Middleware for quality-based context distribution in mobile systems

The continuous advancements and enhancements of wireless systems are enabling new compelling scenarios where mobile services can adapt according to the current execution context, represented by the computational resources available at the local device, current physical location, people in physical proximity, and so forth. Such services called context-aware require the timely delivery of all relevant information describing the current context, and that introduces several unsolved complexities, spanning from low-level context data transmission up to context data storage and replication into the mobile system. In addition, to ensure correct and scalable context provisioning, it is crucial to integrate and interoperate with different wireless technologies (WiFi, Bluetooth, etc.) and modes (infrastructure-based and ad-hoc), and to use decentralized solutions to store and replicate context data on mobile devices. These challenges call for novel middleware solutions, here called Context Data Distribution Infrastructures (CDDIs), capable of delivering relevant context data to mobile devices, while hiding all the issues introduced by data distribution in heterogeneous and large-scale mobile settings. This dissertation thoroughly analyzes CDDIs for mobile systems, with the main goal of achieving a holistic approach to the design of such type of middleware solutions. We discuss the main functions needed by context data distribution in large mobile systems, and we claim the precise definition and clean respect of quality-based contracts between context consumers and CDDI to reconfigure main middleware components at runtime. We present the design and the implementation of our proposals, both in simulation-based and in real-world scenarios, along with an extensive evaluation that confirms the technical soundness of proposed CDDI solutions. Finally, we consider three highly heterogeneous scenarios, namely disaster areas, smart campuses, and smart cities, to better remark the wide technical validity of our analysis and solutions under different network deployments and quality constraints.

Genetic analysis of tumour initiation and progression in a Drosophila model of epithelial cancer

Neoplastic overgrowth depends on the cooperation of several mutations ultimately leading to major rearrangements in cellular behaviour. The molecular crosstalk occurring between precancerous and normal cells strongly influences the early steps of the tumourigenic process as well as later stages of the disease. Precancerous cells are often removed by cell death from normal tissues but the mechanisms responsible for such fundamental safeguard processes remain in part elusive. To gain insight into these phenomena I took advantage of the clonal analysis methods available in Drosophila for studying the phenotypes due to loss of function of the neoplastic tumour suppressor lethal giant larvae (lgl). I found that lgl mutant cells growing in wild-type imaginal wing discs are subject to the phenomenon of cell competition and are eliminated by JNK-dependent cell death because they express very low levels of dMyc oncoprotein compared to those in the surrounding tissue. Indeed, in non-competitive backgrounds lgl mutant clones are able to overgrow and upregulate dMyc, overwhelming the neighbouring tissue and forming tumourous masses that display several cancer hallmarks. These phenotypes are completely abolished by reducing dMyc abundance within mutant cells while increasing it in lgl clones growing in a competitive context re-establishes their tumourigenic potential. Similarly, the neoplastic growth observed upon the oncogenic cooperation between lgl mutation and activated Ras/Raf/MAPK signalling was found to be characterised by and dependent on the ability of cancerous cells to upregulate dMyc with respect to the adjacent normal tissue, through both transcriptional and post-transcriptional mechanisms, thereby confirming its key role in lgl-induced tumourigenesis. These results provide first evidence that the dMyc oncoprotein is required in lgl mutant tissue to promote invasive overgrowth in developing and adult epithelial tissues and that dMyc abundance inside versus outside lgl mutant clones plays a key role in driving neoplastic overgrowth.

