Early Iron Age Pottery: A Quantitative Approach, Proceedings of the International Round Table organized by the Swiss School of Archaeology in Greece (Athens, November 28-30, 2008)

Quantitative approaches in ceramology are gaining ground in excavation reports, archaeological publications and thematic studies. Hence, a wide variety of methods are being used depending on the researchers' theoretical premise, the type of material which is examined, the context of discovery and the questions that are addressed. The round table that took place in Athens on November 2008 was intended to offer the participants the opportunity to present a selection of case studies on the basis of which methodological approaches were discussed. The aim was to define a set of guidelines for quantification that would prove to be of use to all researchers. Contents: 1) Introduction (Samuel Verdan); 2) Isthmia and beyond. How can quantification help the analysis of EIA sanctuary deposits? (Catherine Morgan); 3) Approaching aspects of cult practice and ethnicity in Early Iron Age Ephesos using quantitative analysis of a Protogeometric deposit from the Artemision (Michael Kerschner); 4) Development of a ceramic cultic assemblage: Analyzing pottery from Late Helladic IIIC through Late Geometric Kalapodi (Ivonne Kaiser, Laura-Concetta Rizzotto, Sara Strack); 5) 'Erfahrungsbericht' of application of different quantitative methods at Kalapodi (Sara Strack); 6) The Early Iron Age sanctuary at Olympia: counting sherds from the Pelopion excavations (1987-1996) (Birgitta Eder); 7) L'aire du pilier des Rhodiens à Delphes: Essai de quantification du mobilier (Jean-Marc Luce); 8) A new approach in ceramic statistical analyses: Pit 13 on Xeropolis at Lefkandi (David A. Mitchell, Irene S. Lemos); 9) Households and workshops at Early Iron Age Oropos: A quantitative approach of the fine, wheel-made pottery (Vicky Vlachou); 10) Counting sherds at Sindos: Pottery consumption and construction of identities in the Iron Age (Stefanos Gimatzidis); 11) Analyse quantitative du mobilier céramique des fouilles de Xombourgo à Ténos et le cas des supports de caisson (Jean-Sébastien Gros); 12) Defining a typology of pottery from Gortyn: The material from a pottery workshop pit, (Emanuela Santaniello); 13) Quantification of ceramics from Early Iron Age tombs (Antonis Kotsonas); 14) Quantitative analysis of the pottery from the Early Iron Age necropolis of Tsikalario on Naxos (Xenia Charalambidou); 15) Finding the Early Iron Age in field survey: Two case studies from Boeotia and Magnesia (Vladimir Stissi); 16) Pottery quantification: Some guidelines (Samuel Verdan)

Assessing the impact of DRGs on patient care and professional practice in Switzerland (IDoC) : a potential model for monitoring and evaluating healthcare reform.

QUESTIONS UNDER STUDY: The starting point of the interdisciplinary project "Assessing the impact of diagnosis related groups (DRGs) on patient care and professional practice" (IDoC) was the lack of a systematic ethical assessment for the introduction of cost containment measures in healthcare. Our aim was to contribute to the methodological and empirical basis of such an assessment. METHODS: Five sub-groups conducted separate but related research within the fields of biomedical ethics, law, nursing sciences and health services, applying a number of complementary methodological approaches. The individual research projects were framed within an overall ethical matrix. Workshops and bilateral meetings were held to identify and elaborate joint research themes. RESULTS: Four common, ethically relevant themes emerged in the results of the studies across sub-groups: (1.) the quality and safety of patient care, (2.) the state of professional practice of physicians and nurses, (3.) changes in incentives structure, (4.) vulnerable groups and access to healthcare services. Furthermore, much-needed data for future comparative research has been collected and some early insights into the potential impact of DRGs are outlined. CONCLUSIONS: Based on the joint results we developed preliminary recommendations related to conceptual analysis, methodological refinement, monitoring and implementation.

Procedure for human saphenous veins ex vivo perfusion and external reinforcement.

