2019

The increasing level of automation and complexity in the automotive industry has led to the establishment of a work environment, where human machine interface and human reliability are becoming critical factors of performance especially for safety critical tasks. Many different methodologies for performing risk assessment considering human factors are already available in the literature, but they were often developed for domains other than the automotive industry (aviation, nuclear and process industry).

Data were collected in an automotive production plant during a campaign of observations performed by safety experts. A period of one week of observations was done during which safety experts monitored the working activity of an assembly line. All accident-precursors identified were reported in a format and immediately analysed and classified according to HFACS.

An algorithm for the creation of mixed level arrays with generalized minimum aberration (GMA) is proposed. GMA mixed level arrays are particularly useful for experiments involving qualitative factors: for these, the number of factor levels is often a consequence of subject matter requirements, while a priori assumptions on a statistical model are not made, apart from assuming lower order effects to be more important than higher order effects. The proposed algorithm creates GMA arrays using mixed integer optimization with conic quadratic constraints.

Algebraic sampling methods are a powerful tool to perform hypothesis testing for non-negative discrete exponential families, when the exact computation of the test statistic null distribution is computationally infeasible. We propose an improvement of the accelerated sampling described by Diaconis and Sturmfels (1998) based on permutations. We thus establish a link between standard permutation and algebraic-statistics-based sampling.

Several studies have shown so far that poor acoustics inside classrooms negatively affects the teaching and learning processes, especially at the lowest grades of education. However, the extent to which noise exposure or excessive reverberation affect well-being of children at school in their early childhood is still unanswered, as well as their awareness of noise disturbance. This work is a pilot study to investigate to which extent classroom acoustics affects the perceived well-being and noise disturbance in first graders.

Among Mixed Poisson processes, counting processes having geometrically distributed increments can be obtained when the mixing random intensity is exponentially distributed. Dealing with shock models and compound counting models whose shocks and claims occur according to such counting processes, we provide various comparison results and aging properties concerning total claim amounts and random lifetimes.

In this note, we use multivariate subordination to introduce a multivariate extension of the generalized asymmetric Laplace motion. The class introduced provides a unified framework for several multivariate extensions of the popular variance gamma process. We also show that the associated time one distribution extends the multivariate generalized asymmetric Laplace distributions proposed in the statistical literature.

The class of marked Poisson processes and its connection with subordinated Lévy processes allow us to propose a new interpretation of multidimensional information flows and their relation to market movements. The new approach provides a unified framework for multivariate asset return models in a Lévy economy. In fact, we are able to recover several processes commonly used to model asset returns as subcases. We consider a first application example using the normal inverse Gaussian specification.

The spread of social media highlights controversial changes in the public sphere: new opportunities of access and expression in fact go alongside aberrant phenomena of extremist propaganda. In recent years, civil society institutions and bodies have responded to the wave of hatred and violence on social media through online awareness-raising campaigns aimed at combating ideological propaganda and offering alternative narratives to vulnerable individuals.

Spatial visualization is a very useful tool to help decision-makers in the urban planning process, i) to define future energy transition pathways, ii) to implement energy efficiency strategies and iii) to integrate renewable energy technologies in the context of sustainable cities. There is thus a need to develop new tools to understand the energy consumption patterns across cities. Statistical methods are often used to understand the driving parameters of energy consumption but rarely used to evaluate future urban refurbishment scenarios.