Embry–Riddle Aeronautical University

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    25521 research outputs found

    Harnessing Wearable Sensors: A Study of Stress Reduction through Meditation

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    Modern technology significantly contributes to the stress experienced by individuals worldwide. Meditation, a widely practiced technique with diverse applications, has long been utilized for stress reduction. In John L. Mason\u27s Guide to Stress Reduction, an elevated pulse rate is identified as a key characteristic of the stress response. This research aims to investigate the influence of meditation on an individual\u27s pulse rate and in turn their stress levels. To achieve this goal, a biometric sensing glove is in development to collect pulse data, with completion anticipated by December 2023. Official data collection will begin once the glove is finalized, with multiple test samples gathered during development. Although data collection is pending due to ongoing device development, significant progress has been made, including 3D printing, circuit schematic design, Arduino and Python code implementation, and circuit prototype testing. This study aligns with recent trends in researching the physiological effects of both Eastern and Western meditation practices. It contributes to the broader field by advancing the development of devices aimed at assisting users with stress reduction and practicing mindfulness. This investigation represents a crucial step towards a more mindful and stress-resilient society

    The Last Frontier

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    A group of experienced trekkers are off for the trek of a lifetime. They believe they have prepared for everything, until the unexpected happens that none of them could have prepared for

    Enhancing aviation safety: Uncovering human error patterns and mitigating risks

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    Human factors are the application of scientific insights concerning people and systems to optimize system performance. Given that human beings are integral to every aspect of systems, the potential for human errors stemming from stress, fatigue, or complacency is significant. In aviation, where errors can lead to catastrophic consequences, it is imperative to take comprehensive preventive measures. Our objective is to analyze major human factors issues contributing to aviation accidents, identifying patterns, and generating recommendations to enhance safety regulations and prevent future accidents. Additionally, we aim to assess the financial impact of accidents on the aviation industry. By thoroughly reviewing various accidents and their primary causes, the paper seeks to understand how to mitigate and reduce future incidents. Improved aviation safety benefits airlines, passengers, airports, governments, and regulatory agencies. They can utilize the findings of this paper to enhance aviation regulations and ensure a safer mode of transportation

    Forecasting Airport Passenger Flow during Periods of Volatility: Comparative Investigation of Time Series vs. Neural Network Models

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    Artificial Intelligence (AI) models, particularly neural networks, are infrequently utilized in the existing airport management literature for conventional forecasting of airport activities. The limited adoption of these models in the airport management literature might be influenced by their perceived complexity. This perception is likely derived from their common application in intricate tasks within the academic literature. Nevertheless, this research calls for a reevaluation of such perceptions and advocates for the inclusion of RNN and multivariate RNN in the forecasting toolkits of airport managers as credible alternatives to traditional time series models. This study endeavors to discern the forecasting performance of neural network models, providing insights into their effectiveness and applicability in addressing the complexities of passenger flow dynamics through a comprehensive evaluation of RNN, LSTM, GRU, Deep LSTM, BLSTM, multivariate RNN and multivariate LSTM, in comparison to standard time series models (ARIMA, SARIMA and SARIMAX). It was anticipated that the application of neural network techniques in TSA passenger flow v forecasting will yield heightened accuracy when compared to conventional standard time series models. Moreover, the integration of non-standard external factors was expected to enhance the forecasting performance of neural network-based models like RNN and LSTM, further distinguishing them from standard time series models. This investigation rigorously evaluates the robustness of these models by subjecting them to highly volatile historical data to forecast airport security checkpoint passenger flow at five prominent U.S. airports during the pandemic-induced challenges. At Atlanta\u27s Hartsfield-Jackson Airport (ATL), the forecasting precision of RNN notably exceeds that of SARIMA by 34% (DM= 3.44, p\u3c 0.01). This highlights the superior capacity of RNN to manage intricate interactions among variables, complex dependencies between factors and non-linear dynamics, thereby demonstrating its readiness for the emerging data-rich aviation environment. The incorporation of exogenous variables enhances the forecasting accuracies of the multivariate RNN/LSTM (DM=6.82,

