An analysis of bimanual actions in natural feeding of semi-wild chimpanzees

Objective The objective of the current study was to investigate the lateral dominance for a bimanually coordinated natural feeding behavior in semi-wild chimpanzees. Materials and Methods We investigated strychnos spp. fruit consumption behaviors in semi-wild chimpanzees as an ecologically comparable feeding behavior to those found in cerebral lateralization studies of non-primate species. Video recordings of thirty-three chimpanzees were assessed while they consumed hard-shelled strychnos fruits. We explored statistical and descriptive measures of hand dominance to highlight lateralized patterns. Results Statistical evaluation of feeding bouts revealed a group-level right-handed bias for bimanual coordinated feeding actions, however few individuals were statistically lateralized. Descriptive analyses revealed that the majority of individuals were lateralized and possessed a right-handed bias for strychnos feeding behavior. Discussion The results provide empirical evidence in supports of an early evolutionary delineation of function for the right and left hemispheres. The present findings suggest that great apes express an intermediate stage along the phylogenetic trajectory of human manual lateralization.

Changes to media consumption affecting production methods

Semi-converged media brands who have content on television and online need converged production teams and practices. The case study of BBC Children's 'Level Up' provides evidence and a useful illustration.

An Empirical Evaluation of Preconditioning Data for Accelerating Convex Hull Computations

The convex hull describes the extent or shape of a set of data and is used ubiquitously in computational geometry. Common algorithms to construct the convex hull on a finite set of n points (x,y) range from O(nlogn) time to O(n) time. However, it is often the case that a heuristic procedure is applied to reduce the original set of n points to a set of s < n points which contains the hull and so accelerates the final hull finding procedure. We present an algorithm to precondition data before building a 2D convex hull with integer coordinates, with three distinct advantages. First, for all practical purposes, it is linear; second, no explicit sorting of data is required and third, the reduced set of s points is constructed such that it forms an ordered set that can be directly pipelined into an O(n) time convex hull algorithm. Under these criteria a fast (or O(n)) pre-conditioner in principle creates a fast convex hull (approximately O(n)) for an arbitrary set of points. The paper empirically evaluates and quantifies the acceleration generated by the method against the most common convex hull algorithms. An extra acceleration of at least four times when compared to previous existing preconditioning methods is found from experiments on a dataset.

An Empirical Evaluation of Preconditioning Data for Accelerating Convex Hull Computations

The convex hull describes the extent or shape of a set of data and is used ubiquitously in computational geometry. Common algorithms to construct the convex hull on a finite set of n points (x,y) range from O(nlogn) time to O(n) time. However, it is often the case that a heuristic procedure is applied to reduce the original set of n points to a set of s < n points which contains the hull and so accelerates the final hull finding procedure. We present an algorithm to precondition data before building a 2D convex hull with integer coordinates, with three distinct advantages. First, for all practical purposes, it is linear; second, no explicit sorting of data is required and third, the reduced set of s points is constructed such that it forms an ordered set that can be directly pipelined into an O(n) time convex hull algorithm. Under these criteria a fast (or O(n)) pre-conditioner in principle creates a fast convex hull (approximately O(n)) for an arbitrary set of points. The paper empirically evaluates and quantifies the acceleration generated by the method against the most common convex hull algorithms. An extra acceleration of at least four times when compared to previous existing preconditioning methods is found from experiments on a dataset.