Salvation goods and Canonization Logic : On two Popular Cults of Southern Italy

(Summary of the production) The idea that religion has to succeed in a «market», selling «salvation goods», has proved to be extremely attractive to scholars in sociology and the study of religion. Max Weber used the term «salvation good» to compare different religious traditions. Pierre Bourdieu employed the term in order to analyze «religious economy». And recently, an American group of researchers advocating «rational choice of religion» put the theme at the forefront of current debates. This book - the fruit of an International Congress in Lausanne in April 2005 - brings together leading specialists in the fields of sociology and the study of religion who discuss the terms «salvation goods» (or religious goods) and «religious market». The authors test the applicability of these concepts by using specific examples and they either deliberately advocate or criticize Weberian, Bourdieusian or rational-choice perspectives.

Extreme Precipitation Modelling Using Geostatistics and Machine Learning Algorithms

The paper presents an approach for mapping of precipitation data. The main goal is to perform spatial predictions and simulations of precipitation fields using geostatistical methods (ordinary kriging, kriging with external drift) as well as machine learning algorithms (neural networks). More practically, the objective is to reproduce simultaneously both the spatial patterns and the extreme values. This objective is best reached by models integrating geostatistics and machine learning algorithms. To demonstrate how such models work, two case studies have been considered: first, a 2-day accumulation of heavy precipitation and second, a 6-day accumulation of extreme orographic precipitation. The first example is used to compare the performance of two optimization algorithms (conjugate gradients and Levenberg-Marquardt) of a neural network for the reproduction of extreme values. Hybrid models, which combine geostatistical and machine learning algorithms, are also treated in this context. The second dataset is used to analyze the contribution of radar Doppler imagery when used as external drift or as input in the models (kriging with external drift and neural networks). Model assessment is carried out by comparing independent validation errors as well as analyzing data patterns.

Cinéma, machine à mondes : essai sur les films à univers multiples

Dans cet ouvrage, l'auteur propose une conceptualisation théorique de la coprésence en un même film de mondes multiples en abordant différents paramètres (hétérogénéité de la facture de l'image, pratiques du montage alterné, typologie des enchâssements, expansion sérielle, etc.) sur la base d'un corpus de films de fiction récents qui appartiennent pour la plupart au genre de la science-fiction (Matrix, Dark City, Avalon, Resident Evil, Avatar,...). Issue de la filmologie, la notion de « diégèse » y est développée à la fois dans le potentiel d'autonomisation dont témoigne la conception mondaine qui semble dominer aujourd'hui à l'ère des jeux vidéo, dans ses liens avec le récit et dans une perspective intermédiale. Les films discutés ont la particularité de mettre en scène des machines permettant aux personnages de passer d'un monde à l'autre : les modes de figuration de ces technologies sont investigués en lien avec les imaginaires du dispositif cinématographique et les potentialité du montage. La comparaison entre les films (Tron et son récent sequel, Totall Recall et son remake) et entre des oeuvres filmiques et littéraires (en particulier les nouvelles de Philip K. Dick et Simlacron 3 de Galouye) constitue un outil d'analyse permettant de saisir la contemporanéité de cette problématique, envisagée sur le plan esthétique dans le contexte de l'imagerie numérique.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

