Inferences on earthquake kinematic properties from data inversion: two different approaches

During my PhD, starting from the original formulations proposed by Bertrand et al., 2000 and Emolo & Zollo 2005, I developed inversion methods and applied then at different earthquakes. In particular large efforts have been devoted to the study of the model resolution and to the estimation of the model parameter errors. To study the source kinematic characteristics of the Christchurch earthquake we performed a joint inversion of strong-motion, GPS and InSAR data using a non-linear inversion method. Considering the complexity highlighted by superficial deformation data, we adopted a fault model consisting of two partially overlapping segments, with dimensions 15x11 and 7x7 km2, having different faulting styles. This two-fault model allows to better reconstruct the complex shape of the superficial deformation data. The total seismic moment resulting from the joint inversion is 3.0x1025 dyne.cm (Mw = 6.2) with an average rupture velocity of 2.0 km/s. Errors associated with the kinematic model have been estimated of around 20-30 %. The 2009 Aquila sequence was characterized by an intense aftershocks sequence that lasted several months. In this study we applied an inversion method that assumes as data the apparent Source Time Functions (aSTFs), to a Mw 4.0 aftershock of the Aquila sequence. The estimation of aSTFs was obtained using the deconvolution method proposed by Vallée et al., 2004. The inversion results show a heterogeneous slip distribution, characterized by two main slip patches located NW of the hypocenter, and a variable rupture velocity distribution (mean value of 2.5 km/s), showing a rupture front acceleration in between the two high slip zones. Errors of about 20% characterize the final estimated parameters.

Assessing the fit of unidimensional IRT models for binary data under model misspecification

Model misspecification affects the classical test statistics used to assess the fit of the Item Response Theory (IRT) models. Robust tests have been derived under model misspecification, as the Generalized Lagrange Multiplier and Hausman tests, but their use has not been largely explored in the IRT framework. In the first part of the thesis, we introduce the Generalized Lagrange Multiplier test to detect differential item response functioning in IRT models for binary data under model misspecification. By means of a simulation study and a real data analysis, we compare its performance with the classical Lagrange Multiplier test, computed using the Hessian and the cross-product matrix, and the Generalized Jackknife Score test. The power of these tests is computed empirically and asymptotically. The misspecifications considered are local dependence among items and non-normal distribution of the latent variable. The results highlight that, under mild model misspecification, all tests have good performance while, under strong model misspecification, the performance of the tests deteriorates. None of the tests considered show an overall superior performance than the others. In the second part of the thesis, we extend the Generalized Hausman test to detect non-normality of the latent variable distribution. To build the test, we consider a seminonparametric-IRT model, that assumes a more flexible latent variable distribution. By means of a simulation study and two real applications, we compare the performance of the Generalized Hausman test with the M2 limited information goodness-of-fit test and the Likelihood-Ratio test. Additionally, the information criteria are computed. The Generalized Hausman test has a better performance than the Likelihood-Ratio test in terms of Type I error rates and the M2 test in terms of power. The performance of the Generalized Hausman test and the information criteria deteriorates when the sample size is small and with a few items.

Temporary teams learning and performance: the case of 3D printing technology in orthopaedic surgery

This dissertation contributes to the scholarly debate on temporary teams by exploring team interactions and boundaries.The fundamental challenge in temporary teams originates from temporary participation in the teams. First, as participants join the team for a short period of time, there is not enough time to build trust, share understanding, and have effective interactions. Consequently, team outputs and practices built on team interactions become vulnerable. Secondly, as team participants move on and off the teams, teams’ boundaries become blurred over time. It leads to uncertainty among team participants and leaders about who is/is not identified as a team member causing collective disagreement within the team. Focusing on the above mentioned challenges, we conducted this research in healthcare organisations since the use of temporary teams in healthcare and hospital setting is prevalent. In particular, we focused on orthopaedic teams that provide personalised treatments for patients using 3D printing technology. Qualitative and quantitative data were collected using interviews, observations, questionnaires and archival data at Rizzoli Orthopaedic Institute, Bologna, Italy. This study provides the following research outputs. The first is a conceptual study that explores temporary teams’ literature using bibliometric analysis and systematic literature review to highlight research gaps. The second paper qualitatively studies temporary relationships within the teams by collecting data using group interviews and observations. The results highlighted the role of short-term dyadic relationships as a ground to share and transfer knowledge at the team level. Moreover, hierarchical structure of the teams facilitates knowledge sharing by supporting dyadic relationships within and beyond the team meetings. The third paper investigates impact of blurred boundaries on temporary teams’ performance. Using quantitative data collected through questionnaires and archival data, we concluded that boundary blurring in terms of fluidity, overlap and dispersion differently impacts team performance at high and low levels of task complexity.