Publications

In recent years there has been growing scientific interest in the triangular relationship between knowledge. complexity and innovation systems. The concept of'innovation systems' carries the idea that innovations do not originate as isolated discrete phenomena, but are generated through the interaction of a number of actors or agents. This set of actors and interactions possess certain specific characteristics that tend to remain over time. Such characteristics are also shared by national, regional, sectoral and technological interaction systems. They can all be represented as sets of [institutional] actors and interactions, whose ultimate goal is the production and diffusion of knowledge. The major theoretical and policy problem posed by these systems is that knowledge is generated not only by individuals and organisations, but also by the often complex pattern of interaction between them. To understand how organisations create new products, new production techniques and new organisational forms is important. An even more fundamental need is to understand how organisations create new knowledge if this knowledge creation lies in the mobilisation and conversion of tacit knowledge. Although much has been written about the importance of knowledge in management, little attention has been paid to how knowledge is created and how the knowledge­ creation process is managed. The third component of the research triangle concerns complexity.

Recently performed experimental programmes at the French VULCANO and the German MOCKA and SICOPS facilities aimed at the further elucidation of various phenomena of molten core-concrete interaction (MCCI). Questions on these phenomena arose during the scientific discussion of MCCI in the last years. The large-scale MOCKA (KIT, Karlsruhe) experiments study the interaction of a simulant oxide (Al2O3, ZrO2, CaO) and metal melt (Fe) with concrete. To allow for a long-term interaction, internal heating was provided by alternating additions of alumino-thermite and Zr metal to the upper oxide layer of the stratified melt. Since the heat generated by the thermite reaction and the exothermal oxidation reaction of Zr is mainly deposited in the oxide phase, prototypic heating of both melt phases is achieved. Recent tests in the MOCKA (KIT, Germany) program are focused on assessing the influence of a typical 6wt.% reinforcement in the concrete on the erosion behaviour. The experiments were performed in siliceous concrete crucibles with an inner diameter of 25cm and a height of 1.3m. In these experiments, the overall downward erosion by the metal melt was of the same order as the sideward one. In addition, the lateral erosion in the overlaid oxide melt region was about the same as in the metal melt region. Experiments with prototypic UO2-containing melts have been conducted in parallel in the VULCANO (CEA, Cadarache) and SICOPS (AREVA, Erlangen) facilities. In VULCANO a plasma arc furnace melts the oxide corium while three 1-L steel induction furnaces melt the steel. All these melts are poured in a concrete cavity where the decay heat is applied to the oxide phase via 40-kHz induction coils. VULCANO VBS tests with oxide and metal have shown an increased ablation in front of the metallic masses. More unexpected results concern a very high steel oxidation (especially in VBS-U1 with limestone-rich concrete in which almost all the steel was oxidised) and the fact that post test examinations did not show the expected horizontal steel layer. Induction heating was also used for decay heat simulation in the laboratory-scale SICOPS tests, using a “cold crucible” technique and a higher frequency of about 1.3MHz. Tests were performed to study the influence of an additional metal phase on the 1D erosion behaviour. For these mixed melt tests, concrete specimen of 10cm diameter, made of siliceous concrete with grain aggregates, were used. Oxide melts were either simulant or prototypic melts. Metallic melt was form by addition of steel (steel 37.1) pellets after oxidic melt has been produced. It was found that the impact of the metal phase depends on heating power and gas release rate. The analysis suggests that the mechanism of concrete attack by metal melt can be understood as a thermal destruction mechanism.

Although there is a substantial body of literature on labour market analysis, most of it ignores the spatial dimension of the labour market. A spatial perspective in analysing labour market processes is important for several reasons. FIRST, labour markets are by no means as homogeneous as conventional labour market theories assume. SECOND, most countries are displaying strong regional variations in the dynamics of unemployment. THIRD, geographical space exerts a frictional effect on labour market processes. Regional unemployment rates appear to be the most important indicators for analysing labour market processes from a spatial perspective. The paper aims to discuss some of the problems that are associated with the use of regional unemployment rates. We will focus attention on conceptual problems, problems of data quality and on some of the new problems that have arisen due to the widespread use of new computer technology. Solutions to many of the problems are obvious, but many of the new problems will require some extra effort for their solution. The tyranny that threatens the research community is that regional unemployment data exercise a power over us that can lead the naive to misinterpretations. The data may mislead even the most righteous among us. A good deal of research effort is often given to overcome the tyranny that is found in the columns and rows that the lay public likes to call statistics. The discussion will be enriched by means of a study utilizing regional unemployment rates at the district level in West Germany.

