960 resultados para data skills
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Patient blood pressure is an important vital signal to the physicians take a decision and to better understand the patient condition. In Intensive Care Units is possible monitoring the blood pressure due the fact of the patient being in continuous monitoring through bedside monitors and the use of sensors. The intensivist only have access to vital signs values when they look to the monitor or consult the values hourly collected. Most important is the sequence of the values collected, i.e., a set of highest or lowest values can signify a critical event and bring future complications to a patient as is Hypotension or Hypertension. This complications can leverage a set of dangerous diseases and side-effects. The main goal of this work is to predict the probability of a patient has a blood pressure critical event in the next hours by combining a set of patient data collected in real-time and using Data Mining classification techniques. As output the models indicate the probability (%) of a patient has a Blood Pressure Critical Event in the next hour. The achieved results showed to be very promising, presenting sensitivity around of 95%.
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This text concerns a program about the Promotion of Social and Communicational Skills and Mediation (PSCSM) developed with children aged between 10 and 13 years in a non-formal educational institution. The program of intervention had, as its purpose, the promotion of social and communicational competencies and mediation, thus enabling the children involved to have a healthy and responsible sociability in the different contexts in which they find themselves: family, school, peer group, amongst others. It was developed over 13 sessions with objectives and activities intentionally planned with the view of promoting competencies of communication, co-operation, responsibility, a critical spirit, solidarity, autonomy, respect, integration, inclusion and the recognition of rights and duties. This work was carried out with an action-research methodology that resorted to various techniques and instruments to gather and record information. The results obtained showed the impact and benefits of the program and they also revealed the necessity of educational institutions investing in the promotion of an ethical literacy and the empowerment of the children and young people for healthy sociability and active citizenship.
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OBJECTIVE: To determine the influence of stress on teaching medical emergencies in an Advanced Cardiac Life Support (ACLS) course and to verify this influence on learning, and the efficiency of emergency care training. METHODS: Seventeen physicians signed up for an ACLS course. Their pulses were taken and blood pressure (BP) verified on the first day, before the beginning of the course, and on the second day, during the theoretical and practical test (TPT). Variations in pulse rates and BP were compared with students' test grades. Then, students answered a questionnaire of variables (QV) about the amount of sleep they had during the course, the quantity of study material and the time spent studying for the course, and a stress scale graphic. RESULTS: Seven students had a pulse variation less than 10% between the 2 periods and 10 had a 10% or more variation. Grades on TPT were, respectively, 91.4±2.4 and 87.3±5.2 (p<0.05). Six students had a BP variation less than 20 mmHg, and in 11 it varied more than 21 mmHg. Grades on the TPT were 92.3±3.3 and 86.2± 8.1, respectively (p<0.05). The QV dates did not significantly influence grades. CONCLUSION: Stress, as an isolated variable, had a negative influence on the learning process and on the efficiency of emergency training in this situation.
