Total mercury levels in selected tissues of some marine crustaceans from Persian Gulf, Iran: variations related to length, weight and sex.

Much of the variation in trace metal tissue concentrations in marine invertebrates has been attributed to the variety in individual organism size, age and sex. This study assessed the relationship between total mercury (Hg) concentrations in edible tissue, exoskeleton and viscera with length, weight and gender for 69 samples of crustaceans, Penaeus semisulcatus (n = 30), Thenus orientalis (n = 21) and Portunus pelagicus (n = 18) from the northern part of the Persian Gulf. Significant increase in the Hg level in muscle and viscera (r > 0.65, p < 0.01) with an increase in length and weight for all three species. No relationship was found between the Hg level in exoskeleton and length or weight. Significantly higher Hg levels (p < 0.01) were found in female P. semisulcatus than in males (muscle and viscera), but no gender differences were found for the other two species.

Developing and Evaluating a University Recommender System.

A challenge for many young adults is to find the right institution to follow higher education. Global university rankings are a commonly used, but inefficient tool, for they do not consider a person's preferences and needs. For example, some persons pursue prestige in their higher education, while others prefer proximity. This paper develops and evaluates a university recommender system, eliciting user preferences as ratings to build predictive models and to generate personalized university ranking lists. In Study 1, we performed offline evaluation on a rating dataset to determine which recommender approaches had the highest predictive value. In Study 2, we selected three algorithms to produce different university recommendation lists in our online tool, asking our users to compare and evaluate them in terms of different metrics (Accuracy, Diversity, Perceived Personalization, Satisfaction, and Novelty). We show that a SVD algorithm scores high on accuracy and perceived personalization, while a KNN algorithm scores better on novelty. We also report findings on preferred university features.