Validation of the sensitivity analysis method of coordinate measurement uncertainty evaluation

The paper presents the results of the tests carried out to validate a new method for evaluating the uncertainty of coordinate measurements categorised as the Sensitivity Analysis (SA). This method concerns measuring dimensions and geometrical deviations. Measurement uncertainty is evaluated on the basis of information given in the Maximum Permissible Error (MPE) formula for a Coordinate Measuring Machine (CMM). Measurement models express the measured characteristics as a function of differences of coordinates of a small number of appropriately selected points of the workpiece. If reverification test results for the CMM used are available, then the estimated uncertainty takes into account the actual accuracy of the CMM. General formulae are given to calculate the uncertainty of measurement of a circle diameter and coaxiality. The relevant experiment is based on ISO 15530-3 recommendations. A calibrated cylindrical square was used for validation. 17 circles’ diameters and 84 different combinations of datum length and distance of the toleranced element from the datum for measuring coaxiality were adopted as validated characteristics. The validation results are presented in tables and graphs and the chi-square test for equality of variances was used to confirm that the method is correct. The validation results are positive

Determination of uncertainty of coordinate measurements on the basis of the formula for EL,MPE

according to ISO/TS 15530-1, developed at University of Bielsko-Biała, is presented. Measurement uncertainty is estimated on the basis of information contained in the formula for the maximum permissible error (EL,MPE) of the applied coordinate measuring system (CMS) and on the basis of its acceptance or reverification test results. Measurement models are of the nature of close mathematical dependencies expressing the measured characteristic in the form of a distance which is a function of coordinates differences of a low number of essential points, properly selected on the workpiece. Measurement models for dimensions and various geometrical deviations were developed. Thanks to the applied vector notation the models are in the form of cross and dot products and they are easily programmable in software such as Matlab, Maple or Python. Detailed examples of the uncertainty analysis for two characteristics (position deviations of the axes of the holes in relation to the datum system) of a car steering knuckle are provided

A novel Spatio-temporal principal component analysis based on Geary's contiguity ratio

Multivariate statistics have gained a respectable place in quantitative research, especially in the economic geography, socio-economic development, urban and regional planning and spatio-temporal analysis. The main goal is to reduce multidimensional data to simple, but meaningful representative information. One of the powerful methods in multivariate statistics is Principal Component Analysis (PCA). The aim of this paper is to define a novel Spatio-Temporal Principal Component Analysis (STPCA). It is the first solution that sensibly combines at the same time variability of the values of the observed features, time of observation of the considered features and place of observation. It is therefore a solution for spatio-temporal data and a very valuable tool for practitioners wishing to obtain useful inferences from a PCA. The inclusion of the time and place of observation, in addition to the variability of the values of features, results in more detailed division of the examined objects into homogeneous clusters. Space and time, which interact with each other, are used on equal terms in the construction of the STPCA. The definition of these principal components is based on the product of two factors. The first factor is equal to the variance of the functional principal components, and the second factor is Geary's contiguity ratio C. The proposed new method of Spatio-Temporal Principal Components was used to show the mutual location of 16 Polish regions characterized by 12 socio-economic features observed in the years 2002–2018 in the system of the first two principal components and to identify homogeneous clusters of these regions in the system of all 15 constructed principal components.</p

Evaluation of Proteasome and Immunoproteasome Levels in Plasma and Peritoneal Fluid in Patients with Endometriosis

Endometriosis is a chronic disease in which the endometrium cells are located outside the uterine cavity. The aim of this study was to evaluate circulating 20S proteasome and 20S immunoproteasome levels in plasma and peritoneal fluid in women with and without endometriosis in order to assess their usefulness as biomarkers of disease. Concentrations were measured using surface plasmon resonance imaging biosensors. Patients with suspected endometriosis were included in the study—plasma was collected in 112 cases and peritoneal fluid in 75. Based on the presence of endometriosis lesions detected during laparoscopy, patients were divided into a study group (confirmed endometriosis) and a control group (patients without endometriosis). Proteasome and immunoproteasome levels in both the plasma (p = 0.174; p = 0.696, respectively) and the peritoneal fluid (p = 0.909; p = 0.284, respectively) did not differ between those groups. There was a statistically significant difference in the plasma proteasome levels between patients in the control group and those with mild (Stage I and II) endometriosis (p = 0.047) and in the plasma immunoproteasome levels in patients with ovarian cysts compared to those without (p = 0.017). The results of our study do not support the relevance of proteasome and immunoproteasome determination as biomarkers of the disease but suggest a potentially active role in the pathogenesis of endometriosis