Statistical methods are usually applied in examining diet-disease associations, whereas factor analysis is commonly used for dietary pattern recognition. Recently, machine learning (ML) has been also proposed as an alternative technique in health classification. In this work, the predictive accuracy of statistical v. ML methodologies as regards the association of dietary patterns on CVD risk was tested. During 2001-2002, 3042 men and women (45 (sd 14) years) were enrolled in the ATTICA study. In 2011-2012, the 10-year CVD follow-up was performed among 2020 participants. Item Response Theory was applied to create a metric of combined 10-year cardiometabolic risk, the 'Cardiometabolic Health Score', that incorporated incidence of CVD, diabetes, hypertension and hypercholesterolaemia. Factor analysis was performed to extract dietary patterns, on the basis of either foods or nutrients consumed; linear regression analysis was used to assess their association with the cardiometabolic score. Two ML techniques (k-nearest-neighbor's algorithm and random-forests decision tree) were applied to evaluate participants' health based on dietary information. Factor analysis revealed five and three factors from foods and nutrients, respectively, explaining 54 and 65 % of the total variation in intake. Nutrient and food pattern regression models showed similar accuracy in correctly classifying an individual according to the cardiometabolic risk (R 2=9·6 % and R 2=8·3 %, respectively). ML techniques were superior compared with linear regression in correct classification of the individuals according to the Health Score (accuracy approximately 38 v. 6 %, respectively), whereas the two ML methods showed equal classification ability. Conclusively, ML methods could be a valuable tool in the field of nutritional epidemiology, leading to more accurate disease-risk evaluation.

Inclusion of SEN students in mainstream classrooms constitutes an ambitious objective of the education system in Cyprus. The legislation currently in effect provides for a clear-cut frame of action, largely based on the willingness of teachers in the mainstream school, who are expected to develop positive attitudes that encourage and support the coeducation of SEN and non-SEN students. However, the impact of infrastructural insufficiency, prejudice and, above all, the lack of specific knowledge as regards special education, seems in many cases to pose obstacles that undermine any efforts made. The present study, based on recent research in the schools in Cyprus, aims at recording teachers' perceptions and determining the factors that influence Cypriot teachers' attitudes towards inclusion after the implementation of the new laws on special education in Cyprus. The findings of this research confirm that the provisions in the new laws have adopted the right course of action, although teachers' feelings of inadequacy, non-SEN students' circumspection and SEN students' hesitation have not yet been satisfactorily addressed.

We lower the upper bound for the threshold for random 3-SAT from 4.6011 to 4.596 through two different approaches, both giving the same result. (Assuming the threshold exists, as is generally believed but still not rigorously shown.) In both approaches, we start with a sum over all truth assignments that appears in an upper bound by Kirousis et al. to the the probability that a random 3-SAT formula is satisfiable. In the first approach, this sum is reformulated as the partition function of a spin system consisting of n sites each of which may assume the values 0 or 1. We then obtain an asymptotic expression for this function that results from the application of an optimization technique from statistical * Research performed while this author was visiting the School of Computer Science of Carleton University in Ottawa, supported by the Greek Ministry of National Economy through a NATO scholarship for conducting postdoctoral studies (contract number 106384/∆OO 1222/2-7-98).

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