Multilevel Analysis in Household Survey: An Application to Health Condition Data

The aim of this thesis is to apply multilevel regression model in context of household surveys. Hierarchical structure in this type of data is characterized by many small groups. In last years comparative and multilevel analysis in the field of perceived health have grown in size. The purpose of this thesis is to develop a multilevel analysis with three level of hierarchy for Physical Component Summary outcome to: evaluate magnitude of within and between variance at each level (individual, household and municipality); explore which covariates affect on perceived physical health at each level; compare model-based and design-based approach in order to establish informativeness of sampling design; estimate a quantile regression for hierarchical data. The target population are the Italian residents aged 18 years and older. Our study shows a high degree of homogeneity within level 1 units belonging from the same group, with an intraclass correlation of 27% in a level-2 null model. Almost all variance is explained by level 1 covariates. In fact, in our model the explanatory variables having more impact on the outcome are disability, unable to work, age and chronic diseases (18 pathologies). An additional analysis are performed by using novel procedure of analysis :"Linear Quantile Mixed Model", named "Multilevel Linear Quantile Regression", estimate. This give us the possibility to describe more generally the conditional distribution of the response through the estimation of its quantiles, while accounting for the dependence among the observations. This has represented a great advantage of our models with respect to classic multilevel regression. The median regression with random effects reveals to be more efficient than the mean regression in representation of the outcome central tendency. A more detailed analysis of the conditional distribution of the response on other quantiles highlighted a differential effect of some covariate along the distribution.

Banking Regulation in Turkey and Russia: An Economic Analysis

The importance of the banks and financial markets relies on the fact that they promote economic efficiency by allocating savings efficiently to profitable investment opportunities.An efficient banking system is a key determinant for the financial stability.The theory of market failure forms the basis for understanding financial regulation.Following the detrimental economic and financial consequences in theaftermath of the crisis, academics and policymakers started to focus their attention on the construction of an appropriate regulatory and supervisory framework of the banking sector. This dissertation aims at understanding the impact of regulations and supervision on banks’ performance focusing on two emerging market economies, Turkey and Russia. It aims at examining the way in which regulations matter for financial stability and banking performance from a law & economics perspective. A review of the theory of banking regulation, particularly as applied to emerging economies, shows that the efficiency of certain solutions regarding banking regulation is open to debate. Therefore, in the context of emerging countries, whether a certain approach is efficient or not will be presented as an empirical question to which this dissertation will try to find an answer.

Multidimensional analysis of complex networks

Complex Networks analysis turn out to be a very promising field of research, testified by many research projects and works that span different fields. Those analysis have been usually focused on characterize a single aspect of the system and a study that considers many informative axes along with a network evolve is lacking. We propose a new multidimensional analysis that is able to inspect networks in the two most important dimensions, space and time. To achieve this goal, we studied them singularly and investigated how the variation of the constituting parameters drives changes to the network as a whole. By focusing on space dimension, we characterized spatial alteration in terms of abstraction levels. We proposed a novel algorithm that, by applying a fuzziness function, can reconstruct networks under different level of details. We verified that statistical indicators depend strongly on the granularity with which a system is described and on the class of networks. We keep fixed the space axes and we isolated the dynamics behind networks evolution process. We detected new instincts that trigger social networks utilization and spread the adoption of novel communities. We formalized this enhanced social network evolution by adopting special nodes (called sirens) that, thanks to their ability to attract new links, were able to construct efficient connection patterns. We simulated the dynamics of the system by considering three well-known growth models. Applying this framework to real and synthetic networks, we showed that the sirens, even when used for a limited time span, effectively shrink the time needed to get a network in mature state. In order to provide a concrete context of our findings, we formalized the cost of setting up such enhancement and provided the best combinations of system's parameters, such as number of sirens, time span of utilization and attractiveness.

CT perfusion image processing: analysis of liver tumors

Perfusion CT imaging of the liver has potential to improve evaluation of tumour angiogenesis. Quantitative parameters can be obtained applying mathematical models to Time Attenuation Curve (TAC). However, there are still some difficulties for an accurate quantification of perfusion parameters due, for example, to algorithms employed, to mathematical model, to patient’s weight and cardiac output and to the acquisition system. In this thesis, new parameters and alternative methodologies about liver perfusion CT are presented in order to investigate the cause of variability of this technique. Firstly analysis were made to assess the variability related to the mathematical model used to compute arterial Blood Flow (BFa) values. Results were obtained implementing algorithms based on “ maximum slope method” and “Dual input one compartment model” . Statistical analysis on simulated data demonstrated that the two methods are not interchangeable. Anyway slope method is always applicable in clinical context. Then variability related to TAC processing in the application of slope method is analyzed. Results compared with manual selection allow to identify the best automatic algorithm to compute BFa. The consistency of a Standardized Perfusion Index (SPV) was evaluated and a simplified calibration procedure was proposed. At the end the quantitative value of perfusion map was analyzed. ROI approach and map approach provide related values of BFa and this means that pixel by pixel algorithm give reliable quantitative results. Also in pixel by pixel approach slope method give better results. In conclusion the development of new automatic algorithms for a consistent computation of BFa and the analysis and definition of simplified technique to compute SPV parameter, represent an improvement in the field of liver perfusion CT analysis.

