Browsing by Author "Harrison, Karl"

Now showing 1 - 6 of 6
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    Applying archaeology to fire investigation techniques: a review
    (Elsevier, 2022-11-03) Harding, Mary-Jane; Harrison, Karl; Icove, David
    Archaeology and Fire Investigation do not appear to have much in common on the surface. Scratch beneath however, and the similarities begin to reveal themselves. Both disciplines require the investigation of physical remains by employing an analytical approach in order to reconstruct sequences of events. Before recovery employing an archaeological stratigraphic recording method at fire scenes, provides the opportunity for a sequential recording of both debris layers, recording objects and their relationship to the immediate environment. This approach is particularly pertinent in Fire Investigation, as the recovery of evidence can lead to destruction in cases of fragile evidence and remains.
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    Ballistic research techniques: visualizing gunshot wounding patterns
    (Springer, 2020-02-14) Stevenson, Tom; Carr, Debra J.; Harrison, Karl; Critchley, Richard; Gibb, Iain E.; Stapley, Sarah A.
    There are difficulties associated with mapping gunshot wound (GSW) patterns within opaque models. Depending on the damage measurement parameters required, there are multiple techniques that can provide methods of “seeing” the GSW pattern within an opaque model. The aim of this paper was to test several of these techniques within a cadaveric animal limb model to determine the most effective. The techniques of interest were flash X-ray, ultrasound, physical dissection, and computed-tomography (CT). Fallow deer hind limbs were chosen for the model with four limbs used for each technique tested. Quarantined 7.62 × 39 mm ammunition was used for each shot, and each limb was only shot once, on an outdoor range with shots impacting at muzzle velocity. Flash X-ray provided evidence of yaw within the limb during the projectile’s flight; ultrasound though able to visualise the GSW track, was too subjective and was abandoned; dissection proved too unreliable due to the tissue being cadaveric so also too subjective; and lastly, CT with contrast provided excellent imaging in multiple viewing planes and 3D image reconstruction; this allowed versatile measurement of the GSW pattern to collect dimensions of damage as required. Of the different techniques examined in this study, CT with contrast proved the most effective to allow precise GSW pattern analysis within a cadaveric animal limb model. These findings may be beneficial to others wishing to undertake further ballistic study both within clinical and forensic fields.
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    Chapter 6: Challenges in Fire Investigation
    (Royal Society of Chemistry, 2021-04-15) Johnson, Stephen; Harrison, Karl; Dean, Jason
    This chapter introduces what fire investigation is and why it is necessary. It explores, through a case study and analysis of key issues, the challenges that are faced by fire investigators and those who support them. It finishes with some of the general problems for fire investigation and the more specific issues related to the chemistry of fire scenes.
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    The effect of seasonality on the application of accumulated degree-days to estimate the early post-mortem interval
    (Elsevier, 2020-08-01) Giles, Stephanie B.; Harrison, Karl; Errickson, David; Márquez-Grant, Nicholas
    This study investigated the effect of seasonal variables on decomposition in the early post-mortem period using 26 donated human cadavers at the University of Tennessee’s Anthropology Research Facility (ARF), USA. The rate and pattern of decomposition in human cadavers (as measured by TBS and the revised TBSsurf methods) did not vary significantly between all seasons. Summer and autumn cadavers had comparable rates of accelerated decomposition despite significant differences in both ADD and temperature (p<0.05). Spring cadavers had the slowest onset of decomposition characteristics, even compared to the few decomposition characteristics expressed in winter. Seasonal variation in humidity, rather than temperature, may be the overarching driving force for decomposition progression in the early post-mortem period. Both TBS and TBSsurf methods were poor predictors of the PMI (R²=0.4) and significantly over-estimated the PMI across all seasons, although to a lesser extent in spring. This study also demonstrated no relationship between known ADD and TBS/TBSsurf (R²=0.025). TBS and TBSsurf are ADD-based PMI estimation models that cannot be validated under experimental conditions. Accounting for seasonal expression of individual decomposition characteristics is needed for improvement of PMI predictability in forensic practice
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    Individuals lacking ridge detail: a case study in adermatoglyphia
    (Wiley, 2020-11-02) Cook, Heather Isla; Harrison, Karl; James, Helen
    Adermatoglyphia is a very rare autosomal‐dominant condition that is genetically inherited and causes an individual to be born without conventional ridge detail on either their palmar or plantar surfaces (the fingers and palms of the hands and the toes and the soles of the feet). While adermatoglyphia has been the focus of medical and genetic research, no previous research has been conducted with regard to the forensic recovery and identification of marks from an adermatoglyphic individual. By observation of ridge detail donated by an adermatoglyphic subject, the study uses different methods in order to capture fingermarks (methods include: inked capture, livescan (biometric) capture, cyanoacrylate fuming, ninhydrin enhancement, and physical developer). Unusually, the purpose of this paper ends up presenting a number of examples of an absence of evidence; unsuccessful attempts made to capture and enhance fingerprint ridge detail. This is determined over a range of standard means including "live" donations by the adermatoglyphic subject onto the Livescan system, and enhancements of latent donations. The subject shows to leave either insubstantial fingermarks with no detail, or no mark whatsoever
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    Solving the inverse problem of post-mortem interval estimation using Bayesian Belief Networks
    (Elsevier, 2022-12-09) Giles, Stephanie; Errickson, David; Harrison, Karl; Márquez-Grant, Nicholas
    Bayesian Belief Networks (BBNs) can be applied to solve inverse problems such as the post-mortem interval (PMI) by a simple and logical graphical representation of conditional dependencies between multiple taphonomic variables and the observable decomposition effect. This study is the first cross-comparison retrospective study of human decomposition across three different geographical regions. To assess the effect of the most influential taphonomic variables on the decomposition rate (as measured by the Total Decomposition Score (TDS)), decomposition data was examined from the Forensic Anthropology Research Facility at the University of Tennessee (n = 312), the Allegheny County Office of the Medical Examiner in Pittsburgh, US (n = 250), and the Crime Scene Investigation department at Southwest Forensics in the UK (n = 81). Two different BBNs for PMI estimations were created from the US and the UK training data. Sensitivity analysis was performed to identify the most influential parameters of TDS variance, with weaker variables (e.g., age, sex, clothing) being excluded during model refinement. The accuracy of the BBNs was then compared by additional validation cases: US (n = 28) and UK (n = 10). Both models conferred predictive power of the PMI and accounted for the unique combination of taphonomic variables affecting decomposition. Both models had a mean posterior probability of 86% (US) and 81% (UK) in favor of the experimental hypothesis (that the PMI was on, or less than, the prior last known alive date). Neither the US nor the UK datasets represented any cases below 'moderate' support for the value of PMI evidence. By applying coherent probabilistic reasoning to PMI estimations, one logical solution is provided to model the complexities of human decomposition that can quantify the combined effect of several uncertainties surrounding the PMI estimation. This approach communicates the PMI with an associated degree of confidence and provides predictive power on unknown PMI cases.