Universidad del Norte, Colombia

About: Universidad del Norte, Colombia is a education organization based out in Barranquilla, Colombia. It is known for research contribution in the topics: Population & Context (language use). The organization has 3562 authors who have published 4355 publications receiving 37861 citations. The organization is also known as: University of the North, Colombia & Uninorte.

A multinational Andean Genetic and Health Program: Growth and development in an hypoxic environment

Abstract: In 1972 a multidisciplinary study sought to assess the health status of the indigenous peoples of the Department of Arica in northern Chile, the Aymara, and to relate disease, morphological, physiological and biochemical variation, to the wide changes in altitude of the region. Presented here are the morphological changes which accompany age, altitude and ethnicity amoung 1047 children and adults, permanent residents of the coast, sierra and altiplano. At comparable ages, high-altitude residents were shorter, lighter and leaner but with more expansive and rounder chests than sea-level controls. None of these effects was systematically related to ethnicity (Spanish-Aymara surname), although when stature was held constant, children with greater Aymara ancestry had largest chest circumferences and longer bones. These results suggest that (1) altitude confers allometric growth changes (expensive growth of the chest and diminished growth of the structures less related to oxygen transport); and (2) size changes associated with altitude are acquired during development while shape changes may be under genetic control. Altitude appears to account for less of the variation in growth in this relatively homogeneous Chilean sample than has been reported for other Andean samples, suggesting other concomitants confounding the effects of hypoxia in Andean South America.

Engaging women into STEM in Latin America: W-STEM project

Abstract: Significant progress has been made during the last decades to achieve gender equality, but there is still much work to do. In particular, the gender gap is pronounced in the science, technology, engineering, and mathematics (STEM) fields at all levels of education and labour market. In those areas, the women participation remains low, although there are differences from country to country. In the Latin American context, there is a need for carrying out studies to collect quality data about the actual situation of women in STEM. Although some available data show a high proportion of women in Latin American university education, they are a minority in STEM programs. Moreover, this problem is particularly severe in Latin America because of the biases or cultural norms that influence female behaviour. In this context, the W-STEM project seeks to improve strategies and mechanisms for attracting, accessing, and guiding women in Latin America in STEM higher education programs. This work aims to describe the main results to prepare a set of attraction campaigns in secondary schools in the Latin American countries involved in the project (Chile, Colombia, Costa Rica, Ecuador, Mexico). In particular, a self-assessment tool about gender equality in higher education institutions in Latin America, an interview protocol for female role models, and a mobile application to show those role models.

Automated Vision-Based Recognition of Construction Worker Actions for Building Interior Construction Operations Using RGBD Cameras

Abstract: In this paper we present a novel method for reliable recognition of construction workers and their actions using color and depth data from a Microsoft Kinect sensor. Our algorithm is based on machine learning techniques, in which meaningful visual features are extracted based on the estimated body pose of workers. We adopt a bag-of-poses representation for worker actions and combine it with powerful discriminative classifiers to achieve accurate action recognition. The discriminative framework is able to focus on the visual aspects that are distinctive and can detect and recognize actions from different workers. We train and test our algorithm by using 80 videos from four workers involved in five drywall related construction activities. These videos were all collected from drywall construction activities inside of an under construction dining hall facility. The proposed algorithm is further validated by recognizing the actions of a construction worker that was never seen before in the training dataset. Experimental results show that our method achieves an average precision of 85.28 percent. The results reflect the promise of the proposed method for automated assessment of craftsmen productivity, safety, and occupational health at indoor environments.