Use of Coarse-resolution models of species' distributions to guide local conservation inferences

Distribution models are used increasingly for species conservation assessments over extensive areas, but the spatial resolution of the modeled data and, consequently, of the predictions generated directly from these models are usually too coarse for local conservation applications. Comprehensive distribution data at finer spatial resolution, however, require a level of sampling that is impractical for most species and regions. Models can be downscaled to predict distribution at finer resolutions, but this increases uncertainty because the predictive ability of models is not necessarily consistent beyond their original scale. We analyzed the performance of downscaled, previously published models of environmental favorability (a generalized linear modeling technique) for a restricted endemic insectivore, the Iberian desman (Galemys pyrenaicus), and a more widespread carnivore, the Eurasian otter ( Lutra lutra), in the Iberian Peninsula. The models, built from presence–absence data at 10 × 10 km resolution, were extrapolated to a resolution 100 times finer (1 × 1 km). We compared downscaled predictions of environmental quality for the two species with published data on local observations and on important conservation sites proposed by experts. Predictions were significantly related to observed presence or absence of species and to expert selection of sampling sites and important conservation sites. Our results suggest the potential usefulness of downscaled projections of environmental quality as a proxy for expensive and time-consuming field studies when the field studies are not feasible. This method may be valid for other similar species if coarse-resolution distribution data are available to define high-quality areas at a scale that is practical for the application of concrete conservation measures

Calibration of the maximum carboxylation velocity (Vcmax) using data mining techniques and ecophysiological data from the Brazilian Semiarid region, for use in Dynamic Global Vegetation Models.

The semiarid region of northeastern Brazil, the Caatinga, is extremely important due to its biodiversity and endemism. Measurements of plant physiology are crucial to the calibration of Dynamic Global Vegetation Models (DGVMs) that are currently used to simulate the responses of vegetation in face of global changes. In a field work realized in an area of preserved Caatinga forest located in Petrolina, Pernambuco, measurements of carbon assimilation (in response to light and CO2) were performed on 11 individuals of Poincianella microphylla, a native species that is abundant in this region. These data were used to calibrate the maximum carboxylation velocity (Vcmax) used in the INLAND model. The calibration techniques used were Multiple Linear Regression (MLR), and data mining techniques as the Classification And Regression Tree (CART) and K-MEANS. The results were compared to the UNCALIBRATED model. It was found that simulated Gross Primary Productivity (GPP) reached 72% of observed GPP when using the calibrated Vcmax values, whereas the UNCALIBRATED approach accounted for 42% of observed GPP. Thus, this work shows the benefits of calibrating DGVMs using field ecophysiological measurements, especially in areas where field data is scarce or non-existent, such as in the Caatinga

Distributed non-cooperative robust economic predictive control for dynamically coupled linear systems.

In this thesis, a tube-based Distributed Economic Predictive Control (DEPC) scheme is presented for a group of dynamically coupled linear subsystems. These subsystems are components of a large scale system and control inputs are computed based on optimizing a local economic objective. Each subsystem is interacting with its neighbors by sending its future reference trajectory, at each sampling time. It solves a local optimization problem in parallel, based on the received future reference trajectories of the other subsystems. To ensure recursive feasibility and a performance bound, each subsystem is constrained to not deviate too much from its communicated reference trajectory. This difference between the plan trajectory and the communicated one is interpreted as a disturbance on the local level. Then, to ensure the satisfaction of both state and input constraints, they are tightened by considering explicitly the effect of these local disturbances. The proposed approach averages over all possible disturbances, handles tightened state and input constraints, while satisfies the compatibility constraints to guarantee that the actual trajectory lies within a certain bound in the neighborhood of the reference one. Each subsystem is optimizing a local arbitrary economic objective function in parallel while considering a local terminal constraint to guarantee recursive feasibility. In this framework, economic performance guarantees for a tube-based distributed predictive control (DPC) scheme are developed rigorously. It is presented that the closed-loop nominal subsystem has a robust average performance bound locally which is no worse than that of a local robust steady state. Since a robust algorithm is applying on the states of the real (with disturbances) subsystems, this bound can be interpreted as an average performance result for the real closed-loop system. To this end, we present our outcomes on local and global performance, illustrated by a numerical example.