This paper directs interest on countryspecific labour market discrimination Roma may suffer in South East Europe. The study lies in the tradition of statistical BlinderOaxaca decomposition analysis. We use microdata from UNDP's 2004 survey of Roma minorities, and apply a Bayesian approach, proposed by Keith and LeSage for the decomposition analysis of wage differentials. This approach is based on a robust Bayesian heteroscedastic linear regression model in conjunction with Markov Chain Monte Carlo estimation. The results obtained indicate the presence of labour market discrimination in Albania and Kosovo, but point to its absence in Bulgaria, Croatia and Serbia.

A general framework to analyze communication media choice behavior in the university setting is proposed which integrates a stated preference experimental design procedure into a discrete choice modeling framework. The framework is empirically tested using hypothetical choice experiments in which traditional mail, courier mail, telephone, facsimile, and electronic mail services were choice options to carry out information communication tasks. For this purpose face‐to‐face interviews were conducted in six universities in Austria and Switzerland. The choice modeling approach developed emphasizes the influence of communication context specific characteristics, individual and organizational characteristics of the communication initiator as well as the individual's perceptions and feelings about the communication media on the formation of preferences. Empirical results are presented using stated preference models of communication media choice behavior for a series of communication situations. Specific emphasis is laid on cross‐national differences in choice behavior.

Most standard color vision tests have been designed to evaluate color vision of the central two degrees, since this is the visual region with greatest sensitivity to color differences. Consequently, low vision observers with central visual field defects may perform...

Building a feedforward computational neural network model (CNN) involves two distinct tasks: determination of the network topology and weight estimation. The specification of a problem adequate network topology is a key issue and the primary focus of this contribution. Up to now, this issue has been either completely neglected in spatial application domains, or tackled by search heuristics (see Fischer and Gopal 1994). With the view of modelling interactions over geographic space, this paper considers this problem as a global optimization problem and proposes a novel approach that embeds backpropagation learning into the evolutionary paradigm of genetic algorithms. This is accomplished by interweaving a genetic search for finding an optimal CNN topology with gradient-based backpropagation learning for determining the network parameters. Thus, the model builder will be relieved of the burden of identifying appropriate CNN-topologies that will allow a problem to be solved with simple, but powerful learning mechanisms, such as backpropagation of gradient descent errors. The approach has been applied to the family of three inputs, single hidden layer, single output feedforward CNN models using interregional telecommunication traffic data for Austria, to illustrate its performance and to evaluate its robustness.

No abstract is provided for this article.

Spatial interaction models describe and predict spatial flows of people, commodities, capital and information. They are one of the oldest and most widely used of all social science models. This chapter provides a coherent state-of-the-art overview of the field that has witnessed the progression from gravity models to entropy maximising and random utility maximising models and finally to models based on neurocomputing principles that represent the most recent innovation in the design of spatial interaction models.

The process of vapor cavity formation in light absorbing liquid under the action of free- running laser radiation is investigated and a mathematical model to simulate the process is presented. To minimize thermal and mechanical damage of tissue, it is proposed to choose an appropriate temporal profile and energy of laser pulses using results of the simulation.

Hintergrund Die Radiosynoviorthese (RSO) ist eine seit langem bewährte, wenig invasive nuklearmedizinische Therapie entzündlicher Gelenkerkrankungen sowie der pigmentierten villonodulären Synovitis nach vorangegangener chirurgischer Synovektomie und der Gelenkbeteiligung bei Hämophilie. Die RSO ist die häufigste, ambulant durchzuführende Radionuklidtherapie in Deutschland. Methoden Es erfolgt eine Darstellung der Methode der RSO sowie eine Erläuterung der Ergebnisse, Grenzen und potentiellen Risiken anhand der aktuellen Literatur. Die prätherapeutische Diagnostik, die Vorbereitung und Durchführung der RSO sowie die Nachsorge nach RSO werden dargestellt. Ergebnisse Ziel der RSO ist die Behandlung der betroffenen Synovialmembran durch die lokal wirksame Bestrahlung, um eine Progression zu verhindern. Bei bis zu>80% der behandelten Patienten kommt es nach einer RSO zu einer deutlichen Reduzierung der entzündlichen Veränderungen und Ergussbildung bzw. der Schmerzsymptomatik und damit zu einer Verbesserung der Gelenkfunktion. Die Ansprechrate ist unter anderem abhängig vom Ausmaß der vorhandenen Degenerationen und der Speicherung der Radiokolloide in der Synovialmembran. Neue Studien zeigen eine im Vergleich zu der intra-artilulären Kortikoidapplikation nach der RSO eine signifikant längere Therapieeffizienz. Schlussfolgerungen Die RSO ist eine etablierte, sichere und nebenwirkungs-arme Therapieoption insbesondere bei Patienten mit einer Synovialitis. Die Indikationsstellung zur RSO sowie die Nachsorge sollte in enger Zusammenarbeit zwischen Nuklearmediziner und dem Überweiser erfolgen. Background Radiosynoviorthesis (RSO) is a tried and tested, less invasive nuclear medicine therapy for inflammatory joint diseases as well as pigmented villonodular synovitis after previous surgical synovectomy and joint involvement in hemophilia. The RSO is the most common outpatient radionuclide therapy in Germany. Methods The RSO method is presented as well as an explanation of the results, limits and potential risks based on the current literature. The pre-therapeutic diagnostics, the preparation and implementation of the RSO as well as the follow-up care after RSO are presented. Results The aim of the RSO is to treat the affected synovial membrane with locally effective radiation to prevent progression. In up to> 80% of the treated patients, there is a significant reduction in inflammatory changes and effusion or pain symptoms and thus an improvement in joint function after a RSO. The response rate depends, among other things, on the extent to which the radiocolloids are stored in the synovial membrane. New studies show a significantly longer therapeutic efficiency compared to intra-articular corticosteroid application after RSO. Conclusions The RSO is a well established, safe treatment option with only few side effects, especially for patients with synovitis. The indication for RSO and follow-up care should be carried out in close cooperation between the nuclear medicine specialist and the referring physician.

