Prediction of blood back spatter from a gunshot in bloodstain pattern analysis

A theoretical model for predicting and interpreting blood-spatter patterns resulting from a gunshot wound is proposed. The physical process generating a backward spatter of blood is linked to the Rayleigh-Taylor instability of blood accelerated toward the surrounding air, allowing the determination of the initial distribution of drop sizes and velocities. Then the motion of many drops in air is considered with governing equations accounting for gravity and air drag. Based on these equations, a numerical solution is obtained. It predicts the atomization process, the trajectories of the back-spatter drops of blood from the wound to the ground, the impact angle, and the impact Weber number on the ground, as well as the distribution and location of bloodstains and their shape and sizes. A parametric study is undertaken to predict patterns of backward blood spatter under realistic conditions corresponding to the experiments conducted in the present work. The results of the model are compared to the experimental data on back spatter generated by a gunshot impacting a blood-impregnated sponge

Exact transverse macro dispersion coefficients for transport in heterogeneous porous media

We study transport through heterogeneous media. We derive the exact large scale transport equation. The macro dispersion coefficients are determined by additional partial differential equations. In the case of infinite Peclet numbers, we present explicit results for the transverse macro dispersion coefficients. In two spatial dimensions, we demonstrate that the transverse macro dispersion coefficient is zero. The result is not limited on lowest order perturbation theory approximations but is an exact result. However, the situation in three spatial dimensions is very different: The transverse macro dispersion coefficients are finite - a result which is confirmed by numerical simulations we performe

Pattern formation during the evaporation of a colloidal nanoliter drop: a numerical and experimental study

An efficient way to precisely pattern particles on solid surfaces is to dispense and evaporate colloidal drops, as for bioassays. The dried deposits often exhibit complex structures exemplified by the coffee ring pattern, where most particles have accumulated at the periphery of the deposit. In this work, the formation of deposits during the drying of nanoliter colloidal drops on a flat substrate is investigated numerically and experimentally. A finite-element numerical model is developed that solves the Navier-Stokes, heat and mass transport equations in a Lagrangian framework. The diffusion of vapor in the atmosphere is solved numerically, providing an exact boundary condition for the evaporative flux at the droplet-air interface. Laplace stresses and thermal Marangoni stresses are accounted for. The particle concentration is tracked by solving a continuum advection-diffusion equation. Wetting line motion and the interaction of the free surface of the drop with the growing deposit are modeled based on criteria on wetting angles. Numerical results for evaporation times and flow field are in very good agreement with published experimental and theoretical results. We also performed transient visualization experiments of water and isopropanol drops loaded with polystyrene microsphere evaporating on respectively glass and polydimethylsiloxane substrates. Measured evaporation times, deposit shape and sizes, and flow fields are in very good agreement with the numerical results. Different flow patterns caused by the competition of Marangoni loops and radial flow are shown to determine the deposit shape to be either a ring-like pattern or a homogeneous bump

Effective velocity for transport in heterogeneous compressible flows with mean drift

Solving transport equations in heterogeneous flows might give rise to scale dependent transport behavior with effective large scale transport parameters differing from those found on smaller scales. For incompressible velocity fields, homogenization methods have proven to be powerful in describing the effective transport parameters. In this paper, we aim at studying the effective drift of transport problems in heterogeneous compressible flows. Such a study was done by Vergassola and Avellaneda in Physica D 106, 148 (1997). There, it was shown that for static compressible flow without mean drift, impacts on the large scale drift do not occur. We will first discuss the impact of a mean drift and show that static compressible flow with mean drift can produce a heterogeneity driven large scale drift (or ballistic transport). For the case of Gaussian stationary random processes, we derive explicit results for the large scale drift. Moreover, we show that the large scale or effective drift depends on the small scale diffusion coefficients and thus on the molecular weights of the particles. This study could be applied to weight-based particle separation. Numerical simulations are presented to illustrate these phenomena. © 2008 American Institute of Physics

What we talk about when we talk about uncertainty. Toward a unified, data-driven framework for uncertainty characterization in hydrogeology

In this manuscript, we compare and discuss different frameworks for hydrogeological uncertainty analysis. Since uncertainty is a property of knowledge, we base this comparison on purely epistemological concepts. In a detailed comparison between different candidates, we make the case for Bayesianism, i.e., the framework of reasoning about uncertainty using probability theory. We motivate the use of Bayesian tools, shortly explain the properties of Bayesian inference, prediction and decision and identify the most pressing current challenges of this framework. In hydrogeology, these challenges are the derivation of prior distributions for the parametric uncertainty, typically hydraulic conductivity values, as well as the most relevant paradigm for generating subsurface structures for assessing the structural uncertainty. We present the most commonly used paradigms and give detailed advice on two specific paradigms; Gaussian multivariate random fields as well as multiple-point statistics, both of which have benefits and drawbacks. Without settling for either of these paradigms, we identify the lack of open-access data repositories as the most pressing current impediment for the advancement of data-driven uncertainty analysis. We detail the shortcomings of the current situation and describe a number of steps which could foster the application of both the Gaussian as well as the multiple-point paradigm. We close the manuscript with a call for a community-wide initiative to create this necessary support

Effects of uncertainty in soil properties on simulated hydrological states and fluxes at different spatio-temporal scales

