Big data analytics and modeling for Human Mobility

The fast development of Information Communication Technologies (ICT) offers new opportunities to realize future smart cities. To understand, manage and forecast the city's behavior, it is necessary the analysis of different kinds of data from the most varied dataset acquisition systems. The aim of this research activity in the framework of Data Science and Complex Systems Physics is to provide stakeholders with new knowledge tools to improve the sustainability of mobility demand in future cities. Under this perspective, the governance of mobility demand generated by large tourist flows is becoming a vital issue for the quality of life in Italian cities' historical centers, which will worsen in the next future due to the continuous globalization process. Another critical theme is sustainable mobility, which aims to reduce private transportation means in the cities and improve multimodal mobility. We analyze the statistical properties of urban mobility of Venice, Rimini, and Bologna by using different datasets provided by companies and local authorities. We develop algorithms and tools for cartography extraction, trips reconstruction, multimodality classification, and mobility simulation. We show the existence of characteristic mobility paths and statistical properties depending on transport means and user's kinds. Finally, we use our results to model and simulate the overall behavior of the cars moving in the Emilia Romagna Region and the pedestrians moving in Venice with software able to replicate in silico the demand for mobility and its dynamic.

Development of new tools and devices for CMB and foreground data analysis and future experiments

The discovery of the Cosmic Microwave Background (CMB) radiation in 1965 is one of the fundamental milestones supporting the Big Bang theory. The CMB is one of the most important source of information in cosmology. The excellent accuracy of the recent CMB data of WMAP and Planck satellites confirmed the validity of the standard cosmological model and set a new challenge for the data analysis processes and their interpretation. In this thesis we deal with several aspects and useful tools of the data analysis. We focus on their optimization in order to have a complete exploitation of the Planck data and contribute to the final published results. The issues investigated are: the change of coordinates of CMB maps using the HEALPix package, the problem of the aliasing effect in the generation of low resolution maps, the comparison of the Angular Power Spectrum (APS) extraction performances of the optimal QML method, implemented in the code called BolPol, and the pseudo-Cl method, implemented in Cromaster. The QML method has been then applied to the Planck data at large angular scales to extract the CMB APS. The same method has been applied also to analyze the TT parity and the Low Variance anomalies in the Planck maps, showing a consistent deviation from the standard cosmological model, the possible origins for this results have been discussed. The Cromaster code instead has been applied to the 408 MHz and 1.42 GHz surveys focusing on the analysis of the APS of selected regions of the synchrotron emission. The new generation of CMB experiments will be dedicated to polarization measurements, for which are necessary high accuracy devices for separating the polarizations. Here a new technology, called Photonic Crystals, is exploited to develop a new polarization splitter device and its performances are compared to the devices used nowadays.

La migrazione del chagas: Costruzione bio-medica e socio-politica delle malattie tropicali dimenticate

Questo studio propone un'esplorazione dei nessi tra processi migratori ed esperienze di salute e malattia a partire da un'indagine sulle migrazioni provenienti dall'America latina in Emilia-Romagna. Contemporaneamente indaga i termini del dibattito sulla diffusione della Malattia di Chagas, “infezione tropicale dimenticata” endemica in America centro-meridionale che, grazie all'incremento dei flussi migratori transnazionali, viene oggi riconfigurata come 'emergente' in alcuni contesti di immigrazione. Attraverso i paradigmi teorico-metodologici disciplinari dell'antropologia medica, della salute globale e degli studi sulle migrazioni, si è inteso indagare la natura della relazione tra “dimenticanza” ed “emergenza” nelle politiche che caratterizzano il contesto migratorio europeo e italiano nello specifico. Si sono analizzate questioni vincolate alla legittimità degli attori coinvolti nella ridefinizione del fenomeno in ambito pubblico; alle visioni che informano le strategie sanitarie di presa in carico dell'infezione; alle possibili ricadute di tali visioni nelle pratiche di cura. Parte della ricerca si è realizzata all'interno del reparto ospedaliero ove è stato implementato il primo servizio di diagnosi e trattamento per l'infezione in Emilia-Romagna. È stata pertanto realizzata una etnografia fuori/dentro al servizio, coinvolgendo i principali soggetti del campo di indagine -immigrati latinoamericani e operatori sanitari-, con lo scopo di cogliere visioni, logiche e pratiche a partire da un'analisi della legislazione che regola l'accesso al servizio sanitario pubblico in Italia. Attraverso la raccolta di narrazioni biografiche, lo studio ha contribuito a far luce su peculiari percorsi migratori e di vita nel contesto locale; ha permesso di riflettere sulla validità di categorie come quella di “latinoamericano” utilizzata dalla comunità scientifica in stretta correlazione con il Chagas; ha riconfigurato il senso di un approccio attento alle connotazioni culturali all'interno di un più ampio ripensamento delle forme di inclusione e di partecipazione finalizzate a dare asilo ai bisogni sanitari maggiormente percepiti e alle esperienze soggettive di malattia.

