During crime scene investigations, numerous traces are secured and may be used as evidence for the evaluation of source and/or activity level propositions. The rapid chemical analysis of a biological trace enables the identification of body fluids and can provide significant donor profiling information, including age, sex, drug abuse, and lifestyle. Such information can be used to provide new leads, exclude from, or restrict the list of possible suspects during the investigative phase. This paper reviews the state-of-the-art labelling techniques to identify the most suitable visual enhancer to be implemented in a lateral flow immunoassay setup for the purpose of trace identification and/or donor profiling. Upon comparison, and with reference to the strengths and limitations of each label, the simplistic one-step analysis of noncompetitive lateral flow immunoassay (LFA) together with the implementation of carbon nanoparticles (CNPs) as visual enhancers is proposed for a sensitive, accurate, and reproducible in situ trace analysis. This approach is versatile and stable over different environmental conditions and external stimuli. The findings of the present comparative analysis may have important implications for future forensic practice. The selection of an appropriate enhancer is crucial for a well-designed LFA that can be implemented at the crime scene for a time- and cost-efficient investigation.
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
Within the film and theater world, special effects make-up is used to adapt the appearance of actors for visual storytelling. Currently the creation of special effects makeup is a time-consuming process which creates a lot of waste that doesn’t fit in with the goals of a sustainable industry. Combine with the trend of the digitization of the movie and theater industry which require faster and more iterative workflows, the current ways of creating special effects makeup requires changing. Within this project we would like to explore if the traditional way of working can be converted to a digital production process. Our research consists of three parts. Firstly, we would like to explore if a mobile face scanning rig can be used to create digital copies of actors, and such eliminate the need to creates molds. Secondly, we would like to see if digital sculpting can replace the traditional methods of sculpting molds, casts and prosthetics. Here we would like to compare both methods in terms of creativity and time consumption. The third part of our project will be to explore the use of 3D printing for the creation of molds and prosthetics.
De druk op dansers is enorm. Lange en intensieve werkdagen, veel reizen en verschillende werkplekken maken het lastig om lichaam en geest goed te verzorgen. Hierdoor liggen blessures en mentale klachten op de loer. In het RAAK project Fit to Perform hebben Het Nationale Ballet (HNB), Scapino Ballet Rotterdam en Codarts de krachten gebundeld om de fysieke en mentale gezondheid van dansers te verbeteren en prestaties te optimaliseren. Het project is gestart in april 2016 en heeft inmiddels de volgende resultaten opgeleverd: 1. De Performing artist and Athlete Health Monitor (PAHM) is ontwikkeld. Dit is een online tool dat real-time informatie over de fysieke en mentale gezondheid van podiumkunstenaars verzamelt, analyseert en visualiseert. Via een persoonlijk dashboard koppelt het systeem de uitkomsten van de fysieke testen en de maandelijkse gezondheidsvragenlijst terug aan de studenten (Codarts) en dansers (Het Nationale Ballet en Scapino Ballet Rotterdam). 2. Binnen Codarts wordt de kennis uit PAHM gebruikt voor a) vernieuwing van het onderwijscurriculum, b) evidence based onderbouwing voor het handelen van het Performing Arts Health Team, c) individuele begeleiding van de studenten op het gebied van fysieke en mentale gezondheid door een health coach, d) het ontwikkelen van een traject rondom Periodisering Op Maat (POM). 3. Vier publicaties in internationale wetenschappelijke tijdschriften en diverse presentaties op (inter)nationale congressen. 4. Borging en uitbreiding van het consortium voor 8 jaar door honorering van een SPRONG subsidie resulterend in de oprichting van PEARL (PErforming artist and Athlete Research Lab). Gedurende het RAAK project zijn is er zowel vanuit het onderwijs (Codarts), het praktijkveld (Het Nationale Ballet) als de wetenschappelijke wereld (congresorganisaties) de vraag gesteld aan de projectgroep om de beschikbare gegevens verder te analyseren. Doel van deze top-up aanvraag is het vergroten van de impact van het Fit to Perform project door het verbeteren van de doorwerking van de resultaten richting: 1. Onderwijs: De 4 internationale publicaties worden omgezet naar begrijpelijke factsheets/ infographics. 2. Onderzoek: Er worden secundaire analyses uitgevoegd op de Fit to perform database om inzicht te krijgen welke vragenlijsten door onderzoekers gebruikt moeten worden in projecten om inzicht te krijgen in de omvang, aard, risicofactoren en preventieve maatregelen van fysieke en mentale klachten bij dansers. De uitkomsten worden gepubliceerd in een internationaal tijdschrift en weergegeven in een toegankelijker factsheet/infographic. 3. Praktijk: Er worden secundaire analyses uitgevoerd op de Fit to Perform database om inzicht te krijgen in welke kenmerken van dansers (leeftijd, aantal jaren danservaring, vooropleiding, rang in het gezelschap etc) van invloed kunnen zijn op het grootste blessurerisico. Deze blessureprofielen worden via een presentatie teruggekoppeld aan Het Nationale Ballet.
