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Exploring Mechanisms Underlying Time-on-Task Effects in Decision-Making Tasks
Get PDFSustained vigilance and complex decision-making are necessary skills for operatorsof both manned and unmanned aerial vehicles. Cognitive fatigue due to time ontask may reduce cognitive resources, producing performance decrements. Weperformed a secondary analysis of a data set from 68 participants who performedthree tasks related to target verification (TV), complex decisions, and imagerecognition (IR) in three consecutive days. Both TV and IR tasks were vigilancetasks, separated by the complex decision task, and both showed an expectedperformance decrement (decreasing hit rate) consistent with fatigue. Surprisingly,both also showed performance improvements (decreasing response time andincreasing correct rejections) inconsistent with fatigue. We propose that anincreasing rate of responding “no signal” is due to a complex combination ofcognitive fatigue, strategic learning, and memory decay and interference
Functional Visualizations of a Hydrogen-Electric Aircraft Propulsion System for Supporting Pilot Decision-Making
Get PDFCognitive Work Analysis (CWA) and Ecological Interface Design (EID) were used todesign a novel flight deck display for a regional turboprop aircraft that is being retrofitted with aHydrogen Aircraft Powertrain Storage System (HAPSS). This research addresses the challengesof managing cognitive complexity in next-generation aviation systems, focusing on designinginterfaces based on system constraints which are modeled through the Abstraction Hierarchy(AH). Visualizations were discussed in interviews with subject matter experts highlighting theneed for further matching the mental model of the pilot by addressing simplicity, workload, anduse case representations on the display. The iterative process included static and dynamic displaytesting and focused on three flying scenarios. Future research should involve controlled humanin-the-loop experiments with a larger participant pool to discuss, iterate, and test the proposeddisplay designs
Leveraging System Definition for Human Factors Integration in the UAM Ecosystem; Cockpit Display for Autonomous Flight
Get PDFAs UAM moves toward autonomy, human operators will remain essential—especially ininterpreting complex variables or managing system limitations. Cockpit displays act as a bridgebetween the pilot and multiple UAM ecosystem components, including navigation, traffic,weather, and diagnostics systems. This paper argues that defining each component’s inputs,outputs, services, and requests is a prerequisite for effective human-machine interface (HMI)design, particularly as information is often pre-processed before reaching the pilot. The initialgeneration of UAM systems is likely to adopt manual flight controls due to regulatoryconservatism, certification readiness, and the need for public trust. However, as full autonomybecomes viable, cockpit interfaces must evolve to support remote supervision and control. In thisphase, display logic diverges depending on whether it is integrated in an onboard cockpit or in aremote pilot station, with differences in interaction latency, sensor access, and situational context.To support this evolution, the paper presents a system-level mapping of UAM data sources andexchange pathways, identifying key bilateral interactions between ecosystem entities. Thisfoundation enables scalable visualization of cockpit-relevant information and human-centeredautomation transitions
Bridging Cognitive Modeling and Pilot Training: A Qn-Actr Model for Complex Flight Tasks
Get PDFIn ab initio pilot training, students must learn substantial skills about how to fly anaircraft, but there has been a lack of research that can clearly model these skills.Cognitive architecture models use production rules to model skills, which can beapplied to the case of pilot training to facilitate skill diagnosis, assessment, andfeedback. In this study, researchers who have both flight training experience andcognitive modelling experience developed cognitive architecture models for takeoffand landing tasks using the Queueing Network-Adaptive Control of ThoughtRational (QN-ACTR) method. The models generated flight performance in XPlaneflight simulation similar to human pilot in-aircraft data from the same tasksrecorded in the same type of aircraft (Cessna 172). This presentation details theproduction rules, assumptions, validation results, and lessons learned. Thesefindings provide the foundation for future work to further develop the models forother flight tasks and pilots with different levels of experience
Mental Visualization in Aviation Training: Propensity, Measurement, & Implications for Narrative in Extended Reality
Get PDFWe are currently investigating the role of mental imagery and narrative structureto leverage episodic memory for training, particularly in extended reality (XR).We hypothesize that (a) learners’ propensity for visualization and (b) the use ofinstructional storytelling contributes to more meaningful and effective trainingexperiences. Episodic memory may be particularly helpful for encoding andretrieving the place and timing of actions in a procedure sequence. Furthermore,we believe that learners’ propensity for mental visualization may underpin part ofthis relationship. Here, we describe results from an exploratory factor analysisthat focused on factor loadings across a battery of measures of learners’immersive tendencies, visuospatial abilities, imagination, and mental simulation.The results supported a three-factor model of learners’ visualization propensitycorresponding to concepts of vividness (evocation), directedness (invocation), andfrequency (avocation) of mental imagery. We discuss these findings in context ofour refinement of measure items, and how this effort may support research onadvanced simulation technologies for aviation training
Nekuomanteion or Mafia Front: Was Acheron Legit?