Lives apart? Experiences of transnational motherhood by Georgian labour migrants to Italy and their children

International labour migration processes of the last decades saw increasing numbers of solo female migrants employed in the developed countries. Many of these women were mothers who left their children in the sending countries and thus gave rise to a controversial phenomenon of transnational motherhood. The present thesis is based on the first empirical study of intergenerational narratives of mothers, Georgian labour migrants to Italy, and their children, left behind in Georgia. Mothers’ international labour migration is a challenge to the traditional ideology of motherhood. Although unconsciously migrant mothers often adhere to “alternative”, “rational”, future-oriented model(s) of parenting, they continue to live their experiences in the framework of traditional understandings of motherhood, which appears to be unequipped to “frame” transnational motherhood as, from its point of view, mothers’ choice to leave their children is reprehensible, yet transnational mothers’ physical absence is not an equivalent of “leaving” their children. Informants’ narratives strongly suggest that long periods of physical separation did not jeopardize bonds between mothers and children in transnational families. While informants’ selection bias is probable, the mother-child bond was not “broken” and the very essence of motherhood remained intact. Many forms of mothers’ and children’s online co-presence were documented during the interviews. Interviews also prove that the Internet cannot be considered a solution to the problem of family separation, experienced painfully by both mothers and children: it may reduce the pain caused by separation, but cannot be a substitute for mothers’ physical absence from their families. Despite the pain caused by separation, mothers’ emigration appeared to be the right decision made for the good of the family. Interviewed mothers almost univocally reported readiness to “keep going on”, and continue working in emigration to help their children until physically able to do so, because, as they put it, “motherhood never ends”.

Data assimilation for advanced cross-scale unstructured-grid ocean modeling.

The coastal ocean is a complex environment with extremely dynamic processes that require a high-resolution and cross-scale modeling approach in which all hydrodynamic fields and scales are considered integral parts of the overall system. In the last decade, unstructured-grid models have been used to advance in seamless modeling between scales. On the other hand, the data assimilation methodologies to improve the unstructured-grid models in the coastal seas have been developed only recently and need significant advancements. Here, we link the unstructured-grid ocean modeling to the variational data assimilation methods. In particular, we show results from the modeling system SANIFS based on SHYFEM fully-baroclinic unstructured-grid model interfaced with OceanVar, a state-of-art variational data assimilation scheme adopted for several systems based on a structured grid. OceanVar implements a 3DVar DA scheme. The combination of three linear operators models the background error covariance matrix. The vertical part is represented using multivariate EOFs for temperature, salinity, and sea level anomaly. The horizontal part is assumed to be Gaussian isotropic and is modeled using a first-order recursive filter algorithm designed for structured and regular grids. Here we introduced a novel recursive filter algorithm for unstructured grids. A local hydrostatic adjustment scheme models the rapidly evolving part of the background error covariance. We designed two data assimilation experiments using SANIFS implementation interfaced with OceanVar over the period 2017-2018, one with only temperature and salinity assimilation by Argo profiles and the second also including sea level anomaly. The results showed a successful implementation of the approach and the added value of the assimilation for the active tracer fields. While looking at the broad basin, no significant improvements are highlighted for the sea level, requiring future investigations. Furthermore, a Machine Learning methodology based on an LSTM network has been used to predict the model SST increments.

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.

