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Mind-set is a term used in the friction ridge discipline to describe a confirmation bias in which an examiner makes early decisions about their interpretation of a mark but fails to update or reconsider those decisions in light of additional information. This most often occurs during the analysis of a mark when an examiner makes decisions (such as orientation or anatomical source of a mark) to help expedite a manual search or set parameters for an automated search, but fails to re-evaluate these decisions if the initial screening of available exemplars does not yield a comparable area, potentially leading to a miss or an erroneous exclusion. Mind-set can also occur when an examiner believes a comparison may be an identification early in the comparison process and employs poor comparison habits to convince themselves it is true, often creating or adapting comparison notes after seeing the exemplar, straining logic to justify their decision, and potentially leading to an erroneous identification. A recent black box study on palmar comparison accuracy and reliability noted both behaviors in the annotations and notes provided by some study participants. Examples are provided in this paper to serve as a reminder to examiners to not allow mind-set to lead them into errors. Particularly given the high false negative error rates reported throughout the literature, examiners need to make re-considering their initial analysis before rendering an exclusion decision part of their comparison routine.
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http://dx.doi.org/10.1016/j.forsciint.2020.110545 | DOI Listing |
Vet Med Sci
September 2025
Faculty of Veterinary Medicine, University of Zagreb, Zagreb, Croatia.
Background: Animal identification is a topic of many studies, with a range of biometric methods currently in use. The cattle muzzle serves as a unique source of biometric traits.
Objectives: The aim of this study was to determine the best method for muzzle visualisation using imprints, the most frequent forms and minutiae points on imprints, and the minimum number of minutiae points required to establish an identity profile.
Sci Rep
August 2025
Structural Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt.
This study investigates the use of machine learning (ML) models to predict the ultimate load capacity of demountable shear connectors in steel-concrete composite structures. A dataset of 239 experimental and numerical records was assembled, incorporating critical features such as bolt diameter, bolt yield and ultimate strengths, concrete and grout compressive strengths, and multiple interfacial friction coefficients. Eight supervised ML algorithms were evaluated: Linear Regression, Ridge, Lasso, K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Decision Tree, Random Forest, and XGBoost.
View Article and Find Full Text PDFACS Appl Mater Interfaces
August 2025
Energy Transport Lab, Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan 48105, United States.
By enabling an atomically smooth and chemically homogeneous interface, state-of-the-art lubricant-infused surfaces minimize contact line pinning, which directly translates to remarkable droplet mobility and ultralow drop friction. A unique feature of these surfaces is the formation of a wrapping layer─a nanometric lubricant film that encapsulates droplets. However, the mechanism that governs the formation of the wrapping oil layer and its thickness remains poorly understood to date.
View Article and Find Full Text PDFForensic Sci Int Synerg
December 2025
Indiana University, Bloomington, USA.
Friction ridge examiners report conclusions to palm impression comparisons similarly to fingerprint impression comparisons, although several key differences exist. These include an extensive search process in palm impressions, differences in minutiae rarity, and orientation challenges that most fingerprint comparisons do not require. Most US laboratories use a three-conclusion scale that includes Identification, Exclusion, and Inconclusive, which have not been calibrated against the actual strength of the evidence in palmprint comparisons.
View Article and Find Full Text PDFTribol Lett
July 2025
Department of Mechanical Engineering and Materials Science, University of Pittsburgh, 3700 O'Hara St., Pittsburgh, PA 15261 USA.
Unlabelled: Surface performance is critically influenced by topography in virtually all real-world applications. The current standard practice is to describe topography using one of a few industry-standard parameters. The most commonly reported number is a, the average absolute deviation of the height from the mean line (at some, not necessarily known or specified, lateral length scale).
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