Analysis of the Slow Food movement impact on the farmers and rural areas’ sustainable development

The evaluation of the farmers’ communities’ approach to the Slow Food vision, their perception of the Slow Food role in supporting their activity and their appreciation and expectations from participating in the event of Mother Earth were studied. The Unified Theory of Acceptance and Use of Technology (UTAUT) model was adopted in an agro-food sector context. A survey was conducted, 120 questionnaires from farmers attending the Mother Earth in Turin in 2010 were collected. The descriptive statistical analysis showed that both Slow Food membership and participation to Mother Earth Meeting were much appreciated for the support provided to their business and the contribution to a more sustainable and fair development. A positive social, environmental and psychological impact on farmers also resulted. Results showed also an interesting perspective on the possible universality of the Slow Food and Mother Earth values. Farmers declared that Slow Food is supporting them by preserving the biodiversity and orienting them to the use of local resources and reducing the chemical inputs. Many farmers mentioned the language/culture and administration/bureaucratic issues as an obstacle to be a member in the movement and to participate to the event. Participation to Mother Earth gives an opportunity to exchange information with other farmers’ communities and to participate to seminars and debates, helpful for their business development. The absolute majority of positive answers associated to the farmers’ willingness to relate to Slow Food and participate to the next Mother Earth editions negatively influenced the UTAUT model results. A factor analysis showed that the variables associated to the UTAUT model constructs Performance Expectancy and Effort Expectancy were consistent, able to explain the construct variability, and their measurement reliable. Their inclusion in a simplest Technology Acceptance Model could be considered in future researches.

Numerical methods for the dispersion analysis of Guided Waves

The use of guided ultrasonic waves (GUW) has increased considerably in the fields of non-destructive (NDE) testing and structural health monitoring (SHM) due to their ability to perform long range inspections, to probe hidden areas as well as to provide a complete monitoring of the entire waveguide. Guided waves can be fully exploited only once their dispersive properties are known for the given waveguide. In this context, well stated analytical and numerical methods are represented by the Matrix family methods and the Semi Analytical Finite Element (SAFE) methods. However, while the former are limited to simple geometries of finite or infinite extent, the latter can model arbitrary cross-section waveguides of finite domain only. This thesis is aimed at developing three different numerical methods for modelling wave propagation in complex translational invariant systems. First, a classical SAFE formulation for viscoelastic waveguides is extended to account for a three dimensional translational invariant static prestress state. The effect of prestress, residual stress and applied loads on the dispersion properties of the guided waves is shown. Next, a two-and-a-half Boundary Element Method (2.5D BEM) for the dispersion analysis of damped guided waves in waveguides and cavities of arbitrary cross-section is proposed. The attenuation dispersive spectrum due to material damping and geometrical spreading of cavities with arbitrary shape is shown for the first time. Finally, a coupled SAFE-2.5D BEM framework is developed to study the dispersion characteristics of waves in viscoelastic waveguides of arbitrary geometry embedded in infinite solid or liquid media. Dispersion of leaky and non-leaky guided waves in terms of speed and attenuation, as well as the radiated wavefields, can be computed. The results obtained in this thesis can be helpful for the design of both actuation and sensing systems in practical application, as well as to tune experimental setup.

