Publications

High-precision cryogenic wheel mechanisms for the JWST NIRSpec instrument

The Grating and Filter Wheel Mechanisms of the JWST NIRSpec instrument allow for reconfiguration of the spectrograph in space in a number of NIR sub-bands and spectral resolutions. Challenging requirements need to be met simultaneously including high launch loads, the large temperature shift to cryo-space, high position repeatability and minimum deformation of the mounted optics. The design concept of the NIRSpec wheel mechanisms is based on the ISOPHOT Filter Wheels but with significant enhancements to support much larger optics. A well-balanced set of design parameters was to be found and a considerable effort was spent to adjust the hardware within narrow tolerances.

Typification of the names <i>Veronica jacquinii</i> Baumg. and <i>Veronica dentata</i> F.W. Schmidt <i>(Scrophulariaceae)</i>

Summary Martínez‐Ortega, M. M., Rico, E. &amp; Fischer, M. A.: Typification of the names Veronica jacquinii Baumg. and Veronica dentata F.W. Schmidt ( Scrophulariaceae ). – Taxon 50: 187–189. 2001. – ISSN 0040‐0262. Lectotypes for the names V. jacquinii Baumg. and V. dentata F.W. Schmidt are here designated. Epitypes are also chosen in order to ensure accurate interpretation of both names.

The dynamic impact of monetary policy on regional housingprices in the US: Evidence based on factor-augmented vectorautoregressions

In this study interest centers on regional differences in the response of housing prices to monetary policy shocks in the US. We address this issue by analyzing monthly home price data for metropolitan regions using a factor-augmented vector autoregression (FAVAR) model. Bayesian model estimation is based on Gibbs sampling with Normal-Gamma shrinkage priors for the autoregressive coefficients and factor loadings, while monetary policy shocks are identified using high-frequency surprises around policy announcements as external instruments. The empirical results indicate that monetary policy actions typically have sizeable and significant positive effects on regional housing prices, revealing differences in magnitude and duration. The largest effects are observed in regions located in states on both the East and West Coasts, notably California, Arizona and Florida.

The timing of unemployment response in Austrian regional labour markets. The classical and an alternative mode of exploratory statistical analysis

No abstract is provided for this article.

Software in the context of luminescence dating: status, concepts and suggestions exemplified by the R package 'Luminescence'

Radionuclide Therapy of Bone Metastases

The skeleton is a potential metastatic target of many malignant tumors. Up to 85% of prostate and breast cancer patients may develop bone metastases causing severe pain syndromes in many of them. In patients suffering from multilocular, mainly osteoblastic lesions and pain syndrome, radionuclide therapy is recommended for pain palliation. Low-energy beta-emitting radionuclides ((153)samarium-ethylenediaminetetrameth-ylenephosphonate (EDTMP) and (89)strontium) deliver high radiation doses to bone metastases and micrometastases in the bone marrow, but only negligible doses to the hematopoietic marrow. The response rate regarding pain syndrome is about 75%; about 25% of the patients may even become pain free. The therapy is repeatable, depending on cell counts. Concomitant treatment with modern bisphosphonates does not interfere with the treatment effects. Clinical trials using a new, not yet approved nuclide ((223)Radium) and/or combinations of chemotherapy and radionuclides are aiming at a more curative approach.

Erste Erfahrungen in der Behandlung von Kindern mit metastasiertem und rezidiviertem Neuroblastom mit Metajodbenzylguanidin*

At four involved hospitals 16 children were treated with Metaiodobenzylguanidine (131J-MIBG). These children had a relapse of neuroblastoma stage III or IV or did not respond sufficiently to chemotherapy.

Recent Developments in Spatial Analysis

In recent years, spatial analysis has become an increasingly active field, as evidenced by the establishment of educational and research programs at many universities. Its popularity is due mainly to new technologies and the development of spatial data infrastructures. This book illustrates some recent developments in spatial analysis, behavioural modelling, and computational intelligence. World renown spatial analysts explain and demonstrate their new and insightful models and methods. The applications are in areas of societal interest such as the spread of infectious diseases, migration behaviour, and retail and agricultural location strategies. In addition, there is emphasis on the uses of new technologoies for the analysis of spatial data through the application of neural network concepts.

Interregional and InternationalTelephone CommunicationAggregate Traffic Model andEmpirical Evidence for Austria

No abstract is provided for this article.

Klinik der Nebennierenerkrankungen

Aufgrund des histologischen Aufbaus der Nebennierenrinde unterscheidet man die 3 verschiedenen Zellschichten: die zona glomerulosa, die mittlere zona fasciculata sowie die innere, dem Nebennierenmark benachbarte zona reticularis. Entsprechend dieser Zellpopulationen...

