The assessment of change in rural landscape and the analysis of the driving forces. Proposal of a spatial model for the rural built environment

The presented study carried out an analysis on rural landscape changes. In particular the study focuses on the understanding of driving forces acting on the rural built environment using a statistical spatial model implemented through GIS techniques. It is well known that the study of landscape changes is essential for a conscious decision making in land planning. From a bibliography review results a general lack of studies dealing with the modeling of rural built environment and hence a theoretical modelling approach for such purpose is needed. The advancement in technology and modernity in building construction and agriculture have gradually changed the rural built environment. In addition, the phenomenon of urbanization of a determined the construction of new volumes that occurred beside abandoned or derelict rural buildings. Consequently there are two types of transformation dynamics affecting mainly the rural built environment that can be observed: the conversion of rural buildings and the increasing of building numbers. It is the specific aim of the presented study to propose a methodology for the development of a spatial model that allows the identification of driving forces that acted on the behaviours of the building allocation. In fact one of the most concerning dynamic nowadays is related to an irrational expansion of buildings sprawl across landscape. The proposed methodology is composed by some conceptual steps that cover different aspects related to the development of a spatial model: the selection of a response variable that better describe the phenomenon under study, the identification of possible driving forces, the sampling methodology concerning the collection of data, the most suitable algorithm to be adopted in relation to statistical theory and method used, the calibration process and evaluation of the model. A different combination of factors in various parts of the territory generated favourable or less favourable conditions for the building allocation and the existence of buildings represents the evidence of such optimum. Conversely the absence of buildings expresses a combination of agents which is not suitable for building allocation. Presence or absence of buildings can be adopted as indicators of such driving conditions, since they represent the expression of the action of driving forces in the land suitability sorting process. The existence of correlation between site selection and hypothetical driving forces, evaluated by means of modeling techniques, provides an evidence of which driving forces are involved in the allocation dynamic and an insight on their level of influence into the process. GIS software by means of spatial analysis tools allows to associate the concept of presence and absence with point futures generating a point process. Presence or absence of buildings at some site locations represent the expression of these driving factors interaction. In case of presences, points represent locations of real existing buildings, conversely absences represent locations were buildings are not existent and so they are generated by a stochastic mechanism. Possible driving forces are selected and the existence of a causal relationship with building allocations is assessed through a spatial model. The adoption of empirical statistical models provides a mechanism for the explanatory variable analysis and for the identification of key driving variables behind the site selection process for new building allocation. The model developed by following the methodology is applied to a case study to test the validity of the methodology. In particular the study area for the testing of the methodology is represented by the New District of Imola characterized by a prevailing agricultural production vocation and were transformation dynamic intensively occurred. The development of the model involved the identification of predictive variables (related to geomorphologic, socio-economic, structural and infrastructural systems of landscape) capable of representing the driving forces responsible for landscape changes.. The calibration of the model is carried out referring to spatial data regarding the periurban and rural area of the study area within the 1975-2005 time period by means of Generalised linear model. The resulting output from the model fit is continuous grid surface where cells assume values ranged from 0 to 1 of probability of building occurrences along the rural and periurban area of the study area. Hence the response variable assesses the changes in the rural built environment occurred in such time interval and is correlated to the selected explanatory variables by means of a generalized linear model using logistic regression. Comparing the probability map obtained from the model to the actual rural building distribution in 2005, the interpretation capability of the model can be evaluated. The proposed model can be also applied to the interpretation of trends which occurred in other study areas, and also referring to different time intervals, depending on the availability of data. The use of suitable data in terms of time, information, and spatial resolution and the costs related to data acquisition, pre-processing, and survey are among the most critical aspects of model implementation. Future in-depth studies can focus on using the proposed model to predict short/medium-range future scenarios for the rural built environment distribution in the study area. In order to predict future scenarios it is necessary to assume that the driving forces do not change and that their levels of influence within the model are not far from those assessed for the time interval used for the calibration.

