Modello bayesiano per la riduzione dell'incertezza nella previsione delle piene del fiume Reno

Many efforts have been devoting since last years to reduce uncertainty in hydrological modeling predictions. The principal sources of uncertainty are provided by input errors, for inaccurate rainfall prediction, and model errors, given by the approximation with which the water flow processes in the soil and river discharges are described. The aim of the present work is to develop a bayesian model in order to reduce the uncertainty in the discharge predictions for the Reno river. The ’a priori’ distribution function is given by an autoregressive model, while the likelihood function is provided by a linear equation which relates observed values of discharge in the past and hydrological TOPKAPI model predictions obtained by the rainfall predictions of the limited-area model COSMO-LAMI. The ’a posteriori’ estimations are provided throw a H∞ filter, because the statistical properties of estimation errors are not known. In this work a stationary and a dual adaptive filter are implemented and compared. Statistical analysis of estimation errors and the description of three case studies of flood events occurred during the fall seasons from 2003 to 2005 are reported. Results have also revealed that errors can be described as a markovian process only at a first approximation. For the same period, an ensemble of ’a posteriori’ estimations is obtained throw the COSMO-LEPS rainfall predictions, but the spread of this ’a posteriori’ ensemble is not enable to encompass observation variability. This fact is related to the building of the meteorological ensemble, whose spread reaches its maximum after 5 days. In the future the use of a new ensemble, COSMO–SREPS, focused on the first 3 days, could be helpful to enlarge the meteorogical and, consequently, the hydrological variability.

Dynamic services in mobile ad hoc networks

The increasing diffusion of wireless-enabled portable devices is pushing toward the design of novel service scenarios, promoting temporary and opportunistic interactions in infrastructure-less environments. Mobile Ad Hoc Networks (MANET) are the general model of these higly dynamic networks that can be specialized, depending on application cases, in more specific and refined models such as Vehicular Ad Hoc Networks and Wireless Sensor Networks. Two interesting deployment cases are of increasing relevance: resource diffusion among users equipped with portable devices, such as laptops, smart phones or PDAs in crowded areas (termed dense MANET) and dissemination/indexing of monitoring information collected in Vehicular Sensor Networks. The extreme dynamicity of these scenarios calls for novel distributed protocols and services facilitating application development. To this aim we have designed middleware solutions supporting these challenging tasks. REDMAN manages, retrieves, and disseminates replicas of software resources in dense MANET; it implements novel lightweight protocols to maintain a desired replication degree despite participants mobility, and efficiently perform resource retrieval. REDMAN exploits the high-density assumption to achieve scalability and limited network overhead. Sensed data gathering and distributed indexing in Vehicular Networks raise similar issues: we propose a specific middleware support, called MobEyes, exploiting node mobility to opportunistically diffuse data summaries among neighbor vehicles. MobEyes creates a low-cost opportunistic distributed index to query the distributed storage and to determine the location of needed information. Extensive validation and testing of REDMAN and MobEyes prove the effectiveness of our original solutions in limiting communication overhead while maintaining the required accuracy of replication degree and indexing completeness, and demonstrates the feasibility of the middleware approach.

The firm. Techno-organizational changes, industrial relations and performances. An enquiry on Reggio Emilia local industrial system

