Extending Bayesian network models for mining and classification of glaucoma

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Glaucoma is a degenerative disease that damages the nerve fiber layer in the retina of the eye. Its mechanisms are not fully known and there is no fully-effective strategy to prevent visual impairment and blindness. However, if treatment is carried out at an early stage, it is possible to slow glaucomatous progression and improve the quality of life of sufferers. Despite the great amount of heterogeneous data that has become available for monitoring glaucoma, the performance of tests for early diagnosis are still insufficient, due to the complexity of disease progression and the diffculties in obtaining sufficient measurements. This research aims to assess and extend Bayesian Network (BN) models to investigate the nature of the disease and its progression, as well as improve early diagnosis performance. The exibility of BNs and their ability to integrate with clinician expertise make them a suitable tool to effectively exploit the available data. After presenting the problem, a series of BN models for cross-sectional data classification and integration are assessed; novel techniques are then proposed for classification and modelling of glaucoma progression. The results are validated against literature, direct expert knowledge and other Artificial Intelligence techniques, indicating that BNs and their proposed extensions improve glaucoma diagnosis performance and enable new insights into the disease process

The Yoccoz-Birkeland livestock population model coupled with random price dynamics

We study a random version of the population-market model proposed by Arlot, Marmi and Papini in Arlot et al. (2019). The latter model is based on the Yoccoz-Birkeland integral equation and describes a time evolution of livestock commodities prices which exhibits endogenous deterministic stochastic behaviour. We introduce a stochastic component inspired from the Black-Scholes market model into the price equation and we prove the existence of a random attractor and of a random invariant measure. We compute numerically the fractal dimension and the entropy of the random attractor and we show its convergence to the deterministic one as the volatility in the market equation tends to zero. We also investigate in detail the dependence of the attractor on the choice of the time-discretization parameter. We implement several statistical distances to quantify the similarity between the attractors of the discretized systems and the original one. In particular, following a work by Cuturi (2013), we use the Sinkhorn distance. This is a discrete and penalized version of the Optimal Transport Distance between two measures, given a transport cost matrix

Biostimulant Action of Dissolved Humic Substances From a Conventionally and an Organically Managed Soil on Nitrate Acquisition in Maize Plants

Conversion of conventional farming (CF) to organic farming (OF) is claimed to allow a sustainable management of soil resources, but information on changes induced on dissolved organic matter (DOM) are scarce. Among DOM components, dissolved humic substances (DHS) were shown to possess stimulatory effects on plant growth. DHS were isolated from CF and OF soil leacheates collected from soil monolith columns: first in November (bare soils) and then in April and June (bare and planted soils). DHS caused an enhancement of nitrate uptake rates in maize roots and modulated several genes involved in nitrogen acquisition. The DHS from OF soil exerted a stronger biostimulant action on the nitrate uptake system, but the first assimilatory step of nitrate was mainly activated by DHS derived from CF soil. To validate the physiological response of plants to DHS exposure, real-time RT-PCR analyses were performed on those genes most involved in nitrate acquisition, such as ZmNRT2.1, ZmNRT2.2, ZmMHA2 (coding for two high-affinity nitrate transporters and a PM H+-proton pump), ZmNADH:NR, ZmNADPH:NR, and ZmNiR (coding for nitrate reductases and nitrite reductase). All tested DHS fractions induced the upregulation of nitrate reductase (NR), and in particular the OF2 DHS stimulated the expression of both tested transcripts encoding for two NR isoforms. Characteristics of DHS varied during the experiment in both OF and CF soils: a decrease of high molecular weight fractions in the OF soil, a general increase in the carboxylic groups content, as well as diverse structural modifications in OF vs. CF soils were observed. These changes were accelerated in planted soils. Similarity of chemical properties of DHS with the more easily obtainable water-soluble humic substance extracted from peat (WEHS) and the correspondence of their biostimulant actions confirm the validity of studies which employ WEHS as an easily available source of DHS to investigate biostimulant actions on agricultural crops

