1,704 research outputs found
Supervised and Semi-Supervised Explainable AI applied to Cardiovascular Magnetic Resonance Mapping Techniques
No full textDeep Learning techniques have demonstrated broad applicability across numerous domains, including medicine. Among these, Cardiovascular Magnetic Resonance Imaging stands out as a field where Deep Learning has the potential to transform myocardial tissue characterization through the automated, objective, and reproducible assessment of modern-day quantitative techniques like T1 and T2 mapping images, which serve as the case study of this Thesis. The clinical implementation of these techniques, however, is hindered by several obstacles, including the need for center-specific reference values -- which strongly depend on the magnetic field strength, acquisition sequence, and chosen parameters -- along with the scarcity of large annotated datasets, class imbalance, and the requirement for transparent and explainable decision-making. This Thesis proposes a Deep Learning approach that utilizes supervised and semi-supervised learning and model ensembling to navigate some of these challenges. It provides explainability tools to support transparent case-level explanation of model predictions, facilitate clinician trust and supporting clinical adoption. Experimental results demonstrate that the proposed framework enhanced predictive accuracy and reliability harnessing actionable insights in Cardiac Imaging
A Survey of Modern Hybrid Particle Swarm Optimization Algorithms
No full textBio-inspired, population-based meta-heuristic for global optimization are very popular algorithms for addressing complex computational problems that traditional methods struggle to solve. Among the existing algorithms, the swarm intelligence algorithm Particle Swarm Optimization (PSO) is one of the most popular, thanks to its simplicity and effectiveness in multiple scenarios. This article focuses on recent hybrid optimization methods that extend the basic functioning of PSO. Hybridization, in this context, is defined as the integration of PSO with a different technique, to take advantage of the strengths of both algorithms. According to our findings, many variants have been proposed. The most frequent solutions consist of the hybridization of PSO with evolutionary operators (e.g. Genetic Algorithms and Differential Evolution); such strategies usually maintain a high degree of diversity into the population, enhancing global search capability, while reducing the risk of stagnation. Meanwhile the most widespread applications are from the areas of energy optimization, structural engineering and machine learning problems, demonstrating the versatility of these hybrid approaches
TUTELA DEL LAVORO E LIBERTA' D'IMPRESA NEI PROCESSI DI ESTERNALIZZAZIONE
No full textL’elaborato analizza le conseguenze lavoristiche della successione fra imprenditori, muovendo da una ricognizione delle varie tipologie di esternalizzazione con le relative esigenze e principali criticità.
L’indagine si concentra in primo luogo sul trasferimento d’azienda, esaminando la normativa e la giurisprudenza europee per passare poi alla disciplina di diritto interno, alle procedure sindacali e a uno specifico focus sul trasferimento delle aziende in crisi.
Successivamente l’autore si sofferma sull’appalto, prendendone in particolare considerazione gli indici di genuinità, i criteri di distinzione dalla somministrazione illecita di manodopera e la tutela delle maestranze in caso di avvicendamento fra imprese.
Da ultimo, la ricerca approfondisce le c.d. “clausole sociali”, sia di prima che di seconda generazione, valutandone la compatibilità con il diritto eurounitario e con la costituzione nonché riflettendo sui possibili rimedi in caso di loro violazione.The author analyzes the labour consequences of the succession between entrepreneurs, starting from a recognition of the various types of outsourcing with the related needs and main critical issues.
The survey focuses primarily on the transfer of businesses, examining European legislation and case-law and then moving on to internal legislation, trade union procedures and a specific focus on the transfer of companies in crisis.
The author then dwells on the contract, taking into account in particular the indications of authenticity, the criteria of distinction from the illicit administration of labour and the protection of workers in the event of turnover between companies.
Finally, the research deepens the "social clauses", both first and second generation, assessing their compatibility with European law and with the constitution and reflecting on possible remedies in case of their violation
Ultra Low Carbon Vehicles: New Parameters for Automotive Design
No full textAs the influence of vehicle emissions on our environment has become better understood, the UK government has recently placed urgent emphasis on the implementation of low carbon
technologies in the automotive industry through: the UK Low Carbon Industrial Strategy. The overall objective is to offer big incentives to consumers and support for the development of infrastructure and engineering solutions. This scheme however does not consider how the development of functional and experiential user value might drive consumer demand, contributing to the adoption of low carbon vehicles (LCVs) in the mass market.
With the emergence of the North East of England as the UK’s first specialised region for the development of ultra-low carbon vehicles (ULCVs), ONE North East, as a development agency for the region's economic and business development, and Northumbria University Ideas-lab have supported a project to facilitate innovation through the collaboration of technology, research and development (R&D) and business. The High Value Low Carbon (HVLC) project aims to envisage
new user value made possible by the integration of low carbon vehicle platforms with new process and network technologies. The HVLC consortium represents vehicle manufacturers and their suppliers as well as technology based companies and through an ongoing process of design concept generation the project offers a hub for innovation led enterprise.