It is not unknown that the evolution of firm theories has been developed along a path paved by an increasing awareness of the organizational structure importance. From the early “neoclassical” conceptualizations that intended the firm as a rational actor whose aim is to produce that amount of output, given the inputs at its disposal and in accordance to technological or environmental constraints, which maximizes the revenue (see Boulding, 1942 for a past mid century state of the art discussion) to the knowledge based theory of the firm (Nonaka & Takeuchi, 1995; Nonaka & Toyama, 2005), which recognizes in the firm a knnowledge creating entity, with specific organizational capabilities (Teece, 1996; Teece & Pisano, 1998) that allow to sustaine competitive advantages. Tracing back a map of the theory of the firm evolution, taking into account the several perspectives adopted in the history of thought, would take the length of many books. Because of that a more fruitful strategy is circumscribing the focus of the description of the literature evolution to one flow connected to a crucial question about the nature of firm’s behaviour and about the determinants of competitive advantages. In so doing I adopt a perspective that allows me to consider the organizational structure of the firm as an element according to which the different theories can be discriminated. The approach adopted starts by considering the drawbacks of the standard neoclassical theory of the firm. Discussing the most influential theoretical approaches I end up with a close examination of the knowledge based perspective of the firm. Within this perspective the firm is considered as a knowledge creating entity that produce and mange knowledge (Nonaka, Toyama, & Nagata, 2000; Nonaka & Toyama, 2005). In a knowledge intensive organization, knowledge is clearly embedded for the most part in the human capital of the individuals that compose such an organization. In a knowledge based organization, the management, in order to cope with knowledge intensive productions, ought to develop and accumulate capabilities that shape the organizational forms in a way that relies on “cross-functional processes, extensive delayering and empowerment” (Foss 2005, p.12). This mechanism contributes to determine the absorptive capacity of the firm towards specific technologies and, in so doing, it also shape the technological trajectories along which the firm moves. After having recognized the growing importance of the firm’s organizational structure in the theoretical literature concerning the firm theory, the subsequent point of the analysis is that of providing an overview of the changes that have been occurred at micro level to the firm’s organization of production. The economic actors have to deal with challenges posed by processes of internationalisation and globalization, increased and increasing competitive pressure of less developed countries on low value added production activities, changes in technologies and increased environmental turbulence and volatility. As a consequence, it has been widely recognized that the main organizational models of production that fitted well in the 20th century are now partially inadequate and processes aiming to reorganize production activities have been widespread across several economies in recent years. Recently, the emergence of a “new” form of production organization has been proposed both by scholars, practitioners and institutions: the most prominent characteristic of such a model is its recognition of the importance of employees commitment and involvement. As a consequence it is characterized by a strong accent on the human resource management and on those practices that aim to widen the autonomy and responsibility of the workers as well as increasing their commitment to the organization (Osterman, 1994; 2000; Lynch, 2007). This “model” of production organization is by many defined as High Performance Work System (HPWS). Despite the increasing diffusion of workplace practices that may be inscribed within the concept of HPWS in western countries’ companies, it is an hazard, to some extent, to speak about the emergence of a “new organizational paradigm”. The discussion about organizational changes and the diffusion of HPWP the focus cannot abstract from a discussion about the industrial relations systems, with a particular accent on the employment relationships, because of their relevance, in the same way as production organization, in determining two major outcomes of the firm: innovation and economic performances. The argument is treated starting from the issue of the Social Dialogue at macro level, both in an European perspective and Italian perspective. The model of interaction between the social parties has repercussions, at micro level, on the employment relationships, that is to say on the relations between union delegates and management or workers and management. Finding economic and social policies capable of sustaining growth and employment within a knowledge based scenario is likely to constitute the major challenge for the next generation of social pacts, which are the main social dialogue outcomes. As Acocella and Leoni (2007) put forward the social pacts may constitute an instrument to trade wage moderation for high intensity in ICT, organizational and human capital investments. Empirical evidence, especially focused on the micro level, about the positive relation between economic growth and new organizational designs coupled with ICT adoption and non adversarial industrial relations is growing. Partnership among social parties may become an instrument to enhance firm competitiveness. The outcome of the discussion is the integration of organizational changes and industrial relations elements within a unified framework: the HPWS. Such a choice may help in disentangling the potential existence of complementarities between these two aspects of the firm internal structure on economic and innovative performance. With the third chapter starts the more original part of the thesis. The data utilized in order to disentangle the relations between HPWS practices, innovation and economic performance refer to the manufacturing firms of the Reggio Emilia province with more than 50 employees. The data have been collected through face to face interviews both to management (199 respondents) and to union representatives (181 respondents). Coupled with the cross section datasets a further data source is constituted by longitudinal balance sheets (1994-2004). Collecting