Light & Electron beam - the perfect combination for the observation and application of photo active materials

The aim of the present PhD thesis is to investigate the properties of innovative nanomaterials for energy conversion. The materials have been deeply studied by means of a wide spectrum of different techniques based on both light and electron sources, in order to get an insight into the correlation between the properties of each material and the activity towards different energy conversion applications. The activity has been carried out in the framework of a collaboration between the “G.Ciamician” Chemistry Department of the University of Bologna and the CNR-IMM Bologna. Four main topics have been explored: in the first part, luminescent silicon nanocrystals (SiNCs) have been discussed, suggesting a new approach to improve their optical properties as active material in complementary optoelectronic devices and photovoltaic cells. The luminescence of SiNCs have been exploited to increase the efficiency of conventional photovoltaic cells by means of an innovative architecture. Specifically, SiNCs were shown to be very promising light emitters in luminescent solar concentrators (LSC). The second part of the work has been focused on the study of high phosphorescent molecular chromophores, suggesting a new approach in their use as optical sensors successfully applied to the field of polymeric materials. This is due to the enhanced emission of light that appears in rigid, constrained or crystalline state, that is commonly called: "Aggregation-Induced Emission (AIE)". Such phenomenon is characteristic for molecular structures such as persulfurated benzene chromophores, hereafter named asterisks. The last two parts were focused on conventional and in-situ Transmission Electron Microscopy (TEM) morphological and structural characterization of photoactive and catalytic materials for energetic applications and in particular water splitting.

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.

Non perturbative aspects of non equilibrium physics in two dimensional models

The present manuscript focuses on out of equilibrium physics in two dimensional models. It has the purpose of presenting some results obtained as part of out of equilibrium dynamics in its non perturbative aspects. This can be understood in two different ways: the former is related to integrability, which is non perturbative by nature; the latter is related to emergence of phenomena in the out of equilibirum dynamics of non integrable models that are not accessible by standard perturbative techniques. In the study of out of equilibirum dynamics, two different protocols are used througout this work: the bipartitioning protocol, within the Generalised Hydrodynamics (GHD) framework, and the quantum quench protocol. With GHD machinery we study the Staircase Model, highlighting how the hydrodynamic picture sheds new light into the physics of Integrable Quantum Field Theories; with quench protocols we analyse different setups where a non-perturbative description is needed and various dynamical phenomena emerge, such as the manifistation of a dynamical Gibbs effect, confinement and the emergence of Bloch oscillations preventing thermalisation.

Diffusive models and chaos indicators for non-linear betatron motion in circular hadron accelerators

Understanding the complex dynamics of beam-halo formation and evolution in circular particle accelerators is crucial for the design of current and future rings, particularly those utilizing superconducting magnets such as the CERN Large Hadron Collider (LHC), its luminosity upgrade HL-LHC, and the proposed Future Circular Hadron Collider (FCC-hh). A recent diffusive framework, which describes the evolution of the beam distribution by means of a Fokker-Planck equation, with diffusion coefficient derived from the Nekhoroshev theorem, has been proposed to describe the long-term behaviour of beam dynamics and particle losses. In this thesis, we discuss the theoretical foundations of this framework, and propose the implementation of an original measurement protocol based on collimator scans in view of measuring the Nekhoroshev-like diffusive coefficient by means of beam loss data. The available LHC collimator scan data, unfortunately collected without the proposed measurement protocol, have been successfully analysed using the proposed framework. This approach is also applied to datasets from detailed measurements of the impact on the beam losses of so-called long-range beam-beam compensators also at the LHC. Furthermore, dynamic indicators have been studied as a tool for exploring the phase-space properties of realistic accelerator lattices in single-particle tracking simulations. By first examining the classification performance of known and new indicators in detecting the chaotic character of initial conditions for a modulated Hénon map and then applying this knowledge to study the properties of realistic accelerator lattices, we tried to identify a connection between the presence of chaotic regions in the phase space and Nekhoroshev-like diffusive behaviour, providing new tools to the accelerator physics community.