Customer Evolution in Sales Channel Migration

This research has been triggered by an emergent trend in customer behavior: customers have rapidly expanded their channel experiences and preferences beyond traditional channels (such as stores) and they expect the company with which they do business to have a presence on all these channels. This evidence has produced an increasing interest in multichannel customer behavior and it has motivated several researchers to study the customers’ channel choices dynamics in multichannel environment. We study how the consumer decision process for channel choice and response to marketing communications evolves for a cohort of new customers. We assume a newly acquired customer’s decisions are described by a “trial” model, but the customer’s choice process evolves to a “post-trial” model as the customer learns his or her preferences and becomes familiar with the firm’s marketing efforts. The trial and post-trial decision processes are each described by different multinomial logit choice models, and the evolution from the trial to post-trial model is determined by a customer-level geometric distribution that captures the time it takes for the customer to make the transition. We utilize data for a major retailer who sells in three channels – retail store, the Internet, and via catalog. The model is estimated using Bayesian methods that allow for cross-customer heterogeneity. This allows us to have distinct parameters estimates for a trial and an after trial stages and to estimate the quickness of this transit at the individual level. The results show for example that the customer decision process indeed does evolve over time. Customers differ in the duration of the trial period and marketing has a different impact on channel choice in the trial and post-trial stages. Furthermore, we show that some people switch channel decision processes while others don’t and we found that several factors have an impact on the probability to switch decision process. Insights from this study can help managers tailor their marketing communication strategy as customers gain channel choice experience. Managers may also have insights on the timing of the direct marketing communications. They can predict the duration of the trial phase at individual level detecting the customers with a quick, long or even absent trial phase. They can even predict if the customer will change or not his decision process over time, and they can influence the switching process using specific marketing tools

Study of the circulation processes in the northern Adriatic sea - coastal area and Venice lagoon inlets

This work is a detailed study of hydrodynamic processes in a defined area, the littoral in front of the Venice Lagoon and its inlets, which are complex morphological areas of interconnection. A finite element hydrodynamic model of the Venice Lagoon and the Adriatic Sea has been developed in order to study the coastal current patterns and the exchanges at the inlets of the Venice Lagoon. This is the first work in this area that tries to model the interaction dynamics, running together a model for the lagoon and the Adriatic Sea. First the barotropic processes near the inlets of the Venice Lagoon have been studied. Data from more than ten tide gauges displaced in the Adriatic Sea have been used in the calibration of the simulated water levels. To validate the model results, empirical flux data measured by ADCP probes installed inside the inlets of Lido and Malamocco have been used and the exchanges through the three inlets of the Venice Lagoon have been analyzed. The comparison between modelled and measured fluxes at the inlets outlined the efficiency of the model to reproduce both tide and wind induced water exchanges between the sea and the lagoon. As a second step, also small scale processes around the inlets that connect the Venice lagoon with the Northern Adriatic Sea have been investigated by means of 3D simulations. Maps of vorticity have been produced, considering the influence of tidal flows and wind stress in the area. A sensitivity analysis has been carried out to define the importance of the advection and of the baroclinic pressure gradients in the development of vortical processes seen along the littoral close to the inlets. Finally a comparison with real data measurements, surface velocity data from HF Radar near the Venice inlets, has been performed, which allows for a better understanding of the processes and their seasonal dynamics. The results outline the predominance of wind and tidal forcing in the coastal area. Wind forcing acts mainly on the mean coastal current inducing its detachment offshore during Sirocco events and an increase of littoral currents during Bora events. The Bora action is more homogeneous on the whole coastal area whereas the Sirocco strengthens its impact in the South, near Chioggia inlet. Tidal forcing at the inlets is mainly barotropic. The sensitivity analysis shows how advection is the main physical process responsible for the persistent vortical structures present along the littoral between the Venice Lagoon inlets. The comparison with measurements from HF Radar not only permitted a validation the model results, but also a description of different patterns in specific periods of the year. The success of the 2D and the 3D simulations on the reproduction both of the SSE, inside and outside the Venice Lagoon, of the tidal flow, through the lagoon inlets, and of the small scale phenomena, occurring along the littoral, indicates that the finite element approach is the most suitable tool for the investigation of coastal processes. For the first time, as shown by the flux modeling, the physical processes that drive the interaction between the two basins were reproduced.

