Factores asociados con el inicio de las relaciones sexuales tempranas en los adolescentes de un colegio público y uno privado de la ciudad de Cartagena

La sexualidad es parte importante del desarrollo de la personalidad, la imagen y la autoestima de los adolescentes, definida por las características biológicas, psicológicas, sociales y culturales, permitiendo en su conjunto una visión del mundo y una relación directa o indirecta con él. Objetivo: Analizar los posibles factores de riesgo asociados con el inicio de las relaciones sexuales tempranas y los riesgos inherentes presentes en este tipo de conductas. Materiales y Métodos: El enfoque de la investigación es cuantitativo de tipo descriptivo, de corte transversal. Con una muestra conformada por 324 adolescentes hombres y mujeres estudiantes de una institución educativa pública y una privada de la ciudad de Cartagena. La recolección de la información se hizo mediante una encuesta estructurada y los datos se analizaron en EpiInfo. Resultados: Los adolescentes iniciaron su vida sexual a muy temprana edad, el tiempo libre es usado en internet, de igual forma se presenta el consumo de sustancias psicoactivas. Conclusión: Un alto porcentaje de adolescentes empezaron a tener relaciones sexuales desde los 10 años, evidenciándose conductas irresponsables y sobresaliendo la desinformación frente a este tipo de actividad, destacándose un porcentaje pequeño que evidenció la aparición de enfermedades de transmisión sexual

A simple network agreement-based approach for combining evidences in a heterogeneous sensor network

In this research we investigate how the evidences provided by both static and mobile nodes that are part of a heterogenous sensor network can be combined to have trustworthy results. A solution relying on a network agreement-based approach was implemented and tested

High-Level Features for Recognizing Human Actions in Daily Living Environments Using Wearable Sensors

Action recognition is important for various applications, such as, ambient intelligence, smart devices, and healthcare. Automatic recognition of human actions in daily living environments, mainly using wearable sensors, is still an open research problem of the field of pervasive computing. This research focuses on extracting a set of features related to human motion, in particular the motion of the upper and lower limbs, in order to recognize actions in daily living environments, using time-series of joint orientation. Ten actions were performed by five test subjects in their homes: cooking, doing housework, eating, grooming, mouth care, ascending stairs, descending stairs, sitting, standing, and walking. The joint angles of the right upper limb and the left lower limb were estimated using information from five wearable inertial sensors placed on the back, right upper arm, right forearm, left thigh and left leg. The set features were used to build classifiers using three inference algorithms: Naive Bayes, K-Nearest Neighbours, and AdaBoost. The F- m e a s u r e average of classifying the ten actions of the three classifiers built by using the proposed set of features was 0.806 ( σ = 0.163)

The COMRADE System for Multirobot Autonomous Landmine Detection in Postconflict Regions

We consider the problem of autonomous landmine detection using a team of mobile robots. Previous research on robotic landmine detection mostly employs a single robot equipped with a landmine detection sensor to detect landmines. We envisage that the quality of landmine detection can be significantly improved if multiple robots are coordinated to detect landmines in a cooperative manner by incrementally fusing the landmine-related sensor information they collect and then use that information to visit locations of potential landmines. Towards this objective, we describe a multirobot system called COMRADES to address different aspects of the autonomous landmine detection problem including distributed area coverage to detect and locate landmines, information aggregation to fuse the sensor information obtained by different robots, and multirobot task allocation (MRTA) to enable different robots to determine a suitable sequence to visit locations of potential landmines while reducing the time required and battery expended. We have used commercially available all-terrain robots called Coroware Explorer that are customized with a metal detector to detect metallic objects including landmines, as well as indoor Corobot robots, both in simulation and in physical experiments, to test the different techniques in COMRADES

Comparison of Machine Learning Models to Predict Risk of Falling in Osteoporosis Elderly

Falls are a multifactorial cause of injuries for older people. Subjects with osteoporosis are more vulnerable to falls. The focus of this study is to investigate the performance of the different machine learning models built on spatiotemporal gait parameters to predict falls particularly in subjects with osteoporosis. Spatiotemporal gait parameters and prospective registration of falls were obtained from a sample of 110 community dwelling older women with osteoporosis (age 74.3 ± 6.3) and 143 without osteoporosis (age 68.7 ± 6.8). We built four different models, Support Vector Machines, Neuronal Networks, Decision Trees, and Dynamic Bayesian Networks (DBN), for each specific set of parameters used, and compared them considering their accuracy, precision, recall and F-score to predict fall risk. The F-score value shows that DBN based models are more efficient to predict fall risk, and the best result obtained is when we use a DBN model using the experts’ variables with FSMC’s variables, mixed variables set, obtaining an accuracy of 80%, and recall of 73%. The results confirm the feasibility of computational methods to complement experts’ knowledge to predict risk of falling within a period of time as high as 12 months

3er. Coloquio: Fortalecimiento de los Colectivos de Docencia

Las memorias del 3er. Coloquio de Fortalecimiento de Colectivos de Docencia deben ser entendidas como un esfuerzo colectivo de la comunidad de académicos de la División de Ciencias y Artes para el Diseño, en medio de la pandemia COVID-19, con el fin de: • Analizar y proponer acciones concretas que promuevan el mejoramiento de la calidad docente en la División. • Proponer acciones que permitan continuar fortaleciendo los cursos con modalidad a distancia (remotos). • Ante un escenario que probablemente demandará en el mediano plazo, transitar del modelo remoto a un modelo híbrido, proponer acciones a considerar para la transición de los cursos. • Planear y preparar cursos de nivelación de conocimientos, para cuando se transite a la impartición de la docencia de manera mixta o presencial, dirigidos a los alumnos que no hayan tenido oportunidad de desarrollar actividades relevantes para su formación, como prácticas de talleres y laboratorios, visitas, o alguna otra actividad relevante