PHA urges 'freshers' to be smart and know their alcohol limits

As 'fresher's week' commences, the Public Health Agency is encouraging students across Northern Ireland to avoid binge drinking and to know their limits if they do choose to drink alcohol.Enjoying new freedoms, at college or university, means taking care of yourself and others and, if you choose to drink, staying within safe alcohol limits. Owen O'Neill, PHA Health and Social Wellbeing Improvement Manager for drugs and alcohol, said: "Some young people may drink more when they leave home, or join their friends in college or university for the first time. They might think that, as young people, they don't have to take care with alcohol, but staying within the safe drinking limits is important for everyone who drinks. Excessive and binge drinking can have lasting effects on health, such as damage to the liver, heart, brain and stomach. Drinking too much can also increase the risk of accidents and antisocial behaviour as well as sexually transmitted infections and unplanned pregnancy"."We would also strongly advise against drinking games. Although they are regarded as a 'bit of fun', in reality they can be very dangerous. As an extreme form of binge drinking, where large quantities of alcohol are consumed in a very short time, drinking games can result in alcohol poisoning, leading to brain damage, coma or death. The PHA encourages students to enjoy their new student life, but urges them to be aware of their alcohol intake and drink responsibly, especially throughout fresher's week, with the many cheap drink promotions currently available."Daily alcohol limits are recommended by the government in order to avoid the risks of excessive and binge drinking in any one session. These are:Men: No more than 3 to 4 units of alcohol a day and no more than 21 units over the course of the week.Women: No more than 2 to 3 units of alcohol a day and no more than 14 units over the course of the week.Examples of units:Can of extra strong lager - 4 unitsBottle of lager - 1.5 unitsPint of standard lager - 2.5 unitsPint of premium larger - 3 unitsSmall pub bottle of wine - 2.25 units70cl bottle of wine - 7 to 10 unitsStandard 275ml of alcopops - 1.5 to 1.8 units70cl bottle of alcopops - 3.75 to 4.5 unitsPub measure of spirits - 1.5 unitsPint of cider - 3 unitsPint of stout - 2.5 unitsIf you do choose to drink alcohol:DON'T:Ever drink and driveDrink on an empty stomachMix alcohol with other drugsDrink in rounds as this may speed up your drinkingLeave your drinks unattendedDO:Take sips rather than gulpsAlternate each alcoholic drink with a non alcoholic drink e.g. water or a soft drinkSet yourself a limit and try to stick to it (refer to daily alcohol limits) Take frequent breaks from drinking to give your body time to recoverTell friends and family where you are going and who you will be withRemember, that for each unit you drink over the daily limit, the risk to your health increases. It's important to spread the units throughout the week - you can't 'save up' your units for the weekend or your holiday. It is also important to drink plenty of water, ideally matching the amount of alcohol you have consumed.So students make smart choices this term - drink sensibly and know your limits!For further information on sensible drinking and alcohol units visit the Public Health Agency's website www.knowyourlimits.info

A New Year's Resolution - be smart about your drinking!

Now 2012 has arrived, the Public Health Agency is encouraging people to make a New Year's resolution to know their limits when it comes to alcohol, not to drink excessively and to cut back for a while, especially if the festive period led to a little too much consumption.Owen O'Neill, PHA Health and Social Wellbeing Improvement Manager and drugs and alcohol lead, explained: "The New Year is a great opportunity for us to be positive about our health, making resolutions that make us look and feel better. If people choose to drink, staying within the safe drinking limits is important. Excessive and binge drinking can have lasting effects on health, such as damage to the liver, heart, brain and stomach. Drinking too much can also increase the risk of accidents and antisocial behaviour as well as sexually transmitted infections and unplanned pregnancy. And it doesn't have to be drinking to extremes - regularly drinking over the recommended limits can have a damaging effect."Remember that for each unit you drink over the daily limit, the risk to your health increases. It's important to spread the units throughout the week and not 'save' them for the weekend and to drink plenty of water, ideally matching the amount of alcohol you have consumed."For those who have consumed a lot of alcohol over the festive season, cutting back in the New Year and being careful can have immediate, positive effects particularly on helping you to look and feel better, being less tired during the day, feeling fitter and perhaps losing weight. Longer term, the benefits include improved mood, sleep, memory and general health, particularly improving liver function, immunity to illness and preventing any damage caused by any excessive drinking getting any worse."Daily alcohol limits are recommended by the government to avoid the dangers of excessive and binge drinking in any one session. These are:MenNo more than 3 to 4 units of alcohol a day and no more than 21 units over the course of the week.WomenNo more than 2 to 3 units of alcohol a day and no more than 14 units over the course of the week.Examples of units:Can of extra strong lager - 4 unitsBottle of lager - 1.5 unitsSmall pub bottle of wine - 2.25 unitsPub measure of spirits - 1.5 unitsPint of stout - 2.5 unitsPint of cider - 3 unitsFor further information on sensible drinking and alcohol units visit the Public Health Agency's website www.knowyourlimits.info

Smart Schools = Smart Economy

Ireland’s national recovery will be rooted in further developing our outstanding education system. Schools and colleges are key contributors to economic growth and national competitiveness, providing successive generations with the skills and abilities necessary for a vibrant economy and inclusive society. Within the educational system, teachers play a central role in developing the potential of our children and young people. Our education system must continue to be responsive to and supportive of the economic life of this country.

Love machine

This poster provides advice on the use of condoms as a method of protection from unplanned pregnancy and sexually transmitted infections (STIs). It also provides contact details for the�Genito Urinary Medicine (GUM) clinics in Northern Ireland.

A Classification of Tools for Learning Logic

A table showing a comparison and classification of tools (intelligent tutoring systems) for e-learning of Logic at a college level.