Concussion Awareness and Educational Outreach Through a Website and Mobile Application

In recent years concussions have become a more apparent problem in youth and adolescent sports. 1 in 5 high school students will sustain a concussion during the season. Due to medical and scientific advances, the diagnosis of concussions is becoming much simpler with key markers that are signs for an injury. Returning to play too soon before an athlete is fully recovered increases the likelihood that serious and irreversible neurological deficits can occur. Symptoms for concussions are necessary to track in order for an athlete to properly report their recovery to a physician. Doctors primarily rely on a patient’s report of their symptoms to evaluate the total effect of the injury, so recovery times are often misdiagnosed. Ursinus College- Concussion Outreach Group has been created so that concussion awareness can be raised. No methods have been found to completely prevent a concussion, so educational outreach is the first step. The group has decided to utilize a website and mobile application in order to promote safe techniques of play, along with awareness of how complex and misunderstood concussions are. In order to have a better diagnosis of a concussion it is important to completely monitor symptoms, which is possible using a mobile application. The website will serve as an information database, anonymous blog for sharing experiences, and will contain profile with data entered into the application. The site will provide full details on the dangers of concussions, as well as an “Ask the Expert” section for personal questions. It is not expected that this project will ultimately stop concussions from occurring. Granting adolescents the knowledge of the dangers that exist with concussions may cause an overall change in the attitudes attributed with concussions, hopefully so that concussions are not so under diagnosed. The effort behind making the website and mobile application is motivated by the need to promote responsible play in sports, educate everyone involved with the athlete why concussions are so serious, help prevent premature return to play or academia, explain the risks behind playing certain sports, and encourage interest in future findings related to concussions

Grismadox gen. nov., a new Neotropical genus of ant-resembling spiders (Araneae, Corinnidae, Castianeirinae), including the description of two new species from Bolivia and Paraguay

A new genus and two new species of ant-resembling castianeirine spiders are described from the Neotropics. Grismadox gen. nov. comprises four species: Grismadox baueri sp. nov., and Grismadox mazaxoides (Perger & Duperré, 2021) comb. nov. from Bolivia, and Grismadox karugua sp. nov. (type species) and Grismadox mboitui (Pett, 2021) comb. nov. from Paraguay. All species are diagnosed and the new species are described and illustrated. Available ecological data suggests that all four species are primarily epigeal and inhabit Grassland and savannah type habitats.Fil: Pett, Brogan L.. University of Exeter; Reino Unido. Colección Cientifíca Para La Tierra; Paraguay. Biodiversity Inventory For Conservation; BélgicaFil: Rubio, Gonzalo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Misiones. Estación Experimental Agropecuaria Cerro Azul; ArgentinaFil: Perger, Robert. Colección Boliviana de Fauna; Bolivi

Deep learning computer vision for robotic disassembly and servicing applications

Fastener detection is a necessary step for computer vision (CV) based robotic disassembly and servicing applications. Deep learning (DL) provides a robust approach for creating CV models capable of generalizing to diverse visual environments. Such DL CV systems rely on tuning input resolution and mini-batch size parameters to fit the needs of the detection application. This paper provides a method for determining the optimal compromise between input resolution and mini-batch size to determine the highest performance for cross-recessed screw (CRS) detection while utilizing maximum graphics processing unit resources. The Tiny-You Only Look Once v2 (Tiny-YOLO v2) DL object detection system was chosen to evaluate this method. Tiny-YOLO v2 was employed to solve the specialized task of detecting CRS which are highly common in electronic devices. The method used in this paper for CRS detection is meant to lay the ground-work for multi-class fastener detection, as the method is not dependent on the type or number of object classes. An original dataset of 900 images of 12.3 MPx resolution was manually collected and annotated for training. Three additional distinct datasets of 90 images each were manually collected and annotated for testing. It was found an input resolution of 1664 x 1664 pixels paired with a mini-batch size of 16 yielded the highest average precision (AP) among the seven models tested for all three testing datasets. This model scored an AP of 92.60% on the first testing dataset, 99.20% on the second testing dataset, and 98.39% on the third testing dataset

Automatic Discovery of Political Meme Genres with Diverse Appearances

Forms of human communication are not static -- we expect some evolution in the way information is conveyed over time because of advances in technology. One example of this phenomenon is the image-based meme, which has emerged as a dominant form of political messaging in the past decade. While originally used to spread jokes on social media, memes are now having an outsized impact on public perception of world events. A significant challenge in automatic meme analysis has been the development of a strategy to match memes from within a single genre when the appearances of the images vary. Such variation is especially common in memes exhibiting mimicry. For example, when voters perform a common hand gesture to signal their support for a candidate. In this paper we introduce a scalable automated visual recognition pipeline for discovering political meme genres of diverse appearance. This pipeline can ingest meme images from a social network, apply computer vision-based techniques to extract local features and index new images into a database, and then organize the memes into related genres. To validate this approach, we perform a large case study on the 2019 Indonesian Presidential Election using a new dataset of over two million images collected from Twitter and Instagram. Results show that this approach can discover new meme genres with visually diverse images that share common stylistic elements, paving the way forward for further work in semantic analysis and content attribution.Comment: 13 pages, 14 figure