WOOD-UP

The fundamental vision of the WOOD-UP project was to develop existing wood gasification plants in South Tyrol towards a polygenerative use in order to be able to produce not only energy but also high-quality charcoal (biochar) for the improvement of soil fertility and for climate protection. The project, funded by the European Regional Development Fund ERDF 2014–2020, was implemented by the Free University of Bolzano together with the Laimburg Research Centre. Based on the life cycle analysis (LCA) or scenario analysis of the entire production chain of wood gasification, strengths and weaknesses of the existing systems were identified with regard to their impact on the environment. Thanks to the results obtained, a number of suggestions for improvement could be formulated.; Il miglioramento verso un assetto poligenerativo degli attuali impianti altoatesini di gassificazione della biomassa legnosa, dove oltre all’energia si possa produrre biochar di qualità da impiegare in agricoltura come ammendante con effetti positivi sulla fertilità dei suoli e sulla mitigazione dei cambiamenti climatici è la visione che ha sostenuto il progetto WOOD-UP. Il progetto, finanziato con fondi FESR 2014-2020, ha visto la collaborazione tra la Libera Università di Bolzano e il Centro di Sperimentazione Laimburg. L’analisi del ciclo di vita e di scenario dell’intera filiera di gassificazione ha evidenziato elementi di forza e di debolezza dell’attuale filiera in termini di impatti ambientali e ha permesso di avanzare proposte di miglioramento sulla base dei risultati ottenuti dalla sperimentazione. ; Grundlegende Vision des Projektes WOOD-UP war die Entwicklung der bestehenden Holzvergasungsanlagen in Südtirol hin zu einer polygenerativen Nutzung, um neben Energie auch hochwertige Holzkohle (Biochar) zur Verbesserung der Bodenfruchtbarkeit und zum Klimaschutz erzeugen zu können. Das mit Mitteln aus dem Europäischen Fonds für regionale Entwicklung EFRE 2014–2020 finanzierte Projekt wurde von der Freien Universität Bozen gemeinsam mit dem Versuchszentrum Laimburg umgesetzt. Anhand der Lebenszyklusanalyse (LCA) bzw. der Szenarioanalyse der gesamten Produktionskette der Holzvergasung wurden Stärken und Schwächen der bestehenden Systeme hinsichtlich ihrer Auswirkungen auf die Umwelt aufgezeigt. Dank der erzielten Versuchsergebnisse konnte eine Reihe von Verbesserungsvorschlägen formuliert werden

Intelligenza artificiale e sicurezza: opportunità, rischi e raccomandazioni

L'IA (o intelligenza artificiale) è una disciplina in forte espansione negli ultimi anni e lo sarà sempre più nel prossimo futuro: tuttavia è dal 1956 che l’IA studia l’emulazione dell’intelligenza da parte delle macchine, intese come software e in certi casi hardware. L’IA è nata dall’idea di costruire macchine che - ispirandosi ai processi legati all’intelligenza umana - siano in grado di risolvere problemi complessi, per i quali solitamente si ritiene che sia necessario un qualche tipo di ragionamento intelligente. La principale area di ricerca e applicazione attuale dell’IA è il machine learning (algoritmi che imparano e si adattano in base ai dati che ricevono), che negli ultimi anni ha trovato ampie applicazioni grazie alle reti neurali (modelli matematici composti da neuroni artificiali) che a loro volta hanno consentito la nascita del deep learning (reti neurali di maggiore complessità). Appartengono al mondo dell’IA anche i sistemi esperti, la visione artificiale, il riconoscimento vocale, l’elaborazione del linguaggio naturale, la robotica avanzata e alcune soluzioni di cybersecurity. Quando si parla di IA c'è chi ne è entusiasta pensando alle opportunità, altri sono preoccupati poiché temono tecnologie futuristiche di un mondo in cui i robot sostituiranno l'uomo, gli toglieranno il lavoro e decideranno al suo posto. In realtà l'IA è ampiamente utilizzata già oggi in molti campi, ad esempio nei cellulari, negli oggetti smart (IoT), nelle industry 4.0, per le smart city, nei sistemi di sicurezza informatica, nei sistemi di guida autonoma (drive o parking assistant), nei chat bot di vari siti web; questi sono solo alcuni esempi basati tutti su algoritmi tipici dell’intelligenza artificiale. Grazie all'IA le aziende possono avere svariati vantaggi nel fornire servizi avanzati, personalizzati, prevedere trend, anticipare le scelte degli utenti, ecc. Ma non è tutto oro quel che luccica: ci sono talvolta problemi tecnici, interrogativi etici, rischi di sicurezza, norme e legislazioni non del tutto chiare. Le organizzazioni che già adottano soluzioni basate sull’IA, o quelle che intendono farlo, potrebbero beneficiare di questa pubblicazione per approfondirne le opportunità, i rischi e le relative contromisure. La Community for Security del Clusit si augura che questa pubblicazione possa fornire ai lettori un utile quadro d’insieme di una realtà, come l’intelligenza artificiale, che ci accompagnerà sempre più nella vita personale, sociale e lavorativa.AI (or artificial intelligence) is a booming discipline in recent years and will be increasingly so in the near future.However, it is since 1956 that AI has been studying the emulation of intelligence by machines, understood as software and in some cases hardware. AI arose from the idea of building machines that-inspired by processes related to human intelligence-are able to solve complex problems, for which it is usually believed that some kind of intelligent reasoning is required. The main current area of AI research and application is machine learning (algorithms that learn and adapt based on the data they receive), which has found wide applications in recent years thanks to neural networks (mathematical models composed of artificial neurons), which in turn have enabled the emergence of deep learning (neural networks of greater complexity). Also belonging to the AI world are expert systems, computer vision, speech recognition, natural language processing, advanced robotics and some cybersecurity solutions. When it comes to AI there are those who are enthusiastic about it thinking of the opportunities, others are concerned as they fear futuristic technologies of a world where robots will replace humans, take away their jobs and make decisions for them. In reality, AI is already widely used in many fields, for example, in cell phones, smart objects (IoT), industries 4.0, for smart cities, cybersecurity systems, autonomous driving systems (drive or parking assistant), chat bots on various websites; these are just a few examples all based on typical artificial intelligence algorithms. Thanks to AI, companies can have a variety of advantages in providing advanced, personalized services, predicting trends, anticipating user choices, etc. But not all that glitters is gold: there are sometimes technical problems, ethical questions, security risks, and standards and legislation that are not entirely clear. Organizations already adopting AI-based solutions, or those planning to do so, could benefit from this publication to learn more about the opportunities, risks, and related countermeasures. Clusit's Community for Security hopes that this publication will provide readers with a useful overview of a reality, such as artificial intelligence, that will increasingly accompany us in our personal, social and working lives

