Cuban Immigrants’ Experience with Acculturation and How They Cope in the United States

Objective: This research examines how Cuban immigrants experience cope and adapt to the United States. Cuban immigration is associated with specific stressors related to the immigration experience and the necessary process of acculturation and assimilation. These major stressors can result in mental health concerns among Cuban immigrants; however, no studies have examined how acculturation may influence Cuban immigrants’ coping skills and resultant mental health concerns. This unique study is the first to examine the coping skills Cuban immigrants use during acculturation and the effects of these skills on Cuban immigrants’ mental health. Methods: Seventeen participants completed a semistructured interview and six participants completed a focus group interview. This study only included Cuban immigrants who immigrated within the last 10 years and who currently reside in Florida. The researcher used a phenomenological qualitative methodology approach to examine how Cuban immigrants managed the acculturation process using coping strategies. Results: Key findings revealed that Cuban immigrants who used coping skills during their first 2 years in the United States had higher levels of acculturation. Participants used coping skills related to technology, family, religion, personal coping skills, friends, career services, mental health, and English proficiency to reduce acculturation stress. Conclusion: These findings highlight how mental health counselors and educators would benefit from accessing training to recognize and provide appropriate care for disorders related to acculturative stress among Cuban immigrants

Unsupervised extraction of adverse drug reaction relationships

En este trabajo se presentan los resultados preliminares de una nueva técnica no supervisada para la extracción de relaciones entre medicamentos y efectos adversos. La identificación de relaciones se consigue a partir de un modelo de representación de conocimiento que extrae pares de entidades con un peso determinado, en función de la significatividad estadística de su coaparición en un mismo documento. Dicho modelo puede ser posteriormente convertido en un grafo. El sistema ha sido evaluado sobre un corpus de referencia, denominado ADE corpus, consiguiendo resultados prometedores al obtener una eficacia muy por encima de un baseline estándar. Las primeras pruebas también muestran un alto potencial para inducir conocimiento nuevo.In this work we present preliminary results obtained by a new unsupervised technique for extracting relations between drugs and adverse drug reactions. The identification of those relations is achieved using a knowledge representation model that generates pairs of entities and assigns them a specific weight, depending on the statistical significance of their co-occurrence in the same document. This model may subsequently be transformed into a graph. The system has been evaluated over the reference ADE corpus, obtaining promising results, since its effectiveness is quite higher than that obtained by a standard basline. First tests also show a high potential for inducing new knowledge.Trabajo financiado parcialmente por los proyectos EXTRECM (TIN2013-46616-C2-2-R), y TwiSE (2013-025-UNED-PROY)

Methodology for studies of natural circulation in closed circuits

This work presents an analysis of stability of the phenomenon of natural circulation for onedimension single-phase flow in a closed loop. The computer program uses a stabilized finite element formulation for the solution of the Navier-Stokes and energy equations in cartesian coordinates. The formulation has been developed and tested in a computer code developed at the Nuclear Engineering Institute (IEN-CNEN) and is now available either for future analysis or design of nuclear system

Identificación de nuevas oportunidades de negocios a nivel internacional para agencias de viajes mayoristas y minoristas

La presente monografía identifica las oportunidades de negocios a nivel internacional para agencias de viajes mayoristas y minoristas, divididas en tres categorías: oportunidades basadas en nuevas tecnologías, formas de comercialización y segmentos de turistas, a su vez, clasificadas según el beneficio que aporta su implementación para mayoristas y minoristas. Se investigó sobre los antecedentes históricos de las agencias, para conocer la problemática actual que enfrentan e identificar las nuevas oportunidades que el mercado facilita, para el avance y adecuación de las agencias a las exigencias del consumidor actual. Con el desarrollo de la investigación, se concluyó que las agencias de viajes están en un período de transición entre ofrecer servicios únicamente presenciales a utilizar la tecnología como apoyo o medio para ofrecerlos, basándose en esto se recomiendan acciones de mejora inmediata y se propone una guía de identificación de oportunidades, para garantizar una mejora continua a la agenciaTrabajo final de carrera, para optar al grado de Licenciado en Turism

Semi-supervised incremental learning with few examples for discovering medical association rules