Parallel modeling of the electric field distribution in the brain

The term "Brain Imaging" identi�es a set of techniques to analyze the structure and/or functional behavior of the brain in normal and/or pathological situations. These techniques are largely used in the study of brain activity. In addition to clinical usage, analysis of brain activity is gaining popularity in others recent �fields, i.e. Brain Computer Interfaces (BCI) and the study of cognitive processes. In this context, usage of classical solutions (e.g. f MRI, PET-CT) could be unfeasible, due to their low temporal resolution, high cost and limited portability. For these reasons alternative low cost techniques are object of research, typically based on simple recording hardware and on intensive data elaboration process. Typical examples are ElectroEncephaloGraphy (EEG) and Electrical Impedance Tomography (EIT), where electric potential at the patient's scalp is recorded by high impedance electrodes. In EEG potentials are directly generated from neuronal activity, while in EIT by the injection of small currents at the scalp. To retrieve meaningful insights on brain activity from measurements, EIT and EEG relies on detailed knowledge of the underlying electrical properties of the body. This is obtained from numerical models of the electric �field distribution therein. The inhomogeneous and anisotropic electric properties of human tissues make accurate modeling and simulation very challenging, leading to a tradeo�ff between physical accuracy and technical feasibility, which currently severely limits the capabilities of these techniques. Moreover elaboration of data recorded requires usage of regularization techniques computationally intensive, which influences the application with heavy temporal constraints (such as BCI). This work focuses on the parallel implementation of a work-flow for EEG and EIT data processing. The resulting software is accelerated using multi-core GPUs, in order to provide solution in reasonable times and address requirements of real-time BCI systems, without over-simplifying the complexity and accuracy of the head models.

Kinematics of the Sicily and Calabria Subduction System from Elastic Block Modeling of GPS Data

We use data from about 700 GPS stations in the EuroMediterranen region to investigate the present-day behavior of the the Calabrian subduction zone within the Mediterranean-scale plates kinematics and to perform local scale studies about the strain accumulation on active structures. We focus attenction on the Messina Straits and Crati Valley faults where GPS data show extentional velocity gradients of ∼3 mm/yr and ∼2 mm/yr, respectively. We use dislocation model and a non-linear constrained optimization algorithm to invert for fault geometric parameters and slip-rates and evaluate the associated uncertainties adopting a bootstrap approach. Our analysis suggest the presence of two partially locked normal faults. To investigate the impact of elastic strain contributes from other nearby active faults onto the observed velocity gradient we use a block modeling approach. Our models show that the inferred slip-rates on the two analyzed structures are strongly impacted by the assumed locking width of the Calabrian subduction thrust. In order to frame the observed local deformation features within the present- day central Mediterranean kinematics we realyze a statistical analysis testing the indipendent motion (w.r.t. the African and Eurasias plates) of the Adriatic, Cal- abrian and Sicilian blocks. Our preferred model confirms a microplate like behaviour for all the investigated blocks. Within these kinematic boundary conditions we fur- ther investigate the Calabrian Slab interface geometry using a combined approach of block modeling and χ2ν statistic. Almost no information is obtained using only the horizontal GPS velocities that prove to be a not sufficient dataset for a multi-parametric inversion approach. Trying to stronger constrain the slab geometry we estimate the predicted vertical velocities performing suites of forward models of elastic dislocations varying the fault locking depth. Comparison with the observed field suggest a maximum resolved locking depth of 25 km.

Numerical modeling and experimental validation of the recrystallization behaviour in the extrusion of 6XXX aluminum alloys

The microstructure of 6XXX aluminum alloys deeply affects mechanical, crash, corrosion and aesthetic properties of extruded profiles. Unfortunately, grain structure evolution during manufacturing processes is a complex phenomenon because several process and material parameters such as alloy chemical composition, temperature, extrusion speed, tools geometries, quenching and thermal treatment parameters affect the grain evolution during the manufacturing process. The aim of the present PhD thesis was the analysis of the recrystallization kinetics during the hot extrusion of 6XXX aluminum alloys and the development of reliable recrystallization models to be used in FEM codes for the microstructure prediction at a die design stage. Experimental activities have been carried out in order to acquire data for the recrystallization models development, validation and also to investigate the effect of process parameters and die design on the microstructure of the final component. The experimental campaign reported in this thesis involved the extrusion of AA6063, AA6060 and AA6082 profiles with different process parameters in order to provide a reliable amount of data for the models validation. A particular focus was made to investigate the PCG defect evolution during the extrusion of medium-strength alloys such as AA6082. Several die designs and process conditions were analysed in order to understand the influence of each of them on the recrystallization behaviour of the investigated alloy. From the numerical point of view, innovative models for the microstructure prediction were developed and validated over the extrusion of industrial-scale profiles with complex geometries, showing a good matching in terms of the grain size and surface recrystallization prediction. The achieved results suggest the reliability of the developed models and their application in the industrial field for process and material properties optimization at a die-design stage.