Aerobic biodegradation of chlorinated solvents and plastics in batch and continuous-flow bioreactors: process development, and identification of suitable chemical pre-Treatments

The purpose of the first part of the research activity was to develop an aerobic cometabolic process in packed bed reactors (PBR) to treat real groundwater contaminated by trichloroethylene (TCE) and 1,1,2,2-tetrachloroethane (TeCA). In an initial screening conducted in batch bioreactors, different groundwater samples from 5 wells of the contaminated site were fed with 5 growth substrates. The work led to the selection of butane as the best growth substrate, and to the development and characterization from the site’s indigenous biomass of a suspended-cell consortium capable to degrade TCE with a 90 % mineralization of the organic chlorine. A kinetic study conducted in batch and continuous flow PBRs and led to the identification of the best carrier. A kinetic study of butane and TCE biodegradation indicated that the attached-cell consortium is characterized by a lower TCE specific degredation rates and by a lower level of mutual butane-TCE inhibition. A 31 L bioreactor was designed and set up for upscaling the experiment. The second part of the research focused on the biodegradation of 4 polymers, with and with-out chemical pre-treatments: linear low density polyethylene (LLDPE), polyethylene (PP), polystyrene (PS) and polyvinyl chloride (PVC). Initially, the 4 polymers were subjected to different chemical pre-treatments: ozonation and UV/ozonation, in gaseous and aqueous phase. It was found that, for LLDPE and PP, the coupling UV and ozone in gas phase is the most effective way to oxidize the polymers and to generate carbonyl groups on the polymer surface. In further tests, the effect of chemical pretreatment on polyner biodegrability was studied. Gas-phase ozonated and virgin polymers were incubated aerobically with: (a) a pure strain, (b) a mixed culture of bacteria; and (c) a fungal culture, together with saccharose as a co-substrate.

Development and characterisation of a neurogenic model of brain cancer

Primary glioblastoma (GB), the most common and aggressive adult brain tumour, is refractory to conventional therapies and characterised by poor prognosis. GB displays striking cellular heterogeneity, with a sub-population, called Glioblastoma Stem Cells (GSCs), intrinsically resistant to therapy, hence the high rate of recurrence. Alterations of the tumour suppressor gene PTEN are prevalent in primary GBM, resulting in the inhibition of the polarity protein Lgl1 due to aPKC hyperactivation. Dysregulation of this molecular axis is one of the mechanisms involved in GSC maintenance. After demonstrating that the PTEN/aPKC/Lgl axis is conserved in Drosophila, I deregulated it in different cells populations of the nervous system in order to individuate the cells at the root of neurogenic brain cancers. This analysis identified the type II neuroblasts (NBs) as the most sensitive to alterations of this molecular axis. Type II NBs are a sub-population of Drosophila stem cells displaying a lineage similar to that of the mammalian neural stem cells. Following aPKC activation in these stem cells, I obtained an adult brain cancer model in Drosophila that summarises many phenotypic traits of human brain tumours. Fly tumours are indeed characterised by accumulation of highly proliferative immature cells and keep growing in the adult leading the affected animals to premature death. With the aim to understand the role of cell polarity disruption in this tumorigenic process I carried out a molecular characterisation and transcriptome analysis of brain cancers from our fly model. In summary, the model I built and partially characterised in this thesis work may help deepen our knowledge on human brain cancers by investigating many different aspects of this complicate disease.

Assessing the fit of unidimensional IRT models for binary data under model misspecification

Model misspecification affects the classical test statistics used to assess the fit of the Item Response Theory (IRT) models. Robust tests have been derived under model misspecification, as the Generalized Lagrange Multiplier and Hausman tests, but their use has not been largely explored in the IRT framework. In the first part of the thesis, we introduce the Generalized Lagrange Multiplier test to detect differential item response functioning in IRT models for binary data under model misspecification. By means of a simulation study and a real data analysis, we compare its performance with the classical Lagrange Multiplier test, computed using the Hessian and the cross-product matrix, and the Generalized Jackknife Score test. The power of these tests is computed empirically and asymptotically. The misspecifications considered are local dependence among items and non-normal distribution of the latent variable. The results highlight that, under mild model misspecification, all tests have good performance while, under strong model misspecification, the performance of the tests deteriorates. None of the tests considered show an overall superior performance than the others. In the second part of the thesis, we extend the Generalized Hausman test to detect non-normality of the latent variable distribution. To build the test, we consider a seminonparametric-IRT model, that assumes a more flexible latent variable distribution. By means of a simulation study and two real applications, we compare the performance of the Generalized Hausman test with the M2 limited information goodness-of-fit test and the Likelihood-Ratio test. Additionally, the information criteria are computed. The Generalized Hausman test has a better performance than the Likelihood-Ratio test in terms of Type I error rates and the M2 test in terms of power. The performance of the Generalized Hausman test and the information criteria deteriorates when the sample size is small and with a few items.

