Forensic acarology: an introduction

Mites can be found in all imaginable terrestrial habitats, in freshwater, and in salt water. Mites can be found in our houses and furnishings, on our clothes, and even in the pores of our skin-almost every single person carries mites. Most of the time, we are unaware of them because they are small and easily overlooked, and-most of the time-they do not cause trouble. In fact, they may even proof useful, for instance in forensics. The first arthropod scavengers colonising a dead body will be flies with phoretic mites. The flies will complete their life cycle in and around the corpse, while the mites may feed on the immature stages of the flies. The mites will reproduce much faster than their carriers, offering themselves as valuable timeline markers. There are environments where insects are absent or rare or the environmental conditions impede their access to the corpse. Here, mites that are already present and mites that arrive walking, through air currents or material transfer become important. At the end of the ninetieth century, the work of Jean Pierre M,gnin became the starting point of forensic acarology. M,gnin documented his observations in 'La Faune des Cadavres' [The Fauna of Carcasses]. He was the first to list eight distinct waves of arthropods colonising human carcasses. The first wave included flies and mites, the sixth wave was composed of mites exclusively. The scope of forensic acarology goes further than mites as indicators of time of death. Mites are micro-habitat specific and might provide evidential data on movement or relocation of bodies, or locating a suspect at the scene of a crime. Because of their high diversity, wide occurrence, and abundance, mites may be of great value in the analysis of trace evidence.

A mixture of experts network structure construction algorithm for modelling and control

This paper introduces a new fast, effective and practical model structure construction algorithm for a mixture of experts network system utilising only process data. The algorithm is based on a novel forward constrained regression procedure. Given a full set of the experts as potential model bases, the structure construction algorithm, formed on the forward constrained regression procedure, selects the most significant model base one by one so as to minimise the overall system approximation error at each iteration, while the gate parameters in the mixture of experts network system are accordingly adjusted so as to satisfy the convex constraints required in the derivation of the forward constrained regression procedure. The procedure continues until a proper system model is constructed that utilises some or all of the experts. A pruning algorithm of the consequent mixture of experts network system is also derived to generate an overall parsimonious construction algorithm. Numerical examples are provided to demonstrate the effectiveness of the new algorithms. The mixture of experts network framework can be applied to a wide variety of applications ranging from multiple model controller synthesis to multi-sensor data fusion.

Neurofuzzy mixture of experts network parallel learning and model construction algorithms

A connection between a fuzzy neural network model with the mixture of experts network (MEN) modelling approach is established. Based on this linkage, two new neuro-fuzzy MEN construction algorithms are proposed to overcome the curse of dimensionality that is inherent in the majority of associative memory networks and/or other rule based systems. The first construction algorithm employs a function selection manager module in an MEN system. The second construction algorithm is based on a new parallel learning algorithm in which each model rule is trained independently, for which the parameter convergence property of the new learning method is established. As with the first approach, an expert selection criterion is utilised in this algorithm. These two construction methods are equivalent in their effectiveness in overcoming the curse of dimensionality by reducing the dimensionality of the regression vector, but the latter has the additional computational advantage of parallel processing. The proposed algorithms are analysed for effectiveness followed by numerical examples to illustrate their efficacy for some difficult data based modelling problems.

A two-locus forensic match probability for subdivided populations

A two-locus match probability is presented that incorporates the effects of within-subpopulation inbreeding (consanguinity) in addition to population subdivision. The usual practice of calculating multi-locus match probabilities as the product of single-locus probabilities assumes independence between loci. There are a number of population genetics phenomena that can violate this assumption: in addition to consanguinity, which increases homozygosity at all loci simultaneously, gametic disequilibrium will introduce dependence into DNA profiles. However, in forensics the latter problem is usually addressed in part by the careful choice of unlinked loci. Hence, as is conventional, we assume gametic equilibrium here, and focus instead on between-locus dependence due to consanguinity. The resulting match probability formulae are an extension of existing methods in the literature, and are shown to be more conservative than these methods in the case of double homozygote matches. For two-locus profiles involving one or more heterozygous genotypes, results are similar to, or smaller than, the existing approaches.