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

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,

Enhancing aviation safety: Uncovering human error patterns and mitigating risks

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

Open Education Week 2024 Social Media - Thursday Table Location

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Probing the Multiplicity of Dusty Wolf-Rayet Stars: The Orbit of WR70

Wolf-Rayet stars are a late stage of evolution for massive stars that have high mass-loss rates and have also lost their outer hydrogen shell. While some stars have dust surrounding them, which forms in cold and dense conditions, these same conditions do not apply to WRs. From this, I am working to understand how dusty WRs move in comparison to other systems, and if that has any impact on their dust production. In an effort to create an orbit for a lesser-studied WR star, archival data that was taken between 1999 and 2012 was reduced using Python, and was then used to begin piecing together the orbit of WR70. Once this data has been completely reduced and compiled, data from CTIO will be gathered, and reduced in a similar manner to try and fill in some gaps in the orbit that are present from the archival data. Once both of these are completed, the radial velocity of the system will be analyzed to understand how the system moves at different points in the orbit. Upon completion of this analysis, the first-ever orbit for WR70 will have been created

Assessing the Viability of a Facility Dog in Higher Education to Reduce Stress and Improve Mental Health

This research study investigates the impact of a facility dog on the mental health and stress levels of college students in a higher education setting. College is a pivotal yet challenging time for many students, marked by significant transitions and increased susceptibility to mental health issues. With mental health disorders predominantly surfacing between the ages of 18-24, a demographic that coincides with the typical college age, higher education institutions are facing a mental health crisis. In response, some have begun integrating therapy and facility dogs into their campuses to provide therapeutic support. A facility dog, distinct from a therapy dog, operates within a single setting, offering continuous therapeutic services. This study focuses on one higher education institution’s facility dog and its effect on students\u27 mental health. Through a two-phase data collection process involving 20 participants, the study employs pre-surveys, the Coleman Dog Attitude Scale (C-DAS), the State-Trait Anxiety Inventory State (STAIS-5), and Trait (STAIT-5) measures, along with biometric data collected via Whoops® bands. Participants will engage in regular contact with Higbee, with their anxiety levels and biometric data monitored before and after interaction periods. The study aims to correlate students\u27 perceptions of and actual biometric responses to regular contact with a facility dog, providing insights into the potential mental health benefits of such interventions in higher education settings

Fostering Equity in Engineering Education

Students in introductory engineering courses face challenges communicating and integrating their ideas in team projects. Often, these challenges with team communication fall along gendered lines, where women students experience marginalization in team settings. This project builds from prior research in the field of engineering education, which integrated frameworks from the domains of engineering education and technical and professional communication to implement this research into a classroom intervention aimed at reducing the gendered disparity in these communication challenges. To help resolve these issues, this project utilizes a new research method called infrastructural rhetorical analysis to develop an educational intervention case study involving the experiences of women in the first-year engineering classroom to determine a concrete classroom intervention that aims to make the most difference with the least amount of resources needed to implement it

The Challenges of Pilot Language Training for Effective Aeronautical Communications in Multicultural Contexts

This paper discusses the main elements that account for effective aeronautical communication in multicultural contexts and identifies the approaches that can offer support to these elements. Most exchanges are performed by people from different linguacultural backgrounds and, although Aviation English is the language to be used in operations, communication dynamics are supposed to require skills that go beyond language proficiency. Data were collected from a questionnaire answered by experienced pilots flying in international airspace and results were analyzed both from a quantitative and a qualitative perspective. The main findings show some of the challenges faced by pilots who are non-native speakers of English regarding elements that should be observed in training practices

Diurnal Immune Cell Migration Patterns Characterized in the Spaceflight Environment

Daily diurnal immune rhythm shapes biological pathways of organisms and closely aligns with optimizing energy usage in response to environmental light-dark cycles. Immune mobilization depends on diurnal signals to regulate immunity. In spaceflight, disrupted circadian rhythms and immune systems are noted. However, crosstalk between these systems has not been fully characterized. To fill this knowledge gap, we utilized a ground-based model of spaceflight to phenotype diurnal immunity in mice. For this, 24-week-old male and female mice were exposed to a combination of single-housed, acute 15cGy 5-ion GCRsim irradiation and continuous hindlimb unloading for 2 weeks on a light:dark [12hr:12hr] cycle throughout. Blood was collected at 24 hours and 2 weeks post irradiation and flow cytometrically profiled. Additionally, ribo-depleted, bulk RNA sequencing characterized unique, diurnal and sex-specific biosignatures. This work expands our understanding of diurnal immunity which is important to consider for personalized medicine directives for astronauts. This work was supported in part by the NASA Human Research Program (HRP) Human Factors Behavioral Performance Element Grant 18 18FLAG 2 0028 to AER and Embry-Riddle Start-up grant to Dr. Amber Paul

Density and Magnetic Field Asymmetric Kelvin‐Helmholtz Instability

The Kelvin‐Helmholtz (KH) instability can transport mass, momentum, magnetic flux, and energy between the magnetosheath and magnetosphere, which plays an important role in the solar‐wind‐ magnetosphere coupling process for different planets. Meanwhile, strong density and magnetic field asymmetry are often present between the magnetosheath (MSH) and magnetosphere (MSP), which could affect the transport processes driven by the KH instability. Our magnetohydrodynamics simulation shows that the KH growth rate is insensitive to the density ratio between the MSP and the MSH in the compressible regime, which is different than the prediction from linear incompressible theory. When the interplanetary magnetic field (IMF) is parallel to the planet\u27s magnetic field, the nonlinear KH instability can drive a double mid‐latitude reconnection (DMLR) process. The total double reconnected flux depends on the KH wavelength and the strength of the lower magnetic field. When the IMF is anti‐parallel to the planet\u27s magnetic field, the nonlinear interaction between magnetic reconnection and the KH instability leads to fast reconnection (i.e., close to Petschek reconnection even without including kinetic physics). However, the peak value of the reconnection rate still follows the asymmetric reconnection scaling laws. We also demonstrate that the DMLR process driven by the KH instability mixes the plasma from different regions and consequently generates different types of velocity distribution functions. We show that the counter‐streaming beams can be simply generated via the change of the flux tube connection and do not require parallel electric fields