Resilient housing : a new resource-oriented approach

The institutional regimes framework has previously been applied to the institutional conditions that support or hinder the sustainability of housing stocks. This resource-based approach identifies the actors across different sectors that have an interest in housing, how they use housing, the mechanisms affecting their use (public policy, use rights, contracts, etc.) and the effects of their uses on the sustainability of housing within the context of the built environment. The potential of the institutional regimes framework is explored for its suitability to the many considerations of housing resilience. By identifying all the goods and services offered by the resource 'housing stock', researchers and decision-makers could improve the resilience of housing by better accounting for the ecosystem services used by housing, decreasing the vulnerability of housing to disturbances, and maximizing recovery and reorganization following a disturbance. The institutional regimes framework is found to be a promising tool for addressing housing resilience. Further questions are raised for translating this conceptual framework into a practical application underpinned with empirical data.

Metabolic activation pattern of distinct hippocampal subregions during spatial learning and memory retrieval.

Activation dynamics of hippocampal subregions during spatial learning and their interplay with neocortical regions is an important dimension in the understanding of hippocampal function. Using the (14C)-2-deoxyglucose autoradiographic method, we have characterized the metabolic changes occurring in hippocampal subregions in mice while learning an eight-arm radial maze task. Autoradiogram densitometry revealed a heterogeneous and evolving pattern of enhanced metabolic activity throughout the hippocampus during the training period and on recall. In the early stages of training, activity was enhanced in the CA1 area from the intermediate portion to the posterior end as well as in the CA3 area within the intermediate portion of the hippocampus. At later stages, CA1 and CA3 activations spread over the entire longitudinal axis, while dentate gyrus (DG) activation occurred from the anterior to the intermediate zone. Activation of the retrosplenial cortex but not the amygdala was also observed during the learning process. On recall, only DG activation was observed in the same anterior part of the hippocampus. These results suggest the existence of a functional segmentation of the hippocampus, each subregion being dynamically but also differentially recruited along the acquisition, consolidation, and retrieval process in parallel with some neocortical sites.

Modelling the effect of temperature on height increment of Scots pine at high latitudes

Abstract

Modelling of crowded polymers elucidate effects of double-strand breaks in topological domains of bacterial chromosomes.

Using numerical simulations of pairs of long polymeric chains confined in microscopic cylinders, we investigate consequences of double-strand DNA breaks occurring in independent topological domains, such as these constituting bacterial chromosomes. Our simulations show a transition between segregated and mixed state upon linearization of one of the modelled topological domains. Our results explain how chromosomal organization into topological domains can fulfil two opposite conditions: (i) effectively repulse various loops from each other thus promoting chromosome separation and (ii) permit local DNA intermingling when one or more loops are broken and need to be repaired in a process that requires homology search between broken ends and their homologous sequences in closely positioned sister chromatid.

A characteristic EEG pattern in 4p-syndrome: case report and review of the literature.

Deletions on the short arm of chromosome 4 cause Wolf-Hirschhorn syndrome (WHS) and Pitt-Rogers-Danks syndrome (PRDS). WHS is associated with severe growth and mental retardation, microcephaly, a characteristic facies and congenital malformations. The PRDS phenotype is similar to WHS but generally less severe. Seizures occur in the majority of WHS and PRDS patients. Sgrò et al. [17] described a stereotypic electroclinical pattern in four unrelated WHS patients, consisting of intermittent bursts of 2-3 Hz high voltage slow waves with spike wave activity in the parietal areas during drowsiness and sleep associated with myoclonic jerks. We report a patient with PRDS and the typical EEG pattern and review 14 WHS patients with similar EEG findings reported in the literature. CONCLUSION: Awareness and recognition of the characteristic electroclinical findings in Wolf-Hirschhorn syndrome and Pitt-Rogers-Danks syndrome might help in the early diagnosis of such patients.

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.

Linear prediction application for modelling the relationships between a large number of stand characteristics of Norway spruce stands

Abstract

Individual-based modelling and simulation: towards a better understanding of growth dynamics from small inocula

Postprint (published version)

Cerebral aneurysm exclusion by CT angiography based on subarachnoid hemorrhage pattern: a retrospective study

Contexte : Identifier les patients avec une hémorragie sous-arachnoïdienne spontanée chez qui l'angio-CT suffit pour exclure des anévrysmes rompus.Méthodes : Une étude rétrospective a été effectuée de tous les patients avec une hémorragie sous-arachnoïdienne qui ont eu un angio-CT ainsi qu'une angiographie par cathéter dans le but d'exclure un anévrysme. Les cas négatifs de l'angio-CT (sans anévrysmes) ont été classés d'après leur schéma hémorragique au CT dans les catégories suivantes : « anévrysmale », « périmésencéphalique » puis « sans hémorragie ».Résultats : Deux-cent-quarante-et-un patients ont été inclus. Une sensibilité de 96.4% et une spécificité de 96.0% ont été observée pour l'exclusion d'anévrysmes par l'angio-CT. Parmi les 78 cas négatifs de l'angio-CT, chacun des 35 cas avec un motif hémorragique périmésencéphalique ou sans hémorragie au CT n'ont pas eu d'anévrysmes démontrés à l'angiographie par cathéter.Conclusions: L'angio-CT est fiable pour exclure les anévrysmes rompus lorsqu'un motif hémorragique périmésencéphalique ou pas d'hémorragie sont visibles au CT à une semaine depuis le début des symptômes.

Automatic Decision-Oriented Mapping of Pollution Data

The paper deals with the development and application of the methodology for automatic mapping of pollution/contamination data. General Regression Neural Network (GRNN) is considered in detail and is proposed as an efficient tool to solve this problem. The automatic tuning of isotropic and an anisotropic GRNN model using cross-validation procedure is presented. Results are compared with k-nearest-neighbours interpolation algorithm using independent validation data set. Quality of mapping is controlled by the analysis of raw data and the residuals using variography. Maps of probabilities of exceeding a given decision level and ?thick? isoline visualization of the uncertainties are presented as examples of decision-oriented mapping. Real case study is based on mapping of radioactively contaminated territories.

Energy-based modelling and simulation of the interconnection of a back-to-back converter and a doubly-fed induction machine

This paper describes the port interconnection of two subsystems: a power electronics subsystem (a back-to-back AC/CA converter (B2B), coupled to a phase of the power grid), and an electromechanical subsystem (a doubly-fed induction machine (DFIM). The B2B is a variable structure system (VSS), due to presence of control-actuated switches: however, from a modelling simulation, as well as a control-design, point of view, it is sensible to consider modulated transformers (MTF in the bond graph language) instead of the pairs of complementary switches. The port-Hamiltonian models of both subsystems are presented and, using a power-preserving interconnection, the Hamiltonian description of the whole system is obtained; detailed bond graphs of all subsystems and the complete system are also provided. Using passivity-based controllers computed in the Hamiltonian formalism for both subsystems, the whole model is simulated; simulations are run to rest the correctness and efficiency of the Hamiltonian network modelling approach used in this work.