Integration of MFA and LCA methodologies: the anthropogenic aluminium cycle in Italy

MFA and LCA methodologies were applied to analyse the anthropogenic aluminium cycle in Italy with focus on historical evolution of stocks and flows of the metal, embodied GHG emissions, and potentials from recycling to provide key features to Italy for prioritizing industrial policy toward low-carbon technologies and materials. Historical trend series were collected from 1947 to 2009 and balanced with data from production, manufacturing and waste management of aluminium-containing products, using a ‘top-down’ approach to quantify the contemporary in-use stock of the metal, and helping to identify ‘applications where aluminium is not yet being recycled to its full potential and to identify present and future recycling flows’. The MFA results were used as a basis for the LCA aimed at evaluating the carbon footprint evolution, from primary and electrical energy, the smelting process and the transportation, embodied in the Italian aluminium. A discussion about how the main factors, according to the Kaya Identity equation, they did influence the Italian GHG emissions pattern over time, and which are the levers to mitigate it, it has been also reported. The contemporary anthropogenic reservoirs of aluminium was estimated at about 320 kg per capita, mainly embedded within the transportation and building and construction sectors. Cumulative in-use stock represents approximately 11 years of supply at current usage rates (about 20 Mt versus 1.7 Mt/year), and it would imply a potential of about 160 Mt of CO2eq emissions savings. A discussion of criticality related to aluminium waste recovery from the transportation and the containers and packaging sectors was also included in the study, providing an example for how MFA and LCA may support decision-making at sectorial or regional level. The research constitutes the first attempt of an integrated approach between MFA and LCA applied to the aluminium cycle in Italy.

Nature and relevance of inherited blueprints on business models choices and innovation: an assessment

One important metaphor, referred to biological theories, used to investigate on organizational and business strategy issues is the metaphor about heredity; an area requiring further investigation is the extent to which the characteristics of blueprints inherited from the parent, helps in explaining subsequent development of the spawned ventures. In order to shed a light on the tension between inherited patterns and the new trajectory that may characterize spawned ventures’ development we propose a model aimed at investigating which blueprints elements might exert an effect on business model design choices and to which extent their persistence (or abandonment) determines subsequent business model innovation. Under the assumption that academic and corporate institutions transmit different genes to their spin-offs, we hence expect to have heterogeneity in elements that affect business model design choices and its subsequent evolution. This is the reason why we carry on a twofold analysis in the biotech (meta)industry: under a multiple-case research design, business model and especially its fundamental design elements and themes scholars individuated to decompose the construct, have been thoroughly analysed. Our purpose is to isolate the dimensions of business model that may have been the object of legacy and the ones along which an experimentation and learning process is more likely to happen, bearing in mind that differences between academic and corporate might not be that evident as expected, especially considering that business model innovation may occur.

Brain-Computer Interfaces for augmented communication: Asynchronous and adaptive algorithms and evaluation with end users

This thesis aimed at addressing some of the issues that, at the state of the art, avoid the P300-based brain computer interface (BCI) systems to move from research laboratories to end users’ home. An innovative asynchronous classifier has been defined and validated. It relies on the introduction of a set of thresholds in the classifier, and such thresholds have been assessed considering the distributions of score values relating to target, non-target stimuli and epochs of voluntary no-control. With the asynchronous classifier, a P300-based BCI system can adapt its speed to the current state of the user and can automatically suspend the control when the user diverts his attention from the stimulation interface. Since EEG signals are non-stationary and show inherent variability, in order to make long-term use of BCI possible, it is important to track changes in ongoing EEG activity and to adapt BCI model parameters accordingly. To this aim, the asynchronous classifier has been subsequently improved by introducing a self-calibration algorithm for the continuous and unsupervised recalibration of the subjective control parameters. Finally an index for the online monitoring of the EEG quality has been defined and validated in order to detect potential problems and system failures. This thesis ends with the description of a translational work involving end users (people with amyotrophic lateral sclerosis-ALS). Focusing on the concepts of the user centered design approach, the phases relating to the design, the development and the validation of an innovative assistive device have been described. The proposed assistive technology (AT) has been specifically designed to meet the needs of people with ALS during the different phases of the disease (i.e. the degree of motor abilities impairment). Indeed, the AT can be accessed with several input devices either conventional (mouse, touchscreen) or alterative (switches, headtracker) up to a P300-based BCI.

Many-Core Architectures: Hardware-Software Optimization and Modeling Techniques

During the last few decades an unprecedented technological growth has been at the center of the embedded systems design paramount, with Moore’s Law being the leading factor of this trend. Today in fact an ever increasing number of cores can be integrated on the same die, marking the transition from state-of-the-art multi-core chips to the new many-core design paradigm. Despite the extraordinarily high computing power, the complexity of many-core chips opens the door to several challenges. As a result of the increased silicon density of modern Systems-on-a-Chip (SoC), the design space exploration needed to find the best design has exploded and hardware designers are in fact facing the problem of a huge design space. Virtual Platforms have always been used to enable hardware-software co-design, but today they are facing with the huge complexity of both hardware and software systems. In this thesis two different research works on Virtual Platforms are presented: the first one is intended for the hardware developer, to easily allow complex cycle accurate simulations of many-core SoCs. The second work exploits the parallel computing power of off-the-shelf General Purpose Graphics Processing Units (GPGPUs), with the goal of an increased simulation speed. The term Virtualization can be used in the context of many-core systems not only to refer to the aforementioned hardware emulation tools (Virtual Platforms), but also for two other main purposes: 1) to help the programmer to achieve the maximum possible performance of an application, by hiding the complexity of the underlying hardware. 2) to efficiently exploit the high parallel hardware of many-core chips in environments with multiple active Virtual Machines. This thesis is focused on virtualization techniques with the goal to mitigate, and overtake when possible, some of the challenges introduced by the many-core design paradigm.

Electrochemical imaging of living cell metabolism: investigation on Warburg effect in cancer

Cancer is one of the principal causes of death in the world; almost 8.2 million of deaths were counted in 2012. Emerging evidences indicate that most of the tumors have an increased glycolytic rate and a detriment of oxidative phosphorylation to support abnormal cell proliferation; this phenomenon is known as aerobic glycolysis or Warburg effect. This switching toward glycolysis implies that cancer tissues metabolize approximately tenfold more glucose to lactate in a given time and the amount of lactate released from cancer tissues is much greater than from normal ones. In view of these fundamental discoveries alterations of the cellular metabolism should be considered a crucial hallmark of cancer. Therefore, the investigation of the metabolic differences between normal and transformed cells is important in cancer research and it might find clinical applications. The aim of the project was to investigate the cellular metabolic alterations at single cell level, by monitoring glucose and lactate, in order to provide a better insight in cancer research. For this purpose, electrochemical techniques have been applied. Enzyme-based electrode biosensors for lactate and glucose were –ad hoc- optimized within the project and used as probes for Scanning Electrochemical Microscopy (SECM). The UME biosensor manufacturing and optimization represented a consistent part of the work and a full description of the sensor preparation protocols and of the characterization methods employed is reported. This set-up (SECM used with microbiosensor probes) enabled the non-invasive study of cellular metabolism at single cell level. The knowledge of cancer cell metabolism is required to design more efficient treatment strategies.