Techniques and Methods for a multi-scale analysis of neuromuscular fatigue

This thesis proposes an integrated holistic approach to the study of neuromuscular fatigue in order to encompass all the causes and all the consequences underlying the phenomenon. Starting from the metabolic processes occurring at the cellular level, the reader is guided toward the physiological changes at the motorneuron and motor unit level and from this to the more general biomechanical alterations. In Chapter 1 a list of the various definitions for fatigue spanning several contexts has been reported. In Chapter 2, the electrophysiological changes in terms of motor unit behavior and descending neural drive to the muscle have been studied extensively as well as the biomechanical adaptations induced. In Chapter 3 a study based on the observation of temporal features extracted from sEMG signals has been reported leading to the need of a more robust and reliable indicator during fatiguing tasks. Therefore, in Chapter 4, a novel bi-dimensional parameter is proposed. The study on sEMG-based indicators opened a scenario also on neurophysiological mechanisms underlying fatigue. For this purpose, in Chapter 5, a protocol designed for the analysis of motor unit-related parameters during prolonged fatiguing contractions is presented. In particular, two methodologies have been applied to multichannel sEMG recordings of isometric contractions of the Tibialis Anterior muscle: the state-of-the-art technique for sEMG decomposition and a coherence analysis on MU spike trains. The importance of a multi-scale approach has been finally highlighted in the context of the evaluation of cycling performance, where fatigue is one of the limiting factors. In particular, the last chapter of this thesis can be considered as a paradigm: physiological, metabolic, environmental, psychological and biomechanical factors influence the performance of a cyclist and only when all of these are kept together in a novel integrative way it is possible to derive a clear model and make correct assessments.

Discrete Event Systems based Design Patterns for Diagnosability Analysis of Automated Manufacturing Systems

The main goal of this thesis is to facilitate the process of industrial automated systems development applying formal methods to ensure the reliability of systems. A new formulation of distributed diagnosability problem in terms of Discrete Event Systems theory and automata framework is presented, which is then used to enforce the desired property of the system, rather then just verifying it. This approach tackles the state explosion problem with modeling patterns and new algorithms, aimed for verification of diagnosability property in the context of the distributed diagnosability problem. The concepts are validated with a newly developed software tool.

Stream sediments analysis for geochemical mapping of Romagna Apennines (Northern Italy): monitoring and management tool of environmental resources at various scales

Geochemical mapping is a valuable tool for the control of territory that can be used not only in the identification of mineral resources and geological, agricultural and forestry studies but also in the monitoring of natural resources by giving solutions to environmental and economic problems. Stream sediments are widely used in the sampling campaigns carried out by the world's governments and research groups for their characteristics of broad representativeness of rocks and soils, for ease of sampling and for the possibility to conduct very detailed sampling In this context, the environmental role of stream sediments provides a good basis for the implementation of environmental management measures, in fact the composition of river sediments is an important factor in understanding the complex dynamics that develop within catchment basins therefore they represent a critical environmental compartment: they can persistently incorporate pollutants after a process of contamination and release into the biosphere if the environmental conditions change. It is essential to determine whether the concentrations of certain elements, in particular heavy metals, can be the result of natural erosion of rocks containing high concentrations of specific elements or are generated as residues of human activities related to a certain study area. This PhD thesis aims to extract from an extensive database on stream sediments of the Romagna rivers the widest spectrum of informations. The study involved low and high order stream in the mountain and hilly area, but also the sediments of the floodplain area, where intensive agriculture is active. The geochemical signals recorded by the stream sediments will be interpreted in order to reconstruct the natural variability related to bedrock and soil contribution, the effects of the river dynamics, the anomalous sites, and with the calculation of background values be able to evaluate their level of degradation and predict the environmental risk.

Comportamento tribologico di materiali per componenti di interesse industriale: Failure analysis e prove tribologiche in laboratorio

