Does international trade produce convergence?

In spite of increasing globalization around the world, the effects of international trade on economic growth are not very clear. I consider an endogenous economic growth model in an open economy with the Home Market Effect (HME) and non-homothetic preferences in order to identify some determinants of the different results in this relationship. The model shows how trade between similar countries leads to convergence in economic growth when knowledge spillovers are present, while trade between very asymmetric countries produces divergence and may become trade in a poverty or growth trap. The results for welfare move in the same direction as economic growth since convergence implies increases in welfare for both countries, while divergence leads to increases in welfare for the largest country and the opposite for its commercial partner in the absence of knowledge spillovers. International trade does not implicate greater welfare as is usual in a static context under CES preferences.

Convergence time to equilibrium distributions of autonomous and periodic non-autonomous graphs

We present some estimates of the time of convergence to the equilibrium distribution in autonomous and periodic non-autonomous graphs, with ergodic stochastic adjacency matrices, using the eigenvalues of these matrices. On this way we generalize previous results from several authors, that only considered reversible matrices.

A distributed middleware for IT/OT convergence in modern industrial environments

The modern industrial environment is populated by a myriad of intelligent devices that collaborate for the accomplishment of the numerous business processes in place at the production sites. The close collaboration between humans and work machines poses new interesting challenges that industry must overcome in order to implement the new digital policies demanded by the industrial transition. The Industry 5.0 movement is a companion revolution of the previous Industry 4.0, and it relies on three characteristics that any industrial sector should have and pursue: human centrality, resilience, and sustainability. The application of the fifth industrial revolution cannot be completed without moving from the implementation of Industry 4.0-enabled platforms. The common feature found in the development of this kind of platform is the need to integrate the Information and Operational layers. Our thesis work focuses on the implementation of a platform addressing all the digitization features foreseen by the fourth industrial revolution, making the IT/OT convergence inside production plants an improvement and not a risk. Furthermore, we added modular features to our platform enabling the Industry 5.0 vision. We favored the human centrality using the mobile crowdsensing techniques and the reliability and sustainability using pluggable cloud computing services, combined with data coming from the crowd support. We achieved important and encouraging results in all the domains in which we conducted our experiments. Our IT/OT convergence-enabled platform exhibits the right performance needed to satisfy the strict requirements of production sites. The multi-layer capability of the framework enables the exploitation of data not strictly coming from work machines, allowing a more strict interaction between the company, its employees, and customers.

Inside standard and hyperspectral low-dose CT variational imaging: parameter identification and material decomposition

The main contribution of this thesis is the proposal of novel strategies for the selection of parameters arising in variational models employed for the solution of inverse problems with data corrupted by Poisson noise. In light of the importance of using a significantly small dose of X-rays in Computed Tomography (CT), and its need of using advanced techniques to reconstruct the objects due to the high level of noise in the data, we will focus on parameter selection principles especially for low photon-counts, i.e. low dose Computed Tomography. For completeness, since such strategies can be adopted for various scenarios where the noise in the data typically follows a Poisson distribution, we will show their performance for other applications such as photography, astronomical and microscopy imaging. More specifically, in the first part of the thesis we will focus on low dose CT data corrupted only by Poisson noise by extending automatic selection strategies designed for Gaussian noise and improving the few existing ones for Poisson. The new approaches will show to outperform the state-of-the-art competitors especially in the low-counting regime. Moreover, we will propose to extend the best performing strategy to the hard task of multi-parameter selection showing promising results. Finally, in the last part of the thesis, we will introduce the problem of material decomposition for hyperspectral CT, which data encodes information of how different materials in the target attenuate X-rays in different ways according to the specific energy. We will conduct a preliminary comparative study to obtain accurate material decomposition starting from few noisy projection data.

