An expert model approach to assess the potential of non-wood forest products for forest owners

Mestrado em Engenharia Florestal e dos Recursos Naturais - Instituto Superior de Agronomia - UL

Regime jurídico do alojamento local: a problemática dos imóveis arrendados de particulares e dos respetivos rendimento não declarados

Dissertação de Mestrado, Fiscalidade, Escola Superior de Gestão, Hotelaria e Turismo, Universidade do Algarve, 2016

Disorder driven transitions in non-equilibrium quantum systems

This thesis presents studies of the role of disorder in non-equilibrium quantum systems. The quantum states relevant to dynamics in these systems are very different from the ground state of the Hamiltonian. Two distinct systems are studied, (i) periodically driven Hamiltonians in two dimensions, and (ii) electrons in a one-dimensional lattice with power-law decaying hopping amplitudes. In the first system, the novel phases that are induced from the interplay of periodic driving, topology and disorder are studied. In the second system, the Anderson transition in all the eigenstates of the Hamiltonian are studied, as a function of the power-law exponent of the hopping amplitude.

In periodically driven systems the study focuses on the effect of disorder in the nature of the topology of the steady states. First, we investigate the robustness to disorder of Floquet topological insulators (FTIs) occurring in semiconductor quantum wells. Such FTIs are generated by resonantly driving a transition between the valence and conduction band. We show that when disorder is added, the topological nature of such FTIs persists as long as there is a gap at the resonant quasienergy. For strong enough disorder, this gap closes and all the states become localized as the system undergoes a transition to a trivial insulator.

Interestingly, the effects of disorder are not necessarily adverse, disorder can also induce a transition from a trivial to a topological system, thereby establishing a Floquet Topological Anderson Insulator (FTAI). Such a state would be a dynamical realization of the topological Anderson insulator. We identify the conditions on the driving field necessary for observing such a transition. We realize such a disorder induced topological Floquet spectrum in the driven honeycomb lattice and quantum well models.

Finally, we show that two-dimensional periodically driven quantum systems with spatial disorder admit a unique topological phase, which we call the anomalous Floquet-Anderson insulator (AFAI). The AFAI is characterized by a quasienergy spectrum featuring chiral edge modes coexisting with a fully localized bulk. Such a spectrum is impossible for a time-independent, local Hamiltonian. These unique characteristics of the AFAI give rise to a new topologically protected nonequilibrium transport phenomenon: quantized, yet nonadiabatic, charge pumping. We identify the topological invariants that distinguish the AFAI from a trivial, fully localized phase, and show that the two phases are separated by a phase transition.

The thesis also present the study of disordered systems using Wegner's Flow equations. The Flow Equation Method was proposed as a technique for studying excited states in an interacting system in one dimension. We apply this method to a one-dimensional tight binding problem with power-law decaying hoppings. This model presents a transition as a function of the exponent of the decay. It is shown that the the entire phase diagram, i.e. the delocalized, critical and localized phases in these systems can be studied using this technique. Based on this technique, we develop a strong-bond renormalization group that procedure where we solve the Flow Equations iteratively. This renormalization group approach provides a new framework to study the transition in this system.

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.

Study of charge, spin, and structural orderings in quantum materials using nuclei and muons as local probes

Quantum Materials are many body systems displaying emergent phenomena caused by quantum collective behaviour, such as superconductivity, charge density wave, fractional hall effect, and exotic magnetism. Among quantum materials, two families have recently attracted attention: kagome metals and Kitaev materials. Kagome metals have a unique crystal structure made up of triangular lattice layers that are used to form the kagome layer. Due to superconductivity, magnetism, and charge ordering states such as the Charge Density Wave (CDW), unexpected physical phenomena such as the massive Anomalous Hall Effect (AHE) and possible Majorana fermions develop in these materials. Kitaev materials are a type of quantum material with a unique spin model named after Alexei Kitaev. They include fractional fluctuations of Majorana fermions and non-topological abelian anyons, both of which might be used in quantum computing. Furthermore, they provide a realistic framework for the development of quantum spin liquid (QSL), in which quantum fluctuations produce long-range entanglements between electronic states despite the lack of classical magnetic ordering. In my research, I performed several nuclear magnetic resonance (NMR), nuclear quadrupole resonance (NQR), and muon spin spectroscopy (µSR) experiments to explain and unravel novel phases of matter within these unusual families of materials. NMR has been found to be an excellent tool for studying these materials’ local electronic structures and magnetic properties. I could use NMR to determine, for the first time, the structure of a novel kagome superconductor, RbV3Sb5, below the CDW transition, and to highlight the role of chemical doping in the CDW phase of AV3Sb5 superconductors. µSR has been used to investigate the effect of doping on kagome material samples in order to study the presence and behaviour of an anomalous phase developing at low temperatures and possibly related to time-reversal symmetry breaking.

