Ideales Internet-Frühwarnsystem

Wie müsste ein ideales und globales Internet-Frühwarnsystem aussehen? Der Beitrag stellt ein Modell für ein Frühwarnsystem vor und führt aus, welche Komponenten dafür ben?tigt werden und warum eine Frühwarnung nur durch kollaborative Ans?tze und eine Vielzahl von Systemen funktionieren kann. Anschlie?end werden einige aktuelle Forschungsvorarbeiten im Bereich Internet-Frühwarnung skizziert.     

Quantification of recombinant interleukin-2 in human serum by a specific immunobioassay

A sensitive and specific assay for recombinant interleukin-2 (rIL-2) in human serum is described. The assay is based on a sequential sandwich immunobioassay that uses a microtiter plate coated with anti-rIL-2 monoclonal antibody (specific for recombinant human IL-2) to capture rIL-2 from serum, and an IL-2 dependent T-cell line that proliferates in a dose-dependent fashion. The lower limit of quantitation of the assay is 2 units/mL (1 unit = approximately 50 pg) using 0.1 mL of serum and the calibration curves ranged from 2 to 50 units/mL. Data are reported on the sensitivity, precision, reproducibility, and specificity of the assay; the stability of rIL-2 in serum; and the recovery of rIL-2 from serum. We also report on the use of the procedure to assay clinical samples from patients with AIDS undergoing treatment with rIL-2.     

Image-based truss recognition for density-based topology optimization approach

Structural and Multidisciplinary Optimization - Topology optimization is a tool that supports the creativity of structural-designers and is used in various industries, from automotive to...     

A participative end-user method for multi-perspective business process elicitation and improvement

A business process can be characterized by multiple perspectives (intentional, organizational, operational, functional, interactional, informational, etc). Business process modeling must allow different stakeholders to analyze and represent process models according to these different perspectives. This representation is traditionally built using classical data acquisition methods together with a process representation language such as BPMN or UML. These techniques and specialized languages can easily become hard, complex and time consuming. In this paper, we propose ISEA, a participative end-user modeling approach that allows the stakeholders in a business process to collaborate together in a simple way to communicate and improve the business process elicitation in an accurate and understandable manner. Our approach covers the organizational perspective of business processes, exploits the information compiled during the elicitation of the organizational perspective and touches lightly an interactional perspective allowing users to create customized interface sketches to test the user interface navigability and the coherence within the processes. Thus, ISEA can be seen as a participative end-user modeling approach for business process elicitation and improvement.     

Mining frequent conjunctive queries in relational databases through dependency discovery

We present an approach for mining frequent conjunctive in arbitrary relational databases. Our pattern class is the simple, but appealing subclass of simple conjunctive queries. Our algorithm, called Conqueror $^+$ , is capable of detecting previously unknown functional and inclusion dependencies that hold on the database relations as well as on joins of relations. These newly detected dependencies are then used to prune redundant queries. We propose an efficient database-oriented implementation of our algorithm using SQL and provide several promising experimental results.     

The use of fuzzy logic and neural networks models for sensory properties prediction from process and structure parameters of knitted fabrics

In a competitive business environment, the textile industrialists intend to propose diversified products according to consumers preference. For this purpose, the integration of sensory attributes in the process parameters choice seems to be a useful alternative. This paper provides fuzzy and neural models for the prediction of sensory properties from production parameters of knitted fabrics. The prediction accuracy of these models was evaluated using both the root mean square error (RMSE) and mean relative percent error (MRPE). The results revealed the models ability to predict tactile sensory attributes based on the production parameters. The comparison of the prediction performances showed that the neural models are slightly powerful than the fuzzy models.