Young infants' triangular communication with their parents in the context of maternal postpartum psychosis: Four case studies

With increasing data on the dynamics of normative couples as they transition to parenthood and become a triad, the need for greater understanding of the impact of parental psychopathology on this transition has become clear. The goal of the current article is to begin exploring this area that has received little attention to date, by describing case examples from a study of clinical families as they transitioned to parenthood. Four representative cases were selected from a pool of 13 mother-father-baby triads, for whom the mother had been hospitalized conjointly with her infant due to a psychotic episode during the postpartum period. The families were observed as part of a clinical consultation that included a semistructured play paradigm known as the Lausanne Trilogue Play (LTP; E. Fivaz-Depeursinge, & A. Corboz-Warnery, 1999). Interactions were scored using standardized measures as well as clinical impressions. All families from the clinical sample were noted to struggle and frequently failed to achieve the goals of play. The impact on the infants in terms of their developing sense of self as well as their defensive strategies in this context are discussed, with clinical implications explored.

Determining 241Pu in environmental samples: case studies in alpine soils.

A procedure was developed for determining 241Pu activity in environmental samples. This beta emitter isotope of plutonium was measured by ultra low level liquid scintillation, after several separation and purification steps that involved the use of a highly selective extraction chromatographic resin (Eichrom-TEVA). Due to the lack of reference material for 241Pu, the method was nevertheless validated using four IAEA reference sediments with information values for 241Pu. Next, the method was used to determine the 241Pu activity in alpine soils of Switzerland and France. The 241Pu/239,240Pu and 238Pu/239,240Pu activity ratios confirmed that Pu contamination in the tested alpine soils originated mainly from global fallout from nuclear weapon tests conducted in the fifties and sixties. Estimation of the date of the contamination, using the 241Pu/241Am age-dating method, further confirmed this origin. However, the 241Pu/241Am dating method was limited to samples where Pu-Am fractionation was insignificant. If any, the contribution of the Chernobyl accident is negligible.

Workshop: Capacity building for national public health advocacy: case studies from national associations

The first statement of the EUPHA on the Future of Public Health in Europe refers to the need for going 'to policymakers, politicians and practitioners in all sectors of society and advise them on how to promote public health throughout society'. WHO-EURO Director General Marc Danzon, quoted in the second EUPHA statement on the responsibility of policy makers indicates that 'learning is not systematically applied in health policy development in our continent'. Statement 3 calls for the integration of public health into the political agenda in all sectors. The first EUPHA president, Louise Gunning-Schepers, quoted in Statement 10 called on EUPHA to become 'a powerful advocate of the public health community'. In addition to the above, the EU is now actively seeking ways to build capacity to implement its health strategy. Learning and building the capacity to achieve our aims The aims and objectives to promote the public's health as reflected in EUPHA's 10 statements are also mirrored in the national public health associations. However, many of EUPHA's national associations have little or limited experience in promoting public health policy at the national level. To assist in the learning of advocacy for public health policies, case studies presenting experiences of national public health organizations in lobbying for national public health policy will be presented and discussed. In addition to sharing experiences, the presentations will identify successful approaches to public health advocacy as well as lessons learned from unsuccessful attempts.

Les méthodes d'études de cas en psychothérapie: perspectives actuelles [Methods of psychotherapy case studies: current perspectives].

There is a renewal of interest among psychotherapy researchers and psychotherapists towards psychotherapy case studies. This article presents two paradigms that have greatly influenced this increasing interest in psychotherapy case studies : the pragmatic case study and the theory-building case study paradigm. The origins, developments and key-concepts of both paradigms are presented, as well as their methodological and ethical specificities. Examples of case studies, along with models developed, are cited. The differential influence of the post-modern schools on both paradigms are presented, as well as their contribution to the field of methods of psychotherapy case studies discussed and assessed in terms of relevance for the researcher and the psychotherapist.

Supporting interdisciplinary case studies: development and implementation of a joint learning environment for students in forensic science and criminal law

Scientific reporting and communication is a challenging topic for which traditional study programs do not offer structured learning activities on a regular basis. This paper reports on the development and implementation of a web application and associated learning activities that intend to raise the awareness of reporting and communication issues among students in forensic science and law. The project covers interdisciplinary case studies based on a library of written reports about forensic examinations. Special features of the web framework, in particular a report annotation tool, support the design of various individual and group learning activities that focus on the development of knowledge and competence in dealing with reporting and communication challenges in the students' future areas of professional activity.

