Em seu livro, “Perception as a Capacity for Knowledge” (2011), John McDowell defende que a garantia fornecida pela percepção é infalível. Para tanto, é preciso entender o papel que a razão tem na constituição de estados perceptivos genuínos. Por meio dela, posicionamos estes estados no espaço lógico das razões. Assim, não só fazemos do estado perceptivo um episódio de conhecimento, mas também obtemos conhecimento de como chegamos a este conhecimento. McDowell sustenta que esta condição para o conhecimento, a posse da capacidade de posicionar um estado perceptivo no espaço lógico das razões, não o compromete com o intelectualismo. Neste artigo, defendo que o internismo de McDowell não está totalmente livre do intelectualismo e que o internismo é mais plausível não só sem intelectualismo,
mas também sem reflexividade.

This paper presents the identifiability analysis of a nonlinear model for a batch bioreactor and the estimation of the identifiable parameters within the prediction error framework. The output data of the experiment are the measurements of the methane gas generated by the process, during 37 days, and knowledge of the initial conditions is limited to the initial quantity of chemical oxygen demand. It is shown by the identifiability analysis that only three out of the eight model parameters can be identified with the available measurements and that identification of the remaining parameters would require further knowledge of the initial conditions. A prediction error algorithm is implemented for the estimation of the identifiable parameters. This algorithm is iterative, relies on the gradient of the prediction error, whose calculation is implemented recursively, and consists of a combination of two classic optimization methods: the conjugated gradient method and the Gauss?Newton method.

PURPOSE: To compare the effect of incomplete caries removal (ICR) and indirect pulp capping (IPC) with calcium hydroxide (CH) or an inert material (wax) on color, consistency and contamination of the remaining dentin of primary molars. METHODS: This double-blind, parallel-design, randomized controlled trial included 30 children presenting one primary molar with deep caries lesion. Children were randomly assigned after ICR to receive IPC with CH or wax. All teeth were then restored with resin composite. Baseline dentin color and consistency were evaluated after ICR, and dentin samples were collected for contamination analyses using scanning electron microscopy. After 3 months, restorations were removed and the three parameters were re-evaluated. In both groups, dentin became significantly darker after 3 months. RESULTS: No cases of yellow dentin were observed after 3 months with CH compared to 33.3% of the wax cases (P < 0.05). A statistically significant difference over time was observed only for CH regarding consistency. CH stimulated a dentin hardening process in a statistically higher number of cases than wax (86.7% vs. 33.3%; P = 0.008). Contamination changed significantly over time in CH and wax without significant difference between groups. It was concluded that CH and wax arrested the carious process of the remaining carious dentin after indirect pulp capping, but CH showed superior dentin color and consistency after 3 months.

AIM: To compare the incidence of gingival fissures after the use of soft and medium-hard toothbrushes. MATERIAL & METHODS: Overall, 35 participants (14-20 years old), with periodontal attachment loss (PAL)

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The Prediction Error Method (PEM) is related to an optimization problem built on input/output data collected from the system to be identified. It is often hard to find the global solution of this optimization problem because the corresponding objective function presents local minima and/or the search space is constrained to a nonconvex set. The shape of the cost function, and hence the difficulty in solving the optimization problem, depends directly on the experimental conditions, more specifically on the spectrum of the input/output data collected from the system. Therefore, it seems plausible to improve the convergence to the global minimum by properly choosing the spectrum of the input; in this paper, we address this problem. We present a condition for convergence to the global minimum of the cost function and propose its inclusion in the input design. We present the application of the proposed approach to case studies where the algorithms tend to get trapped in nonglobal minima.