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.

Gene expression databases and data mining.

The DNA microarray technology has arguably caught the attention of the worldwide life science community and is now systematically supporting major discoveries in many fields of study. The majority of the initial technical challenges of conducting experiments are being resolved, only to be replaced with new informatics hurdles, including statistical analysis, data visualization, interpretation, and storage. Two systems of databases, one containing expression data and one containing annotation data are quickly becoming essential knowledge repositories of the research community. This present paper surveys several databases, which are considered "pillars" of research and important nodes in the network. This paper focuses on a generalized workflow scheme typical for microarray experiments using two examples related to cancer research. The workflow is used to reference appropriate databases and tools for each step in the process of array experimentation. Additionally, benefits and drawbacks of current array databases are addressed, and suggestions are made for their improvement.

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.

The tobacco industry's past role in weight control related to smoking.

BACKGROUND: Smoking is thought to produce an appetite-suppressing effect by many smokers. Thus, the fear of body weight gain often outweighs the perception of health benefits associated with smoking cessation, particularly in adolescents. We examined whether the tobacco industry played a role in appetite and body weight control related to smoking and smoking cessation. METHODS: We performed a systematic search within the archives of six major US and UK tobacco companies (American Tobacco, Philip Morris, RJ Reynolds, Lorillard, Brown & Williamson and British American Tobacco) that were Defendants in tobacco litigation settled in 1998. Findings are dated from 1949 to 1999. RESULTS: The documents revealed the strategies planned and used by the industry to enhance effects of smoking on weight and appetite, mostly by chemical modifications of cigarettes contents. Appetite-suppressant molecules, such as tartaric acid and 2-acetylpyridine were added to some cigarettes. CONCLUSION: These tobacco companies played an active and not disclaimed role in the anti-appetite effects of smoking, at least in the past, by adding appetite-suppressant molecules into their cigarettes.

The tobacco industry's past role in weight control related to smoking

Résumé (français)ContexteLe tabagisme est connu pour produire un effet coupe-faim chez la plupart des fumeurs. La crainte de la prise de poids à l'arrêt de la cigarette l'emporte souvent sur la perception des bénéfices sur la santé associés à l'abandon du tabac, particulièrement chez les adolescents. Nous avons examiné quel a été le rôle de l'industrie du tabac dans cet effet de la fumée sur l'appétit et sur le contrôle du poids.MéthodesNous avons réalisé une recherche systématique dans les archives de six grandes compagnies de tabac américaines et du Royaume-Uni (American Tobacco, Philip Morris, RJ Reynolds, Lorillard, Brown & Williamson et British American Tobacco) qui avaient été mises en cause dans les procès du tabac en 1998 aux Etats-Unis. Les résultats concernent les années 1949 à 1999.RésultatsLes archives de ces industries montrent qu'elles ont mis au point et ont utilisé diverses stratégies pour améliorer les effets du tabagisme sur le poids et l'appétit, la plupart du temps en réalisant des modifications chimiques des cigarettes. Des substances coupe-faim, telles que l'acide tartrique et le 2-acétylpyridine ont notamment été ajoutées aux cigarettes.ConclusionEn tout cas par le passé, ces compagnies de tabac ont activement modifié leurs cigarettes afin d'en augmenter l'effet de diminution de l'appétit, notamment en y ajoutant des substances coupe-faim.

Nanoparticle usage and protection measures in the manufacturing industry : a representative survey

Addressing the risks of nanoparticles requires knowledge about release into the environment and occupational exposure. However, such information currently is not systematically collected; therefore, this risk assessment lacks quantitative data. The goal was to evaluate the current level of nanoparticle usage in Swiss industry as well as health, safety, and environmental measures, and the number of potentially exposed workers. A representative, stratified mail survey was conducted among 1626 clients of the Swiss National Accident Insurance Fund (SUVA), which insures 80,000 manufacturing firms, representing 84% of all Swiss manufacturing companies (947 companies answered the survey for a 58.3% response rate). The extrapolation to all Swiss manufacturing companies results in 1309 workers (95% confidence interval [CI]: 1073 to 1545) potentially exposed to nanoparticles in 586 companies (95% CI: 145 to 1027). This corresponds to 0.08% of workers (95% CI: 0.06% to 0.09%) and to 0.6% of companies (95% CI: 0.2% to 1.1%). The industrial chemistry sector showed the highest percentage of companies using nanoparticles (21.2%). Other important sectors also reported nanoparticles. Personal protection equipment was the predominant protection strategy. Only a few applied specific environmental protection measures. This is the first nationwide representative study on nanoparticle use in the manufacturing sector. The information gained can be used for quantitative risk assessment. It can also help policymakers design strategies to support companies developing a safer use of nanomaterial. Notingthe current low use of nanoparticles, there is still time to proactively introduce protective methods. If the predicted "nano-revolution" comes true, now is the time to take action. [Supplementary materials are available for this article. Go to the publisher's online edition of Journal of occupational and Environmental Hygiene for the following free supplemental resource: a pdf file containing a detailed description of the approach to statistical analyses, English translation of the questionnaire, additional information for Figure 1, and additional information for the SUVA-code.] [Authors]