Non-thermal phenomena in galaxy clusters: the LOFAR revolution

Turbulence introduced into the intra-cluster medium (ICM) through cluster merger events transfers energy to non-thermal components (relativistic particles and magnetic fields) and can trigger the formation of diffuse synchrotron radio sources. Owing to their steep synchrotron spectral index, such diffuse sources can be better studied at low radio frequencies. In this respect, the LOw Frequency ARray (LOFAR) is revolutionizing our knowledge thanks to its unprecedented resolution and sensitivity below 200 MHz. In this Thesis we focus on the study of radio halos (RHs) by using LOFAR data. In the first part of this work we analyzed the largest-ever sample of galaxy clusters observed at radio frequencies. This includes 309 Planck clusters from the Second Data Release of the LOFAR Two Metre Sky Survey (LoTSS-DR2), which span previously unexplored ranges of mass and redshift. We detected 83 RHs, half of which being new discoveries. In 140 clusters we lack a detected RH; for this sub-sample we developed new techniques to derive upper limits to their radio powers. By comparing detections and upper limits, we carried out the first statistical analysis of populations of clusters observed at low frequencies and tested theoretical formation models. In the second part of this Thesis we focused on ultra-steep spectrum radio halos. These sources are almost undetected at GHz frequencies, but are thought to be common at low frequencies. We presented LOFAR observations of two interesting clusters hosting ultra-steep spectrum radio halos. With complementary radio and X-ray observations we constrained the properties and origin of these targets.

Data driven regularization models of non-linear ill-posed inverse problems in imaging

Imaging technologies are widely used in application fields such as natural sciences, engineering, medicine, and life sciences. A broad class of imaging problems reduces to solve ill-posed inverse problems (IPs). Traditional strategies to solve these ill-posed IPs rely on variational regularization methods, which are based on minimization of suitable energies, and make use of knowledge about the image formation model (forward operator) and prior knowledge on the solution, but lack in incorporating knowledge directly from data. On the other hand, the more recent learned approaches can easily learn the intricate statistics of images depending on a large set of data, but do not have a systematic method for incorporating prior knowledge about the image formation model. The main purpose of this thesis is to discuss data-driven image reconstruction methods which combine the benefits of these two different reconstruction strategies for the solution of highly nonlinear ill-posed inverse problems. Mathematical formulation and numerical approaches for image IPs, including linear as well as strongly nonlinear problems are described. More specifically we address the Electrical impedance Tomography (EIT) reconstruction problem by unrolling the regularized Gauss-Newton method and integrating the regularization learned by a data-adaptive neural network. Furthermore we investigate the solution of non-linear ill-posed IPs introducing a deep-PnP framework that integrates the graph convolutional denoiser into the proximal Gauss-Newton method with a practical application to the EIT, a recently introduced promising imaging technique. Efficient algorithms are then applied to the solution of the limited electrods problem in EIT, combining compressive sensing techniques and deep learning strategies. Finally, a transformer-based neural network architecture is adapted to restore the noisy solution of the Computed Tomography problem recovered using the filtered back-projection method.

Multi-scalar critical theories: first steps for a non perturbative functional renormalization group analysis

In this work the fundamental ideas to study properties of QFTs with the functional Renormalization Group are presented and some examples illustrated. First the Wetterich equation for the effective average action and its flow in the local potential approximation (LPA) for a single scalar field is derived. This case is considered to illustrate some techniques used to solve the RG fixed point equation and study the properties of the critical theories in D dimensions. In particular the shooting methods for the ODE equation for the fixed point potential as well as the approach which studies a polynomial truncation with a finite number of couplings, which is convenient to study the critical exponents. We then study novel cases related to multi field scalar theories, deriving the flow equations for the LPA truncation, both without assuming any global symmetry and also specialising to cases with a given symmetry, using truncations based on polynomials of the symmetry invariants. This is used to study possible non perturbative solutions of critical theories which are extensions of known perturbative results, obtained in the epsilon expansion below the upper critical dimension.

