1) Nanoflares, the basic units of impulsive energy release, may produce much of the solar background emission. Extrapolation of the energy frequency distribution of observed microflares, which follows a power law to lower energies, can give an estimation of the importance of nanoflares for heating the solar corona. If the power-law index is greater than 2, then the nanoflare contribution is dominant. Wemodel a time series of
extreme-ultraviolet emission radiance as random flares with a power-law exponent of the flare event distribution. The model is based on three key parameters: the flare rate, the flare duration, and the power-law exponent of the flare intensity frequency distribution. We use this model to simulate emission line radiance detected in 171 A, observed by Solar Terrestrial Relation Observatory/Extreme- Ultraviolet Imager and Solar Dynamics Observatory/Atmospheric Imaging Assembly. The observed light curves are matched with simulated light curves using an Artificial Neural Network, and the parameter values are determined across the active region, quiet Sun, and coronal hole. The damping rate of nanoflares is compared with the radiative losses cooling time. The effect of background emission, data cadence, and network sensitivity on the key parameters of the model is studied. Most of the observed light curves have a power-law exponent, ƒ¿, greater than the critical value 2. At these sites, nanoflare heating could be significant.

2) Small-scale extreme-ultraviolet (EUV) dimming often surrounds sites of energy release in the quiet Sun. This paper describes a method for the automatic detection of these small-scale EUV dimmings using a feature-based classifier. The method is demonstrated using sequences of 171 A images taken by the STEREO/Extreme UltraViolet Imager (EUVI) on 2007 June 13 and by Solar Dynamics Observatory/Atmospheric Imaging Assembly on 2010 August 27. The feature identification relies on recognizing structure in sequences of space-time 171 A images using the Zernike moments of the images. The Zernike moments space-time
slices with events and non-events are distinctive enough to be separated using a support vector machine (SVM) classifier. The SVM is trained using 150 events and 700 non-event space-time slices.We find a total of 1217 events in the EUVI images and 2064 events in the AIA images on the days studied. Most of the events are found between latitudes .35. and +35.. The sizes and expansion speeds of central dimming regions are extracted using a region grow algorithm. The histograms of the sizes in both EUVI and AIA follow a steep power law with slope of about .5. The AIA slope extends to smaller sizes before turning over. The mean velocity of 1325 dimming regions seen by AIA is found to be about 14 km/s.