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Este proyecto propone extender y generalizar los procesos de estimación e inferencia de modelos aditivos generalizados multivariados para variables aleatorias no gaussianas, que describen comportamientos de fenómenos biológicos y sociales y cuyas representaciones originan series longitudinales y datos agregados (clusters). Se genera teniendo como objeto para las aplicaciones inmediatas, el desarrollo de metodología de modelación para la comprensión de procesos biológicos, ambientales y sociales de las áreas de Salud y las Ciencias Sociales, la condicionan la presencia de fenómenos específicos, como el de las enfermedades.Es así que el plan que se propone intenta estrechar la relación entre la Matemática Aplicada, desde un enfoque bajo incertidumbre y las Ciencias Biológicas y Sociales, en general, generando nuevas herramientas para poder analizar y explicar muchos problemas sobre los cuales tienen cada vez mas información experimental y/o observacional.Se propone, en forma secuencial, comenzando por variables aleatorias discretas (Yi, con función de varianza menor que una potencia par del valor esperado E(Y)) generar una clase unificada de modelos aditivos (paramétricos y no paramétricos) generalizados, la cual contenga como casos particulares a los modelos lineales generalizados, no lineales generalizados, los aditivos generalizados, los de media marginales generalizados (enfoques GEE1 -Liang y Zeger, 1986- y GEE2 -Zhao y Prentice, 1990; Zeger y Qaqish, 1992; Yan y Fine, 2004), iniciando una conexión con los modelos lineales mixtos generalizados para variables latentes (GLLAMM, Skrondal y Rabe-Hesketh, 2004), partiendo de estructuras de datos correlacionados. Esto permitirá definir distribuciones condicionales de las respuestas, dadas las covariables y las variables latentes y estimar ecuaciones estructurales para las VL, incluyendo regresiones de VL sobre las covariables y regresiones de VL sobre otras VL y modelos específicos para considerar jerarquías de variación ya reconocidas. Cómo definir modelos que consideren estructuras espaciales o temporales, de manera tal que permitan la presencia de factores jerárquicos, fijos o aleatorios, medidos con error como es el caso de las situaciones que se presentan en las Ciencias Sociales y en Epidemiología, es un desafío a nivel estadístico. Se proyecta esa forma secuencial para la construcción de metodología tanto de estimación como de inferencia, comenzando con variables aleatorias Poisson y Bernoulli, incluyendo los existentes MLG, hasta los actuales modelos generalizados jerárquicos, conextando con los GLLAMM, partiendo de estructuras de datos correlacionados. Esta familia de modelos se generará para estructuras de variables/vectores, covariables y componentes aleatorios jerárquicos que describan fenómenos de las Ciencias Sociales y la Epidemiología.
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Los eventos transitorios únicos analógicos (ASET, Analog Single Event Transient) se producen debido a la interacción de un ión pesado o un protón de alta energía con un dispositivo sensible de un circuito analógico. La interacción del ión con un transistor bipolar o de efecto de campo MOS induce pares electrón-hueco que provocan picos que pueden propagarse a la salida del componente analógico provocando transitorios que pueden inducir fallas en el nivel sistema. Los problemas más graves debido a este tipo de fenómeno se dan en el medioambiente espacial, muy rico en iones pesados. Casos típicos los constituyen las computadoras de a bordo de satélites y otros artefactos espaciales. Sin embargo, y debido a la continua contracción de dimensiones de los transistores (que trae aparejado un aumento de sensibilidad), este fenómeno ha comenzado a observarse a nivel del mar, provocado fundamentalmente por el impacto de neutrones atmosféricos. Estos efectos pueden provocar severos problemas a los sistemas informáticos con interfaces analógicas desde las que obtienen datos para el procesamiento y se han convertido en uno de los problemas más graves a los que tienen que hacer frente los diseñadores de sistemas de alta escala de integración. Casos típicos son los Sistemas en Chip que incluyen módulos de procesamiento de altas prestaciones como las interfaces analógicas.El proyecto persigue como objetivo general estudiar la susceptibilidad de sistemas informáticos a ASETs en sus secciones analógicas, proponiendo estrategias para la mitigación de los errores.Como objetivos específicos se pretende: -Proponer nuevos modelos de ASETs basados en simulaciones en el nivel dispositivo y resueltas por el método de elementos finitos.-Utilizar los modelos para identificar las secciones más propensas a producir errores y consecuentemente para ser candidatos a la aplicación de técnicas de endurecimiento a radiaciones.-Utilizar estos modelos para estudiar la naturaleza de los errores producidos en sistemas de procesamiento de datos.-Proponer soluciones novedosas para la mitigación de estos efectos en los mismos circuitos analógicos evitando su propagación a las secciones digitales.-Proponer soluciones para la mitigación de los efectos en el nivel sistema.Para llevar a cabo el proyecto se plantea un procedimiento ascendente para las investigaciones a realizar, comenzando por descripciones en el nivel físico para posteriormente aumentar el nivel de abstracción en el que se encuentra modelado el circuito. Se propone el modelado físico de los dispositivos MOS y su resolución mediante el Método de Elementos Finitos. La inyección de cargas en las zonas sensibles de los modelos permitirá determinar los perfiles de los pulsos de corriente que deben inyectarse en el nivel circuito para emular estos efectos. Estos procedimientos se realizarán para los distintos bloques constructivos de las interfaces analógicas, proponiendo estrategias de mitigación de errores en diferentes niveles.Los resultados esperados del presente proyecto incluyen hardware para detección de errores y tolerancia a este tipo de eventos que permitan aumentar la confiabilidad de sistemas de tratamiento de la información, así como también nuevos datos referentes a efectos de la radiación en semiconductores, nuevos modelos de fallas transitorias que permitan una simulación de estos eventos en el nivel circuito y la determinación de zonas sensibles de interfaces analógicas típicas que deben ser endurecidas para radiación.