reliable data that in turn provide reliable results needs always a great effort to which are connected uncertain results. Data at micro level are often subjected to a trade off: the wider is the geographical context to which the population surveyed belong the lesser is the amount of information usually collected (low level of resolution); the narrower is the focus on specific geographical context, the higher is the amount of information usually collected (high level of resolution). For the Italian case the evidence about the diffusion of HPWP and their effects on firm performances is still scanty and usually limited to local level studies (Cristini, et al., 2003). The thesis is also devoted to the deepening of an argument of particular interest: the existence of complementarities between the HPWS practices. It has been widely shown by empirical evidence that when HPWP are adopted in bundles they are more likely to impact on firm’s performances than when adopted in isolation (Ichniowski, Prennushi, Shaw, 1997). Is it true also for the local production system of Reggio Emilia? The empirical analysis has the precise aim of providing evidence on the relations between the HPWS dimensions and the innovative and economic performances of the firm. As far as the first line of analysis is concerned it must to be stressed the fundamental role that innovation plays in the economy (Geroski & Machin, 1993; Stoneman & Kwoon 1994, 1996; OECD, 2005; EC, 2002). On this point the evidence goes from the traditional innovations, usually approximated by R&D investment expenditure or number of patents, to the introduction and adoption of ICT, in the recent years (Brynjolfsson & Hitt, 2000). If innovation is important then it is critical to analyse its determinants. In this work it is hypothesised that organizational changes and firm level industrial relations/employment relations aspects that can be put under the heading of HPWS, influence the propensity to innovate in product, process and quality of the firm. The general argument may goes as follow: changes in production management and work organization reconfigure the absorptive capacity of the firm towards specific technologies and, in so doing, they shape the technological trajectories along which the firm moves; cooperative industrial relations may lead to smother adoption of innovations, because not contrasted by unions. From the first empirical chapter emerges that the different types of innovations seem to respond in different ways to the HPWS variables. The underlying processes of product, process and quality innovations are likely to answer to different firm’s strategies and needs. Nevertheless, it is possible to extract some general results in terms of the most influencing HPWS factors on innovative performance. The main three aspects are training coverage, employees involvement and the diffusion of bonuses. These variables show persistent and significant relations with all the three innovation types. The same do the components having such variables at their inside. In sum the aspects of the HPWS influence the propensity to innovate of the firm. At the same time, emerges a quite neat (although not always strong) evidence of complementarities presence between HPWS practices. In terns of the complementarity issue it can be said that some specific complementarities exist. Training activities, when adopted and managed in bundles, are related to the propensity to innovate. Having a sound skill base may be an element that enhances the firm’s capacity to innovate. It may enhance both the capacity to absorbe exogenous innovation and the capacity to endogenously develop innovations. The presence and diffusion of bonuses and the employees involvement also spur innovative propensity. The former because of their incentive nature and the latter because direct workers participation may increase workers commitment to the organizationa and thus their willingness to support and suggest inovations. The other line of analysis provides results on the relation between HPWS and economic performances of the firm. There have been a bulk of international empirical studies on the relation between organizational changes and economic performance (Black & Lynch 2001; Zwick 2004; Janod & Saint-Martin 2004; Huselid 1995; Huselid & Becker 1996; Cappelli & Neumark 2001), while the works aiming to capture the relations between economic performance and unions or industrial relations aspects are quite scant (Addison & Belfield, 2001; Pencavel, 2003; Machin & Stewart, 1990; Addison, 2005). In the empirical analysis the integration of the two main areas of the HPWS represent a scarcely exploited approach in the panorama of both national and international empirical studies. As remarked by Addison “although most analysis of workers representation and employee involvement/high performance work practices have been conducted in isolation – while sometimes including the other as controls – research is beginning to consider their interactions” (Addison, 2005, p.407). The analysis conducted exploiting temporal lags between dependent and covariates, possibility given by the merger of cross section and panel data, provides evidence in favour of the existence of HPWS practices impact on firm’s economic performance, differently measured. Although it does not seem to emerge robust evidence on the existence of complementarities among HPWS aspects on performances there is evidence of a general positive influence of the single practices. The results are quite sensible to the time lags, inducing to hypothesize that time varying heterogeneity is an important factor in determining the impact of organizational changes on economic performance. The implications of the analysis can be of help both to management and local level policy makers. Although the results are not simply extendible to other local production systems it may be argued that for contexts similar to the Reggio Emilia province, characterized by the presence of small and medium enterprises organized in districts and by a deep rooted unionism, with strong supporting institutions, the results and the implications here obtained can also fit well. However, a hope for future researches on the subject treated in the present work is that of collecting good quality information over wider geographical areas, possibly at national level, and repeated in time. Only in this way it is possible to solve the Gordian knot about the linkages between innovation, performance, high performance work practices and industrial relations.