A conceptualisation of a governance model for biobanks in the digital society

Biobanks are key infrastructures in data-driven biomedical research. The counterpoint of this optimistic vision is the reality of biobank governance, which must address various ethical, legal and social issues, especially in terms of open consent, privacy and secondary uses which, if not sufficiently resolved, may undermine participants’ and society’s trust in biobanking. The effect of the digital paradigm on biomedical research has only accentuated these issues by adding new pressure for the data protection of biobank participants against the risks of covert discrimination, abuse of power against individuals and groups, and critical commercial uses. Moreover, the traditional research-ethics framework has been unable to keep pace with the transformative developments of the digital era, and has proven inadequate in protecting biobank participants and providing guidance for ethical practices. To this must be added the challenge of an increased tendency towards exploitation and the commercialisation of personal data in the field of biomedical research, which may undermine the altruistic and solidaristic values associated with biobank participation and risk losing alignment with societal interests in biobanking. My research critically analyses, from a bioethical perspective, the challenges and the goals of biobank governance in data-driven biomedical research in order to understand the conditions for the implementation of a governance model that can foster biomedical research and innovation, while ensuring adequate protection for biobank participants and an alignment of biobank procedures and policies with society’s interests and expectations. The main outcome is a conceptualisation of a socially-oriented and participatory model of biobanks by proposing a new ethical framework that relies on the principles of transparency, data protection and participation to tackle the key challenges of biobanks in the digital age and that is well-suited to foster these goals.

Uncovering the encoding and transmission of behaviourally relevant information in neural activity

Most cognitive functions require the encoding and routing of information across distributed networks of brain regions. Information propagation is typically attributed to physical connections existing between brain regions, and contributes to the formation of spatially correlated activity patterns, known as functional connectivity. While structural connectivity provides the anatomical foundation for neural interactions, the exact manner in which it shapes functional connectivity is complex and not yet fully understood. Additionally, traditional measures of directed functional connectivity only capture the overall correlation between neural activity, and provide no insight on the content of transmitted information, limiting their ability in understanding neural computations underlying the distributed processing of behaviorally-relevant variables. In this work, we first study the relationship between structural and functional connectivity in simulated recurrent spiking neural networks with spike timing dependent plasticity. We use established measures of time-lagged correlation and overall information propagation to infer the temporal evolution of synaptic weights, showing that measures of dynamic functional connectivity can be used to reliably reconstruct the evolution of structural properties of the network. Then, we extend current methods of directed causal communication between brain areas, by deriving an information-theoretic measure of Feature-specific Information Transfer (FIT) quantifying the amount, content and direction of information flow. We test FIT on simulated data, showing its key properties and advantages over traditional measures of overall propagated information. We show applications of FIT to several neural datasets obtained with different recording methods (magneto and electro-encephalography, spiking activity, local field potentials) during various cognitive functions, ranging from sensory perception to decision making and motor learning. Overall, these analyses demonstrate the ability of FIT to advance the investigation of communication between brain regions, uncovering the previously unaddressed content of directed information flow.

Bridging innovation, nature and tradition: assessment of strategies for rural tourism development in Maramureş (Romania)

In the frame of EU rural policy, always more oriented towards environmental concerns and green livelihoods, Romania stands out for the predominance of rural areas and high nature value farming. The country has to face the challenge of joining the modernization process of rural farming systems with the valorization of local assets. Tourism has emerged as one of the main drivers of change and contributors for a sustainable exploitation of local resources. Rural tourism (RT) can foster the enhancement of the territorial capital (TC), the preservation of public goods (PGs) and the promotion of a more environmental oriented livelihood. The research focuses on a case study area, two valleys from Maramureş, where environmental approaches as diversification strategies are partially explored. The work investigates the role of tourism initiatives for the promotion of green oriented practices. The first part of the work is based on a literature review and interdisciplinary analysis of secondary data to identify the key issues: from rural development policy, to the concept of TC, of PGs and RT. The Romanian development programmes and related strategies are investigated; afterwards the characteristics of the County and the role of RT as diversification and valorisation policies is considered. The second part is based on the collection of primary data through interviews to different local stakeholders (farmers owners of rural guesthouses, local administrators, networks and artisans). The main frequencies are analyzed, a cluster analysis is computed to evaluate the similarities within the most representative groups and a comparative analysis is carried out between the two Valleys. The frame of the analysis is based on a set of indicators following the dimensions of the TC, to assess the characteristics of the local stakeholders and to outline the perception about the local PGs and on the adopted strategies to manage the territory. Final considerations are elaborated and few scenarios are outlined, giving relevance to the importance of improving awareness and creating embeddedness among public-private local stakeholders and resources as a tool for a socio-economic and environmental development of the area.

