Bayesian models for finding and grouping junctions

In this paper, we propose two Bayesian methods for detecting and grouping junctions. Our junction detection method evolves from the Kona approach, and it is based on a competitive greedy procedure inspired in the region competition method. Then, junction grouping is accomplished by finding connecting paths between pairs of junctions. Path searching is performed by applying a Bayesian A* algorithm that has been recently proposed. Both methods are efficient and robust, and they are tested with synthetic and real images.

The posterior chamber phakic refractive lens (PRL): a review

Implantation of phakic intraocular lenses (pIOLs) is a reversible refractive procedure, preserving the patient’s accommodative function with minimal induction of higher order aberrations compared with corneal photoablative procedures. Despite this, as an intraocular procedure, it has potential risks such as cataracts, chronic uveitis, pupil ovalization, corneal endothelial cell loss, pigmentary dispersion syndrome, pupillary block glaucoma, astigmatism, or endophthalmitis. Currently, only two models of posterior chamber pIOLs are commercially available, the implantable collammer lens (STAAR Surgical Co.) and the phakic refractive lens (PRL; Zeiss Meditec). The number of published reports on the latter is very low, and some concerns still remain about its long-term safety. The present article reviews the published literature on the outcomes after PRL implantation in order to provide a general overview and evaluate its real potential as a surgical refractive option.

Individual differences in predicting occupational success: The effect of population heterogeneity

Using a sample of 339 university graduates from the University of Alicante (Spain) three years after completion of their studies, we studied the relationships between general intelligence (GI), personality traits, emotional intelligence (EI), academic performance, and occupational attainment and compared the results of conventional regression analysis with the results obtained from applying regression mixture models. The results reveal the influence of unobserved population heterogeneity (latent class) on the relationship between predictors and criteria and the improvement in the prediction obtained from applying regression mixture models compared to applying a conventional regression model.