Background: Association Rules are one of the main ways to represent structural patterns underlying raw data. They represent dependencies between sets of observations contained in the data. The associations established by these rules are very useful in the medical domain, for example in the predictive health field. Classic algorithms for association rule mining give rise to huge amounts of possible rules that should be filtered in order to select those most likely to be true. Most of the proposed techniques for these tasks are unsupervised. However, the accuracy provided by unsupervised systems is limited. Conversely, resorting to annotated data for training supervised systems is expensive and time-consuming. The purpose of this research is to design a new semi-supervised algorithm that performs like supervised algorithms but uses an affordable amount of training data. Methods: In this work we propose a new semi-supervised data mining model that combines unsupervised techniques (Fisher's exact test) with limited supervision. Starting with a small seed of annotated data, the model improves results (F-measure) obtained, using a fully supervised system (standard supervised ML algorithms). The idea is based on utilising the agreement between the predictions of the supervised system and those of the unsupervised techniques in a series of iterative steps. Results: The new semi-supervised ML algorithm improves the results of supervised algorithms computed using the F-measure in the task of mining medical association rules, but training with an affordable amount of manually annotated data. Conclusions: Using a small amount of annotated data (which is easily achievable) leads to results similar to those of a supervised system. The proposal may be an important step for the practical development of techniques for mining association rules and generating new valuable scientific medical knowledge.This work has been partially supported by projects DOTT-HEALTH (PID2019-106942RB-C32, MCI/AEI/FEDER, UE). (Design of the study. Analysis and interpretation of data) and EXTRAE II (IMIENS 2019). (Design of the study. Analysis and interpretation of data. HUF corpus manual tagging. Writing of the manuscript), PI18CIII/00004 “Infobanco para uso secundario de datos basado en estándares de tecnología y conocimiento: implementación y evaluación de un infobanco de salud para CoRIS (Info-bank for the secondary use of data based on technology and knowledge standards: implementation and evaluation of a health info-bank for CoRIS) – SmartPITeS” (Data collection and HUF corpus construction), and PI18CIII/00019 - PI18/00890 - PI18/00981 “Arquitectura normalizada de datos clínicos para la generación de infobancos y su uso secundario en investigación: solución tecnológica (Clinical data normalized architecture for the genaration of info-banks and their secondary use in research: technological solution) – CAMAMA 4” (Data collection and HUF corpus construction) from Fondo de Investigación Sanitaria (FIS) Plan Nacional de I+D+i.S

EXTracción de RElaciones entre Conceptos Médicos en fuentes de información heterogéneas (EXTRECM)

En este proyecto se plantea la extracción de relaciones entre conceptos médicos en documentos científicos, historiales médicos e información de carácter general en Internet, en varias lenguas utilizando técnicas y herramientas de Procesamiento de Lenguaje Natural y Recuperación de Información. El proyecto se propone demostrar, mediante dos casos de uso, los beneficios de la aplicación de este tipo de tecnologías lingüísticas al dominio de la salud.This project addresses extraction of medical concepts relationship in scientific documents, medical records and general information on the Internet, in several languages by using advanced Natural Language Processing and Information Retrieval techniques and tools. The project aims to show, through two use cases, the benefits of the application of language technology in the health sector.TIN2013-46616-C2-1-R, TIN2013-46616-C2-2-R

Disentangling categorical relationships through a graph of co-occurrences

The mesoscopic structure of complex networks has proven a powerful level of description to understand the linchpins of the system represented by the network. Nevertheless, themapping of a series of relationships between elements, in terms of a graph, is sometimes not straightforward. Given that all the information we would extract using complex network tools depend on this initial graph, it is mandatory to preprocess the data to build it on in the most accurate manner. Here we propose a procedure to build a network, attending only to statistically significant relations between constituents. We use a paradigmatic example of word associations to show the development of our approach. Analyzing the modular structure of the obtained network we are able to disentangle categorical relations, disambiguating words with success that is comparable to the best algorithms designed to the same end.We acknowledge financia support through Grant No. FIS2009-13364-C02-01, Holopedia (Grant No. TIN2010-21128-C02-01), MOSAICO (Grant No. FIS2006-01485), PRODIEVO (Grant No. FIS2011-22449), and Complexity-NET RESINEE, all of them from Ministerio de Educación y Ciencia in Spain, as well as support from Research Networks MODELICO-CM (Grant No. S2009/ESP-1691) and MA2VICMR (Grant No. S2009/TIC-1542) from Comunidad de Madrid, and Network 2009-SGR-838 from Generalitat de Catalunya