Sistema de reutilización de agua para inodoro que permita recolectar aguas grises de duchas o lavabos

Implementar un sistema de recolección de aguas grises de duchas y lavabos para reutilizar en inodoros.Se presenta el diseño y construcción de un sistema de reutilización de aguas grises para inodoro, con la finalidad de ayudar a disminuir el consumo de agua y reutilizando el líquido para otros objetivos, específicamente para descargar el inodoro. Adicionalmente, con la implementación de este sistema se pretende solucionar la falta de agua en ciertas épocas del año en sectores en zonas donde no existe un constante abastecimiento de líquido que es muy necesario para el uso diario. En el desarrollo de la solución se analizan criterios y limitaciones de diseño. Se plantearon varias alternativas de solución y se utilizaron diferentes métodos para seleccionar la mejor opción. Para el inodoro se obtuvo un modelo que no afecta la funcionalidad de sistema original el cual ofrece como ventajas que requiere poco mantenimiento es seguro para el usuario. Este sistema cuenta con sensores sencillos de manipular e instalar. Posee un microcontrolador fácil de optimizar en caso de mejorar la funcionalidad del sistema. Los dispositivos que se implementaron en el sistema son fáciles de adquirir y su costo es sumamente accesible, en comparación con otros dispositivos en el mercado; además, su implementación y mantenimiento es muy sencillo.Ingenierí

Intervención educativa en los conocimientos sobre reanimación cardiopulmonar básica (RCP) en los estudiantes de Enfermería Con Mención En Pacientes Críticos de quinto año del POLISAL, UNAN Managua, en el periodo del segundo semestre 2015

La investigación que lleva por título Intervención educativa en los conocimientos sobre reanimación cardiopulmonar básica (RCP) en los estudiantes de Enfermería Con Mención En Pacientes Críticos de quinto año del POLISAL, UNAN Managua, en el periodo del segundo semestre 2015, buscó describir las variables socio demográficas de los estudiantes de enfermería con mención en pacientes críticos de quinto año del POLISAL, analizando los conocimientos que poseían sobre reanimación cardiopulmonar básica en adulto luego se desarrolló una intervención educativa que mejoro los conocimientos estableciendo una relación entre los conocimientos que poseían antes de la intervención y después de la intervención educativa obteniendo como resultado un cambio significativo en el conocimiento de los estudiantes evidenciado en el incremento de la nota promedio del 56.7 del pre-test al 81.9 en el post-test , aumentando 25.2 puntos la nota de los estudiantes. El análisis estadístico pertinente con los datos obtenidos permitió aceptar la hipótesis de investigación, que indica que el conocimiento de los estudiantes mejoro con la intervención educativa. Se plantearon las recomendaciones sobre el desarrollo de estrategias encaminadas a mantener siempre las actualizaciones en los conocimientos, de igual manera capacitar sobre la técnica de reanimación cardiopulmonar básica en adultos 2015 a los estudiantes de enfermería de las demás menciones del POLISAL y certificar a los docentes de enfermería del POLISAL UNAN Managua sobre la técnica de reanimación cardiopulmonar básica actualizada y a los estudiantes actualizar sus conocimientos de forma autodidactica en las distintas áreas y temáticas de su profesión

Ensemble Forecasting of Major Solar Flares: Methods for Combining Models

One essential component of operational space weather forecasting is the prediction of solar flares. With a multitude of flare forecasting methods now available online it is still unclear which of these methods performs best, and none are substantially better than climatological forecasts. Space weather researchers are increasingly looking towards methods used by the terrestrial weather community to improve current forecasting techniques. Ensemble forecasting has been used in numerical weather prediction for many years as a way to combine different predictions in order to obtain a more accurate result. Here we construct ensemble forecasts for major solar flares by linearly combining the full-disk probabilistic forecasts from a group of operational forecasting methods (ASAP, ASSA, MAG4, MOSWOC, NOAA, and MCSTAT). Forecasts from each method are weighted by a factor that accounts for the method's ability to predict previous events, and several performance metrics (both probabilistic and categorical) are considered. It is found that most ensembles achieve a better skill metric (between 5\% and 15\%) than any of the members alone. Moreover, over 90\% of ensembles perform better (as measured by forecast attributes) than a simple equal-weights average. Finally, ensemble uncertainties are highly dependent on the internal metric being optimized and they are estimated to be less than 20\% for probabilities greater than 0.2. This simple multi-model, linear ensemble technique can provide operational space weather centres with the basis for constructing a versatile ensemble forecasting system -- an improved starting point to their forecasts that can be tailored to different end-user needs