Analytic models of self-gravitating rotating gaseous tori with central black hole

In this thesis project, I present stationary models of rotating fluids with toroidal distributions that can be used to represent the active galactic nuclei (AGN) central obscurers, i.e. molecular tori (Combes et al., 2019), as well as geometrically thick accretion discs, like ADAF discs (Narayan and Yi, 1995) or Polish doughnuts (Abramowicz, 2005). In particular, I study stationary rotating systems with a more general baroclinic distribution (with a vertical gradient of the angular velocity), which are often more realistic and less studied, due to their complexity, than the barotropic ones (with cylindrical rotation), which are easier to construct. In the thesis, I compute analytically the main intrinsic and projected properties of the power-law tori based on the potential-density pairs of Ciotti and Bertin (2005). I study the density distribution and the resulting gravitational potential for different values of α, in the range 2 < α < 5. For the same models, I compute the surface density of the systems when seen face-on and edge-on. I then apply the stationary Euler equations to obtain rotational velocity and temperature distributions of the self-gravitating models in the absence of an external gravitational potential. In the thesis I also consider the power-law tori with the presence of a central black hole in addition to the gas self-gravity, and solving analytically the stationary Euler equations, I compute how the properties of the system are modified by the black hole and how they vary as a function of the black hole mass. Finally, applying the Solberg-Høiland criterion, I show that these baroclinic stationary models are linearly stable in the absence of the black hole. In the presence of the black hole I derive the analytical condition for stability, which depends on α and on the black hole mass. I also study the stability of the tori in the hypothesis that they are weakly magnetized, finding that they are always unstable to this instability.

Determination of local density of states in amorphous oxide semiconductors with Kelvin Probe Force Microscopy

Amorphous semiconductors are important materials as they can be deposited by physical deposition techniques on large areas and even on plastic substrates. Therefore, they are crucial for transistors in large active matrices for imaging and transparent wearable electronics. The most widely applied candidate for amorphous thin film transistors production is Indium Gallium Zinc Oxide (IGZO). It is attracting much interest because of its optical transparency, facile processing by sputtering deposition and notable improved charge carrier mobility with respect to hydrogenated amorphous silicon a-Si:H. Degradation of the device and long-term performance issues have been observed if IGZO thin film transistors are subjected to electrical stress, leading to a modification of IGZO channel properties and subthreshold slope. Therefore, it is of great interest to have a reliable and precise method to study the conduction band tail, and the density of states in amorphous semiconductors. The aim of this thesis is to develop a local technique using Kelvin Probe Force Microscopy to study the evolution of IGZO DOS properties. The work is divided into three main parts. First, solutions to the non-linear Poisson-Boltzmann equation of a metal-insulator-semiconductor junction describing the charge accumulation and its relation to DOS properties are elaborated. Second macroscopic techniques such as capacitance voltage (CV) measurements and photocurrent spectroscopy are applied to obtain a non-local estimate of band-tail DOS properties in thin film transistor samples. The third part of my my thesis is dedicated to the KPFM measurements. By fitting the data to the developed numerical model, important parameters describing the amorphous conduction band tail are obtained. The results are in excellent agreement with the macroscopic characterizations. KPFM result is comparable also with non-local optoelectronic characterizations, such as photocurrent spectroscopy.

Local and nonlocal integro-differential reaction-diffusion models for protein dynamics in Alzheimer's disease

In this work, integro-differential reaction-diffusion models are presented for the description of the temporal and spatial evolution of the concentrations of Abeta and tau proteins involved in Alzheimer's disease. Initially, a local model is analysed: this is obtained by coupling with an interaction term two heterodimer models, modified by adding diffusion and Holling functional terms of the second type. We then move on to the presentation of three nonlocal models, which differ according to the type of the growth (exponential, logistic or Gompertzian) considered for healthy proteins. In these models integral terms are introduced to consider the interaction between proteins that are located at different spatial points possibly far apart. For each of the models introduced, the determination of equilibrium points with their stability and a study of the clearance inequalities are carried out. In addition, since the integrals introduced imply a spatial nonlocality in the models exhibited, some general features of nonlocal models are presented. Afterwards, with the aim of developing simulations, it is decided to transfer the nonlocal models to a brain graph called connectome. Therefore, after setting out the construction of such a graph, we move on to the description of Laplacian and convolution operations on a graph. Taking advantage of all these elements, we finally move on to the translation of the continuous models described above into discrete models on the connectome. To conclude, the results of some simulations concerning the discrete models just derived are presented.

