A biophysical model for valence-based navigational learning: the role of D-Serine

Recent experiments have revealed the fundamental importance of neuromodulatory action on activity-dependent synaptic plasticity underlying behavioral learning and spatial memory formation. Neuromodulators affect synaptic plasticity through the modification of the dynamics of receptors on the synaptic membrane. However, chemical substances other than neuromodulators, such as receptors co-agonists, can influence the receptors' dynamics and thus participate in determining plasticity. Here we focus on D-serine, which has been observed to affect the activity thresholds of synaptic plasticity by co-activating NMDA receptors. We use a computational model for spatial value learning with plasticity between two place cell layers. The D-serine release is CB1R mediated and the model reproduces the impairment of spatial memory due to the astrocytic CB1R knockout for a mouse navigating in the Morris water maze. The addition of path-constraining obstacles shows how performance impairment depends on the environment's topology. The model can explain the experimental evidence and produce useful testable predictions to increase our understanding of the complex mechanisms underlying learning.

Structural health of a road tunnel intersecting a large and active rockslide

This work presents the case of the San Lorenzo road tunnel, a transportation infrastructure located in the northern part of Italy, involved in the so-called Passo della Morte landslide. This tunnel crosses a large rockslide characterized by slow movements. Damages like water seepage inside the tunnel and concrete lining detachments have surfaced through the years, increasing the risk. This work develops the objective of tracing back the landslide-induced stresses directly responsible for the cracks’ pattern on the most damaged segments of the tunnel. The first section of this work gives information about the global framework: site geography and its strategic relevance, geological setting, hydrological and climate conditions will be provided. The road tunnel infrastructure and its interaction with the landslide phenomena will be discussed together with the active monitoring system, which has been working for more than 20 years. In the second part the several steps and tools used to add more details about the road damages are reported. A visualization of the actual state of the most damaged portions of the road has been reached. Then the attention has been addressed to the stresses acting on the road tunnel’s aforesaid portions, developing a FEM model of a section of the tunnel through a selected software. This latter process can be deemed as a beginning for further developments. Some preliminary results are shown to demonstrate the goodness of the assumptions made. The possible future set by this work aims at constant enlargement of information to be provided to the FEM software, and at the validation of the obtained results through the monitoring data interpretative tools.

Green Network - Un progetto urbano per lo scalo San Donato a Bologna

Da alcuni anni Bologna è interessata da un processo di trasformazione,che investe l’economia, la società e la città fisica. Le mutazioni delle condizioni tecnologiche, economiche e ambientali inducono trasformazioni nel modo di concepire la pianificazione. In questo contesto di trasformazioni gli spazi vuoti si offrono come straordinaria risorsa di nuove opportunità progettuali in termini di qualità urbana, inoltre si pongono come occasione per avviare una seria politica di sostenibilità in grado di ridisegnare una città più attrattiva ed efficiente nei suoi servizi. La probabile prossima dismissione dello scalo merci San Donato si presenta quindi come concreta possibilità d’intervento in un’area periurbana diventata ormai vera e propria centralità.

Machine Learning applicato al Web Semantico: Statistical Relational Learning vs Tensor Factorization

Obiettivo della tesi è analizzare e testare i principali approcci di Machine Learning applicabili in contesti semantici, partendo da algoritmi di Statistical Relational Learning, quali Relational Probability Trees, Relational Bayesian Classifiers e Relational Dependency Networks, per poi passare ad approcci basati su fattorizzazione tensori, in particolare CANDECOMP/PARAFAC, Tucker e RESCAL.

Learning from experience: Analysis of the civil litigation regarding building structures on the court of bologna in the years 2011/2012

The present work is aimed to the study and the analysis of the defects detected in the civil structure and that are object of civil litigation in order to create an instruments capable of helping the different actor involved in the building process. It is divided in three main sections. The first part is focused on the collection of the data related to the civil proceeding of the 2012 and the development of in depth analysis of the main aspects regarding the defects on existing buildings. The research center “Osservatorio Claudio Ceccoli” developed a system for the collection of the information coming from the civil proceedings of the Court of Bologna. Statistical analysis are been performed and the results are been shown and discussed in the first chapters.The second part analyzes the main issues emerged during the study of the real cases, related to the activities of the technical consultant. The idea is to create documents, called “focus”, addressed to clarify and codify specific problems in order to develop guidelines that help the technician editing of the technical advice.The third part is centered on the estimation of the methods used for the collection of data. The first results show that these are not efficient. The critical analysis of the database, the result and the experience and throughout, allowed the implementation of the collection system for the data.