Turbulence models in the atmospheric boundary layer under convective conditions

In this work a modelization of the turbulence in the atmospheric boundary layer, under convective condition, is made. For this aim, the equations that describe the atmospheric motion are expressed through Reynolds averages and, then, they need closures. This work consists in modifying the TKE-l closure used in the BOLAM (Bologna Limited Area Model) forecast model. In particular, the single column model extracted from BOLAM is used, which is modified to obtain other three different closure schemes: a non-local term is added to the flux- gradient relations used to close the second order moments present in the evolution equation of the turbulent kinetic energy, so that the flux-gradient relations become more suitable for simulating an unstable boundary layer. Furthermore, a comparison among the results obtained from the single column model, the ones obtained from the three new schemes and the observations provided by the known case in literature ”GABLS2” is made.

Investigating strategies and indicators for sustainable campus: Comparison and synergies between University of Bologna and Delft University of Technology

Sustainable development is one of the biggest challenges of the twenty fist-century. Various university has begun the debate about the content of this concept and the ways in which to integrate it into their policy, organization and activities. Universities have a special responsibility to take over a leading position by demonstrating best practices that sustain and educate a sustainable society. For that reason universities have the opportunity to create the culture of sustainability for today’s student, and to set their expectations for how the world should be. This thesis aim at analyzing how Delft University of Technology and University of Bologna face the challenge of becoming a sustainable campus. In this context, both universities have been studied and analyzed following the International Sustainable Campus Network (ISCN) methodology that provides a common framework to formalize commitments and goals at campus level. In particular this work has been aimed to highlight which key performance indicators are essential to reach sustainability as a consequence the following aspects has been taken into consideration: energy use, water use, solid waste and recycling, carbon emission. Subsequently, in order to provide a better understanding of the current state of sustainability on University of Bologna and Delft University of Technology, and potential strategies to achieve the stated objective, a SWOT Analysis has been undertaken. Strengths, weaknesses, opportunities and threats have been shown to understand how the two universities can implement a synergy to improve each other. In the direction of framing a “Sustainable SWOT” has been considered the model proposed by People and Planet, so it has been necessary to evaluate important matters as for instance policy, investment, management, education and engagement. Regarding this, it has been fundamental to involve the main sustainability coordinators of the two universities, this has been achieved through a brainstorming session. Partnerships are key to the achievement of sustainability. The creation of a bridge between two universities aims to join forces and to create a new generation of talent. As a result, people can become able to support universities in the exchange of information, ideas, and best practices for achieving sustainable campus operations and integrating sustainability in research and teaching. For this purpose the project "SUCCESS" has been presented, the project aims to create an interactive European campus network that can be considered a strategic key player for sustainable campus innovation in Europe. Specifically, the main key performance indicators have been analyzed and the importance they have for the two universities and their strategic impact have been highlighted. For this reason, a survey was conducted with people who play crucial roles for sustainability within the two universities and they were asked to evaluate the KPIs of the project. This assessment has been relevant because has represented the foundation to develop a strategy to create a true collaboration.

European and Chinese legislation regulating the portrayal of women in television advertising: A comparison

This dissertation has two main purposes. On the one hand, it aims at comparing the gender stereotypes presented in the television commercials in China and in Europe. Considering the cultural, historical and socio-economical differences between these two contexts, it is interesting to examine the gender role models offered and used by the advertising industry in European Union and China in order to see if the gender stereotypes are similar and to evaluate to which extent they reflect, challenge or reinforce the gender roles of the society where they are broadcasted. On the other hand, the objective of this dissertation is to establish the degree of adequateness and effectiveness of the existing regulatory framework through an analysis of the positive and negative aspects of the regulatory acts issued to safeguard a fair representation of genders in the EU Member States and in China.

Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks

In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.

Dynamic characterisation of four nine-story large-panel R.C. buildings in Bishkek (Kyrgyzstan): A comparison between experimental ambient vibration analysis and numerical finite element modeling

With the outlook of improving seismic vulnerability assessment for the city of Bishkek (Kyrgyzstan), the global dynamic behaviour of four nine-storey r.c. large-panel buildings in elastic regime is studied. The four buildings were built during the Soviet era within a serial production system. Since they all belong to the same series, they have very similar geometries both in plan and in height. Firstly, ambient vibration measurements are performed in the four buildings. The data analysis composed of discrete Fourier transform, modal analysis (frequency domain decomposition) and deconvolution interferometry, yields the modal characteristics and an estimate of the linear impulse response function for the structures of the four buildings. Then, finite element models are set up for all four buildings and the results of the numerical modal analysis are compared with the experimental ones. The numerical models are finally calibrated considering the first three global modes and their results match the experimental ones with an error of less then 20%.

Models for the assessment of resilience in water distribution networks

The following thesis work focuses on the use and implementation of advanced models for measuring the resilience of water distribution networks. In particular, the functions implemented in GRA Tool, a software developed by the University of Exeter (UK), and the functions of the Toolkit of Epanet 2.2 were investigated. The study of the resilience and failure, obtained through GRA Tool and the development of the methodology based on the combined use of EPANET 2.2 and MATLAB software, was tested in a first phase, on a small-sized literature water distribution network, so that the variability of the results could be perceived more clearly and with greater immediacy, and then, on a more complex network, that of Modena. In the specific, it has been decided to go to recreate a mode of failure deferred in time, one proposed by the software GRA Tool, that is failure to the pipes, to make a comparison between the two methodologies. The analysis of hydraulic efficiency was conducted using a synthetic and global network performance index, i.e., Resilience index, introduced by Todini in the years 2000-2016. In fact, this index, being one of the parameters with which to evaluate the overall state of "hydraulic well-being" of a network, has the advantage of being able to act as a criterion for selecting any improvements to be made on the network itself. Furthermore, during these analyzes, was shown the analytical development undergone over time by the formula of the Resilience Index. The final intent of this thesis work was to understand by what means to improve the resilience of the system in question, as the introduction of the scenario linked to the rupture of the pipelines was designed to be able to identify the most problematic branches, i.e., those that in the event of a failure it would entail greater damage to the network, including lowering the Resilience Index.