Traditionally, firearm and toolmark examiners manually evaluate the similarity of features on two bullets using comparison microscopy. Advances in microscopy have made it possible to collect 3D topographic data, and several automated comparison algorithms have been introduced for the comparison of bullet striae using these data. In this study, open-source approaches for cross-correlation, congruent matching profile segments, consecutive matching striations, and a random forest model were evaluated. A statistical characterization of these automated approaches was performed using four datasets of consecutively manufactured firearms to provide a challenging comparison scenario. Each automated approach was applied to all samples in a pairwise fashion, and classification performance was compared. Based on these findings, a Bayesian network was empirically learned and constructed to leverage the strengths of each individual approach, model the relationship between the automated results, and combine them into a posterior probability for the given comparison. The network was evaluated similarly to the automated approaches, and the results were compared. The developed Bayesian network classified 99.6% of the samples correctly, and the resultant probability distributions were significantly separated more so than the automated approaches when used in isolation.

Are professionals better at assessing the evidential strength of different types of forensic conclusions compared to students? In an online questionnaire 96 crime investigation and law students, and 269 crime investigation and legal professionals assessed three fingerprint examination reports. All reports were similar, except for the conclusion part which was stated in a categorical (CAT), verbal likelihood ratio (VLR) or numerical likelihood ratio (NLR) conclusion with high or low evidential strength. The results showed no significant difference between the groups of students and professionals in their assessment of the conclusions. They all overestimated the strength of the strong CAT conclusion compared to the other conclusion types and underestimated the strength of the weak CAT conclusion. Their background (legal vs. crime investigation) did have a significant effect on their understanding. Whereas the legal professionals performed better compared to the crime investigators, the legal students performed worse compared to crime investigation students.

This study assists the interpretation of glass and paint evidence by filling an existing gap in the background occurrence that reflects the socioeconomic and demographic circumstances in the United States. The collection was performed in a college US city (Morgantown, West Virginia) to determine the effect of the type of clothing worn at different seasons on the presence of glass and paint. Tape lifts and sole scrapings (1038) were collected from 210 participants and up to six clothing and footwear areas per individual. Glass fragments were analyzed via polarized light microscopy (PLM), refractive index (RI), micro-X-Ray fluorescence (μXRF), and scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS), while paint specimens were examined by light microscopy and infrared spectroscopy (μFTIR). Higher occurrences of glass and paint were found in the winter season. The winter collection yielded 10 glass fragments and 68 paint particles, whereas the summer collection resulted in one glass fragment and 23 paint particles. The percentage of individuals with traces varied between seasons; 7% of individuals in the winter and 0.9% in the summer had glass, whereas 36% of individuals in the winter and 19% in the summer bore paint. Lastly, when considering the overall garment and footwear areas, glass was detected in 1.4% of the winter set, compared to 0.2% in the summer collection; paint was found in 9.2% of the winter collection, whereas only 4.2% was found in the summer set. There were no instances where both glass and paint were detected on the clothing and footwear of the same individual.

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A new method for the evaluation of duct tape ends is proposed. This method is based on the breaks of the loops in the warp yarns, when duct tape with a scrim of chain-stitched warp yarns and weft-insertion is torn. After tearing, the loop at the end of each warp yarn can be in one of four states: open, closed, complex or missing. Additionally, the horizontal position of each warp yarn can be expressed in terms of weft yarns. The evidential strength of these loopbreaking patterns is expressed in terms of likelihood ratios. We construct a likelihood ratio system to determine these likelihood ratios. This consists of three dynamic Bayesian networks, which are based on the main assumption that the loopbreaking patterns are a stochastic process which comply to the Markov property. A dataset is used to train and test the LR-system. Based on these results, it is found that the loopbreaking patterns contain very strong evidence. More data, especially on observation errors, is needed to evaluate the system further.