Variational Quantum Splines: Moving Beyond Linearity for Quantum Activation Functions

Activation functions within neural networks play a crucial role in Deep Learning since they allow to learn complex and non-trivial patterns in the data. However, the ability to approximate non-linear functions is a significant limitation when implementing neural networks in a quantum computer to solve typical machine learning tasks. The main burden lies in the unitarity constraint of quantum operators, which forbids non-linearity and poses a considerable obstacle to developing such non-linear functions in a quantum setting. Nevertheless, several attempts have been made to tackle the realization of the quantum activation function in the literature. Recently, the idea of the QSplines has been proposed to approximate a non-linear activation function by implementing the quantum version of the spline functions. Yet, QSplines suffers from various drawbacks. Firstly, the final function estimation requires a post-processing step; thus, the value of the activation function is not available directly as a quantum state. Secondly, QSplines need many error-corrected qubits and a very long quantum circuits to be executed. These constraints do not allow the adoption of the QSplines on near-term quantum devices and limit their generalization capabilities. This thesis aims to overcome these limitations by leveraging hybrid quantum-classical computation. In particular, a few different methods for Variational Quantum Splines are proposed and implemented, to pave the way for the development of complete quantum activation functions and unlock the full potential of quantum neural networks in the field of quantum machine learning.

[collective Health Paradigms: A Brief Reflection].

The article discusses the possibility of applying Kuhn's concept of paradigm to collective health. The concept and its use in epidemiology, planning and the social sciences are reviewed briefly. The study stresses the multi-paradigmatic character of collective health, resulting from the convergence of multiple epistemologies and the involvement of diverse fields such as the biological sciences, philosophy, the social sciences and humanities.

Non-gaussian Spatial Correlations Dramatically Weaken Localization.

We perform variational studies of the interaction-localization problem to describe the interaction-induced renormalizations of the effective (screened) random potential seen by quasiparticles. Here we present results of careful finite-size scaling studies for the conductance of disordered Hubbard chains at half-filling and zero temperature. While our results indicate that quasiparticle wave functions remain exponentially localized even in the presence of moderate to strong repulsive interactions, we show that interactions produce a strong decrease of the characteristic conductance scale g^{*} signaling the crossover to strong localization. This effect, which cannot be captured by a simple renormalization of the disorder strength, instead reflects a peculiar non-Gaussian form of the spatial correlations of the screened disordered potential, a hitherto neglected mechanism to dramatically reduce the impact of Anderson localization (interference) effects.

Encephalization And Diversification Of The Cranial Base In Platyrrhine Primates.

The cranial base, composed of the midline and lateral basicranium, is a structurally important region of the skull associated with several key traits, which has been extensively studied in anthropology and primatology. In particular, most studies have focused on the association between midline cranial base flexion and relative brain size, or encephalization. However, variation in lateral basicranial morphology has been studied less thoroughly. Platyrrhines are a group of primates that experienced a major evolutionary radiation accompanied by extensive morphological diversification in Central and South America over a large temporal scale. Previous studies have also suggested that they underwent several evolutionarily independent processes of encephalization. Given these characteristics, platyrrhines present an excellent opportunity to study, on a large phylogenetic scale, the morphological correlates of primate diversification in brain size. In this study we explore the pattern of variation in basicranial morphology and its relationship with phylogenetic branching and with encephalization in platyrrhines. We quantify variation in the 3D shape of the midline and lateral basicranium and endocranial volumes in a large sample of platyrrhine species, employing high-resolution CT-scans and geometric morphometric techniques. We investigate the relationship between basicranial shape and encephalization using phylogenetic regression methods and calculate a measure of phylogenetic signal in the datasets. The results showed that phylogenetic structure is the most important dimension for understanding platyrrhine cranial base diversification; only Aotus species do not show concordance with our molecular phylogeny. Encephalization was only correlated with midline basicranial flexion, and species that exhibit convergence in their relative brain size do not display convergence in lateral basicranial shape. The evolution of basicranial variation in primates is probably more complex than previously believed, and understanding it will require further studies exploring the complex interactions between encephalization, brain shape, cranial base morphology, and ecological dimensions acting along the species divergence process.

Mineral Nutrition Of Campos Rupestres Plant Species On Contrasting Nutrient-impoverished Soil Types.