Non-Markovian stochastic processes and their applications: from anomalous diffusion to time series analysis

This work provides a forward step in the study and comprehension of the relationships between stochastic processes and a certain class of integral-partial differential equation, which can be used in order to model anomalous diffusion and transport in statistical physics. In the first part, we brought the reader through the fundamental notions of probability and stochastic processes, stochastic integration and stochastic differential equations as well. In particular, within the study of H-sssi processes, we focused on fractional Brownian motion (fBm) and its discrete-time increment process, the fractional Gaussian noise (fGn), which provide examples of non-Markovian Gaussian processes. The fGn, together with stationary FARIMA processes, is widely used in the modeling and estimation of long-memory, or long-range dependence (LRD). Time series manifesting long-range dependence, are often observed in nature especially in physics, meteorology, climatology, but also in hydrology, geophysics, economy and many others. We deepely studied LRD, giving many real data examples, providing statistical analysis and introducing parametric methods of estimation. Then, we introduced the theory of fractional integrals and derivatives, which indeed turns out to be very appropriate for studying and modeling systems with long-memory properties. After having introduced the basics concepts, we provided many examples and applications. For instance, we investigated the relaxation equation with distributed order time-fractional derivatives, which describes models characterized by a strong memory component and can be used to model relaxation in complex systems, which deviates from the classical exponential Debye pattern. Then, we focused in the study of generalizations of the standard diffusion equation, by passing through the preliminary study of the fractional forward drift equation. Such generalizations have been obtained by using fractional integrals and derivatives of distributed orders. In order to find a connection between the anomalous diffusion described by these equations and the long-range dependence, we introduced and studied the generalized grey Brownian motion (ggBm), which is actually a parametric class of H-sssi processes, which have indeed marginal probability density function evolving in time according to a partial integro-differential equation of fractional type. The ggBm is of course Non-Markovian. All around the work, we have remarked many times that, starting from a master equation of a probability density function f(x,t), it is always possible to define an equivalence class of stochastic processes with the same marginal density function f(x,t). All these processes provide suitable stochastic models for the starting equation. Studying the ggBm, we just focused on a subclass made up of processes with stationary increments. The ggBm has been defined canonically in the so called grey noise space. However, we have been able to provide a characterization notwithstanding the underline probability space. We also pointed out that that the generalized grey Brownian motion is a direct generalization of a Gaussian process and in particular it generalizes Brownain motion and fractional Brownain motion as well. Finally, we introduced and analyzed a more general class of diffusion type equations related to certain non-Markovian stochastic processes. We started from the forward drift equation, which have been made non-local in time by the introduction of a suitable chosen memory kernel K(t). The resulting non-Markovian equation has been interpreted in a natural way as the evolution equation of the marginal density function of a random time process l(t). We then consider the subordinated process Y(t)=X(l(t)) where X(t) is a Markovian diffusion. The corresponding time-evolution of the marginal density function of Y(t) is governed by a non-Markovian Fokker-Planck equation which involves the same memory kernel K(t). We developed several applications and derived the exact solutions. Moreover, we considered different stochastic models for the given equations, providing path simulations.

A comprehensive study of the 26th December 2004 Sumatra earthquake: possible implications on Earth rotation and investigations on the coseismic and postseismic stress diffusion associated with the seismic rupture

In this work a multidisciplinary study of the December 26th, 2004 Sumatra earthquake has been carried out. We have investigated both the effect of the earthquake on the Earth rotation and the stress field variations associated with the seismic event. In the first part of the work we have quantified the effects of a water mass redistribution associated with the propagation of a tsunami wave on the Earth’s pole path and on the length-of-day (LOD) and applied our modeling results to the tsunami following the 2004 giant Sumatra earthquake. We compared the result of our simulations on the instantaneous rotational axis variations with some preliminary instrumental evidences on the pole path perturbation (which has not been confirmed yet) registered just after the occurrence of the earthquake, which showed a step-like discontinuity that cannot be attributed to the effect of a seismic dislocation. Our results show that the perturbation induced by the tsunami on the instantaneous rotational pole is characterized by a step-like discontinuity, which is compatible with the observations but its magnitude turns out to be almost one hundred times smaller than the detected one. The LOD variation induced by the water mass redistribution turns out to be not significant because the total effect is smaller than current measurements uncertainties. In the second part of this work of thesis we modeled the coseismic and postseismic stress evolution following the Sumatra earthquake. By means of a semi-analytical, viscoelastic, spherical model of global postseismic deformation and a numerical finite-element approach, we performed an analysis of the stress diffusion following the earthquake in the near and far field of the mainshock source. We evaluated the stress changes due to the Sumatra earthquake by projecting the Coulomb stress over the sequence of aftershocks taken from various catalogues in a time window spanning about two years and finally analyzed the spatio-temporal pattern. The analysis performed with the semi-analytical and the finite-element modeling gives a complex picture of the stress diffusion, in the area under study, after the Sumatra earthquake. We believe that the results obtained with the analytical method suffer heavily for the restrictions imposed, on the hypocentral depths of the aftershocks, in order to obtain the convergence of the harmonic series of the stress components. On the contrary we imposed no constraints on the numerical method so we expect that the results obtained give a more realistic description of the stress variations pattern.

