Matrix-assisted laser desorption/ionisation time of-flight mass spectrometry (MALDI-TOF MS) is a fast and reliable method for the identification of bacteria from agar media. Direct identification from positive blood cultures should decrease the time to obtaining the result. In this study, three different processing methods for the rapid direct identification of bacteria from positive blood culture bottles were compared. In total, 101 positive aerobe BacT/ALERT bottles were included in this study. Aliquots from all bottles were used for three bacterial processing methods, i.e. the commercially available Bruker's MALDI Sepsityper kit, the commercially available Molzym's MolYsis Basic5 kit and a centrifugation/washing method. In addition, the best method was used to evaluate the possibility of MALDI application after a reduced incubation time of 7 h of Staphylococcus aureus- and Escherichia coli-spiked (1,000, 100 and 10 colony-forming units [CFU]) aerobe BacT/ALERT blood cultures. Sixty-six (65%), 51 (50.5%) and 79 (78%) bottles were identified correctly at the species level when the centrifugation/washing method, MolYsis Basic 5 and Sepsityper were used, respectively. Incorrect identification was obtained in 35 (35%), 50 (49.5%) and 22 (22%) bottles, respectively. Gram-positive cocci were correctly identified in 33/52 (64%) of the cases. However, Gram-negative rods showed a correct identification in 45/47 (96%) of all bottles when the Sepsityper kit was used. Seven hours of pre-incubation of S. aureus- and E. coli-spiked aerobe BacT/ALERT blood cultures never resulted in reliable identification with MALDI-TOF MS. Sepsityper is superior for the direct identification of microorganisms from aerobe BacT/ALERT bottles. Gram-negative pathogens show better results compared to Gram-positive bacteria. Reduced incubation followed by MALDI-TOF MS did not result in faster reliable identification.
Research demonstrated a large variety regarding effects of light (e.g. health, performance, or comfort effects). Since human health is related to each individual separately, the lighting conditions around these individuals should be analysed individually as well. This paper provides, based on a literature study, an overview identifying the currently used methodologies for measuring lighting conditions in light effect studies. 22 eligible articles were analysed and this resulted in two overview tables regarding the light measurement methodologies. In 70% of the papers, no measurement details were reported. In addition, light measurements were often averaged over time (in 84% of the papers) or location level (in 32% of the papers) whereas it is recommended to use continuous personal lighting conditions when light effects are being investigated. Conclusions drawn in light effect studies based on personal lighting conditions may be more trusting and valuable to be used as input for an effect-driven lighting control system.
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Objectives: Aiming to reduce distributed denial-of-service (DDoS) attacks by alerting the consciences of Internet users, this paper evaluates the effectiveness of four warning banners displayed as online ads (deterrent—control, social, informative, and reorienting) and the contents of their two linked landing pages. Methods: We implement a 4 x 2 quasi-experimental design on a self-selected sample of Internet users to measure the engagement generated by the ads and the pages. Engagement is measured on the ads as the ratio of clicks to impressions, and on the pages as percentage of page scrolled, average session duration, video interaction rate, and URLs click rate. Results: Social ads generate significantly more engagement than the rest with low to medium effect sizes. Data reveal no differences in engagement between both landing page designs. Conclusions: Social messages may be a better alternative for engaging with potential cyber offenders than the traditional deterrent messages. Correspondence: Netherlands Institute for the Study of Crime and Law Enforcement (NSCR), De Boelelaan 1077, 1081 HV, Amsterdam, The Netherlands. Email: AMoneva@nscr.n This is a post-peer-review, pre-copyedit version of an article published in Journal of Experimental Criminology. The final authenticated version is available online at: https://link.springer.com/article/10.1007/s11292-022-09504-2
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Automated driving nowadays has become reality with the help of in-vehicle (ADAS) systems. More and more of such systems are being developed by OEMs and service providers. These (partly) automated systems are intended to enhance road and traffic safety (among other benefits) by addressing human limitations such as fatigue, low vigilance/distraction, reaction time, low behavioral adaptation, etc. In other words, (partly) automated driving should relieve the driver from his/her one or more preliminary driving tasks, making the ride enjoyable, safer and more relaxing. The present in-vehicle systems, on the contrary, requires continuous vigilance/alertness and behavioral adaptation from human drivers, and may also subject them to frequent in-and-out-of-the-loop situations and warnings. The tip of the iceberg is the robotic behavior of these in-vehicle systems, contrary to human driving behavior, viz. adaptive according to road, traffic, users, laws, weather, etc. Furthermore, no two human drivers are the same, and thus, do not possess the same driving styles and preferences. So how can one design of robotic behavior of an in-vehicle system be suitable for all human drivers? To emphasize the need for HUBRIS, this project proposes quantifying the behavioral difference between human driver and two in-vehicle systems through naturalistic driving in highway conditions, and subsequently, formulating preliminary design guidelines using the quantified behavioral difference matrix. Partners are V-tron, a service provider and potential developer of in-vehicle systems, Smits Opleidingen, a driving school keen on providing state-of-the-art education and training, Dutch Autonomous Mobility (DAM) B.V., a company active in operations, testing and assessment of self-driving vehicles in the Groningen province, Goudappel Coffeng, consultants in mobility and experts in traffic psychology, and Siemens Industry Software and Services B.V. (Siemens), developers of traffic simulation environments for testing in-vehicle systems.