L’attività di ricerca della presente tesi di dottorato ha riguardato sistemi tribologici complessi di interesse industriale per i quali sono stati individuati, mediante failure analysis, i meccanismi di usura dominanti. Per ciascuno di essi sono state studiate soluzioni migliorative sulla base di prove tribologiche di laboratorio. Nella realizzazione di maglie per macchine movimentazione terra sono ampiamente utilizzati i tradizionali acciai da bonifica. La possibilità di utilizzare i nuovi microlegati a medio tenore di carbonio, consentirebbe una notevole semplificazione del ciclo produttivo e benefici in termini di costi. Una parte della tesi ha riguardato lo studio del comportamento tribologico di tali acciai. E’ stato anche affrontato lo studio tribologico di motori idraulici, con l’obiettivo di riuscire a migliorarne la resistenza ad usura e quindi la vita utile. Sono state eseguite prove a banco, per valutare i principali meccanismi di usura, e prove di laboratorio atte a riprodurre le reali condizioni di utilizzo, valutando tecniche di modificazione superficiale che fossero in grado di ridurre l’usura dei componenti. Sono state analizzate diverse tipologie di rivestimenti Thermal Spray in termini di modalità di deposizione (AFS-APS) e di leghe metalliche depositate (Ni,Mo,Cu/Al). Si sono infine caratterizzati contatti tribologici nel settore del packaging, dove l’utilizzo di acciai inox austenitici è in alcuni casi obbligatorio. L’acciaio inossidabile AISI 316L è ampiamente utilizzato in applicazioni in cui siano richieste elevate resistenze alla corrosione, tuttavia la bassa resistenza all’usura, ne limitano l’impiego in campo tribologico. In tale ambito, è stata analizzata una problematica tribologica relativa a macchine automatiche per il dosaggio di polveri farmaceutiche. Sono state studiate soluzioni alternative che hanno previsto sia la completa sostituzione dei materiali della coppia tribologica, sia l’individuazione di tecniche di modificazione superficiale innovative quali la cementazione a bassa temperatura anche seguita dalla deposizione di un rivestimento di carbonio amorfo idrogenato a-C:H

A Bayesian changepoint analysis on spatio-temporal point processes

Changepoint analysis is a well established area of statistical research, but in the context of spatio-temporal point processes it is as yet relatively unexplored. Some substantial differences with regard to standard changepoint analysis have to be taken into account: firstly, at every time point the datum is an irregular pattern of points; secondly, in real situations issues of spatial dependence between points and temporal dependence within time segments raise. Our motivating example consists of data concerning the monitoring and recovery of radioactive particles from Sandside beach, North of Scotland; there have been two major changes in the equipment used to detect the particles, representing known potential changepoints in the number of retrieved particles. In addition, offshore particle retrieval campaigns are believed may reduce the particle intensity onshore with an unknown temporal lag; in this latter case, the problem concerns multiple unknown changepoints. We therefore propose a Bayesian approach for detecting multiple changepoints in the intensity function of a spatio-temporal point process, allowing for spatial and temporal dependence within segments. We use Log-Gaussian Cox Processes, a very flexible class of models suitable for environmental applications that can be implemented using integrated nested Laplace approximation (INLA), a computationally efficient alternative to Monte Carlo Markov Chain methods for approximating the posterior distribution of the parameters. Once the posterior curve is obtained, we propose a few methods for detecting significant change points. We present a simulation study, which consists in generating spatio-temporal point pattern series under several scenarios; the performance of the methods is assessed in terms of type I and II errors, detected changepoint locations and accuracy of the segment intensity estimates. We finally apply the above methods to the motivating dataset and find good and sensible results about the presence and quality of changes in the process.

Life cycle assessment in construction field: A life cycle cost analysis of reinforcement concrete bridge

The present work is included in the context of the assessment of sustainability in the construction field and is aimed at estimating and analyzing life cycle cost of the existing reinforced concrete bridge “Viadotto delle Capre” during its entire life. This was accomplished by a comprehensive data collection and results evaluation. In detail, the economic analysis of the project is performed. The work has investigated possible design alternatives for maintenance/rehabilitation and end-of-life operations, when structural, functional, economic and also environmental requirements have to be fulfilled. In detail, the economic impact of different design options for the given reinforced concrete bridge have been assessed, whereupon the most economically, structurally and environmentally efficient scenario was chosen. The Integrated Life-Cycle Analysis procedure and Environmental Impact Assessment were also discussed in this work. The scope of this thesis is to illustrate that Life Cycle Cost analysis as part of Life Cycle Assessment approach could be effectively used to drive the design and management strategy of new and existing structures. The final objective of this contribution is to show how an economic analysis can influence decision-making in the definition of the most sustainable design alternatives. The designers can monitor the economic impact of different design strategies in order to identify the most appropriate option.