Evaluating evidence and providing opinions are at the heart of forensic science, and forensic experts are expected to provide opinions that are based on logically sound and transparent scientific reasoning, and that honour the boundaries of their area of expertise. In order to meet these objectives, many fields of science explicitly apply Bayes\' theorem, which describes the logically correct way to update probabilities on the basis of observations. Making a distinction between \'investigative\' and evaluative\' modes of operating helps to implement the theorem into daily casework. Use of these principles promotes the logic and transparency of the reasoning that leads to expert\'s opinion and helps the expert to stay within her remit. Despite these important benefits, forensic pathology seems slow to adopt these principles. In this article, we explore this issue and suggest a way forward. We start with a short introduction to Bayes\' theorem and its benefits, followed by a discussion of why its application is actually second nature to medical practitioners. We then discuss the difference between investigative and evaluative opinions, and how they enable the forensic pathologist to reconcile Bayes\' theorem with the different phases of a forensic investigation. Throughout the text, practical examples illustrate the various ways in which the logically correct way of evidence interpretation can be implemented, and how it may help the forensic pathologist to provide an appropriate and relevant opinion.

For the past two to three decades, forensic DNA evidence has been analyzed with a limited number of short tandem repeats (STRs), and these STRs are usually assumed to be independent for statistical calculations. With the development and implementation of the MPS technologies, more autosomal markers, both single nucleotide polymorphisms (SNPs) and STRs, can be analyzed. A number of these markers are physically very close to each other, and it may not be appropriate to assume all these markers are genetically unlinked or in linkage equilibrium. In this study, publicly accessible genomic data from five representative populations were used to evaluate the genetic linkage and linkage disequilibrium (LD) between autosomal markers represented in six major commercial panels (in total, 362 markers). Among the 3041 syntenic marker pairs, 1524 pairs had sex-average genetic distances <50 cM, and thus, these marker pairs can be considered as genetically linked. Among the 143 marker pairs with physical distances <1 Mb, 19 LD haplotype blocks (comprising 39 SNPs in total) were detected for at least one of the tested populations. Statistical methods for interpreting linked markers and/or markers in LD were suggested for various case scenarios.

Bayesian networks have shown to be a useful tool for the evaluation of forensic findings given activity level propositions. In this paper, we demonstrate how case specific experiments can be used to assign probabilities to the states of the nodes of a Bayesian network for the evaluation of fingermarks given activity level propositions. The transfer, persistence and recovery of fingermarks on knives is studied in experiments where a knife is either used to stab a victim or to cut food, representing the activities that were disputed in the case of the murder of Meredith Kercher. Two Bayesian networks are constructed, exploring the effect of different uses of the experimental data by assigning the probabilities based on the results of the experiments. The evaluation of the findings using the Bayesian networks demonstrates the potential for fingermarks in addressing activity level propositions.

Nowadays, forensic age estimation takes an important role in worldwide forensic and medico-legal institutes that are solicited by judicial or administrative authorities for providing an expert report on the age of individuals. The authorities\' ultimate issue of interest is often the probability that the person is younger or older than a given age threshold, which is usually the age of majority. Such information is fundamental for deciding whether a person being judged falls under the legal category of an adult. This is a decision that may have important consequences for the individual, depending on the legal framework in which the decision is made. The aim of this paper is to introduce a normative approach for assisting the authority in the decision-making process given knowledge from available findings reported by means of probabilities. The normative approach proposed here has been acknowledged in the forensic framework, and represents a promising structure for reasoning that can support the decision-making process in forensic age estimation. The paper introduces the fundamental elements of decision theory applied to the specific case of age estimation, and provides some examples to illustrate its practical application.

The two-stage evaluative process is an established framework utilized by forensic document examiners (FDEs) for reaching a conclusion about the source(s) of handwritten evidence. In the second, or discrimination, stage, the examiner attempts to estimate the rarity of observations in a relevant background population. Unfortunately, control samples from a relevant background population are often unavailable, leaving the FDE to reach this determination based on subjective experience. Automated handwriting feature recognition systems are capable of performing both feature comparison and discrimination, yet these systems have not been subjected to empirical validation studies. In the present study, we repurposed a commercially available automated system to generate empirical distributions for ranking feature dissimilarity scores among pairs of handwritten phrases. The blinded results of this automated process were used to survey an international cohort of 36 FDEs regarding their strength of support for same- and different-writer propositions. The survey served to cross-validate FDE decision-making under the two-stage approach. Results from the survey demonstrated a clear pattern of response consistent with ground truth. Predictive regression analyses indicated that the automated feature dissimilarity scores and the log of their cumulative distribution functions accounted for 72% of the variability in FDE opinions. This study demonstrated that feature dissimilarity scores acquired using automated processes and their distributions are closely aligned with FDE decision-making processes supporting the heuristic value of the two-stage evaluative framework.