    Toward the Unified Theory of SAID-Linked Subauroral Arcs

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    We present a unified approach to subauroral arcs within intense subauroral ion drifts (SAID), which explains the observed transition of a precursor Stable Auroral Red (SAR) arc into Strong Thermal Emission Velocity Enhancement (STEVE). This approach is based on the short-circuiting concept of fasttime SAID as an integral part of a magnetospheric voltage generator between the innermost boundaries of the freshly injected plasma sheet electrons and ring current ions. Here, enhanced plasma turbulence rapidly heats the bulk plasma and accelerates suprathermal non-Maxwellian “tails.” Heat and suprathermal electron transport rapidly elevate the ionospheric electron temperature—the source of a bright SAR arc. Through a substorm, the density altitude profile within the evolving ionospheric SAID channel transforms into a “fresh” F-region trough with the E-region valley. The ionospheric feedback instability within the depleted-density SAID channel generates small-scale, field-aligned currents with parallel electric fields sufficient to produce the suprathermal electron population, exciting the STEVE and Picket Fence emissions. This approach also explains the inner electromagnetic structure of intense SAID, which is consistent with fine optical structures in STEVE and Picket Fence

    A case study of management shortcomings: Lessons from the B737-Max aviation accidents

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    This case study looked into the shortcomings in of Boeing’s upper management in the engineering and fielding of the B737-Max. Under pressure to build a plane with identical flying characteristics to the existing B737 family, Boeing included modifications but purposely concealed those changes from regulators and pilots. This decision resulted in fatal accidents in 2018 and 2019 and caused the deaths of 346 passengers. Unlike previous aviation accidents, these mishaps were entirely preventable and a direct result of Boeing’s organizational failures and management shortcomings. This case study analyzed the behavior, decision making process, and reasons which led Boeing to push for the certification of the B737-Max despite these known flaws in the design. Additionally, this poster studied the consequences and punitive actions that followed the investigation of the two crashes. The poster concludes by offering recommendations to the aviation industry on how accidents such as this can be avoided in the future

    On Progress in Exploring Controlled Viscous Limit-Cycle Oscillations in Modified Glauert Airfoil

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    The paper reports on the progress in the development of a novel robust, nonlinear flow control technology that employs an array of synthetic-jet actuators (SJAs) embedded in 2-DOF, elastically mounted, optimized Modified Glauert (MG) airfoil design in order to control limit cycle oscillations (LCO) at low subsonic flow regimes. The focus here is on the conceptual design of the wind energy harvesting system that employs, e.g., a piezoelectric device to extract energy from plunging LCO, with the closed-loop controller being capable to sustain the required LCO amplitudes over a wide range of wind speeds. The current high-fidelity studies first include validation of the static-airfoil aerodynamic predictions against results obtained from the concurrent experimental campaign. Next, a set of parametric 1-DOF and 2-DOF numerical analyses examine open-loop and closed-loop LCO control strategies that employ the ability of MG airfoil to sustain LCO at subcritical velocities due to natural separation-induced flutter

    Using Fourier Analysis to Reveal Pulsation Frequencies of roAp Stars

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    The goals of this investigation are to determine the principal pulsation and get an understanding of the atmospheric fluctuation of TYC 3218-888-2, a rapidly oscillating chemically peculiar A-type star (roAP star). The Southeastern Association for Research in Astronomy (SARA-RM) telescope was used to carefully collect time-series photometry. Our investigation\u27s basic premise holds that by analyzing the frequency spectrum of the star\u27s light curve, we may interpret crucial information about the star\u27s upper atmosphere and photosphere. We analyzed the star\u27s pulsation through the use of multi-aperture photometry (AstroImage J) and Fourier Analysis techniques (Period 04). The determined principal pulsation of TYC 3218-888-2, is 0.8743 mHz, with an amplitude of 4.58 mmag. We also found a pulsation with a large amplitude of 9.194 mmag and frequency of 0.0529mHz that lies between the gamma Doradus and Delat scuti frequency ranges. This was the preliminary detection, so we need more observations to confirm this pulsation. Additionally, we unintentionally discovered that one of the comparison stars was a variable star while interpreting all the light curves of the comparison stars. This presents progress on the way to determining the main pulsation and other frequencies of this roAp stars. Keywords - roAp Stars, Pulsations, Fourier Analysis, Multi-aperture Photometry, Atmospheric Structure, light curve

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