Medical students often lack key skills in academic writing, yet good academic writing is often a prerequisite for employment, promotion and enculturation into the profession. This article focuses on the rhetorical strategies used for the presentation of academic stance by student writers of dentistry research reports. Adopting a contrastive, corpus-based approach, we compare student writing with that of comparable professionally-written research reports for evidence of hedging, boosting, self-mention and attitude markers. Our findings indicate that professional reports exhibit a narrower set of linguistic devices than used by student writers, who tend to use a much wider range of the four stance feature types analysed for discussion of both others' and their own personal stance, both across whole texts and by section. We discuss pedagogical implications for ESP professionals working to more closely align student writing with that of professional norms.

Real-time identification of wildlife is an upcoming and promising tool for the preservation of wildlife. In this research project, we aimed to use object detection and image classification for the racer snakes of the Galápagos Islands, Ecuador. The final target of this project was to build an artificial intelligence (AI) platform, in terms of a web or mobile application, which would serve as a real-time decision making and supporting mechanism for the visitors and park rangers of the Galápagos Islands, to correctly identify a snake species from the user’s uploaded image. Using the deep learning and machine learning algorithms and libraries, we modified and successfully implemented four region-based convolutional neural network (R-CNN) architectures (models for image classification): Inception V2, ResNet, MobileNet, and VGG16. Inception V2, ResNet and VGG16 reached an overall accuracy of 75%.

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