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

Methods and tools for causal discovery and causal inference

Causality is a complex concept, which roots its developments across several fields, such as statistics, economics, epidemiology, computer science, and philosophy. In recent years, the study of causal relationships has become a crucial part of the Artificial Intelligence community, as causality can be a key tool for overcoming some limitations of correlation-based Machine Learning systems. Causality research can generally be divided into two main branches, that is, causal discovery and causal inference. The former focuses on obtaining causal knowledge directly from observational data. The latter aims to estimate the impact deriving from a change of a certain variable over an outcome of interest. This article aims at covering several methodologies that have been developed for both tasks. This survey does not only focus on theoretical aspects. But also provides a practical toolkit for interested researchers and practitioners, including software, datasets, and running examples. This article is categorized under: Algorithmic Development > Causality Discovery Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Machine Learning

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

Social network analytics and visualization: Dynamic topic-based influence analysis in evolving micro-blogs

Influence Analysis is one of the well-known areas of Social Network Analysis. However, discovering influencers from micro-blog networks based on topics has gained recent popularity due to its specificity. Besides, these data networks are massive, continuous and evolving. Therefore, to address the above challenges we propose a dynamic framework for topic modelling and identifying influencers in the same process. It incorporates dynamic sampling, community detection and network statistics over graph data stream from a social media activity management application. Further, we compare the graph measures against each other empirically and observe that there is no evidence of correlation between the sets of users having large number of friends and the users whose posts achieve high acceptance (i.e., highly liked, commented and shared posts). Therefore, we propose a novel approach that incorporates a user's reachability and also acceptability by other users. Consequently, we improve on graph metrics by including a dynamic acceptance score (integrating content quality with network structure) for ranking influencers in micro-blogs. Additionally, we analysed the topic clusters' structure and quality with empirical experiments and visualization.Fundaçao para a Ciência e a Tecnologia, Grant/Award Number: UIDB/50014/202

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,