Detrimental effects of exuberant and restrained immune responses against the central nervous system in the context of multiple sclerosis and glioblastoma multiforme

Glioblastoma multiforme (GBM) is the most common and most aggressive astrocytic tumor of the central nervous system (CNS) in adults. The standard treatment consisting of surgery, followed by a combinatorial radio- and chemotherapy, is only palliative and prolongs patient median survival to 12 to 15 months. The tumor subpopulation of stem cell-like glioma-initiating cells (GICs) shows resistance against radiation as well as chemotherapy, and has been suggested to be responsible for relapses of more aggressive tumors after therapy. The efficacy of immunotherapies, which exploit the immune system to specifically recognize and eliminate malignant cells, is limited due to strong immunosuppressive activities of the GICs and the generation of a specialized protective microenvironment. The molecular mechanisms underlying the therapy resistance of GICs are largely unknown. rnThe first aim of this study was to identify immune evasion mechanisms in GICs triggered by radiation. A model was used in which patient-derived GICs were treated in vitro with fractionated ionizing radiation (2.5 Gy in 7 consecutive passages) to select for a more radio-resistant phenotype. In the model cell line 1080, this selection process resulted in increased proliferative but diminished migratory capacities in comparison to untreated control GICs. Furthermore, radio-selected GICs downregulated various proteins involved in antigen processing and presentation, resulting in decreased expression of MHC class I molecules on the cellular surface and diminished recognition potential by cytotoxic CD8+ T cells. Thus, sub-lethal fractionated radiation can promote immune evasion and hamper the success of adjuvant immunotherapy. Among several immune-associated proteins, interferon-induced transmembrane protein 3 (IFITM3) was found to be upregulated in radio-selected GICs. While high expression of IFITM3 was associated with a worse overall survival of GBM patients (TCGA database) and increased proliferation and migration of differentiated glioma cell lines, a strong contribution of IFITM3 to proliferation in vitro as well as tumor growth and invasiveness in a xenograft model could not be observed. rnMultiple sclerosis (MS) is the most common autoimmune disease of the CNS in young adults of the Western World, which leads to progressive disability in genetically susceptible individuals, possibly triggered by environmental factors. It is assumed that self-reactive, myelin-specific T helper cell 1 (Th1) and Th17 cells, which have escaped the control mechanisms of the immune system, are critical in the pathogenesis of the human disease and its animal model experimental autoimmune encephalomyelitis (EAE). It was observed that in vitro differentiated interleukin 17 (IL-17) producing Th17 cells co-expressed the Th1-phenotypic cytokine Interferon-gamma (IFN-γ) in combination with the two respective lineage-associated transcription factors RORγt and T-bet after re-isolation from the CNS of diseased mice. Pathogenic molecular mechanisms that render a CD4+ T cell encephalitogenic have scarcely been investigated up to date. rnIn the second part of the thesis, whole transcriptional changes occurring in in vitro differentiated Th17 cells in the course of EAE were analyzed. Evaluation of signaling networks revealed an overrepresentation of genes involved in communication between the innate and adaptive immune system and metabolic alterations including cholesterol biosynthesis. The transcription factors Cebpa, Fos, Klf4, Nfatc1 and Spi1, associated with thymocyte development and naïve T cells were upregulated in encephalitogenic CNS-isolated CD4+ T cells, proposing a contribution to T cell plasticity. Correlation of the murine T-cell gene expression dataset to putative MS risk genes, which were selected based on their proximity (± 500 kb; ensembl database, release 75) to the MS risk single nucleotide polymorphisms (SNPs) proposed by the most recent multiple sclerosis GWAS in 2011, revealed that 67.3% of the MS risk genes were differentially expressed in EAE. Expression patterns of Bach2, Il2ra, Irf8, Mertk, Odf3b, Plek, Rgs1, Slc30a7, and Thada were confirmed in independent experiments, suggesting a contribution to T cell pathogenicity. Functional analysis of Nfatc1 revealed that Nfatc1-deficient CD4+ T cells were restrained in their ability to induce clinical signs of EAE. Nfatc1-deficiency allowed proper T cell activation, but diminished their potential to fully differentiate into Th17 cells and to express high amounts of lineage cytokines. As the inducible Nfatc1/αA transcript is distinct from the other family members, it could represent an interesting target for therapeutic intervention in MS.rn