A designerly journey into textiles and HCI leads to thinking about data as a material for a cybernetic future.
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Non-invasive, rapid, on-site detection and identification of body fluids is highly desired in forensic investigations. The use of fluorescence-based methods for body fluid identification, have so far remain relatively unexplored. As such, the fluorescent properties of semen, serum, urine, saliva and fingermarks over time were investigated, by means of fluorescence spectroscopy, to identify specific fluorescent signatures for body fluid identification. The samples were excited at 81 different excitation wavelengths ranging from 200 to 600 nm and for each excitation wavelength the emission was recorded between 220 and 700 nm. Subsequently, the total emitted fluorescence intensities of specific fluorescent signatures in the UV–visible range were summed and principal component analysis was performed to cluster the body fluids. Three combinations of four principal components allowed specific clustering of the body fluids, except for fingermarks. Blind testing showed that 71.4% of the unknown samples could be correctly identified. This pilot study shows that the fluorescent behavior of ageing body fluids can be used as a new non-invasive tool for body fluid identification, which can improve the current guidelines for the detection of body fluids in forensic practice and provide the robustness of methods that rely on fluorescence.
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From the article: Abstract Adjustment and testing of a combination of stochastic and nonstochastic observations is applied to the deformation analysis of a time series of 3D coordinates. Nonstochastic observations are constant values that are treated as if they were observations. They are used to formulate constraints on the unknown parameters of the adjustment problem. Thus they describe deformation patterns. If deformation is absent, the epochs of the time series are supposed to be related via affine, similarity or congruence transformations. S-basis invariant testing of deformation patterns is treated. The model is experimentally validated by showing the procedure for a point set of 3D coordinates, determined from total station measurements during five epochs. The modelling of two patterns, the movement of just one point in several epochs, and of several points, is shown. Full, rank deficient covariance matrices of the 3D coordinates, resulting from free network adjustments of the total station measurements of each epoch, are used in the analysis.
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