Case studies and mathematical models of ecological speciation. 1. Cichlids in a crater lake.

A recent study of a pair of sympatric species of cichlids in Lake Apoyo in Nicaragua is viewed as providing probably one of the most convincing examples of sympatric speciation to date. Here, we describe and study a stochastic, individual-based, explicit genetic model tailored for this cichlid system. Our results show that relatively rapid (<20,000 generations) colonization of a new ecological niche and (sympatric or parapatric) speciation via local adaptation and divergence in habitat and mating preferences are theoretically plausible if: (i) the number of loci underlying the traits controlling local adaptation, and habitat and mating preferences is small; (ii) the strength of selection for local adaptation is intermediate; (iii) the carrying capacity of the population is intermediate; and (iv) the effects of the loci influencing nonrandom mating are strong. We discuss patterns and timescales of ecological speciation identified by our model, and we highlight important parameters and features that need to be studied empirically to provide information that can be used to improve the biological realism and power of mathematical models of ecological speciation.

The Economics of Reducing the County Road System: Three Case Studies in Iowa - Executive Summary, HR-242, 1986

This Executive Summary presents a brief summary of the main research report, "The Economics of Reducing the County Road System: Three Case Studies in Iowa" (DOT/OST/P-34/86-035). The case studies are described, as well as the analytic methodology and research findings.

The Economics of Reducing the County Road System: Three Case Studies in Iowa, HR-242, 1986

This research project looked at the economic benefits and costs associated with alternative strategies for abandoning low volume rural highways and bridges. Three test counties in Iowa were studied, each 100 square miles in size: Hamilton County having a high agricultural tax base and a high percentage of paved roads and few bridges; Shelby County having a relatively low agricultural tax base, hilly terrain and a low percentage of paved road and many bridges; and Linn County having a high agricultural tax base, a high percentage of paved roads and a large number of non-farm households. A questionnaire survey was undertaken to develop estimates of farm and household travel patterns. Benefits and costs associated with the abandonment of various segments of rural highway and bridge mileages in each county were calculated. "Benefits" calculated were reduced future reconstruction and maintenance costs, whereas "costs" were the added cost of travel resulting from the reduced mileage. Some of the findings suggest limited cost savings from abandonment of county roads with no property access in areas with large non-farm rural population; relatively high cost savings from the abandonment of roads with no property access in areas with small rural population; and the largest savings from the conversion of public dead-end gravel roads with property or residence accesses to private drives.

The Football Stadium - A Place Where Right-Wing Extremists Recruit ? Case Studies of Three Swiss Clubs

Der Sammelband ,,Right-wing extremism" hat ein doppeltes Ziel. Zum einen soll er das Nationale Forschungsprogramm 40plus und seine Projekte präsentieren (die alle mit Beiträgen präsent sind), zum anderen sollen diese nationalen Beiträge in eine internationale Perspektive gestellt werden, sodass in der Übersicht und Umschau eine Verortung der schweizerischen Forschung (und damit auch des NFP40plus selbst) und ihrer Resultate möglich wird. Eingeladen wurden dazu führende europäische Forscher auf dem Gebiet des Rechtsextremismus.

Parents - child role reversal in trilogue play: case studies of trajectories from pregnancy to toddlerhood

Role reversal, whereby a child attempts to meet her parent's adult needs for parenting, intimacy, or companionship, has been identified as a risk factor for developmental disturbances. It has been defined from diverse perspectives as a child attachment strategy, a parent - toddler relational disturbance, and a boundary disturbance between parents and child. The recently discovered infant's triangular capacity, namely the sharing of her attention and affects with both parents, allows one to analyse the infant's contribution to early family dynamics. Role reversal was detected in 4 out of 45 father - mother - infant interactions observed in trilogue play from pregnancy to toddlerhood. The developmental trajectories towards role reversal are explored by means of case analyses. Results are compared with cases of problematic triangulation encountered in the same sample. In role reversal, family interactions are rigidly organized around a "two against one" coalition, whereby the normative hierarchy between parents and child is reversed. The child's triangular capacity is overactivated, controlling the tension between her parents by provocation - animation strategies

Determining Pu-241 in environmental samples: case studies in alpine soils

A procedure was developed for determining Pu-241 activity in environmental samples. This beta emitter isotope of plutonium was measured by ultra low level liquid scintillation, after several separation and purification steps that involved the use of a highly selective extraction chromatographic resin (Eichrom-TEVA). Due to the lack of reference material for Pu-241, the method was nevertheless validated using four IAEA reference sediments with information values for Pu-241. Next, the method was used to determine the Pu-241 activity in alpine soils of Switzerland and France. The Pu-241/Pu-239,Pu-240 and Pu-238/Pu-239,Pu-240 activity ratios confirmed that Pu contamination in the tested alpine soils originated mainly from global fallout from nuclear weapon tests conducted in the fifties and sixties. Estimation of the date of the contamination, using the Pu-241/Am-241 age-dating method, further confirmed this origin. However, the Pu-241/Am-241 dating method was limited to samples where Pu-Am fractionation was insignificant. If any, the contribution of the Chernobyl accident is negligible.

