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Purpose There are limited studies on the effects of coffee consumption, which has become part of popular culture and is highly common among young people, on the attention levels of female university students. This paper aims to examine the effects of caffeine consumption given at different doses (0, 100 and 200 mg) to female university students (n = 100) studying at the faculty of health sciences on attention performance with the Stroop Color and Word Test (SCWT). Design/methodology/approach Participants' test completion time, number of errors and corrections were tested. In addition, the frequencies of weekly coffee consumption were also recorded. Findings It was found that consumption of coffee containing 200 mg of caffeine reduced the time to complete the sections of the SCWT test (p < 0.05), as well as the total test completion time (55.81 ± 8.67 s) compared to no coffee consumption (58.25 ± 9.46 s) and coffee consumption with 100-mg caffeine (57.56 ± 8.87 s) (p < 0.05; effect size: 0.268). The effect of caffeine on attention level was induced by 200-mg caffeine consumption, but was unapparent at 100-mg dose. In addition, SCWT completion times were reduced in the student group with more weekly coffee consumption (=3 cups/week) at 200-mg caffeine dose. Originality/value In this context, caffeine dose is a factor that should be taken into account in determining the performance of individuals in the SCWT test. It is thought that coffee consumption can increase the concentration of students and have positive effects on academic achievement.

The ordered assembly of multicellular structures mimicking native tissues has lately come into prominence for various applications of biomedicine. In this respect, three-dimensional bioprinting (3DP) of cells and other biologics through additive manufacturing techniques has brought the possibility to develop functional in vitro tissue models and perhaps creating de novo transplantable tissues or organs in time. Bioinks, which can be defined as the printable analogues of the extracellular matrix, represent the foremost component of 3DP. In this chapter, we attempt to elaborate the major classes of bioinks which are prevalently being evaluated for the 3DP of a wide range of tissue models.

Recent advances in computer vision have almost solved the problem of in the wild face detection, using complex techniques like convolutional neural networks. On the contrary many open source computer vision frameworks like OpenCV have not yet made the switch to these complex techniques and tend to depend on well established algorithms for face detection, like the cascade classification pipeline suggested by Viola and Jones. However the accuracy of these open source face detection models on public datasets like FDDB stays rather low, mainly due to the relatively high number of false positive detections produced. We propose several adaptations to the current existing LBP/AdaBoost cascade classification pipeline of OpenCV. This is done by improving the training sample generation and annotation procedure, and by applying an active learning strategy. This boosts the accuracy of in the wild face detection on the FDDB dataset quite drastically, closing the gap towards the accuracy levels gained by state-of-the-art CNN-based face detectors. The proposed changes allow us to provide a new face detection model to the OpenCV framework, achieving a remarkably high precision at an acceptable recall rate, offering a model that ensures detected faces are actually faces, a critical requirement for further processing pipelines like person identification, etc. ispartof: pages:396-404 ispartof: Proceedings of VISAPP 2017 vol:5 pages:396-404 ispartof: VISAPP location:Porto, Portugal date:5 Jan - 5 Jan 2017 status: published