No full textOracles of the dead, or nekuomanteia as they were often called in Ancient Greek, have come to be a more frequently discussed element of Classical Antiquity in recent years, and some scholars might go as far as to argue that they were a legitimate part of Greek and Roman society, with some believing that famous consultations of these oracles from authors such as Plutarch and Herodotus are but mythologized legends that recall real types of consultations from Antiquity. However, upon closer examination of the primary sources and archaeological evidence, one begins to doubt whether the modern interpretation of the ancient nekuomanteion is the same as what the ancients had in mind. This essay will specifically examine the evidence for one of the most well-known nekuomanteia: Acheron, in Thesprotia (modern-day Greece), and address the following questions: Did the Greeks actually understand Acheron to be a functioning death oracle, or did it simply function as an exaggerated setting for a good ghost story, or perhaps as a convenient entrance to the underworld for literary purposes? If Acheron did operate as an oracle of the dead, then how might it have functioned? Was it centered around a building, cave, or body of water
Biofunctional Characterization of a Polyherbal Formulation
No full textPolyherbal formulations (PHFs) have greater medicinal importance due to their enhanced bioactivities. To check whether a combination of several plants enhances bioactivities, the current study was designed. Methanol, aqueous and n-hexane solvents were used to prepare PHF of Syzgium cumini, Origanum vulgare, Aloe vera barbadensis Miller and Gymnema sylvestre. DPPH (2, 2-diphenyl-1-picrylhydrazyl scavenging) assay was used to analyze antioxidant activity. The total phenolic content (TPC) and total flavonoid content (TFC) estimations were done by Folin-Ciocalteu reagent and AlCl3 colourimetric techniques, respectively. Bactericidal activity was measured by agar well diffusion method, anti-diabetic profile was assessed by starch-iodine assay, anti-inflammatory potential was checked by BSA denaturation method and cytotoxicity was evaluated by hemolytic assay. Structural characterization was done by Fourier transform infrared spectroscopy (FTIR) and HPLC (high-performance liquid chromatography). Methanolic PHF extract showed maximum TPC (355.59±0.2432mg GAE/mL), TFC (485.88±0.58 µg CE/mL) and antioxidant activity (58.34±0.36 %). In bactericidal activity, PHF had variable (mild to moderate) antimicrobial potential against E coli and S. aureus. The methanol extract showed maximum enzyme inhibition (35 %). Maximum hemolytic activity was shown by aqueous extract (2.14 %). In anti-inflammatory potential, the methanol sample had 29.89 % inhibitory potential. Structural characterization by FTIR and HPLC indicated the presence of alcohols, alkenes, ethers, aromatic amines, alkanes, carboxyl acids, chlorogenic acid, gallic acid, kaempherol and benzoic acid that contributed to the bioactivities of PHF. It is concluded that polyherbal combinations excel in therapeutic attributes that warrant further in vivo studies
The Opioid Crisis: Analyzing Access to Resources, Mental Health and the Influence on Overdose Deaths in Urban and Rural Communities
Get PDFBackground: Opioid use contributes significantly to deaths throughout the United States. Opioid overdose deaths have increased in recent years and the opioid crisis has far reaching effects on many communities. There are several factors that contribute to opioid overuse and death, including access to resources and social factors.Objective: In this study we aimed to assess how social factors affect overdoses by examining the differences between rural and urban communities over time.Methods: We utilized data from County Health Rankings and conducted a retrospective review of data from 2016 and 2023 to compare several factors that we hypothesized affected opioid overdoses. We divided counties into rural or urban to compare how access to resources and social factors differ and contribute to overdose deaths.Results: We found no correlation between overdose deaths in rural and urban communities in 2016 and 2023. There was a weak, positive correlation between frequent mental