Sistemi liquido cristallini complessi: simulazioni al calcolatore e studi ESR

The aim of this PhD thesis was to study at a microscopic level different liquid crystal (LC) systems, in order to determine their physical properties, resorting to two distinct methodologies, one involving computer simulations, and the other spectroscopic techniques, in particular electron spin resonance (ESR) spectroscopy. By means of the computer simulation approach we tried to demonstrate this tool effectiveness for calculating anisotropic static properties of a LC material, as well as for predicting its behaviour and features. This required the development and adoption of suitable molecular models based on a convenient intermolecular potentials reflecting the essential molecular features of the investigated system. In particular, concerning the simulation approach, we have set up models for discotic liquid crystal dimers and we have studied, by means of Monte Carlo simulations, their phase behaviour and self­-assembling properties, with respect to the simple monomer case. Each discotic dimer is described by two oblate Gay­Berne ellipsoids connected by a flexible spacer, modelled by a harmonic "spring" of three different lengths. In particular we investigated the effects of dimerization on the transition temperatures, as well as on the characteristics of molecular aggregation displayed and the relative orientational order. Moving to the experimental results, among the many experimental techniques that are typically employed to evaluate LC system distinctive features, ESR has proved to be a powerful tool in microscopic scale investigation of the properties, structure, order and dynamics of these materials. We have taken advantage of the high sensitivity of the ESR spin probe technique to investigate increasingly complex LC systems ranging from devices constituted by a polymer matrix in which LC molecules are confined in shape of nano- droplets, as well as biaxial liquid crystalline elastomers, and dimers whose monomeric units or lateral groups are constituted by rod-like mesogens (11BCB). Reflection-mode holographic-polymer dispersed liquid crystals (H-PDLCs) are devices in which LCs are confined into nanosized (50­-300 nm) droplets, arranged in layers which alternate with polymer layers, forming a diffraction grating. We have determined the configuration of the LC local director and we have derived a model of the nanodroplet organization inside the layers. Resorting also to additional information on the nanodroplet size and shape distribution provided by SEM images of the H-PDLC cross-section, the observed director configuration has been modeled as a bidimensional distribution of elongated nanodroplets whose long axis is, on the average, parallel to the layers and whose internal director configuration is a uniaxial quasi- monodomain aligned along the nanodroplet long axis. The results suggest that the molecular organization is dictated mainly by the confinement, explaining, at least in part, the need for switching voltages significantly higher and the observed faster turn-off times in H-PDLCs compared to standard PDLC devices. Liquid crystal elastomers consist in cross-linked polymers, in which mesogens represent the monomers constituting the main chain or the laterally attached side groups. They bring together three important aspects: orientational order in amorphous soft materials, responsive molecular shape and quenched topological constraints. In biaxial nematic liquid crystalline elastomers (BLCEs), two orthogonal directions, rather than the one of normal uniaxial nematic, can be controlled, greatly enhancing their potential value for applications as novel actuators. Two versions of a side-chain BLCEs were characterized: side­-on and end­-on. Many tests have been carried out on both types of LCE, the main features detected being the lack of a significant dynamical behaviour, together with a strong permanent alignment along the principal director, and the confirmation of the transition temperatures already determined by DSC measurements. The end­-on sample demonstrates a less hindered rotation of the side group mesogenic units and a greater freedom of alignment to the magnetic field, as already shown by previous NMR studies. Biaxial nematic ESR static spectra were also obtained on the basis of Molecular Dynamics generated biaxial configurations, to be compared to the experimentally determined ones, as a mean to establish a possible relation between biaxiality and the spectral features. This provides a concrete example of the advantages of combining the computer simulation and spectroscopic approaches. Finally, the dimer α,ω-bis(4'-cyanobiphenyl-4-yl)undecane (11BCB), synthesized in the "quest" for the biaxial nematic phase has been analysed. Its importance lies in the dimer significance as building blocks in the development of new materials to be employed in innovative technological applications, such as faster switching displays, resorting to the easier aligning ability of the secondary director in biaxial phases. A preliminary series of tests were performed revealing the population of mesogenic molecules as divided into two groups: one of elongated straightened conformers sharing a common director, and one of bent molecules, which display no order, being equally distributed in the three dimensions. Employing this model, the calculated values show a consistent trend, confirming at the same time the transition temperatures indicated by the DSC measurements, together with rotational diffusion tensor values that follow closely those of the constituting monomer 5CB.

Neuroprotective strategies against neurodegeneration in cellular model systems

In the present study we analyzed new neuroprotective therapeutical strategies in PD (Parkinson’s disease) and AD (Alzheimer’s disease). Current therapeutic strategies for treating PD and AD offer mainly transient symptomatic relief but it is still impossible to block the loss of neuron and then the progression of PD and AD. There is considerable consensus that the increased production and/or aggregation of α- synuclein (α-syn) and β-amyloid peptide (Aβ), plays a central role in the pathogenesis of PD, related synucleinopathies and AD. Therefore, we identified antiamyloidogenic compounds and we tested their effect as neuroprotective drug-like molecules against α-syn and β-amyloid cytotoxicity in PC12. Herein, we show that two nitro-catechol compounds (entacapone and tolcapone) and 5 cathecol-containing compounds (dopamine, pyrogallol, gallic acid, caffeic acid and quercetin) with antioxidant and anti-inflammatory properties, are potent inhibitors of α-syn and β-amyloid oligomerization and fibrillization. Subsequently, we show that the inhibition of α-syn and β-amyloid oligomerization and fibrillization is correlated with the neuroprotection of these compounds against the α-syn and β-amyloid-induced cytotoxicity in PC12. Finally, we focused on the study of the neuroprotective role of microglia and on the possibility that the neuroprotection properties of these cells could be use as therapeutical strategy in PD and AD. Here, we have used an in vitro model to demonstrate neuroprotection of a 48 h-microglial conditioned medium (MCM) towards cerebellar granule neurons (CGNs) challenged with the neurotoxin 6-hydroxydopamine (6-OHDA), which induces a Parkinson-like neurodegeneration, with Aβ42, which induces a Alzheimer-like neurodegeneration, and glutamate, involved in the major neurodegenerative diseases. We show that MCM nearly completely protects CGNs from 6-OHDA neurotoxicity, partially from glutamate excitotoxicity but not from Aβ42 toxin.

