873 publications from this institution
Economists have long stressed the importance of human capital to the process of economic growth. However, recent cross-country studies have shown that economic growth appears to be unrelated to increases in human capital (Griliches 2000). Benhabib and Spiegel (1994),...
The systems FeO-UO2-ZrO2 (in inert atmosphere) and Fe2O3-U3O8-ZrO2 (in air) were studied. For the FeO-UO2-ZrO2 system, the eutectic temperature was found t
The focus of this paper is on cross-region R&D collaboration funded by the fifth EU Framework Programme (FP5). The objective is to measure distance
During the last thirty years there has been much research effort in regional science devoted to modelling interactions over geographic space. Theoretical approaches for studying these phenomena have been modified considerably. This paper suggests a 'new modelling approach, based upon a general nested sigmoid neural network model. Its feasibility is illustrated in the context of modelling interregional telecommunication traffic in Austria and its performance is evaluated in comparison with the classical regression approach of the gravity type. The application of this neural network approach may be viewed as a three-stage process. The first stage refers to the identification of an appropriate network from the family of two-layered feedforward networks with 3 input nodes, one layer of (sigmoidal) intermediate nodes and one (sigmoidal) output node (logistic activation function). There is no general procedure to address this problem. We solved this issue experimentally. The input-output dimensions have been chosen in order to make the comparison with the gravity model as close as possible. The second stage involves the estimation of the network parameters of the selected neural network model. This is perlormed via the adaptive setting of the network parameters (training, estimation) by means of the application of a least mean squared error goal and the error back propagating technique, a recursive learning procedure using a gradient search to minimize the error goal. Particular emphasis is laid on the sensitivity of the network perlormance to the choice of the initial network parameters as well as on the problem of overlitting. The final stage of applying the neural network approach refers to the testing of the interregional teletraffic flows predicted. Prediction quality is analysed by means of two perlormance measures, average relative variance and the coefficient of determination, as well as by the use of residual analysis. The analysis shows that the neural network model approach outperlorms the classical regression approach to modelling telecommunication traffic in Austria.
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. (authors' abstract)
Die nuklearmedizinische Diagnostik des Skelettes unterliegt durch die Einführung von SPECT/CT und PET/CT in die klinische Diagnostik einem Wandel in mehrfacher Hinsicht:
As interest in questions of the knowledge economy has grown, knowledge spillovers have received increased attention in recent years. For the purpose of this paper we use externalities and knowledge spillovers interchangeably to denote the non-pecuniary benefit of...
This paper employs a spatial Durbin panel data model, an extension of the cross-sectional spatial Durbin model to a panel data framework, to quantify the impact of a set of sociodemographic and socioeconomic factors that influence opioid-related mortality in the US. The empirical model uses a pool of 49 US states over six years from 2014 to 2019, and a nearest-neighbor matrix that represents the topological structure between the states. Calculation of direct (own-state) and indirect (cross-state spillover) effects estimates is based on Bayesian estimation and inference reflecting a proper interpretation of the marginal effects for the model that involves spatial lags of the dependent and independent variables. The study provides evidence that opioid mortality depends not only on the characteristics of the state itself (direct effects), but also on those of nearby states (indirect effects). Direct effects are important, but externalities (spatial spillovers) are more important. The sociodemographic structure (age and race) of a state is important whereas economic distress of a state is less so, as indicated by the total impact estimates. The methodology and the research findings provide a useful template for future empirical work using other geographic locations or shifting interest to other epidemics.
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 article views spatial analysis as a research paradigm that provides a unique set of specialised techniques and models for a wide range of research questions in which the prime variables of interest vary significantly over space. The heart of spatial analysis is concerned with the analysis and modeling of spatial data. Spatial point patterns and area referenced data represent the most appropriate perspectives for applications in the social sciences. The researcher analysing and modeling spatial data tends to be confronted with a series of problems such as the data quality problem, the ecological fallacy problem, the modifiable areal unit problem, boundary and frame effects, and the spatial dependence problem. The problem of spatial dependence is at the core of modern spatial analysis and requires the use of specialised techniques and models in the data analysis. The discussion focuses on exploratory techniques and model-driven [confirmatory] modes of analysing spatial point patterns and area data. In closing, prospects are given towards a new style of data-driven spatial analysis characterized by computational intelligence techniques such as evolutionary computation and neural network modeling to meet the challenges of huge quantities of spatial data characteristic in remote sensing, geodemographics and marketing. (author's abstract)
In primary aldosteronism the type of adrenal lesion was correctly identified in 28 of 40 patients (70%) by standard adrenal scintigraphy. Suppression scint
Seit über sechs Jahrzehnten werden Radionuklide für die Schmerztherapie bei Knochen- und Gelenkerkrankungen eingesetzt. Dabei ist das Target bei malignen Knochenprozessen, besonders Knochenmetastasen, das überwiegend osteoblastische Knochengewebe, bei...
No abstract is provided for this article.
<h3>INTRODUCTION.</h3> Within the last few years our conceptions of the physical state of protoplasm have undergone a series of changes in consequence of which a number of long-debated physiologic problems have been settled. No one advance has contributed nor promised to contribute more toward the understanding of certain cell phenomena, both from their morphologic and physiologic aspects, than the recognition of the colloidal nature of protoplasm. It is our purpose in the following pages to show, if possible, that the fertilization of the ovum by the spermatozoön represents another cell process which finds its explanation in the colloidal nature of the egg protoplasm, and it is to the arguments which point in this direction that we wish to call attention. As it is impossible in the limited time allowed to meet all the various criticisms which might be raised against our theory of fertilization, we would refer the interested listener
Die ArtenVeronica pectinata, V. cuneifolia, V. macrostachya, V. orientalis undV. multifida (SubsektionenAnatolicae-Lycicae Riek undOrientales [Römpp]R
No abstract is provided for this article.
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 crossnational differences in choice behavior.