The mainstay of contemporary therapies for extensive occlusive arterial disease is venous bypass graft. However, its durability is threatened by intimal hyperplasia (IH) that eventually leads to vessel occlusion and graft failure. Mechanical forces, particularly low shear stress and high wall tension, are thought to initiate and to sustain these cellular and molecular changes, but their exact contribution remains to be unraveled. To selectively evaluate the role of pressure and shear stress on the biology of IH, an ex vivo perfusion system (EVPS) was created to perfuse segments of human saphenous veins under arterial regimen (high shear stress and high pressure). Further technical innovations allowed the simultaneous perfusion of two segments from the same vein, one reinforced with an external mesh. Veins were harvested using a no-touch technique and immediately transferred to the laboratory for assembly in the EVPS. One segment of the freshly isolated vein was not perfused (control, day 0). The two others segments were perfused for up to 7 days, one being completely sheltered with a 4 mm (diameter) external mesh. The pressure, flow velocity, and pulse rate were continuously monitored and adjusted to mimic the hemodynamic conditions prevailing in the femoral artery. Upon completion of the perfusion, veins were dismounted and used for histological and molecular analysis. Under ex vivo conditions, high pressure perfusion (arterial, mean = 100 mm Hg) is sufficient to generate IH and remodeling of human veins. These alterations are reduced in the presence of an external polyester mesh.

Beyond the 'dyad': a qualitative re-evaluation of the changing clinical consultation.

OBJECTIVE: To identify characteristics of consultations that do not conform to the traditionally understood communication 'dyad', in order to highlight implications for medical education and develop a reflective 'toolkit' for use by medical practitioners and educators in the analysis of consultations. DESIGN: A series of interdisciplinary research workshops spanning 12 months explored the social impact of globalisation and computerisation on the clinical consultation, focusing specifically on contemporary challenges to the clinician-patient dyad. Researchers presented detailed case studies of consultations, taken from their recent research projects. Drawing on concepts from applied sociolinguistics, further analysis of selected case studies prompted the identification of key emergent themes. SETTING: University departments in the UK and Switzerland. PARTICIPANTS: Six researchers with backgrounds in medicine, applied linguistics, sociolinguistics and medical education. One workshop was also attended by PhD students conducting research on healthcare interactions. RESULTS: The contemporary consultation is characterised by a multiplicity of voices. Incorporation of additional voices in the consultation creates new forms of order (and disorder) in the interaction. The roles 'clinician' and 'patient' are blurred as they become increasingly distributed between different participants. These new consultation arrangements make new demands on clinicians, which lie beyond the scope of most educational programmes for clinical communication. CONCLUSIONS: The consultation is changing. Traditional consultation models that assume a 'dyadic' consultation do not adequately incorporate the realities of many contemporary consultations. A paradox emerges between the need to manage consultations in a 'super-diverse' multilingual society, while also attending to increasing requirements for standardised protocol-driven approaches to care prompted by computer use. The tension between standardisation and flexibility requires addressing in educational contexts. Drawing on concepts from applied sociolinguistics and the findings of these research observations, the authors offer a reflective 'toolkit' of questions to ask of the consultation in the context of enquiry-based learning.

Challenges to the implementation of behavioural survellance for HIV/STI in Europe

Background: Well-conducted behavioural surveillance (BS) is essential for policy planning and evaluation. Data should be comparable across countries. In 2008, the European Centre for Disease Prevention and Control (ECDC) began a programme to support Member States in the implementation of BS for Second Generation Surveillance. Methods: Data from a mapping exercise on current BS activities in EU/EFTA countries led to recommendations for establishing national BS systems and international coordination, and the definition of a set of core and transversal (UNGASS-Dublin compatible) indicators for BS in the general and eight specific populations. A toolkit for establishing BS has been developed and a BS needs-assessment survey has been launched in 30 countries. Tools for BS self-assessment and planning are currently being tested during interactive workshops with country representatives. Results: The mapping exercise revealed extreme diversity between countries. Around half had established a BS system, but this did not always correspond to the epidemiological situation. Challenges to implementation and harmonisation at all levels emerged from survey findings and workshop feedback. These include: absence of synergy between biological and behavioural surveillance and of actors having an overall view of all system elements; lack of awareness of the relevance of BS and of coordination between agencies; insufficient use of available data; financial constraints; poor sustainability, data quality and access to certain key populations; unfavourable legislative environments. Conclusions: There is widespread need in the region not only for technical support but also for BS advocacy: BS remains the neglected partner of second generation surveillance and requires increased political support and capacity-building in order to become effective. Dissemination of validated tools for BS, developed in interaction with country experts, proves feasible and acceptable.