Sistemi riconfigurabili a basso consumo per applicazioni di monitoraggio distribuito

The term Ambient Intelligence (AmI) refers to a vision on the future of the information society where smart, electronic environment are sensitive and responsive to the presence of people and their activities (Context awareness). In an ambient intelligence world, devices work in concert to support people in carrying out their everyday life activities, tasks and rituals in an easy, natural way using information and intelligence that is hidden in the network connecting these devices. This promotes the creation of pervasive environments improving the quality of life of the occupants and enhancing the human experience. AmI stems from the convergence of three key technologies: ubiquitous computing, ubiquitous communication and natural interfaces. Ambient intelligent systems are heterogeneous and require an excellent cooperation between several hardware/software technologies and disciplines, including signal processing, networking and protocols, embedded systems, information management, and distributed algorithms. Since a large amount of fixed and mobile sensors embedded is deployed into the environment, the Wireless Sensor Networks is one of the most relevant enabling technologies for AmI. WSN are complex systems made up of a number of sensor nodes which can be deployed in a target area to sense physical phenomena and communicate with other nodes and base stations. These simple devices typically embed a low power computational unit (microcontrollers, FPGAs etc.), a wireless communication unit, one or more sensors and a some form of energy supply (either batteries or energy scavenger modules). WNS promises of revolutionizing the interactions between the real physical worlds and human beings. Low-cost, low-computational power, low energy consumption and small size are characteristics that must be taken into consideration when designing and dealing with WSNs. To fully exploit the potential of distributed sensing approaches, a set of challengesmust be addressed. Sensor nodes are inherently resource-constrained systems with very low power consumption and small size requirements which enables than to reduce the interference on the physical phenomena sensed and to allow easy and low-cost deployment. They have limited processing speed,storage capacity and communication bandwidth that must be efficiently used to increase the degree of local ”understanding” of the observed phenomena. A particular case of sensor nodes are video sensors. This topic holds strong interest for a wide range of contexts such as military, security, robotics and most recently consumer applications. Vision sensors are extremely effective for medium to long-range sensing because vision provides rich information to human operators. However, image sensors generate a huge amount of data, whichmust be heavily processed before it is transmitted due to the scarce bandwidth capability of radio interfaces. In particular, in video-surveillance, it has been shown that source-side compression is mandatory due to limited bandwidth and delay constraints. Moreover, there is an ample opportunity for performing higher-level processing functions, such as object recognition that has the potential to drastically reduce the required bandwidth (e.g. by transmitting compressed images only when something ‘interesting‘ is detected). The energy cost of image processing must however be carefully minimized. Imaging could play and plays an important role in sensing devices for ambient intelligence. Computer vision can for instance be used for recognising persons and objects and recognising behaviour such as illness and rioting. Having a wireless camera as a camera mote opens the way for distributed scene analysis. More eyes see more than one and a camera system that can observe a scene from multiple directions would be able to overcome occlusion problems and could describe objects in their true 3D appearance. In real-time, these approaches are a recently opened field of research. In this thesis we pay attention to the realities of hardware/software technologies and the design needed to realize systems for distributed monitoring, attempting to propose solutions on open issues and filling the gap between AmI scenarios and hardware reality. The physical implementation of an individual wireless node is constrained by three important metrics which are outlined below. Despite that the design of the sensor network and its sensor nodes is strictly application dependent, a number of constraints should almost always be considered. Among them: • Small form factor to reduce nodes intrusiveness. • Low power consumption to reduce battery size and to extend nodes lifetime. • Low cost for a widespread diffusion. These limitations typically result in the adoption of low power, low cost devices such as low powermicrocontrollers with few kilobytes of RAMand tenth of kilobytes of program memory with whomonly simple data processing algorithms can be implemented. However the overall computational power of the WNS can be very large since the network presents a high degree of parallelism that can be exploited through the adoption of ad-hoc techniques. Furthermore through the fusion of information from the dense mesh of sensors even complex phenomena can be monitored. In this dissertation we present our results in building several AmI applications suitable for a WSN implementation. The work can be divided into two main areas:Low Power Video Sensor Node and Video Processing Alghoritm and Multimodal Surveillance . Low Power Video Sensor Nodes and Video Processing Alghoritms In comparison to scalar sensors, such as temperature, pressure, humidity, velocity, and acceleration sensors, vision sensors generate much higher bandwidth data due to the two-dimensional nature of their pixel array. We have tackled all the constraints listed above and have proposed solutions to overcome the current WSNlimits for Video sensor node. We have designed and developed wireless video sensor nodes focusing on the small size and the flexibility of reuse in different applications. The video nodes target a different design point: the portability (on-board power supply, wireless communication), a scanty power budget (500mW),while still providing a prominent level of intelligence, namely sophisticated classification algorithmand high level of reconfigurability. We developed two different video sensor node: The device architecture of the first one is based on a low-cost low-power FPGA+microcontroller system-on-chip. The second one is based on ARM9 processor. Both systems designed within the above mentioned power envelope could operate in a continuous fashion with Li-Polymer battery pack and solar panel. Novel low power low cost video sensor nodes which, in contrast to sensors that just watch the world, are capable of comprehending the perceived information in order to interpret it locally, are presented. Featuring such intelligence, these nodes would be able to cope with such tasks as recognition of unattended bags in airports, persons carrying potentially dangerous objects, etc.,which normally require a human operator. Vision algorithms for object detection, acquisition like human detection with Support Vector Machine (SVM) classification and abandoned/removed object detection are implemented, described and illustrated on real world data. Multimodal surveillance: In several setup the use of wired video cameras may not be possible. For this reason building an energy efficient wireless vision network for monitoring and surveillance is one of the major efforts in the sensor network community. Energy efficiency for wireless smart camera networks is one of the major efforts in distributed monitoring and surveillance community. For this reason, building an energy efficient wireless vision network for monitoring and surveillance is one of the major efforts in the sensor network community. The Pyroelectric Infra-Red (PIR) sensors have been used to extend the lifetime of a solar-powered video sensor node by providing an energy level dependent trigger to the video camera and the wireless module. Such approach has shown to be able to extend node lifetime and possibly result in continuous operation of the node.Being low-cost, passive (thus low-power) and presenting a limited form factor, PIR sensors are well suited for WSN applications. Moreover techniques to have aggressive power management policies are essential for achieving long-termoperating on standalone distributed cameras needed to improve the power consumption. We have used an adaptive controller like Model Predictive Control (MPC) to help the system to improve the performances outperforming naive power management policies.

