This paper investigates the effect of mobile phone screen size (1.65 inches - 2.75 inches) on video based learning. It first examines the educational benefits of video as a teaching medium and surveys the usage and issues related with video based learning. After which, it investigates the value of video for mobile learning. It reports on an empirical investigation that studied the effect that screen-size has on video-based m-learning. Findings indicate that regardless of the screen size of a mobile phone, students tended to have a positive overall opinion of m-learning and watching the video significantly increased their knowledge of the subject area. However, if an m-learning environment that relies heavily on video based material is displayed on a mobile device with a small screen, such as an average mobile phone, then the effectiveness of the learning experience may be inhibited. Paper identifies the underlying reasons why mobile phone screen size may be a problem for video based m-learning. The implications of this finding are discussed.

High-content screening (HCS) technologies are becoming increasingly used in both large-scale drug discovery and basic research programs. These automated imaging and analysis technologies enable the researcher to elucidate the complex biology that underlies the functions of genes, proteins, and other biomolecules at the cellular level. HCS combines the power of automated digital microscopy and advanced software-based image analysis algorithms to detect and quantify biological changes in cells and tissues. This technology is a particularly powerful tool when used to interrogate the cellular effects of exogenously applied agents such as RNAi and/or small molecules. HCS allows for the evaluation of cellular perturbations that occur both at the level of the single cell and within cellular populations. In a multivariate approach, multiple cellular parameters are collected, allowing for more complex analysis. However, in these scenarios, data flow and management still represent substantial bottlenecks in HCS projects. HCS data include a diversity of information from multiple sources such as details pertaining to screening libraries (e.g., siRNA and small molecules), image stacks acquired from automated microscopes (of which there may be up to several million), and the image analysis data. From this, postprocessing algorithms are required to generate statistical, quality control bioinformatic information and ultimately a final hit list. To accomplish these individual tasks, numerous tools can be used to perform each analytical step; however, management of the entire information flow currently requires the use of commercially available proprietary software, the scope of which is often limited, or bespoke customized scripts. In this article, the authors introduce an open-source research tool that allows for the management of the entire data flow of the HCS data chain, by handling and linking information and providing many powerful postprocessing and visualization tools.

A Projection Augmented Model (PA model) is a type of projection based haptic Augmented Reality display. It consists of a real physical model, onto which a computer image is projected to create a realistic looking object. Users can physically touch the surface of a PA model with their bare hands, which has clear experiential value for the types of applications for which they are being developed. However, the majority of PA models are front-projected and do not provide haptic feedback for material properties (e.g. temperature and physical texture), which suggests a user's sense of objectpresence will be reduced when this type of PA model is touched. (Object-presence measures the subjective feeling that the object the PA model represents exists in a person's environment, as opposed to a white physical model and a projected computer image.) Alternatively, if people consider PA models to be essentially computer generated objects (i.e. it is the projected image that gives the 'dummy' physical model meaning), then the act of being able to touch computer generated information may increase object-presence. The empirical investigation reported in this paper found that object-presence was lower when this type of PA model was touched. The implications these results have for both PA models and other types of displays, are discussed.

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This paper reports on a study that investigated the effect touching a Projection Augmented model, and interacting with it using a spatially-coincident device, has on the perception of size. It was found that touching increased the accuracy of size estimates, however interaction using a spatially-coincident device did not.