Danza, virtudes humanas y bienestar psicológico

[ES] El objetivo de este trabajo es hacer un análisis de la contribución de la danza en los profesionales y practicantes de la misma en el desarrollo de las virtudes humanas, las cuales suponen factores determinantes del bienestar psicológico. La investigación se basa en una metodología descriptiva-cuantitativa a través de la recogida de información en un cuestionario de 29 ítems cerrados. La finalidad es medir la relación de las variables de edad, género, perfil, tipo de danza y motivación en danza, con el supuesto de práctica danza-desarrollo virtudes humanas. Los resultados obtenidos permiten afirmar que los profesionales y practicantes de danza perciben esta relación como alta, lo cual abre una línea de investigación interesante para la Educación Social en cuanto a la danza como un nuevo ámbito o herramienta a través de la cual poder promocionar el desarrollo de las virtudes humanas y con ello incentivar el bienestar psicológico de los individuos.

Analysis of multivariate stochastic signals sampled by on-line particle analyzers: Application to the quantitative assessment of occupational exposure to NOAA in multisource industrial scenarios (MSIS)

In multisource industrial scenarios (MSIS) coexist NOAA generating activities with other productive sources of airborne particles, such as parallel processes of manufacturing or electrical and diesel machinery. A distinctive characteristic of MSIS is the spatially complex distribution of aerosol sources, as well as their potential differences in dynamics, due to the feasibility of multi-task configuration at a given time. Thus, the background signal is expected to challenge the aerosol analyzers at a probably wide range of concentrations and size distributions, depending of the multisource configuration at a given time. Monitoring and prediction by using statistical analysis of time series captured by on-line particle analyzers in industrial scenarios, have been proven to be feasible in predicting PNC evolution provided a given quality of net signals (difference between signal at source and background). However the analysis and modelling of non-consistent time series, influenced by low levels of SNR (Signal-Noise Ratio) could build a misleading basis for decision making. In this context, this work explores the use of stochastic models based on ARIMA methodology to monitor and predict exposure values (PNC). The study was carried out in a MSIS where an case study focused on the manufacture of perforated tablets of nano-TiO2 by cold pressing was performed