The dietary changes observed are only in partial agreement with predictions of the nutrition transition literature. The relationship between the economic and diet changes was shaped by the local context which should be considered when implementing CCT programs, like the Bolsa Família.

In this article a feedforward error backpropagation artificial neural network is investigated and the analysis of its illogical behaviour is presented. The problem of illogical behavior arises in various models of artificial neural networks. In the presented work a classifying artificial neural network (CANN) is considered and several learning algorithms were implemented and compared. CANN was designed for automatic differentiaition of cyanobacterial strains during environmental monitoring and some of trained networks demonstrated illogical behavior in further testing. Several original techniques were elaborated for estimation of the quality and accuracy of classification in addition to the traditional ones. Novel visualization methods were suggested for classification and generalization results representation.

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This chapter focuses on the articulatory phonetics of English vowels; thus, it identifies descriptive parameters for vowel articulation in English, differentiates monophthongs and diphthongs, classifies the vowels of American English using these parameters, and addresses vowel reduction in American English. The theoretical material is followed by a pedagogical consideration of how the specifics of the articulatory characteristics of English vowels can be addressed in the classroom to facilitate comprehension and production of English vowels by English language learners. Supplementary materials are suggested for readers offering sample activities that could be used by language practitioners in ESL classrooms for this goal, as well as for exploring other dialects of English, including specific regional dialects falling under the umbrella of General American English, the variety addressed in this chapter.