ABRINDO CAIXAS PRETAS EM AULAS DE FÍSICA: Uma reflexão educacional a partir dos conceitos de Bruno Latour.

As contribuições, em uma linha que se poderia chamar de “sociologia da ciência”, de Bruno Latour permitem-se dialogar com questões educacionais, em particular no que tange à consolidação histórica de conceitos científicos e ao fazer ciência, haja vista a ampla defesa que a literatura expõe no sentido de uma educação científica pautada na abordagem historicoepistemológica, que apreenta o conhecimento científico como construção sociocultural. Neste sentido, é de nosso especial interesse a concepção latouriana das caixas pretas, que representam conceitos e instrumentos, de uma dada disciplina científica, que alcançaram a posição de objetos (teóricos, como leis e equações, ou experimentais, como equipamentos de laboratório) considerados\ud seguros até evidência em contrário. Exemplos de caixas pretas são abundantes: tipicamente, figuram como tal os instrumentos de medida, os conceitos e modelos que, a partir do momento em que sejam aceitos como válidos (pelos membros de uma comunidade de cientistas), fazem-se ponto de partida para novas descobertas. Quando um físico realiza experimentos em seu laboratório, está considerando válido um grande conjunto de princípios e confiando que seus instrumentos fornecem uma medida fiel para certas grandezas, suposição essa indispensável à prática científica. Frequentemente, esse cientista fará uso de instrumentos cujo princípio de funcionamento foge à alçada de seu conhecimento, e é sobre esse fato que Latour funda seu conceito de caixa preta (o qual se estende mesmo aos objetos da especialidade do nosso pesquisador). Neste ensaio, teremos por objetivo mostrar que (e como) a abordagem histórico epistemológica das aulas de ciências pode, em alguns aspectos, traduzir-se como o convite a abrir certas caixas pretas.FAPES

Bayesian Federated Learning: A Survey

Federated learning (FL) demonstrates its advantages in integrating distributed infrastructure, communication, computing and learning in a privacy-preserving manner. However, the robustness and capabilities of existing FL methods are challenged by limited and dynamic data and conditions, complexities including heterogeneities and uncertainties, and analytical explainability. Bayesian federated learning (BFL) has emerged as a promising approach to address these issues. This survey presents a critical overview of BFL, including its basic concepts, its relations to Bayesian learning in the context of FL, and a taxonomy of BFL from both Bayesian and federated perspectives. We categorize and discuss client- and server-side and FL-based BFL methods and their pros and cons. The limitations of the existing BFL methods and the future directions of BFL research further address the intricate requirements of real-life FL applications.Comment: Accepted by IJCAI 2023 Survey Track, copyright is owned to IJCA

Fluid flows through unsaturated porous media: An alternative simulation procedure

This article studies fluid flows through an unsaturated porous matrix, modeled under a mixture theory viewpoint, which give rise to nonlinear hyperbolic systems. An alternative procedure is employed to simulate these nonlinear nonhomogeneous hyperbolic systems of two partial differential equations representing mass and momentum conservation for the fluid (liquid) constituent of mixture. An operator splitting technique is employed so that the nonhomogeneous system is split into a time-dependent ordinary portion and a homogeneous one. This latter is simulated by employing Glimm’s scheme and an approximate Riemann solver is used for marching between two consecutive time steps. This Riemann solver conveniently approximates the solution of the associated Riemann problem by piecewise constant functions always satisfying the jump condition – giving rise to an approximation easier to implement with lower computational cost. Comparison with the standard procedure, employing the complete solution of the associated Riemann problem for implementing Glimm’s scheme, has shown good agreement

Total Mass TCI driven by Parametric Estimation

This paper presents the Total Mass Target Controlled Infusion algorithm. The system comprises an On Line tuned Algorithm for Recovery Detection (OLARD) after an initial bolus administration and a Bayesian identification method for parametric estimation based on sparse measurements of the accessible signal. To design the drug dosage profile, two algorithms are here proposed. During the transient phase, an Input Variance Control (IVC) algorithm is used. It is based on the concept of TCI and aims to steer the drug effect to a predefined target value within an a priori fixed interval of time. After the steady state phase is reached the drug dose regimen is controlled by a Total Mass Control (TMC) algorithm. The mass control law for compartmental systems is robust even in the presence of parameter uncertainties. The whole system feasibility has been evaluated for the case of Neuromuscular Blockade (NMB) level and was tested both in simulation and in real cases

Análise da alavancagem das empresas de capital aberto do agronegócio brasileiro: uma abordagem usando logit multinomial

This study intends to verify which variables affect the financial leverage of Brazilian agribusiness companies, considering the migration in the indebtedness ranges as proposed in the model of Matarazzo (1998). 26 companies were selected in accordance to the following links of the agribusiness chain flow: a) agricultural production; b) input supplying; and c) processing and distribution. The study was conducted using a multinomial logit model, based on annual data from 1999 to 2005. The results indicate that the variables tangibility of assets, growth opportunities, size and profitability were statistically significant in the explanation of the debt structure of Brazilian agribusiness companies.Financial leverage, Capital structure, Agribusiness, Multinomial logit, Agribusiness,

Análise dos determinantes do endividamento das empresas de capital aberto do agronegócio brasileiro

Studies involving capital structure and the identification of its determinants are relevant issues in the field of corporate finance management research. In this regard, the present study intends to evaluate the determinants of corporate leverage in the Brazilian agribusiness sector using the model of Rajan and Zingales (1995). In the definition of the sample there were selected 26 companies that are classified in one of three subdivisions of the Brazilian agribusiness sector: a) the agriculture or cattle raising; b) inputs or production factors and c) processing and distribution sector, using as reference the CNA classification. The study used data from the Economatica® database, with the adoption of panel data methods. The results indicated that the variables tangibility of assets, growth opportunities, size and profitability were statiscally significant as determinant factors of the debt structure of Brazilian agribusiness companies. It is also possible to conclude that the model estimated by panel data generated results that are compatible with those suggested by the pecking order theory.Debt, capital structure, agribusiness, Pecking Order Theory., Agribusiness, Environmental Economics and Policy, Q14, G32,

Modeling Events and Interactions through Temporal Processes -- A Survey

In real-world scenario, many phenomena produce a collection of events that occur in continuous time. Point Processes provide a natural mathematical framework for modeling these sequences of events. In this survey, we investigate probabilistic models for modeling event sequences through temporal processes. We revise the notion of event modeling and provide the mathematical foundations that characterize the literature on the topic. We define an ontology to categorize the existing approaches in terms of three families: simple, marked, and spatio-temporal point processes. For each family, we systematically review the existing approaches based based on deep learning. Finally, we analyze the scenarios where the proposed techniques can be used for addressing prediction and modeling aspects.Comment: Image replacement

Open challenges for Machine Learning based Early Decision-Making research

More and more applications require early decisions, i.e. taken as soon as possible from partially observed data. However, the later a decision is made, the more its accuracy tends to improve, since the description of the problem to hand is enriched over time. Such a compromise between the earliness and the accuracy of decisions has been particularly studied in the field of Early Time Series Classification. This paper introduces a more general problem, called Machine Learning based Early Decision Making (ML-EDM), which consists in optimizing the decision times of models in a wide range of settings where data is collected over time. After defining the ML-EDM problem, ten challenges are identified and proposed to the scientific community to further research in this area. These challenges open important application perspectives, discussed in this paper