Onze omgeving is volop in ontwikkeling: overal in dorpen, wijken en buurten in Nederland bestaan er werkgroepen met plannen voor een meer leefbare, duurzame of gezonde wijk, oftewel een zachtere stad. Bij het werken aan de zachte stad is het belangrijk om na te denken over manieren waarop alle bewoners die met de plannen te maken krijgen, erin meegenomen kunnen worden. Met behulp van de ‘social fingerprint’-aanpak kan bewonersparticipatie zinvol worden vormgegeven.
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Fingerprints found at a crime scene can be key in criminal investigations. A method to accurately determine the age of the fingerprint, potentially crucial to linking the fingerprint to the crime, is not available at the moment. In this paper, we show that the use of the enantiomeric ratio of d/l-serine in fingerprints could pose as interesting target for age estimation techniques. We developed a UPLC-MS/MS method to determine the enantiomer ratios of histidine, serine, threonine, alanine, proline, methionine and valine from fingerprint residue. We found a significant change only in the relative ratio of d-serine with increasing fingerprint age after analysis of fingerprints up to 6 months old.
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Fingerprints are widely used in forensic science for individualization purposes. However, not every fingermark found at a crime scene is suitable for comparison, for instance due to distortion of ridge detail, or when the reference fingerprint is not in the database. To still retrieve information from these fingermarks, several studies have been initiated into the chemical composition of fingermarks, which is believed to be influenced by several donor traits. Yet, it is still unclear what donor information can be retrieved from the composition of one's fingerprint, mainly because of limited sample sizes and the focus on analytical method development. It this paper, we analyzed the chemical composition of 1852 fingerprints, donated by 463 donors during the Dutch music festival Lowlands in 2016. In a targeted approach we compared amino acid and lipid profiles obtained from different types of fingerprints. We found a large inter-variability in both amino acid and lipid content, and significant differences in L-(iso)leucine, L-phenylalanine and palmitoleic acid levels between male and female donors. In an untargeted approach we used full-scan MS data to generate classification models to predict gender (77.9% accuracy) and smoking habit (90.4% accuracy) of fingerprint donors. In the latter, putatively, nicotine and cotinine are used as predictors.
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The amino acid profile obtained from a fingerprint may provide valuable information on its donor. Unfortunately, the collection of chemicals from the fingerprint is often destructive to the fingerprint ridge detail. Herein we detail the use of cross-linkable solutions of dextran-methacrylate to form hydrogels capable of collecting amino acids from surfaces followed by extraction and quantification with UPLC-MS. This method allows for the amino acid profile analysis of fingerprints while allowing for their increased visualization at a later stage using the standard method of cyanoacrylate fuming followed by basic-yellow dyeing.
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Traces of condom lubricants in fingerprints can be valuable information in cases of sexual assault. Ideally, not only confirmation of the presence of the condom but also determination of the type of condom brand used can be retrieved. Previous studies have shown to be able to retrieve information about the condom brand and type from fingerprints containing lubricants using various analytical techniques. However, in practice fingerprints often appear latent and need to be detected first, which is often achieved by cyanoacrylate fuming. In this study, we developed a desorption electrospray ionization mass spectrometry (DESI-MS) method which, combined with principal component analysis and linear discriminant analysis (PCA-LDA), allows for high accuracy classification of condom brands and types from fingerprints containing condom lubricant traces. The developed method is compatible with cyanoacrylate (CA) fuming. We collected and analyzed a representative dataset for the Netherlands comprising 32 different condoms. Distinctive lubricant components such as polyethylene glycol (PEG), polydimethylsiloxane (PDMS), octoxynol-9 and nonoxynol-9 were readily detected using the DESI-MS method. Based on the analysis of lubricant spots, a 99.0% classification accuracy was achieved. When analyzing lubricant containing fingerprints, an overall accuracy of 90.9% was obtained. Full chemical images could be generated from fingerprints, showing the distribution of lubricant components such as PEG and PDMS throughout the fingerprint, while still allowing for classification. The developed method shows potential for the development of DESI-MS based analyses of CA treated exogenous compounds from fingerprints for use in forensic science.
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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.
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Forensic reports use various types of conclusions, such as a categorical (CAT) conclusion or a likelihood ratio (LR). In order to correctly assess the evidence, users of forensic reports need to understand the conclusion and its evidential strength. The aim of this paper is to study the interpretation of the evidential strength of forensic conclusions by criminal justice professionals. In an online questionnaire 269 professionals assessed 768 reports on fingerprint examination and answered questions that measured self-proclaimed and actual understanding of the reports and conclusions. The reports entailed CAT, verbal LR and numerical LR conclusions with low or high evidential strength and were assessed by crime scene investigators, police detectives, public prosecutors, criminal lawyers, and judges. The results show that about a quarter of all questions measuring actual understanding of the reports were answered incorrectly. The CAT conclusion was best understood for the weak conclusions, the three strong conclusions were all assessed similarly. The weak CAT conclusion correctly emphasizes the uncertainty of any conclusion type used. However, most participants underestimated the strength of this weak CAT conclusion compared to the other weak conclusion types. Looking at the self-proclaimed understanding of all professionals, they in general overestimated their actual understanding of all conclusion types.
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The security of online assessments is a major concern due to widespread cheating. One common form of cheating is impersonation, where students invite unauthorized persons to take assessments on their behalf. Several techniques exist to handle impersonation. Some researchers recommend use of integrity policy, but communicating the policy effectively to the students is a challenge. Others propose authentication methods like, password and fingerprint; they offer initial authentication but are vulnerable thereafter. Face recognition offers post-login authentication but necessitates additional hardware. Keystroke Dynamics (KD) has been used to provide post-login authentication without any additional hardware, but its use is limited to subjective assessment. In this work, we address impersonation in assessments with Multiple Choice Questions (MCQ). Our approach combines two key strategies: reinforcement of integrity policy for prevention, and keystroke-based random authentication for detection of impersonation. To the best of our knowledge, it is the first attempt to use keystroke dynamics for post-login authentication in the context of MCQ. We improve an online quiz tool for the data collection suited to our needs and use feature engineering to address the challenge of high-dimensional keystroke datasets. Using machine learning classifiers, we identify the best-performing model for authenticating the students. The results indicate that the highest accuracy (83%) is achieved by the Isolation Forest classifier. Furthermore, to validate the results, the approach is applied to Carnegie Mellon University (CMU) benchmark dataset, thereby achieving an improved accuracy of 94%. Though we also used mouse dynamics for authentication, but its subpar performance leads us to not consider it for our approach.
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