No abstract is provided for this article.
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.
Zusammenfassung Hintergrund Die Radiosynoviorthese (RSO) ist ein seit Jahrzehnten etabliertes sicheres Verfahren zur effektiven Behandlung der Synovialitis. Dennoch sind unerwünschte Wirkungen möglich. Diese zu kennen ist für den therapierenden Nuklearmediziner essenziell. Ziel der Arbeit In dem Artikel werden Nebenwirkungen und Komplikationen der RSO dargelegt. Material und Methoden Laut Definition der EMA sind Nebenwirkungen zu unterteilen in „adverse events“ (AE), das sind Ereignisse, die bei der Verabreichung eines Arzneimittels auftreten, ohne dass ein kausaler Zusammenhang mit dem Arzneimittel bestehen muss und adverse drug reactions (ADR), das sind durch das verabreichte Arzneimittel verursachte Reaktionen. In einer Literaturübersicht werden Nebenwirkungen in o. g. Sinne dargestellt und in den klinischen Kontext gestellt. Besonderes Augenmerk wird zudem auf Schmerzen im Rahmen der RSO und das Vorgehen bei Patienten mit Bakerzyste gelegt. Ergebnisse Es ist eine Prävalenz ernster AE durch die RSO von 4,5 pro 100 000 Therapien dokumentiert. Die häufigsten gemeldeten Nebenwirkungen sind infektiöse Prozesse nach RSO, die damit nicht dem Radiopharmakon oder dem gleichzeitig injizierten Kortikoid zugeordnet werden können, sondern durch das invasive Vorgehen begründet sind. Von den auftretenden Nebenwirkungen werden überwiegend die ernsten AE, die im Rahmen einer RSO auftreten, gemeldet, weniger nicht-ernste AE. Dennoch ist sogar bei einer 100-fach höheren Rate an ernstzunehmenden Komplikationen (aufgrund der vermutlich hohen Dunkelziffer) deren Wahrscheinlichkeit unter 0,5 %. Diskussion Die RSO ist eine sichere, nebenwirkungsarme Therapie, wenn sie nach den Regeln der ärztlichen Kunst und unter Berücksichtigung der aktuellen Leitlinien durchgeführt wird. Mit ernsthaften Komplikationen ist nach den vorliegenden Daten in ca. einer von 20 000 Therapien zu rechnen.
A novel rough set approach is proposed in this paper to discover classification rules through a process of knowledge induction which selects decision rules with a minimal set of features 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 minimal 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.
No abstract is provided for this article.
The Toxic and Sedative Bufadienolides of Kalanchoe daigremontiana H AMET et P ERR . From the toxic Crassulacee Kalanchoe daigremontiana five biosynthetically related bufadienolides have been isolated and structurally elucidated by X‐ray and 1 H‐ and 13 C‐NMR spectroscopy. The compounds were identified as bersaldegenin 1,3,5‐orthoacetate ( 2 ), bersaldegenin 1‐acetate ( 4 ), bersaldegenin 3‐acetate ( 3 ), 1β,3β,5,11α,14‐pentahydroxy‐12,19‐dioxo‐5β,14β‐bufa‐20,22‐dienolide 1,3,5‐orthoacetate ( = daigremontianin; 1 ), and 3β‐acetoxy‐1β,5,14‐trihydroxy‐5β,14β‐bufa‐20,22‐dienolide ( = daigredorigenin‐3‐acetate, 5 ). According to the pharmacological investigations, the two orthoacetates 1 and 2 comprise the positive inotropic, sedative, and toxic activity of the plant.
Ein Großteil der Infekte in der Traumatologie ist dem „röntgenologisch erweiterten“ klinischen Blick des Therapeuten zugänglich. Die relativ aufwendigen szintigraphischen Untersuchungsmethoden werden, meist ergänzend, bei...
In this chapter we give an introduction to spatial data analysis, and distinguish it from other forms of data analysis. By spatial data we mean data that contain locational as well as attribute information. We focus on two broad types of spatial data: area data and...
The focus of this paper is on the neural network modeling 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.
This study suggests a two-step approach to identifying and interpreting regional convergence clubs in Europe. The first step calculates Bayesian probabilities for various assignments of regions to two clubs using a general stochastic space-time dynamic panel relationship between growth rates and initial levels of income as well as endowments of physical, knowledge and human capital. This approach produces club assignments that are unconditional on specific parameter estimates. The second step uses the club assignments in a dynamic space-time panel data model to assess long-run dynamic direct and spillover responses of regional income levels to changes in initial period endowments for clubs that were identified. Correctly determining the dynamic partial derivative impacts of changes in initial endowments on regional income levels is an important contribution of our study. The dynamic trajectories of regional income levels over time allow us to draw inferences regarding the timing and magnitude of regional income responses to changes in (physical capital, human capital and knowledge capital) endowments for the clubs that have been identified in the first step. We find different responses to endowments by regions in two clubs that appear consistent with low- and high-income regions as clubs.
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The Near-Infrared Spectrograph (NIRSpec) onboard the James Webb Space Telescope can be reconfigured in space for astronomical observation in a range of filter bands as well as spectral resolutions. This will be achieved using a Filter wheel (FWA) which carries 7 transmission filters and a Grating wheel (GWA) which carries six gratings and one prism. The large temperature shift between warm launch and cryogenic operation (30K) and high launch vibration loads on the one hand side and accurate positioning capability and minimum deformation of optical components on the other hand side must be consolidated into a single mechanical design which will be achieved using space-proven concepts derived from the successful ISO filter wheel mechanisms which were manufactured and tested by Carl Zeiss. Carl Zeiss Optronics has been selected by Astrium GmbH for the implementation of both NIRSpec wheel mechanisms. Austrian Aerospace and Max-Planck-Institut fur Astronomie Heidelberg (MPIA) will contribute major work shares to the project. The project was started in October 2005 and the preliminary designs have been finalized recently. Critical performance parameters are properly allocated to respective hardware components, procurements of long-lead items have been initiated and breadboard tests have started. This paper presents an overview of the mechanism designs, discusses its properties and the approach for component level tests.
No abstract is provided for this article.
No abstract is provided for this article.
Neural spatial interaction models are receiving much attention in recent years because of their powerful universal approximation properties. They are essentially devices for non-parametric statistical inference, providing an elegant formalism. Neural spatial interaction models have shown considerable successes in a variety of application contexts. The paper discusses a novel modular methodology for neural spatial interaction model identification. We briefly introduce the motivation for the two main constituent components of the methodology: model selection and model adequacy testing. Then we discuss the issues involved in model selection and make a clear distinction between the problems of estimation and model specification. Though major emphasis will be laid on the case of unconstraint spatial interaction, some attention will be paid also to the singly constrained case. The methodology will be illustrated in a real world context. KEYWORDS: Neural Spatial Interaction Models; Model Selection and Model Adequacy Testing; Unconstraint Spatial Interaction.
