Proposal and Validation of a Scale of Composite Measure Reactivity Score to Characterize the Reactivity in Horses During Handling

This study aimed to identify the parameters related to the expression of the reactivity in horses during handling and based on that proposed and validated a scale of composite measure reactivity score to characterize horse's reactivity. To this end, the first stage (S1) proposed the scale and the second (S2) validated it. In S1, 364 Lusitano horses were evaluated, 188 were adult breeding mares (4–12 years old), and 176 were foals (males/females, aged from 2 months to 2 years). During hooves trimming, vermifuge application, palpation scores were assigned to behaviors of movement, ears and eyes position, breathing, vocalization, and urination. A response parameter called reactivity was attributed to each animal, ranging from score 1 (nonreactive/calm) to score 4 (very reactive/aggressive). The verification of the possible parameters (age, behavior), which explains the response parameter (reactivity), was taken using ordinal proportional odds model. Movement, breathing, ears and eyes position, vocalization, and age appear to explain the reactivity of horses during handling (P < .01). Therefore, based on these parameters, it was possible to propose two scales of composite measure reactivity score: one to characterize the mares and another the foals. On S2, the proposed scale was validated by the simultaneous application of Forced Human Approach Test, another commonly used test to evaluate the reactivity in horses, with a correlation of 0.97 (P < .05). The assessment of the reactivity of horses during handling by a composite measure reactivity score scale is valid, and easy to apply, without disrupting daily routine and override the impact of individual differences.

Analysis of Dyscalculia Evidences through Artificial Intelligence Systems

Dyscalculia is usually perceived of as a specific learning difficulty for mathematics or, more appropriately, arithmetic. Because definitions and diagnoses of dyscalculia are in their infancy and sometimes are contradictory. However, mathematical learning difficulties are certainly not in their infancy and are very prevalent and often devastating in their impact. Co-occurrence of learning disorders appears to be the rule rather than the exception. Co-occurrence is generally assumed to be a consequence of risk factors that are shared between disorders, for example, working memory. However, it should not be assumed that all dyslexics have problems with mathematics, although the percentage may be very high, or that all dyscalculics have problems with reading and writing. Because mathematics is very developmental, any insecurity or uncertainty in early topics will impact on later topics, hence to need to take intervention back to basics. However, it may be worked out in order to decrease its degree of severity. For example, disMAT, an app developed for android may help children to apply mathematical concepts, without much effort, that is turning in itself, a promising tool to dyscalculia treatment. Thus, this work will focus on the development of a Decision Support System to estimate children evidences of dyscalculia, based on data obtained on-the-fly with disMAT. The computational framework is built on top of a Logic Programming approach to Knowledge Representation and Reasoning, grounded on a Case-based approach to computing, that allows for the handling of incomplete, unknown, or even self-contradictory information.