Polytechnic University of Milan

About: Polytechnic University of Milan is a education organization based out in Milan, Italy. It is known for research contribution in the topics: Finite element method & Population. The organization has 18231 authors who have published 58416 publications receiving 1229711 citations. The organization is also known as: PoliMi & L-NESS.

Self-adaptive software needs quantitative verification at runtime

Abstract: Continually verify self-adaptation decisions taken by critical software in response to changes in the operating environment.

Vendor Selection Using Principal Component Analysis

Abstract: SUMMARY Purchasing managers need to periodically evaluate supplier performance in order to retain those suppliers which meet their requirements in terms of several performance criteria. The evaluation element typically consists of identifying the attributes, criteria, or factors relevant to the decision and then measuring or rating each vendor by considering each of the relevant factors. A critical part of the overall supplier selection process is the determination of the relative importance of each of the factors. This article presents an alternative decision model to evaluate the relative performance of suppliers that have multiple outputs and inputs. This approach is based on a multivariate statistical method, principal component analysis, that uses information obtained from eigenvalues to combine different ratio measures defined by every input and every output. The method has been employed to aggregate multiple performance measures for a real-world data set of suppliers of a medium-sized firm operating in the bottling machinery industry.

Linear and nonlinear parameters for the analysisof fetal heart rate signal from cardiotocographic recordings

Abstract: Antepartum fetal monitoring based on the classical cardiotocography (CTG) is a noninvasive and simple tool for checking fetal status. Its introduction in the clinical routine limited the occurrence of fetal problems leading to a reduction of the precocious child mortality. Nevertheless, very poor indications on fetal pathologies can be inferred from the even automatic CTG analysis methods, which are actually employed. The feeling is that fetal heart rate (FHR) signals and uterine contractions carry much more information on fetal state than is usually extracted by classical analysis methods. In particular, FHR signal contains indications about the neural development of the fetus. However, the methods actually adopted for judging a CTG trace as "abnormal" give weak predictive indications about fetal dangers. We propose a new methodological approach for the CTG monitoring, based on a multiparametric FHR analysis, which includes spectral parameters from autoregressive models and nonlinear algorithms (approximate entropy). This preliminary study considers 14 normal fetuses, eight cases of gestational (maternal) diabetes, and 13 intrauterine growth retarded fetuses. A comparison with the traditional time domain analysis is also included. This paper shows that the proposed new parameters are able to separate normal from pathological fetuses. Results constitute the first step for realizing a new clinical classification system for the early diagnosis of most common fetal pathologies.