The measurement set-ups for measuring economic phenomena are economic systems that cannot be controlled or shielded. Economists have to infer the desired quantitative facts about the phenomenon under study from the data generated by these systems. Accurate measurement results are therefore not obtained by adjustments and improvements of the measurement system, but must result from building accurate models of these systems. By taking into account the background noise when modelling the phenomenon, the impossibility of control for bias and error is compensated. As a result, accuracy is obtained by first finding accurate representations of the relevant economic system incorporating both phenomenon and its environment. Secondly, these representations are applied in the methods for achieving precision of the measurement results. Because the unobserved facts about the relevant economic phenomena are inferred from data generated by the underlying system, measurement in economics is always associative in which the inferences are based on the models that function as representations of this association.

The paper contains an overview of ethical dilemmas that appear in measurement-related research and engineering practice. They are presented in a broader con-text of ethical issues that have been currently discussed in empirical sciences and engineering in general.

This paper focuses on the data processing method for reference materials preparation. By analyzing the data of reference materials (RMs), the process of repeatability and stability verification, uncertainty estimation, traceability of the value of a quantity and the characterization of RMs are detailed in the paper.

In the paper the working context analysis and the required project hardware and software specifications of an innovative cytometry system able to operate in the space environment are presented. This new flow cytometer is the object of the “Space micro Environment Portable Active Cytometry Terminal” research project supported by the Italian Space Agency.

In nowadays, there is a huge volume of information on the web, which is disseminated to the users in a chaotic way. In order to be accessed, the information must be clustered and classified in appropriate knowledge models. These models will provide and form the initial conditions of a knowledge structure.
A confusing situation exists in the Digital Economy or in the so-called E-Commerce Arena. The way in which economic values and business strategies are created is changing fundamentally and this transforms the general structure of the Economy. A simple survey shows that there is no single, comprehensive and cogent taxonomy of web business models, due to the fact that every classification approach is depending on the importance appointed to the various aspects of the emerging “new economy”.
This paper, takes under consideration a web business model taxonomy, proposed by M. Rappa. This taxonomy classifies a web site into a business model according its position in the value chain.
Based in the above taxonomy the paper suggests a system, which through information filtering, text retrieval and knowledge techniques, will extract the site content, analyse it and finally classify the e-commerce site. The system uses retrieval techniques, which are emphasised in statistical correlations of words in site content, and scoring content collections with model profiles under the Vector Space Model (VSM).
The anticipated outcome of the proposed system is to identify whether a site is actually offers commerce services through the web. In case of an E-commerce identification, the system proceeds in a business model classification based in Rappa’s taxonomy. The system performance concerning the E-Commerce identification and the business model classification was close to 95% and 88% respectively.