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A novel rough set approach is proposed in this paper to discover classification rules through a process of knowledge induction which selects optimal decision rules with a minimal set of features necessary and sufficient for classification of real-valued data. A rough set knowledge discovery framework is formulated for the analysis of interval-valued information systems converted from real-valued raw decision tables. The optimal feature selection method for information systems with interval-valued features obtains all classification rules hidden in a system through a knowledge induction process. Numerical examples are employed to substantiate the conceptual arguments.

The need to account for spatial autocorrelation is well known in spatial analysis. Many spatial statistics and spatial econometric texts detail the way spatial autocorrelation can be identified and modelled in the case of object and field data. The literature on spatial autocorrelation is much less developed in the case of spatial interaction data. The focus of interest in this paper is on the problem of spatial autocorrelation in a spatial interaction context. The paper aims to illustrate that eigenfunction-based spatial filtering offers a powerful methodology that can efficiently account for spatial autocorrelation effects within a Poisson spatial interaction model context that serves the purpose to identify and measure spatial separation effects to interregional knowledge spillovers as captured by patent citations among high-technology-firms in Europe.

This is on the one hand an announcement of the two-volume Fourth Edition of the Excursion Flora for Austria expanded by also including the remaining parts of the Eastern Alps (chapter 1), and on the other hand a rough survey of the flora of the Eastern Alps in connection with the main vegetation types (chapter 2). The geographical scope includes, besides Austria, the entire Eastern Alps from the Rhine valley in E Switzerland (Grisons) to the Vipava valley in SW Slovenia. Volume 1 mainly contains comprehensive introductory chapters like introductions to plant morphology, taxonomy, and nomenclature, as well as a sketch of ecomorphology and habitat ecology, a survey of vegetation types (phytosociology) and floristic peculiarities of the different natural regions, a rough history of floristic research, a detailed glossary including the meaning of epithets, etc., and drawings of several plant species characteristic of the flora covered. The structure of the keys concentrated in volume 2 is explained: besides the descriptive traits, they include for each taxon comprehensive ecological and plant geographical data, as well as information about Red Lists of the countries involved, plant uses, and taxonomical problems. Genus names are given not only in German, but also in the Romansh (Rumantsch Grischun), Italian, and Slovenian languages. In chapter 2, some important chorotypes including endemics are characterised, and an overview of floristic diversity (lists of exemplary taxa) in accordance with the main and most characteristic vegetation types is presented.

This paper explores the contribution of knowledge capital to total factor productivity differences among regions within a regression framework. The dependent variable is total factor productivity, defined as output (in terms of gross value added) per unit of labour and physical capital combined, while the explanatory variable is a patent stock measure of regional knowledge endowments. We provide an econometric derivation of the relationship, which in the presence of unobservable knowledge capital leads to a spatial regression model relationship. This model form is extended to account for technological dependence between regions, which allows us to quantify disembodied knowledge spillover impacts arising from both spatial and technological proximity. A six-year panel of 198 NUTS-2 regions spanning the period from 1997 to 2002 was used to empirically test the model, to measure both direct and indirect effects of knowledge capital on regional total factor productivity, and to assess the relative importance of knowledge spillovers from spatial versus technological proximity.

Networks have become increasingly important vehicles for the diffusion of innovation. But relatively little is known about how location and policies affect the development of such networks, or their effects on the competitiveness of firms in various nations. Bringing...

(1) Die nomenklatorischen Autorenbezeichnungen nach dem Taxonnamen beziehen sich nicht auf das Taxon, wie man meinen könnte (und tatsächlich viele meinen), sondern auf den Namen und damit auf ein einziges Individuum, den nomenklatorischen Typus (Prinzip II und Art. 7 ICN [ehem. ICBN]). (2) Änderungen der Definition (Umgrenzung) des Taxons haben – solange der nomenklatorische Typus eingeschlossen ist – keine Namensänderung zur Folge (Art. 47 ICN). (3) Daraus folgt, dass Taxanamen samt Autor nicht eindeutig sein können; sie sind homotypische Homonyme, was weithin nicht oder zu wenig beachtet wird, auch nicht (4) in Lehrbüchern und Florenwerken. (5) Das führte vor dem Jahr 2000 (Saint-Louis-ICBN) zu unrichtigen Auslegungen des Art. 46 ICN. (6) Eindeutige Bezeichnungen der Taxa sind nur mittels taxonomischer Referenz möglich. Solche Bezeichnungen heißen Taxonyme (Koperski & al. 2000). Nomenklatorische Autorenbezeichnungen hingegen sind überflüssig, mitunter sogar irreführend. (7) Dreizehn Gründe für das verbreitete Missverständnis um die nomenklatorischen Autoren und die Notwendigkeit, stattdessen Taxonyme anzugeben, werden analysiert.

