Caratterizzazione di sequenze di DNA mediante catene di Markov

Questa tesi si inserisce nell’ambito di studio dei modelli stocastici applicati alle sequenze di DNA. I random walk e le catene di Markov sono tra i processi aleatori che hanno trovato maggiore diffusione in ambito applicativo grazie alla loro capacità di cogliere le caratteristiche salienti di molti sistemi complessi, pur mantenendo semplice la descrizione di questi. Nello specifico, la trattazione si concentra sull’applicazione di questi nel contesto dell’analisi statistica delle sequenze genomiche. Il DNA può essere rappresentato in prima approssimazione da una sequenza di nucleotidi che risulta ben riprodotta dal modello a catena di Markov; ciò rappresenta il punto di partenza per andare a studiare le proprietà statistiche delle catene di DNA. Si approfondisce questo discorso andando ad analizzare uno studio che si ripropone di caratterizzare le sequenze di DNA tramite le distribuzioni delle distanze inter-dinucleotidiche. Se ne commentano i risultati, al fine di mostrare le potenzialità di questi modelli nel fare emergere caratteristiche rilevanti in altri ambiti, in questo caso quello biologico.

Analisi di una piattaforma di sviluppo per BPMN: Activiti

Si tratta di un'analisi della piattaforma di sviluppo per BPMN Activiti. Viene prima spiegata la notazione del Business Process Modeling e poi viene descritto il funzionamento e la struttura di Activiti. Infine viene spiegato come usare le API fornite insieme al motore.

New markov chain based methods for single and cross-domain sentiment classification

Nowadays communication is switching from a centralized scenario, where communication media like newspapers, radio, TV programs produce information and people are just consumers, to a completely different decentralized scenario, where everyone is potentially an information producer through the use of social networks, blogs, forums that allow a real-time worldwide information exchange. These new instruments, as a result of their widespread diffusion, have started playing an important socio-economic role. They are the most used communication media and, as a consequence, they constitute the main source of information enterprises, political parties and other organizations can rely on. Analyzing data stored in servers all over the world is feasible by means of Text Mining techniques like Sentiment Analysis, which aims to extract opinions from huge amount of unstructured texts. This could lead to determine, for instance, the user satisfaction degree about products, services, politicians and so on. In this context, this dissertation presents new Document Sentiment Classification methods based on the mathematical theory of Markov Chains. All these approaches bank on a Markov Chain based model, which is language independent and whose killing features are simplicity and generality, which make it interesting with respect to previous sophisticated techniques. Every discussed technique has been tested in both Single-Domain and Cross-Domain Sentiment Classification areas, comparing performance with those of other two previous works. The performed analysis shows that some of the examined algorithms produce results comparable with the best methods in literature, with reference to both single-domain and cross-domain tasks, in $2$-classes (i.e. positive and negative) Document Sentiment Classification. However, there is still room for improvement, because this work also shows the way to walk in order to enhance performance, that is, a good novel feature selection process would be enough to outperform the state of the art. Furthermore, since some of the proposed approaches show promising results in $2$-classes Single-Domain Sentiment Classification, another future work will regard validating these results also in tasks with more than $2$ classes.

Multiphase flow in porous media: meta-modeling techniques for sensitivity analysis and risk assessment

One of the biggest challenges that contaminant hydrogeology is facing, is how to adequately address the uncertainty associated with model predictions. Uncertainty arise from multiple sources, such as: interpretative error, calibration accuracy, parameter sensitivity and variability. This critical issue needs to be properly addressed in order to support environmental decision-making processes. In this study, we perform Global Sensitivity Analysis (GSA) on a contaminant transport model for the assessment of hydrocarbon concentration in groundwater. We provide a quantification of the environmental impact and, given the incomplete knowledge of hydrogeological parameters, we evaluate which are the most influential, requiring greater accuracy in the calibration process. Parameters are treated as random variables and a variance-based GSA is performed in a optimized numerical Monte Carlo framework. The Sobol indices are adopted as sensitivity measures and they are computed by employing meta-models to characterize the migration process, while reducing the computational cost of the analysis. The proposed methodology allows us to: extend the number of Monte Carlo iterations, identify the influence of uncertain parameters and lead to considerable saving computational time obtaining an acceptable accuracy.

Modeling Ultrafast Spectroscopy of the NADH Dimer: From UV-VIS to X-rays

Ultrafast pump-probe spectroscopy is a conceptually simple and versatile tool for resolving photoinduced dynamics in molecular systems. Due to the fast development of new experimental setups, such as synchrotron light sources and X-ray free electron lasers (XFEL), new spectral windows are becoming accessible. On the one hand, these sources have enabled scientist to access faster and faster time scales and to reach unprecedent insights into dynamical properties of matter. On the other hand, the complementarity of well-developed and novel techniques allows to study the same physical process from different points of views, integrating the advantages and overcoming the limitations of each approach. In this context, it is highly desirable to reach a clear understanding of which type of spectroscopy is more suited to capture a certain facade of a given photo-induced process, that is, to establish a correlation between the process to be unraveled and the technique to be used. In this thesis, I will show how computational spectroscopy can be a tool to establish such a correlation. I will study a specific process, which is the ultrafast energy transfer in the nicotinamide adenine dinucleotide dimer (NADH). This process will be observed in different spectral windows (from UV-VIS to X-rays), accessing the ability of different spectroscopic techniques to unravel the system evolution by means of state-of-the-art theoretical models and methodologies. The comparison of different spectroscopic simulations will demonstrate their complementarity, eventually allowing to identify the type of spectroscopy that is best suited to resolve the ultrafast energy transfer.

Kinetic modeling for the synthesis of ethyl levulinate

This work is focused on studying the kinetics of esterification of levulinic acid in an isothermal batch reactor using ethanol as a reactant and as a protic polar solvent at the same time and in the presence of an acid catalyst (sulfuric acid). The choice of solvent is important as it affects the kinetics and thermodynamics of the reaction system moreover, the knowledge of the reaction kinetics plays an important role in the design of the process. This work is divided into two stages; The first stage is the experimental part in which the experimental matrix was developed by changing the process variables one at a time (temperature, molar ratio between reactants, and catalyst concentration) in order to study their influence on the kinetics; the second stage is using the obtained data from the experiments to build the modeling part in order to estimate the thermodynamics parameters.

Conditional entropy and predictability for non-reversible Markov systems

The purpose of this thesis is to clarify the role of non-equilibrium stationary currents of Markov processes in the context of the predictability of future states of the system. Once the connection between the predictability and the conditional entropy is established, we provide a comprehensive approach to the definition of a multi-particle Markov system. In particular, starting from the well-known theory of random walk on network, we derive the non-linear master equation for an interacting multi-particle system under the one-step process hypothesis, highlighting the limits of its tractability and the prop- erties of its stationary solution. Lastly, in order to study the impact of the NESS on the predictability at short times, we analyze the conditional entropy by modulating the intensity of the stationary currents, both for a single-particle and a multi-particle Markov system. The results obtained analytically are numerically tested on a 5-node cycle network and put in correspondence with the stationary entropy production. Furthermore, because of the low dimensionality of the single-particle system, an analysis of its spectral properties as a function of the modulated stationary currents is performed.