Detección de hábitos de estudio que presentan los alumnos de primer grado de la Escuela Secundaría Oficial No. 19 Cuauhtémoc.

La presente investigación tuvo como propósito identificar los hábitos de estudio de los alumnos que aplican en su proceso de aprendizaje por el índice de materias que reprueban durante el curso escolar, es por eso que al tener conocimiento de dicho problema quise realizar mi trabajo de Tesis y demostrar que sí el alumno presenta esta deficiencia es porque no tiene las bases bien cimentadas de lo que son los hábitos de estudio. El estudiante se encuentra en un proceso de cambios, el cual le perturba algunas veces en sus cambios de crecimiento y desarrollo, ocasionando la poca concentración en el estudio y éste repercute en sus calificaciones

Metabolic syndrome predictors of brain gray matter volume in an age-stratified community sample of 776 Mexican- American adults: Results from the genetics of brain structure image archive

Introduction: This project aimed to investigate the association between biometric components of metabolic syndrome (MetS) with gray matter volume (GMV) obtained with magnetic resonance imaging (MRI) from a large cohort of community-based adults (n = 776) subdivided by age and sex and employing brain regions of interest defined previously as the “Neural Signature of MetS” (NS-MetS). Methods: Lipid profiles, biometrics, and regional brain GMV were obtained from the Genetics of Brain Structure (GOBS) image archive. Participants underwent T1-weighted MR imaging. MetS components (waist circumference, fasting plasma glucose, triglycerides, HDL cholesterol, and blood pressure) were defined using the National Cholesterol Education Program Adult Treatment Panel III. Subjects were grouped by age: early adult (18–25 years), young adult (26–45 years), and middle-aged adult (46–65 years). Linear regression modeling was used to investigate associations between MetS components and GMV in five brain regions comprising the NS-MetS: cerebellum, brainstem, orbitofrontal cortex, right insular/limbic cluster and caudate. Results: In both men and women of each age group, waist circumference was the single component most strongly correlated with decreased GMV across all NS-MetS regions. The brain region most strongly correlated to all MetS components was the posterior cerebellum. Conclusion: The posterior cerebellum emerged as the region most significantly associated with MetS individual components, as the only region to show decreased GMV in young adults, and the region with the greatest variance between men and women. We propose that future studies investigating neurological effects of MetS and its comorbidities—namely diabetes and obesity—should consider the NS-MetS and the differential effects of age and sex

Multi-objective optimisation using sharing in swarm optimisation algorithms

Many problems in the real world are multi-objective by nature, this means that many times there is the need to satisfy a problem with more than one goal in mind. These type of problems have been studied by economists, mathematicians, between many more, and recently computer scientists. Computer scientists have been developing novel methods to solve this type of problems with the help of evolutionary computation. Particle Swarm Optimisation (PSO) is a relatively new heuristic that shares some similarities with evolutionary computation techniques, and that recently has been successfully modified to solve multi-objective optimisation problems. In this thesis we first review some of the most relevant work done in the area of PSO and multi-objective optimisation, and then we proceed to develop an heuristic capable to solve this type of problems. An heuristic, which probes to be very competitive when tested over synthetic benchmark functions taken from the specialised literature, and compared against state-of-the-art techniques developed up to this day; we then further extended this heuristic to make it more competitive. Almost at the end of this work we incursion into the area of dynamic multi-objective optimisation, by testing the capabilities and analysing the behaviour of our technique in dynamic environments

Working title: “Multi-Objective Optimization using Niching

In this report we present the progress made to our research since our last thesis group meeting held on October 2004. First we will introduce some of the main concepts in order to familiarize the reader (this section can be skipped, but is included for completeness), then we will speak about the work we have don

Particle Swarm Optimization and Fitness Sharing to solve Multi-Objective Optimization Problems

Abstract- The particle swarm optimization algorithm has been shown to be a competitive heuristic to solve multi-objective optimization problems. Also, fitness sharing concepts have shown to be significant when used by multi-objective optimization methods. In this paper we introduce an algorithm that makes use of these two main concepts, particle swarm optimization and fitness sharing to tackle multi-objective optimization problems.