World climate change and global warming increase are two urgent and strategic issues that national and international governments have to face, and different scenarios aimed to estimate the world energy demand were realized by several research centers: each scenario distinguishes itself by energy policies over the years, and the desirable one requires many efforts to keep the temperature increase below 2°C above pre-industrial level. These efforts imply challenging targets on both primary and final energy employment, and this thesis is focused on two of them: improvement of renewable energy exploitation and reduction of final energy consumption, and energy conversion systems able to efficiently achieve these targets are cogenerated distributed plants, in particular the small scale. Nevertheless, in order to achieve significant primary energy saving, combined heat and power plants need to be designed with a substantial thermal power exploitation, as well as the design need reliable and congruent system models to evaluate the plant performances. The methodology carried out in this doctorate course was focused on the analysis of these topics and it was made by two main elements, an energy conversion system model, which describes the peculiar studied case, and a multi-variable multi-objective optimization algorithm, which depends on the specific application. In particular, two different applications of the methodology were realized, one aimed at designing the more efficient energy interaction between energy system and user and one aimed at validate thermodynamic models and experimental data congruence; the first application concerned combined heat and power plants based on internal combustion engine and gas turbine, while the second application was performed on micro gas turbines and pyro-gasification biomass plant. The methodology showed to be a potentially powerful tool about conversion energy systems analysis, due to the relevant primary energy saving related to designed cogenerated power plant and to the analysis of reliability performed on mathematical models of energy conversion systems.
In latest years, the possibility to exploit the high amount of spectral information has made hyperspectral remote sensing a very promising approach to detect changes occurred in multi-temporal images. Detection of changes in images of the same area collected at different times is of crucial interest in military and civilian applications, spanning from wide area surveillance and damage assessment to geology and land cover. In military operations, the interest is in rapid location and tracking of objects of interest, people, vehicles or equipment that pose a potential threat. In civilian contexts, changes of interest may include different types of natural or manmade threats, such as the path of an impending storm or the source of a hazardous material spill. In this PhD thesis, the focus is on Anomalous Change Detection (ACD) in airborne hyperspectral images. The goal is the detection of small changes occurred in two images of the same scene, i.e. changes having size comparable with the sensor ground resolution. The objects of interest typically occupy few pixels of the image and change detection must be accomplished in a pixel-wise fashion. Moreover, since the images are in general not radiometrically comparable, because illumination, atmospheric and environmental conditions change from one acquisition to the other, pervasive and uninteresting changes must be accounted for in developing ACD strategies. ACD process can be distinguished into two main phases: a pre-processing step, which includes radiometric correction, image co-registration and noise filtering, and a detection step, where the pre-processed images are compared according to a defined criterion in order to derive a statistical ACD map highlighting the anomalous changes occurred in the scene. In the literature, ACD has been widely investigated providing valuable methods in order to cope with these problems. In this work, a general overview of ACD methods is given reviewing the most known pre-processing and detection methods proposed in the literature. The analysis has been conducted unifying different techniques in a common framework based on binary decision theory, where one has to test the two competing hypotheses H0 (change absent) and H1 (change present) on the basis of an observation vector derived from the radiance measured on each pixel of the two images. Particular emphasis has been posed on statistical approaches, where ACD is derived in the framework of Neymann Pearson theory and the decision rule is carried out on the basis of the statistical properties assumed for the two hypotheses distribution, the observation vector space and the secondary data exploited for the estimation of the unknown parameters. Typically, ACD techniques assume that the observation represents the realization of jointly Gaussian spatially stationary random process. Though such assumption is adopted because of its mathematical tractability, it may be quite simplistic to model the multimodality usually met in real data. A more appropriate model is that adopted to derive the well known RX anomaly detector which assumes the local Gaussianity of the hyperspectral data. In this framework, a new statistical ACD method has been proposed considering the local Gaussianity of the hyperspectral data. The assumption of local stationarity for the observations in the two hypotheses is taken into account by considering two different models, leading to two different detectors. In addition, when data are collected by airborne platforms, perfect co-registration between images is very difficult to achieve. As a consequence, a residual misregistration (RMR) error should be taken into account in developing ACD techniques. Different techniques have been proposed to cope with the performance degradation problem due to the RMR, embedding the a priori knowledge on the statistical properties of the RMR in the change detection scheme. In this context, a new method has been proposed for the estimation of the first and second order statistics of the RMR. The technique is based on a sequential strategy that exploits the Scale Invariant Feature Transform (SIFT) algorithm cascaded with the Minimum Covariance Determinant algorithm. The proposed method adapts the SIFT procedure to hyperspectral images and improves the robustness of the outliers filtering by means of a highly robust estimator of multivariate location. Then, the attention has been focused on noise filtering techniques aimed at enforcing the consistency of the ACD process. To this purpose, a new method has been proposed to mitigate the negative effects due to random noise. In particular, this is achieved by means of a band selection technique aimed at discarding spectral channels whose useful signal content is low compared with the noise contribution. Band selection is performed on a per-pixel basis by exploiting the estimates of the noise variance accounting also for the presence of the signal dependent noise component. Finally, the effectiveness of the proposed techniques has been extensively evaluated by employing different real hyperspectral datasets containing anomalous changes collected in different acquisition conditions and on different scenarios, highlighting advantages and drawbacks of each method. In summary, the main issues related to ACD in multi-temporal hyperspectral images have been examined in this PhD thesis. With reference to the pre-processing step, two original contributions have been offered: i) an unsupervised technique for the estimation of the RMR noise affecting hyperspectral images, and ii) an adaptive approach for ACD which mitigates the negative effects due to random noise. As to the detection step, a survey of the existing techniques has been carried out, highlighting the major drawbacks and disadvantages, and a novel contribution has been offered by presenting a new statistical ACD method which considers the local Gaussianity of the hyperspectral data.
This article explores the capacity of the concept of multi-level governance to respond to the analytical, empirical & normative challenges posed to political science by EU decision-making processes & structures. After exploring the relationship between governance & multi-level governance (MLG), the article tackles the question of the empirical fruitfulness of this latter concept. It argues that the concept is best understood as describing those decision-making processes in which territorial jurisdictions with different competencies end up collaborating on a par, thus upsetting & recasting traditional territorial hierarchies. It also discusses whether MLG, by involving different groups of citizens & fostering the diffusion of shared norms & convictions, contributes to more legitimate decision-making. The conclusions, on all counts, are somewhat mixed. Although MLG appears to describe effectively a new class of phenomena & to suggest intereasing novel hypotheses as to the transformation of the structures of democratic rule, it has not yet succeeded in generating new testable propositions nor in suggesting new convincing criteria of legitimacy. References. Adapted from the source document.
Abstract ; L'articolo esamina il conflitto tra le fazioni fiorentine dei Bianchi e dei Neri, che coinvolse Dante, quale espressione di una cultura della vendetta largamnte diffusa nelle società cittadine italiane. Di tale conflitto si ripercorrono le origini, legate alla faida tra Cerchi e Donati, gli sviluppi, connessi ai legami di solidarietà e ai rapporti di inimicizia tra queste e altre famiglie cittadine e le strategie messe in atto dalle parti fino all'arrivo di Carlo di Valois (1301) che ne segnò l'esito finale, portando tra l'altro al bando del poeta ; SeriesInformation ; Reti Medievali Rivista, Vol 18, N° 1 (2017)