In Brazil, the campos rupestres occur over the Brazilian shield, and are characterized by acidic nutrient-impoverished soils, which are particularly low in phosphorus (P). Despite recognition of the campos rupestres as a global biodiversity hotspot, little is known about the diversity of P-acquisition strategies and other aspects of plant mineral nutrition in this region. To explore nutrient-acquisition strategies and assess aspects of plant P nutrition, we measured leaf P and nitrogen (N) concentrations, characterized root morphology and determined the percentage arbuscular mycorrhizal (AM) colonization of 50 dominant species in six communities, representing a gradient of soil P availability. Leaf manganese (Mn) concentration was measured as a proxy for carboxylate-releasing strategies. Communities on the most P-impoverished soils had the highest proportion of nonmycorrhizal (NM) species, the lowest percentage of mycorrhizal colonization, and the greatest diversity of root specializations. The large spectrum of leaf P concentration and variation in root morphologies show high functional diversity for nutritional strategies. Higher leaf Mn concentrations were observed in NM compared with AM species, indicating that carboxylate-releasing P-mobilizing strategies are likely to be present in NM species. The soils of the campos rupestres are similar to the most P-impoverished soils in the world. The prevalence of NM strategies indicates a strong global functional convergence in plant mineral nutrition strategies among severely P-impoverished ecosystems.

Resolvendo a equação de Schrödinger utilizando procedimentos numéricos fundamentais

A combination of the variational principle, expectation value and Quantum Monte Carlo method is used to solve the Schrödinger equation for some simple systems. The results are accurate and the simplicity of this version of the Variational Quantum Monte Carlo method provides a powerful tool to teach alternative procedures and fundamental concepts in quantum chemistry courses. Some numerical procedures are described in order to control accuracy and computational efficiency. The method was applied to the ground state energies and a first attempt to obtain excited states is described.

Uma observação sobre o lugar da teoria da ligação e do critério temático dentro do programa minimalista

In this paper I present some evidencie that forces us to conclude that within the Minimalist Program (Chomsky 1993; 1995), Binding Theory (BT) should be computed after LF (Logical Form). I show that derivations leading to structures containing violations of BT-Principles must converge at LF, since less economical alternative derivations respecting those principles are also ungrammatical. Being irrelevant to the notion of convergence, BT must apply after LF. A similar reasoning reveals that the Theta-Criterion should have the status of a bare output condition appling at LF, since less economical derivations are allowed by the computational system to prevent violations of it.

A comunicação corporal na pre-escola : caminhos e descaminhos

Universidade Estadual de Campinas . Faculdade de Educação Física

Desvelando os significados da vivencia da ginastica geral para adolescentes de uma instituição salesiana de proteção a criança e ao adolescente

Universidade Estadual de Campinas. Faculdade de Educação Física

Educação fisica, saude coletiva e a luta do MST : reconstruindo relações a partir das violencias

Universidade Estadual de Campinas . Faculdade de Educação Física

Correlation between margin fit and microleakage in complete crowns cemented with three luting agents

Microleakage can be related to margin misfit. Also, traditional microleakage techniques are time-consuming. This study evaluated the existence of correlation between in vitro margin fit and a new microleakage technique for complete crowns cemented with 3 different luting agents. Thirty human premolars were prepared for full-coverage crowns with a convergence angle of 6 degrees, chamfer margin of 1.2 mm circumferentially, and occlusal reduction of 1.5 mm. Ni-Cr cast crowns were cemented with either zinc phosphate (ZP) (S.S. White), resin-modified glass-ionomer (RMGI) (Rely X Luting Cement) or a resin-based luting agent (RC) (Enforce). Margin fit (seating discrepancy and margin gap) was evaluated according to criteria in the literature under microscope with 0.001 mm accuracy. After thermal cycling, crowns were longitudinally sectioned and microleakage scores at tooth-cement interface were obtained and recorded at ×100 magnification. Margin fit parameters were compared with the one-way ANOVA test and microleakage scores with Kruskal-Wallis and Dunn's tests (alpha=0.05). Correlation between margin fit and microleakage was analyzed with the Spearman's test (alpha=0.05). Seating discrepancy and marginal gap values ranged from 81.82 µm to 137.22 µm (p=0.117), and from 75.42 µm to 78.49 µm (p=0.940), respectively. Marginal microleakage scores were ZP=3.02, RMGI=0.35 and RC=0.12 (p<0.001), with no differences between RMGI and RC scores. The correlation coefficient values ranged from -0.27 to 0.30 (p>0.05). Conclusion: Margin fit parameters and microleakage showed no strong correlations; cast crowns cemented with RMGI and RC had lower microleakage scores than ZP cement.