Developing a radiomic based machine learning model to identify patients with resected non-small-cell lung cancer at high risk of relapse

Background There is a wide variation of recurrence risk of Non-small-cell lung cancer (NSCLC) within the same Tumor Node Metastasis (TNM) stage, suggesting that other parameters are involved in determining this probability. Radiomics allows extraction of quantitative information from images that can be used for clinical purposes. The primary objective of this study is to develop a radiomic prognostic model that predicts a 3 year disease free-survival (DFS) of resected Early Stage (ES) NSCLC patients. Material and Methods 56 pre-surgery non contrast Computed Tomography (CT) scans were retrieved from the PACS of our institution and anonymized. Then they were automatically segmented with an open access deep learning pipeline and reviewed by an experienced radiologist to obtain 3D masks of the NSCLC. Images and masks underwent to resampling normalization and discretization. From the masks hundreds Radiomic Features (RF) were extracted using Py-Radiomics. Hence, RF were reduced to select the most representative features. The remaining RF were used in combination with Clinical parameters to build a DFS prediction model using Leave-one-out cross-validation (LOOCV) with Random Forest. Results and Conclusion A poor agreement between the radiologist and the automatic segmentation algorithm (DICE score of 0.37) was found. Therefore, another experienced radiologist manually segmented the lesions and only stable and reproducible RF were kept. 50 RF demonstrated a high correlation with the DFS but only one was confirmed when clinicopathological covariates were added: Busyness a Neighbouring Gray Tone Difference Matrix (HR 9.610). 16 clinical variables (which comprised TNM) were used to build the LOOCV model demonstrating a higher Area Under the Curve (AUC) when RF were included in the analysis (0.67 vs 0.60) but the difference was not statistically significant (p=0,5147).

Crowd-shipping and autonomous vehicles: an optimization model for the last mile delivery

Il seguente elaborato propone un modello innovativo per la gestione della logistica distributiva nell’ultimo miglio, congiungendo l’attività di crowd-shipping con la presenza di Autonomous Vehicles, per il trasporto di prodotti all’interno della città. Il crowd-shipping utilizza conducenti occasionali, i quali deviano il loro tragitto in cambio di una ricompensa per il completamento dell’attività. Dall’altro lato, gli Autonomous Vehicles sono veicoli elettrici a guida autonoma, in grado di trasportare un numero limitato di pacchi e dotati di un sistema di sicurezza avanzato per garantire la fiducia nel trasporto. In primo luogo, nel seguente elaborato verrà mostrato il modello di ottimizzazione che congiunge i due attori principali in un unico ambiente, dove sono presenti un numero determinato di prodotti da muovere. Successivamente, poiché il problema di ottimizzazione è molto complesso e il numero di istanze valutabili è molto basso, verranno presentate due soluzioni differenti. La prima riguarda la metaeuristica chiamata Ant System, che cerca di avvicinarsi alle soluzioni ottime del precedente modello, mentre la seconda riguarda l’utilizzo di operatori di Local Search, i quali permettono di valutare soluzioni per istanze molto più grandi rispetto alla metaeuristica. Infine, i due modelli euristici verranno utilizzati per analizzare uno scenario che cerca di riprodurre una situazione reale. Tale scenario tenta di allocare strategicamente le risorse presenti e permette di dimostrare che gli Autonomous Vehicles riescono a supportare gli Occasional Drivers anche quando il numero di prodotti trasportabili è elevato. Inoltre, le due entità proposte riescono a soddisfare la domanda, garantendo un servizio che nel futuro potrebbe sostituire il tradizionale sistema di logistica distributiva last mile.

