Corporate Social Responsibility Practices of Colombian Companies as Perceived by Industrial Engineering Students

Science and Engineering Ethics - This work describes the perceptions that Industrial Engineering students have regarding Colombian firms’ corporate social responsibility (CSR) practices. It...     

Do dogs rescue their owners from a stressful situation? A behavioral and physiological assessment

Animal Cognition - Rescue behavior is considered a type of pro-social response, defined as a voluntary action directed to benefit another individual who is in a stressful or dangerous situation. In...     

Age influences domestic dog cognitive performance independent of average breed lifespan

Animal Cognition - Across mammals, increased body size is positively associated with lifespan. However, within species, this relationship is inverted. This is well illustrated in dogs (Canis...     

Satisfiability Testing for Boolean Formulas Using Δ-trees

The tree-based data structure of -tree for propositional formulas is introduced in an improved and optimised form. The -trees allow a compact representation for negation normal forms as well as for a number of reduction strategies in order to consider only those occurrences of literals which are relevant for the satisfiability of the input formula. These reduction strategies are divided into two subsets (meaning- and satisfiability-preserving transformations) and can be used to decrease the size of a negation normal form A at (at most) quadratic cost. The reduction strategies are aimed at decreasing the number of required branchings and, therefore, these strategies allow to limit the size of the search space for the SAT problem.     

Variability and detection of invariant structure

Two experiments investigated learning of nonadjacent dependencies by adults and 18–month–olds. Each learner was exposed to three–element strings (e.g., pel–kicey–jic) produced by one of two artificial languages. Both languages contained the same adjacent dependencies, so learners could distinguish the languages only by acquiring dependencies between the first and third elements (the nonadjacent dependencies). The size of the pool from which the middle elements were drawn was systematically varied to investigate whether increasing variability (in the form of decreasing predictability between adjacent elements) would lead to better detection of nonadjacent dependencies. Infants and adults acquired nonadjacent dependencies only when adjacent dependencies were least predictable. The results point to conditions that might lead learners to focus on nonadjacent versus adjacent dependencies and are important for suggesting how learning might be dynamically guided by statistical structure.