Resultados de investigación

Climatic information recorded by the physician Francisco Fernández Navarrete in Granada (southern Spain) during the first third of the 18th century. Observations are included in the book “Cielo y suelo granadino” (Sky and soil in Granada), and consist of qualitative comments relating climatic conditions to illness and diseases from 1706 to 1730, as well as instrumental observations (using an “English barometer” and a “Florentine thermometer”) from December 1728 to February 1730.

Textos proporcionados para la ponencia del mismo título en el Congreso Internacional EVROPA RENASCENS "Latín y vernáculo en los siglos de Oro" (Jaén-Baeza, 20-24 de noviembre de 2017).

Energy meters provide valuable information that can be used to determine important features such as energy consumption of electrical devices and consumption habits in corporate, residential or public institutions. This information is crucial to establish energy saving strategies. With this aim, different approaches have been proposed in the literature, including non-intrusive load monitoring techniques, which enable the energy disaggregation of appliances and devices through a centralized measurement taken at panel level using a metering infrastructure. Generally, the accuracy of these techniques increases as more information is available on the analyzed signals or through subsequent post-computed values. Active power, reactive power, or even current harmonics measurements can be used for this task. However, the use of these and other recently proposed power and current features increases the dimensionality and, therefore, the complexity of the algorithms involved in the disaggregation process. Therefore, it is necessary to apply advanced techniques to reduce the dimensionality of the data, as well as the possible linear dependence between variables. This paper compares the performance of 8 data pretreatment methods and 6 dimensionality reduction techniques to data retrieved by an advanced metering infrastructure in a real environment consisting of 10 different home appliances. Results obtained from the comparative analysis show that the information provided by raw data can be enhanced by using pretreatment techniques and dimensionality reduction methods, especially when a custom combination of active power and current harmonics measures is considered. Keywords: Energy monitoring, energy data disaggregation, pretreatment methods, dimensionality reduction, harmonics, home appliances.

Archivo de datos experimentales

Resumen: Base de datos municipalizada para el conjunto de España con información para la identificación y caracterización de los sistemas agroalimentarios territorializados. Contenido: Información municipalizada sobre: superficie de áreas protegidas, % superficie municipal de áreas protegidas, Superficie Agraria Útil (SAU), % de superficie municipal correspondiente a Superficie Agraria Útil (SAU), Superficie de olivar (ha), % de superficie municipal correspondiente a olivar, Superficie de viñedo (ha), % de superficie municipal correspondiente a viñedo, Superficie de arrozales (ha), % de superficie municipal correspondiente a arrozales, Superficie de mosaico de cultivos (ha), % de superficie municipal correspondiente a mosaico de cultivos, Superficie de frutales (ha), % de superficie municipal correspondiente a frutales, Superficie de cereales de secano (ha), % de superficie municipal correspondiente a cereales de secano, Superficie agroforestal (ha), % de superficie municipal agroforestal, Superficie de prados (ha), % de superficie municipal de prados, número de grupos de desarrollo rural, número de Denominaciones de Origen Protegidas.

Datos de entrevistas para trabajo sobre asistencia sexual en salud, en relación a trabajo investigación máster sexología.

An excel-based sheet to calculate training zones and heart rate intensity according to the Karvonen formula (1). The user will download the heart rate data second-by-second from the device (ej.: Polar, Garmin, FitBit…). After providing the participant's maximum heart rate (maxHR) and resting heart rate (resting HR), the user will be able to set the different work zones according to their preferences using the heart rate reserve (%HRR). Once these data are entered, the excel will return the time (hh:mm:ss and % format) that the person has been working in the different zones according to Karvonen's formula. In addition, it includes Tanaka's formula (2) to calculate the maxHR in case it is not known by the user. (1) Karvonen, M. J., Kentala, E., and Mustala, O. (1957). The effects of training on heart rate: a longitudinal study. Ann. Med. Exp. Biol. Fenn., 35:307–315. (2) Tanaka, H et al. “Age-predicted maximal heart rate revisited.” Journal of the American College of Cardiology vol. 37,1 (2001): 153-6.