An Isoflavone From Dipteryx Alata Vogel Is Active Against The In Vitro Neuromuscular Paralysis Of Bothrops Jararacussu Snake Venom And Bothropstoxin I, And Prevents Venom-induced Myonecrosis.

Snakebite is a neglected disease and serious health problem in Brazil, with most bites being caused by snakes of the genus Bothrops. Although serum therapy is the primary treatment for systemic envenomation, it is generally ineffective in neutralizing the local effects of these venoms. In this work, we examined the ability of 7,8,3'-trihydroxy-4'-methoxyisoflavone (TM), an isoflavone from Dipteryx alata, to neutralize the neurotoxicity (in mouse phrenic nerve-diaphragm preparations) and myotoxicity (assessed by light microscopy) of Bothrops jararacussu snake venom in vitro. The toxicity of TM was assessed using the Salmonella microsome assay (Ames test). Incubation with TM alone (200 μg/mL) did not alter the muscle twitch tension whereas incubation with venom (40 μg/mL) caused irreversible paralysis. Preincubation of TM (200 μg/mL) with venom attenuated the venom-induced neuromuscular blockade by 84% ± 5% (mean ± SEM; n = 4). The neuromuscular blockade caused by bothropstoxin-I (BthTX-I), the major myotoxic PLA2 of this venom, was also attenuated by TM. Histological analysis of diaphragm muscle incubated with TM showed that most fibers were preserved (only 9.2% ± 1.7% were damaged; n = 4) compared to venom alone (50.3% ± 5.4% of fibers damaged; n = 3), and preincubation of TM with venom significantly attenuated the venom-induced damage (only 17% ± 3.4% of fibers damaged; n = 3; p < 0.05 compared to venom alone). TM showed no mutagenicity in the Ames test using Salmonella strains TA98 and TA97a with (+S9) and without (-S9) metabolic activation. These findings indicate that TM is a potentially useful compound for antagonizing the neuromuscular effects (neurotoxicity and myotoxicity) of B. jararacussu venom.

Bacterial Diversity Assessment In Soil Of An Active Brazilian Copper Mine Using High-throughput Sequencing Of 16s Rdna Amplicons.

Mining activities pose severe environmental risks worldwide, generating extreme pH conditions and high concentrations of heavy metals, which can have major impacts on the survival of organisms. In this work, pyrosequencing of the V3 region of the 16S rDNA was used to analyze the bacterial communities in soil samples from a Brazilian copper mine. For the analysis, soil samples were collected from the slopes (geotechnical structures) and the surrounding drainage of the Sossego mine (comprising the Sossego and Sequeirinho deposits). The results revealed complex bacterial diversity, and there was no influence of deposit geographic location on the composition of the communities. However, the environment type played an important role in bacterial community divergence; the composition and frequency of OTUs in the slope samples were different from those of the surrounding drainage samples, and Acidobacteria, Chloroflexi, Firmicutes, and Gammaproteobacteria were responsible for the observed difference. Chemical analysis indicated that both types of sample presented a high metal content, while the amounts of organic matter and water were higher in the surrounding drainage samples. Non-metric multidimensional scaling (N-MDS) analysis identified organic matter and water as important distinguishing factors between the bacterial communities from the two types of mine environment. Although habitat-specific OTUs were found in both environments, they were more abundant in the surrounding drainage samples (around 50 %), and contributed to the higher bacterial diversity found in this habitat. The slope samples were dominated by a smaller number of phyla, especially Firmicutes. The bacterial communities from the slope and surrounding drainage samples were different in structure and composition, and the organic matter and water present in these environments contributed to the observed differences.