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Driven by concerns about rising energy costs, security of supply and climate change a new wave of Sustainable Energy Technologies (SET’s) have been embraced by the Irish consumer. Such systems as solar collectors, heat pumps and biomass boilers have become common due to government backed financial incentives and revisions of the building regulations. However, there is a deficit of knowledge and understanding of how these technologies operate and perform under Ireland’s maritime climate. This AQ-WBL project was designed to address both these needs by developing a Data Acquisition (DAQ) system to monitor the performance of such technologies and a web-based learning environment to disseminate performance characteristics and supplementary information about these systems. A DAQ system consisting of 108 sensors was developed as part of Galway-Mayo Institute of Technology’s (GMIT’s) Centre for the Integration of Sustainable EnergyTechnologies (CiSET) in an effort to benchmark the performance of solar thermal collectors and Ground Source Heat Pumps (GSHP’s) under Irish maritime climate, research new methods of integrating these systems within the built environment and raise awareness of SET’s. It has operated reliably for over 2 years and has acquired over 25 million data points. Raising awareness of these SET’s is carried out through the dissemination of the performance data through an online learning environment. A learning environment was created to provide different user groups with a basic understanding of a SET’s with the support of performance data, through a novel 5 step learning process and two examples were developed for the solar thermal collectors and the weather station which can be viewed at http://www.kdp 1 .aquaculture.ie/index.aspx. This online learning environment has been demonstrated to and well received by different groups of GMIT’s undergraduate students and plans have been made to develop it further to support education, awareness, research and regional development.
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Almost half of Ireland’s commercial stocks face overexploitation. As traditional species decrease in abundance and become less profitable, the industry is increasingly turning to alternate species. Atlantic saury (Scomberesox saurus saurus (Walbaum)) has been identified as a potential species for exploitation. Very little information is available on its biology or population dynamics, especially for Irish waters. This thesis aims to obtain sound scientific data, which will help to ensure that a future Atlantic saury fishery can be sustainably managed. The research has produced valuable data, some of which contradicts previous studies. Growth of Atlantic saury measured using otolith microstructure is found to be more than twice that previously calculated from annual structures on scales and otoliths. This results in a significant reduction of the expected life span from five to about two years. Investigation of maturity stage at age indicates that Atlantic saury will reproduce for the first time at age one and will survive for one or at most two reproduction seasons. It is concluded that a future Irish fishery will target mostly fish prior to their first reproduction. Finally the thesis gives some insights into the population structure of Atlantic saury, by analysis of otolith morphometric. Significant differences are detected between Northeastern Atlantic and western Mediterranean Sea specimens of the 0+ age class (less than one year old). The implications of these results for the management of an emerging fishery are discussed.
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Ground-based measurements of the parameters of atmosphere in Tbilisi during the same period, which are provided by the Mikheil Nodia Institute of geophysics, were used as calibration data. Satellite data monthly averaging, preprocessing, analysis and visualization was performed using Giovanni web-based application. Maps of trends and periodic components of the atmosphere aerosol optical thickness and ozone concentration over the study area were calculated.
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Magdeburg, Univ., Fak. für Informatik, Diss., 2011
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Data Mining, Learning from data, graphical models, possibility theory
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Scheduling, job shop, uncertainty, mixed (disjunctive) graph, stability analysis
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Similarity-based operations, similarity join, similarity grouping, data integration