English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Fractured metal fragments with rough and irregular surfaces are often foundat crime scenes. Current forensic practice visually inspects the complex jaggedtrajectory of fractured surfaces to recognize a ``match'' using comparativemicroscopy and physical pattern analysis. We developed a novel computationalframework, utilizing the basic concepts of fracture mechanics and statisticalanalysis to provide quantitative match analysis for match probability and errorrates. The framework employs the statistics of fracture surfaces to becomenon-self-affine with unique roughness characteristics at relevant microscopiclength scale, dictated by the intrinsic material resistance to fracture and itsmicrostructure. At such a scale, which was found to be greater than twograin-size or micro-feature-size, we establish that the material intrinsicproperties, microstructure, and exposure history to external forces on anevidence fragment have the premise of uniqueness, which quantitativelydescribes the microscopic features on the fracture surface for forensiccomparisons. The methodology utilizes 3D spectral analysis of overlappingtopological images of the fracture surface and classifies specimens with veryhigh accuracy using statistical learning. Cross correlations of image-pairs intwo frequency ranges are used to develop matrix variate statistical models forthe distributions among matching and non-matching pairs of images, and providesa decision rule for identifying matches and determining error rates. A set ofthirty eight different fracture surfaces of steel articles were correctlyclassified. The framework lays the foundations for forensic applications withquantitative statistical comparison across a broad range of fractured materialswith diverse textures and mechanical properties.

Fracture Mechanics-Based Quantitative Matching of Forensic Evidence Fragments