Assessing brain connectivity through electroencephalographic signal processing and modeling analysis

Brain functioning relies on the interaction of several neural populations connected through complex connectivity networks, enabling the transmission and integration of information. Recent advances in neuroimaging techniques, such as electroencephalography (EEG), have deepened our understanding of the reciprocal roles played by brain regions during cognitive processes. The underlying idea of this PhD research is that EEG-related functional connectivity (FC) changes in the brain may incorporate important neuromarkers of behavior and cognition, as well as brain disorders, even at subclinical levels. However, a complete understanding of the reliability of the wide range of existing connectivity estimation techniques is still lacking. The first part of this work addresses this limitation by employing Neural Mass Models (NMMs), which simulate EEG activity and offer a unique tool to study interconnected networks of brain regions in controlled conditions. NMMs were employed to test FC estimators like Transfer Entropy and Granger Causality in linear and nonlinear conditions. Results revealed that connectivity estimates reflect information transmission between brain regions, a quantity that can be significantly different from the connectivity strength, and that Granger causality outperforms the other estimators. A second objective of this thesis was to assess brain connectivity and network changes on EEG data reconstructed at the cortical level. Functional brain connectivity has been estimated through Granger Causality, in both temporal and spectral domains, with the following goals: a) detect task-dependent functional connectivity network changes, focusing on internal-external attention competition and fear conditioning and reversal; b) identify resting-state network alterations in a subclinical population with high autistic traits. Connectivity-based neuromarkers, compared to the canonical EEG analysis, can provide deeper insights into brain mechanisms and may drive future diagnostic methods and therapeutic interventions. However, further methodological studies are required to fully understand the accuracy and information captured by FC estimates, especially concerning nonlinear phenomena.

Innovation in aquaculture. Modeling the adoption and implementation processes of innovations in the Italian aquaculture sector

Since the turn of the century, fisheries have maintained a steady growth rate, while aquaculture has experienced a more rapid expansion. Aquaculture can offer EU consumers more diverse, healthy, and sustainable food options, some of which are more popular elsewhere. To develop the sector, the EU is investing heavily. The EU supports innovative projects that promote the sustainable development of seafood sectors and food security. Priority 3 promotes sector development through innovation dissemination. This doctoral dissertation examined innovation transfer in the Italian aquaculture sector, specifically the adoption of innovative tools, using a theoretical model to better understand the complexity of these processes. The work focused on innovation adoption, emphasising that it is the end of a well-defined process. The Awareness Knowledge Adoption Implementation Effectiveness (AKAIE) model was created to better analyse post-adoption phases and evaluate technology adoption implementation and impact. To identify AKAIE drivers and barriers, aquaculture actors were consulted. "Perceived complexity"—barriers to adoption that are strongly influenced by contextual factors—has been used to examine their perspectives (i.e. socio-economic, institutional, cultural ones). The new model will contextualise the sequence based on technologies, entrepreneur traits, corporate and institutional contexts, and complexity perception, the sequence's central node. Technology adoption can also be studied by examining complexity perceptions along the AKAIE sequence. This study proposes a new model to evaluate the diffusion of a given technology, offering the policy maker the possibility to be able to act promptly across the process. The development of responsible policies for evaluating the effectiveness of innovation is more necessary than ever, especially to orient strategies and interventions in the face of major scenarios of change.

Temporalities of migration: time, data infrastructures and intervention

The dissertation explores the intersections between the temporalities of migration management and border-crossers’ temporalities. First, I analyze the relation between acceleration and (non)knowledge production by focusing on the “accelerated procedures” for asylum. These procedures are applied to people whose asylum applications are deemed as suspicious and likely to be rejected. I argue that the shortened timeframes shaping these procedures are a tool for hindering asylum seekers’ possibilities to collect and produce evidence supporting their cases, eventually facilitating and speeding up their removal for Member States’ territory. Second, I analyze the encounters between migration management and border-crossers during the identification practices carried out the Hotspots and during the asylum process in terms of “temporal collisions”. I develop the notion of “hijacked knowledge” to illustrate how these “temporal collisions” negatively affect border-crossers’ possibilities of action, by producing a significant lack of knowledge and awareness about the procedures to which they are subjected and their temporal implications. With the concept of “reactive calibration”, on the other hand, I suggest that once migrants become aware of the temporalities of control, they try to appropriate them by aligning their bodies, narrations and identities to those temporalities. The third part of the dissertation describes the situated intervention developed as part of my ethnographic activity. Drawing on participatory design, design justice and STS making and doing, I designed a role-playing game - My documents, check them out - seeking to involve border-crossers in the re-design of the categories usually deployed in migration management.