Search

Highlights: [•] We analyse the effect of climate change discourses on forest conservation in Europe. [•] Multiple discourses conceptualise conservation and climate change differently. [•] Distinct discourse coalitions use different discourses and storylines. [•] European level discourses are clearly related to political ideas and interests. [•] Local level discourses are dominated by uncertainty and pragmatism. [ABSTRACT FROM AUTHOR]

Abstract: Carbon monoxide intoxication (COI) can result in severe neuropsychiatric lesions that are however granted little attention in literature. Following the description of affective and neurological symptoms in a 37-year-old female patient five years following COI, we will review, across the literature (Medline1974–2006), the long-term neuropsychiatric consequences, etiopathogenic hypotheses, prognoses and treatments to apply. Subjective symptoms are reported by the quasi-totality of patients for over more than 30 years following COI. More than half of patients are diagnosed as suffering from cognitive impairments and other neurological symptoms after years following COI. Affective disorders are observed in almost three-fourths of patients and personality disorders in more than half. Numerous cerebral lesions and perfusion disorders can be observed through IRM, PET scan and SPECT and related to the clinical symptomatology of the patient. COI may constitute a risk factor in the waking of long-term neuropsychiatric disorders in a context of environmental and neurobiological complex factor interaction. A close follow-up must be envisaged with neuropsychiatric assessments and regular neuroimagery in order to adapt at best therapeutic interventions to the patient''s clinical status. First and foremost prevention and education remain the key solution to the reduction of morbidity and mortality of COI. [Copyright &y& Elsevier]

Abstract: This paper focuses on the application of Kohonen self-organizing maps (SOM) and principal component analysis (PCA) to thoroughly analyse and interpret multidimensional data from a biological process. The process is aimed at enhanced biological phosphorus removal (EBPR) from wastewater. In this work, SOM and PCA are firstly applied to the data set in order to identify and analyse the relationships among the variables in the process. Afterwards, K-means algorithm is used to find out how the observations can be grouped, on the basis of their similarity, in different classes. Finally, the information obtained using these intelligent tools is used for process interpretation and diagnosis. In the data set analysed, both techniques yielded similar results regarding the relationships among the variables and the clustering of the observations (i.e., the same groups of observations were identified) and, therefore, identical process interpretation could be made. The cluster analysis allowed relating the observations to process behaviour, clearly distinguishing start-up, desirable and poor process conditions. The results demonstrate that the applied techniques are highly effective to compress multidimensional data sets and to extract relevant information from the process, making the interpretation and diagnosis much easier and evident. [Copyright &y& Elsevier]