Ensemble Forecasting of Major Solar Flares: Methods for Combining Models

One essential component of operational space weather forecasting is the prediction of solar flares. With a multitude of flare forecasting methods now available online it is still unclear which of these methods performs best, and none are substantially better than climatological forecasts. Space weather researchers are increasingly looking towards methods used by the terrestrial weather community to improve current forecasting techniques. Ensemble forecasting has been used in numerical weather prediction for many years as a way to combine different predictions in order to obtain a more accurate result. Here we construct ensemble forecasts for major solar flares by linearly combining the full-disk probabilistic forecasts from a group of operational forecasting methods (ASAP, ASSA, MAG4, MOSWOC, NOAA, and MCSTAT). Forecasts from each method are weighted by a factor that accounts for the method's ability to predict previous events, and several performance metrics (both probabilistic and categorical) are considered. It is found that most ensembles achieve a better skill metric (between 5\% and 15\%) than any of the members alone. Moreover, over 90\% of ensembles perform better (as measured by forecast attributes) than a simple equal-weights average. Finally, ensemble uncertainties are highly dependent on the internal metric being optimized and they are estimated to be less than 20\% for probabilities greater than 0.2. This simple multi-model, linear ensemble technique can provide operational space weather centres with the basis for constructing a versatile ensemble forecasting system -- an improved starting point to their forecasts that can be tailored to different end-user needs.Comment: Accepted for publication in the Journal of Space Weather and Space Climat

Active Region Photospheric Magnetic Properties Derived from Line-of-sight and Radial Fields

The effect of using two representations of the normal-to-surface magnetic field to calculate photospheric measures that are related to active region (AR) potential for flaring is presented. Several AR properties were computed using line-of-sight (Blos) and spherical-radial (Br) magnetograms from the Spaceweather HMI Active Region Patch (SHARP) products of the Solar Dynamics Observatory, characterizing the presence and features of magnetic polarity inversion lines, fractality, and magnetic connectivity of the AR photospheric field. The data analyzed corresponds to ≈4,000 AR observations, achieved by randomly selecting 25% of days between September 2012 and May 2016 for analysis at 6-hr cadence. Results from this statistical study include: i) the Br component results in a slight upwards shift of property values in a manner consistent with a field-strength underestimation by the Blos component; ii) using the Br component results in significantly lower inter-property correlation in one-third of the cases, implying more independent information about the state of the AR photospheric magnetic field; iii) flaring rates for each property vary between the field components in a manner consistent with the differences in property-value ranges resulting from the components; iv)flaring rates generally increase for higher values of properties, except Fourier spectral power index that has flare rates peaking around a value of 5=3. These findings indicate that there may be advantages in using Br rather than Blos in calculating flare-related AR magnetic properties, especially for regions located far from central meridian

Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning

We propose a forecasting approach for solar flares based on data from Solar Cycle 24, taken by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) mission. In particular, we use the Spaceweather HMI Active Region Patches (SHARP) product that facilitates cut-out magnetograms of solar active regions (AR) in the Sun in near-realtime (NRT), taken over a five-year interval (2012 – 2016). Our approach utilizes a set of thirteen predictors, which are not included in the SHARP metadata, extracted from line-of-sight and vector photospheric magnetograms. We exploit several Machine Learning (ML) and Conventional Statistics techniques to predict flares of peak magnitude >M1 and >C1, within a 24 h forecast window. The ML methods used are multi-layer perceptrons (MLP), support vector machines (SVM) and random forests (RF). We conclude that random forests could be the prediction technique of choice for our sample, with the second best method being multi-layer perceptrons, subject to an entropy objective function. A Monte Carlo simulation showed that the best performing method gives accuracy ACC=0.93(0.00), true skill statistic TSS=0.74(0.02) and Heidke skill score HSS=0.49(0.01) for >M1 flare prediction with probability threshold 15% and ACC=0.84(0.00), TSS=0.60(0.01) and HSS=0.59(0.01) for >C1 flare prediction with probability threshold 35%