain entails across the territory. As a result, the research uncovers praxes which reveal the anatomy of the Piave basin by challenging modes of representation. While diverse forms of powers constitute the current conjuncture, it argues for an analysis which is constitutive of cities and their more-than-urban geographies, in order to address both the specificity of conflicts at the local scale and the larger web of political, economic and environmental processes in the broader one. By shifting urbanism focus into large ‘operationalised landscapes’, through the lens of landscape as a ‘way of seeing’, working and reconstituting knowledge, this work aims at empowering territorial research as a design tool. In the midst of the ongoing multiplicity of crises, the thesis argues for a widening need to describe the machinic territory as a way through which define future coexisting strategies to inhabit the territory
IL RUOLO DELL'INFORMATIVA CONTABILE DURANTE CIRCOSTANZE STRAORDINARIE.
No full textLa mia tesi di dottorato combina principalmente due filoni di ricerca. Il primo è strettamente legato al campo dell’economia aziendale e utilizza la pandemia di Covid-19 come contesto sperimentale. Infatti, l’avvento della pandemia ad inizio 2020 ha iniziato una fase di forte incertezza nei mercati finanziari mondiali, a livelli tali da stravolgerne il loro normale funzionamento. Soprattutto, è opinione diffusa da accademici e addetti ai lavori che la recente pandemia di Covid-19 sia stata estremamente diversa da qualsiasi altro evento simile accaduto in passato, ed abbia comportato conseguenze imprevedibili e mai sperimentate fino ad ora (almeno da un punto di vista economico-finanziario). Avendo considerato ciò, un particolare sforzo è stato richiesto al mondo della ricerca per ripensare a tutta la conoscenza consolidata in ambito economico sotto i nuovi riflettori della pandemia. Nella mia tesi di dottorato, contribuisco alla letteratura collegata alla pandemia di Covid-19 con due lavori (che costituiscono il Capitolo 1 e 2 della tesi rispettivamente). Queste due produzioni indagano empiricamente come l’informazione contabile e finanziaria abbia spiegato il sottostante economico delle aziende durante una fase di mercato caratterizzata da incertezza estrema, derivata dall’avvento della pandemia. Il secondo filone di ricerca a cui riferisce questa tesi è legato alla finanza aziendale. Recentemente, la diffusione delle tecniche di predizione e classificazione di tipo Machine Learning ha consentito ai ricercatori di riformulare molti problemi storici di tipo economico o finanziario sfruttando i vantaggi portati da queste nuove tecniche, raggiungendo risultati migliori e più soddisfacenti rispetto ai modelli tradizionali. Nel Capitolo 3 della tesi, introduco l’utilizzo delle tecniche di tipo Machine Learning nel problema economico-finanziario della predizione di un futuro stato di bancarotta. Allo stesso tempo, provo ad evolvere la teoria processata dai modelli stessi seguendo un approccio data-analitico.My doctoral thesis merges two main streams of research. The first one is tied to the accounting field and uses the Covid-19 pandemic as research setting. For instance, the incumbency of the Covid-19 pandemic at the early stages of 2020 starts a phase of heightened uncertainty in global financial markets, such that the usual state of thing does no longer hold. Most importantly, it is a shared belief among academics and practitioners that the pandemic has been unlike anything experienced in the past, involving unprecedented and unexperienced consequences, at least from an economic point of view. Having this considered, an effort by researchers is deemed necessary to understand the new mechanisms regulating a pandemic-affected market and to rethink the well consolidated knowledge under the new lights of this turbulent market phase. In my doctoral thesis, we contribute to the Covid19-related accounting literature with two research papers (which are Chapter 1 and 2 of the thesis respectively). These two papers investigate empirically the extent to which the accounting informs the market about the underlying firms’ fundamental during phases characterized by heightened fundamental uncertainty (derived from the Covid-19 pandemic). The second stream of research is more tied to the corporate finance field. Over the recent years, the spread of machine learning based techniques has enabled researchers to reframe many economic or financial problems exploiting the advantages brought by these new techniques, reaching far better solutions than traditional models. In Chapter 3 of my doctoral thesis I introduce the use of machine learning into the accounting-based default prediction literature, advancing also the financial knowledge processed inside the models following a data-analytic approach
Enhancing variant calling and interpretation pipelines using data-driven in-silico simulation and artificial-intelligence
No full textDNA is a complex molecule that stores the genetic information needed for the development and functioning of each living organism.
Each organism has its own unique sequence of DNA, also known as the genome, which differs from those of all other existing individuals due to the presence of genetic variants.
These differences in the genome sequence can be inherited from parental DNA or arise during the life of each individual. Some variants are responsible for differences in appearance among individuals of the same species, while some others are deeply associated to their health status.
Since the importance of identifying variants to monitor the disease onset or predisposition in a certain individual has been recognised, the techniques for studying DNA have significantly improved. Increasingly sophisticated sequencing techniques are employed to determine the DNA sequence extracted from biological tissues, while bioinformatics pipelines are used both for variant calling, to identify the exact position of variants along the genome, and variant interpretation, to finally assess their impact on an individual's health.
With the rapidly decreasing cost of sequencing technologies, gold-standard samples, for which the positions of variants in the genome are known, have become available. Subsequently, these samples have been used to optimise variant calling pipelines, in some cases achieving identification performance good enough to allow those pipelines to be used in clinical practice. As regards variant interpretation pipelines, many tools have been deployed that automatically collect relevant clinical information about variants. Based on this information, it is possible to infer the variant's impact on health and provide the assessment as a suggestion for clinicians.
Despite these recent advancements, critical challenges still limit the effective use of bioinformatics pipelines for identifying variants and determine their clinical significance.
On the one hand, variant calling remains difficult due to the lack of a comprehensive gold-standard sample dataset that accounts for the extensive variability in both biological and technical characteristics of samples. As a result, optimising variant calling for each specific sample scenario is challenging, leading to less than optimal discovery performance.
On the other hand, variant interpretation requires to collect clinically relevant information about variants from databases and the literature. However, the process of extracting information from the literature is carried out manually. Consequently, it is highly time-consuming, non-scalable, and error-prone. This limitation affects the ability to correctly infer the effects of the variants on health, as the variant information from the literature is often incomplete or biased.
Currently, to address the problem of gold-standard samples shortage, in-silico simulation tools are employed. However, these tools lack the ability to control all the characteristics of real samples. Moreover, if they are not configured properly, they fail to realistically represent samples. Instead, to address the challenges of manual literature curation, artificial intelligence tools have been developed to analyse textual data. While these tools can identify relevant terms and filter scientific papers, they cannot fully automate the complex literature screening process needed to extract all clinically relevant information about variants
How we will live together?
Get PDFThe City and Territory course entitled “How we will live together?” (titled borrowed from the 17th Venice Architecture Biennial held in 2021) of the Turin Polytechnic’s Bachelor of Architecture degree was an opportunity to build and test a working group of researchers and students who would spend four months discussing the question of a project marking the transition from multiculturalism to ‘multi-naturalism’, as Eduardo Viveiro de Castro suggested in the 2020 Taipei Biennial. It was an opportunity to construct a new narrative of the city of Turin that overlaps climatic aspects, environ- mental resources, urban practices, and spatial characteristics. The question at the core of the course was: how can we learn, map, and analyse something that is not human, and how can this become the subject of a territorial project that puts coexistence at the centre of its agenda. The aim of this paper is therefore to analyse the experiences made within the course that tried to trace design strat- egies that emerged from an unprecedented reading of the city
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
- …