A two-week workshop to promote cardiovascular disease prevention programs in countries with limited resources.

Training is a crucial tool for building the capacity necessary for prevention and control of cardiovascular diseases (CVDs) in developing countries. This paper summarizes some features of a 2-week workshop aimed at enabling local health professionals to initiate a comprehensive CVD prevention and control program in a context of limited resources. The workshops have been organized in the regions where CVD prevention programs are being contemplated, in cooperation with health authorities of the concerned regions. The workshop's content includes a broad variety of issues related to CVD prevention and control, and to program development. Strong emphasis is placed on "learning by doing," and groups of 5-6 participants conduct a small-scale epidemiological study during the first week; during the second week, they draft a virtual program of CVD prevention and control adapted to the local situation. This practice-oriented workshop focuses on building expertise among anticipated key players, strengthening networks among relevant health professionals, and advocating the urgent need to tackle the emerging CVD epidemic in developing countries.

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.

Early Iron Age Pottery: A Quantitative Approach. Proceedings of the International Round Table organized by the Swiss School of Archaeology in Greece (Athens, Novembre 28-30, 2008).

Quantitative approaches in ceramology are gaining ground in excavation reports, archaeological publications and thematic studies. Hence, a wide variety of methods are being used depending on the researchers' theoretical premise, the type of material which is examined, the context of discovery and the questions that are addressed. The round table that took place in Athens on November 2008 was intended to offer the participants the opportunity to present a selection of case studies on the basis of which methodological approaches were discussed. The aim was to define a set of guidelines for quantification that would prove to be of use to all researchers. Contents: 1) Introduction (Samuel Verdan); 2) Isthmia and beyond. How can quantification help the analysis of EIA sanctuary deposits? (Catherine Morgan); 3) Approaching aspects of cult practice and ethnicity in Early Iron Age Ephesos using quantitative analysis of a Protogeometric deposit from the Artemision (Michael Kerschner); 4) Development of a ceramic cultic assemblage: Analyzing pottery from Late Helladic IIIC through Late Geometric Kalapodi (Ivonne Kaiser, Laura-Concetta Rizzotto, Sara Strack); 5) 'Erfahrungsbericht' of application of different quantitative methods at Kalapodi (Sara Strack); 6) The Early Iron Age sanctuary at Olympia: counting sherds from the Pelopion excavations (1987-1996) (Birgitta Eder); 7) L'aire du pilier des Rhodiens à Delphes: Essai de quantification du mobilier (Jean-Marc Luce); 8) A new approach in ceramic statistical analyses: Pit 13 on Xeropolis at Lefkandi (David A. Mitchell, Irene S. Lemos); 9) Households and workshops at Early Iron Age Oropos: A quantitative approach of the fine, wheel-made pottery (Vicky Vlachou); 10) Counting sherds at Sindos: Pottery consumption and construction of identities in the Iron Age (Stefanos Gimatzidis); 11) Analyse quantitative du mobilier céramique des fouilles de Xombourgo à Ténos et le cas des supports de caisson (Jean-Sébastien Gros); 12) Defining a typology of pottery from Gortyn: The material from a pottery workshop pit, (Emanuela Santaniello); 13) Quantification of ceramics from Early Iron Age tombs (Antonis Kotsonas); 14) Quantitative analysis of the pottery from the Early Iron Age necropolis of Tsikalario on Naxos (Xenia Charalambidou); 15) Finding the Early Iron Age in field survey: Two case studies from Boeotia and Magnesia (Vladimir Stissi); 16) Pottery quantification: Some guidelines (Samuel Verdan).

Business IT Alignment from Business Model to Enterprise Architecture

In this paper, we show how business model modelling can be connected to IT infrastructure, drawing parallels from enterprise architecture models such as ArchiMate. We then show how the proposed visualization based on enterprise architecture, with a strong focus on business model strategy, can help IT alignment, at both the business model and the IT infrastructure level.