Oxidative Stress And Susceptibility To Mitochondrial Permeability Transition Precedes The Onset Of Diabetes In Autoimmune Non-obese Diabetic Mice.

Beta cell destruction in type 1 diabetes (TID) is associated with cellular oxidative stress and mitochondrial pathway of cell death. The aim of this study was to determine whether oxidative stress and mitochondrial dysfunction are present in T1D model (non-obese diabetic mouse, NOD) and if they are related to the stages of disease development. NOD mice were studied at three stages: non-diabetic, pre-diabetic, and diabetic and compared with age-matched Balb/c mice. Mitochondria respiration rates measured at phosphorylating and resting states in liver and soleus biopsies and in isolated liver mitochondria were similar in NOD and Balb/c mice at the three disease stages. However, NOD liver mitochondria were more susceptible to calcium-induced mitochondrial permeability transition as determined by cyclosporine-A-sensitive swelling and by decreased calcium retention capacity in all three stages of diabetes development. Mitochondria H2O2 production rate was higher in non-diabetic, but unaltered in pre-diabetic and diabetic NOD mice. The global cell reactive oxygen species (ROS), but not specific mitochondria ROS production, was significantly increased in NOD lymphomononuclear and stem cells in all disease stages. In addition, marked elevated rates of 2',7'-dichlorodihydrofluorescein (H2DCF) oxidation were observed in pancreatic islets from non-diabetic NOD mice. Using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) and lipidomic approach, we identified oxidized lipid markers in NOD liver mitochondria for each disease stage, most of them being derivatives of diacylglycerols and phospholipids. These results suggest that the cellular oxidative stress precedes the establishment of diabetes and may be the cause of mitochondrial dysfunction that is involved in beta cell death.

Development And Characterization Of A Cationic Lipid Nanocarrier As Non-viral Vector For Gene Therapy.

The aim of the present work was to produce a cationic solid lipid nanoparticle (SLN) as non-viral vector for protein delivery. Cationic SLN were produced by double emulsion method, composed of softisan(®) 100, cetyltrimethylammonium bromide (CTAB), Tween(®) 80, Span(®) 80, glycerol and lipoid(®) S75 loading insulin as model protein. The formulation was characterized in terms of mean hydrodynamic diameter (z-ave), polydispersity index (PI), zeta potential (ZP), stability during storage time, stability after lyophilization, effect of toxicity and transfection ability in HeLa cells, in vitro release profile and morphology. SLN were stable for 30days and showed minimal changes in their physicochemical properties after lyophilization. The particles exhibited a relatively slow release, spherical morphology and were able to transfect HeLa cells, but toxicity remained an obstacle. Results suggest that SLN are nevertheless promising for delivery of proteins or nucleic acids for gene therapy.

Atividade fisica e aptidão fisica de adolescentes : similaridades e contrastes em uma decada

Universidade Estadual de Campinas . Faculdade de Educação Física

Metodologia para a suavização de dados biomecanicos por função não parametrica ponderada local robusta

Universidade Estadual de Campinas. Faculdade de Educação Física

Weekend/weekday differences in concentrations of ozone, nox, and non-methane hydrocarbon in the metropolitan area of São Paulo

Ozone and inhalable particulate matter are the major air pollutants in the Metropolitan Area of São Paulo, Brazil, a region that has more than 19 million inhabitants and approximately 7 million registered vehicles. Proximity of roadways, adjacent land use, and local circulation are just some of the factors that can affect the results of monitoring of pollutant concentrations. The so-called weekend effect (higher ozone concentrations on weekends than on weekdays) might be related to the fact that concentrations of ozone precursors, such as nitrogen oxides (NOx) and Non Methane-Hydrocarbon (NMHC), are relatively lower on weekends. This phenomenon has been reported in some areas of the United States since the 1970s. The differences between the concentrations of ozone in period of weekend and weekday, were obtained from analysis of data hourly average of CETESB for 2004, studied the precursors to the formation of troposphere ozone, the meteorological variables and traffic profile for RMSP. Because of the proximity to sources of emissions from the station Pinheiros showed higher concentrations of NO and NO² and greater variations to the periods weekend and weekday. With fewer vehicles circulating during the weekend, and consequently less emission of pollutants, it has cleaner air and less concentration of NO and NO², there is the ideal setting to the formation of troposphere ozone, despite the lower concentration of NO². The proximity with the source emissions, aided by the increased availability of solar radiation and the presence of ozone precursors, were factors conditions for the occurrence of weekend effect.

