Dialyse et écologie: est-il possible de faire mieux à l'avenir [Dialysis and ecology: can we do better in the future?].

Development of dialysis has saved the lives of many patients. However, haemodialysis and peritoneal dialysis are very demanding in resources such as water and electricity, and generate a large amount of waste. In this article, we will review the environmental aspects of dialysis. Different solutions will be discussed, such as recycling of water discharged during reverse osmosis, the integration of solar energy, recycling of waste plastics, and the use of other techniques such as sorbent dialysis. In a world where natural resources are precious and where global warming is a major problem, it is important that not only dialysis, but all branches of medicine become more attentive to ecology.

The significant behavioral aspects of adopted children within successful adoption in Romania

The human behavior is giving the entire picture of a person. The behavior is based on a complex combination of personal innate factors, experience and education and the social network within which the individual is integrated. Each moment the complex individual factors face external and internal environmental aspects which in turn, generate proactive and retroactive reactions. The adoption process is an important and challenging social experiment, bringing to the adopted child a new affiliation, social network, experiences and educational standards. This comes in his/her life after the trauma of losing his biological affiliation and social and emotional support. Which are the significant characteristics of the adopted child's behavior? Which are the behavioral characteristics of the adoptive child that guarantee the success of the domestic adoptions in Romania? These are the questions we are going to answer here based on the research done within FISAN(6) project. The 32 children, 11-16 years old, adopted by Romanian families, during the early ages (0-4 years), were evaluated with complex evaluation tools for children and parents. The adoptive families participating in the research live in the Western counties of Romania.Successful adoption is defined as secure attachment of the adopted child. The conclusions emphasize the importance of: the pro-social behavior, the loving behavior showed to the parents, the assertive behavior, the differentiations between parents and people outside the family, and between the two parents, and the humor. These behaviors displayed by the adopted children are the ingredients of a successful integration within the adoptive family.

Conceptual models for designing information systems supporting the strategic analysis of technology environments

1 6 STRUCTURE OF THIS THESIS -Chapter I presents the motivations of this dissertation by illustrating two gaps in the current body of knowledge that are worth filling, describes the research problem addressed by this thesis and presents the research methodology used to achieve this goal. -Chapter 2 shows a review of the existing literature showing that environment analysis is a vital strategic task, that it shall be supported by adapted information systems, and that there is thus a need for developing a conceptual model of the environment that provides a reference framework for better integrating the various existing methods and a more formal definition of the various aspect to support the development of suitable tools. -Chapter 3 proposes a conceptual model that specifies the various enviromnental aspects that are relevant for strategic decision making, how they relate to each other, and ,defines them in a more formal way that is more suited for information systems development. -Chapter 4 is dedicated to the evaluation of the proposed model on the basis of its application to a concrete environment to evaluate its suitability to describe the current conditions and potential evolution of a real environment and get an idea of its usefulness. -Chapter 5 goes a step further by assembling a toolbox describing a set of methods that can be used to analyze the various environmental aspects put forward by the model and by providing more detailed specifications for a number of them to show how our model can be used to facilitate their implementation as software tools. -Chapter 6 describes a prototype of a strategic decision support tool that allow the analysis of some of the aspects of the environment that are not well supported by existing tools and namely to analyze the relationship between multiple actors and issues. The usefulness of this prototype is evaluated on the basis of its application to a concrete environment. -Chapter 7 finally concludes this thesis by making a summary of its various contributions and by proposing further interesting research directions.

Interdisciplinary research for the analysis and risk assesment : the case of the municipality of Uspantan Guatemala

Short description of the proposed presentation * lees than 100 words This paper describes the interdisciplinary work done in Uspantán, Guatemala, a city vulnerable to natural hazards. We investigated local responses to landslides that happened in 2007 and 2010 and had a strong impact on the local community. We show a complete example of a systemic approach that incorporates physical, social and environmental aspects in order to understand risks. The objective of this work is to present the combination of social and geological data (mapping), and describe the methodology used for identification and assessment of risk. The article discusses both the limitations and methodological challenges encountered when conducting interdisciplinary research. Describe why it is important to present this topic at the Global Platform in less than 50 words This work shows the benefits of addressing risk in an interdisciplinary perspective, in particular how integrating social sciences can help identify new phenomena and natural hazards and assess risk. It gives a practical example of how one can integrate data from different fields. What is innovative about this presentation? * The use of mapping to combine qualitative and quantitative data. By coupling approaches, we could associate a hazard map with qualitative data gathered by interviews with the population. This map is an important document for the authorities. Indeed, it allows them to be aware of the most dangerous zones, the affected families and the places where it is most urgent to intervene.

Circadian and circaseptan patterns of natality and perinatal mortality of infants with different birth weights

OBJECTIVE. Data on human natality, stillbirth and perinatal mortality from Switzerland (1979-1987), available in four birthweight categories, are reexamined to assess any about-weekly (circaseptan) and changes in about-daily (circadian) patterns in central Europe over a century and a halfDESIGN. Retrospective analyses on archived data.SETTING. Federal Office of Statistics for Switzerland.RESULTS. In addition to prominent circadians, weekly patterns are also documented.CONCLUSION. Exogenous variations, prominent in early extrauterine life, such as changes of scheduling in obstetrics, may contribute to circadian and cireaseptan natality patterns. Information on these patterns serves in the optimization of neonatal care. Partly endogenous, partly physical environmental aspects, at least of about-weekly patterns, remain to be elucidated in series consisting exclusively of spontaneous parturitions.

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.