Transportation of hazardous materials via pipeline. A historical overview

The transportation of hazardous materials via pipelines is often considered a safer alternative to other transportation modalities such as railway, road and ship. However, pipelines often cross industrial and highly populated areas, so that their failure can pose a significant risk to the surrounding environment and the exposed population: the possible release of flammable and/or toxic materials in such areas can generate catastrophic events with very severe consequences. A number of accidents have actually occurred in the past years, and even when no deaths or injured are reported, significant damages to the surrounding environment often occur. This suggests that, given the extremely wide extension of the network worldwide, and the very high amounts of transported materials, a careful analysis is still required. In addition, the construction of pipelines also involves the contribution of expertise from a range of technical areas. As a consequence, the occurrence of accidents and the impact of their consequences, depend on the combination of a large number of parameters. In the present paper, an analysis of data relative to pipelines transporting hazardous materials has been carried out, and the influence of specific issues connected with their type and operation, has been assessed

Solar Antineutrinos from Fluctuating Magnetic Fields at Kamiokande

We consider the effect of a strongly chaotic magnetic field at the narrow bottom of the convective zone of the Sun together with resonant matter oscillations on the production of electron Majorana antineutrinos. Even for moderate levels of noise, we show that it is possible to obtain a small but significant probability for $\nu_e\to \bar{\nu}_e$ conversions (1-3%) at the energy range 2-10 MeV for large regions of the mixing parameter space while still satisfying present (Super)-Kamiokande antineutrino bounds and observed total rates. In the other hand it would be possible to obtain information about the solar magnetic internal field if antineutrino bounds reach the 1% level and a particle physics solution to the SNP is assumed. The mechanism presented here has the advantage of being independent of the largely unknown magnetic profile of the Sun and the intrinsic neutrino magnetic moment.Comment: 10 pags. latex. 3 figures, 3 ps files. epsfig.sty neccesary. Version with minor typo errors correcte

Influence of the Ground-State Topology on the Domain-Wall Energy in the Edwards-Anderson +/- J Spin Glass Model

We study the phase stability of the Edwards-Anderson spin-glass model by analyzing the domain-wall energy. For the bimodal distribution of bonds, a topological analysis of the ground state allows us to separate the system into two regions: the backbone and its environment. We find that the distributions of domain-wall energies are very different in these two regions for the three dimensional (3D) case. Although the backbone turns out to have a very high phase stability, the combined effect of these excitations and correlations produces the low global stability displayed by the system as a whole. On the other hand, in two dimensions (2D) we find that the surface of the excitations avoids the backbone. Our results confirm that a narrow connection exists between the phase stability of the system and the internal structure of the ground-state. In addition, for both 3D and 2D we are able to obtain the fractal dimension of the domain wall by direct means.Comment: 4 pages, 3 figures. Accepted for publication in Rapid Communications of Phys. Rev.

Bring Your Own Data to X-PLAIN

Exploring and understanding the motivations behind black-box model predictions is becoming essential in many different applications. X-PLAIN is an interactive tool that allows human-in-the-loop inspection of the reasons behind model predictions. Its support for the local analysis of individual predictions enables users to inspect the local behavior of different classifiers and compare the knowledge different classifiers are exploiting for their prediction. The interactive exploration of prediction explanation provides actionable insights for both trusting and validating model predictions and, in case of unexpected behaviors, for debugging and improving the model itself

An assesment for UAS traffic awareness operations

Technology evolution in the field of Unmanned Aircraft Systems (UAS) will affect the Air Traffic Management (ATM) performance regarding to new military and civil applications. UAS, as new airspace users, will represent new challenges and opportunities to design the ATM system of the future. The goal of this future ATM network is to keep intact (or improve) the network in terms of security, safety, capacity and efficiency level. On the other hand, most UAS are, at present, designed for military purposes and very few civil applications have been developed mainly because the lack of a regulation basis concerning their certification, airworthiness and operations. Therefore, UAS operations have always been solutions highly dependent on the mission to be accomplished and on the scenario of flight. The generalized development of UAS applications is still limited by the absence of systems that support the development of the actual operations. Moreover, the systematic development of UAS missions leads to many other operational risks that need to be addressed. All this elements may delay, increase the risk and cost in the implementation of a new UAS application

Mixture models and exploratory analysis in networks

Networks are widely used in the biological, physical, and social sciences as a concise mathematical representation of the topology of systems of interacting components. Understanding the structure of these networks is one of the outstanding challenges in the study of complex systems. Here we describe a general technique for detecting structural features in large-scale network data which works by dividing the nodes of a network into classes such that the members of each class have similar patterns of connection to other nodes. Using the machinery of probabilistic mixture models and the expectation-maximization algorithm, we show that it is possible to detect, without prior knowledge of what we are looking for, a very broad range of types of structure in networks. We give a number of examples demonstrating how the method can be used to shed light on the properties of real-world networks, including social and information networks.Comment: 8 pages, 4 figures, two new examples in this version plus minor correction

Discurso de odio e ilegalización de partido político

Traballo fin de grao (UDC.DER). Dereito. Curso 2016/201