Mason Journals (George Mason Univ.)
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Quantifying the macroeconomics impacts of satellite failure on global supply chains
No full textGlobal supply chains have grown hand-in-hand with modern telecommunication systems,particularly satellite communication services which can provide connectivity anywhere in theworld. Satellite communication services have thus become indispensable to the functioning ofmodern economies. However, the potential macroeconomic impacts of satellite failures on thesesupply chains remain underexplored. This research addresses the knowledge gap by examininghow satellite disruptions influence global trade, production, and economic stability.
Recent studies have underscored the critical role of satellites in tracking shipments, managinginventories, and facilitating financial transactions. Yet, comprehensive analyses on the economicripple effects stemming from satellite malfunctions are scarce. To fill this gap, a Multi RegionalInput-Output (MRIO) model is used, combining econometric modeling with case studies of pastsatellite failures. The methodology integrates satellite data interruption scenarios with globaltrade models to simulate potential outcomes on supply chain efficiency and economic indicators.
The study reveals that satellite failures can cause significant delays in shipping, increased costsdue to rerouting and manual tracking, and disruptions in communication channels critical forjust-in-time production systems. For instance, a one-week satellite outage risks an estimatedglobal trade loss of $50 billion, highlighting the vulnerability of interconnected economies.Additionally, industries heavily reliant on real-time data exhibited the most pronounceddisruptions.
The findings suggest that satellite failures can cause significant congestion in global supplychains. This research underscores the interconnectedness of modern supply systems and thepropagating effects of one point of failure
Synthesis of Inhibitors of the Impα Protein Binding as a Potential Treatment for Venezuelan Equine Encephalitis Virus (VEEV) infection Binding
No full textVenezuelan Equine Encephalitis Virus (VEEV) is a mosquito-borne pathogen that commonly infects equines but has transmitted to humans in certain cases. The lack of small molecule antivirals for treatment against VEEV prompted an in silico high throughput screen (HTS), which led to a hit molecule named DP9. A series of DP9 analogues were synthesized using a two-step reaction protocol starting from a condensation reaction with hippuric acid followed by amidation of the resulting lactone adduct. Preliminary results led us to keep select portions of DP9 fixed while we modified the "tail" region by using different amine nucleophiles. A diverse series of 14 DP9 analogues were synthesized in yields ranging from 50-100% and fully characterized by 1H and 13C NMR and ESI-MS. The modulation of amine tails explores different physiochemical properties of the “tail” region by incorporating different lengths, sizes, types of aromatic rings, and basic amines groups. DP9 analogues inhibitory activity against VEEV were quantified using an AlphaScreen assay to show inhibition of the an NLS-sequence peptide binding to the Impα protein
HERMIT: A Robotic Hand Mirror Therapy Device, Utilizing a Spiral CAM Linkage for Children with Cerebral Palsy
No full textChildren with hemiplegic cerebral palsy (CP) often have limited hand function, hindering their ability to perform daily activities. Although constraint-induced therapy and hand-arm bimanual intensive training have shown clinical efficacy, they are intensive and may not be suitable for children who are more severely impaired. Mirror therapy, on the other hand, may be a more feasible option, but typically leaves the affected hand inactive, limiting sensory feedback, a crucial component in motor control and learning. Robotics can enhance mirror therapy by allowing the movement of the unaffected hand to control a robotic device on the affected hand, thus providing necessary sensory feedback. However, robotic mirror therapy has primarily been studied in adult stroke patients, and its efficacy in children with CP has not been rigorously evaluated. Moreover, most developed devices are unilateral and focus on proximal joints rather than the hand. To address this challenge, we present the preliminary design of a hand end-effector robot for mirror therapy (HERMIT) in children with CP. Each side of the robot features a novel spiral cam linkage mechanism. Additionally, the measured movement on one side of the robot, detected via a sensor, controls the movement on the other side, which is driven by a motor. Furthermore, HERMIT enables the user to attach their fingers and thumb from the palmar side. HERMIT also sits on a sliding aluminum frame, allowing for adjustability depending on should-width. Future studies aim to validate the effectiveness of robotic mirror therapy in children with CP
Calibrating Low-Cost Air Quality Sensors for High Accuracy PM2.5 Measurements Using Machine and Deep Learning, Enabling Monitoring of Air Quality for a Wider Range of Geographic Regions
No full textIn recent years, air quality has decreased substantially because of increasing levels of harmful pollutants from various industrial practices, vehicle emissions, and wildfires. This has raised public health concerns, especially those around respiratory conditions. Particulate matter with diameters less than 2.5 µm or PM2.5 is considered a leading air pollutant, causing over 8 million deaths annually worldwide. Hence, accurate monitoring and prediction of air pollution, specifically PM2.5, is essential for public health protection; however, current air quality monitoring methods face limitations. Currently, air quality data is collected from ground-level monitoring stations, which utilize Environmental Protection Agency (EPA) sensors. While these sensors provide accurate data, their high cost prevents them from widespread use, restricting the geographic regions that can be monitored. On the other hand, low-cost air quality sensors can be distributed across different areas and fill geographic gaps in sensor coverage, but they are not very accurate. This trade-off between cost and accuracy presents a challenge in effectively monitoring widespread air quality. To address this challenge, this study presents a new approach to improving the accuracy of air quality measurements of low-cost sensors by calibrating them using various machine learning (ML) and deep learning (DL) techniques, allowing them to attain the high accuracy of EPA sensors while still remaining inexpensive. ML/DL models were trained on spatiotemporal data that was collected from ground-level monitoring stations, including PM2.5 levels, temperature, and humidity from the low-cost sensors and PM2.5 levels from the EPA sensors. Then, using these models, the temperature, humidity, and PM2.5 levels from the low-cost sensors were used to predict their corresponding highly accurate PM2.5 measurements from EPA sensors with RSMEs as low as 5.53. Better accuracy in low-cost sensors can lead to more extensive and reliable air quality monitoring networks, providing real-time data that can be used for public health protection. This approach can be applied globally, enabling better response strategies to air pollution and its associated health risks
Philip Dwyer and Mark Micale, Eds., The Darker Angels of Our Nature: Refuting the Pinker Theory of History and Violence
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