Classical limit of the Nelson model

Since the development of quantum mechanics it has been natural to analyze the connection between classical and quantum mechanical descriptions of physical systems. In particular one should expect that in some sense when quantum mechanical effects becomes negligible the system will behave like it is dictated by classical mechanics. One famous relation between classical and quantum theory is due to Ehrenfest. This result was later developed and put on firm mathematical foundations by Hepp. He proved that matrix elements of bounded functions of quantum observables between suitable coherents states (that depend on Planck's constant h) converge to classical values evolving according to the expected classical equations when h goes to zero. His results were later generalized by Ginibre and Velo to bosonic systems with infinite degrees of freedom and scattering theory. In this thesis we study the classical limit of Nelson model, that describes non relativistic particles, whose evolution is dictated by Schrödinger equation, interacting with a scalar relativistic field, whose evolution is dictated by Klein-Gordon equation, by means of a Yukawa-type potential. The classical limit is a mean field and weak coupling limit. We proved that the transition amplitude of a creation or annihilation operator, between suitable coherent states, converges in the classical limit to the solution of the system of differential equations that describes the classical evolution of the theory. The quantum evolution operator converges to the evolution operator of fluctuations around the classical solution. Transition amplitudes of normal ordered products of creation and annihilation operators between coherent states converge to suitable products of the classical solutions. Transition amplitudes of normal ordered products of creation and annihilation operators between fixed particle states converge to an average of products of classical solutions, corresponding to different initial conditions.

Improvement of photon transport model by including coupled photon-electron transport and kernel refinement

The first part of this work deals with the inverse problem solution in the X-ray spectroscopy field. An original strategy to solve the inverse problem by using the maximum entropy principle is illustrated. It is built the code UMESTRAT, to apply the described strategy in a semiautomatic way. The application of UMESTRAT is shown with a computational example. The second part of this work deals with the improvement of the X-ray Boltzmann model, by studying two radiative interactions neglected in the current photon models. Firstly it is studied the characteristic line emission due to Compton ionization. It is developed a strategy that allows the evaluation of this contribution for the shells K, L and M of all elements with Z from 11 to 92. It is evaluated the single shell Compton/photoelectric ratio as a function of the primary photon energy. It is derived the energy values at which the Compton interaction becomes the prevailing process to produce ionization for the considered shells. Finally it is introduced a new kernel for the XRF from Compton ionization. In a second place it is characterized the bremsstrahlung radiative contribution due the secondary electrons. The bremsstrahlung radiation is characterized in terms of space, angle and energy, for all elements whit Z=1-92 in the energy range 1–150 keV by using the Monte Carlo code PENELOPE. It is demonstrated that bremsstrahlung radiative contribution can be well approximated with an isotropic point photon source. It is created a data library comprising the energetic distributions of bremsstrahlung. It is developed a new bremsstrahlung kernel which allows the introduction of this contribution in the modified Boltzmann equation. An example of application to the simulation of a synchrotron experiment is shown.

Unlocking the Potential of Women in the Rural Development Process of Republic of Srpska: The Role of Extension Service

In Bosnia Herzegovina the development of clear policy objectives and endorsement of a long-term, coherent and mutual agricultural and rural development policy have also been affected by structural problems: a lack of reliable information on population and other relevant issues, the absence of an adequate land registry system and cadastre. Moreover in BiH the agricultural and rural sectors are characterized by many factors that have typically affected transition countries such as land fragmentation, lack of agricultural mechanization and outdated production technologies, and rural aging, high unemployment and out-migration. In such a framework the condition and role of women in rural areas suffered for the lack of gender disaggregated data and a consequent poor information that lead to the exclusion of gender related questions in the agenda of public institutions and to the absence of targeted policy interventions. The aim of the research is to investigate the role and condition of women in the rural development process of Republic of Srpska and to analyze the capacity of extension services to stimulate their empowerment. Specific research questions include the status of women in the rural areas of Republic of Srpska, the role of government in fostering the empowerment of rural women, and the role of the extension service in supporting rural women. The methodology - inspired by the case study method developed by R. Yin - is designed along the three specific research questions that are used as building blocks. Each of the three research questions is investigated with a combination of methodological tools - including surveys, experts interviews and focus groups - aimed to overcome the lack of data and knowledge that characterize the research objectives.