The Relationship between Physical Growth and Infant Behavioral Development in Rural Guatemala

The present study investigated the relationship between a number of anthropometric indices and behavioral development during the first 2 years of life in rural Guatemala. Length and weight were the indices most strongly correlated with behavioral development. If the effect of the infant\u27s length and weight was statistically controlled for, none of the other anthropometric variables explained a significant proportion of the variance in behavioral development. Con- trolling for length (or weight) assessed at the same age as the behavioral assessment, length (or weight) for younger ages was not significantly correlated with behavioral development. Changes in length or weight over time were correlated with changes in behavioral performance. We were unable to explain the association between physical growth and behavioral development by a number of variables including gestational age, nutrient intake, prevalence of disease, and familial characteristics

Preface

Prefac

Ostrowski–Sugeno type fuzzy integral inequalities

Here we present Ostrowski–Sugeno Fuzzy type inequalities. These are Ostrowski-like inequalities in the context of Sugeno fuzzy integral and its special properties. They give tight upper bounds to the deviation of a function from its Sugeno-fuzzy averages. This work is greatly inspired by [1, 4]. It follows [2]

What does fuzzy logic bring to AI?

International audienceThe term “fuzzy logic” often refers to a particular control-engineering methodology that exploits a numerical representation of commensense control rules in order to synthesize, via interpolation, a control law. This approach has many features in common with neural networks. It is currently concerned mainly with the efficient encoding and approximation of numerical functions and has less and less relationship to knowledge representation issues. This is, however, a very narrow view of fuzzy logic that has little to do with AI. Scanning the fuzzy set literature, one realizes that fuzzy logic may also refer to two other topics: multiplevalued Iogics and approximate reasoning. Although the multiple-valued logic stream is very mathematically oriented, the notion of approximate reasoning as imagined by Zadeh is much more closely related to the program of AI research: he wrote in 1979 that “the theory of approximate reasoning is concerned with the deduction of possibly imprecise conclusions from a set of imprecise premises.” In the following, we use the term “fuzzy logic” to mean any kind of fuzzy set-based method intended to be used in reasoning systems. Fuzzy logic is 30 years old and has a long-term misunderstanding with AI. As a consequence, fuzzy logic methods have not been considered to belong to mainstream AI tools until now, although an important part of fuzzy logic research concentrates on issues in approximate reasoning and reasoning under uncertainty. Some reasons for this situation may be found in the antagonism which existed for a long time between purely symbolic methods advocated by AI and the numerically oriented approaches that were involved in fuzzy rule-based systems. Besides, fuzzy sets were a new emerging approach not yet firmly settled, but apparently challenging the monopoly of probability theory on being the unique proper framework for handling uncertainty. In spite of the fact that fuzzy sets have received more recognition recently, there is still a lack of appreciation by AI researchers of what fuzzy logic really is, as, for instance, recently exemplified by Elkan [ 1994]