Revisão e verificação das propriedades psicométricas da dimensão instabilidade de humor do inventário dimensional clínico da personalidade

El presente estudio tuvo como objetivo examinar la dimensión Inestabilidad del Humor del Inventario Dimensional Clínico de la Personalidad (IDCP), y la investigación de sus propiedades psicométricas. Para ello se desarrollaron nuevos ítems que fueron aplicados a 230 participantes, con edades comprendidas entre 18 y 63 años (M=23,0, DP=9,44), la mayoría de sexo femenino (76,4%). Todos los participantes respondieron el IDCP, la versión brasileña de la versión revisada del Inventario de Personalidad NEO, y la versión brasileña del Inventario de Personalidad para el DSM-5 (PID-5). Como resultado, 306 nuevos ítems fueron desarrollados con base en cuatro fuentes de referencia, y seleccionados mediante análisis de contenido, y 27 ítems que conformaron la versión final para la aplicación de la dimensión. Después de recoger los datos y de realizar el análisis estadístico, la dimensión analizada constaba de tres factores, además de una puntuación total. Los coeficientes de consistencia interna fueron adecuados e iguales a 0,85 para el conjunto final de 16 ítems con un rango de variación entre 0,78 y 0,81 por cada factor. De otra parte, se encontraron las intracorrelaciones esperadas, así como correlaciones coherentes con los instrumentos utilizados. Los datos permiten inferir evidencias de validez de la escala revisada, así como una demostración satisfactoria de la consistencia interna.O presente estudo teve como objetivo revisar a dimensão Instabilidade de Humor do Inventário Dimensional Clínico da Personalidade (IDCP), investigando suas propriedades psicométricasPara tanto, novos itens foram desenvolvidos, para então serem aplicados. Participaram 230 sujeitos, com idade variando entre 18 e 63 anos (M=23,0; DP=9,44), sendo a maior parte do sexo feminino (76,4%). Todos os participantes responderam o IDCP, a versão brasileira do Inventário de Personalidade NEO-PI Revisado e a versão brasileira do Personality Inventory for DSM-5 (PID-5). Como resultado, foram desenvolvidos 306 novos itens com base em quatro fontes de referencia na área e selecionados, por meio de análise de conteúdo, 27 para compor a versão final de aplicação da dimensão. Após a coleta de dados e análises estatísticas, a dimensão revisada ficou composta por três fatores, além de um escore total. Os coeficientes de consistência interna mostraram-se adequados sendo igual a 0,85 para o conjunto final de 16 itens e variando de 0,78 e 0,81 por fator. Além disso, foram encontradas intracorrelações esperadas, bem como correlações coerentes com os instrumentos aplicados. Os dados permitem inferir evidências de validade para a dimensão revisada, além de demonstrar índices satisfatórios de consistência interna.The present study aimed to review the Mood Instability dimension of the Dimensional Clinical Personality Inventory (DCPI) and to examine its psychometric properties. To this end, new items were developed that were applied to 230 subjects, aged between 18 and 63 years (M = 23.0, SD = 9.44), with a majority of females (76.4%). All participants answered the DCPI, the Brazilian version of the NEO Personality Inventory-Revised and the Brazilian version of the Personality Inventory for DSM-5 (PID-5). As a result, 306 new items were developed based on four sources of reference in the area and selected by means of content analysis and 27 items to compose the final version of the dimension to be applied. After data collection and statistical analysis, the reviewed dimension was composed of three factors plus a total score. The internal consistency coefficients were adequate and equal to .85 for the final set of 16 items, with a variation range between .78 and .81 per factor. Moreover, the expected intracorrelations were found, as well as consistent correlations with the instruments used. Data allow inferring validity evidence for the scale reviewed, as well as demonstrating satisfactory internal consistency

A family of non-parametric density estimation algorithms

A new methodology for density estimation is proposed. The method- ology, which builds on the one developed in [15], normalizes the data points through the composition of simple maps. The parameters of each map are determined through the maximization of a local quadratic approximation to the log-likelihood. Various candidates for the el- ementary maps of each step are proposed; criteria for choosing one includes robustness, computational simplicity and good behavior in high-dimensional settings. A good choice is that of localized radial expansions, which depend on a single parameter: all the complex- ity of arbitrary, possibly convoluted probability densities can be built through the composition of such simple maps.Fil: Tabak, E. G. . University Of New York; Estados UnidosFil: Turner, Cristina Vilma. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Centro de Investigación y Estudios de Matemática de Córdoba(p); Argentin

Space exploration: The interstellar goal and Titan demonstration

Automated interstellar space exploration is reviewed. The Titan demonstration mission is discussed. Remote sensing and automated modeling are considered. Nuclear electric propulsion, main orbiting spacecraft, lander/rover, subsatellites, atmospheric probes, powered air vehicles, and a surface science network comprise mission component concepts. Machine, intelligence in space exploration is discussed

Challenges in biomedical data science: data-driven solutions to clinical questions

Data are influencing every aspect of our lives, from our work activities, to our spare time and even to our health. In this regard, medical diagnosis and treatments are often supported by quantitative measures and observations, such as laboratory tests, medical imaging or genetic analysis. In medicine, as well as in several other scientific domains, the amount of data involved in each decision-making process has become overwhelming. The complexity of the phenomena under investigation and the scale of modern data collections has long superseded human analysis and insights potential