The relationship of ethnicity, socio-economic factors and malnutrition in primary school children in North of Iran: A cross-sectional study

related factors based on three ethnic groups among primary school children in north of Iran in 2010. Methods: This cross-sectional study was carried out through multistage cluster random sampling on 5698 subjects (2505 Fars-native, 2154 Turkman, and 1039 Sistani) in 112 schools. Well-trained staffs completed the questionnaire and measured students' weight and height. Malnutrition estimated the Z-score less than -2SD for underweight, stunting and wasting were calculated using the cutoffs from WHO references. Results: Generally, malnutrition was observed in 3.20%, 4.93% and 5.13% based on underweight, stunting and wasting respectively. It was more common in girls than in boys and in Sistani than in other ethnic groups. The correlation between malnutrition based on underweight and stunting and ethnicity was statisti-cally significant (P=0.001). Results of logistic regression analyses showed that the risk of malnutrition was in rural area 1.34 times more than urban area, in girls 1.17 times more than boys, in Sistani ethnic group 1.82 times more than Fars-native ethnic group, in low economic families 2.01 times more than high economic families. Conclusion: Underweight, stunting and wasting are the health problems in primary school children in north of Iran with a higher prevalence in girls, in rural areas, and in Sistani ethnic group

Detecting multiple authorship of United States Supreme Court legal decisions using function words

This paper uses statistical analysis of function words used in legal judgments written by United States Supreme Court justices, to determine which justices have the most variable writing style (which may indicated greater reliance on their law clerks when writing opinions), and also the extent to which different justices' writing styles are distinguishable from each other.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS378 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Recognition of partially occluded threat objects using the annealed Hopefield network

Recognition of partially occluded objects has been an important issue to airport security because occlusion causes significant problems in identifying and locating objects during baggage inspection. The neural network approach is suitable for the problems in the sense that the inherent parallelism of neural networks pursues many hypotheses in parallel resulting in high computation rates. Moreover, they provide a greater degree of robustness or fault tolerance than conventional computers. The annealed Hopfield network which is derived from the mean field annealing (MFA) has been developed to find global solutions of a nonlinear system. In the study, it has been proven that the system temperature of MFA is equivalent to the gain of the sigmoid function of a Hopfield network. In our early work, we developed the hybrid Hopfield network (HHN) for fast and reliable matching. However, HHN doesn't guarantee global solutions and yields false matching under heavily occluded conditions because HHN is dependent on initial states by its nature. In this paper, we present the annealed Hopfield network (AHN) for occluded object matching problems. In AHN, the mean field theory is applied to the hybird Hopfield network in order to improve computational complexity of the annealed Hopfield network and provide reliable matching under heavily occluded conditions. AHN is slower than HHN. However, AHN provides near global solutions without initial restrictions and provides less false matching than HHN. In conclusion, a new algorithm based upon a neural network approach was developed to demonstrate the feasibility of the automated inspection of threat objects from x-ray images. The robustness of the algorithm is proved by identifying occluded target objects with large tolerance of their features