distress and overdose in both 2016 (r = .405, p \u3c .001) and 2023 (r=.390, p \u3c .001). There was a negative correlation between median household income and overdose in both 2016 (r = -.333, p = .004) and 2023 (r= -.477, p \u3c .001). There was a negative correlation between social association and overdoses in 2023 only (r= -.339, p = .003). Indicators of stress were found to account for 66.9% of variance in overdose mortality in 2023 (F1,80 = 64.664, p\u3c0.001) and 51.2% in 2016 (F2,71 = 12.619, p\u3c0.001). Overdose mortality was significantly different in Ohio between 2016 and 2023 (t = 11.094, p\u3c0.001).Discussion: We expected that less access to resources, including primary care as seen in rural areas, would increase mortality. However, opioid use disorders are complex and efforts to limit mortality rates should focus on multiple factors that are associated with overdose
Prevention of Substance Related Deaths Through Mental Health Counseling
Get PDFBackground: Substance abuse, particularly alcohol and drug use, remains a critical public safety issue in Ohio. Those suffering with abuse disorders are likely to often have accompanying mental health disorders, however, there lies a significant gap in treatment. Objective: This study examines trends in alcohol-related driving deaths, drug overdose deaths, excessive drinking, and mental health provider availability from 2016 to 2023 in rural and urban counties of Ohio.Methods: Data collected from the public source, County Health Rankings and Roadmaps, was analyzed using statistical methods to assess changes between 2016 to 2023 for the 88 counties of Ohio. Urban versus rural counties were defined using United States Department of Agriculture (USDA) distinctions. Results: Key findings include a significant statewide decrease in alcohol-related driving deaths (p=0.013), coupled with an increase in excessive drinking rates (p\u3c0.001). Mental health provider rates improved significantly in both rural and urban counties, narrowing the disparity between the two regions by 2023. However, no significant relationship was found between mental health provider availability and alcohol-related fatalities (r=0.054, p=0.616). Additionally, drug overdose death rates showed no significant difference between rural and urban counties in 2023
Applying Machine Learning Methods to Generate Understandings of Differential Item Functioning in a Flu Knowledge Assessment
Get PDFCurrent influenza trends, including the severity of the 2025 flu season and the prevalence of H5 bird flu in livestock, necessitate efforts to better understand how to educate students about its transmission. Although validated assessments of influenza knowledge exist, these have not been evaluated for affective and demographic biases. We explore differential item functioning (DIF) effects in four items focused on specific aspects of flu transmission derived from a validated influenza knowledge assessment. In doing so, we introduce and utilize a machine learning framework for exploration of DIF which offers greater flexibility than traditional statistical approaches in terms of studying generalizability of effects within and across study sites and across different modeling approaches. Both statistical (logistic regression) and machine learning approaches revealed that the largest DIF effects—perceived complications and barriers to preventative practice—were affective in nature. Demographic factors such as gender, ethnicity, and presence of health professionals in the students’ families, tended to emerge from the algorithmic models (random forest and neural networks), whereas the data models (like logistic regression) tended to overlook these smaller effects. While not a direct replacement for statistical approaches, we encourage researchers interested in understanding equity to treat machine learning as an additional resource in our toolboxes. To better understand how to educate students about communicable diseases such as H5 bird flu, moving beyond model-specific inferential methods toward model-agnostic machine learning-based methods will enhance our ability to detect biases in our assessments, and to focus on those biases which persist across different samples and diverse modeling paradigms