Model predictive control in thermal management of multiprocessor systems-on-chip

MultiProcessor Systems-on-Chip (MPSoC) are the core of nowadays and next generation computing platforms. Their relevance in the global market continuously increase, occupying an important role both in everydaylife products (e.g. smartphones, tablets, laptops, cars) and in strategical market sectors as aviation, defense, robotics, medicine. Despite of the incredible performance improvements in the recent years processors manufacturers have had to deal with issues, commonly called “Walls”, that have hindered the processors development. After the famous “Power Wall”, that limited the maximum frequency of a single core and marked the birth of the modern multiprocessors system-on-chip, the “Thermal Wall” and the “Utilization Wall” are the actual key limiter for performance improvements. The former concerns the damaging effects of the high temperature on the chip caused by the large power densities dissipation, whereas the second refers to the impossibility of fully exploiting the computing power of the processor due to the limitations on power and temperature budgets. In this thesis we faced these challenges by developing efficient and reliable solutions able to maximize performance while limiting the maximum temperature below a fixed critical threshold and saving energy. This has been possible by exploiting the Model Predictive Controller (MPC) paradigm that solves an optimization problem subject to constraints in order to find the optimal control decisions for the future interval. A fully-distributedMPC-based thermal controller with a far lower complexity respect to a centralized one has been developed. The control feasibility and interesting properties for the simplification of the control design has been proved by studying a partial differential equation thermal model. Finally, the controller has been efficiently included in more complex control schemes able to minimize energy consumption and deal with mixed-criticalities tasks

Development and applications of hollow fiber flow field-flow fractionation in the bioanalytical field. Studies of aggregation phenomena in complex protein samples

