28 resultados para María de Cervelló , Santa, 1230-1290-Sermones
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Duración (en horas): Más de 50 horas. Destinatario: Estudiante y Docente
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10 p.
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Trabajo de Investigación Predoctoral 2006
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8 cartas (mecanografiadas) ; 225x285 mmm. Ubicación: Caja 1 - Carpeta 20
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1 carta (manuscrita) : 275x214mm. Ubicación: Caja 1 - Carpeta 81
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7 cartas (mecanografiadas y manuscritas) ; entre 180x130mm y 214x139mm. Ubicación: Caja 1 - Carpeta 82
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10 cartas (mecanografiadas); entre 210x255mm y 210x310mm. [La carta fechada el 10-11-1942 esta incompleta, falta la primera hoja]
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11 cartas (mecanografiadas y manuscritas); entre 170x225mm y 215x275mm
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8 cartas (mecanografiadas y manuscritas); entre 150x210mm y 215x275mm .- 1 Felicitación de Navidad (manuscrita y sin fecha) ; 110mmx160mm
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2 cartas (mecanografiada y manuscrita); entre 215x155mm y 210x315mm.
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Duración (en horas): De 21 a 30 horas Destinatario: Estudiante y Docente
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Actas coordinadas por Maurilio Pérez González
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Recent player tracking technology provides new information about basketball game performance. The aim of this study was to (i) compare the game performances of all-star and non all-star basketball players from the National Basketball Association (NBA), and (ii) describe the different basketball game performance profiles based on the different game roles. Archival data were obtained from all 2013-2014 regular season games (n = 1230). The variables analyzed included the points per game, minutes played and the game actions recorded by the player tracking system. To accomplish the first aim, the performance per minute of play was analyzed using a descriptive discriminant analysis to identify which variables best predict the all-star and non all-star playing categories. The all-star players showed slower velocities in defense and performed better in elbow touches, defensive rebounds, close touches, close points and pull-up points, possibly due to optimized attention processes that are key for perceiving the required appropriate environmental information. The second aim was addressed using a k-means cluster analysis, with the aim of creating maximal different performance profile groupings. Afterwards, a descriptive discriminant analysis identified which variables best predict the different playing clusters. The results identified different playing profile of performers, particularly related to the game roles of scoring, passing, defensive and all-round game behavior. Coaching staffs may apply this information to different players, while accounting for individual differences and functional variability, to optimize practice planning and, consequently, the game performances of individuals and teams.