Cardioprotective effect of ornitho-kinin in an anesthetized, open-chest chicken model of acute coronary occlusion

The generation of bradykinin (BK; Arg-Pro-Pro-Gly-Phe-Ser-Pro-Phe-Arg) in blood and kallidin (Lys-BK) in tissues by the action of the kallikrein-kinin system has received little attention in non-mammalian vertebrates. In mammals, kallidin can be generated by the coronary endothelium and myocytes in response to ischemia, mediating cardioprotective events. The plasma of birds lacks two key components of the kallikrein-kinin system: the low molecular weight kininogen and a prekallikrein activator analogous to mammalian factor XII, but treatment with bovine plasma kallikrein generates ornitho-kinin [Thr6,Leu8]-BK. The possible cardioprotective effect of ornitho-kinin infusion was investigated in an anesthetized, open-chest chicken model of acute coronary occlusion. A branch of the left main coronary artery was reversibly ligated to produce ischemia followed by reperfusion, after which the degree of myocardial necrosis (infarct size as a percent of area at risk) was assessed by tetrazolium staining. The iv injection of a low dose of ornitho-kinin (4 µg/kg) reduced mean arterial pressure from 88 ± 12 to 42 ± 7 mmHg and increased heart rate from 335 ± 38 to 402 ± 45 bpm (N = 5). The size of the infarct was reduced by pretreatment with ornitho-kinin (500 µg/kg infused over a period of 5 min) from 35 ± 3 to 10 ± 2% of the area at risk. These results suggest that the physiological role of the kallikrein-kinin system is preserved in this animal model in spite of the absence of two key components, i.e., low molecular weight kininogen and factor XII.

Stress distribution in the cervical region of an upper central incisor in a 3D finite element model

The aim of this study was to evaluate the stress distribution in the cervical region of a sound upper central incisor in two clinical situations, standard and maximum masticatory forces, by means of a 3D model with the highest possible level of fidelity to the anatomic dimensions. Two models with 331,887 linear tetrahedral elements that represent a sound upper central incisor with periodontal ligament, cortical and trabecular bones were loaded at 45º in relation to the tooth's long axis. All structures were considered to be homogeneous and isotropic, with the exception of the enamel (anisotropic). A standard masticatory force (100 N) was simulated on one of the models, while on the other one a maximum masticatory force was simulated (235.9 N). The software used were: PATRAN for pre- and post-processing and Nastran for processing. In the cementoenamel junction area, tensile forces reached 14.7 MPa in the 100 N model, and 40.2 MPa in the 235.9 N model, exceeding the enamel's tensile strength (16.7 MPa). The fact that the stress concentration in the amelodentinal junction exceeded the enamel's tensile strength under simulated conditions of maximum masticatory force suggests the possibility of the occurrence of non-carious cervical lesions such as abfractions.

A novel polar-based human face recognition computational model

Motivated by a recently proposed biologically inspired face recognition approach, we investigated the relation between human behavior and a computational model based on Fourier-Bessel (FB) spatial patterns. We measured human recognition performance of FB filtered face images using an 8-alternative forced-choice method. Test stimuli were generated by converting the images from the spatial to the FB domain, filtering the resulting coefficients with a band-pass filter, and finally taking the inverse FB transformation of the filtered coefficients. The performance of the computational models was tested using a simulation of the psychophysical experiment. In the FB model, face images were first filtered by simulated V1- type neurons and later analyzed globally for their content of FB components. In general, there was a higher human contrast sensitivity to radially than to angularly filtered images, but both functions peaked at the 11.3-16 frequency interval. The FB-based model presented similar behavior with regard to peak position and relative sensitivity, but had a wider frequency band width and a narrower response range. The response pattern of two alternative models, based on local FB analysis and on raw luminance, strongly diverged from the human behavior patterns. These results suggest that human performance can be constrained by the type of information conveyed by polar patterns, and consequently that humans might use FB-like spatial patterns in face processing.