Recent advances in the fast growing area of therapeutic/diagnostic proteins and antibodies - novel and highly specific drugs - as well as the progress in the field of functional proteomics regarding the correlation between the aggregation of damaged proteins and (immuno) senescence or aging-related pathologies, underline the need for adequate analytical methods for the detection, separation, characterization and quantification of protein aggregates, regardless of the their origin or formation mechanism. Hollow fiber flow field-flow fractionation (HF5), the miniaturized version of FlowFFF and integral part of the Eclipse DUALTEC FFF separation system, was the focus of this research; this flow-based separation technique proved to be uniquely suited for the hydrodynamic size-based separation of proteins and protein aggregates in a very broad size and molecular weight (MW) range, often present at trace levels. HF5 has shown to be (a) highly selective in terms of protein diffusion coefficients, (b) versatile in terms of bio-compatible carrier solution choice, (c) able to preserve the biophysical properties/molecular conformation of the proteins/protein aggregates and (d) able to discriminate between different types of protein aggregates. Thanks to the miniaturization advantages and the online coupling with highly sensitive detection techniques (UV/Vis, intrinsic fluorescence and multi-angle light scattering), HF5 had very low detection/quantification limits for protein aggregates. Compared to size-exclusion chromatography (SEC), HF5 demonstrated superior selectivity and potential as orthogonal analytical method in the extended characterization assays, often required by therapeutic protein formulations. In addition, the developed HF5 methods have proven to be rapid, highly selective, sensitive and repeatable. HF5 was ideally suitable as first dimension of separation of aging-related protein aggregates from whole cell lysates (proteome pre-fractionation method) and, by HF5-(UV)-MALS online coupling, important biophysical information on the fractionated proteins and protein aggregates was gathered: size (rms radius and hydrodynamic radius), absolute MW and conformation.

Polymeric membranes for CO2 separation: effect of aging, humidity and facilitated transport

Polymeric membranes represent a promising technology for gas separation processes, thanks to low costs, reduced energy consumption and limited waste production. The present thesis aims at studying the transport properties of two membrane materials, suitable for CO2 purification applications. In the first part, a polyimide, Matrimid 5218, has been throughout investigated, with particular reference to the effect of thermal treatment, aging and the presence of water vapor in the gas transport process. Permeability measurements showed that thermal history affects relevantly the diffusion of gas molecules across the membrane, influencing also the stability of the separation performances. Subsequently, the effect of water on Matrimid transport properties has been characterized for a wide set of incondensable penetrants. A monotonous reduction of permeability took place at increasing the water concentration within the polymer matrix, affecting the investigated gaseous species to the same extent, despite the different thermodynamic and kinetic features. In this view, a novel empirical model, based on the Free Volume Theory, has been proposed to qualitatively describe the phenomenon. Moreover, according to the accurate representation of the experimental data, the suggested approach has been combined with a more rigorous thermodynamic tool (NELF Model), allowing an exhaustive description of water influence on the single parameters contributing to the gas permeation across the membrane. In the second part, the study has focused on the synthesis and characterization of facilitated transport membranes, able to achieving outstanding separation performances thanks to the chemical enhancement of CO2 permeability. In particular, the transport properties have been investigated for high pressure CO2 separation applications and specific solutions have been proposed to solve stability issues, frequently arising under such severe conditions. Finally, the effect of different process parameters have been investigated, aiming at the identification of the optimal conditions capable to maximize the separation performance.

Computational insight into materials properties

The aim of the work was to explore the practical applicability of molecular dynamics at different length and time scales. From nanoparticles system over colloids and polymers to biological systems like membranes and finally living cells, a broad range of materials was considered from a theoretical standpoint. In this dissertation five chemistry-related problem are addressed by means of theoretical and computational methods. The main results can be outlined as follows. (1) A systematic study of the effect of the concentration, chain length, and charge of surfactants on fullerene aggregation is presented. The long-discussed problem of the location of C60 in micelles was addressed and fullerenes were found in the hydrophobic region of the micelles. (2) The interactions between graphene sheet of increasing size and phospholipid membrane are quantitatively investigated. (3) A model was proposed to study structure, stability, and dynamics of MoS2, a material well-known for its tribological properties. The telescopic movement of nested nanotubes and the sliding of MoS2 layers is simulated. (4) A mathematical model to gain understaning of the coupled diffusion-swelling process in poly(lactic-co-glycolic acid), PLGA, was proposed. (5) A soft matter cell model is developed to explore the interaction of living cell with artificial surfaces. The effect of the surface properties on the adhesion dynamics of cells are discussed.

Energy-efficient Communication and Estimation in Wireless Sensor Networks

This thesis focuses on the energy efficiency in wireless networks under the transmission and information diffusion points of view. In particular, on one hand, the communication efficiency is investigated, attempting to reduce the consumption during transmissions, while on the other hand the energy efficiency of the procedures required to distribute the information among wireless nodes in complex networks is taken into account. For what concerns energy efficient communications, an innovative transmission scheme reusing source of opportunity signals is introduced. This kind of signals has never been previously studied in literature for communication purposes. The scope is to provide a way for transmitting information with energy consumption close to zero. On the theoretical side, starting from a general communication channel model subject to a limited input amplitude, the theme of low power transmission signals is tackled under the perspective of stating sufficient conditions for the capacity achieving input distribution to be discrete. Finally, the focus is shifted towards the design of energy efficient algorithms for the diffusion of information. In particular, the endeavours are aimed at solving an estimation problem distributed over a wireless sensor network. The proposed solutions are deeply analyzed both to ensure their energy efficiency and to guarantee their robustness against losses during the diffusion of information (against information diffusion truncation more in general).