Dynamic Modelling of Material Flows and Sustainable Resource Use: Case Studies in Regional Metabolism and Space Life Support Systems

Sustainable resource use is one of the most important environmental issues of our times. It is closely related to discussions on the 'peaking' of various natural resources serving as energy sources, agricultural nutrients, or metals indispensable in high-technology applications. Although the peaking theory remains controversial, it is commonly recognized that a more sustainable use of resources would alleviate negative environmental impacts related to resource use. In this thesis, sustainable resource use is analysed from a practical standpoint, through several different case studies. Four of these case studies relate to resource metabolism in the Canton of Geneva in Switzerland: the aim was to model the evolution of chosen resource stocks and flows in the coming decades. The studied resources were copper (a bulk metal), phosphorus (a vital agricultural nutrient), and wood (a renewable resource). In addition, the case of lithium (a critical metal) was analysed briefly in a qualitative manner and in an electric mobility perspective. In addition to the Geneva case studies, this thesis includes a case study on the sustainability of space life support systems. Space life support systems are systems whose aim is to provide the crew of a spacecraft with the necessary metabolic consumables over the course of a mission. Sustainability was again analysed from a resource use perspective. In this case study, the functioning of two different types of life support systems, ARES and BIORAT, were evaluated and compared; these systems represent, respectively, physico-chemical and biological life support systems. Space life support systems could in fact be used as a kind of 'laboratory of sustainability' given that they represent closed and relatively simple systems compared to complex and open terrestrial systems such as the Canton of Geneva. The chosen analysis method used in the Geneva case studies was dynamic material flow analysis: dynamic material flow models were constructed for the resources copper, phosphorus, and wood. Besides a baseline scenario, various alternative scenarios (notably involving increased recycling) were also examined. In the case of space life support systems, the methodology of material flow analysis was also employed, but as the data available on the dynamic behaviour of the systems was insufficient, only static simulations could be performed. The results of the case studies in the Canton of Geneva show the following: were resource use to follow population growth, resource consumption would be multiplied by nearly 1.2 by 2030 and by 1.5 by 2080. A complete transition to electric mobility would be expected to only slightly (+5%) increase the copper consumption per capita while the lithium demand in cars would increase 350 fold. For example, phosphorus imports could be decreased by recycling sewage sludge or human urine; however, the health and environmental impacts of these options have yet to be studied. Increasing the wood production in the Canton would not significantly decrease the dependence on wood imports as the Canton's production represents only 5% of total consumption. In the comparison of space life support systems ARES and BIORAT, BIORAT outperforms ARES in resource use but not in energy use. However, as the systems are dimensioned very differently, it remains questionable whether they can be compared outright. In conclusion, the use of dynamic material flow analysis can provide useful information for policy makers and strategic decision-making; however, uncertainty in reference data greatly influences the precision of the results. Space life support systems constitute an extreme case of resource-using systems; nevertheless, it is not clear how their example could be of immediate use to terrestrial systems.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Waste management policies for industrial symbiosis development: case studies in European countries

Industrial symbiosis (IS) emerged as a self-organizing business strategy among firms that are willing to cooperate to improve their economic and environmental performance. The adoption of such cooperative strategies relates to increasing costs of waste management, most of which are driven by policy and legislative requirements. Development of IS depends on an enabling context of social, informational, technological, economical and political factors. The power to influence this context varies among the agents involved such as the government, businesses or coordinating entities. Governmental intervention, as manifested through policies, could influence a wider range of factors; and we believe this is an area which is under-researched. This paper aims to critically appraise the waste policy interventions from supra-national to sub-national levels of government. A case study methodology has been applied to four European countries i.e. Denmark, the UK, Portugal and Switzerland, in which IS emerged or is being fostered. The findings suggest that there are commonalities in policy instruments that may have led to an IS enabling context. The paper concludes with lessons learnt and recommendations on shaping the policy context for IS development.