Whilst new technological developments in areas such as power generation, nano materials, hydrogen fuel cells, printed electronics and networked communications will all impact on future automotive design, the mass adoption of low carbon technologies represents a paradigm shift for the motorist. This paper aims to describe how the mapping of new parameters will lead to new transport scenarios that will create the space for new collaborative research on user experiences supported by innovative technologies and related services
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Matteo Maria Boiardo (1441-1494)
No full textMatteo Maria Boiardo – contemporary of Sir Thomas Malory and forerunner of Shakespeare, Spenser, Milton, Cervantes, Tolkien, and C.S. Lewis – is best known as the author of the Orlando Innamorato [Orlando in Love], a poem in ottava rima organically merging Carolingian epic and Arthurian romance. He was also a lyric and pastoral poet, playwright, and translator of classical texts into the Italian vernacular. Matteo was active in Italy during the years of 1463 throughout 1494, the year of his death
Uno sguardo all'Est. Lavoro e diritti fondamentali in Russia e negli altri Paesi della Comunità di Stati indipendenti
No full textNel contributo l'autore, dopo aver esaminato i diritti fondamentali del lavoro presenti nelle Costituzioni dei Paesi della Comunità di Stati indipendenti, si sofferma sul diritto del lavoro russo. La trattazione, a carattere diacronico, abbraccia i rami principali del diritto del lavoro: il rapporto individuale, le relazioni collettive, il mercato del lavoro. L'autore rileva una sorprendente continuità nelle linee essenziali del diritto del lavoro russo, pur nel susseguirsi di sistemi socioeconomici completamente diversi. Egli conclude che il diritto del lavoro russo appartiene saldamente alla matrice europea della materia.The contribution of the author starts by examining fundamental labour rights recognised by the Constitutions of the Countries of the Community of Independent States. Then the author focuses upon Russian Labour Law, going through the history of the main branches of the discipline: the individual relationship, industrial relations, the labour market. The article finds a surprising continuity in the essential lines of Russian Labour Law, in spite of the completely different socio-economic systems that were experimented in Russia during the twentieth century. The author concludes that Russian Labour Law clearly belongs to the European matrix of the discipline
Corpo, spazio, architettura. Fenomenologia dell’esperienza spaziale Matteo Vegetti, Fabrizia Bandi (eds.), Editrice Morcelliana, Brescia 2024
No full textThe author reviews "Corpo, spazio, architettura: Fenomenologia dell’esperienza spaziale," edited by Matteo Vegetti and Fabrizia Bandi. The book examines the philosophical underpinnings of spatial experience through a phenomenological lens, engaging with both traditional and contemporary notions of how space, body, and architecture interconnect. The author highlights the text\u27s exploration of how architects and designers might integrate phenomenological insights into their work, transforming space from mere physical dimensions into lived, experienced reality. The review appreciates the book’s thematic organization, which starts with foundational theories from Merleau-Ponty and expands through discussions on virtual spaces and the future of architecture in the digital age. Through this anthology, readers gain a comprehensive view of the architectural implications of phenomenological thought, making it a critical resource for anyone involved in the conceptualization and design of space
Assessing Cardiac Functionality by Means of Interpretable AI and Myocardial Strain
No full textCardiac Imaging is a powerful methodology for the accurate assessment of heart functionality. Among the possible approaches, Myocardial Strain assesses the functionality of the heart by tracking the movement and deformation of myocardium during the cardiac cycle. This information, that can be acquired also by means of Cardiac Magnetic Resonance, can pave the way to the development of predictive models using machine learning. In this work, we developed a predictive model of left ventricular ejection fraction, which is a measure of the heart’s function to pump oxygen-rich blood to the body, trained using strain data. Specifically, we developed a fully interpretable model based on a rule-based Fuzzy Inference System, coupled with a novel methodology for the disambiguation of the rules. Our results show that the developed model is able to accurately estimate the ejection fraction, and can provide physicians with additional insights about the role of strain features
Improving the Efficiency and the Validity of Molecular Transformers
No full textSince their advent, Transformer models have been applied across a wide range of fields, including cheminformatics. In this context, drug discovery has benefited from using Molecular Transformers by leveraging diverse string representations of molecules, such as the Simplified Molecular Input Line Entry Systems (SMILES), for a variety of tasks. In this study, we present a model focused on the optimization of a formerly developed Molecular Transformer specifically dedicated to metabolism prediction. Metabolism refers to all the biotransformations a drug undergoes once inside the human body, directly influencing its therapeutic effect and potential toxicity, and therefore represents a key topic in medicinal chemistry. Framing molecular transformation prediction as a sequence-to-sequence translation task has shown promise, but suffers from limitations such as low validity of generated molecules and high computational cost. To address this limitation, we here propose an optimized model that integrates pre-training, transfer learning, and fine-tuning techniques, already improving validity and reducing computation time. Finally, by separating the metabolism prediction task from the SMILES syntax learning, we ensure broader applicability of the proposed model across diverse datasets and a variety of SMILES-based tasks beyond metabolic transformations, expanding its potential utility
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