Soil properties show high heterogeneity at different spatial scales and their correct characterization remains a crucial challenge over large areas. The aim of the study is to quantify the impact of different types of uncertainties that arise from the unresolved soil spatial variability on simulated hydrological states and fluxes. Three perturbation methods are presented for the characterization of uncertainties in soil properties. The methods are applied on the soil map of the upper Neckar catchment (Germany), as an example. The uncertainties are propagated through the distributed mesoscale hydrological model (mHM) to assess the impact on the simulated states and fluxes. The model outputs are analysed by aggregating the results at different spatial and temporal scales. These results show that the impact of the different uncertainties introduced in the original soil map is equivalent when the simulated model outputs are analysed at the model grid resolution (i.e. 500 m). However, several differences are identified by aggregating states and fluxes at different spatial scales (by subcatchments of different sizes or coarsening the grid resolution). Streamflow is only sensitive to the perturbation of long spatial structures while distributed states and fluxes (e.g. soil moisture and groundwater recharge) are only sensitive to the local noise introduced to the original soil properties. A clear identification of the temporal and spatial scale for which finer-resolution soil information is (or is not) relevant is unlikely to be universal. However, the comparison of the impacts on the different hydrological components can be used to prioritize the model improvements in specific applications, either by collecting new measurements or by calibration and data assimilation approaches. In conclusion, the study underlines the importance of a correct characterization of uncertainty in soil properties. With that, soil maps with additional information regarding the unresolved soil spatial variability would provide strong support to hydrological modelling applications

Hydrodebridement of wounds: effectiveness in reducing wound bacterial contamination and potential for air bacterial contamination

<p>Abstract</p> <p>Background</p> <p>The purpose of this study was to assess the level of air contamination with bacteria after surgical hydrodebridement and to determine the effectiveness of hydro surgery on bacterial reduction of a simulated infected wound.</p> <p>Methods</p> <p>Four porcine samples were scored then infected with a broth culture containing a variety of organisms and incubated at 37°C for 24 hours. The infected samples were then debrided with the hydro surgery tool (Versajet, Smith and Nephew, Largo, Florida, USA). Samples were taken for microbiology, histology and scanning electron microscopy pre-infection, post infection and post debridement. Air bacterial contamination was evaluated before, during and after debridement by using active and passive methods; for active sampling the SAS-Super 90 air sampler was used, for passive sampling settle plates were located at set distances around the clinic room.</p> <p>Results</p> <p>There was no statistically significant reduction in bacterial contamination of the porcine samples post hydrodebridement. Analysis of the passive sampling showed a significant (<it>p </it>< 0.001) increase in microbial counts post hydrodebridement. Levels ranging from 950 colony forming units per meter cubed (CFUs/m³) to 16780 CFUs/m³were observed with active sampling of the air whilst using hydro surgery equipment compared with a basal count of 582 CFUs/m³. During removal of the wound dressing, a significant increase was observed relative to basal counts (<it>p </it>< 0.05). Microbial load of the air samples was still significantly raised 1 hour post-therapy.</p> <p>Conclusion</p> <p>The results suggest a significant increase in bacterial air contamination both by active sampling and passive sampling. We believe that action might be taken to mitigate fallout in the settings in which this technique is used.</p

Evidence Based Estimation of Macrodispersivity for Groundwater Transport Applications

The scope of this work is to discuss the proper choice of macrodispersion coefficients in modeling contaminant transport through the advection dispersion equation (ADE). It is common to model solute concentrations in transport by groundwater with the aid of the ADE. Spreading is quantified by macrodispersivity coefficients, which are much larger than the laboratory observed pore-scale dispersivities. In the frame of stochastic theory, longitudinal macrodispersivity is related to the hydraulic conductivity spatial variability via its statistical moments (mean, variance, integral scales), which are generally determined by geostatistical analysis of field measurements. In many cases, especially for preliminary assessment of contaminant spreading, these data are not available and ad hoc values are adopted by practitioners. The present study aims at recommending dispersivity values based on a thorough analysis of tens of field experiments. Aquifers are classified as of weak, medium, and high heterogeneity and for each class a range of macrodispersivity values is recommended. Much less data are available for the transverse macrodispersivities, which are significantly smaller than the longitudinal one. Nevertheless, a few realistic values based on field data, are recommended for applications. Transport models using macrodispersivities can predict mean concentrations, different from the local ones. They can be used for estimation of robust measures, like plumes spatial moments, longitudinal mass distribution and breakthrough curves at control planes

Numerical simulation of blood flow and pressure drop in the pulmonary arterial and venous circulation

A novel multiscale mathematical and computational model of the pulmonary circulation is presented and used to analyse both arterial and venous pressure and flow. This work is a major advance over previous studies by Olufsen et al. (Ann Biomed Eng 28:1281–1299, 2012) which only considered the arterial circulation. For the first three generations of vessels within the pulmonary circulation, geometry is specified from patient-specific measurements obtained using magnetic resonance imaging (MRI). Blood flow and pressure in the larger arteries and veins are predicted using a nonlinear, cross-sectional-area-averaged system of equations for a Newtonian fluid in an elastic tube. Inflow into the main pulmonary artery is obtained from MRI measurements, while pressure entering the left atrium from the main pulmonary vein is kept constant at the normal mean value of 2 mmHg. Each terminal vessel in the network of ‘large’ arteries is connected to its corresponding terminal vein via a network of vessels representing the vascular bed of smaller arteries and veins. We develop and implement an algorithm to calculate the admittance of each vascular bed, using bifurcating structured trees and recursion. The structured-tree models take into account the geometry and material properties of the ‘smaller’ arteries and veins of radii ≥ 50 μ m. We study the effects on flow and pressure associated with three classes of pulmonary hypertension expressed via stiffening of larger and smaller vessels, and vascular rarefaction. The results of simulating these pathological conditions are in agreement with clinical observations, showing that the model has potential for assisting with diagnosis and treatment for circulatory diseases within the lung