1,790 research outputs found

    S21RS SGB No. 6 (Election Code)

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    A Bill To amend the Election Cod

    Digital Analysis of Heartbeats from Remote Machines

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    The “Bring Out Your Exceptions” project is a robust online automated data collection and aggregation utility. Specifically designed to handle application to application communication so that system health analysis can be performed easily within minutes by both trained and untrained personnel. The utility, once set-up, receives relevant data (be it crash errors or current system health) from remote systems without human interaction being required. This will allow for faster turn-around on patch development and addressing future errors without reliance on a client requesting help. Created using a combination of tools and languages such as Javascript, GoLang, Node.JS, RabbitMQ, and MongoDB, the “Bring Out Your Exceptions” project successfully handles communications from remote systems and parses the necessary information before storing it for future retrieval and analysis. Combined further with the use of Kibana, an aesthetically pleasing interface is produced for the user in which statistics about the underlying data are readily presented with real-time analysis as it enters the system. The “Bring Out Your Exceptions” project is capable of future growth that allows it to generically accept data from any system rather than the current pre-defined system and also due to its use of Kibana is extremely user-friendly for data analysis. Line graphs, pie charts, and bar charts are all easily added and configured with a few clicks of the mouse and allows for accurate and quick representation of underlying data from remote systems which helps to streamline both the development process of future solutions as well as to enrich current knowledge of ongoing issues.https://scholarscompass.vcu.edu/capstone/1179/thumbnail.jp

    Co-Attentive Cross-Modal Deep Learning for Medical Evidence Synthesis and Decision Making

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    Modern medicine requires generalised approaches to the synthesis and integration of multimodal data, often at different biological scales, that can be applied to a variety of evidence structures, such as complex disease analyses and epidemiological models. However, current methods are either slow and expensive, or ineffective due to the inability to model the complex relationships between data modes which differ in scale and format. We address these issues by proposing a cross-modal deep learning architecture and co-attention mechanism to accurately model the relationships between the different data modes, while further reducing patient diagnosis time. Differentiating Parkinson's Disease (PD) patients from healthy patients forms the basis of the evaluation. The model outperforms the previous state-of-the-art unimodal analysis by 2.35%, while also being 53% more parameter efficient than the industry standard cross-modal model. Furthermore, the evaluation of the attention coefficients allows for qualitative insights to be obtained. Through the coupling with bioinformatics, a novel link between the interferon-gamma-mediated pathway, DNA methylation and PD was identified. We believe that our approach is general and could optimise the process of medical evidence synthesis and decision making in an actionable way

    Visual Focus and Sports Performance

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    Understanding how an athlete’s mind and body interact is vital in finding ways to promote maximal athletic performance. Athletes are required to smoothly connect their external environment to internal motor networks for executing sports specific tasks. A study by Corbetta and Shulman aimed to explain how quiet eye (QE) connects with athletic performance. Their study defined QE as the “final fixation to a target during the preparation phase of a goal-directed movement” (2002). Corbetta and Shulman found that QE measurements were longer when maximizing “goal-directed” attention and minimizing “stimulus-driven” attention (2002). PURPOSE: This study analyzes the distance between where an athlete looks and where they kick to understand how gaze correlates with kick accuracy. We hypothesize that shorter kick-to-gaze distances predict higher accuracy kicks due to minimizing external gaze deviation so that internal networks create purer signals that increase penalty kick execution. METHODS: Participants completed a series of 24 penalty kicks, performing 6 penalty kicks within each of four conditions: No Keeper/No Target, Keeper/No Target, No Keeper/Target, and Keeper/Target. Having a target indicates that the participant was required to look at a red cardstock (RC) posted to the center of the crossbar before completing their kick. Eye movements were recorded, along with penalty kick quality and accuracy. Kick quality was measured using ball velocity, while kick accuracy was determined by whether the goal was scored. Eye movement patterns were collected using TOBII eyeglass equipment, which recorded fixation duration and count along with visit duration and count among various areas of interest. There were 7 areas of interest total. 6 areas divide the goal into Top Left (1), Bottom Left (2), Top Center (RC) (3), Bottom Center (4), Top Right (5), Bottom Right (6) regions and one area of interest was assigned for the ball (7). RESULTS: Analyzing where participants looked prior to their kick identified that athletes spend the greatest time looking towards the center region of the goal for all experimental conditions; No Keeper/No Target = 51.3% of kicks, Keeper/No Target =55.1%, No Keeper/Target = 76.9%, and Keeper/Target = 74.3%. Regarding ball landing location, the distance from center decreased the longer a participant looked at the ball prior to kick. Furthermore, the longer a participant looked at the goal prior to kick was found to directly associate with distance from the center. CONCLUSION: With accuracy being defined as in the goal but away from center, our results suggest that the longer a participant spent looking at the ball, the less accurate their kicks. Additionally, the more time an athlete spent looking in the direction of the goal, their accuracy increased. This provides partial support for our hypothesis and suggests that focusing on a target, as opposed to ball, prior to kick led to greater accuracy for our participants. Given participants were skilled soccer players (mean years played = 10.9; SD = 4.2), future studies could examine if this pattern is consistent among novice players

    S21RS SGB No. 4 (SG Bylaws)

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    A Bill To amend the Student Government Bylaws and Rules of Orde

    Foreign Bodies and Bowel Obstructions

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    Foreign body ingestion most commonly occurs in the pediatric population, with approximately 80–90% of objects passing spontaneously in individuals who are evaluated by medical professionals. Objects may be lodged in a variety of anatomic locations. Only about 10% of foreign bodies progress past the stomach. Of the 10–20% of objects that fail to pass, less than 1% requires surgical intervention. Small bowel obstructions are a rare presentation of foreign body ingestions. There are case reports, guidelines, and retrospective reviews in the literature regarding the management of ingested foreign bodies. In patients who do not have spontaneous passage of foreign bodies, endoscopic and surgical techniques have been utilized for successful retrieval. The timing and indication for endoscopic intervention is dependent upon several factors, including the type and location of the foreign body and is also contingent upon patient symptoms. Numerous case reports and studies describe the successful endoscopic removal of foreign bodies in the upper and lower gastrointestinal tract. Although the type and location of an ingested object is critical for determining the success of endoscopic intervention, the patient’s clinical exam and stability is also an aspect to consider when deciding on management of bowel obstructions caused by foreign bodies
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