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Fern gametophytes and young sporophytes often provide too little material for DNA extraction and are particularly difficult to identify to genus. Here we developed an efficient procedure called 'Tissue-direct PCR', in which a slice of fern tissue is mixed with PCR reagents and primers, allowing certain genomic regions to be amplified directly in the thermal cycler. For these diminutive and featureless stages of ferns, Tissue-direct PCR combined with amplifying plant barcodes promises to make the identification of immature ferns easy and rapid. Tissue-direct PCR would also be very helpful for large-scale ecological studies surveying distribution and population structure.

© 2015, Journal of Zhejiang University Science Editorial Office and Springer-Verlag Berlin Heidelberg. Building and using maps is a fundamental issue for bionic robots in field applications. A dense surface map, which offers rich visual and geometric information, is an ideal representation of the environment for indoor/outdoor localization, navigation, and recognition tasks of these robots. Since most bionic robots can use only small light-weight laser scanners and cameras to acquire semi-dense point cloud and RGB images, we propose a method to generate a consistent and dense surface map from this kind of semi-dense point cloud and RGB images. The method contains two main steps: (1) generate a dense surface for every single scan of point cloud and its corresponding image(s) and (2) incrementally fuse the dense surface of a new scan into the whole map. In step (1) edge-aware resampling is realized by segmenting the scan of a point cloud in advance and resampling each sub-cloud separately. Noise within the scan is reduced and a dense surface is generated. In step (2) the average surface is estimated probabilistically and the non-coincidence of different scans is eliminated. Experiments demonstrate that our method works well in both indoor and outdoor semi-structured environments where there are regularly shaped objects.

Despite the success of Learning Analytics (LA), there are two obstacles to its application in educational games, including transparency in assessing educational outcomes in real-time gameplay, and clarity in representing those results to players. Open learner model (OLM) is a valuable instrument with capability to improve learning that meets such challenges. However, OLMs usually suffer issues concerning interactivity and transparency, which mostly regard the assessment mechanism that is used to evaluate learners’ knowledge. Tackling down transparency issues would offer context for interpreting and comparing learner model information, as well as promoting interactivity. As there is lack of studies investigating the potential of OLMs in educational games, we argue that this work can provide a valuable starting point for applying OLMs or adaptive visualizations of players’ learner models within gameplay sessions, which, in turn, can help to address both issues of application of LA to game research and OLMs. As a case study, we introduce the proposed approach into our adaptive computational thinking game.

One of the core components of any visual surveillance system is object classification, where detected objects are classified into different categories of interest. Although in airports or train stations, abandoned objects are mainly luggage or trolleys, none of the existing works in the literature have attempted to classify or recognize trolleys. In this paper, we analyze and classify images of trolleys, bags, persons, and groups of people by using various shape features. We conducted a set of experiments with a number of uncluttered (images collected from the Internet with clear background) and cluttered images (images segmented out from the background in real videos) using various criteria. Our experimental results show that the features extracted enable 100% recognition accuracy for the trolley category. For our four-class object recognition problem, we achieved an overall recognition accuracy of 83.3% and an average false positive rate of 6%.