Technological innovation in Emilia-Romagna: knowledge, practice, strategies

The present study aims at assessing the innovation strategies adopted within a regional economic system, the Italian region Emilia-Romagna, as it faced the challenges of a changing international scenario. As the strengthening of the regional innovative capabilities is regarded as a keystone to foster a new phase of economic growth, it is important also to understand how the local industrial, institutional, and academic actors have tackled the problem of innovation in the recent past. In this study we explore the approaches to innovation and the strategies adopted by the main regional actors through three different case studies. Chapter 1 provides a general survey of the innovative performance of the regional industries over the past two decades, as it emerges from statistical data and systematic comparisons at the national and European levels. The chapter also discusses the innovation policies that the regional government set up since 2001 in order to strengthen the collaboration among local economic actors, including universities and research centres. As mechanics is the most important regional industry, chapter 2 analyses the combination of knowledge and practices utilized in the period 1960s-1990s in the design of a particular kind of machinery produced by G.D S.p.A., a world-leader in the market of tobacco packaging machines. G.D is based in Bologna, the region’s capital, and is at the centre of the most important Italian packaging district. In chapter 3 the attention turns to the institutional level, focusing on how the local public administrations, and the local, publicly-owned utility companies have dealt with the creation of new telematic networks on the regional territory during the 1990s and 2000s. Finally, chapter 4 assesses the technology transfer carried out by the main university of the region – the University of Bologna – by focusing on the patenting activities involving its research personnel in the period 1960-2010.

Detection and Modeling of Boundary Layer Height

This thesis tackles the problem of the automated detection of the atmospheric boundary layer (BL) height, h, from aerosol lidar/ceilometer observations. A new method, the Bayesian Selective Method (BSM), is presented. It implements a Bayesian statistical inference procedure which combines in an statistically optimal way different sources of information. Firstly atmospheric stratification boundaries are located from discontinuities in the ceilometer back-scattered signal. The BSM then identifies the discontinuity edge that has the highest probability to effectively mark the BL height. Information from the contemporaneus physical boundary layer model simulations and a climatological dataset of BL height evolution are combined in the assimilation framework to assist this choice. The BSM algorithm has been tested for four months of continuous ceilometer measurements collected during the BASE:ALFA project and is shown to realistically diagnose the BL depth evolution in many different weather conditions. Then the BASE:ALFA dataset is used to investigate the boundary layer structure in stable conditions. Functions from the Obukhov similarity theory are used as regression curves to fit observed velocity and temperature profiles in the lower half of the stable boundary layer. Surface fluxes of heat and momentum are best-fitting parameters in this exercise and are compared with what measured by a sonic anemometer. The comparison shows remarkable discrepancies, more evident in cases for which the bulk Richardson number turns out to be quite large. This analysis supports earlier results, that surface turbulent fluxes are not the appropriate scaling parameters for profiles of mean quantities in very stable conditions. One of the practical consequences is that boundary layer height diagnostic formulations which mainly rely on surface fluxes are in disagreement to what obtained by inspecting co-located radiosounding profiles.