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This chapter discusses how the industrial ecological systems can help in dealing with environmental issues in developing countries, and it presents three case studies from India that highlight some of the unique environmental issues of developing world. Industrial ecology explores the assumption that the industrial system can be seen as a certain kind of ecosystem. The scope of industrial ecology goes well beyond waste exchange to the optimization of resources flowing through the economic system. Among the various specific aspects of developing countries, which have to be born in mind, is the fact that the pattern of resource flows in developing countries, and hence, the resultant environmental threat could be very different than what it is in the industrialized west. Typically, the flow of materials through the large, organized manufacturing facilities in the developing countries could be very small in relation to the overall material flow as the small, informal ?industry? plays a key role and forms a very significant portion of the economic activity. The case studies of the Tirupur textile industries, and the leather industry in India, illustrate how redefining the problem from a perspective of resource conservation, and on the basis of resource flow data could point to totally new directions for strategy planning. The case study of the Damodar Valley region amplifies the importance of looking beyond formal industry to solve an environmental problem. It shows that even for globally critical programs, such as climate change program in developing countries, it is just not enough to estimate the emissions from the formal industrial sectors.

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 politics of contaminated sites management : institutional regime change and actor's mode of participation in the environmental management of the Bonfol Chemical Waste Landfill in Switzerland

By the end of the 1970s, contaminated sites had emerged as one of the most complex and urgent environmental issues affecting industrialized countries. The authors show that small and prosperous Switzerland is no exception to the pervasive problem of sites contamination, the legacy of past practices in waste management having left some 38,000 contaminated sites throughout the country. This book outlines the problem, offering evidence that open and polycentric environmental decision-making that includes civil society actors is valuable. They propose an understanding of environmental management of contaminated sites as a political process in which institutions frame interactions between strategic actors pursuing sometimes conflicting interests. In the opening chapter, the authors describe the influences of politics and the power relationships between actors involved in decision-making in contaminated sites management, which they term a "wicked problem." Chapter Two offers a theoretical framework for understanding institutions and the environmental management of contaminated sites. The next five chapters present a detailed case study on environmental management and contaminated sites in Switzerland, focused on the Bonfol Chemical Landfill. The study and analysis covers the establishment of the landfill under the first generation of environmental regulations, its closure and early remediation efforts, and the gambling on the remediation objectives, methods and funding in the first decade of the 21st Century. The concluding chapter discusses the question of whether the strength of environmental regulations, and the type of interactions between public, private, and civil society actors can explain the environmental choices in contaminated sites management. Drawing lessons from research, the authors debate the value of institutional flexibility for dealing with environmental issues such as contaminated sites.

Automatic Mapping and Classification of Spatial Environmental Data

The paper deals with the development and application of the generic methodology for automatic processing (mapping and classification) of environmental data. General Regression Neural Network (GRNN) is considered in detail and is proposed as an efficient tool to solve the problem of spatial data mapping (regression). The Probabilistic Neural Network (PNN) is considered as an automatic tool for spatial classifications. The automatic tuning of isotropic and anisotropic GRNN/PNN models using cross-validation procedure is presented. Results are compared with the k-Nearest-Neighbours (k-NN) interpolation algorithm using independent validation data set. Real case studies are based on decision-oriented mapping and classification of radioactively contaminated territories.

Multiple Kernel Learning of Environmental Data. Case study: Analysis and Mapping of Wind Fields

The paper presents the Multiple Kernel Learning (MKL) approach as a modelling and data exploratory tool and applies it to the problem of wind speed mapping. Support Vector Regression (SVR) is used to predict spatial variations of the mean wind speed from terrain features (slopes, terrain curvature, directional derivatives) generated at different spatial scales. Multiple Kernel Learning is applied to learn kernels for individual features and thematic feature subsets, both in the context of feature selection and optimal parameters determination. An empirical study on real-life data confirms the usefulness of MKL as a tool that enhances the interpretability of data-driven models.

Ensemble methods for environmental data modelling with support vector regression

This paper investigates the use of ensemble of predictors in order to improve the performance of spatial prediction methods. Support vector regression (SVR), a popular method from the field of statistical machine learning, is used. Several instances of SVR are combined using different data sampling schemes (bagging and boosting). Bagging shows good performance, and proves to be more computationally efficient than training a single SVR model while reducing error. Boosting, however, does not improve results on this specific problem.

Stage fright: its experience as a problem and coping with it.

PURPOSE AND METHOD: This questionnaire survey of 190 university music students assessed negative feelings of music performance anxiety (MPA) before performing, the experience of stage fright as a problem, and how closely they are associated with each other. The study further investigated whether the experience of stage fright as a problem and negative feelings of MPA predict the coping behavior of the music students. Rarely addressed coping issues were assessed, i.e., self-perceived effectiveness of different coping strategies, knowledge of possible risks and acceptance of substance-based coping strategies, and need for more support.RESULTS: The results show that one-third of the students experienced stage fright as a problem and that this was only moderately correlated with negative feelings of MPA. The experience of stage fright as a problem significantly predicted the frequency of use and the acceptance of medication as a coping strategy. Breathing exercises and self-control techniques were rated as effective as medication. Finally, students expressed a strong need to receive more support (65%) and more information (84%) concerning stage fright.CONCLUSION: Stage fright was experienced as a problem and perceived as having negative career consequences by a considerable percentage of the surveyed students. In addition to a desire for more help and support, the students expressed an openness and willingness to seriously discuss and address the topic of stage fright. This provides a necessary and promising basis for optimal career preparation and, hence, an opportunity to prevent occupational problems in professional musicians. [Authors]

Importation of Acinetobacter baumannii into a burn unit: a recurrent outbreak of infection associated with widespread environmental contamination.

A burn patient was infected with Acinetobacter baumannii on transfer to the hospital after a terrorist attack. Two patients experienced cross-infection. Environmental swab samples were negative for A. baumannii. Six months later, the bacteria reemerged in 6 patients. Environmental swab samples obtained at this time were inoculated into a minimal mineral broth, and culture results showed widespread contamination. No case of infection occurred after closure of the unit for disinfection.

La dépendance à l'égard de l'environnement lors de lésions cérébrales: conduites d'imitation, de préhension et d'utilisation [Environmental dependence in brain lesions: imitative behavior, prehension and utilization].

This paper reviews the literature on clinical signs such as imitation behavior, grasp reaction, manipulation of tools, utilization behavior, environmental dependency, hyperlexia, hypergraphia and echolalia. Some aspects of this semiology are of special interest because they refer to essential notions such as free-will and autonomy.

Influence of local environmental olfactory cues on place learning in rats

The aim of the present study was to assess the influence of local environmental olfactory cues on place learning in rats. We developed a new experimental design allowing the comparison of the use of local olfactory and visual cues in spatial and discrimination learning. We compared the effect of both types of cues on the discrimination of a single food source in an open-field arena. The goal was either in a fixed or in a variable location, and could be indicated by local olfactory and/or visual cues. The local cues enhanced the discrimination of the goal dish, whether it was in a fixed or in a variable location. However, we did not observe any overshadowing of the spatial information by the local olfactory or visual cue. Rats relied primarily on distant visuospatial information to locate the goal, neglecting local information when it was in conflict with the spatial information.