Drug consumption estimates are of relevance because of public health effects as well as associated criminal activities. Wastewater analysis of drug residues enables the estimation of drug consumption and drug markets. Short-term and long-term trends of cocaine, MDMA (ecstasy), amphetamine (speed) and methamphetamine (crystal meth), were studied for the city of Amsterdam. MDMA (+41%) and cocaine (+26%) showed significantly higher weekend vs. week consumption, while no differences were observed for the other drugs. The consumption of MDMA, cocaine, amphetamine and methamphetamine significantly increased between 2011 and 2019. Weekly trends emerging from wastewater analyses were supported by qualitative and quantitative data from a recreational drug use monitoring scheme. However, information collected in panel interviews within nightlife networks and surveys among visitors of pubs, clubs and festivals only partially reflected the long term increase in consumption as registered from wastewater analysis. Furthermore, methamphetamine use was not well presented in survey data, panel studies and test service samples, but could be monitored trough wastewater analysis. This illustrates that wastewater analysis can function as an early warning if use and user groups are small or difficult to reach trough other forms of research. All in all, this study illustrates that wastewater-based epidemiology is complementary to research among user groups, and vice versa. These different types of information enable to connect observed trends in total drug consumption to behaviour of users and the social context in which the use takes place as well as validate qualitative signals about (increased) consumption of psychoactive substances. Such a multi angular approach to map the illicit drug situation on local or regional scale can provide valuable information for public health.
MULTIFILE
Automation surprises in aviation continue to be a significant safety concern and the community’s search for effective strategies to mitigate them are ongoing. The literature has offered two fundamentally divergent directions, based on different ideas about the nature of cognition and collaboration with automation. In this paper, we report the results of a field study that empirically compared and contrasted two models of automation surprises: a normative individual-cognition model and a sensemaking model based on distributed cognition. Our data prove a good fit for the sense-making model. This finding is relevant for aviation safety, since our understanding of the cognitive processes that govern human interaction with automation drive what we need to do to reduce the frequency of automation-induced events.
Studying images in social media poses specific methodological challenges, which in turn have directed scholarly attention toward the computational interpretation of visual data. When analyzing large numbers of images, both traditional content analysis as well as cultural analytics have proven valuable. However, these techniques do not take into account the contextualization of images within a socio-technical environment. As the meaning of social media images is co-created by online publics, bound through networked practices, these visuals should be analyzed on the level of their networked contextualization. Although machine vision is increasingly adept at recognizing faces and features, its performance in grasping the meaning of social media images remains limited. Combining automated analyses of images with platform data opens up the possibility to study images in the context of their resonance within and across online discursive spaces. This article explores the capacities of hashtags and retweet counts to complement the automated assessment of social media images, doing justice to both the visual elements of an image and the contextual elements encoded through the hashtag practices of networked publics.
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.
Structural and functional knowledge of proteins, which are essential in biological processes, is fundamental for our understanding of the Chemistry of Life. Structural biology - the field that studies the structure and function of proteins – has seen several revolutions over the last few years. Single particle analysis (SPA), where individual macromolecular assemblies are imaged under cryogenic conditions within highly automated electron microscopes, has been used to elucidate the structures of many novel and important proteins and complexes. Deep-learning–based computational techniques provided systematic predictions of an million three-dimensional protein structures. Cryo-electron tomography (ET) combined with sub-tomogram averaging (STA) enabled the investigation of conformational states of large macromolecular complexes. We expect in situ structural biology, where macromolecular assemblies are studied within the interior of focused-ion-beam milled frozen cells, to become the next revolution in our field. Such revolution would require well prepared vitreous samples (cells, tissue slices, organoids): the sample should be cooled fast enough to prevent the formation of crystalline ice. Previously, we developed the technology to prepare SPA samples using jets of cryogenic fluid directed onto the sample. This device, the VitroJet, has been further developed into a commercial product by CryoSol-World and has been sold worldwide. Here, we wish to advance the jetting technology such that it can vitrify cells. Crucial aspects are the speed of the jets and the timing and reproducibility of the fronts of the cryogens arriving onto the sample. We will design, build, characterise and refine a next generation of the ethane cup, a core component within the VitroJet. If successful, we should be able to increase its vitrification potential as well as its reproducibility by more than one order of magnitude. This technology will enable in situ structural biology studies necessary to understand the Chemistry of Life.
