48 resultados para Forensic science


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We present the results of an initial investigation into the efficacy of using testate amoebae for the discrimination of soils from wet ground and puddles, as little attention has been given to these organisms in forensic science. The preservation of testate amoebae in these sediments is generally good, although test concentrations are low. Statistical analysis suggests that restate amoebae assemblages are somewhat spatially distinct and have potential to be used for soil discrimination. A case study is presented where mineralogical (X-ray diffraction) and restate amoebae analyses are used in conjunction to clarify the scene of crime in a 'cold case' murder enquiry. Testate amoebae were recovered from dried sediment residues on clothing 10 years after the murder. Despite these promising results, further experimental work is crucial to examine the spatial and temporal variation of amoebae assemblages in water films, wet ground and puddles before they can be added to the armoury of methods available to the forensic biologist.

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Geomorphology plays a critical role in two areas of geoforensics: searching the land for surface or buried objects and sampling or imaging rural scenes of crime and control locations as evidence. Most of the associated geoscience disciplines have substantial bodies of work dedicated to their relevance in forensic investigations, yet geomorphology (specifically landforms, their mapping and evolution, soils and relationship to geology and biogeography) have had no such exposure. This is strange considering how fundamental to legal enquiries the location of a crime and its evolution are, as this article will demonstrate. This work aims to redress the balance by showing how geomorphology is featured in one of the earliest works on forensic science methods, and has continued to play a role in the sociology, archaeology, criminalistics and geoforensics of crime. The application geomorphology has in military/humanitarian geography and environmental/engineering forensics is briefly discussed as these are also regularly reviewed in courts of law

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The burial of objects (human remains, explosives, weapons) below or behind concrete, brick, plaster or tiling may be associated with serious crime and are difficult locations to search. These are quite common forensic search scenarios but little has been published on them to-date. Most documented discoveries are accidental or from suspect/witness testimony. The problem in locating such hidden objects means a random or chance-based approach is not advisable. A preliminary strategy is presented here, based on previous studies, augmented by primary research where new technology or applications are required. This blend allows a rudimentary search workflow, from remote desktop study, to non-destructive investigation through to recommendations as to how the above may inform excavation, demonstrated here with a case study from a homicide investigation. Published case studies on the search for human remains demonstrate the problems encountered when trying to find and recover sealed-in and sealed over locations. Established methods include desktop study, photography, geophysics and search dogs:these are integrated with new technology (LiDAR and laser scanning; photographic rectification; close quarter aerial imagery; ground-penetrating radar on walls and gamma-ray/neutron activation radiography) to propose this possible search strategy.

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Statistics are regularly used to make some form of comparison between trace evidence or deploy the exclusionary principle (Morgan and Bull, 2007) in forensic investigations. Trace evidence are routinely the results of particle size, chemical or modal analyses and as such constitute compositional data. The issue is that compositional data including percentages, parts per million etc. only carry relative information. This may be problematic where a comparison of percentages and other constraint/closed data is deemed a statistically valid and appropriate way to present trace evidence in a court of law. Notwithstanding an awareness of the existence of the constant sum problem since the seminal works of Pearson (1896) and Chayes (1960) and the introduction of the application of log-ratio techniques (Aitchison, 1986; Pawlowsky-Glahn and Egozcue, 2001; Pawlowsky-Glahn and Buccianti, 2011; Tolosana-Delgado and van den Boogaart, 2013) the problem that a constant sum destroys the potential independence of variances and covariances required for correlation regression analysis and empirical multivariate methods (principal component analysis, cluster analysis, discriminant analysis, canonical correlation) is all too often not acknowledged in the statistical treatment of trace evidence. Yet the need for a robust treatment of forensic trace evidence analyses is obvious. This research examines the issues and potential pitfalls for forensic investigators if the constant sum constraint is ignored in the analysis and presentation of forensic trace evidence. Forensic case studies involving particle size and mineral analyses as trace evidence are used to demonstrate the use of a compositional data approach using a centred log-ratio (clr) transformation and multivariate statistical analyses.

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Obtaining as much particulate material as possible from questioned items is desirable in forensic science as this allows a range of analyses to be undertaken and the retention of material for others to check. A method of maximising particulate recovery is described using a kidnap case, where minimal staining on clothing (socks) remained as possible indications of where the victim had been held captive. Police intelligence led to a hostage scene that was sampled. Brushing of the socks recovered about 50 sand grains with some silt: ultrasonic agitation and centrifuging recovered over 300 grains of sand, silt and clay. These were visually compared to scene and control samples, allowing exclusion of 52 samples and the retention of one comparison sample as well as other possibles, saving time and money, but maximising sample quantity and quality. © 2011 Elsevier Ireland Ltd.