RESUMEN

A physical fit is an important observation that can result from the forensic analysis of trace evidence as it conveys a high degree of association between two items. However, physical fit examinations can be time-consuming, and potential bias from analysts may affect judgment. To overcome these shortcomings, a data analysis algorithm using mutual information and a decision tree has been developed to support practitioners in interpreting the evidence. We created these tools using data obtained from physical fit examinations of duct tape and textiles analyzed in previous studies, along with the reasoning behind the analysts' decisions. The relative feature importance is described by material type, enhancing the knowledge base in this field. Compared with the human analysis, the algorithms provided accuracies above 90%, with an improved rate of true positives for most duct tape subsets. Conversely, false positives were observed in high-quality scissor cut (HQ-HT-S) duct tape and textiles. As such, it is advised to use these algorithms in tandem with human analysis. Furthermore, the study evaluated the accuracy of physical fits when only partial sample lengths are available. The results of this investigation indicated that acceptable accuracies for correctly identifying true fits and non-fits occurred when at least 35% of a sample length was present. However, lower accuracies were observed for samples prone to stretching or distortion. Therefore, the models described here can provide a valuable supplementary tool but should not be the sole means of evaluating samples.

RESUMEN

Several organizations have outlined the need for standardized methods for conducting physical fit comparisons. This study answers this call by developing and evaluating a systematic and transparent approach for examining, documenting, and interpreting textile physical fits, using qualitative feature descriptors and a quantitative metric (Edge Similarity Score, ESS) for the physical fit examination of textile materials. Here, the results from 1027 textile physical fit comparisons are reported. This includes the evaluation of inter and intraanalyst variation when using this method for hand-torn and stabbed fabrics. ESS higher than 80% and ESS lower than 20%, respectively, support fit and nonfit conclusions. The results show that analyst accuracy ranges from 88% to 100% when using this criterion. The estimated false-positive rate for this dataset (2% false positives, 10 of 477 true nonfit pairs) demonstrates the importance of assessing the quality of a physical fit during an examination and reveals that potential errors are low, but possible in textile physical fit examinations. The risk of error must be accounted for in the interpretation and verification processes. Further analysis shows that factors such as the separation method, construction, and design of the samples do not substantially influence the ESS values. Additionally, the proposed method is independently evaluated by 15 practitioners in an interlaboratory exercise that demonstrates satisfactory reproducibility between participants. The standardized terminology and documentation criteria are the first steps toward validating approaches to streamline the peer review process, minimize bias and subjectivity, and convey the probative value of the evidence.

RESUMEN

This paper describes the construction and use of a machine-learning model to provide objective support for a physical fit examination of duct tapes. We present the ForensicFit package that can preprocess and database raw tape images. Using the processed tape image, we trained a convolutional neural network to compare tape edges and predict membership scores (i.e., fit or non-fit category). A dataset of nearly 2000 tapes and 4000 images was evaluated, including various quality grades: low, medium, and high, as well as two separation methods, scissor-cut and hand-torn. The model predicts medium-quality and high-quality scissor-cut tape more accurately than hand-torn, whereas for low-quality tape predicts the hand-torn tapes more accurately. These results are consistent with previous studies performed on the same datasets by analyst examinations. A method of pixel importance was also implemented to show which pixels are used to make the decision. This method can confirm some fit features that correspond with analyst-identified features, like edge morphology and backing pattern. This pilot study demonstrates the feasibility of computational algorithms to build physical fit databases and automated comparisons using deep neural networks, which can be used as a model for other materials.

RESUMEN

Coagulopathy can occur in trauma, and it can affect septic patients as a host tries to respond to infection. Sometimes, it can lead to disseminated intravascular coagulopathy (DIC) with a high potential for mortality. New research has delineated risk factors that include neutrophil extracellular traps and endothelial glycocalyx shedding. Managing DIC in septic patients focuses on first treating the underlying cause of sepsis. Further, the International Society on Thrombolysis and Haemostasis (ISTH) has DIC diagnostic criteria. "Sepsis-induced coagulopathy" (SIC) is a new category. Therapy of SIC focuses on treating the underlying infection and the ensuing coagulopathy. Most therapeutic approaches to SIC have focused on anticoagulant therapy. This review will discuss SIC and DIC and how they are relevant to prolonged casualty care (PCC).

Asunto(s)

RESUMEN

This study expands upon a previously developed method that quantifies the similarity of the compared tape edges by systematically studying the effect of several separation methods and tape grades on the quality of a fit. Analysts examined more than 3300 pairs of hand-torn or scissor-cut duct tapes from three different tape grades while they were kept blind from the ground truth to minimize bias. The samples were examined following a three-step methodology: 1) qualitative assessment of the overall edge alignment and description of edge pattern, 2) macroscopic evaluation of the edges' features, 3) bin by bin subunit assessment of tape edges and estimation of the edge similarity score. A report template was designed to maintain records of the decision-making process. In the second and third steps, eight comparison features were defined and documented using auto-populated cell options. Generally, misidentification rates were low, with no false positives reported. Coinciding with previous research, low scores (under 20%) provided the most support for a non-fit conclusion, while high scores (80% or higher) supported a fit conclusion. A statistical analysis of the separation method and quality of tape revealed a potential interaction between these factors and showed that they significantly impact the edge scores for true fitting pairs, but not the true non-fits' scores. The developed comparison and documentation criteria can assist practitioners with a more straightforward, consistent, and transparent interpretation and reporting approach.