The solar corona is the origin of very dynamic events that are mostly produced in active regions (AR) and coronal holes (CH). The exact location of these large-scale features can be determined by applying image-processing approaches to extreme-ultraviolet (EUV) data.We here investigate the problem of segmentation of solar EUV images into ARs, CHs, and quiet-Sun (QS) images in a firm Bayesian way. On the basis of Bayes' rule, we need to obtain both prior and likelihood models. To find the prior model of an image, we used a Potts model in non-local mode. To construct the likelihood model, we combined a mixture of a Markov-Gauss model and non-local means. After estimating labels and hyperparameters with the Gibbs estimator, cellular learning automata were employed to determine the label of each pixel.We applied the proposed method to a Solar Dynamics Observatory/ Atmospheric Imaging Assembly (SDO/AIA) dataset recorded during 2011 and found that the mean value of the filling factor of ARs is 0.032 and 0.057 for CHs. The power-law exponents of the size distribution of ARs and CHs were obtained to be -1.597 and -1.508, respectively, with the maximum likelihood estimator method. When we compare the filling factors of our method with a manual selection approach and the SPoCA algorithm, they are highly compatible.

This study aimed to develop pH sensitive polymethacrylic acid-chitosan-polyethylene glycol (PCP) nanoparticles for oral insulin delivery. This was achieved by dispersion polymerization of methacrylic acid (MAA), polyethylene glycol (PEG) and chitosan (CS) in the presence of a cross linking agent, ethylene glycoldimethacrylate (EGDMA), and a polymer initiator, potassium per sulphate. Method development was carried out based on fractional factorial design by varying process parameters such as ratio of MAA to CS, ratio of MAA to EGDMA and the initial amount of insulin used to prepare PCP nanoparticles. PCP nanoparticles were characterized with different techniques including FTIR, DLS, and scanning electron microscopy (SEM). Insulin was incorporated into the nanoparticles by a diffusion filling method. It was found that the PCP nanoparticles exhibited good protein encapsulation efficiency (up to 99.9%). The findings revealed that the nanoparticles were spherical with smooth surfaces. The particle size average was determined to be 172 nm by DLS and 86 nm by SEM. The in vitro release profiles of PCP nanoparticles were investigated both in acidic (simulated gastric fluids, pH: 1.2) and neutral buffered solutions (simulated intestinal fluids, pH: 7.4). In order to have the best performance of nanoparticles, the process parameters were optimized using a support vector regression (SVR) method in combination with genetic algorithms (GA). The results revealed that the optimum settings were as follows: MAA/CS mole ratio (%): 297.35, CS/EGDMA mole ratio (%): 51.4, and the initial insulin amount (mg): 50.3. The findingsshowed that nanoparticles exhibited a pH responsive release profile where the extent of drug release in simulated intestinal medium was almost two fold more than the simulated gastric media. Global sensitivity analysis was also used to identify the impact of different variables on the PCP nanoparticle characteristics. This study introduces a new approach to rational design of nanoparticles according to the properties of interest.