Barriers to Communication:Reflections on Methodological Approaches

A cross-national comparative analysis of regional labour markets

No abstract is provided for this article.

A global search procedure for parameter estimation in neural spatial interaction modelling

No abstract is provided for this article.

Spectral Pattern Recognition and Fuzzy ARTMAPClassification: Design Features, System Dynamicsand Real World Simulations

Classification of terrain cover from satellite radar imagery represents an area of considerable current interest and research. Most satellite sensors used for land applications are of the imaging type. They record data in a variety of spectral channels and at a variety of ground resolutions. Spectral pattern recognition refers to classification procedures utilizing pixel-by-pixel spectral information as the basis for automated land cover classification. A number of methods have been developed in the past to classify pixels [resolution cells] from multispectral imagery to a priori given land cover categories. Their ability to provide land cover information with high classification accuracies is significant for work where accurate and reliable thematic information is needed. The current trend towards the use of more spectral bands on satellite instruments, such as visible and infrared imaging spectrometers, and finer pixel and grey level resolutions will offer more precise possibilities for accurate identification. But as the complexity of the data grows, so too does the need for more powerful tools to analyse them. It is the major objective of this study to analyse the capabilities and applicability of the neural pattern recognition system, called fuzzy ARTMAP, to generate high quality classifications of urban land cover using remotely sensed images. Fuzzy ARTMAP synthesizes fuzzy logic and Adaptive Resonance Theory (ART) by exploiting the formal similarity between the computations of fuzzy subsethood and the dynamics of category choice, search and learning. The paper describes design features, system dynamics and simulation algorithms of this learning system, which is trained and tested for classification (8 a priori given classes) of a multispectral image of a Landsat-5 Thematic Mapper scene (270 x 360 pixels) from the City of Vienna on a pixel-by-pixel basis. Fuzzy ARTMAP performance is compared with that of an error-based learning system based upon the multi-layer perceptron, and the Gaussian maximum likelihood classifier as conventional statistical benchmark on the same database. Both neural classifiers outperform the conventional classifier in terms of classification accuracy. Fuzzy ARTMAP leads to out-of-sample classification accuracies, very close to maximum performance, while the multi-layer perceptron - like the conventional classifier - shows difficulties to distinguish between some land use categories. (authors' abstract)

The dynamic impact of monetary policy on regional housing prices in the United States

This paper uses a factor-augmented vector autoregressive model to examine the impact of monetary policy shocks on housing prices across metropolitan and micropolitan regions. To simultaneously estimate the model parameters and unobserved factors we rely on Bayesian estimation and inference. Policy shocks are identified using high-frequency suprises around policy announcements as an external instrument. Impulse reponse functions reveal differences in regional housing price responses, which in some cases are substantial. The heterogeneity in policy responses is found to be significantly related to local regulatory environments and housing supply elasticities. Moreover, housing prices responses tend to be similar within states and adjacent regions in neighboring states.

Friedrich Ehrendorfer 70 ? a life of pioneering devotion to botany and biosystematics

From Conventional to Knowledge Based Geographical Information Systems

Artificial intelligence (Al) has received an explosion of interest during the last five years in various fields. There is no longer any question that expert systems and neural networks will be of central importance for developing the next generation of more intelligent geographic information systems. Such knowledge based geographic information systems will especially play a key role in spatial decision and policy analysis related to issues such as environmental monitoring and management, land use planning, motor vehicle navigation and distribution logistics. This paper sketches briefly the major characteristics of conventional geographic information systems, and then looks at some of the potentials of Al principles and techniques in a GIS environment where emphasis is laid on expert systems and artificial neural networks technologies and techniques.

ARTIFICIAL NEURAL NETWORKS: A NEW APPROACH TO MODELING INTERREGIONAL TELECOMMUNICATION FLOWS*

. During the last thirty years there has been much research effort in regional science devoted to modeling interactions over geographic space. Theoretical approaches for studying these phenomena have been modified considerably. This paper suggests a new modeling approach, based upon a general nested sigmoid neural network model. Its feasibility is illustrated in the context of modeling 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 performed 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 performance to the choice of the initial network parameters, as well as on the problem of overfitting. The final stage of applying the neural network approach refers to the testing of the interregional teletraffic flows predicted. Prediction quality is analyzed by means of two performance 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 outperforms the classical regression approach to modeling telecommunication traffic in Austria.