Learning in neural networks has attracted considerable interest in recent years. Our focus is on learning in single hidden‐layer feedforward networks which is posed as a search in the network parameter space for a network that minimizes an additive error function of statistically independent examples. We review first the class of single hidden‐layer feedforward networks and characterize the learning process in such networks from a statistical point of view. Then we describe the backpropagation procedure, the leading case of gradient descent learning algorithms for the class of networks considered here, as well as an efficient heuristic modification. Finally, we analyze the applicability of these learning methods to the problem of predicting interregional telecommunication flows. Particular emphasis is laid on the engineering judgment, first, in choosing appropriate values for the tunable parameters, second, on the decision whether to train the network by epoch or by pattern (random approximation), and, third, on the overfitting problem. In addition, the analysis shows that the neural network model whether using either epoch‐based or pattern‐based stochastic approximation outperforms the classical regression approach to modeling telecommunication flows.

The housing sector in Austria is quite different from that in a purely free market society. Government involvement in housing production and consumption has a strong tradition in Austria. Rent control and tenant security legislation, in varying forms, have been...

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 spatial externalities caused by disembodied knowledge diffusion between technologically similar regions. The transition from theory to econometrics yields a reduced-form empirical model that in the 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 that avoid incorrect inferences about the presence or absence of significant capital externalities among technologically similar regions.

In this paper, a Poisson gravity model is introduced that incorporates spatial dependence of the explained variable without relying on restrictive distributional assumptions of the underlying data generating process. The model comprises a spatially filtered component - including the origin, destination and origin-destination specific variables - and a spatial residual variable that captures origin- and destination-based spatial autocorrelation. We derive a 2-stage nonlinear least squares estimator that is heteroscedasticity-robust and, thus, controls for the problem of over- or underdispersion that often is present in the empirical analysis of discrete data or, in the case of overdispersion, if spatial autocorrelation is present. This estimator can be shown to have desirable properties for different distributional assumptions, like the observed flows or (spatially) filtered component being either Poisson or Negative Binomial. In our spatial autoregressive model specifcation, the resulting parameter estimates can be interpreted as the implied total impact effects defined as the sum of direct and indirect spatial feedback effects. Monte Carlo results indicate marginal finite sample biases in the mean and standard deviation of the parameter estimates and convergence to the true parameter values as the sample size increases. In addition, the paper illustrates the model by analysing patent citation flows data across European regions.

This paper develops a multivariate regime switching monetary policy model for the US economy. To exploit a large dataset we use a factor-augmented VAR with discrete regime shifts, capturing distinct business cycle phases. The transition probabilities are modelled as time-varying, depending on a broad set of indicators that influence business cycle movements. The model is used to investigate the relationship between business cycle phases and monetary policy. Our results indicate that the effects of monetary policy are stronger in recessions, whereas the responses are more muted in expansionary phases. Moreover, lagged prices serve as good predictors for business cycle transitions.

The paper is concerned with the impact of market research prior to integration, on the structures of noncompetitive industries in integrated economy. The analysis focuses on separated, single commodity, monopolistic markets with stochastic demand. Monopolistic firms are considered in dynamic multiperiod model, where intertemporal links are determined by expenditures on market research in a present period and benefits from this activity (i.e., smaller variance of the prediction error) in the future. Assuming that each firm maximizes its total discounted expected utility from profit in indefinite time, we show that the optimal market research strategy is stationary and depends on market size. Consequently, in the period following integration firms operating prior to integration in small markets (such as Slovenia, Czech Republic, Hungary or Estonia) are expected to have much less information about the integrated market than their competitors operating before integration on European market. This informational asymmetry may affect the structure of the industry in integrated economy. In the extreme case, the firm operating before integration in the small market can be ruled out from the integrated market. (authors' abstract)