Causality in Europeanization Research: A Discursive Institutional Analytical Strategy

Discourse analysis as a methodology is perhaps not readily associated with substantive causality claims. At the same time the study of discourses is very much the study of conceptions of causal relations among a set, or sets, of agents. Within Europeanization research we have seen endeavours to develop discursive institutional analytical frameworks and something that comes close to the formulation of hypothesis on the effects of European Union (EU) policies and institutions on domestic change. Even if these efforts so far do not necessarily amount to substantive theories or claims of causality, it suggests that discourse analysis and the study of causality are by no means opposites. The study of Europeanization discourses may even be seen as an essential step in the move towards claims of causality in Europeanization research. This paper deals with the question of how we may move from the study of discursive causalities towards more substantive claims of causality between EU policy and institutional initiatives and domestic change

Organic Farming Development and Agricultural Institutions in Europe: A Study of Six Countries

Cooperation between general agricultural institutions and the organisation of the organic farming sector are key factors for the development of organic farming. This study analyses the relationships of organic farming organisations with other farmers' organisations, agencies of agricultural policy and food market firms in six European countries. On this basis it identifies a path for successful development of organic farming which is adaptable to the special conditions of all European countries. This book presents the most systematic and in-depth comparison of the dynamics of organic farming development to date, providing concrete suggestions for a line of action for everyone with an interest in developing organic farming

Detecting Premature Ventricular Contraction by using Regulated Discriminant Analysis with very sparse training data

Pathological electrocardiogram is often used to diagnose abnormal cardiac disorders where accurate classification of the cardiac beat types is crucial for timely diagnosis of dangerous conditions. However, accurate, timely, and precise detection of arrhythmia-types like premature ventricular contraction is very challenging as these signals are multiform, i.e. a reliable detection of these requires expert annotations. In this paper, a multivariate statistical classifier that is able to detect premature ventricular contraction beats is presented. This novel classifier can be trained with a very sparse amount of expert annotated data. To enable this, the dimensionality of the feature vector is kept very low, it uses strong designed features and a regularization mechanism. This approach is compared to other classifiers by using the MIT-BIH arrhythmia database. It has been found that the average accuracy, specificity, and sensitivity are above 96%, which is superior given the sparse amount of training data