La Carbon Footprint implementata da Maschio Gaspardo

I principi dello sviluppo sostenibile vengono recepiti dalle imprese e dalle organizzazioni con sensibilit\ue0 diverse e adottando strumenti diversi, in dipendenza dal settore di attivit\ue0 e dalle specifiche caratteristiche dell\u2019azienda. La Carbon Footprint \ue8 un indicatore che permette di calcolare le emissioni di gas serra in atmosfera, dimostrando cos\uec la propria eco-sostenibilit\ue0 e andando incontro alle richieste dei consumatori, sempre pi\uf9 disposti a privilegiare prodotti e servizi a minor impatto ambientale. Maschio Gaspardo \ue8 un\u2019azienda leader mondiale nella produzione di macchine agricole, con sede nel comune di Campodarsego (PD) e altri 16 stabilimenti produttivi in vari Paesi europei, asiatici e nord-americani. Da molti anni Maschio Gaspardo ha avviato una politica eco-sostenibile sia a livello di prodotto, attraverso la realizzazione di macchine agricole ad alta efficienza, che a livello di processo, dotando i propri stabilimenti di sistemi di autoproduzione da fonti rinnovabili e di risparmio energetico. Inoltre l\u2019azienda ha investito sul rinnovo del parco veicoli per ridurre i consumi di carburante e le emissioni in atmosfera. L\u2019azienda ha intrapreso uno studio di Carbon Footprint relativo ai tre stabilimenti italiani, prendendo in considerazione sia la raccolta e l\u2019elaborazione dei dati sui consumi energetici che l\u2019analisi dei processi industriali. E\u2019 stato cos\uec possibile constatare la riduzione dell\u2019impronta di carbonio equivalente e un adeguato monitoraggio delle emissioni di gas serra. Al termine del processo di valutazione, Maschio Gaspardo ha ottenuto la certificazione internazionale Carbon Trust Standard come prima azienda mondiale produttrice di macchine agricole e prima industria metalmeccanica in Italia

Time to detect deterioration for linear regression of mean deviation (<i>MD</i>), point-wise linear regression (<i>PLR</i>), <i>ANSWERS</i> and <i>ANSWER</i> at false positive rates between 0 and 15%.

<p>The number of contiguous points in point-wise linear regression are shown in the square points.</p

The ratio of the hit rates for <i>ANSWER</i> and <i>ANSWERS</i> (in columns) against those of linear regression of mean deviation (<i>MD</i>), point-wise linear regression (<i>PLR</i>) of differential light sensitivity and <i>ANSWER</i> (in rows).

<p>The false positive rate (<i>FP</i>) at which the ratio was estimated is also given. The ratio is calculated for criteria giving 5% false positive rates, or at a false positive rate closest to 5% for point-wise linear regression where the false positive rate cannot be continuously estimated. The comparison was carried out with series lengths of 5, 7, 9 and 11.</p