Este trabajo presenta la aplicación de un conjunto de articulos con propias teorías de los robots manipuladores y el modelamiento de pinzas. Para ello el brazo humano se modela como un robot manipulador redundante. En particular se aplica el concepto de índices de desempeño para predecir posturas óptimas del brazo durante la realización de tareas. En el estudio se incluyen tanto estructuras estáticas, como tambien los analisis de estabilidad del brazo y los materiales para su respectiva realizacion.
This work presents the application of a set of articles with the own theories of the manipulative robots and the modeling of tweezers. For this, the bearing is modeled as a redundant manipulator robot. In particular, the concept of performance indices is applied to predict the optimal postures of the task during the performance of tasks. The study includes both static structures, as well as safety management analyzes and materials for their respective realization.
The usage of software has grown as computers become popular. There have emerged, both in academia and in the market, technological solutions for several areas, among them education. On the other hand, classroom teaching and learning continues to suffer from classical educational problems such as lack of student and teacher motivation and lack of clear educational goals. And although software supports learning across a range of disciplines and ages, children's audiences, especially in mathematics, have been little contemplated with the benefits that technological solutions can bring. Therefore, the use of pedagogical approaches, such as Bloom's Taxonomy and Formative Assessments, together with gamification techniques, such as Octalysis, can be used to develop a technological solution that contemplates this public. The present work aims to propose the development of a software to assist the teaching and learning of mathematics for children in the classroom.
In Email Analytics, our main focus on criminal and civil investigation from large email dataset. It is very difficult to deal with challenging task for investigator due to large size of email dataset. This paper offer an interactive email analytics various to current and manually intensive technique is used for search evidence from large email dataset. In investigation process, many emails are irrelevant to the investigation so it will force investigator to search carefully through email in order to find relevant emails manually. This process is very costly in terms of money and times. To help to investigation process. We combine Elasticsearch, Logstash and Kibana for data storing, data preprocessing, data visualization and data analytics and displaying results. In this process reduce the number of email which are irrelevant for investigation. It shows the relationship between them and also analyzing the email corpus based on topic relation using text mining.