. This paper presents a theoretical neoclassical growth model with two kinds of capital, and technological interdependence among regions. Technological interdependence is assumed to operate through disembodied knowledge diffusion between technologically similar regions. The transition from theory to econometrics yields a reduced-form empirical model that in spatial econometrics literature is known as spatial Durbin model. Technological dependence between regions is formulated by a connectivity matrix that measures closeness of regions in a technological space spanned by 120 distinct technological fields. We use a system of 158 regions across 14 European countries over the period from 1995 to 2004 to empirically test the model. The paper illustrates the importance of an impact-based model interpretation, in terms of the LeSage and Pace (2009) approach, to correctly quantify the magnitude of spillover effects, in order to avoid incorrect inferences about the presence or absence of significant capital externalities and the role technological interdependence plays in regional growth processes in Europe.

This chapter deals with systemic radionuclide treatment of otherwise refractory bone pain in patients with bone metastases caused by different tumour diseases. It describes the different radiopharmaceuticals approved in Europe, both the indications and...

Veronica quezelii M.Fischer aus hochalpinen Felswänden in Lycien (Kleinasien) ist eine neue Art aus der Verwandtschaft vonV. kotschyana Benth.; sie is

Abstract . This paper presents a theoretical neoclassical growth model with two kinds of capital, and technological interdependence among regions. Technological interdependence is assumed to operate through disembodied knowledge diffusion between technologically similar regions. The transition from theory to econometrics yields a reduced-form empirical model that in spatial econometrics literature is known as spatial Durbin model. Technological dependence between regions is formulated by a connectivity matrix that measures closeness of regions in a technological space spanned by 120 distinct technological fields. We use a system of 158 regions across 14 European countries over the period from 1995 to 2004 to empirically test the model. The paper illustrates the importance of an impact-based model interpretation, in terms of the LeSage and Pace (2009) approach, to correctly quantify the magnitude of spillover effects, in order to avoid incorrect inferences about the presence or absence of significant capital externalities and the role technological interdependence plays in regional growth processes in Europe.

One of the major intellectual achievements and, at the same time, perhaps the most useful contribution of spatial analysis to social science literature has been the development of spatial interaction models. Spatial interaction can be broadly defined as movement of...

This chapter describes the special characteristics of spatial data and some features of the field of spatial analysis. The origins of spatial analysis lie in the development in the early 1960s of quantitative geography and regional science. Despite the recognition that spatial analysis is central to the purpose of many geographic information systems (GIS), the lack of integration of the technology and spatial data analysis, and the relative simplicity of many GIS, are seen as a major impediment to their full utilisation. The ability of GIS to handle and analyse spatial data is usually seen as the characteristic that most distinguishes them from other information, computer-aided design, and map production, systems. Spatial analysis goes beyond sampling, manipulation, exploratory and confirmatory analysis of data into spatial modelling which encompasses a large and diverse set of models. The chapter also presents an overview of the key concepts discussed in this book.

The focus of this paper is on the neural network modelling approach that has gained increasing recognition in GIScience in recent years. The novelty about neural networks lies in their ability to model non-linear processes with few, if any, a priori assumptions about the nature of the data-generating process. The paper discusses some important issues that are central for successful application development. The scope is limited to feedforward neural networks, the leading example of neural networks. It is argued that failures in applications can usually be attributed to inadequate learning and/or inadequate complexity of the network model. Parameter estimation and a suitably chosen number of hidden units are, thus, of crucial importance for the success of real world neural network applications. The paper views network learning as an optimization problem, reviews two alternative approaches to network learning, and provides insights into current best practice to optimize complexity so to perform well on generalization tasks.

Pattern recognition in urban areas is one of the most challenging issues in classifying satellite remote sensing data. Parametric pixel-by-pixel classification algorithms tend to perform poorly in this context. This is because urban areas comprise a complex spatial...

This paper includes an operational definition of relevance numbers as used in evaluation matrices and relevance trees. This definition enables a better understanding of the concept, a derivation of the formalism, and an analysis of the error propagation in relevance trees. For technical systems a special measure of utility based on distance in parameter space is introduced, which can be used to obtain relevance numbers from a computerized model of that process.

Although aldosterone is the most recent of the adrenocortical steroids to be characterized, clinical and experimental evidence for its presence has existed for many years. As early as 1943, Albright in his famous Harvey Lecture on Cushing’s Syndrome postulated...

An attempt has been made in this volume to bring together a number of very different approaches to the exploration of the relationship between technological change and economic development, with particular consideration of the spatial implications. The investigation...