Fingermarks are highly relevant in criminal investigations for individualization purposes. In some cases, the question in court changes from ‘Who is the source of the fingermarks?’ to ‘How did the fingermark end up on the surface?’. In this paper, we explore evaluation of fingermarks given activity level propositions by using Bayesian networks. The variables that provide information on activity level questions for fingermarks are identified and their current state of knowledge with regards to fingermarks is discussed. We identified the variables transfer, persistency, recovery, background fingermarks, location of the fingermarks, direction of the fingermarks, the area of friction ridge skin that left the mark and pressure distortions as variables that may provide information on how a fingermark ended up on a surface. Using three case examples, we show how Bayesian networks can be used for the evaluation of fingermarks given activity level propositions.
Standard mass-production is a well-known manufacturing concept. To make small quantities or even single items of a product according to user specifications at an affordable price, alternative agile production paradigms should be investigated and developed. The system presented in this article is based on a grid of cheap reconfigurable production units, called equiplets. A grid of these equiplets is capable to produce a variety of different products in parallel at an affordable price. The underlying agent-based software for this system is responsible for the agile manufacturing. An important aspect of this type of manufacturing is the transport of the products along the available equiplets. This transport of the products from equiplet to equiplet is quite different from standard production. Every product can have its own unique path along the equiplets. In this article several topologies are discussed and investigated. Also, the planning and scheduling in relation to the transport constraints is subject of this study. Some possibilities of realization are discussed and simulations are used to generate results with the focus on efficiency and usability for different topologies and layouts of the grid and its internal transport system. Closely related with this problem is the scheduling of the production in the grid. A discussion about the maximum achievable load on the production grid and its relation with the transport system is also included.
STUDY DESIGN: Cross-sectional study.OBJECTIVES: This study: (1) investigated the accuracy of bioelectrical impedance analysis (BIA) and skinfold thickness relative to dual-energy X-ray absorptiometry (DXA) in the assessment of body composition in people with spinal cord injury (SCI), and whether sex and lesion characteristics affect the accuracy, (2) developed new prediction equations to estimate fat free mass (FFM) and percentage fat mass (FM%) in a general SCI population using BIA and skinfolds outcomes.SETTING: University, the Netherlands.METHODS: Fifty participants with SCI (19 females; median time since injury: 15 years) were tested by DXA, single-frequency BIA (SF-BIA), segmental multi-frequency BIA (segmental MF-BIA), and anthropometry (height, body mass, calf circumference, and skinfold thickness) during a visit. Personal and lesion characteristics were registered.RESULTS: Compared to DXA, SF-BIA showed the smallest mean difference in estimating FM%, but with large limits of agreement (mean difference = -2.2%; limits of agreement: -12.8 to 8.3%). BIA and skinfold thickness tended to show a better estimation of FM% in females, participants with tetraplegia, or with motor incomplete injury. New equations for predicting FFM and FM% were developed with good explained variances (FFM: R2 = 0.94; FM%: R2 = 0.66).CONCLUSIONS: None of the measurement techniques accurately estimated FM% because of the wide individual variation and, therefore, should be used with caution. The accuracy of the techniques differed in different subgroups. The newly developed equations for predicting FFM and FM% should be cross-validated in future studies.